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Über dieses Buch

This book is composed of the Proceedings of the International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2013), held at Central Institute of Technology, Raipur, Chhattisgarh, India during June 14–16, 2013. The book records current research articles in the domain of computing, networking, and informatics. The book presents original research articles, case-studies, as well as review articles in the said field of study with emphasis on their implementation and practical application. Researchers, academicians, practitioners, and industry policy makers around the globe have contributed towards formation of this book with their valuable research submissions.

Inhaltsverzeichnis

Frontmatter

Image and Template Security for Palmprint

The wide spread use of biometrics in real world causes more security and privacy concerns to be raised, because conventional biometric systems store biometric templates as it is in database without any security, and this may lead to the possibility of tracking personal information stored in database; moreover, biometric templates are not revocable and are unusable throughout their life time once they are lost or stolen. To overcome this non-revocability of biometrics, we proposed two methods for image security and template security in this paper (these methods are also applicable for some of the biometric traits); for image security, we used chaotic mixing with watermarking technique; first chaotic mixing is applied to the host image, and then, this resultant image is embedded in a sample (cover or carrier) image, and if the intruder gets the chaotic mixed image, he/she cannot get back the original host image, and for template security, we shuffled the palmprint template according to the input random number. This shuffling scheme increases the imposter matching score leaving genuine matching score.

Munaga V. N. K. Prasad, B. Adinarayana

Extending Network Lifetime by Time-Constrained Data Aggregation in Wireless Sensor Networks

The most important challenge in wireless sensor network is to reduce the energy consumption of each node and increase the network lifetime. Many networking schemes are used to minimize the amount of data transmission by data aggregation. Three main factors affecting the lifetime of sensor nodes are as follows: (1) the consumed energy for sending data from the leaves to the sink, (2) queuing delay during aggregation, and (3) the tree’s delay, which is equal to the tree’s depth, should be considered. We analyze the optimal time allotted for intermediate node data aggregation and optimal delay at each higher aggregation node. The adaptive scheme then dynamically adjusts the time constrain at the sensor node.

K. B. Ashwini, G. T. Raju

The Complex Network Analysis of Power Grid: A Case Study of the West Bengal Power Network

Complex network analysis is a new multidisciplinary approach to characterize the structure and function of power grid as a complex network to establish the communication topology between the grid stations. By taking this into consideration, we are trying to design a reliable system which continuously supplies power to the grid station which can able to avoid the cascading failures, i.e., blackouts. In this article, we model the power grid as an undirected graph through which the different connectivity of power grid is represented as a measure to evaluate structure and function of power grid. The goal of this paper is to characterize the topological structure of the West Bengal of India power grid and evaluate the performance of electricity infrastructures.

Himansu Das, Gouri Sankar Panda, Bhagaban Muduli, Pradeep Kumar Rath

Comparison and Analysis of Node Deployment for Efficient Coverage in Sensor Network

Wireless sensor network (WSN) is composed of sensor nodes, which have capability of perception, computing, sensing, and communication. In wireless sensor network, the number of nodes deployed in a region is directly proportional to the cost of network, performance, and robustness. Sensor node deployment is an essential issue to be resolved in WSNs. A proper node deployment method can reduce the complexity of problems in WSNs. In this paper, we calculate the efficiency of some popular regular deployment patterns such as square grid, triangular lattice, and rhomb in terms of the number of sensors required to provide coverage and connectivity. We have shown comparison between these patterns in terms of total coverage area and net efficient coverage area ratio for varying number of nodes. Simulations have been done using MATLAB R2010a.

Ram Shringar Raw, Shailender Kumar, Sonia Mann, Sambit Bakshi

Performance Analysis of Routing Protocols for VANETs with Real Vehicular Traces

In this study, we have evaluated the performance vehicular ad hoc network (VANET) with real vehicular traces. The vehicular movements are generated with IDM_IM mobility model. This mobility model used to emulate the movement pattern of nodes, i.e., vehicles on streets defined by maps. Our objective is to provide a comparative performance analysis among various ad hoc routing protocols, i.e., LAR1, AODV, and DSR protocols. The simulation work has been conducted using the Glomosim 2.03 simulator. The results show that LAR1 protocol achieves maximum packet delivery ratio is 100 % in the sparsely populated network. The results show that LAR1 outperforms DSR and AODV in terms of packet delivery ratio.

Sanjoy Das, Ram Shringar Raw, Indrani Das, Rajib Sarkar

A Bluetooth-Based Autonomous Mining System

This chapter encompasses the description of a Bluetooth-based automated wireless communication system. The proposed system would equip underground and open-cast mines with a wireless system which would enable communication within the various layers of the mine and also enhance the security facilities for the miners. It would allow a two-way communication process between the ‘administrators’ or the people in charge outside the mine, within its premises, and the supervisors present inside the mine at that time. The system would be implemented by placing servers in each part of the mine such that each server covers a designated area. The Bluetooth signals are to be transmitted to and from the servers using a network of intermediate ‘epidemic’ servers which would pass on information along the line. Every server in the mine would have its own independent series of ‘gossip’ servers to communicate with the targeted server located on the surface or inside the mine.

Saikat Roy, Soumalya Sarkar, Avranil Tah

Transistor Representation of a Low-Power Reversible 32-Bit Comparator

In recent years, reversible logic has emerged as a major area of research due to its ability to reduce the power dissipation, which is the main requirement in the low-power digital circuit design. It has wide applications such as low-power CMOS design, nanotechnology, digital signal processing, communication, DNA computing, and optical computing. In this paper, two new 3 × 3 reversible gates are proposed and these are being used to realize the classical set of logic gates in the reversible domain. An important aspect of the two newly proposed reversible gates is that a novel optimized 1-bit comparator can be realized. The proposed reversible 1-bit comparator is better and optimized in terms of the number of reversible gates used, the number of transistor counts, and the number of garbage outputs. Also, a 4-bit comparator has been designed by cascading 1-bit comparators in series. Using this, a 32-bit reversible comparator has been proposed. Proposed circuits have been simulated using Modelsim.

A. V. AnanthaLakshmi, G. F. Sudha

Performance Enhancement of Brillouin Distributed Temperature Sensor Using Optimized Fiber

The improvement of signal-to-noise ratio (SNR) and the suppression of stimulated Brillouin scattering (SBS) effects in a long-range distributed sensor are presented in this paper. We have designed a simple Brillouin distributed temperature sensor using phase modulation and optimization technique. Global evolutionary computing-based optimization technique [particle swarm optimization (PSO)] is applied for fiber and receiver optimization. The simulated results of the sensing system are reported in this paper. The combination of phase modulation and the global evolutionary computing technique improved the SBS threshold power to an extent of 6.8 and 6.3 dBm for 50 and 75 km of sensing range, respectively. However, with both receiver and fiber optimization, a 20 dBm improvement of SNR for an input power of 5 dBm and 75 km of sensing range is reported.

P. K. Sahu, Himansu Shekhar Pradhan

To Study the Architectural Designs of a Proposed Comprehensive Software Extractor for Reengineering Tool: A Literature Survey

Software is a critical issue nowadays. The selection and design suitable technique/process/method for maintenance phase for the same, we required suitable reengineering tool and technique, extractor one of the most important component of reengineering tool, for design appropriate extractor, we survey the several extractors, which read the source code find out the architecture of code and design level problems and remove the problems make a new update architecture diagram. We studied several extractors and not found any suitable tool for achieve completeness architecture recovery. In this paper, we tried to survey available extractor to find their merits and demerits. The requirement set is proposed for the constructing the new extractor those who take the merits of existing extractor and some required based on our study.

Rashmi Yadav, Abhay Kothari, Ravindra Patel

Detection of Web-Based Attacks by Analyzing Web Server Log Files

In today’s scenario, Web traffic is increasing everyday in the world and has overtaken P2P traffic. The Websites are getting hacked on daily basis. These rises in hacking activity pose a greater threat than the network attacks as they threaten to steal crucial and important information from Website. This information can be related to the users, employee, and other important data stored in applications and database linked to the Website. Increase in Web network traffic has opened new and more efficient attack vectors for the hackers and attackers to work with. Attackers take advantage of the vulnerability in traditional firewalls deployed on Website. These firewalls are not designed to protect Web applications; lots of Websites are getting attacked by malicious scripts and users. In this paper, many Web attacks are carried out on Web applications hosted on local server to analyze the log file created after the attacks. A Web application log file allows a detailed analysis of a user action. We have simulated some Web attacks using MATLAB. Results extracted from this process helps in the recognition of majority of the attacks and helps in prevention from further exploitation.

Nanhay Singh, Achin Jain, Ram Shringar Raw, Rahul Raman

A Survey of Energy-Aware Routing Protocols and Mechanisms for Mobile Ad Hoc Networks

Mobile ad hoc networks are wireless networks with no fixed infrastructure. They contain mobile nodes that communicate over multi-hop wireless links. However, nodes in MANETs are powered by limited supply of battery energy. Therefore, efficient energy conservation is of prime importance for the overall functioning of an ad hoc network. Many energy-aware routing protocols have been developed, which aim at achieving energy efficiency via routing procedures. Various mechanisms have been developed, which maximize the lifetime of nodes and in overall the network lifetime. In this paper, we study recent mechanisms and protocols proposed which provide energy conservation in mobile ad hoc networks.

Charu Gandhi, Vivek Arya

Lexical Ontology-Based Computational Model to Find Semantic Similarity

Finding semantic similarity between two words or concepts has been considered as a challenging task in the field of natural language processing. Some lexical ontology-based approaches have been developed for this purpose. However, these approaches have been tested only for English language. Based on survey, there is no computational model for calculating semantic similarity between Hindi concepts. We cannot ignore Hindi language, because it is the third most spoken language of the world. In this paper, we present a computational model for calculating semantic similarity between words/concepts with the help of lexical ontology, which has been tested for Hindi language. Further, experiments have been carried out on a benchmark data set translated from English to Hindi. In our proposed computational model, Hindi WordNet has been used to get relational information between Hindi concepts. Existing popular semantic similarity approaches have been used to calculate semantic similarity. Miller and Charles’s benchmark data set was used to evaluate our proposed approach. We calculated the semantic similarity between 20 word pairs by using three different semantic similarity measures. Accuracy of the results was measured by calculating correlation coefficient between these similarity measures and human judgment. Our proposed model is useful in following ways. Firstly, it allows us to study and analyze the results of available semantic similarity methods on Hindi words. Secondly, it provides a general module along with algorithms, which can be tuned to develop similar modules for any other language.

Jagendra Singh, Aditi Sharan

Energy-Efficient Cluster-Based Aggregation Protocol for Heterogeneous Wireless Sensor Networks

The main goal of the data aggregation protocol is to gather and aggregate data in a wireless sensor network (WSN) in an energy-efficient manner. Prolonging the network lifetime depends on the efficient management of sensor nodes energy resource by minimizing the number of transmissions through aggregating similar data from nearby region. The clustering technique and aggregating the correlated data greatly minimize the energy consumed in collecting and disseminating the data. Reducing the energy consumption of the nodes to prolong the network lifetime is considered a critical challenge while designing protocol for WSN. In this work, we propose a novel Energy-Efficient Cluster-Based Aggregation Protocol (EECAP) for heterogeneous WSN. The main focus in this proposed work is to achieve energy efficiency by proper selection of nodes for cluster heads by considering residual energy of a node. We present experimental results by calculating the lifetime of network using various parameters such as average residual energy of nodes, number of dead nodes after each round, and death of first and last nodes in the network. The results are compared with Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol and Stable Election Protocol (SEP). The EECAP performs better in both homogeneous and heterogamous WSNs.

Prakashgoud R. Patil, Umakant P. Kulkarni

Digital Watermarking Based on Magic Square and Ridgelet Transform Techniques

This paper proposes two algorithms for embedding and extraction of the watermark into the cover image based on magic square and ridgelet transform techniques. Spread-spectrum communication systems use the spread sequences that have good correlation properties. Magic square technique is used as a spread-spectrum technique to spread the watermark. Ridgelet transform is the next-generation wavelets as it is effective through line singularities characteristic. Ridgelet transform generates sparse image representation where the most significant coefficient represents the most energetic direction of an image with straight edges. The experiments indicated that these algorithms enabled the cover images to have the good invisibility and made them robust to the general image compression attacks such as JPEG, GIF.

Rama Seshagiri Rao Channapragada, Munaga V. N. K. Prasad

Circle of Trust: One-Hop-Trust-Based Security Paradigm for Resource-Constraint MANET

Mobile Ad Hoc Networks (MANETs) suffer from acute crisis of resources in terms of battery power, computational ability, and so on. This together with its inherent salient nature makes it very difficult to design effective and efficient security solutions for the MANET. In this kind of dynamic environment, the nodes cannot rely on the conventional measures pertaining to the wired networks. Thus, approaches that depend on trust establishment and evaluation among the nodes are being considered as significant strides toward data protection, secure routing, and other secure network activities. Most of these models can be deemed as rather unscalable due to an excessive exhaustion of resources. In this paper, we limit the region of concern for each node to its one-hop locality and thereby considerably reduce the network overhead. This simple approach to security depending on the principle of mutual trust and prioritization of self-experience has been shown to be effective against a pool of common attacks and feasible with respect to the architectural demand of MANET.

K. M. Imtiaz-Ud-Din, Touhid Bhuiyan, Shamim Ripon

Design of a Biometric Security System Using Support Vector Machine Classifier

Biometric security systems are employed as authenticating devices in several firms and organizations possessing restricted zones within their campus. Biometric systems are also used as electronic attendance registers in various institutes and organizations. Pattern recognition is one of the main constituents of biometric systems. Support vector machine (SVM) is one of the state-of-the-art tools for linear and nonlinear pattern classification. In this paper, design of a SVM-based biometric security system using speech and face as inputs are discussed. Details about the performance of the proposed system for speech and face recognition are reported in this paper. The proposed biometric system as well as approaches can be extended for fingerprint and iris recognition too.

