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2017 | Buch

Advances in Computing and Data Sciences

First International Conference, ICACDS 2016, Ghaziabad, India, November 11-12, 2016, Revised Selected Papers

herausgegeben von: Prof. Mayank Singh, P.K. Gupta, Prof. Dr. Vipin Tyagi, Dr. Arun Sharma, Dr. Tuncer Ören, William Grosky

Verlag: Springer Singapore

Buchreihe : Communications in Computer and Information Science

insite
SUCHEN

Über dieses Buch

This book constitutes the refereed proceedings of the First International Conference on Advances in Computing and Data Sciences, ICACDS 2016, held in Ghaziabad, India, in November 2016.

The 64 full papers were carefully reviewed and selected from 502 submissions. The papers are organized in topical sections on Advanced Computing; Communications; Informatics; Internet of Things; Data Sciences.

Inhaltsverzeichnis

Frontmatter

Advanced Computing

Frontmatter
A Survey on Location Recommendation Systems

Location based service is an integral part of day to day life. Many location recommendation algorithms are proposed in the past. This paper investigates various approaches currently available for implementing location recommendation systems.

Meera Narvekar, Snehal Nayak, Jagdish Bakal
A Comparative Study and Performance Analysis of Classification Techniques: Support Vector Machine, Neural Networks and Decision Trees

A support vector machine (SVM) is a classification technique in the field of data mining, used for the classification of both linear as well as non-linear data. It learns the decision surface from two different classes of input samples and then performs analysis of new input samples. A neural network is able to learn without the explicit description of the problem or the need of a programmer. Another type of classification technique is the decision tree. In this paper, we are doing a comparative study of the above mentioned classification techniques by analyzing their performance on data sets. We will be comparing the inputs and the observed outputs.

Kumarshankar Raychaudhuri, Manoj Kumar, Sanjana Bhanu
A Novel Technique for Segmenting Platelets by k-Means Clustering

Platelet is a major component of various blood cells present in blood that helps in clotting of blood. Platelet count often becomes a crucial diagnostic parameter to identify several diseases like dengue, yellow fever, etc. The traditional process of counting platelets by examining blood slides under a conventional optical microscope is subjected to human errors due to manual inspection. In addition, the overhead on pathologist increases manifold when huge numbers of blood samples are to be tested. In this work, we have developed an Android-based mobile app, which takes as input the microscopic image of blood smear and gives as output the total platelet count present in the image. This system reduces the dependency on expert pathologists and avoids manual errors. A comparative study between platelet counts obtained from expert lab technicians and the one given by our developed app have shown it to be robust and efficient for automated platelet counting.

Kaushiki Roy, Ratnadeep Dey, Debotosh Bhattacharjee, Mita Nasipuri, Pramit Ghosh
Additive Noise Removal by Combining Non Local Means Filtering and a Local Fuzzy Filter – A Fusion Approach

Additive noise is one among the prominent types of noises which degrades the quality of images. A very large number of algorithms, in spatial, frequency and wavelet domain have been proposed to enhance images corrupted with additive noise. All the methods suggested have their own advantages as well as disadvantages. With the availability of parallel processing capability, in low end workstations and systems, fusion of two or more de-noising methods has become a topic of interest. In this paper, we have implemented one of the recent contributions to mean filter - a fuzzy filter. Also, as a complementary filter, the basic Non Local Means filter is implemented. Experiments were carried out by fusing the results obtained through the two filters. The results obtained establish the merit of the fusion approach.

Raju G., Farha Fatina Wahid, Sugandhi K.
An Incremental Verification Paradigm for Embedded Systems

Embedded Systems complexity is enhancing many folds in most of the product domains. Changing requirements and uncertainty during early stages of development are of greatest concern for the developing community, as they enhance system development complexities. Verification encompasses all aspects of system development process. This paper proposes an incremental paradigm that incorporates early integration and reduces uncertainty during initial phases of development under changing conditions of requirements. The method can be represented by a cascaded V-model. The verification methodology implementation issues are presented.

Hara Gopal Mani Pakala
Artificial Intelligence Based Recommender Systems: A Survey

In recent years, Artificial Intelligence (AI) techniques like (a) fuzzy sets, (b) Artificial Neural Networks (ANNs), (c) Artificial Immune Systems (AIS) (d) Swarm Intelligence (SI), and (e) Evolutionary Computing (EC) are used to improve recommendation accuracy as well as mitigate the current challenges like Scalability, Sparsity, Cold-start etc. Aim of the survey is to incorporate the recommender system in light of the AI techniques. Various AI techniques are presented and recommender system’s challenges are also presented. Moreover, we have tried to study the ability of AI techniques to deal with the above mentioned challenges while designing recommender systems. Furthermore, pros and cons of AI techniques are discussed in detail.

Goldie Gabrani, Sangeeta Sabharwal, Viomesh Kumar Singh
Assembling Swarm with Limited Visibility in Presence of Line Obstacles

In this paper, we have proposed a distributed algorithm for assembling of swarm of autonomous mobile robots on the left boundary of a rectangular region in presence of opaque horizontal line obstacles. The robots are having limited visibility capabilities and they are randomly scattered inside the region together with the obstacles. The robots do not have any message exchange among themselves. In the proposed algorithm, the robots follow the CORDA model for computation. In addition to that, synchronous/semi-synchronous timing model and full compass model are also followed. Our algorithm guarantees successful assembling of all the robots on the left boundary of the given region in a collision free manner.

Pratibha Tokas, Aravind Mekala, Deepanwita Das
Clustering Proficient Students Using Data Mining Approach

Every educational institution strives to be the best in terms of quality. Quality is measured using many parameters. One such parameter of measuring quality is proficient students. Hence the objective of this study is to Cluster proficient students of an educational institution using data mining approach. Clustering is based on knowledge, skill and ability concept known as KSA. A model and an algorithm are proposed to accomplish the task of Clustering. A student data set consisting of 1,434 students from an institution located in Bangalore are collected for the study and were subjected to preprocessing. To evaluate the performance of the proposed algorithm, it is compared with other Clustering algorithm on the basis of precision and recall. The results obtained are tabulated. The performance of the proposed algorithm was better in comparison with other algorithms.

