Proceedings of the Second International Conference on Computer and Communication Technologies

This paper represents a new technique to recognize human gait using gait flow image (GFI) and extension neural network (ENN). GFI is a gait period-based technique, based on optical flow. ENN combines the extension theory and neural networks. So a novel ENN-based gait recognition method is proposed, which outperforms all existing methods. All the study is done on, CASIA-A database, which includes 20 persons. The results derived using ENN are compared with support vector machines (SVM) and nearest neighbor (NN) classifiers. ENN proved to have 98 % accuracy and lesser iterations as compared to other traditional methods.

Network management is crucial to implement large wireless sensor network. The network may contain hundreds to thousands of node. Furthermore, it is imperative to know the connectivity and location of the nodes to envision the framework of the network. Compared to GPS and other localization techniques, the virtual coordinate (VC) system is an affordable and efficient solution. In previous studies, the hop count from all anchor nodes was used to define the VC of a node, but the studies do not address the chance of having the same virtual coordinates. This paper introduces a distance-based virtual coordinate system (D-VCS) that uses physical distance along the shortest path from all anchor nodes to obtain distinctive virtual coordinates (VC). In the current study, we tested and analyzed the proposed D-VCS and compared it with the hop-based VCS mentioned in a previous study. We introduced a metric for connectivity error which quantitatively analyzed the precision of the introduced system. After completing the study, we observed that the TPM obtained from D-VCS shows lesser error compared to hop-based VCS. Furthermore, there was a mean deviation in connectivity error of approximately 23 % between both systems.

Health care facilities in rural India are in a state of utter indigence. Over three-fifths of those who live in rural areas have to travel more than five km to reach a hospital and health care services are becoming out of reach for economically challenged communities in India. Since rural communities experience about 22.9 % of deaths due to heart disease [

1

], there is a need to improve remote ECG monitoring devices to cater to the needs of rural India. The existing wearable ECG devices experience several issues in accurately detecting the type of heart disease someone has due to the presence of motion artifacts. Hence, even though wearable devices are finding their place in today’s health care systems, the above-mentioned issues discourage a doctor in depending upon it. So to enhance the existing wearable ECG device, we designed a context aware system to collect the body movement activity (BMA). In this paper, an innovative BMA classifier has been designed to classify the physical activities of user from the real-time data received from a context aware device. The test results of the BMA classifier integrated with the complete system show that the algorithm developed in this work is capable of classifying the user activity such as walking, jogging, sitting, standing, climbing upstairs, coming downstairs, and lying down, with an accuracy of 96.66 %.

Current e-Learning solutions enable viewing and interaction with participants who are geographically distant. However, these systems often are prone to delay, jitter, and packet loss owing to network fluctuation. The dynamic nature of bandwidth congestion requires us to dynamically adapt the quality of video streaming. In this paper, we attempt to alter the quality of streaming on the fly, by monitoring packet loss due to network congestion. To achieve this, we setup a simplistic classroom architecture consisting of one local and two remote classrooms with the camera feeds of the participants displayed in each other’s location. The camera feeds are flagged in accordance to their predetermined priority level, and the flagging is altered in accordance to the dynamic interactions in the classroom. In the advent of network congestion, the packet loss of the recipient is monitored, and the capture resolutions of all the other feeds are altered dynamically, to make allowance for the prominent streams to remain unaffected. The effectiveness of the system is measured using participant feedback. Results indicate that the participants do not feel any perceivable change in the quality of the multimedia content presented to them.

Optimum resource allocation problem of cooperative wireless communication is discussed in this paper. Looking at the variety in services offered in wireless network and time-varying nature of the channel, it is need of the time to go for dynamic resource allocation which not only improves the performance but also enhances reliability, coverage, and user satisfaction in cooperative network. Efficiency and fairness are two different perspectives of resource allocation which are very difficult to achieve at the same time. We have presented three approaches for performing trade-off between efficiency and fairness in systematic manner. First approach is based on converting the data rate achieved by the user in terms of utility and then maximizes the total utility of all the users. It is shown that a properly design utility function is able to result any desired trade-off. Second approach is based on putting the restrictions of minimum resources that must be assigned to the user and maximum resources that can be given to the user. The resources that assign to any user vary between this limit. The minimum and maximum are determined by the parameters

A

(0 <

A

≤ 1) and

B

(>1). In the third approach,

E

-

F

function is presented which is able to provide trade-off based upon the values of

E

(1 <

E

≤ 2) and

F

(0 ≤

F

≤ 1). In all the three cases, the total resources available for distribution are kept constant. Fairness is measured by Jain’s fairness Index, and loss of efficiency is measured in terms of price of fairness.

Numerous schemes for data aggregation are done by encryption for privacy of data, and thus, homomorphism has been reviewed and designed on the wireless sensor network (WSN) to improve security. WSN is an emerging and challenging technology in the area of the research because of its vital scope with low power energy associated with it. Several application sensors collect information or data from different sensed nodes and will be aggregated to a host computer or a base station. Data aggregation happens in a network between intermediate nodes which causes to reduce energy consumption on these nodes to make efficient network performance. The algorithm used in existing aggregation system does not provide sufficient security functionality and is more vulnerable to several attacks. Furthermore, some compromised nodes also inject false data which leads to a falsify data aggregation which is forwarded to base station. The enlargement of WSNs is thrombosed due the limited energy constraints. The main focus of this paper is done mainly on enhancing energy in WSN by working on the enhancement of some routing protocol. This paper also explains on eliminating of data vulnerability, security aggregation, and false data injecting attack by presenting different robust surveys and protocols. In this paper, initial discussions are done on WSN with a detailed overview of sensor, and reviews are made on providing security of wireless sensor network. The proposed novel idea is used in aggregating data during exchanging of messages and to preserve its data privacy and also to overcome problem in network construction (NC) and its security. The NC is done by clustering its topology, and assigning heads to each cluster with huge communication range, also security pattern generation is created for protecting the data information.

Generally, Intrusion detection system (IDS) is installed in industrial environment for protecting network that works based on signature, where they are not capable of detecting most unidentified attacks. The detection of undefined attack and intrusion is not more helpful to identify the several kinds of attack, where intrusion-based attack has become a challenging task to detect intruder on network. A skilled attacker can obtain a sensible information and data from the system after knowing the weakness. Distributed denial of service (DDoS) is a major thread over the security and most enlarging thread in recent days. There are so many types of Denial of Service (DoS) such as Teardrop, Smurf, Ping of Death, and Clone attack. The aim of the cyber defense system is to detect the main cause of the several counter attacks on the enterprise network. On the way to fix these issues, we are proposing a novel idea that relies on honey pot technique and packet data analysis which are trained by the sample of malware after using the Intrusion detection technique in both ways separately as Network and Anomaly intrusion detection system. Some approaches are not being easily implemented in the network of real enterprises, because of practicability training system which is trained by the sample of malware or deep analysis of packet inspection or depends on the host-based technique that requires a big capacity for storage over the enterprise. The honey pots are one of the most successful techniques to collect the sample of malware for the purpose of analysis and identification of attacks. Honey pot is a novel technology which consists of massive energy and possibilities in the field of security. It helps reading the behavior of the attack and attacker information.