J. Manikandan, V. K. Agrawal, B. Venkataramani

Impact of Distance Measures on the Performance of Clustering Algorithms

Distance measure plays a vital role in clustering algorithms. Selecting the right distance measure for a given dataset is a challenging problem. In this paper, the effect of six distance measures on three clustering algorithms,

K

-means, single linkage, and average linkage is investigated. The distance measures include Euclidean, Euclidean squared, Manhattan, Mahalanobis, cosine similarity, and Pearson correlation. We describe all the distance measures pointing out their strengths and weaknesses. The performance of clustering algorithms on distance measures are evaluated on two artificial and four real-life datasets. Experimental results show the impact of distance measures when used for different datasets.

Vijay Kumar, Jitender Kumar Chhabra, Dinesh Kumar

Gender Identification Using Gait Biometrics

Soft biometrics-based gender classification is an interesting and a challenging area of neural networking and has potential application in visual surveillance as well as human–computer interaction. In this paper, we have investigated gender recognition from human gait in image sequence. For the above purpose, we have extracted silhouette of 15 males and 15 females from the database collected from CASIA Gait Database (Dataset B). The computer-vision-based gender classification is then carried out on the basis of standard deviation, center of mass, and height from head to toe. Experimental results demonstrate that the present gender recognition systems achieve superior recognition performance of 96.8 % on feed-forward back-propagation (FFBP) network. Data on different networks have also been trained and tested. The above study indicates that gait-based gender recognition is one of the best reliable biometric technologies that can be used to monitor people without their cooperation. Controlled environments such as banks, military installations, and even airports need to quickly detect threats and provide differing levels of access to different user groups.

Richa Shukla, Reenu Shukla, Anupam Shukla, Nirupama Tiwari

A Survey on Business Context

Today, business processes are modeled without taking into deeper consideration the context of the business environment where they will be later executed. As a consequence, initiation, management, and delayed adjustments of business processes are demanding, time-consuming, and very often impossible tasks. In this paper, we summarize crucial advantages and disadvantages of different context modeling approaches, and we define the context related to the scope of the business environment particularly. If we could assign a business context to a business process, we could undermine negative trends in today’s business, such as interoperability issues, inconsistencies, and heterogeneous interpretations of different business processes.

Danijel Novakovic, Christian Huemer

Enhanced Caching for Geocast Routing in Vehicular Ad Hoc Network

In vehicular ad hoc network (VANET), the major challenge for routing protocol is to find a route from the sender to the destination without any preconfigured information under constantly varying link circumstances. Topology-based routing is strictly avoided because of frequent changes in the topology. The approach of position-based routing relies only on geographical position information to deal with the problem of dynamic topology changes. Also, in most of the intelligent transport system (ITS) applications such as collision warning, advertising, alerts message, information needs to be disseminated in a predefined geographical region. That is why, position-based geocast routing is a suitable candidate for VANETs since position information is already available from navigation systems. In this paper, we have proposed a novel geocast routing protocol named “Enhanced Caching for Geocast routing (ECGR)” that takes the advantage of “Geocast in Vehicular Environments: Caching and Transmission Range Control for Improved Efficiency (CTRC)” and can be used in various ITS applications. The main contribution of our work is to introduce a novel coverage determination algorithm. This algorithm improves the caching methodology of CTRC and almost eliminates the packet loss due to high-speed movement of vehicles. Our new protocol also improves the throughput of the system by eliminating the range-forwarding approach of CTRC and using full radio transmission range of the vehicles to forward packets.

Omprakash Kaiwartya, Sushil Kumar

Cooperation Enforcement and Collaboration Inducement in Mobile Ad Hoc Networks

Limited onboard battery power is being a major constraint in operation of a mobile ad hoc network. If stricter provisions are not imposed, nodes tend to show selfish behavior in packet forwarding. At the same time, redemption procedure should be proclaimed so that the efficient network services can be ensured. Taking all these aspects in view, we propose a novel reputation establishing and activeness calculation method. Incorporating this method, we propose a mechanism based on game theory and reputation system which not only enforce cooperation but also induce collaboration among nodes. This mechanism is built on strategy which in turn is derived from trust and activity level of nodes. Genetic algorithm is employed for evolution of these strategies. Simulation result shows that this mechanism prevents selfish behavior and induces collaboration among nodes.

Ghyani Umesh Kumar Maurya, Sushil Kumar

Uncoupling of Mobile Cloud Computing Services: An Architectural Perspective

Mobile cloud computing, an extension of cloud computing paradigm for providing services of existing cloud facility to user application in mobile/portable devices, has been recently presented to overcome inherent limitations of these devices. Mobile cloud application, initiated in these devices, can be provided with support of existing cloud services. However, this paper observes that such activities in existing architecture result in tightly coupling between applications and services, which is highly undesirable in dynamic environments. A new architecture of mobile cloud computing, where interactions between mobile cloud applications and leased cloud services will be decoupled, is introduced in this paper. The paper also explains how uncoupling is accomplished in proposed architecture, i.e., how appropriate uncoupling medium can be suitably placed in this architecture.

Sohini De, Suddhasil De

Non-subsampled Contourlet Transform-Based Image Denoising in Ultrasound Images Using Elliptical Directional Windows and Block-Based Noise Estimation

Speckle noise reduction is an important preprocessing stage for ultrasound medical image processing. In this paper, a despeckling algorithm is proposed based on non-subsampled contourlet transform (NSCT). This transform has the property of high directionality, anisotropy, and translation invariance which can be controlled by non-subsampled filter banks. This paper aims to estimate the noise-free coefficients in the directional subband by applying minimum mean square estimate (MMSE). Signal variance is estimated from the elliptical directional window, and noise variance is estimated from block-based approach and is compared with the MAD approach. Experimental results of proposed method are compared with existing methods in terms of signal-to-noise ratio (SNR) and edge preservation index.

J. Jai Jaganath Babu, Gnanou Florence Sudha

Marathi Parts-of-Speech Tagger Using Supervised Learning

In this paper, we present a parts-of-speech tagger for inflectional and derivational morphologically rich language Marathi. Marathi is spoken by the native people of Maharashtra. The general approach used for the development of tagger is statistical-based hidden Markov model (HMM). We establish a methodology of parts-of-speech (POS) tagging for Marathi using HMM. The main concept of HMM is to calculate probabilities to determine which is the best sequence of tags that correspond to observation sequence of words. In this paper, we show the development of the tagger. Moreover, we have also shown the evaluation done.

Jyoti Singh, Nisheeth Joshi, Iti Mathur

Design and Evaluation of N-Module Reconfigurable Systems

Reconfigurable system is defined as the ability of a single system to get reconfigured to perform multiple applications. Reconfigurable systems are intended to reduce the hardware resources required for a system, power dissipation, and overall cost of the system. Static random access memory (SRAM)-based field-programmable gate arrays (FPGAs) are the best choice for designing reconfigurable systems. In this paper, different approaches of designing a reconfigurable system on Virtex-5 FPGA are discussed. In order to assess the significance of reconfigurable systems, three sets of N-module reconfigurable system are proposed and their performance results are reported in this paper.

Kunal Yogeshkumar Parikh, J. Manikandan, V. K. Agrawal

Genre-Based Classification of Song Using Perceptual Features

Genre-based classification of song is one of the major steps in the music retrieval system. In this work, we have presented perception-based song genre classification. Many of the past researchers have been using combination of perception-based features and other popular features such as zero-crossing, short-time energy. We have used three perceptual features that capture the ordering of sound in frequency scale (pitch-based features), the pace of a musical piece (tempo-based features), and repetition of a pattern in the audio signal. In order to capture the repeating pattern in a signal, we have used cooccurrence matrix. The experimental result using multilayer perceptron network as a classifier indicates the effectiveness of our proposed scheme.

Arijit Ghosal, Rudrasis Chakraborty, Bibhas Chandra Dhara, Sanjoy Kumar Saha

Novel Distributed Dual Beamforming for Randomly Distributed Sensor by Phase Tracking Using Bilateral Probability Function

Beamforming is being used in sensor networks to enhance the communication range. The beamforming is achieved by phase synchronization of participating sensors with channels phase constant. Therefore, the channel state information is required to from beam in the intended direction. The improvement in data rate due to dual beamforming has not been considered in the literature. In this paper, the beamforming for the situation of transmitting same set of data to different destinations and non-availability of CSI for both channels are considered. For the above scenario, two novel methods of channel phase tracking are proposed in this paper. First method uses two sets of single-beam array and second uses dual-beam array. The simultaneous tracking of two channels by two sets of array suffers from co-channel interference. Though the dual-beam array does not suffer from co-channel interference, the convergence problem exists and is solved by multiplicative combining of both feedbacks. It is observed from simulation results that about 65 % convergence is possible in both directions. By the property of dual-beam array, the data rate is also increased and is about 66 % minimum as compared to transmitting data in two time slots.

G. Vaikundam, G. F. Sudha

Electrical Network Modeling of Amino Acid String and Its Application in Cancer Cell Prediction

Since genomic data and the collective functions of amino acids directly dictate the phenotype of the cell thus more accurate interpretation of the cause of biological phenomena, the study of amino acid is an important area of research in cancer genomics. In the present case, an equivalent electrical network model of amino acid chain has been designed based on their physicochemical properties and the network behavior has been examined on several Homo sapiens cancer and non-cancer cells. The network response yields promising results in cancer prediction.

T. Roy, S. Das, S. Barman

Generation of AES-like 8-bit Random S-Box and Comparative Study on Randomness of Corresponding Ciphertexts with Other 8-bit AES S-Boxes

In Advanced Encryption Standard (

AES

), the standard S-Box is usually generated using a particular irreducible polynomial in GF(2

8

), though it can be generated by 29 others. The focus of the present paper is to show that it is possible to generate secured, AES-like S-Boxes randomly, using a PRNG like BBS and to compare its security with 7 other S-Boxes generated by 7 arbitrarily selected irreducible polynomials from the set. A comparative study has been made by testing the randomness of the ciphertexts generated by the S-Boxes using National Institute of Standards and Technology (

NIST

)

Test Suite

, which estimates a

P

-

value

to accept or reject the randomness of a bit sequence. It has been found that besides using modular arithmetic, a secured S-Box can be generated by using PRNGs. Moreover, the initial seed of BBS acts as a secondary key of AES.

S. Das

BCube-IP: BCube with IP Address Hierarchy

BCube and DCell propose data transfer using source routing for data center networks (DCNs). One potential problem with their approach is the use of two different address spaces, one for identifying end hosts and another for routing. In this paper, we propose a variant of BCube called BCube-IP based on an IP address hierarchy. BCube-IP overcomes the limitations of using two different address spaces in BCube. Along with the improved performance of source routing, we demonstrate the use of location-based routing for data transfer in BCube-IP.

A. R. Ashok Kumar, S. V. Rao, Diganta Goswami

Image Retrieval Using Fuzzy Color Histogram and Fuzzy String Matching: A Correlation-Based Scheme to Reduce the Semantic Gap

The research interest in the recent years has progressed to improve the performance of image retrieval (IR) systems by reducing the semantic gap between the low-level features and the high-level concept. In this paper, we proposed an approach to combine the two modalities in IR systems, i.e., content and text, while considering the semantics between the query image and the textual query provided by the user. For content matching, color feature is extracted and is represented using fuzzy color histogram (FCH). For text matching, fuzzy string matching with edit distance is used. Furthermore, we find the correlation between the query image and the textual query provided by the user to reduce the semantic gap. Using this correlation, we combined the two modalities with late fusion approach. The proposed approach is assessed on standard annotated database. Higher values of precision and recall show better performance of the proposed approach. Moreover, the use of correlation helps in reducing the semantic gap and providing good results through better ranking of the similar images.

Nidhi Goel, Priti Sehgal

A Multi-Objective Optimization Approach for Lifetime and Coverage Problem in Wireless Sensor Network

Coverage in wireless sensor networks has been an area of interest in recent years. Energy efficiency is also very crucial factor to extend the lifetime of wireless sensor network. In wireless sensor network, main source of energy consumption is sensing and communication. In this paper, we model the coverage problem as multi-objective optimization problem for maximizing the lifetime of network and minimizing the energy consumption while keeping the coverage as a quality of service measure. The network lifetime maximization can be achieved by maximizing the number of cover sets while keeping the desired coverage fraction. We use NSGA-II an evolutionary multi-objective approach to solve this problem successfully.

Anil Kumar Sagar, D. K. Lobiyal

Evaluation of English-to-Urdu Machine Translation

This paper is based on the Evaluation of English-to-Urdu Machine Translation. Evaluation measures the quality characteristic of the Machine Translation output and is based on two approaches: Human Evaluation and Automatic Evaluation. In this paper, we are mainly concentrating over Human Evaluation. Machine Translation is an emerging research area in which human beings play a very crucial role. Since language is so vast and because of its diverse nature, the accuracy is not maintained. To maintain this accuracy, Human Evaluation is taken as a base. Human Evaluation can be used with different parameters to judge the quality of sentences.

Vaishali Gupta, Nisheeth Joshi, Iti Mathur

A Novel Edge Detection Technique for Multi-Focus Images Using Image Fusion

Since last three decades, edge detection techniques have been drawing the attention of researchers due to its applications in 3D reconstruction, motion recognition, morphing, restoration, watermarking, image compression, and so on. Again, detecting edges in multi-focused images are one of the most challenging tasks. This paper deals with a novel edge detection approach for multi-focused images by means of complex wavelet-based image fusion. An illumination-invariant hyperbolic tangent filter (HBT) is applied followed by an adaptive thresholding to get the real edges. The shift invariance and directionally selective diagonal filtering as well as the ease of implementation of dual-tree complex wavelet transform (DT-CWT) ensure robust sub-band fusion. It helps in avoiding the ringing artifacts that are more pronounced in discrete wavelet transform (DWT). The fusion using DT-CWT also solves the problem of low contrast and blocking effects. To fulfill the symmetry of sub-sampling structure and bi-orthogonal property, a Q-shift dual-tree CWT has been implemented. In the adaptive thresholding technique, the threshold value varies smartly over the image. This helps to combat with a potent illumination gradient, shadowing, and multi-focus blurring of an image.