M. V. Ashok, A. Apoorva
Designing of a Gender Based Classifier for Western Music

A musical piece constitutes of vocals and background music which is repetitive in nature. Their separation is an essential job in many applications, like music information retrieval (MIR), gender recognition and lyrical recognition. In this paper, we propose a classifier which cleaves the music/song on the basis of gender of the singer without having to listen to it. The basic idea is the music/vocal using REPET algorithm and using the vocals extracted for parameter extraction. The parameters used here are pitch, ZCR and MFCC. Based on these values, a classifier was designed using fuzzy inference system (FIS). A dataset of 43 songs was prepared to check the validity of the system. These songs were experimented upon and showed the accuracy of 82.6% by using the classifier designed.

Anam Tasleem, Satbir Singh, Balwinder Singh, Hitesh Pahuja
Detecting Malwares Using Dynamic Call Graphs and Opcode Patterns

Classification and detection of malware includes detecting instances and variants of the existing known malwares. Traditional signature based approaches fails when byte level content of the malware undergoes modification. Different static, dynamic and hybrid approaches exist and are classified based on the form in which the executable is analyzed. Static approaches include signature based methods that uses byte or opcode sequences, printable string information, control flow graphs based on code and so on. Dynamic approaches analyze the runtime behavior of the malwares and constructs features. Hybrid methods provide an effective combination of static and dynamic approaches. This work compares the classification accuracy of static approach that employs opcode sequence analysis and dynamic approach that uses the call graph generated from the function calls made by the program and an integrated approach that combines both these approaches. Integrated approach shows an improvement of 2.89% than static and 0.82% than dynamic approach.

K. P. Deepta, A. Salim
Development of Secured Trust SLA Model from SLA Life Cycle Phases

This is the era of Cloud Computing which shares on-demand computing resources and disposes off efficiently. It has a wide range of benefits for business and consumers. In spite of several benefits, there embeds numerous challenges such as data integrity, authenticity, data security, data locking, access control, data confidentiality, auditability, trust on cloud service provider, well management of service level agreement (SLA). Trust – is a key differentiator in defining the success or failure of many business companies. Service Level Agreement builds trust between cloud providers and cloud consumers. This research proposes a secured trust SLA model that builds trust on cloud service provider and helps in providing data security, confidentiality and integrity for cloud user.

Manjula Shanbhog, Krista Chaudhary, Mayank Singh, Shailendra Mishra
Dimensionality Reduction by Distance Covariance and Eigen Value Decomposition

Dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimension. In this paper, we investigate how much dimensionality reduction can be achieved with distance covariance and eigen value decomposition. The proposed method starts with data normalization, calculation of euclidean distances matrices of each attributes, and a recentralization followed by eigen value decomposition on the distance covariance matrix. We repeated experiment with a different normalization technique. Applied these reduction techniques on few public data sets, and results were compared with that of conventional Principal Component Analysis. The distance covariance matrix for dimension reduction yields better performance than that of PCA on the basis of the classification efficiency parameters.

S. Nithya, A. Salim
Exploration of GBP2MP Network Performance for Next Generation Using Artificial Neural Network (ANN)

New era of world needed fast communication network for that optical fiber communication is promising solution. Optical networks are used in closed systems to open systems for various application like video on demand, voice over internet, video conference and real time broadcast. So fast performance criteria prediction of optical fiber network is time and cost saving solution. The aim of this paper is to determine the performance characteristic of Gigabit point to multipoint (GBP2MP) optical fiber network using artificial neural network. In artificial neural model (ANN), the input is frequency and fiber length in kilometres. Performance of optical network checked in term of minimum bit error rate (BER) parameters and results are discussed the performance of optical fiber network (OFN) when varying the length of fiber and frequency respectively.

Sanjeev Verma, Anita Thakur
Hybrid Segmentation Technique Using Wavelet Packet and Watershed Transform for Medical Images

Image segmentation is necessary but significant element in less intensity image investigation, pattern recognition, and in robotic systems. It is one of the most complex and demanding tasks in image processing. Image segmentation is the process of separating an image into various regions such that each region is identical. This paper proposes a new medical image segmentation method that integrates multi-resolution wavelet packet decomposition with the watershed transform for MRI image. The wavelet packet transform (WPT) is applied to the input image, creating detail and approximation coefficients. If watershed technique alone is used for segmentation, then over cluster is present. To overcome this, the proposed technique which combines wavelet packet and watershed algorithm is developed. First, the wavelet packet transform is applied to produce multi-resolution images, followed by applying watershed for segmentation to the approximation sub-bands. Finally, Inverse WPT is implemented to obtain the segmented image. Due to wavelet packet decomposition, the quantity of the disturbance can be decreased and leads to a tough segmentation. This proposed work concludes that wavelet packet and watershed transform facilitate to get the elevated precision even in strident images.

K. RajMohan, G. Thirugnanam, P. Mangaiyarkarasi
Image Modelling: A Feature Detection Approach for Steganalysis

The most popular challenges in steganalysis is to identify the characterstics, to discover the stego-images. In this, we derive a steganalysis measure using Gaussian distribution, for image modeling. By using Gaussian distribution model the distribution of DCT coefficients and quantify a ratio of two Fourier coefficients of the distribution of DCT coefficients [9]. This derive steganalysis measure is evaluated against three steganographic methods i.e. first one is LSB (Least Significant Bit), the second one is SSIS (Spread Spectrum Image Steganography), and the last one is Steg-Hide tool, which is based on graph theoretic approach. Classification of image features dataset is done by using different classification techniques such as SVM.