A new parametrization of the small-size silicon cluster is proposed in this paper to improve the quality of predicted energy value by potential energy function in force-field modeling. ASBO-based concept has applied to evolve the parameters under different circumstances and cluster structure. The performance of new parameters is compared with the other well-established parameters in stillinger–weber energy function and its variants. Under known and unknown environment, effects of higher dimension in energy predicting capability are also analyzed. A significant improvement is observed in predicting the small cluster energy value with a proposed solution compared to values obtained with existing parameters. PSO with dynamic weight (DWPSO) is also applied to analyze the comparative capability of ASBO in solution exploration and convergence characteristics, and there is a remarkable improvement observed with ASBO-based solution.

Internet has gained a wide popularity in recent years. The people’s interaction and sharing of their views about a particular subject and providing feedback to them have increased rapidly. The feedbacks are mainly in the form of numeric rating and free text words. The numeric rating can be easily processed but to process free text words is an important task. In this paper, different approaches are reviewed and based on that a self-learning feedback analysis system is proposed, which analyzes the feedback and provides an accurate result that helps in decision making.

The fetal ECG is a useful tool in the assessment of condition of fetus heart before and during labor time and also contains more information than sonography. Early detection of fetal heart defect helps the selection of appropriate treatment before and during pregnancy. FECG signal obtained by non-invasive method is affected from the background noise and MECG interference as FECG signal is weak relative to MECG signal and competing noise. This interference produced by MECG signal and other artifacts can be canceled by application of adaptive filters using LMS and RLS algorithms. In this paper, we have purposed an adaptive filter algorithm which has shown better results than standard LMS algorithm for the detection of Fetus ECG Signal.

This work is based on strategies of query optimization of object-oriented database and subsequent techniques for implementing those strategies. First, an extension of direct navigation method optimization, this proposed methodology can handle multiple attributes of objects through direct navigation method and second is an extension of cache-based optimization for complex queries. Proposed algorithms work on complex queries which involved number of objects and attributes. The experimental result shows that proposed algorithms are capable of handling multiple attributes and gives more optimized result.

In this paper, cylindrical aluminium structures are tested for examination of buckling phenomenon. Testing of the material structures are accomplished by comparison made on the basis of linearity and non-linearity. The computation of various parameters viz. displacement, external work done, energy loss, and total energy consumed are being performed to evaluate the performance and optimal operating conditions of the aluminium structure. The results are visualized with ABAQUS CAE software. It is observed that non-linear buckling is found superior in terms of structural deformations and energy loss as compared to linear buckling. This non-linear buckling has proved to be an eminent technique to reduce the adverse effects of the buckling phenomenon prominent in aluminium structures.

Cloud computing is a great design of technology ever made which provides services, applications, and resources through a network. Cloud computing gives the opportunity to use a very large amount of resources on demand. There are many cloud infrastructures as a service (IaaS) frameworks that exist for users, developers, and administrators and they have to make a decision about which environment is best suited for them. In this paper, we focus to analyze and evaluate the performance of the open-source OpenStack for IaaS cloud computing, and give the comparison between OpenStack and VMware. We outline some of our initial findings by providing such a testbed on OpenStack. The experimental results showed the advantages of OpenStack solution in cloud computing where infrastructure is provided as a service.

In 2D shape recognition and retrieval approach using shape feature extraction, statistical shape analysis methods such as PCA, ICA and NMF are commonplace, and these methods using subspace approach, have not been adequately investigated for recognition and retrieval of 2D shapes. The main hurdle in achieving higher recognition efficiency seems to be the shape sensitivity. In this paper we suggest, subspace method approach. The main idea is to use modular technique to improve the recognition and retrieval efficiency. Normally in the earlier methods proposed so far, a complete image is considered in training and matching process, in modular method approach partial image is used for training and matching the 2D images. The recognition and retrieval process is carried out in two phase, in the first phase uses the ridgelet transform applied. The second phase PCA is used for dimensionality reduction and to extract the effective features. For recognition and retrieval a study was conducted by using seventeen different distance measure technique. The training and testing process is conducted using leave-one-out strategy. The retrieval process is carried out by considering standard test “bull eyes” score. The proposed method is tested on the standard dataset MPEG-7. The experiment results of Subspace ridgelet PCA are compared with Subspace PCA method.

There are multiple option pricing methodologies and yet they still occupy an important place in academic research. Pricing an option is regarded as one of the most challenging questions in finance. Though the Black Scholes model is more popular to price an option, the binomial model is also very effective. However, the binomial model for pricing an option is computationally challenging. Therefore, an algorithmic implementation of the binomial option pricing algorithm is inefficient. This paper proposes using the Vector class template of C++ to make the binomial pricing model more efficient.

The notion of intuitionistic fuzzy sets (IFSs) helps to an observer to incorporate the hesitancy value in the degree of membership function. The hesitancy factor comes from his basic knowledge, past experience, situation, depth of the standard terminologies, and many more characteristics; so the degree of membership function involved uncertainty under IFSs. Hence, the uncertainty included with an observer in the choice of membership grade under IFSs needs to be further improved by a moderator parameter to make the uncertain behavior more accurate. This can be done by introducing the concept of moderator intuitionistic fuzzy set (MIFS) as a generalization of IFSs. Furthermore, some properties and operators are defined over MIFSs similar to IFSs. Finally, a real-life problem of medical diagnosis is considered to apply the proposed approach effectively.

Data mining is the process of discovering knowledge from the vast data sources. In Data mining, classification and clustering are the two broad branches of study. In Clustering,

K

-means algorithm is one of the bench mark algorithms used for numerous applications. The popularity of

k

-means algorithm is due to its efficient and low usage of memory. One of the short comings of

k

-means algorithm is degradation of performance, when applied to imbalance distributed data. The results of cluster size generated by

k

-means are relatively uniform, in spite of the input data with non-uniform cluster sizes, which is defined as “uniform effect” in the literature. This paper proposes several novel algorithms to solve the above said problem. The proposed algorithms are compared with each other. The experiments conducted with the proposed algorithm on eleven UCI datasets with evaluation metrics show that proposed algorithms are effective to solve the problem of “uniform effect.”

Sexual harassment of women traveling on railway carriages is a widespread problem. The predominant method used to address this problem is for women to travel in groups, utilizing Safety-in-Numbers, to hinder and discourage attacks against them. However, when the number of women in a railway carriage is low, there is no safety system in place to detect the imminent danger and proactively alert security personnel. In this work, we consider a system that keeps track of the number of passengers in a railway carriage using a wireless sensor network and automatically notifies security personnel when the number of passengers goes below a certain threshold. Here, we consider different scenarios and evaluate if our solution approach will work in the different cases considered. We also evaluated different techniques for automatically counting the number of people aboard a railway carriage. Our initial experimental results show that we are able to estimate the number of people in a room (considered in lieu of a railway carriage) with a high degree of accuracy using the background subtraction method. We hope that the proposed architecture in concert with the people counting technique will be able to significantly improve the safety of women traveling in railway carriages.