Priya Ranjan Muduli, Umesh Chandra Pati

Event Detection Refinement Using External Tags for Flickr Collections

Social media sites like Flickr, Twitter, YouTube, and Facebook are more and more flourishing with user-thrown pieces like videos, photographs, and multimedia content. Information about the real-world events are made available in social media. Problem arises when people search for multimedia content and extract information from the huge data. Usually, users are interested in gathering information about social events which may be a game, a conference, dance show, music concert, etc. Automatic event detection is one of the interesting subproblems. Our proposed methodology uses the context allied with social media content, the user-provided tags, and significant terms for each event from the Internet as features for event detection. Our proposed method groups the images to events simultaneously.

S. Sheba, B. Ramadoss, S. R. Balasundaram

Proposed Threshold Based Certificate Revocation in Mobile Ad Hoc Networks

Certification system plays an important role in mobile ad hoc networks (MANETs) to achieve network security. Handling the issue of certificate revocation in wired network is somewhat easy to compare the MANETs. In wired network, when the certificate of a malicious node get revoked, the certificate authorities add the information about the revoked node into certificate revocation lists (CRLs) or broadcast the CRL to each and every node present in the network or either store them on accessible repositories. Whereas the certificate revocation is a challenging task in MANETs and also this conventional method of certificate revocation is not useful for MANETs due to the absence of centralized repositories and trusted authorities. In this paper, we propose a threshold-based certificate revocation scheme for MANETs, which will revoke the certificate of malicious nodes as soon as it detects the first misbehavior of nodes. The proposed scheme also solves the improper certificate revocation, which can occur due to false accusations made by malicious node and also the problem of window of opportunity where revoked certificates are get assigned as a valid to new nodes.

Priti Swapnil Rathi, Parikshit N. Mahalle

“Bin SDR”: Effective Algorithm for Wireless Sensor–Actor Network

To propose and design new Bin SDR algorithm for wireless sensor–actor network. The proposed algorithm will improve the network lifetime of WSAN in terms of energy consumed, end-to end delay and throughput between sensor nodes. Performance is tested theoretically and practically. All the results are tested with previous algorithms.

M. E. Sanap, Rachana A. Satao

An Elliptic-Curve-Based Hierarchical Cluster Key Management in Wireless Sensor Network

In wireless sensor networks (WSN), because of the absence of physical protection and unattended deployment, the wireless connections are prone to different types of attacks. Hence, security is a measure concern in WSN. Moreover, the limited energy, memory, and computation capability of sensor nodes lead to difficulty in implementing security mechanisms effectively. In this paper, we proposed an elliptic-curve-based hierarchical cluster key management scheme (ECHCKM), which is very much secure, has better time complexity, and consumes reasonable amount of energy. The proposed work uses digital signature scheme and encryption–decryption mechanisms using elliptic curve cryptography (ECC). As it is using ECC, the same level of security can be achieved with smaller key size compared to other mechanisms. The result shows that the proposed work is faster than Hamed and Khamy’s work, and it also guards against different types of attacks. Energy consumption, number of messages exchanged, and key storage are three other aspects addressed in this work.

Srikanta Kumar Sahoo, Manmanth Narayan Sahoo

Probabilistic Approach-Based Congestion-Aware Swarm-Inspired Load-Balancing Multipath Data Routing in MANETs

The major causes of network congestion are the irrational allocation of network resources. Solution is to make more effectively use of the network resources by adjusting the traffic depending on choice may be probabilistic. In mobile ad hoc networks, congestion creates delay in transmission and also loss of a packet that causes wastage of time and energy. New challenges have come considering the major limitations, like node’s limited processing power, balance the load of network. To overcome the above problems, we use the ants swarm intelligence based on the concept of evolutionary cooperation to reinforce good quality routes. In this paper, we propose a scheme to identify the congestion among nodes. In this scheme, we develop a mathematical model considering the swarm-based ant intelligence, which provide an efficient congestion control routing mechanism using ant’s probabilistic transition rule.

Subhankar Joardar, Debasis Giri, Vandana Bhattacherjee

Integration of Eco-Friendly POF-Based Splitter and Optical Filter for Low-Cost WDM Network Solutions

The green technology wavelength division multiplexing based on polymer optical fiber (GT-WDM-POF) network solutions is presented in this paper. Green technology polymer optical fiber (GT-POF) splitter has been fabricated by environment-friendly handmade fusion technique, as an effective transmission media to split and recombine a number of different wavelengths. Two different wavelengths from ecologically friendly light emitting diode (LED) were fully utilized to transmit two different sources of systems; Ethernet connection and video transmission system. Red LED which in 650 nm wavelength capable to download and upload data through Ethernet cable while green LED in 520 nm wavelength transmits a video signal. Special filter has been placed between the splitter and receiver-end to ensure GT-WDM-POF network system can select and generate a single signal as desired. The material, fabrication method, system, and application approach in this study are based on the environment-friendly solution to reduce the power consumption and wastage without affecting the system performance. Our GT-POF splitter and GT-WDM-POF network solution proposed in this paper are the first reported up to this time.

Archana Rathore

Sensor Cloud: The Scalable Architecture for Future Generation Computing

In this paper, we propose a hybrid computational paradigm associated with cloud computing and wireless sensor network. Attempts have been made to devise a protocol that establishes communication between sensors and cloud resources. The objective is to increase the availability of real-time sensor information across the users. Further, it will be possible to share the information of various application-specific sensor network deployed at various locations. We suggest a virtualization for sensor versus cloud and cloud versus users. This will facilitate the user to visualize sensor network and cloud.

Subasish Mohapatra, Banshidhar Majhi, Srikanta Patnaik

Partial Fingerprint Matching Using Minutiae Subset

This paper proposes an algorithm based on correlating a minutiae subset for matching partial fingerprints. From minutiae-sparse partial fingerprints, we first construct a super-template by combining the minutiae points from these partial fingerprints. Then, the subtemplates constructed through randomized selection will be matched against the super-template. In the experiment, we improve the recognition performance from 0.32 to 0.12 % in equal error rate after applying the minutiae subsets algorithm. Also, the experimental results show that the proposed method provides a good trade-off between speed and accuracy. Since the minutia-based fingerprint representation is an ANSI–NIST standard, our approach has the advantage of being directly applicable to existing databases. We present results of testing on FVC2004s DB1 database.

S Asha, C Chellappan

Genetic Algorithm-Based Approach for Adequate Test Data Generation

Software testing is an important phase in software development. It involves two activities, test data generation and test execution. Test data generation is a NP-complete problem as we have to find a lot of test data to validate our system. Also those test data should be adequate in nature. In this paper, we present a method to generate test data automatically from initial test data and then testing these test data against the software under test (SUT) for adequacy criteria. First, we generate a test data set randomly. Then, we apply genetic algorithm to find a better test data set iteratively. We stop at the position where our test data set satisfies the stopping condition or it completed maximum iterations. We test the generated test data against the software to check its adequacy. The test data generated by our approach are more capable of finding the synchronization and loop faults. A case study is given to illustrate our approach.

Swagatika Swain, D. P. Mohapatra

ISA: An Intelligent Search Algorithm for Peer-to-Peer Networks

Finding an object in distributed peer-to-peer networks among a large volume of data needs the application of a proper intelligent search method. Designing such an intelligent search method will significantly affect the efficiency of the network taking into account sending a search query to nodes which have more probably stored the desired object. Machine learning techniques such as learning automaton can be used as an appropriate tool for this purpose. This paper tries to present a search method based on the learning automaton for the peer-to-peer networks, in which each node is selected according to values stored in its memory for sending the search queries rather than being selected randomly. The probable values are stored in tables, and they indicate the history of the node in previous searches for finding the desired object. For evaluation, simulation is used to demonstrate that the proposed algorithm outperforms K-random walk method which randomly sends the search queries to the nodes.

Mahdi Ghorbani, Mohammad Jooyan, Mostafa Safarpour

Modified Graph-Cut Algorithm with Adaptive Shape Prior

In this paper, a new method to adaptively apply shape prior in “modified graph-cut segmentation technique” has been proposed. The modified graph-cut technique has a better performance in terms of speed and accuracy when compared to the conventional graph-cut approach, introducing adaptive shape prior to this novel (modified) graph-cut approach yields a more efficient and effective result. Adaptive shape prior takes care of noise or object occlusion in a graph-cut segmentation process, it can be realized via a shape probability map, whose presence helps to showcase regions where the presence of a shape is required in an image. If employing adaptive shape prior to a conventional graph-cut technique yielded a better result than the classical approach, it is evident that applying adaptive shape prior to a modified graph-cut would yield a far better result.

Adonu Celestine, J. Dinesh Peter

Analysis on Optimization of Energy Consumption in Mobile Ad Hoc Networks

Mobile ad hoc network (MANET) is a decentralized and an infrastructure less network, which is the collection of wireless nodes that can exchange information dynamically among them. Minimizing the energy consumption is one among the essential requirement in MANET because of the limited battery power and difficulties in replacing the battery. This paper presents the survey on the optimization of energy consumption in MANET using various energy-efficient algorithms as well as the various metrics used to evaluate the energy efficiency in MANET. The various energy-efficient algorithms are analyzed, and their usages were discussed. The main objective of all these techniques is to minimize the energy consumed by the batteries and to maximize the life span of the network.

A. Karmel, C. Jayakumar

Accuracy of Atomic Transaction Scenario for Heterogeneous Distributed Column-Oriented Databases

As Internet usage is rapidly growing, e-commerce, e-business and corporate world revenue also increases. These areas not only require scalable and consistent databases but also require inter-database transaction support as well. In this paper, we present a scalable architecture along with a distributed algorithm to support atomic transactions across heterogeneous distributed column-oriented databases. Our methodology does not compromise on any assumption on the accuracy of failure territories. Hence, it reveals suited for a class of heterogeneous distributed systems. To achieve such a target, our architectural model exploits an innovative methodology for distributed atomic transactions. Here, we analyse this methodology between different site or node transactions by the analytical performance to make the protocol sufficient.

Ramesh Dharavath, Amit Kumar Jain, Chiranjeev Kumar, Vikas Kumar

Implantable CPW-fed Double-Crossed-Type Triangular Slot Antenna for ISM Band

In this paper, an implantable CPW-fed double-crossed-type triangular slot antenna mounted over human tissue is proposed for biomedical applications. The proposed antenna covers the ISM band of 2.45 GHz. The radiation parameters such as return loss, VSWR, Z-parameter are analyzed using the method of moments software (IE3D).The proposed antenna has substantial merits like miniaturization, lower return loss, and better impedance matching and high gain over other implanted antennas.

S. Ashok Kumar, T. Shanmuganantham

Training a Feed-Forward Neural Network Using Artificial Bee Colony with Back-Propagation Algorithm

Training a feed-forward neural network (FNN) is an optimization problem over continuous space. Back-propagation algorithm (BP) is the conventional and most popular gradient-based local search optimization technique. The major problem more often BP suffers is the poor generalization performance by getting stuck at local minima. The artificial bee colony (ABC) is one of the popular global optimization algorithms of swarm intelligence and is used to train the weights of the neural network, but it also suffers from slow convergence speed. Nevertheless, a hybrid algorithm by combining artificial bee colony and back-propagation (ABC-BP) is proposed to train the FNN. The results of the proposed algorithm are compared with hybrid real-coded genetic algorithms with back-propagation (GA-BP) to train the FNN using five benchmark datasets taken from the UCI machine learning repository. The simulation results indicate that ABC-BP hybrid algorithm gives promising results in terms of significantly improved convergence rate and classification rate. Hence, the proposed algorithm can be efficiently used for training the FNN.

Partha Pratim Sarangi, Abhimanyu Sahu, Madhumita Panda

Navigation of Autonomous Mobile Robot Using Adaptive Neuro-Fuzzy Controller

This paper presents a new sensor-based technique for autonomous mobile robot navigation in uncertain environments. In recent day, computational intelligent techniques, such as artificial neural network (ANN), fuzzy inference system (FIS), and adaptive neuro-fuzzy inference system (ANFIS), are mainly considered as applicable techniques from modeling point of view. ANFIS has taken the integrate performance of neural network and fuzzy inference system. In this architecture, different obstacle range data, such as front obstacle distance (FOD), left obstacle distance (LOD), right obstacle distance (ROD), and heading angle (HA) from each ultrasonic range finders, are given as input to the adaptive fuzzy controller and output from the controller is steering angle for the mobile robot. Simulation experiments using MATLAB demonstrate that the proposed ANFIS navigational controller can be effectively applied to navigate the mobile robot safely in unknown environments and reach to target objects.

Prases Kumar Mohanty, Dayal R. Parhi

Performance Estimation of Fuzzy Logic-Based Mobile Relay Nodes in Dense Multihop Cellular Networks

In relay-assisted cellular networks, relay nodes are usually deployed in a cellular cell without taking the information about the place where it needs to be deployed. So sometime it will ultimately leads to wastage of resources. In this paper, we have focused on this problem and proposed a fuzzy-based methodology to find the optimum quantity and requirement of these relay nodes in cellular networks. Proposed methodology tackles with two problems, which are “where to deploy,” “how many relay nodes to deploy.” In cellular cell, users residing near the base station get higher data services and users residing near the boundary of cellular cell get lower data services. So this introduces unfairness for far users in terms of data rate. Relay-assisted networks are introduced to solve this problem. As the number of users is increasing day by day, so it is necessary to provide adaptive positioning of relay nodes for getting optimal services within limited infrastructure cost. In other words, relay-assisted cellular networks should be adaptive for traffic offered by specific area. In this paper, we have taken three parameters that strongly affect the position of relay nodes. These three parameters include user density in specific area, amount of high-speed data requirement from a certain area on regular basis and signal strength to tackle with dead zones over the entire cellular cell.