Anuj Rani, Manoj Kumar, Payel Goel
Implementation of Medical Image Watermarking Technique Using FPGA

Protection and confidentiality are essential for Medical Image Watermarking because thorough analysis on health pictures is needed for proper diagnosis. In this paper we address this problem and designed a watermarking algorithm for medical images. Protection resolutions are increasingly joined alongside biomedical images. The patient data and patient fingerprint are added to the medical images, without disturbing the essential information in an image. Consequently, watermarking algorithms that involve embedding the patient report and finger print are proposed. In this method we are separating Region of Interest and Region of noninterest pixels from medical image using MATLAB. We have used RONI for adding watermark (patient report and fingerprint) to the medical cover image. In this paper we designed a new method to protect and authenticate medical images. We developed Independent Component Analysis on A Field programmable Gate Array (FPGA) and applied to RONI image that enables the addition of watermark. In this way X ray or scan report are utilized for watermark creation and for patient authentication. The aim of proposed technique is assembly of Independent Component Analysis watermarking to insert two autonomous data’s for accuracy and authentication.

S. Sivakannan, G. Thirugnanam, P. Mangaiyarkarasi
IoV: The Future of Automobiles

Internet of Things is an extension of WSN (Wireless Sensor Networks) in which every WSN is connected via the Internet such that in the end every “thing” is connected to the Internet, which will eventually ensure that the world becomes a “connected village”. Any “thing” in IoT should be able to sense, compute and communicate. Of those “things”, automobiles are an integral part. That’s why this field can be aptly called “Internet of Vehicles”. To integrate all these vehicles together and to ensure QoS and QoE for the end users is now a challenging need that is put forth the telecom providers and car manufacturers. This paper surveys on different applications that can be created with vehicles of day-to-day usage.

Preethi Pattabiraman, R. Dhaya, B. Sasidhar, R. Kanthavel
Measuring Branch Coverage for the SOA Based Application Using Concolic Testing

This work describes the working of white-box testing for the Service-Oriented Architecture (SOA) based Application. Now, a days it is very essential to perform code coverage testing to understand the quality of software. This paper deals with the measurement of branch coverage percentage for the BPEL architecture that orchestrates all the services, which are distributed geographically. Here, we are testing the code coverage of BPEL architecture, which actually shows the invocations of services when it is required. This work integrates the existing open source tools, to automate the computation of branch coverage for SOA based application. This paper shows a novel technique for generation of test cases and computing branch coverage through our proposed tool.

A. Dutta, S. Godboley, D. P. Mohapatra
Mutation Analysis of Stateflow to Improve the Modelling Analysis

Formal methods possess great analyzing capability that has led to an increasing use by engineers in the development and verification-validation life-cycle of hardware and software critical systems. Mutation Analysis has been very effective in model design and safety analysis. In this paper primary idea is to integrate the mutation analysis of stateflow to the Integrated Mutation Analysis Tool. This enhanced property of the IMAT tool after integration will be able to analyze the functionalities of stateflow models of the highly critical systems. The effectiveness of the Stateflow mutation analysis can be validated using the case-study of Autopilot Mode Transition Logic.

Prachi Goyal, Manju Nanda, J. Jayanthi
Prioritization of Near-Miss Incidents Using Text Mining and Bayesian Network

Near-Miss incidents can be treated as events to signal the weakness of safety management system (SMS) at the workplace. Analyzing near-misses will provide relevant root causes behind such incidents so that effective safety related interventions can be developed beforehand. Despite having a huge potential towards workplace safety improvements, analysis of near-misses is scant in the literature owing to the fact that near-misses are often reported as text narratives. The aim of this study is therefore to explore text-mining for extraction of root causes of near-misses from the narrative text descriptions of such incidents and to measure their relationships probabilistically. Root causes were extracted by word cloud technique and causal model was constructed using a Bayesian network (BN). Finally, using BN’s inference mechanism, scenarios were evaluated and root causes were listed in a prioritized order. A case study in a steel plant validated the approach and raised concerns for variety of circumstances such as incidents related to collision, slip-trip-fall, and working at height.

Abhishek Verma, Deeshant Rajput, J. Maiti
Security Integration in Big Data Life Cycle

We are living in a modern age, where technology is all around us. On single click user can do anything just like book a ticket, shopping, take an appointment to anyone, see medical reports, etc. Technology is so accessible because smart phones ownership. Large amount of data about users which is generated from various sources such as social networking sites, sensors devices, medical data etc. is called big data. With the increased use of big data, there arise many issues; especially security issues which may badly impact a person’s or an organization’s privacy. Yazan et al., presented threat and security attack model for big data security lifecycle. In this paper authors presents a critical review of the work and describes some security issues of big data. An approach to secure threat model for big data lifecycle has been proposed as a main contribution of the paper.

Kanika, Alka Agrawal, R. A. Khan
Trust Based Energy Efficient Clustering Protocol in Wireless Sensor Networks for Military Applications

Wireless sensor network finds its prominence in military applications because the sensing nature of sensors deduces uncertainty. As the sensors may work in harsh environments, minimal or no manual attention can be given to the sensors. Hence they are prone to adversary attacks. With the objective of obtaining security in these environments, trust based energy efficient method TBEM is suggested for clustered wireless sensor networks. By energy efficient and trust based clustering, security and decrease in energy deprivation in Wireless sensor networks can be achieved. The method is compared with existing Low Energy Adaptive Clustering Hierarchy LEACH protocol. The achieved minimal false positives and false negatives are presented for performance analysis.

T. P. Rani, C. Jayakumar
Using Morphological Features to Simplify Complex Sentences in Punjabi Language

In this research paper, a technique for automatic identification and simplification of long and complex sentences in Punjabi language has been proposed. In this research, mainly compound and complex sentences have been simplified. Various morphological features of non-finite verb and type of conjunctions used in complex sentences are used for identification of complex sentences. Sentence structure and conjunction are used for identification of compound sentences. Structure of clauses has been used to mark the start and end position of the dependent and independent clauses present in complex sentences. Syntactic technique has been used for simplification of compound and complex sentences. Author tested this system on compound and complex sentences and obtained an accuracy of 98% in case of compound sentences and 78% for complex sentence.