Feedbacks in colleges and universities are often taken by means of online polls, OMR sheets, and so on. These methods require Internet access and are machine dependent. But feedbacks through SMS can be more efficient due to its flexibility and ease of usage. However, reliability of these text messages is a matter of concern in terms of accuracy, so we introduce the concept of text preprocessing techniques which includes tokenization, parts of speech (POS), sentence split, lemmatization, gender identification, true case, named entity recognition (NER), parse, conference graph, regular expression NER, and sentiment analysis to improve more accurate results and giving importance even to insignificant details in the text. Our experimental analysis on sentiment trees and ranking of feedbacks produces exact polarities to an extent. By this way, we can determine better feedback results that can be supplied to the faculty to enhance their teaching process.

Seizures in epileptic patients affect tremendously their daily life in terms of accidents during driving a vehicle, swimming, using stairs, etc. Automatic seizure detectors are used to detect seizure as early as possible so that an alarm can be given to patient or their family for using anti-epileptic drugs (AEDs). In this paper, an algorithm has been proposed for automatic seizure onset detection by analysis of electroencephalogram (EEG) signals. The method is based on few wavelet transform-based features and two statistical features without wavelet decomposition for improving the performance of detector. The mean, energy, and entropy were calculated on different wavelet decomposed subbands, and mean absolute deviation and interquartile range were calculated on raw signal. Classification between seizure and nonseizure types of EEG signals was done successfully by linear classifier. The algorithm was applied to CHB-MIT EEG dataset for seizure onset detection and achieved 100 % sensitivity with mean latency of 1.9 s.

MapReduce is now a significant parallel processing model for large-scale data-intensive applications using clusters with commodity hardware. Scheduling of jobs and tasks, and identification of TaskTrackers which are slow in Hadoop clusters are the focus research in the recent years. MapReduce performance is currently limited by its default scheduler, which does not adapt well in heterogeneous environments. In this paper, we propose a scheduling method to identify the TaskTrackers which are running slowly in

map

and

reduce

phases of the MapReduce framework in a heterogeneous Hadoop cluster. The proposed method is integrated with the MapReduce default scheduling algorithm. The performance of this method is compared with the unmodified MapReduce default scheduler. We observe that the proposed approach shows improvements in performance to the default scheduler in the heterogeneous environments. Performance improvement was observed as the overall job execution times for different workloads from HiBench benchmark suite were reduced.

This paper discusses about the design and development of a semi-autonomous robot that is intended for harvesting coconuts. The robot is composed of two parts: the climbing mechanism and the harvesting mechanism. The harvesting mechanism consists of a robotic arm with three degrees of freedom and has a circular saw as the end effector. It also consists of a camera that is fixed on the wrist of the robotic arm, with which the video of the tree top is sent to the ground station in real time. For this a Linux-based ARM board is used. The climbing mechanism consists of a circular chassis. Three wheels that are powered by DC motors with sufficient torque are set at equal distance around the coconut tree. There are another three motors that ensure sufficient tightening of the climbing mechanism to the coconut tree. The climbing part also has a special mechanism to rotate around the coconut tree, so that the robotic arm gets full coverage around the coconut tree. The entire movement of the robot is controlled from the ground station, using a remote controller.

This paper includes a methodical way to suppress impulse noise in digital images using the concept of popular Hermite interpolation. Hermite interpolator is a spline where each piece is a third-degree polynomial specified in Hermite form by its values and first derivatives at the end points of the corresponding domain interval. Our proposed technique is mostly divided into two phases: (a) noise cancellation and (b) edge preservation. The principle of Hermite interpolation has been applied in edge preservation process. Computational outcomes of this approach give out up to 90 % of impulse noise suppression.

For monitoring and controlling unwanted events like intrusion of burglars/thieves at home/locality/city, the security systems are much required. In this paper, an efficient and low-cost wireless security system is proposed with ZigBee and GSM modems. In the proposed system, city is divided into sections and sections into colonies/sectors. The section security system comprises multiple cluster nodes with one cluster head. Cluster nodes and cluster head are linked via ZigBee network. The cluster heads of all sections communicate with central control room (main server) of the city via GSM in order to make its coverage area large. The network optimization for efficient routing of the cluster node and cluster head is done using PSO algorithm. Cluster node is an embedded device connected with ZigBee modem and switches in each room as per user’s requirement. If there is an intrusion at home, in any locality of the city, the resident is to press the switch. This information is communicated by cluster node through ZigBee network to the corresponding cluster head which in turn communicates to the central control room (main server) via GSM network. Cluster head is equipped with audio alarm system like a hooter and this system is activated as soon as the switch in the cluster node is pressed. The identity of cluster node is conveyed to the cluster head via communication network. For future enhancement, the system in the cluster node may be fitted with other sensors like temperature, smoke, fire, etc., for detection of various hazardous conditions.

The reviews of the products are increasing rapidly on the web due to the rapid growth and uses of the Internet. The products review makes very big impact on consumer’s interest in buying or not buying a product. However, there are various products, which have thousands of user-generated reviews. Mining this enormous online reviews and finding the important reviews for a user became a challenging task. It is very hard for consumers to find out the true quality of a particular product due to the presence of large number of reviews for a single product. To solve this problem, we are proposing a ranking mechanism which can be efficiently used to rank different reviews in accordance to their aspects rating. Here, the ranking mechanism uses the numerous ratings of the aspect and calculates the aggregate score of the review. This paper demonstrates the ranking of various reviews by means of their aspects rating through ranked voting method. Both the practicability and the benefits of the suggested approach are illustrated through an example.

Image Segmentation is an open research area in which Multi-level thresholding is a topic of current research. To automatically detect the threshold, histogram-based methods are commonly used. In this paper, histogram-based bi-level and multi-level segmentation are proposed for gray scale image using spider monkey optimization (SMO). In order to maximize Kapur’s and Otus’s objective functions, SMO algorithm is used. To test the results of SMO algorithm, we use standard test images. The standard images are pre-tested and Benchmarked with Particle Swarm Optimization (PSO) Algorithm. Results confirm that new segmentation method is able to improve upon result obtained by PSO in terms of optimum threshold values and CPU time.

Virtual multiple input multiple output (V-MIMO) systems employ a variety of scheduling algorithms to group multiple users and allocate the same set of physical resources to improve the spectral efficiency by exploiting the multiplexing capability in cellular uplink (UL) communication. This paper proposes an efficient dynamic scheduling algorithm for SC-FDMA-based UL network. It estimates the possible interference caused due to adding a new user to existing multiuser group and optimally selects users such that the interference level is within the limit for the receiver to perform flawless detection. A threshold for interference level based on the average SINR of the receiver ensures that users can be dynamically added to an existing group provided the total interference after adding new user is under the limit. Our extensive simulation results based on 3GPP LTE UL network shows that the proposed algorithm has much better performance than the existing random dynamic scheduling technique.