Devendra Gurjar, Ajay Bhardwaj, Ashutosh Singh

Local Binary Pattern as a Texture Feature Descriptor in Object Tracking Algorithm

In this paper, we address a real-time object tracking algorithm considering local binary pattern (LBP) as a feature descriptor. In addition to texture feature, Ohta color features are included in the feature vector of the covariance tracking algorithm. The performance of the proposed algorithm is compared with some other competitive object tracking algorithms such as the RGB feature-based covariance method and color histogram method. The comparisons of the performance among these algorithms include detection rate and computational time. These methods have been applied to four different challenging situations, and the resulting experimental results show the robustness of the proposed technique against occlusion, camera motion, appearance, and change in illumination condition.

Prajna Parimita Dash, Dipti Patra, Sudhansu Kumar Mishra

A Sensor-Based Technique for Speed Invariant Human Gait Classification

Analyzing the human gait and obtaining the walking patterns can be an important biometric signature through which one could confirm an individual’s identity. In this paper, a nonvision-based approach using rotation sensor has been applied to acquire the oscillations from eight major joints of human body. These joints are, both the shoulders, elbows which constitute the upper body, and both hips and knees, which constitute the lower body. The gait patterns (from these eight oscillations) for male and female were obtained for different gait speeds varying from 3 to 5 km/h. The 3-km/h data was used as reference gait speed for training to classify the data at other gait speeds (4 and 5 km/h). This speed invariant human gait classification was done using a naïve Bayesian classifier along with applying Euclidean distance method and K-nearest neighbor technique. We have achieved encouraging classification results with those techniques.

Anup Nandy, Soumabha Bhowmick, Pavan Chakraborty, G. C. Nandi

High-Speed 100 Gbps/Channel DWDM System Design and Simulation

A high-speed 100 Gbps/channel dense wavelength-division multiplexing (DWDM) system for a distance of 450 km is proposed here. The input power applied is 10 dBm, with adjacent channel spacing of 400 GHz. Simple return-to-zero (RZ) data format is used for avoiding the system complexity. The system is designed and simulated using OptiSystem 9. The proposed system will be very much useful for future optical networks and also for long-haul optical communication applications.

Bijayananda Patnaik, P. K. Sahu

Performance Analysis of Contention-Based Ranging Mechanism for Idle-Mode Mobility

In this paper, the contention performance of message- and code-based initial ranging is investigated for two different scenarios or cases when idle mode is activated at the mobile stations (MSs). First scenario assumes a chosen set of MSs to follow mobility waypoint between the base stations (BSs), while other MSs remain stationary. Second scenario (worst case) presumes that all the MSs served by the BSs follow either mobility waypoint or random waypoint between the BSs. Under these scenarios, during periodic location update, MSs undergo contention-based network re-entry process. When more than one MSs undergo location update, it causes high contention amid MSs. Hence, this paper investigates the contention performance of message- and code-based ranging during periodic network re-entry. Simulations validate that the MSs in worst-case scenario with code ranging perform better in terms of idle-mode energy consumption (57.97 %) and percentage of time in idle mode (68.11 %) than message ranging under Rayleigh fading channel.

Rajesh Anbazhagan, Nakkeeran Rangaswamy

A Novel Approach to Face Detection Using Advanced Support Vector Machine

This paper presents a robust algorithm for face detection in still grayscale images. The structure and characteristics of the human nose are used to find possible face regions. Line detection filters are employed for this purpose; furthermore, among the several candidates detected in an image, a human face is identified by employing a trained support vector machine (SVM). The proposed method is robust to deal with illumination problems. The accuracy of this method is higher than 90 %, if testing for less than 10 faces in a simple background with adequate illumination. Owing to its simplicity, it can be transferred from a PC to an embedded device, making it a potential for customized and miniature systems.

Swastik Mohapatra, Asutosh Kar, Satyanarayan Dash, Sidhant Mohanty, Prasant Swain

Concept Based Clustering of Documents with Missing Semantic Information

Today, every new document added to the Web is augmented with semantic information (i.e., information about the content) which identifies the class of the document. The information is either added as keywords, or implicitly known from structural information like title, body text, or added as objects and their relationship (rich data format). But, the documents that enriched the Web five or ten years back do not contain semantic information. The objective of this paper is to cluster documents with missing semantic information. It is performed by adopting frequent term-based method exploiting the lexical and structural relation between keywords in the document. Similarity histogram clustering algorithm has been used to cluster the documents after deriving semantic information on concepts which identifies the class of the document. The results illustrate that the concept-based clustering performs well compared to statistical clustering k-means but suffers from proper subset selection of frequent terms.

E. Anupriya, N. Ch. S. N. Iyengar

Theoretical Validation of New Class Cohesion Metric Against Briand Properties

Class cohesion is an object-oriented software quality attribute and refers to the extent to which the members of a class are related. Software developers use class cohesion measures to assess the quality of their products and to guide the restructuring of poorly designed classes. Several class cohesion metrics are proposed in the literature, and a few of them are theoretically validated against the necessary properties of class cohesion. Metrics that violate class cohesion properties are not well defined, and their utility as indictors of the relatedness of class members is questionable. The purpose of this paper is to theoretically validate proposed class cohesion metrics using class cohesion properties. Results show that metrics differ considerably in satisfying the cohesion properties; some of them satisfy all properties, while others satisfy none.

Sandip Mal, Kumar Rajnish

RF-SEA-Based Feature Selection for Data Classification in Medical Domain

Dimensionality reduction is an essential problem in data analysis that has received a significant amount of attention from several disciplines. It includes two types of methods, i.e., feature extraction and feature selection. In this paper, we introduce a simple method for supervised feature selection for data classification tasks. The proposed hybrid feature selection mechanism (HFS), i.e., RF-SEA (ReliefF-Shapley ensemble analysis) which combines both filter and wrapper models for dimension reduction. In the first stage, we use the filter model to rank the features by the ReliefF(RF) between classes and then choose the highest relevant features to the classes with the help of the threshold. In the second stage, we use Shapley ensemble algorithm to evaluate the contribution of features to the classification task in the ranked feature subset and principal component analysis (PCA) is carried out as preprocessing step before both the steps. Experiments with several medical datasets proves that our proposed approach is capable of detecting completely irrelevant features and remove redundant features without significantly hurting the performance of the classification algorithm and also experimental results show obviously that the RF-SEA method can obtain better classification performance than singly Shapley-value-based or ReliefF (RF)-algorithm based method.

S. Sasikala, S. Appavu alias Balamurugan, S. Geetha

Optimizing Delay for MAC in Randomly Distributed Wireless Sensor Networks

Medium access control is a fundamental problem in wireless sensor network. It determines the performance of sensor network. The prime design goal of a medium access control method is to provide high throughput, reduce the delay, and minimize collision and energy consumption. In this paper, we present a general model for MAC protocol to reduce the delay in channel accessing in high density randomly distributed wireless sensor network. The proposed model is simulated using MATLAB. The simulation results show that the average delay for sensors with sufficient memory is lower than sensors without memory. Further, the proposed model is tested using S-MAC protocol, and the better result is obtained.

Ajay Sikandar, Sushil Kumar, Ghyani Umesh Kumar Maurya

An Ontology-Based Software Development Environment Using Upgraded Functionalities of Clojure

The development of a system which supports a multiprogramming paradigm is very challenging these days. The current programming languages provide only a single programming paradigm, due to which software programmers have to mix and match different programming languages for modeling a business process. Clojure is a programming language which has multiple programming techniques, such as functional, object-oriented, and concurrent. In the proposed system, the ontology paradigm is also added to Clojure with the help of metaprogramming. Thus, Clojure has become an efficient software development environment, with added features of ontology, using which one can represent the semantics of complex business processes.

Mary Alias, C. R. Rene Robin

Using a Cluster for Efficient Scalability Evaluation of Multithreaded and Event-Driven Web Servers

Scalability of a Web server is pertinently influenced by the architecture of Web server software system and/or Web server cluster. In this paper, a Web server cluster system is used to evaluate the vertical (scale-up) and horizontal (scale-out) scalability properties of multithreaded

Apache

and event-driven

μserver

Web server. Servers are scaled up by adding more processor cores to the server node and scaled out by adding more server nodes to the cluster. A number of experiments are thus performed on nine different cluster configurations. The relative capacity is measured against the number of active processor cores. The analysis of calculated relative capacity shows that scalability makes a transition from ‘near-linear’ to sub-linear for both Web servers as more processor cores and/or server nodes are added. While comparing the throughput, it was found that

μserver

exhibits slightly better performance than

Apache

on all nine server cluster configurations; although the increments in scalability achieved by means of vertical (scale-up) and horizontal (scale-out) scalability are almost equivalent.

Syed Mutahar Aaqib, Lalitsen Sharma

An Overview of Detection Techniques for Metamorphic Malware

Malicious code designed to destroy or steal information from victim’s computer intentionally is known as malware. Signature-based detection is used to detect malware in antiviruses, but malware writers are using encryption and obfuscation to deter these detections. Techniques used to evade detection are broadly classified as polymorphism and metamorphism. Polymorphic malwares encrypt virus payload and decryption engine with different encryption keys, but virus payload remains the same. Metamorphic malware uses code obfuscation techniques, which regenerate distinct variant of same malware family. Detection of metamorphic malware is a challenging task, in the presence of code obfuscation. This paper gives an overview on different metamorphic malware detection techniques and analyzes their strengths and weaknesses.

Pratiksha Natani, Deepti Vidyarthi

Temporal Forensics of MPEG Video Using Discrete Wavelet Transform and Support Vector Machine

Detection of video forgery is challenging, as finding traces of tampering is a complex task. Digital video forensic is used as an important tool to detect video forgery. Compression history of a video is analyzed to carry out temporal forensics of motion-compensated video such as

MPEG-2, MPEG-4, H.263, H.264

. Wang and Farid demonstrated that double MPEG compression can detect temporal fingerprints of video forgery, by visually inspecting

I

-frame prediction error sequence (PES) in the discrete Fourier transform (DFT) domain. This method is prone to human error and can be stressful while analyzing large number of videos. In order to overcome the drawback of this method, here we propose a novel technique to automatically detect video forgery using discrete wavelet transform (DWT) and support vector machine (SVM). A new statistical parameter “γ” related to the difference vector of first-level DWT coarse and detail sub-bands is used for the automatic detection of temporal attacks. Here, we use SVM, which is an effective and efficient classification tool to analyze/recognize data patterns. Experiment is conducted using various SVM kernel functions such as linear, polynomial, quadratic, radial basis function (RBF), and multilayer perceptron (MLP) to classify forged videos. The experimental results demonstrated that the proposed method can efficiently detect video forgery.

Sunil Jaiswal, Sunita Dhavale

Securing the Root Through SELinux

The protection of the root user is an important requirement for Linux systems. Recent developments in the area of cyber security have tackled this issue with the use of mandatory access control (MAC) mechanisms. Though MAC policies confine the root as per organizational requirements, yet security problems arise during the management of critical components. This gives rise to the need for incorporation of additional authentication mechanisms into the current scheme for the protection of security-sensitive components under the administration of root. We propose a scheme which uses MAC policies as a base for external device authentication of the root user.

Ananya Chatterjee, Arun Mishra

Automatic Ontology Extraction from Heterogeneous Documents for E-Learning Applications

In this paper, we present our approach to build domain ontology for e-learning purposes from heterogeneous documents by using the automatic extraction technique. Ontologies have been frequently employed in order to solve problems for shared distributed knowledge and the effective integration of information across many applications. The process of ontology building is a very lengthy and error-prone work. Therefore, a number of research studies to build ontologies semi-automatically from existing documents have been developed. This paper proposes a novel method which is used to build ontology, using the existing knowledge base of heterogeneous documents for complex application domains without the need of human intervention. This method improves the system performance and accuracy and reduces the time for the ontology building process from a collection of documents.

J. Jeslin Shanthamalar, C. R. Rene Robin

An Appraisal of Service-Based Virtual Networks and Virtualization Tools Paves the Way Toward Future Internet

Network virtualization has received important attention to overcome ossification due to the coexisting heterogeneous networks, networking applications, and protocol stack capability in the future Internet. Network virtualization plays a crucial role in solving such problems, and the service-oriented architecture (SOA) presents a promising approach to supporting network virtualization and facilitates service deployment for the future Internet. In this paper, we study the problem of key technologies like network service description and discovery for applying SOA and support network virtualization in the future Internet. This work appraises their virtualization tools and technologies as well as a wide array of past and state-of-the-art projects on network virtualization followed by a discussion on major challenges in virtual network environment.

Bhisham Sonkar, Devendra Chaphekar, Gupteshwar Gupta

Comparative Analysis and Research Issues in Classification Techniques for Intrusion Detection

Intrusion detection is one of the major research problems in network security. It is the process of monitoring and analyzing the events occurring in a computer system in order to detect different security violations. Mining approach can play a very important role in developing an intrusion detection system. In this paper, we present the comparison of different classification techniques to detect and classify intrusions into normal and abnormal behaviors. The algorithms used are J48, Naive Bayes, JRip, and OneR. We use the WEKA tool to evaluate these algorithms. The experiments and assessments of these methods are performed with NSL-KDD intrusion detection dataset. Our main aim was to show the comparison of the different classification algorithms and find out which algorithm will be most suitable for the intrusion detection. We also summarize the research challenges in classification process.