Sanjeev Kumar Sharma

Communications

Frontmatter
A Comparative Study of Internet Protocols in MANET

In present era, a growing ease to work in an more sophisticated environment, independent of geographical locations, ease to use resources at distant devices on an anytime-anywhere basis with higher space availability is seen. Taking this into consideration a technology has introduced, namely cloud computing. To emerge with this situation a factor of cloud computing is established that provides an environment with no or minimum infrastructure physically to establish a communication in between the two communicating devices. Besides this, to provide an operating unit of smartness ad-hoc technology along with IPv6 protocol came into existence that is being proven as one of the most encouraging and satisfactory way to use technology and meet today’s IT needs and deeds. To attain this infrastructure OPNET IT GURU EDUCATIONAL VERSION 14.5 Modeler is being used.

Shweta Singh, Arun Kumar Tripathi
A Novel Bulk Drain Connected 6T SRAM Cell

Static Random Access Memory (SRAM) being the fastest on chip memory is scaled everyday making a challenge to reduce the leakage current and to sustain its consistency. This paper focuses on the six transistor (6T) realization of the SRAM cell. Two existing SRAM architectures are analyzed and their drawbacks in the deep submicron regions are identified. A novel bulk-drain connected architecture of a six transistor (6T) SRAM cell is proposed. The proposed structure exhibits the capability of minimizing the leakage current in deep submicron region and at the same time maintaining the optimum hold and read noise margins. The performance of the proposed circuit is compared with the conventional 6T SRAM cell using a PTM 32 nm CMOS technology parameters. It is found that the proposed cell structure reduces the leakage current by 44% in comparison to conventional SRAM architecture.

Shobhit Kareer, Anchit Kumar, Kirti Gupta, Neeta Pandey
Blood Pressure Control During Anaesthesia With and Without Transport Delay

Regular monitoring of the anaesthetic drug dosage during a surgery is required to avoid the patient’s inter operative awareness due to inadequate levels of anaesthesia. The traditional methods of assessing the anaesthetic depth levels such as heart rate, blood pressure, pupil size, sweating, etc are not very accurate as these responses may differ from patient to patient depending on the type of surgery and the anaesthetic drug administered. Sometimes during the process of anesthesia some time delay may occurs and this time delay is very dangerous for our life during anesthesia. In this paper Transport delay that comes in the process of annesthesia have investigated and analysed. Z-Transform have been shown its incapability for fractional time delay processes. So, Modified Z-Transform is preffered for fractional time delay processes. Further effect of this time lag (Transportation delay) on system performance is minimized with PID controller. Relative, absolute stability have been calculated through which blood pressure is effectivly controlled during annesthesia.

Varun Gupta, Abhas Kanungo, Piyush Chandra Ojha, Pankaj Kumar
Brain-Bot: An Unmanned Ground Vehicle (UGV) Using Raspberry Pi and Brain Computer Interface (BCI) Technology

Since the last decade, internet has become an essential part of our lives and all the major devices no matter in what field and utility they are used for, connectivity through Internet has become an essential requirement for the users. One of the two major objectives this project was to build an Unmanned Ground Vehicle (UGV) providing with a set of different functionalities such as: GPS tracking, Real-Time Face Detection, Location Tagging, Live Streaming, etc. functionalities such that these functionalities could be used from anywhere in the world irrespective of the distance between the Bot and the controller and this Bot can be controlled through any device so that there is no need of a specialized controller in order to control and access it. The second objective was to introduce a methodology through which people suffering from motor disabilities can interact with physical devices using the Brain Computer Interface (BCI).

Vishal Chaurasia, Vikas Mishra, Lokesh Jain
Contact Dynamics Emulation Using Leap Motion Controller

Recent developments in Human-Computer Interaction technologies can be harnessed effectively to facilitate better cognitive learning and this is what this project aims to achieve in the field of Contact Dynamics. The project aims to enable a better comprehension of the concepts of Contact Dynamics to a layman through a technology that has been developed to foster interactive learning viz. the Leap Motion Controller. The project involves the usage of the Leap Motion Controller, a hand motion sensing device, to understand ‘Dynamics’ i.e., a branch of classical mechanics associated with the application of forces on bodies and the effect they have on their motion, along with the help of a conventional personal computer.

Akshat Bhardwaj, Akshay Grover, Praveen Saini, Mayank Singh
Dynamic Two Level Threshold Estimation for Zero Motion Prejudgment: A Step Towards Fast Motion Estimation

In most video sequences, especially containing slow motion, a large number of blocks are stationary. Early determination of these blocks may save large number of computations in any motion estimation (ME) algorithm. The decision for declaring a block to be stationary can be made by comparing the block distortion with a predetermined threshold whose large or small values may affect the speed and accuracy of a ME algorithm. Accurate prediction of this threshold proposes a challenging problem. In this manuscript, a dynamic two level threshold estimation technique has been proposed. This two level scheme not only detects constant variations in the neighboring blocks but is also capable of detecting stationary blocks with abrupt variations. Performance of the proposed technique is evaluated by implementing ZMP before ME process in adaptive rood pattern search (ARPS) algorithm. Simulation results show better performance of proposed technique in comparison to single level dynamic threshold predictor and fixed threshold predictor.

Shaifali M. Arora, Kavita Khanna
Emerging Technologies, Applications and Futuristic Scope of Cognitive Vehicular Network for 5G Wireless Systems

Research and study on fifth generation (5G) wireless communication systems is growing at a massive rate and has the potential to provide enhanced quality-of-experience (QoE), high spectral availability, less power utilization and less end-to-end latency to the wireless system designers. New wireless applications demand high data rates and mobility thereby urging for a deep excavation on 5G wireless systems. This article presents a heterogeneous network system based framework for 5G wireless systems. It proposes a potential cellular architecture that discusses various emerging and promising schemes for 5G wireless network such as cognitive radio oriented network, massive multiple-input multiple-output (MIMO), effective and energy-efficient communication systems, and visible light communication. The focus of the proposed prototype is on making the most effective use of quality of service (QoS), energy efficiency and spectral efficiency in heterogeneous wireless network and is expected to facilitate the interpretation of critical technical challenges towards spectrum efficient 5G wireless network.