In this work, electromechanical oscillations in single-area power systems can be effectively reduced by the influence of energy storage unit, and it helps in the load leveling process and performance improvement of the system. This proposed paper describes the application of super magnetic energy storage (SMES) unit and redox flow battery (RFB) in single-area non-reheat, single, and double reheat thermal power system. The commonly used industrial PID controller act as a control strategy and the optimal gain values are obtained using three different cost functions with stochastic particle swarm optimization technique (SPSO). The dynamic performance of the investigated power system is obtained and examined with one percent step load perturbation.

There has been a tremendous increase in demand for low-cost and compact antennas using techniques to improve their electromagnetic properties. To improve negative permittivity and negative permeability, the design and simulation of left-handed metamaterials (LHM) are used and presented. Our purpose is the betterment of return loss of antenna with the use of new incorporated LHM structure. The incorporated design has dimensions of 32 mm × 32 mm. The design has improved return loss and bandwidth while maintaining the VSWR and gain to the same level required for mobile applications as BLUETOOTH at 2.4 GHz. The proposed design is compared with conventional patch antenna which shows a significant improvement in return loss up to −41.06 dB and bandwidth 29 MHz.

High throughput, low delay in message delivery, and energy-efficient operation are certain challenges that medium access control (MAC) protocol for wireless sensor network (WSN) has to meet. Traffic patterns and load of network may change during the lifetime of the network and thus the adaptability in duty cycle, wakeup interval, and reliability of transport are mandatory. This paper presents a new adaptive mechanism which is effective in changing traffic conditions, i.e., when a burst of traffic is generated from a particular region. The acknowledgments per packet level guarantees reliability, lowering the cost on retransmissions. Also the adaptive wakeup interval of a node and corresponding adaptations in the preamble length makes it more energy efficient.

Security provisions are a significant influence in the conception of security system for wireless mesh networks (WMNs). It is therefore necessary to guard the identities of individual clients to avoid personal privacy concerns. Numerous susceptibilities exist in different protocols for WMNs (i.e. Overhead, storage, availability of resources). These ambiguities can be discussed by probable attackers to bring down the network performance. In this manuscript, we offer a secure authentication and signature routing protocol (SASR) based on diffie-helman model and threshold signature for reducing response time and improve the security at mesh node. The proposed approach validates the certification of the mesh nodes effectively and paves the path for secure communication. Since the protocol uses Diffie-Helman key mode and threshold signature, very little key is enough for obtaining the needed protection. This thins out the bandwidth allocation for key, so the security constraints will not move the bandwidth by any means, which is an additional advantage over other systems.

The intrusion detection system (IDS) research field has grown tremendously in the past decade. Current IDS uses all data features to detect intrusions. Some of the features may be irrelevant and redundant to the detection process. The purpose of this study is to identify a minimal subset of relevant features to design effective intrusion detection system. A proposed minimal subset of features is built by selecting common features from six search methods with correlation feature selection method. The paper presents empirical comparison between 7 reduced subsets and the given complete set of features. The simulation results have shown slightly better performance using only 12 proposed features compared to others.

Shield Effectiveness (SE) is a most significant parameter that determines the electromagnetic compatibility (EMC) characteristics of the system. Calculation of this parameter is the prerequisite for understanding the standards of the system. Many techniques are implemented for the betterment of this parameter. In this paper, implementation of various perforated shield materials are proposed and their corresponding frequency responses are evaluated. Distance between the source of electromagnetic interference (EMI) and the system under test (SUT) has a direct impact on SE. Investigations by varying this distance is presented in this paper. The functioning of the material as shield in the presence of such environment is given.

A novel method for automatic classification of magnetic resonance image (MRI) under categories of normal and Parkinson’s disease (PD) is then classified according to the severity of the medical specialty drawbacks. In recent years, with the advancement in all fields, human suffers from numerous specialty disorders like brain disorder, epilepsy, Alzheimer, Parkinson, etc. Parkinson’s involves the malfunction and death of significant nerve cells within the brain, known as neurons. As metal progresses, the quantity of Dopastat made within the brain decreases, defeat someone, and make them unable to manage movements commonly. In the planned system,

T

2 (spin-spin relaxation time)—weighted MR images are obtained from the potential PD subjects. For categorizing the MRI knowledge, bar graph options and gray level co-occurrence matrix (GLCM) options are extracted. The options obtained are given as input to the SVM classifier that classifies the information into traditional or PD classes. The system shows a satisfactory performance of quite 87 %.

Hyperspectral image analysis is considered as a promising technology in the field of remote sensing over the past decade. There are various processing and analysis techniques developed that interpret and extract the maximum information from high-dimensional hyperspectral datasets. The processing techniques significantly improve the performance of standard algorithms. This paper uses variational mode decomposition (VMD) as the processing algorithm for hyperspectral data scenarios followed by classification based on sparse representation. Variational Mode Decomposition decomposes the experimental data set into few different modes of separate spectral bands, which are unknown. These modes are given as raw input to the classifier for performance analysis. Orthogonal matching pursuit (OMP), the sparsity-based algorithm is used for classification. The proposed work is experimented on the standard dataset, namely Indian pines collected by the airborne visible/infrared imaging spectrometer (AVIRIS). The classification accuracy obtained on the hyperspectral data before and after applying Variational Mode Decomposition was analyzed. The experimental result shows that the proposed work leads to an improvement in the overall accuracy from 84.82 to 89.78 %, average accuracy from 85.03 to 89.53 % while using 40 % data pixels for training.

Path planning is one of the prime robot problems which essentially call for smooth navigation of the robot through an optimal path by avoiding barriers of any kind. In this work, Fuzzy Logic approaches are attempted and compared for obstacle avoidance through an unknown environment. In this approach, it considers inputs from sensors placed on the robot, which include the distance from nearest obstacle towards left, front and right besides the information on the angular variation from the target. The fuzzy rules are employed to control the velocity of left and right wheels of the robot. A defuzzification procedure is applied to the left and right velocity wheels, and results are compared with the defuzzified values obtained from Sugeno-weighted average method. The second approach ignores the four inputs and follows the same fuzzy technique. A comparison of the two approaches indicates that the first method is more precise. Finally path planning using Sugeno-based fuzzy logic controller has implemented in I robot Create (mobile robot) by interfacing with Arduino Uno.

This paper highlights the local binary pattern (LBP) method in the unsupervised texture segmentation task. It has been made into a really dominant measure of image texture, showing outstanding results in terms of computational complexity and accuracy. The LBP operator is a theoretically simple yet very efficient approach for texture analysis. The LBP concept is slightly modified, i.e., instead of considering the center pixel value for generation of binary values, the present paper utilized average of all the eight neighboring pixels of the center pixel. The binary code generated is separated into “Diamond-LBP Code (DLBPC)” and “Corner LBP code (CLBPC).” The proposed new variant local binary pattern (NVLBP) segmentation approach is simple, rotationally invariant and easy to understand. This method also resulted in good segmentation which is noticed from the entropy, standard deviation, contrast, and discrepancy values.