Himadri Chauhan, Vipin Kumar, Sumit Pundir, Emmanuel S. Pilli

An Apriori-Based Vertical Fragmentation Technique for Heterogeneous Distributed Database Transactions

Many distributed applications have become very useful in these days, such as reservation enquiry in railways and airways, status of postal, and Internet banking. All these are related to common database, through which they are connected to provide the result of user’s query. But, the purpose of the above-distributed application will fail if we do not work cleverly on distributed database design. If we design the database with some motive to improve the performance of the application, the tolerance of the distributed database increases and it can handle several queries concurrently. So, improving the performance of a database system is a challenging research area. Earlier approaches have suggested fragmentation solution based on the data access and frequency of the queries. Previously distributed design used attribute matrix approach to generate a new matrix upon which they applied to their clustering algorithm to find out different fragments, which involves little bit complicated computation. In this paper, we present a vertical fragmentation technique, which uses attribute usage matrix instead of attribute affinity matrix by applying

apriori

algorithm on usage matrix for partitioning the relations. Our result comprises the proposed technique, which reduces the overhead of complicated computations.

Ramesh Dharavath, Vikas Kumar, Chiranjeev Kumar, Amit Kumar

A Speech Recognition Technique Using MFCC with DWT in Isolated Hindi Words

Human speech recognition is indeed a challenging task through which several words spoken by different individuals could be analyzed and synthesized for man and machine interaction. It allows us to build up a framework for understanding the variability of different phonemes, which can be applied to identify an individual’s word. We create a Hindi speech repository of 5 persons where five words were spoken by 10 times for five different persons. An attempt had been taken to investigate the correctness of speech recognition technique of isolated 5 different Hindi words. We apply a discrete wavelet transforms for extracting the frequency coefficients of spatiotemporal speech signal with respect to time. The mel-second frequency cepstral coefficients (MFCC) considered as feature vector are obtained by applying DWT decomposition on accumulating speech signals. A feature extraction technique, principal component analysis (PCA), is applied on MFCC feature space for reducing the dimensionality of feature vector. We apply a minimum distance-based classifier for comparing an unknown test speech signal among all the training set of Hindi words. The recognition of isolated Hindi words is exploited in two conditions such as applying DWT and without DWT decomposition technique. In this paper, comparative analysis of various methods is carried out. Among all the methods, we found that MFCC with DWT decomposition provides higher recognition accuracy as compared to other techniques.

Neha Baranwal, Ganesh Jaiswal, G. C. Nandi

Mechanism for Preventing Registration Flooding Attack in SIP

The transition of voice communication from public switched telephone networks (PSTN) to IP network has offered numerous advantages, at the same time, myriad of security threats. Common among these threats is DoS attacks which was not possible in PSTN with closed architecture. This paper examines the denial-of-service (DoS) attacks on session initiation protocol (SIP) server using SIP particularly with REGISTER messages, focusing on the design of a framework to protect SIP server from such attacks. The proposed scheme introduces an intermediate server between SIP server and the User Agents, which is used to filter out attacks.

Bosco Sebastian, Paromita Choudhury, C. D. Jaidhar

A Combined Approach: Proactive and Reactive Failure Handling for Efficient Job Execution in Computational Grid

Resource management using failure handling is a very important and complex problem in grid computing environment. There may be a wide range of failures due to distributed and dynamic nature of grids. Particularly, the failure of resources affects job execution during runtime fatally. We propose a new framework called “combined proactive and reactive failure handling” for efficient job execution in computational grid. Proactive or reactive approach alone is unlikely to provide a reliable solution for fault management in grid. Hence, our system handles the resource failure before (proactive phase) and after allocating the job (reactive phase). In proactive phase, our system selects more suitable resources using reliability and the current status of the resource, which reduces the failure probabilities during job execution and hence it minimizes the number of rescheduling. In reactive phase, the system recovers the failed job with last checkpoint using suitable recovery method with minimum recovery time. Simulation results show that in order to improve the performance of both user and resource, it is important to consider the combined proactive and reactive approach for efficient job execution, rather than considering them separately.

P. Latchoumy, P. Sheik Abdul Khader

A Comparative Analysis of Keyword- and Semantic-Based Search Engines

Keyword-based Search engines are not able to provide relevant search result because they suffer from the fact that they do not know the meaning of the terms and expression used in the web pages and the relationship between them. This paper compares the semantic search performance of both keyword-based and semantic web-based search engines. Initially, two keyword-based search engines (Google and Yahoo) and three semantic search engines (Hakia, DuckDuckGo, and Bing) are selected to compare their search performance on the basis of precision ratio and how they handle natural language queries. Ten queries, from various topics was run on each search engine, the first twenty documents on each retrieval output was classified as being “relevant” or “nonrelevant”. Afterward, precision ratios were calculated for the first 20 document retrieved to evaluate performance of these search engines. Also, comparison of some popular semantic search engines is provided with their features.

Yogender Singh Negi, Suresh Kumar

Slicing MATLAB Simulink/Stateflow Models

MATLAB Simulink/Stateflow is widely used industrial tools for developing complex embedded systems. The resulting Simulink/Stateflow models consist of more than ten thousand blocks. To ensure the quality of such models, automated static analyses and slicing methods are necessary to cope up with this complexity. In this article, we present an approach for slicing Simulink models using dependence graphs. With slicing, the complexity of a model can be reduced to a given point of interest by removing unrelated model elements.

Adepu Sridhar, D. Srinivasulu

Link Mining Using Strength of Frequent Pattern of Interaction

This work addresses the important problem of discovery and analysis of social networks and link between frequent people from surveillance video where large amount of video data are collected routinely. A computer vision approach enabled to solve the problem of face recognition at lower level with the help of video data obtained from the fixed camera. Camera systems should have the capability to acquiring high-resolution face images of people under challenging conditions. We perform “opportunistic” face recognition on captured images. We present a novel frequent pattern-mining-based approach to solve this frequent association problem between social networks. Our approach is illustrated with promising results from a fully integrated camera system.

Seema Mishra, G. C. Nandi

Integration of HSV Color Histogram and LMEBP Joint Histogram for Multimedia Image Retrieval

In this paper, HSV color histogram and local maximum edge binary patterns (LMEBP) joint histogram are integrated for content-based image retrieval (CBIR). The local region of image is represented by LMEBP, which are evaluated by taking into consideration the magnitude of local difference between the center pixel and its neighbors. This LMEBP differs from the existing LBP in a manner that it extracts the information based on distribution of edges in an image. Further, the joint histogram is constructed between uniform two rotational invariant first three LMEBP patterns. The color feature is extracted by calculating the histogram on hue (

H

), saturation (

S

), and value (

V

) spaces. The experimentation has been carried out for proving the worth of our algorithm. It is further mentioned that the database considered for experiment is Corel-1K. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to previously available spatial and transform domain methods on their respective databases.

K. Prasanthi Jasmine, P. Rajesh Kumar

DBC Co-occurrence Matrix for Texture Image Indexing and Retrieval

In this paper, a new image indexing and retrieval algorithm using directional binary code (DBC) co-occurrence matrix is proposed. The exits DBC collect the directional edges, which are calculated by applying the first-order derivatives in 0º, 45º, 90º, and 135º directions. The feature vector length of DBC for a particular direction is 512, which are more for image retrieval. To avoid this problem, we collect the directional edges by excluding the center pixel and further applied the rotation invariant property. Finally, we calculated the co-occurrence matrix to form the feature vector. The retrieval results of the proposed method have been tested by conducting the experiment on Brodatz texture database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP, DBC, and other transform domain features.

K. Prasanthi Jasmine, P. Rajesh Kumar

M-Band and Rotated M-Band Dual-Tree Complex Wavelet Transform for Texture Image Retrieval

A new set of two-dimensional (2D) M-band dual-tree complex wavelet transform (M_band_DT_CWT) and rotated M_band_DT_CWT is designed to improve the texture retrieval performance. Unlike the standard dual-tree complex wavelet transform (DT_CWT), which gives a logarithmic frequency resolution, the M-band decomposition gives a mixture of logarithmic and linear frequency resolution. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we propose a novel approach for texture image retrieval using M_band_DT_CWT and rotated M_band_DT_CWT (M_band_DT_RCWT) by computing the energy, standard deviation, and their combination on each sub-band of the decomposed image. To check the retrieval performance, texture database of 1,856 textures is created from Brodatz album. Retrieval efficiency and accuracy using proposed features are found to be superior to other existing methods.

K. Prasanthi Jasmine, P. Rajesh Kumar

A Rank-Based Hybrid Algorithm for Scheduling Data- and Computation-Intensive Jobs in Grid Environments

Scheduling is one of the most important challenges in grid computing environments. Most existing scheduling algorithms in grids only focus on one type of grid job, which can be data intensive or computation intensive. However, merely considering one type of job in scheduling does not result in proper scheduling in the viewpoint of all system and sometimes causes wasting of resources on the other side. To address the problem of simultaneously considering both types of jobs, a rank-based hybrid scheduling (RBHS) algorithm is proposed in this paper. On the one hand, RBHS algorithm takes both data server and computational resource availability of the network into account, and on the other hand, considering the corresponding requirements of each job, it assigns a factor called

Z

to the job. Using the

Z

factor, the importance of two dimensions (being data or computation intensive) for each job is determined, and then, the job is scheduled to the available resources. Results obtained from simulating different scenarios in hypothetical grid environments show that the proposed algorithm outperforms other existing algorithms.

Mohsen Abdoli, Reza Entezari-Maleki, Ali Movaghar

Performance Evaluation of Video Communications Over 4G Network

With exponential increase in the volumes of video traffic in cellular networks, there is an increasing need for optimizing the quality of video delivery. Fourth-generation networks (Long-Term Evolution-Advanced or LTE-A) are being introduced in many countries worldwide, which allow a downlink speed of up to 1 Gbps and uplink of 100 Mbps over a single base station. In this paper, we characterize the performance of LTE-A physical layer in terms of transmitted video quality when the channel conditions and LTE settings are varied. We test the performance achieved as the channel quality is changed and HARQ features are enabled in physical layer. Blocking and blurring metrics were used to model image quality.

Gaurav Pande

Online Hybrid Model for Online Fraud Prevention and Detection

The current trend of online business enables better and faster service for users and makes it more profitable for merchants. On the other side, the Internet has become the most popular platform for fraudsters to commit online fraud with ease. Several solutions have been proposed in the literature to overcome these online frauds. But, complete and efficient way out from this problem is still in research. In this paper, we have proposed online hybrid model (OHM) which extensively prevents the possibilities of online fraud, and further, if any possibility is present, then it detects and fixes this possibility. The OHM approach is proposed exclusively for in-auction, non-delivery/merchandise and identity theft frauds. OHM further is applicable to several other online frauds. We have evaluated the performance of this model and have shown that OHM is a robust and highly effective online fraud prevention and detection approach.

Ankit Mundra, Nitin Rakesh

An Efficient Approach to Analyze Users’ Interest on Significant Web Access Patterns with Period Constraint

In recent times, Web usage mining has attracted significant attention due to its large number of applications. Existing Web usage mining approaches determine the significance of a Web access pattern by computing its support or utility, considering the entire span time of the database. The discovered frequent and high-utility patterns are treated as significant patterns and they reflect users’ interest based on support and utility constraints. However, in reality, users’ interest of a pattern is dynamic and varies from time to time. Because of this, there may be changes in web page access and its browsing time in a web access pattern at any point of time. Hence, it is essential and useful to analyze how changes in users’ interest affect the significance of the discovered patterns at any point of time in the database. With this idea, we propose an efficient algorithm to address the problems restricted in the existing approaches such as (1) discovery of Web access patterns with support and/or utility constraints (2) analyzing users’ interest of the discovered patterns with period constraint. The proposed algorithm uses a structure called web access pattern with support and utility (WAPSU) tree to represent the database in a compressed form and mines frequent and/or high-utility patterns from the tree efficiently.

M. Thilagu, R. Nadarajan

Efficient Privacy Preserving Distributed Association Rule Mining Protocol Based on Random Number

The rapid advances in recent years in the field of data mining have lead to concern about privacy. The main aim of privacy preserving data mining is to find the global mining results without leaking individual information. For satisfying the privacy constraints, algorithms based on cryptography techniques, data perturbation, information hiding,

k

-anonymization, secure scalar product, and secret sharing technique are used. In this paper, we propose secure protocol for association rule mining using vector dot product over vertically distributed data among multiple parties. Our method is secure, more efficient and requires less communication cost.

Reena Kharat, Madhuri Kumbhar, Preeti Bhamre

Directional Local Quinary Patterns: A New Feature Descriptor for Image Indexing and Retrieval

A novel evaluation algorithm using directional local quinary pattern (DLQP) for image retrieval (IR) was proposed here. The proposed method extracts the directional edge information between reference pixel and its surrounding neighbors by computing gray-level difference based on local quinary value (−2, −1, 0, 1, 2) instead of binary and ternary values in 0º, 45º, 90º, and 135º directions of an image which are absent in (Ojala, Pattern Recogn 29:51–59, 1996), (Takala, Block-based methods for image retrieval using local binary patterns, SCIA 2005, LNCS 3450:822–891, 2005), (Baochay, Pattern Recogn:2337–2344, 2010), (Marko, Pattern Recogn:42:425–436, 2009). The performance of the proposed method is tested on Corel 5000 (DB1) database. The performance yields better recognition rate when compared with standard local binary pattern (LBP), center-symmetric local binary pattern (CS-LBP), directional binary pattern (DBC), and other existing transform domain methods.