Zain Hashim, Nishu Gupta
Intuitionistic Fuzzy PROMETHEE Technique for Multi-criteria Decision Making Problems Based on Entropy Measure

Entropy measures play an important role in the field of fuzzy set theory and generalized by various authors for different purposes. In the present communication, intuitionistic fuzzy entropy measure based on sine function is developed. Further, the modified intuitionistic fuzzy PROMETHEE (IF-PROMETHEE) technique for multi-criteria decision making problems is discussed with the help of proposed entropy measure and the intuitionistic fuzzy preferences. Finally, the effectiveness of the technique is illustrated through a problem of selection of the antiretroviral drugs for HIV/AIDS to reduce the infection of HIV.

Pratibha Rani, Divya Jain
Ku Band Microstrip Rectangular Patch

This Paper presents a microstrip rectangular patch antenna for satellite communication. This antenna resonates at 15.88 GHz which comes under Ku band. The advantage of this antenna is 5.5 dBi gain and the antenna size is reduced up to 15% to conventional antenna. This microstrip antenna size reduced by Optimization technique. The proposed antenna design has good directivity and sharp narrow band. VSWR of the antenna is almost equal to 1. Impedance matching is perfect and it is almost 50 Ω. Edge side cut Feeding technique is used in this paper which is simple to design. Simulation has been performed by HFSS software.

Praful Ranjan
Least Time Based Weighted Load Balancing Using Software Defined Networking

The network is changing very rapidly due to Software Defined Networking. To properly implement networks, we need many network devices like routers, switches, load balancers, firewalls and intrusion detection (IDS)/prevention (IPS). In traditional networking, these devices are very costly, inflexible & vendor specific. All these devices are made up of data plane & control plane. These planes are tightly coupled. You cannot force the vendors to sell you only the data plane so that you can use control plane of your choice. But SDN is changing this by separating the data plane & control plane. Now you can ask for the data plane from the vendor & can use open source control planes such as POX, RYU & Opendaylight. By writing network applications such as load balancer, firewall on top of these control planes, your data plane will start behaving like load balancer or firewall. Thus SDN offers us networking programmability. In our case, we created one load balancing application based on least time based Weighted Round Robin strategy. Load balancers devices are basically used for distributing large amount of client traffic among several servers depending upon load balancing strategy. The various strategies can be round robin, random, weighted round robin or server load balancing. We compared our new load balancing strategy with round robin strategy. For our experimental setup, we used Mininet Emulator Tool & POX Controller as our control plane.

Karamjeet Kaur, Sukhveer Kaur, Vipin Gupta
QoS and QoE in Wireless Multicast Communication

During last decade the deployment of multimedia and real-time applications over the network has grown with large interest. There are some parameters like service level agreements and quality of service such as delay, jitter, and packet loss on which the progress of these multimedia and real time applications depend. Though Multicasting is UDP based, but it is a very efficient group communication technique, it enhances efficiency by controlling network traffic and by reducing server and CPU load. Multicast optimizes performance by eliminating traffic redundancy and it is also helpful in distributed applications as well. Multicasting is a novel area of research in which broad scope of development is possible. In this paper, we are working on Quality of Services and Quality of Experiences and will provide an adaptive approach for data packet in terms of jitter by using the queuing mechanism. Our goal is to improve the performance of Multicast communication. The performance of the Multicast communication is calculated in terms of the throughput, packet loss, link utilization, delay and mean opinion score.

Mahendra Kumar Jangir, Karan Singh
Signed Social Networks: A Survey

People hold both sorts of emotions-positive and negative against each other. Online social media serves as a platform to show these relationships, whether friendly or unfriendly, like or dislike, agreement or dissension, trust or distrust. These types of interactions lead to the emergence of Signed Social Networks (SSNs) where positive sign represents friend, like, trust, agreement and negative sign represents foe, dislike, distrust and disagreement. Although an immense body of work has been dedicated to the field of social networks; the field of SSNs remains not much explored. This survey first frames the concept of signed networks and offers a brief discourse on the two most prevalent theories of social psychology applied to study them. Then, we address the various state-of-the-art issues which relates the real world scenarios with signed networks. Grounded along the network attributes, this survey talks about the different metrics used to analyze these networks and the real world datasets used for observational purposes. This paper, makes an attempt to follow the contours of research in the area to provide readers with a comprehensive understanding of SSNs elaborating the open research areas.

Nancy Girdhar, K. K. Bharadwaj
Text Document Clustering Based on Neural K-Mean Clustering Technique

Data clustering is a significant tool for applications like search engines and document browsers. It gives the user an overall vision of the information contained in the data sets. The well-known techniques of data clustering do not look for exact problems of clustering like high dimensionality of the dataset, large size of the datasets and to understand the ability of the cluster description. The work done before does not have the inbuilt property of clustering, so, here for extracting the features from the document, the clusters of different classes present in the document are taken. In the proposed work, this problem can be solved by using similarity technique on neighbors in addition to K- means method.

Daljeet Kaur, Jagpuneet Kaur Bajwa

Informatics

Frontmatter
An Accelerated Approach for Generalized Entropy Based MRI Image Segmentation

Image segmentation, a major research class is frequently governed by the way of two foremost parameters, associated with a specified segmentation procedure: threshold decision and seed-point determination. Various methods such as the histogram based processes, entropy headquartered process, business measure approaches and so on. These are well recognized for threshold choice within the image segmentation issues. In this article, threshold determination is done on the basis of extraordinary entropy measures on each of grey scale and color images. Comparative analysis of the Shannon and non-Shannon entropies (Renyi, Havrda-Charvat, Kapur and Vajda) is done to receive an appropriate threshold worth for the perfect image segmentation. It’s concluded via the simulation experiments performed on MRI images, that the role of the smallest minima obtained within the entropy versus grey-degree plot is different for every entropy measure. The threshold values received from these plots is accordingly elegant on the specific definition of the entropy chosen, which in flip influences segmentation outcome. It’s further observed that the segmentation results acquired, making use of Havrda-Charvat entropy measure is healthier than other entropy measures.