Optical character recognition (OCR) transforms printed text to editable format and digital writing on smart devices. Learning to write programs has made learners trace an alphabet to learn the flow of writing and OCR by itself is less effective as it ignores the directional flow of writing and only focuses on the final image. Our research designed a unique android-based multilingual game-like writing app that enhances the writing experience. A key focus of the research was to compare and identify character recognition algorithms that are effective on low-cost android tablets with limited processing capabilities. We integrate a quadrant-based direction checking system with artificial neural networks and compare it to the existing systems. Our solution has the dual advantage of evaluating the writing direction and significantly increasing the accuracy compared to the existing systems. This program is used as the literacy tool in many villages in rural India.

This paper proposes an extraction-based hybrid model for a single text document summarization. The hybrid model is depending on the linear combination of statistical measures like sentence position, TF-IDF, aggregate similarity, centroid, and sentiment analysis. Our idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message; hence, it can play vital role in text document summarization. As we know for any sentence, emotions (calling sentiments) may be negative, positive, or neutral. Sentence which has strong sentiment are more important for us which may be either negative or positive.

Land use land cover (LULC) information extraction is a crucial exercise for agricultural land. The present study highlights the advantages of remote sensing, GIS, and GPS techniques for LULC mapping from high-resolution remote sensing data. High spatial resolution (5.8 m) satellite imagery of IRS-P6 Resourcesat-II LISS-IV having three spectral bands was utilized for LULC classification and for data processing ENVI 4.4 tool and Arc GIS10 software were used. Eight training samples for LULC classes have been selected from the image. Supervised classification using maximum likelihood (ML), Mahalanobis distance (MD), and minimum distance to means (MDM) were applied. The performances of above classifiers were evaluated in terms of the classification accuracy with respect to the collected real-time ground truth information. The evaluation result shows that the overall accuracies of LULC classifications are approximately 84.40, 77.98, and 74.31 % with Kappa coefficients 0.82, 0.74, and 0.70 for the ML, MD, and MDM, respectively. It is noticed that ML has a better accuracy than the MD and MDM classifiers and it is a more effective method for complex and noisy remote sensing data because of its unified approach for estimation of parameters.

Today in the era of ICT, security of data and services on the WWW has become the most important issue for web service providers. Loopholes in the security systems of WWW may break the integrity, reliability, and availability of data and services. Today, intrusion detection systems based on data mining is the best security framework for the Internet. In this paper a novel intrusion detection system is proposed which is based on the fuzzy min-max neural network and the genetic algorithm. The proposed model is trained using fuzzy min-max neural network and the learning system is optimized by application of genetic algorithm. The developed system is tested on the KDD Cup dataset. The parameters classification accuracy and classification error were used as a final performance evaluator of the learning process. The experimental results show that the proposed model gives superior performance over other existing frameworks.

In this paper, we propose a fault tolerance real-time task scheduling algorithm with energy minimization. A fault in a system can be recovered at runtime without participation of external agent. It maintains enough time redundancy so that task can be re-executed in presence of fault. It can be achieved by checkpointing policy which gives reliability in a system. For reliable fault tolerance in a system, optimal number of checkpoints is applied and save the system from complete re-execution. Energy minimization can be achieved by dynamic voltage scaling (DVS). In this paper, existing real-time scheduling algorithm has been modified for fault tolerance and energy minimization. To minimize energy consumption voltage level is adjusted with respect to deadline of the system and check the schedulability of test on each task. The worst-case execution time is associated with voltage level for each task. The result shows that energy consumption is reduced with maximum task scheduling in a system.

Separable reversible data hiding techniques facilitate hiding and extraction of data in the encrypted domain. This paper proposes a novel method to embed binary data in an encrypted cover image that provides complete independence of data extraction and covers image recovery. This allows the content owner to retrieve the host signal without any distortion regardless of the embedded data and the data hider to perform lossless extraction of embedded message. The method also provides a high data-embedding capacity using Residue number system (RNS) technique and ensures that only an authorized person can access the contents of the plain image.

Imbalance in data is quite obvious while studying intrusion detection system (IDS). Classification algorithms are used to identify the attacks in IDS, which has many parameters for its performance evaluation. Due to imbalance in data, the classification results need to be revisited given that IDS generally evaluates detection rate and false alarm rate which belongs to two different classes. This paper validates a new metric NPR used for ranking the classifiers for IDS. The metric is made functional on KDD data set and then the classifiers are ranked and compared with results on another data set.

Face Recognition is considered to be as one of the finest aspects of Computer Vision, also various Feature Extraction and classification techniques including Neural Network Architectures have made it even more interesting. In this paper, an attempt towards developing a model for better feature representation/extraction and cascading it with neural networks classifier is presented. In order to derive better use of face recognition system for faster and better surveillance, analysis is carried out which provides a greater knowledge on the entire process and clarifies on various parameters effecting the system. Most popular Single-Layer Neural Networks such as generalized regression neural network (GRNN) and probabilistic neural network (PNN) are used with different subspace methods to provide a distinguished analysis. The experimental results in this work have revealed that the combination of subspace method with neural networks has increased the robustness and speed of face recognition system. Performance analysis of the proposed model is carried out by conducting the experiments on three benchmarking databases such as ORl, Yale and Feret.

The Bearing is utilized to give free direct development to the moving part or with the expectation of complimentary revolution around a fixed axis. Bearings are considered a main part in various mechanical systems. Multi component vibration signals are generated when the machine works. Accelerometers are used to capture generated vibration signal. Vibration signal analysis is effectively used to diagnose bearing faults. There are various methods using empirical mode decomposition (EMD) as their fundamental method to diagnose bearing faults. The proposed method consists of analyzing the kurtosis of residue obtained after removing higher frequency components of the original signal. The proposed technique identifies the boisterous frequency segment in the signal through the iterative procedure. The experimental data were collected from Case Western Reserve University, Ohio. The simulation is done over MATLAB 7.8.1.

This paper centralizes the idea of shortest path routing using a new approach to Dijkstra’s algorithm. Our new algorithm gives best solution as compared to the previously proposed algorithms using fuzzy mathematics with well-defined explanation in terms of complexity. The algorithm is valid for negative-weight graphs as well as a number of obstacles in the path of routing. It will search out the shortest path for routing and the shortest route which costs minimum. The minimum cost consuming shortest route is valuable routing for Ultra Large Scale Integrated chip.