Santosh Kumar Vipparthi, S. K. Nagar

Data Mining Approach for Developing Various Models Based on Types of Attack and Feature Selection as Intrusion Detection Systems (IDS)

Information security is one of the important issues to protect data or information from unauthorized access. Classification techniques play very important role in information security to classify data as legitimate or normal data. Nowadays, network traffic includes large amount of irrelevant information that increases complexity of classifier and affect the classification result, so we need to develop robust model that can classify the data with high accuracy. In this paper, various types of classification techniques are applied on NSL-KDD data with Tenfold cross-validation technique in two different viewpoints. First, the classification techniques are applied for two class problem as binary classification (normal and attack), and second, it is applied for five class problem as multiclass classification. Empirical result shows that random forest technique outperforms in case of two class problem as well as five class problem on NSL-KDD data set. Due to large amount of redundant data, we have also applied feature selection techniques on random forest tree model which is best model as binary classifier as well as multiclass classifier. Model produces highest accuracy with 15 features in case of binary classification. Performance of the various models are also evaluated using other performance measures like true-positive rate (TPR), false-positive rate (FPR), precision, F-measure and receiver operating characteristic (ROC) curve and the results are found to be satisfactory.

H. S. Hota, Akhilesh Kumar Shrivas

Facial Expression Recognition Using Local Binary Patterns with Different Distance Measures

Facial expression recognition is a well-known activity in the domain of human–computer interaction and computer vision. In this work, we have applied face detection algorithm on the images to get the facial part only; then, we have used local binary pattern (LBP) operator to get the facial features. Finally, to match the test image with the different expressions, various distance measures which are Euclidian distance, taxicab distance, chessboard distance, Bray-Curtis distance and chi-square distance have been applied. The maximum facial expression recognition rate of Bray-Curtis distance measure reaches 92.85 % for person-dependent expression recognition, which is better than other distance measures. The experiments were performed on JAFFE which is a standard dataset, and result shows that the facial expression recognition with LBP and Bray-Curtis for person-dependent recognition is an effective method.

Sarika Jain, Sunny Bagga, Ramchand Hablani, Narendra Chaudhari, Sanjay Tanwani

Cluster-Based Routing for Optimal Communication in Port Logistics

Wireless communication-based logistics in ports is a promising alternative for increasing the capacity of handling goods and containers. Efficient communication among various departments helps in reducing the delay caused for the transportation. In this paper, a novel routing protocol is proposed for routing the information about containers to different departments in ports so as to decrease the delay in transportation and hence increase the efficiency of port management. In the proposed work, a wireless network of all the departments in ports is formed, which have sensors acting as routers, radio frequency identification (RFID)-tagged containers as end devices and personal area network (PAN) coordinator as trust center. RFID readers read the information from RFID-tagged containers and pass information to the routers which in turn communicate with other routers for performing the tasks. For efficient communication to happen, a cluster-based routing algorithm is proposed in this paper. The proposed algorithm is analyzed in a simulation environment using network simulator and is found to have good performance in terms of delay and hop count when compared to the existing hierarchical tree routing protocol.

J. Thejo Kishan, M. M. Manohara Pai, Radhika M. Pai

Secure Adaptive Traffic Lights System for VANETs

Adaptive traffic signal control system can reduce waiting time of vehicles at intersection by dynamically changing traffic signals based on density of vehicles present on roads at intersection. One of the approaches used to find density of vehicles on roads at intersection is proposed in [

1

] called as CDRIVE. In this, cluster-head vehicle updates density information as vehicles join the cluster and cluster is formed based on direction of the vehicle, it takes after intersection. However, a malicious vehicle can join the cluster by providing wrong direction information. Therefore, to ensure correct density estimation, the participating vehicles in density estimation should be authenticated ones; otherwise, density estimation estimated by cluster head may not reflect correct density and hence may result in incorrect changing of signals. In this work, we propose secure adaptive traffic lights system (SATS) protocol for VANETs which is an enhancement to CDRIVE by adding security to it. By this, the malicious vehicles are prevented to join the cluster. This is achieved by using cluster symmetric key for cluster joining and cluster communication. Cluster symmetric key is obtained from roadside unit (RSU) present near intersection. It has been seen that our proposed algorithm achieves security parameters such as authentication, privacy, source non-repudiation, and density estimation estimated by cluster-head vehicle is correct. Further, it is analyzed that the computational overhead by adding security features does not affect the functioning of CDRIVE.

Kishore Biradar, Radhika M. Pai, M. M. Manohara Pai, Joseph Mouzana

Analysis of Image Segmentation Techniques on Morphological and Clustering

Image segmentation is one of the important processing steps in image, video, and computer vision applications. Research has been done for finding many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another. And also, there is no general image segmentation algorithm that works for all images. In this paper, we reviewed different morphological and clustering techniques for image segmentation. Extensive evaluation methods of these techniques are presented. The advantages and disadvantages of these techniques are also analyzed and discussed in this paper.

M. Sivagami, T. Revathi

Performance Impact of TCP and UDP on the Mobility Models and Routing Protocols in MANET

The comparison of the performance of User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) is evaluated from different angles to revise and analyze the behavior of Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Destination-Sequenced Distance Vector (DSDV) routing protocols with Random Waypoint, Reference Point Group, and Manhattan mobility models. The results are obtained by simulating the network under different conditions in NS2 to analyze results, which has given the performance metrics, such as throughput, packet delivery ratio, and end-to-end delay using application traffic agent and data traffic. Our work indicates that TCP performs well for throughput in some mobility model and different routing protocols than UDP.

Sunil Kumar Singh, Rajesh Duvvuru, Jyoti Prakash Singh

A Survey on Video Segmentation

This paper presents a short survey on video segmentation. Due to the growth in multimedia information, an effective video indexing and video retrieval is necessary. This can be achieved when an effective video segmentation tools and algorithms are available. MPEG-compressed videos are mostly used by researchers for video segmentation.

Shot boundary detection

,

color histogram characteristics

,

DC-images

,

motion vector

and

motion compensation

,

threshold

-

based detector

, etc. are mostly used for video segmentation.

Dalton Meitei Thounaojam, Amit Trivedi, Kh. Manglem Singh, Sudipta Roy

A New Paradigm for Open Source Software Development

Any software development process requires different expertise, such as software engineering and project management. While software management works on the issues related to design, coding, testing, and maintenance, project management deals with planning, monitoring, risk management, etc. In software management, we try to work on the problem domain for a wider scope so that we can solve all related problems by applying a single model. Today, we have different models available for software development, and some are suited for open source software system. Agile methodology is one of those methods for the development of software project. This paper gives refined model based on Agile methodology for open source software systems (OSS).

Sushil Kumar, Ranjeet Ranjan, Amit Kumar Trivedi

A Real-Time Signature Verification Technology Using Clustering and Statistical Analysis

The aim of this paper is to present the detailed description of the working and application of ‘real-time signature verification’ using clustering and statistical analysis. The entitled system works faster and efficiently involving two processes, i.e., ‘preprocessing’ and ‘feature matching’ such that ‘noise reduction,’ ‘image normalization,’ and ‘skeletonization’ are carried out in preprocessing after which the input image is stored in the database. When the sample inputs new signature, ‘feature matching’ takes place, comprising brute force and sift algorithm, and matches the signature according to the cluster values present in the stored sample signature. The approach is fast efficient and authentic, which can be implemented in certain core areas. We have proposed integrated signature verification system, in which the brute force and sift algorithm are implemented in order to perform the matching. An integrated verification system not only provides a way to compare and match an online signature against an online signature but also improves the system performance in those cases where both static and dynamic features are available.

Joshane Kelsy, Rajib Sarkar

Component-Aspect Separation-Based Slicing of Aspect-Oriented Programs

In this paper, we have introduced a slicing technique for AOP based on the separation of aspect and non-aspect (component) sections and separately find their slices and finally combine these two to get the resultant slice. In this paper, we have used AspectJ to find the dynamic information of the program.

Jagannath Singh, Durga Prasad Mohapatra, Pabitra Mohan Khilar

Evaluation of Software Understandability Using Rough Sets

Understandability is one of the important characteristics of software quality, because it may influence the maintainability of the software. Cost and reuse of the software is also affected by understandability. In order to maintain the software, the programmers need to properly understand the source code. The understandability of the source code depends upon the psychological complexity of the software, and it requires cognitive abilities to understand the source code. The understandability of source code is getting affected by so many factors. In this paper, we have taken different factors in an integrated view. We have chosen rough set approach to calculate the understandability based on outlier detection. Generally, the outlier is having an abnormal behavior. Here, we have taken that outlier may be easily understandable or difficult to understand. Here, we have taken a few factors, which affect understandability, and bring forward an integrated view to determine understandability.

D. Srinivasulu, Adepu Sridhar, Durga Prasad Mohapatra

HCDLST: An Indexing Technique for Current and Recent-Past Sliding Window Spatio-Temporal Data

There are several applications such as wireless communication and geographical information system that use both spatial and temporal data. Since last decade, researchers are working on the current and limited past data for query processing that leads to the development of data stream management system (DSMS). Mostly, DSMSs are application specific. Earlier spatial and temporal data were managed by historical data management systems and did not have support for real-time data processing. In this paper, we propose an idea for indexing sliding window spatio-temporal data, which efficiently updates evolution of the objects’ positions and maintains the current and recent-past data for query processing. It also efficiently deletes the obsolete data that have no further use. We also propose a hash table and a doubly circular linked list-based technique which efficiently manages the index updates and time range queries for real-time data management.

Kuleshwar Sahu, Sangharatna J. Godboley, S. K. Jain

Solving Planar Graph Coloring Problem Using PSO with SPV Rule

In this paper, we have proposed a new particle swarm optimization algorithm named Enhanced PSO. The proposed strategy consists of the concept of the smallest position value rule. In this case, the solution having the smallest position value will be served first. The newly proposed PSO algorithm provides yet another solution for the planar graph coloring problem (GCP) using four colors to get smaller average iterations and higher correction coloring rate.

Vaibhav Bhardwaj, Sudhanshu Prakash Tiwari

Weather Prediction Using Error Minimization Algorithm on Feedforward Artificial Neural Network

Precise weather forecast is very important today, as day-today life is widely dependent on weather. The paper focuses on precise weather outlook using feedforward neural network (FFNN) and provides learning to the network using error correction method. This paper explores the idea of creating network by taking appropriate neurons in the hidden layer so as to obtain the best result. FFNN is frequently used to solve many problems from various disciplines such as image recognition, clustering, function approximation, biological application, and forecast/prediction.

Arti R. Naik, Pathan Mohd. Shafi, Shyamsunder P. Kosbatwar

Mining Association Rules Using Adaptive Particle Swarm Optimization

Association rule mining is a technique of data mining that is very widely used to deduce inferences from large databases. Particle swarm optimization is one of the several methods for mining association rules and has its own pitfalls. In this paper, an adaptive particle swarm optimization (APSO) that yields a finer solution by performing a diversified search over the entire search space is presented. The algorithmic parameters such as inertia weight and acceleration coefficients are adjusted dynamically to avoid possible local optima and to improve the convergence speed. The evolutionary state estimation (ESE) approach is adopted to identify the evolutionary states that the particle undergoes for each generation. The parameters are adjusted according to the estimated state in order to provide a better balance between global exploration and local exploitation. Additionally, an elitist learning strategy (ELS) is developed for the best particle to jump out of possible local optima. APSO provides a faster convergence mechanism and avoids premature convergence when compared to normal PSO.

K. Indira, S. Kanmani, V. Ashwini, B. Rangalakshmi, P. Divya Mary, M. Sumithra

Study of Framework of Mobile IP and MANET Integration

Mobile ad hoc network (MANET) is the collection of mobile nodes that can communicate in limited area without any infrastructure. Every ad hoc network acts as router, to search the destination address that is not connected directly. Limited coverage area of the MANET creates a problem for node to communicate globally and with the nodes that are away from the area of MANET. The feature of MANET is utilized properly when nodes are able to connect with other networks. Mobile IP is a standard protocol which supports the mobility in wireless Internet environment to keep connected mobile host roam. Mobile IP provisioning to MANET nodes can play an important role in order to utilize foreign network’s resource. Different frameworks of integration are suggested by researchers.

Devendra Chaphekar, Bhisham Sonkar, Gupteshwar Gupta

Delay Analysis of Various Links Using OPNET Simulator

The main objective of this paper is to design network model as similar as possible to the Ananya hotels network using OPNET to optimize the network delay in its services. The entire application is connected via 56 K lines. The employees and customers both are experiencing a high delay while accessing personal hotel reservation application. In such situations, simulation and modeling is a rapid and low-priced way of studying multiple scenarios and identifying the best possible configuration. OPNET IT Guru Simulator (academic edition) 9.1 was used to simulate the entire network. Thus, the effects of varying some network parameters such as various links and delay were observed in the delay performance metric. Several simulations in OPNET models have been performed in order to adapt the model to the real equipments and configurations. Several simulation graphs were obtained and used to analyze the network performance. We compared the results obtained via simulation with a live application scenario in the laboratory and adjusted the simulation configuration accordingly.

Pooja Singh, Chitosia Anamika, C. K. Jha, Anup Bhola

GenSeeK: A Novel Parallel Multiple Pattern Recognition Algorithm for DNA Sequences

DNA sequences are huge in size, and the genome databases are growing exponentially every year. One of the key elements in computational biology is genomic data. There are many real-time applications, such as DNA profiling and real-time crime investigation, which requires the biological subjects DNA sequences at real time. To retrieve this, data in real time require lot of computational power and resources. Throughput is one of the main bottleneck for applications such as DNA sequence searching or pattern matching. This paper presents a new DNA sequence multiple pattern recognition algorithm which computes on compressed space. This algorithm is efficient in terms of computational complexity and the amount of resources required during the computation in real time, the main reason for this behavior is that it does the computations on compressed sequences. This algorithm is implemented using index-based technique, and the sequential code is optimized. The proposed algorithm is mainly focused on achieving good comparison per character ratio as well as high throughput. The parallel version of the algorithm is implemented using multicore for achieving high throughput. The techniques used in development of this algorithm can be directly translated into huge DNA database search.