Anushikha Jain, Maninder Singh Nehra, Manoj Kuri
Automatic Face Naming Using Image Processing: A Review Study Based on Face Name Detection in Images or Videos

Automatic character identification in images is essential for semantic image analysis such as indexing, summarization and retrieval. Face identification, however greatly characteristic to individuals, is an enormously troublesome errand in modernized vision. Structure is enthused about modified naming of people in different images or montage material with their character. A noteworthy testing issue due to the extensive assortment in intrapersonal appearance assortments in light of the fact that photometric variables, for instance, distinctive lighting tints, viewpoint point, and scale, or due to changes in expressions, or impediments, hairstyles, so appearances of each characters and the inadequacy vulnerability of available comment. The objective of this paper is to clarify the characteristics required in programmed face naming procedure and to review some of their recent studies.

Pragya Baluni, S. K. Verma, Y. P. Raiwani
Directional Contourlet Based Multi-resolution Image Fusion Method for INSAT Images

Among the accessibility of multi-detector information in numerous fields, image fusion has received growing consideration in the researchers for a extensive spectrum of applications. Image fusion is the procedure to merges statistics from various images of the identical view. These pictures perhaps taken from various detectors, obtained at dissimilar times. In this paper, an image fusion technique rely on Directional Contourlet transform is proposed to improve the quality of image and meet the needs of application of vision. Two or more images to be fused should be decomposed using Contourlet with multi-resolution frequencies. The resulting sub-images are fused using Directive Contrast rule to obtain the combined image. As the wavelet transform has several special features in evaluation with scalar wavelets on image processing, but it flushes it to keep the inherent information. The efficacy of the proposed scheme has been explained using various image sets such as the multi-focus pictures, multi-detector satellite image. The proposed Directional Contourlet transform based fusion method has compared with wavelet transform image fusion method qualitatively and quantitatively. Experimental results concluded that the proposed scheme performs superior for image fusion in comparison with wavelet transform.

P. Santhi, G. Thirugnanam, P. Mangaiyarkarasi
Feature Extraction Methods for Human Gait Recognition – A Survey

Human gait recognition opens a wide variety of challenging problems for research community. Feature extraction has a significant role in designing human gait recognition systems. Numerous features have been defined based on gait video frames. Spatial as well as temporal descriptors have equal importance within gait features. In this paper, we present a survey of prominent feature extraction methods incorporated in human gait recognition systems and their respective recognition accuracies are reported. Also, a description of popular gait databases is presented.

Sugandhi K., Farha Fatina Wahid, Raju G.
Modeling the Decline of Orkut with Popularity in Facebook

Social networking services are gaining popularity at a very fast rate. Social networking websites like Facebook, Twitter, Myspace and many more have been active among people for a long time, thus providing users many features to enjoy online. Apart from great success of these networking websites, there have been various reasons for the downfall of a known website- Orkut. This paper concludes various reasons for the downfall of Orkut and helps in understanding the popularity of Facebook. The outcome of this paper will help in understanding whether Facebook will have a downfall like Orkut or there may be some other reasons for its downfall.

Tanuja Jha, Shilpi Burman Sharma, Shubham Krishna Chaturvedi
Optimizing Support Vector Machines for Multi-class Classification

The accuracy obtained when classifying multi-class data depends on the classifier and the features used for training the classifier. The parameters passed to the classifier and feature selection techniques can help improve accuracy. In this paper we propose certain dataset and classifier optimization to help improve the accuracy when classifying multi-class data. These optimization also help in reducing the training time.

J. K. Sahoo, Akhil Balaji
Scalable Online Analytics on Cloud Infrastructures

The need for low latency analysis of high velocity real time continuous data streams has led to the emergence of Stream Processing Systems (SPSs). Contemporary SPSs allow a stream processing application to be hosted on Cloud infrastructures and dynamically scaled so as to adapt to the fluctuating data rates. However, the run time scalability incorporated in these SPSs are in their early adaptations and are based on simple local/global threshold based controls. This work studies the issues with the local and global auto scaling techniques that may lead to performance inefficiencies in real time traffic analysis on Cloud platforms and presents an efficient hybrid auto scaling strategy StreamScale which addresses the identified issues. The proposed StreamScale auto-scaling algorithm accounts for the gaps in the local/global scaling approaches and effectively identifies (de)parallelization opportunities in stream processing applications for maintaining QoS at reduced costs. Simulation based experimental evaluation on representative stream application topologies indicate that the proposed StreamScale auto-scaling algorithm exhibits better performance in comparison to both local and global auto-scaling approaches.

Jyoti Sahni, Deo Prakash Vidyarthi
User Search Intention in Interactive Data Exploration: A Brief Review

Data exploration finds relevant data efficiently even if a user doesn’t know exactly what he/she is aiming for. These exploratory search paradigms are not well-supported in traditional database systems, thus interactive data exploratory (IDE) system evolved. A naïve data user exhibits various kinds of search behavior while formulating exploratory queries. In IDE system, each user interaction leads to more relevant results, due to its highly interactive and user-centric approach. To understand why and what user is searching for, more efficient data exploration systems need to be designed. This paper aims to discuss various factors affecting the user search behavior, how an IDE system support user. Various practices for measuring system support’s effectiveness are also highlighted. Finally, proposed a strategy for modeling the user search intentions for exploratory queries in IDE systems.

Archana Dhankar, Vikram Singh

Internet of Things

Frontmatter
A Novel Ultra Low Power Current Comparator

A novel ultra low power current comparator has been proposed in this paper. The current comparator utilizes Dynamic Threshold Metal Oxide Semiconductor (DTMOS) technique to reduce the power dissipation, by reducing the supply voltage. The circuit is capable of working at a supply voltage as low as ±0.2 V. The circuit has been implemented in 0.18 µm (Taiwan Semiconductos Manufacturing Company) TSMC technology parameters.