In this paper, a compact Asymmetric Coplanar Waveguide (ACPW)-fed monopole antenna with Split Ring Resonator (SRR) in the ground plane is presented. Inclusion of SRR in the ground plane of ACPW offers a considerable bandwidth enhancement. SRR and Complementary SRR (CSRR) in the ground plane of ACPW are analyzed and bandwidth enhancements of 37.4 and 21.5 % at 2.52 GHz are obtained respectively, in comparison with ACPW-fed monopole antenna. The antennas offer an omni-directional pattern. Peak Gain of the antenna is found to be ~2 dBi for all the configurations in the frequency band of interest.

The digital Image processing has emerged as an effective tool for analyzing the digital images of various fields and applications of engineering. Threshold technique is the most useful and well known among segmentation methods because of its robustness, simplicity, and high precision. This paper is an attempt to make an efficient segmentation Natural calamity images by Healthy Bacteria Foraging Optimization Algorithm.

The concept of green computing is eminently applied in the field of technology in order to reduce carbon dioxide emission and to have minimal impact on environment. Practice of green computing is used in designing, manufacturing, and discarding of computers, servers, monitors, printers, other storage devices, etc. Delve into this field of technology is abiding into key areas such as energy efficient use of computers and scheming algorithms that can be implemented for efficient management of computer technologies. It has been observed that energy consumed by IT section in an organization is around 60 % of the total energy consumed. It is expected that using green computing organization will be able to create value for both customer as well as in business. This paper focuses on review of the literature on sustainable Information technology and key area of focus is realization of green computing for a safe environment.

In this article, the determinantal definition for the Hermite-Sheffer polynomials is established using linear algebra tools. Further, the Hermite-Sheffer matrix polynomials are introduced by means of their generating function.

Traffic congestion in cities is a major problem mainly in developing countries; to encounter this, many models of traffic system have been proposed by different scholars. Different ways have been proposed to make the traffic system smarter, reliable, and robust. This paper presents the various approaches made to enhance the traffic system across the globe. A comparative study has been made of different potential researches in which intelligent traffic system (ITS) emerges as an important application area. Important key points of each research are highlighted and judged on the basis of implementing them in developing countries like India. A model is also proposed which uses infrared proximity sensors and a centrally placed microcontroller and uses vehicular length along a length to implement intelligent traffic monitoring system.

All living things are connected to the space and time, which really shows a necessity to improve their sophistication for leading a better life. Exploration and prediction in space and time has been the tough chore to the researchers and developers. Development in the technology helps to elevate the persevering difficulties. Two interdisciplinary approaches in the computer science that has become pre-eminent in the effective analysis of space and time are data mining and visual analytics. Visual analytics is one interactive user interface where we can explore and visualize the data using visual analytic tools. So, visual analytics with the complex data requires a competent approach for accuracy which is nevertheless a data mining process. But the real scenario is, techniques and tools are more developed but may not nail in terms of accuracy and speed for handling complex-and time-oriented data. The main cause of the dearth may be more new tools and techniques developed by more researchers are not deliberated. The mission of the research paper is to study the techniques and tools of data mining and visual analytic in space and time.

Dimensionality reduction techniques have been immensely used in hyperspectral image classification tasks and is still a topic of great interest. Feature extraction based on image fusion and recursive filtering (IFRF) is a recent work which provides a framework for classification and produces good classification accuracy. In this paper, we propose an alternative approach to this technique by employing an efficient preprocessing technique based on average interband blockwise correlation coefficient followed by a stage of dimensionality reduction. The final stages involve recursive filtering and support vector machine (SVM) classifier. Our method highlights the utilization of an automated procedure for the removal of noisy and water absorption bands. Results obtained using experimentation of the proposed method on Aviris Indian Pines database indicate that a very low number of feature dimensions provide overall accuracy around 98 %. Four different dimensionality reduction techniques (LDA, PCA, SVD, wavelet) have been employed and notable results have been obtained, especially in the case of SVD (OA = 98.81) and wavelet-based approaches (OA = 98.87).

Distributed file system (DFS) is one of the main components of a cloud computing system used to provide scalable storage solutions for Big Data applications. hadoop distributed file system (HDFS) is one of the core components of Apache Hadoop project and many IT companies are using HDFS to store and manage Big Data. HDFS provides both command line and web-based interface to the users for storing and accessing data. The web-based user interface (WBUI) is used only for browsing the file system whereas the command line interface (CLI) is used for creating a file and performing read or write operations on the file. The CLI provides many more facilities and note that CLI is not user friendly as the user has to remember and type the commands to access the HDFS. In this paper, we propose a new customized web user interface (CWBUI) for the HDFS. We have developed CWBUI using servlets and java server pages (JSP) and deployed the same in a Hadoop cluster. The CWBUI is found to be very helpful in using the HDFS in an interactive manner without the need of typing commands in the user interface.

In today’s democratic society, the notion that all citizens have equal opportunity to express their views and opinions and right to information irrespective of any disability are elementary principles. The Web is becoming the centerpiece of this new information age. However, web sites are prone to accessibility issues and in some way or the other are inaccessible to people with disabilities. This paper provides a useful touchstone for those developers who have been standing at the shores of designing accessible web sites and even for those who have taken a deep dive in web designing in making Web accessible to all. This paper presents development of a validating and analysis tool to rate a web site on accessibility checkpoints by W3C (WCAG 2.0). Efforts are made in attaining web accessibility using web scrapping as a technology. Paper also contains tightly bound relations between various HTML attributes and tags using Bayesian Network.

The problem of high dimensionality in multi-label domain is an emerging research area to explore. A strategy is proposed to combine both multiple regression and hybrid k-Nearest Neighbor algorithm in an efficient way for high-dimensional multi-label classification. The hybrid kNN performs the dimensionality reduction in the feature space of multi-labeled data in order to reduce the search space as well as the feature space for kNN, and multiple regression is used to extract label-dependent information from the label space. Our multi-label classifier incorporates label dependency in the label space and feature similarity in the reduced feature space for prediction. It has various applications in different domains such as in information retrieval, query categorization, medical diagnosis, and marketing.

Wireless ad hoc networks are dynamic and infrastructureless networks. The dynamicity of the network and ignorance of the link availability are the basic reason for inefficient routing and hence causes unsuccessful data transmission between the peers. The prediction of link expiration can overcome the problem of link failure during routing. In this paper, we modified the routing mechanism by considering the network dynamicity in terms of Link Expiration Time (LET). The proposed routing technique is reactive in nature and performs route discovery based on LET with minimized control overhead. The simulation results show that the proposed scheme outperforms the existing Dynamic Source Routing (DSR) in terms of control overhead, hop count, etc., and provides better link duration time than the existing protocol.

In this paper, we present a high gain, dual beam pentagonal patch antenna. The pentagonal geometry is inspired from a rectangle patch antenna and a triangle patch antenna. The architecture of the antenna resembles an irregular pentagon comprised a triangle placed on top of a rectangle. FEM is used for the analysis of the antenna design. The pentagonal antenna gave good reflection and radiation characteristics with a 2:1 VSWR bandwidth of 8.1 % at resonant frequency. As we combined the geometry of a rectangle and a triangle for the pentagonal design, the single pentagon patch antenna radiated two directional beams with a peak gain of 6.1 dBi at resonant frequency. Even though the pentagon patch had slightly less gain than the rectangular patch, it had an additional advantage since it radiated in two directions. The proposed pentagonal antenna architecture can be used in applications where there is a need to produce a dual beam without the need of any additional circuits for switching.