Kaliuday Balleda, D. Satyanvesh, P. K. Baruah

Improvement of PAPR in OFDM Systems Using SLM Technique and Digital Modulation Schemes

Orthogonal frequency division multiplexing (OFDM) is an attractive transmission technique for high data rate and reliable communications over multipath fading channels. One of the main drawbacks in OFDM is increase in peak-to-average power ratio (PAPR) of the signal, which causes degradation of bit error rate (BER) performance, nonlinear distortion effects, and need of high-power amplifiers. To reduce PAPR, there are several distortion- and distortionless-based schemes. In this paper, distortionless-based scheme, selective level mapping (SLM), is considered for the reduction of PAPR as it gives significant improvement in PAPR. For encoding, turbo codes are used as they are a class of high-performance forward error correction (FEC) codes. This paper consists of comparison of PAPR improvement for various digital modulation techniques.

Srinu Pyla, K. Padma Raju, N. BalaSubrahmanyam

Radioactive Pollution Monitoring Using Triangular Deployment in Wireless Sensor Network

The deployment mechanism in wireless sensor networks (WSNs) affects the coverage, connectivity, and lifetime of network. Depending upon the application, the sensor nodes can be deployed in a random or deterministic fashion. In this paper, we have proposed a comprehensive framework for radioactive pollution detection and monitoring using WSNs. In the proposed framework, nodes are deployed in a triangular grid form to cover whole region with minimum number of nodes. This framework includes proposals for the WSNs architecture and communication protocols to detect the radioactive radiation threats quickly. This paper also presents a new deterministically deployed cluster head selection algorithm (D

2

CH) for choosing cluster head; therefore, it enhances the lifetime of the network. Analysis and simulation results demonstrate the correctness and effectiveness of the proposed work.

Ankit Khare, Nitin Nitin

Securing Networks Using Situation-Based Firewall Policy Computations

In the last decade, the number of users connecting with Web has increased by multiple folds. As the number of users is increasing, naturally, the network’s computability load will also increase, which creates the challenges to network in terms of security threats and attacks. At this juncture, the network security plays key-role by using the firewall which is considered as one of most immediate requirements assuring the users about the network security. With expansion of the size of network, it is also required to improve the security road map implementation; otherwise, it would be tough for users to rely on network-based services. In this article, a novel idea has been proposed to cater the avoidance of threats and attack in organization’s network by expanding the scope of firewall policies. The chapter shows the importance of network design and firewall rules compatibility to make network free from threats and attacks.

Vijender Kumar Solanki, Kumar Pal Singh, M. Venkatesan, Sudhanshu Raghuwanshi

Color Image Quantization Scheme Using DBSCAN with K-Means Algorithm

Color image quantization (CIQ) is one of the important and well-accepted application areas in the field of data compression where a truly colored image is mainly represented by less number of selected significant color pixels. CIQ is performed in two major phases, i.e., color palette design and pixel mapping. The performance of any CIQ depends on the construction of a proper color palette, and this construction process is computationally expensive. In this paper, we have proposed a color palette design algorithm where we have incorporated two different types of clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and K-means. Initially, we have decomposed the color image into several non-overlapping blocks, and subsequently, we have employed DBSCAN on each block. This process has concerned for some sort of initial screening of representative color pixels. Further, we have obtained the desired size of color palette, employing K-means on the earlier selected representative color pixels. We have tested the proposed scheme on a set of benchmark test images and obtained the satisfactory results in terms of the visual quality of the reconstructed images. In case of designing the color palette, the proposed scheme requires less computational time compare with the conventional K-means algorithm.

Kumar Rahul, Rohit Agrawal, Arup Kumar Pal

A Novel Approach to Text Steganography Using Font Size of Invisible Space Characters in Microsoft Word Document

Steganography is the hidden way of communication, where one individual communicates with another through cover medium without giving any doubt about the secret communication to the intermediary. In this paper, we propose a novel approach to text steganography in Microsoft Word document. The idea behind this technique is that slight variation in font size of invisible character space from other characters is not reflected in the document and in the required disk size for the document. Thus, steganography can be intelligently achieved. The embedding rate is very high in this technique, which increases with the increase in blank space character between words. The secret data hiding and revealing technique is presented.

Susmita Mahato, Dilip Kumar Yadav, Danish Ali Khan

Personalizing News Documents Using Modified Page Rank Algorithms

World Wide Web is a global village and a rich source of information. The number of users accessing Websites is increasing day by day. For effective and efficient handling, personalization coupled with recommendation techniques provides personalized contents at the disposal of users. News personalization is an area of Web personalization that deals with the extraction of interesting news documents from various news service providers. While surfing the Websites, user interactions with Websites are recorded in Web usage file. These Web log information are useful for constructing user profile which in turn acts as a rich source for news personalization. Since the growth of World Wide Web has resulted in a large amount of data that is now in general freely available for user access, the different types of data have to be managed and organized such that they can be accessed by different users efficiently. Therefore, the application of page rank techniques on the Web is now the focus of an increasing number of researchers. Several page ranking algorithms are used to re-rank the Web pages in the Web. However, these algorithms have to be modified such that they better suit the demands of the user. In this paper, we have proposed modified topic-sensitive and trust-based page rank algorithms for better ordering of news documents to the users.

S. Akhilan, S. R. Balasundaram

Understanding Query Vulnerabilities for Various SQL Injection Techniques

SQL injections pose a lot of risk to e-commerce sites as well as Web pages that are database driven. There are various kinds of SQL injections. For each type, there are different ways of interpreting the errors and cracking the query for exploiting the Web site. This paper discusses how to understand the errors for each type of injection. This will help us find exhaustive solutions to every kind of injection strategy. This paper also suggests few remedies to defend and prevent such attacks.

U. Chandrasekhar, Digvijay Singh

Effective Ontology Alignment: An Approach for Resolving the Ontology Heterogeneity Problem for Semantic Information Retrieval

For more precise and better information retrieval on the semantic web, where meaningful but sometimes irrelevant information is retrieved; using ontology mapping, there could be an improvement in getting more relevant information. Ontology mapping is the process of finding the similarity between the concepts in a heterogeneous environment. This paper presents an approach for ontology mapping. Two different ontologies of a particular domain are considered, and the concepts that are similar to each other from both of the ontology, are retrieved, i.e., Ontology alignment. Also, the similarity is being calculated if the two concepts are not matched even by expanding the term. The conceptual analysis of the technique shows that the results obtained through the proposed approach provides the semantic terms of the same domain.

Ankita Kandpal, R. H. Goudar, Rashmi Chauhan, Shalini Garg, Kajal Joshi

Classification Technique for Improving User Access on Web Log Data

In the present era, Internet is playing a significant role in our everyday life; therefore, it is very thorny to survive without it. Web log file that keeps track of the users’ access on net, if mined, can provide us precious information about the surfers. Similarly, the rapid growth of data mining applications has shown the necessity for machine learning algorithms to be applied to large-scale data. In this paper, we are using the naïve Bayesian (NB) classification technique using Weka for identifying the frequent access pattern. The main objective of this paper is to categorize browsing behavior of the user based on their position. This paper performs experiment and classifies the user access behavior from the large databases, which could result in increasing the efficiency and effectiveness of the system by reducing the browsing time of the user or results in fast retrieval of information from the system.

Bina Kotiyal, Ankit Kumar, Bhaskar Pant, R. H. Goudar

A Review on Methods for Query Personalization

Partitioning a set of data (or objects) into a set of meaningful subclasses called clusters is clustering. This paper explains time line of effective algorithms, which personalizes the query using some clustering methods. The methods discussed are as follows: biclique clustering method, concept-based clustering method, personalized concept-based clustering method, content-based query clustering method, k-means clustering method for OLAP queries, personalization based on user preferences, rank-based Web search personalization and agent-based Web search personalization. Personalization has been taken into account in many fields such as data mining, Web search, making the users’ preferences available to them effectively. We can understand how the clustering methods improved toward user personalization. Adding few more attributes such as users, queries, and concepts shall improve personalization of search queries.

Shivangi Sharma, Prachi Gupta

A Wideband Compact Microstrip Antenna for DCS/PCS/WLAN Applications

A novel compact slit-loaded broadband microstrip patch antenna is presented for various wireless applications. The proposed antenna has been fabricated and the impedance bandwidth and radiation pattern are measured. The compact antenna size is 40 × 60 × 1.6 mm which can be integrated conveniently with other RF circuits. The simulated and measured antenna characteristics along with radiation pattern and gain are presented in the paper. It is stated that the proposed designed antenna can completely cover the required bandwidths of digital communication system (DCS 1.71–1.88 GHz), personal communication system (PCS 1.85–1.88 GHz) and IEEE 802.11 b/g (2.4–2.485 GHz) with satisfactory radiation characteristics. The experimental result shows that the proposed antenna presents a bandwidth 60.25 % covering the range of 1.431–2.665 GHz with the maximum radiation efficiency 90 %.

Vinod Kumar Singh, Zakir Ali, Ashutosh Kumar Singh, Shahanaz Ayub

Analysis of Type-2 Fuzzy Systems for WSN: A Survey

Lifetime enhancement has always been a crucial issue in the field of wireless sensor networks (WSNs). In the recent past, a number of issues related to WSNs have been addressed through fuzzy-based algorithms. Fuzzy-based algorithms have shown their potential in data fusion, clustering, and energy-efficient routing. In this survey, we have presented the performances of type-2 fuzzy-based algorithms for solving the various issues of WSNs.

Megha Sharma, Ashutosh Kumar Singh

Fusion of Entropy-Based Color Space Selection and Statistical Color Features for Ripeness Classification of Guavas

This paper presents a novel and non-destructive approach to the color appearance characterization and classification of guava ripeness. Guava ripeness is modeled using extracted statistical color features and support vector machines (SVM) are adopted to perform the classification task. Also, the role of different color spaces in entropy calculation for estimating resolving power in the characterization of ripeness levels of guava is investigated. This approach is applied to 270 guava images from three types of ripeness, i.e., under ripe, ripe, and over ripe. Entropy-based color space selection is carried out using nonparametric

Kruskal–Wallis

procedure. Statistical curve-fitting color features are derived from the histogram of selected color space. Experimental results show that in spite of the complexity and high variability in color appearance of guava, the modeling of guava images with statistical color curve-fitting parameters allows the capture of differentiating color features between the guava ripeness levels. The classification accuracy using six

normpdf

curve-fitting parameters (mean, sigma, mean_LB, mean_UB, sigma_LB, sigma_UB) is 90.37 % for testing data.

Suchitra Khoje, S. K. Bodhe

Optimal Positioning of Base Station in Wireless Sensor Networks: A Survey

Location of the base station plays an important role in a wireless sensor networks because the position of the base station governs the lifetime of wireless sensor networks. The main motive of this paper is to give the basic idea of the all possible approaches to find the optimal position of base station having different aspects, i.e., some approaches are suitable for low-density networks, and some can effectively work for highly deployed networks. Approximately all the recent works consider the energy consumption factor of the sensor node as well as base station to propose new algorithm based on WSN.

Prerna Meena, Devendra Gurjar, Ashutosh Kumar Singh, Shekhar Verma

Testing and Implementation Process in Automation of a University

Automation represents one of the major trends of the twentieth century. In many cases, automation has provided the desire benefits and has extended functionality well beyond existing human capabilities. In this era of modern education, automation of higher education institutes is required for the quality education and for the fast administrative work at the institution. Here, we mainly focus on the university. During installation of automation software for any particular department of the university, we have to test it before and during the implementation process. Testing verifies the errors and bugs of the software. Testing effectiveness can be achieved by the use of different testing methods and strategies. Genetic algorithms (GAs) have been successfully applied in the area of software testing. Aim of this paper is to propose GA-based technique to test the software product for the automation of a university.

Vaibhav Sharma, Jyoti Singh, A. S. Zadgaonkar

Performance of Spectral Efficiency and Blocking Probability Using Distributed Dynamic Channel Allocation

Modern cellular mobile communication systems are characterized by a high degree of capacity. Efficient management of the wireless channels by effective channel allocation algorithms is crucial for the performance of any cellular system. To provide a better channel usage performance, allocation of channels to individual cells can be done either by a centralized method or by a distributed method. A modified Distributed Dynamic Channel Allocation (DDCA) algorithm is proposed with three-cell cluster model is to increase the spectral efficiency for an increase in channels and blocking probability reduces for increase in offered load.

Y. S. V. Raman, S. Sri Gowri, B. Prabhakara Rao

An Effective Content-Based Image Retrieval Using Color, Texture and Shape Feature

Content based image retrieval is an active area of research for more than a decade. In this paper, we have proposed an effective way of extracting color, texture, and shape features from image and combine them in a way that ensures higher retrieval efficiency. For extraction of color features, images are divided into non-overlapping blocks, and dominant color of each block is determined using

k

-means algorithm. For extracting gray-level co-occurrence matrix (GLCM)-based texture features, each pixel in the image is replaced by average value of its neighborhood pixels. These average values are further quantized into 16 levels, for better and efficient representation of texture in the database. Finally, Fourier descriptors are extracted from the segmented image and are used to represent the shape of objects, as they have better representation capability and robust to noise, than other shape descriptors. The feature vector formed by combining all these is used to represent image in the database. We have tested our approach on wang dataset. Experimental results show that the present scheme has achieved higher retrieval accuracy on representative color image databases.

Milind V. Lande, Praveen Bhanodiya, Pritesh Jain

Underwater Communication with IDMA Scheme

Underwater communication finds numerous application including oceanographic data collection, pollution, monitoring, offshore exploration, disaster prevention, assisted navigation, tactical, surveillance, and mine reconnaissance. The enabling technology for these applications is acoustic wireless networking. A major challenge for the deployment of UW-ASN is the development of a multiple-access technique and modulation technique tailored for the underwater environment. Typical frequency ranges from 1 Hz to 1 MHz resulting in low data rates. The ever-increasing demand of short- and long-distance communication is leading to further research in the field of underwater communication. In this paper, performance analysis has been done for underwater acoustic (UWA) communication with IDMA scheme using BPSK modulation techniques. fThe simulated system is subjected to additive white Gaussian noise (AWGN) in the channel in uncoded environment using random interleavers in MATLAB environment.