Veepsa Bhatia, Neeta Pandey
A Secure Multi-owner Multi-copy Facility with Privacy Preserving on Public Storage Cloud Dynamic Data

Most of the organization produced huge sensitive data and uses expensive applications. Both of these are quite expensive due to requirements of high storage capacity and installing the same application on different computers. Cloud Service Providers (CSP’s) offered paid Storage-as-a-service (SaaS) and Platform-as-a-service (PaaS) services that enable to store large data and uses many applications by less amount. Sometimes organizations need to be replicated data on multiple servers for increasing levels of scalability, availability, and durability and need to verify the intactness and consistency of dynamic multiple data copies. Implemented system provides evidence that all data copies are actually stored and remain intact. Moreover, implemented system supports dynamic behavior on cloud data, where the data owners are capable of archiving, accessing, updating and scaling the data copies stored by CSP. In addition, the system allows owners to appoint third party verifier and used to identify corrupted copies and reconstructs them.

Priya Sontakke, Amrita Manjrekar
A Technique to Reduce Problem of Delay in Key Rekeying in Mobile Networks

With the recent advancement in technology, necessary enhancement in communication speed is needed as well. Automatic key management plays an expressive role in large virtual private networks (VPNs). Establishment of VPN occurs when a Secured Gateway and an End User negotiate security association (SA) in mobile networks. In this paper we first describe the basic concept of IPsec, Internet Key Exchange (IKE) and then describe the mechanism to decrease time delay during re-keying when communication starts after intrusion. A Secured Gateway is used during negotiation between peers on an untrusted network. The path between end user and secured gateway should be minimum as possible for better communication. Here, we are proposing a methodology in which there are multiple secured gateways where one gateway sends its whole VPN session information to the next secured gateway in order to create the backup before occurrence of failure and thus avoid the re-keying and re-authentication after any interruption in running communication.

Rajwinder Kaur, Karan Verma
Automated Learning Based Water Management and Healthcare System Using Cloud Computing and IoT

Utilization of Water is increasing day-by-day in Homes, factories, Schools, Universities, Industries etc. which are consuming water on regular basis. Today, most of the places are suffering from the scarcity of water which is becoming the most imperative issue for human being because water is one of the most important natural resource present on earth. About 70% of the earth’s surface is covered with water out of which only 1% is available as fresh water. In that case there is a need to use water efficiently. A vast amount of water is being wasted without being consumed anywhere due to existing ineffective water supply system. Unnecessary leakage of large amount of water during supply, unsystematic supply to various regions i.e. not according to consumption, need or requirement, degraded water quality, time taking process to find the fault and then fixing it are the major drawbacks in the existing supply system. In this research we are designing and implementing an efficient water management system based on smart wireless sensor technology, using IOT, automatic learning with multiple modules and cloud computing to enhance the water supply system.

Punit Gupta, Dilpreet Singh, Anuj Purwar, Mohit Patel
Challenging Issues of Video Surveillance System Using Internet of Things in Cloud Environment

In video surveillance system, traditional systems are susceptible to environmental variation i.e. change in light, motion in the background due to water, fluctuation or reflection of light etc. This paper focuses on the challenging issues, application areas, freely available resources (dataset, tools) and benefits of video surveillance system, risk occurred in visual surveillance system. Here, we are exploring the study of major challenges along with application area. This paper also presents some basic steps of proposed framework using Internet of Things in cloud (IoTC) environment. Such surveillance systems can be developed according to suitability and requirement of society, army, navigation, robotics, healthcare, transportation, social media, etc.

Dileep Kumar Yadav, Karan Singh, Swati Kumari
Dynamic Orchestration Model for Complex Business Processes: An Application to e-SCMS

Design and development of an efficient and trustworthy e-business application using orchestration has number of issues. Some of them considered here are reducing complexity, higher flexibility and improving efficiency, quality attributes, ensuring trust, and processing over large datasets. The present paper considers the application of dynamic orchestration in e- Supply Chain Management System (SCMS), which is based on hybrid approach. An orchestration enabled e-SCMS process model is also proposed. The paper considers (a) layered-system architecture and (b) Quality of Service (QoS) attributes. The attribute enables an optimal web service selection scheme and takes into account the trust parameter of the service providers. The scheme also reduces the complexity. The results show reduced complexity, improved efficiency, and higher flexibility. An evaluation when considering the different criteria shows that the presented orchestration architecture offers an improved solution than existing approaches for designing a complex e-SCMS business process.

Reena Gupta, Raj Kamal, Ugrasen Suman
Efficient Vehicle Detection and Classification for Traffic Surveillance System

Video surveillance systems is a key component of any security system. Making an intelligent system that can detect and track multiple moving objects from video and also deals with dynamic backgrounds, illumination problem and environment conditions is a challenging task. The proposed system is designed for real-time vehicle detection and classification. The traffic is increasing day by day due to increase in number of vehicles. Vehicle detection, classification, and counting is a very important application by which highway monitoring, traffic planning, analysis of the traffic flow, etc. can be easily done. In this paper, vehicle detection is done by background subtraction and from each detected vehicles Scale-Invariant Feature Transform (SIFT) features is extracted. Vehicles are classified using the neural network and Support Vector Machine (SVM). SVM showed better generalization than Artificial Neural Networks.

Vijay Ukani, Sanjay Garg, Chirag Patel, Hetali Tank
Graphical and Theoretical Approach of Thermodynamics in Cyclic Theory of Universe

In cyclic theory of universe the scenario is just inverse of the theory of inflation. The cyclic theory model almost solves the problem of homogeneity, isotropy and flatness. It also has relevant cause and effect scenarios of different phenomenon like reheating, contraction of branes and it also shows the thermodynamic relevance. The basic approach of the paper is to satisfy the thermodynamics laws and to formulate the situation of ultimate fate of universe. Each and every phenomena that has been described in cyclic theory of universe has been taken into account while drawing the hypothetical graphs of thermodynamics. While treating thermodynamics with cosmology and astrophysics, many factors have been neglected like shape of universe, the cosmological constant, invariant scalar factor and factors related to the depth of cosmology. The paper also shows the behaviour of pressure, volume and temperature by using arbitrary values. The time and space has been neglected in graphical representation.