The retrieval of an image can be done by extracting the local texture information. The procedure involves the calculation of patterns of texture present in an image. Local binary patterns became the first of its kind in which the gray value of a pixel in the middle position is compared with all of its surrounding pixels. The patterns of the texture obtained are converted into a histogram and the same becomes a feature vector in the process of comparison and subsequent retrieval process. One among several modifications to LBP was directional extrema patterns, which employs a technique of deriving the data by comparison of the pixel at the middle with two immediate pixels in four possible directions. To increase the performance further, an integrated approach is proposed for a CBIR system in which CDLEP is integrated with Gabor filters. The precision and recall values are found to decide the performance of the proposed framework.

Preemptions are necessary to obtain feasible schedule for real-time processing. A nonpreemptive scheduler can block higher priority transactions affecting schedulability of the system. This paper presents deferred preemptive technique scheduling algorithm using EDF to overcome the drawbacks of fully preemptive scheduler that substantially reduces number of preemptions in comparison with fully preemptive scheduling.

In this proliferation world, quantity of information available is increasing in a rapid speed. Due to enormous increase in availability of data, many researches are trying to fetch the most relevant information from the available sources. Many organizations and institutions rely heavily on internet for their sources and solutions, chances for intrusion grow significantly. So, there arise a need for a mechanism that protects their network and data from other external forces. The threat for organization’s network and data can be minimized by deploying firewalls. In this paper, an intelligent packet filtering mechanism in a firewall based on Bi-Layer Particle Swarm Optimization has been proposed which minimizes the search space. With the reduced search space, finding a match between the field values of the incoming packet and the rules in the rule set can be done in an effective manner.

The process of storing data with the help of digits obtained from constant expansion is an interesting one, which stores your data in the digits of a constant, instead of consuming hard drive space. The pattern for a file is looked up in the constant and identified by the index of this pattern into it along with its length. Hence, instead of storing actual data, only the meta-data, i.e., the index and length of a file are stored in the disk. The reverse process takes place while retrieving a file for reading with the help of file location stored in the meta-data. This is achieved by performing preprocessing of digits of the constant and doing pattern recognition in it.

The collection of labeled data to train a classifier is very difficult, time-consuming, and expensive in the area of remote sensing. To solve the classification problem with few labeled data, many semi-supervised techniques have been developed and explored for the classification of remote sensing images. Self-learning and co-training techniques are widely explored for the semi-supervised classification of remote sensing images. In this paper, a co-training model with clustering is proposed for the classification of remote sensing images. To show effectiveness of the proposed technique, experiments have been performed on two different spectral views of hyperspectral remote sensing images using support vector machine as supervised classifier and semi-supervised fuzzy c-means as clustering technique. The experimental results show that co-training with clustering technique performs better than the traditional co-training algorithm and self-learning semi-supervised technique for the classification of remotely sensed images.

An All-IP network is probably getting highly feasible since all applications and services in the telecommunication are already getting IP enabled. Internet Protocol version 6 or IPv6 is a later version of IP suite as it is designed to handle the increasing number of Internet applications. Security has become a major concern for the IP next generation network architecture and is considered as one of the most fundamental requirements for business continuity and service delivery. Several initiatives have been made by researchers to integrate secure IPv4/IPv6 address translation between IPv4 and IPv6 networks. But, not much progress has been reported in the recent past. Hence, in this research, an Integrated Secure Architecture for IPv4/IPv6 Address Translation between IPv4 and IPv6 Networks, with an IPv4/IPv6-Enabled Gateway Translator (IP46EGT), has been proposed to achieve MAC-level, VPN-IPSec, and Certificate level security. Network performance is evaluated and the generated results are tabulated and graphically presented.

Credit card is the well-accepted manner of remission in financial field. With the rising number of users across the globe, risks on usage of credit card have also been increased, where there is danger of stealing credit card details and committing frauds. Traditionally, machine learning area has been developing algorithms that have certain assumptions on underlying distribution of data, such as data should have predetermined and fixed distribution. Real-word situations are different than this constrained model; rather applications often face problems such as unbalanced data distribution. Additionally, data picked from non-stationary environments are also frequent that results in the sudden drifts in the concepts. These issues have been separately addressed by the researchers. This paper aims to propose a universal framework using logistic regression model that intelligently tackles issues in the incremental learning for the assessment of credit risks.

“

Sa_Re_Ga_Ma_Pa_Da_Ni_Sa” is the soul of Indian Music. As we play these 7 nodes at different frequency, different length, and at different reparations on one or more nodes, we will listen n number of feelings, expressions, and emotions. As musician can create such number of tones manually and get famous, all world remember such tone forever. As frequency, node duration, node energy are the parameters for creating different tones for 7 nodes, we can use genetic evolutionary theory for revolutionary musical tones evolution. In this paper, we are proposing novel method for musical tone generation using machine learning algorithm with the help of Narmour Structure Analysis.

For increased profitability and sustainability, the E-commerce systems should be backed by efficient and effective Supply Chain Management and Value Chain Management Systems. However, it is very difficult to build such systems as the functions are complex and systems are CAPEX and OPEX intensive. To overcome these problems, an inexpensive supply chain management system was designed and developed successfully with Real-time Advance Inexpensive Network Computing. The results of the research work are presented in this article.

Over the past decade, Indian e-commerce sector witnessed a huge growth. Currently this industry has approximately 40 million customers and it is expanding. These people express their experiences with various products, services in several websites, blogs, and social networking sites. To identify and extract any subjective knowledge from these huge unstructured user data, we need to develop a method that can collect, analyze, and classify user opinions. Two popular learning techniques (Supervised and Unsupervised) can be used to classify an opinion into two classes—“Positive” or “Negative.” In this paper, we propose an integrated framework for product review collection and unsupervised classification. The categorization of reviews is generated by the average semantic orientation of the phrases of suggestions or opinions in the review that holds adjectives as well as adverbs. A review can be categorized as an “

Endorsed

” one when the average semantic orientation is “Positive” otherwise it is an “

Opposed

” (“Negative”) one. Our proposed method has been tested on some real-life datasets collected from an Indian e-commerce website. The experimental results obtained show the efficiency of our proposed method for classification of product reviews.

Free-Space Optics (FSO) have been emerging communication field because of several advantages like cost-effective, higher bandwidth, and license free. It is basically line of sight communication and more suitable for last mile connectivity. Signal degradation occurs mostly due to atmospheric interference like rain, fog, etc. Diversity is an efficient solution to overcome these effects. In this paper, we have applied the concept of combined diversity (spatial and wavelength) with help of two receiver antenna to mitigate effects of fog and rain attenuation over transmission. An Equal Gain Combining for array gain enhancement applied at the receiver side. Results also demonstrated improvement in BER performance under strong turbulence.