Tanuja Pande, Kulbhushan Gupta, M. Shukla, Prachi Tripathi, Ashutosh Singh

M-ARY PSK Modulation Technique for IDMA Scheme

There are a number of factors that enter into the choice of a modulation scheme for use in a wireless application. Performance of a cellular system is dependent on the efficiency of the modulation scheme in use. The performance of M-ary PSK modulations scheme for IDMA technique-based wireless communication system on data bits transmission over additive white Gaussian noise (AWGN) channel are analyzed in terms of bit error probability (BER) as a function of SNR. Based on the results obtained in this study, MPSK are showing better performance for lower modulation order whereas these are inferior with higher M. It will use MatLab 7.9 for simulation and evaluation of BER for IDMA system models, so that the system can go for more suitable phase shift keying (PSK) modulation technique to suit the channel quality. Thus, it can deliver the optimum and efficient data rate to mobile terminal.

Pratibha Verma, Sanjiv Mishra, M. Shukla, Ashutosh Singh

A Novel Approach for Eye Gaze and Tilt Estimation

Conventional iris biometric system, in its localization module, detects iris boundary through integrodifferential intensity change among concentric circles drawn on pupil center. The pupil is detected as the darkest region in human eye. However, this crude approach performs well for constrained iris images captured in near infrared (NIR) spectrum, but may fail for low-quality color eye images captured in visible spectrum (VS). This paper proposes a novel approach that estimates eye gaze and tilt without precise knowledge of pupil location. Rather the proposed technique color-segments the sclera region to find few low-cost nodal points within eye region. The proposed method localizes sclera through a novel color segmentation method applied in YC

b

C

r

color space. In the next phase, during content retrieval process of the sclera, typically six nodal points are extracted whose relative positions define the gaze and tilt of the eye of the subject. The Proposed method has been experimented on 100 randomly chosen images from UBIRISv2 unconstrained VS iris database. The experiment yielded 96 % accuracy in proper sclera-localization and extracting the six low-cost nodal points.

Sambit Bakshi, Rahul Raman, Pankaj K. Sa

Enhanced Single-Pass Algorithm for Efficient Indexing Using Hashing in Map Reduce Paradigm

Today the data in the world has reached beyond the sky limits and with the advancement of data-intensive applications there is a need to collect, analyze, process, and retrieve enormous datasets efficiently. This large datasets are popularly termed as “BIG DATA” which was coined by Roger Magoulas, director of market research at O’Reilly Media. To deal with these large datasets different approaches by various data scientists around the world grew and as a result scalable effectuations of information retrieval (IR) operations have become a necessity. MapReduce [

1

,

2

] programming model (Apache’s Hadoop [

3

], an open source implementation of MapReduce) has emerged as a very effective tool to handle large volume of data in distributed environment. Here with our work we are extending the technique of indexing large data using Single-Pass with hash implementation over MapReduce framework.

Piyush Kumar Sinha, Prashant Joshi, Pooja Pundir, Manisha Negi, R. H. Goudar

Data Structures for IP Lookups, A Comparative Analysis with Scalability to IPV6

IP forwarding is a process where the routing table needs to be looked up to find which output port a packet should be forwarded through. To search the routing table efficiently we need to represent the routing table by a good data structure which is the major challenge for research in IP forwarding. Three major approaches have been proposed over the years namely Trie-based algorithms (which use linear search on prefix values), binary search on prefix values and binary search on prefix lengths. This paper compares the performance of these approaches and discusses their scalability to IPV6.

Soumyadeep Ghosh, Oaindrila Das, Arindam Majumdar

Multiobjective Clustering Using Support Vector Machine: Application to Microarray Cancer Data

Microarray technology facilitates the analysis and interpretation of the microarray expression profile of a huge amount of genes across different experimental conditions or tissue samples simultaneously. In this paper, a clustering technique is implemented on microarray cancer data using multiobjective genetic algorithm with non-dominated sorting GA (MOGA-NSGA-II). The two objective functions for this multiobjective clustering are optimization of cluster compactness as well as separation. The multiobjective technique is first used to produce a set of non-dominated solutions. We find high-confidence points for these non-dominated set using a fuzzy voting technique. SVM classifier is further trained by the selected training points which have high-confidence value. Finally, the remaining points are classified by trained SVM classifier. The performance of the proposed multiobjective clustering method has been compared to other microarray clustering algorithms for two publicly available cancer data sets, viz. ovarian and colon cancer data to establish its superiority.

Anita Bai

Prediction of Warning Level in Aircraft Accidents using Classification Techniques: An Empirical Study

This paper focuses on evaluation of risk and safety in civil aviation industry. There is a huge amount of knowledge and data aggregation in Aviation Company. This paper aims to study the performance of different classification algorithms on accident reports of the Federal Aviation Administration (FAA) Accident/incident Data System database, contains number of accident data records for all categories of aviation between the years of 1950 to 2012. The classification algorithms such as DT, KNN, SVM, NN, and NB are used to predict the warning level of the component as the class attribute. We have explored the use of different classification techniques on aviation components data. The rules construct are proved in terms of their accuracy and these results are seen to be very meaningful. This study also proved that the NB classifiers will performance better than other classifiers on airline data. This work may be useful for Aviation Company to make better prediction.

A. B. Arockia Christopher, S. Appavu alias Balamurugan

Fuzzy TOPSIS Method Applied for Ranking of Teacher in Higher Education

Higher education institutions including technical institutions are facing problems for providing quality education. A survey reported that there are crises of good and qualified teachers in higher education system. To detain quality teachers and to reject others an exposited opinion is required, but due to conflicting criteria on them it is very difficult to decide rank of quality Teachers, hence a suitable techniques for selecting and ranking of existing teachers is required. Multicriteria decision making (MCDM) is a technique which can be used in this scenario. Fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) is a MCDM method in which various criteria can be fuzzified using fuzzy logic to deal the problem in precise manner. In this research work, FTOPSIS method is applied on the sample data collected from different higher education intuitions and weights are obtained with the help of another MCDM method called analytic hierarchy process (AHP) method. A small sample of 10 teachers and 5 criteria are considered to demonstrate FTOPSIS method to find out ranking among them, obtained results are verified with those collected from various experts and found to be satisfactory.

H. S. Hota, L. K. Sharma, S. Pavani

Performance Analysis of Transformation Methods in Multi-Label Classification

The association between the instance query example and the class labels are mutually exclusive in traditional single label examples. But in real life applications like musical categorization, functional genomics, text, and document categorization, one instance query example may belong to a subset of class labels, i.e., mutually inclusive. Because of the highly correlated label structure, the traditional single label classification (SLC) algorithms will not be sufficient. We need effective algorithms to work with multiple labels. The multi-label classification (MLC) algorithms are classified into two ways: (1) transform the multi-label problem into single label binary problem and (2) make the existing single label algorithms to cope with multi-label problems. In this paper, we present theoretical concepts behind multi-label classification (MLC) and also we did a comparative analysis of transformation methods with two tools MEKA and MULAN over different application domains. 6 Example based, 6 Label based, and 4 Ranking-based measures are used to evaluate the efficacy of the different transformation methods.

P. K. A. Chitra, S. Appavu Alias Balamurugan

Predictive Data Mining Techniques for Forecasting Tamil Nadu Electricity Board (TNEB) Load Demand: An Empirical Study

In the smart grid environment, the electricity consumers will act in response to electricity demand. And the construction of large power generation plants such as thermal, nuclear, atomic, and wind power stations and plants takes many years. In any field, demand has to be forecasted for smooth running of all works in the society. So, the government must determine the electricity needs well in advance. Many statistical and mathematical methods have been developed to forecast the energy demand in the market environment since restructuring of the electricity power industry. In this paper, predictive data mining models named support vector machine, multilayer perceptron, linear regression, and Gaussian processes are analyzed using real-time electricity data with data mining tool Weka for forecasting the electricity load demand. Many accuracy parameters such as mean absolute error, root-mean-squared error, root relative squared error, and relative absolute error were analyzed to find the best of those four models for forecasting the electricity load demand.

T. M. Usha, S. Appavu Alias Balamurugan

Novel Approach for Finding Patterns in Product-Based Enhancement Using Labeling Technique

The main problem nowadays is that even if the meetings go perfectly to formulate and sell a product, there are chances that the product may fail. So how to understand the product capacity and its future scope depends upon the team of individual managers such as finance, sales, technical, accounts. We need to make a method so that anonymous entries of all the managers can be taken into consideration about the product and then a central administrator or the chief manager can then log on to the system where entries have taken place and then understand the comments and requirements of the various sub-ordinates for the product. So for that concern, we have defined a method or series of steps that can help the company executives understand that where the need is and how to bring about the changes in the product. This particular mechanism can also be used for software fault prediction and other activities such as trip planning and meeting schedule. For that, we can create a tree-based structure mechanism and define the sessions so that each session can accommodate the individual’s opinion, and after that session, if rectification is performed, we can take the opinion in the second session itself so that we can understand the product more precisely. But the main disadvantage is that the commands such as propose, acknowledgment, and negative response do not have a fixed structure or a notion that can differentiate them from one another. In the proposed work, fixed nodal values or labels can be assigned so that the labels association can be formulated and confidence values can be instantiated.

Hemant Palivela, H. K. Yogish, N. Shalini, S. N. Raghavendra

Optimal Path and Best-Effort Delivery in Wireless Sensor Networks

We propose an intelligent position-based VIP routing algorithm in wireless sensor networks (WSNs). This paper addresses the problem of finding an optimal path with guaranteed delivery of packets and low overhead. In this position-based routing, the physical position of the nodes is known through the use of GPS or some other types of positioning services. Each node knows its own position, one-hop neighbor position, and destination node position. Because of limited transmission range and limited energy, intermediate nodes take into participation and contribute to packet forwarding. This algorithm used a greedy forwarding approach to forward the packets toward destination and a clustering approach to save energy. Network area is divided into fixed size of zones, and a VIP node selects a best forwarding node in the next zone by using a directional routing (DIR) greedy approach. The objective is to deal with issues of network design and provide an optimal path without unnecessary transmission. We compare VIP routing algorithm with an existing routing algorithm. Both theoretical analysis and simulation result show an excellent performance under low node mobility.

Vipin Kumar, Sushil Kumar

Spanning-Tree-Based Position-Based Routing in WSNs

This paper presents the spanning-tree-based position-based routing (STBPR) algorithm that finds the best path from source to destination. In wireless sensor networks (WSNs), sensor nodes are densely deployed in the area and intermediate nodes collaborate in forwarding the packets toward the destination. In flooding, there are unnecessary transmissions that increase the overhead on the overall network. The proposed routing algorithm uses the position of the nodes, and based on the position, it uses directional routing (DIR) greedy routing approach that restricts the flooding. The main objective is to design an efficient algorithm that finds a shortest path, by avoiding the unnecessary transmissions. In this routing algorithm, we are using the concept of maximum spanning tree (MST) in each zone. The area is divided into a number of zones and finds a MST in each zone that reduces the number of links in a zone.

Vipin Kumar, Sushil Kumar

Feature Extraction and Classification of Microarray Cancer Data Using Intelligent Techniques

Feature extraction plays an important role to improve the performance of the classifier. Microarray consists of a large amount of features with small number of samples. In this paper, we address the dimension reduction of DNA features in which relevant features are extracted among thousands of irrelevant ones through dimensionality reduction. This enhances the speed and accuracy of the classifiers. Principal component analysis (PCA) is a very powerful statistical technique to represent the d-dimensional data in a lower-dimensional space without any significant loss of information. The aim is to project the original I-dimensional space into an

$$ I_{0} $$

I

0

-dimensional linear subspace, where

$$ I > I_{0} $$

I

>

I

0

such that the variance in the data is maximally explained within the smaller

$$ I_{0} $$

I

0

-dimensional space to solve the curse of dimensionality problem (where number of features are large with less samples). Support vector machine (SVM) is implemented, and its performance is measured in terms of predictive accuracy, specificity, and sensitivity. First, we implement PCA for significant feature extraction and then SVM to train the reduced feature set. In the second part, we attempt to validate our results on two public data sets (ovarian and colon).

Anita Bai, Anima Pradhan

Survey of Route Choice Models in Transportation Networks

Finding a shortest and convenient route by taking into account all the relevant factors is not an easy task. The accuracy and efficiency of the route depend on precision with which all the variables in the problem are incorporated. A lot of research has been undertaken on developing such route guidance system, and several procedures have been developed for this purpose. In the present paper, a survey of different route choice models has been discussed.

Madhavi Sharma, Jitendra Kumar Gupta, Archana Lala

Secure Routing Technique in MANET: A Review

We are here to introduce the different approach that prevents security threat in MANET routing protocol. In recent year, secure routing protocol has been extensively studied. A mobile ad hoc network (MANET) is an infrastructure-less network where each node acts as an administrator for establishing end-to-end connection and a host for the source or destination. Security in MANET routing protocol is a mechanism to transfer data packet safely. Mostly, the routing protocols are based on cryptography scheme, security association, key distribution, authentication, and so on. The security research area is still open as many of the provided solutions are designed keeping a limited size scenario and limited kind of attacks and vulnerabilities. MANET is the future network because it is practically versatile, easy to use, inexpensive and can instantly update and reconfigure itself. In this paper, we have highlighted some of the typical vulnerabilities that are caused by characteristics of mobile ad hoc networks such as dynamic topology, limited resources (e.g., bandwidth, power), and lack of central management’s points.

Aartika Chandrakar, Rajib Sarkar

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