Hyper connectivity is what the world’s reality is today. The day is not far away where clients, consumers, and suppliers “go online” to work, play, or consume; no we live in a realm where one and all is just interconnected to each other and with Internet. This new age carries with it an acceleration of formulation and disruption. It is a domain occupied with huge opportunity for folks eager to welcome this and capable to cope with it. Everything around us, transversely each business, firms are ascertaining novel clients, making new income generation method, constructing novel environments, and formulating new business prototypes on a connected stage at an extraordinary place. This paper discusses the technologies responsible for uniting the physical, cyber, and hyper world, i.e., the Internet of things, the Internet of everything along with its high-level representation to realize IoT, WoT, SWoT, W2oT, IoE.

In Last one decade researchers are focus on cloud computing presenting many novel approach for improving storage spaces in digital world. In this paper, we introduce architecture and algorithms that can be implemented to facilitate well-equipped infrastructure within cloud environment for load balancing and resource allocation. This architecture is specially developed for virtual storage drive over cloud. This architecture supports a number of end users, which are authenticating to send requests to the server. This request may demand some resources for its processing, thus, here comes the concept of resource allocation where it is required to allocate and schedule the available resources to process the request of the client. A numerous algorithms have been introduced here for easy configuration for the proposed architecture. The conclusion of this research paper is to provide such a cloud framework which can be implemented for efficient resource allocation and load balancing for multiple clouds.

Basic challenge in current Online Social Networks (OSNs) is to grant total control and access to its millions of customers (users) over the data and/or messages shared or highlighted on their personal accounts or private spaces. This control would allow users to have a check on its content and in turn would help in building a strong system facilitating OSN users to directly control the data/content. We need to update our training data on regular basis else it will misclassify any unwanted message which is not in our training data resulting into a negative impact on the accuracy of system. Hence to overcome this limitation we are proposing a new approach where-in an adjustable defined system that allows users to apply text filtering algorithms at preprocessing stage so as to categorize the message and trust value calculation. In this technique it will calculate the trust value for each message and give the trustworthiness of users. If that trust value is less than predefined threshold then it will block that user.

A computational grid environment consists of several loosely coupled pool of virtualized heterogeneous resources. The resources are geographically dispersed and their interactions with other components in the grid are independent of location. The grid architecture follows a Client-Broker-Resource system. The broker is as an intermediary between the clients and the resources. The broker allocates the resources to the clients based on the response received by each resource. In this scenario, prioritization of client’s request rather to prioritize the resource, which may fulfill clients’ request, is a major issue. Eventually, prioritization of resources balances workload in grid. Thus, the objective of this paper is to prioritize the resources, in order to allocate jobs in computational grid, using analytic hierarchy process (AHP) methodology. This technique plays major role in our proposed nearest deadline first scheduled (NDFS) algorithm. This paper also demarks the resources with proper ranking in Unicore grid environment.

The Mobile ad hoc wireless network is infrastructure less mobile network that allows communication among several mobile devices. The energy and delay of each mobile node affect the network performance in higher load. So, it becomes important to take into account these factors during the path selection. Here, we propose a method to incorporate the concept of fuzzy logic system to existing AODV routing protocol, and to select the optimal path by considering the remaining energy and delay of each node along with hop count. The experiment result shows that the proposed fuzzy-based modify-AODV (M-AODV) routing protocol outperforms the existing AODV in terms of average end-to-end delay and packet delivery ratio.

To accomplish the effectual software testing there is a requirement for optimization of test cases. The most challenging task in software testing is the generation of optimal test cases. There are various methods that are being used for generation of test cases and the test case optimization. The paper manifests the two different algorithms for test case generation and optimization of those test cases. The algorithms discussed are based on multi-objective optimization technique and successfully shows the desired results. The Cuckoo search algorithm based on the breeding behavior of Cuckoo bird is used here for the generation of test cases for a discussed problem and another algorithm based on the flashing phenomenon of fireflies is used for the optimization of the generated test cases. The second algorithm used verifies if every node in the given control flow graph is covered by given test cases.

Electroencephalography (EEG) finds variety of uses in the fields ranging from medicine to research. EEG has long been used to study the different responses of the brain. In this paper, EEG has been applied to study the imagined vowel sounds. An algorithm is developed to differentiate three classes of imagined vowel sounds namely /a/, /u/, and ‘rest or no action’ in pairwise manner. The algorithm is tested on three subjects S1, S2, and S3 and high performance is achieved. With classification accuracy ranging from 85 to 100 %, the algorithm shows the potential to be used in Brain Computer Interfaces (BCIs) and synthetic telepathy systems. High classification performance is obtained. Sensitivity ranges from 90 to 100 %. Specificity ranges from 80 to 100 %. Positive predictive value ranges from 81.82 to 100 %. Negative predictive value ranges from 88.89 to 100 %.

Due to proliferating harmonic pollution in the power system, analysis and monitoring of harmonic variation in real-time have become important. In this paper, a novel approach for estimation of fundamental frequency in power system is discussed. In this method, the fundamental frequency component of the signal is extracted using Empirical Wavelet Transform. The extracted component is then projected onto fourier basis, where the frequency is estimated to a resolution of 0.001 Hz. The proposed approach gives an accurate frequency estimate compared with some existing methods.

From the last couple of decades, the web services on the Internet changed the perspectives of the usage of a normal user as well as the vendor. Recommender systems are the intelligent agents that provide suggestions regarding the navigation in the web site for a user, based on preferences mentioned by the user in the past usage. Although there were several hybrid recommenders available with content-based and collaborative strategies, they were unable to process semantics about temporal and conceptual aspects. This paper incorporates the domain knowledge of the web site and the semantics for the temporal constructs into the hybrid recommender system. The proposed recommender parse the personalized ontology constructed for a user based on temporal navigation patterns and suggests the pages. The effectiveness of this approach is demonstrated by the experiments varying the scale of the data set and analyzed with the user’s satisfaction toward the quality of recommendations.

Wireless mesh networks (WMN) possess characteristics such as self-healing, self-configuring and self-discovery. Due to this nature WMN has emerged as the most widely used popular network. Since these devices are operated using battery resources, several works have been carried out for minimizing energy consumption during routing process, thereby increasing network lifetime. WMNs are more vulnerable for attackers due to its wide usage. Many works can be found to detect the intruder during routing without considering energy as a metric. There exist possibilities of intruder to attack the battery resource thereby reducing network efficiency in energy-aware routing. Hence in this work we propose a novel approach to detect an intruder by self-monitoring mechanism of node considering metrics such as packet size, data rate, remaining energy and draining rate of a energy resources of a node. The proposed model consists of three modules, namely self-intrusion detector, monitor and evaluator. It detects and helps in making decisions to participate in the network transmission. The working of the model is analyzed and shows that the proposed model detects intruder effectively, thereby resulting in increase of WMN efficiency.