Nitin Chandola, Mayank Chaturvedi, Rohit Singh Rawat, Yogesh Dhami
Supplier Performance Evaluation for Manufacturing Industries: Re-exploring with Big Data Analysis

In the present era of globalization, every industry needs to explore methods for effective supplier selection. This paper re-defines the supplier selection problem in industries as a big-data problem and reviews the pre-existing approaches for supplier ranking. The major focus is on introducing Big-Data for supplier selection problem in industries. The approaches used are majorly looked for its implementation time and importantly, processing big-data in a way to prevent error tendencies and discrepancies in results. This article reviews AHP and PCA-based methods for supplier ranking problem re-defined as a real-time big-data problem. It also proposes further solutions and methodologies for better results.

Purnima Matta, Akash Tayal

Data Sciences

Frontmatter
A Distributed, Scalable Computing Facility for Big Data Analytics in Atmospheric Physics

Technological advancements in computing and communication have led to a flood of data from different domains like healthcare, social networks, Internet commerce and finance. Over the past few years a larger chunk of data comes from the domain of scientific applications, using simulated experiments or collected using sensors. This development calls for new architectural models for data acquisition, storage, and large-scale data analytics.In this paper, we present a distributed and scalable computing facility, using low cost machines, which support analytics of large scientific data sets, constituting three sequential modules, namely data pre-processing, data analytics and data post-processing. These three modules together form a big data value chain which is illustrated through a case study related to Atmospheric physics.

Reena Bharathi, S. C. Shirwaikar, Vilas Kharat
Abstracting Communication Methodology in IoT Sensors to Eliminate Redundancy and Cycles

Internet of Things is a huge collection of sensors or actuators that are interconnected either through fixed or dynamic networks. Fixed networks has nearly fixed location of sensors and are distantly placed, while dynamic networks are specialized as, inclusion of, the movement of the sensor objects. Information, when requested in these networks follows multi-path and observe cycles when reaching to the Requester Node (RN). This causes redundancy, slow networks and flooding of garbage packets in the networks. Therefore, in this article a methodology has been proposed for information requisition in IOT with the objective to eliminate cycle and redundancy.

Sharad Saxena
Analysis of Application Layer Protocols in Internet of Things

In the vision of Internet of Things (IoT), everyday objects such as domestic appliances, actuators and embedded systems of any kind soon will be connected with each other using Internet. Especially, with the emerging trends and devices evolving in this technology, choosing an optimized communication protocol for a particular application scenario is highly essential. Hence, in this work an analysis of existing communication protocols namely IETF Constrained Application Protocol (CoAP), IBM Message Queuing Telemetry Transport (MQTT) and W3C HyperText Transfer Protocol (HTTP) was carried out to understand their significance and classify them based on the application scenario. To perform a real-time analysis of these protocols, a test environment for inventory management system is designed. It involves sensors and communication boards, which generates data about the availability status of the every product. Various test cases were executed on all the three protocols by varying the request types in the process of retrieving the sensor data. To justify the performance of each protocol, metrics such as response time, latency and throughput are determined and analyzed. Finally, using the obtained results the protocols were classified and justified that each protocol works optimized for particular request type.

S. Sasirekha, S. Swamynathan, S. Chandini, K. Keerthana
Classification of Emotions from Images Using Localized Subsection Information

Emotional intelligence has important social significance and literature indicates that facial features are an important factor in determining the emotional state. It has been an intense study field to build systems that are capable to recognize emotions automatically based on facial expressions. Various approaches have been proposed but still there is a scope of improvement in detection accuracy because of diverse form of expressions exhibiting the same emotion. A widely used approach in the object detection field is Histogram of Oriented gradients. In this paper extensive experiments are conducted using various subsection sizes of images of histogram of oriented gradients and also along with Local Binary Pattern to extract the features for classification of emotions from facial images. Quantitative analysis of the approach in comparison with others is done to show its applicability and effectiveness.

Abhishek Singh Kilak, Namita Mittal
Comparative Study of Classification Techniques for Weather Data

Data mining techniques are widely used to analyze the large amount of data. Classification is an important technique which classifies data of various real world applications. This paper aims to compare the performance of classification algorithms for weather data using Waikato Environment for Knowledge Analysis (WEKA). Performance analysis done using cross fold and training set method. The best algorithm found was J48 Decision Tree classifier with highest accuracy and minimum error as compared to others.

Shweta Panjwani, S. Naresh Kumar, Laxmi Ahuja
Content Based Component Retrieval Based on Neural Network (NN) Classification Method

With the development of multimedia data, there is urgent need of high bandwidth for retrieval process. Selection of extracted features play an important role in retrieval process. Good selection features also save time and enhance the accuracy rate. The main objective of this proposed work is to classify the reusable components using neural network (NN) and genetic algorithm (GA). From result analysis it has been seen that proposed work has provided good results in terms of recall and precision rate.

Rupali Garg, Jagpuneet Kaur Bajwa
Data Mining Classification Models for Industrial Planning

The data mining models are an excellent tool to help companies that live from the sale of items they produce. With these models combined with Lean Production, it becomes easier to remove waste and optimize industrial production. This project is based on the phases of the methodology CRISP-DM. Several methods were applied to this data namely, average, mean and standard deviation, quartiles and Sturges rule. Classification Techniques were used in order to understand which model has the best probability of hitting the correct result. After performing the tests, model M1 was the one with the best chance to accomplish a great level of classification having 99.52% of accuracy.

Ricardo Bragança, Filipe Portela, A. Vale, Tiago Guimarães, Manuel Santos
Designing a Smart-Contract Application Layer for Transacting Decentralized Autonomous Organizations

This keynote paper addresses existing problems with traditional non-machine readable contracts that are based on trust. Such contracts have mostly a ceremonial purpose between transacting business parties and when conflicts occur, traditional contracts are often not enforcible. On the other hand, so called smart contracts that are machine readable and supported by blockchain-technology transactionalities, do not require qualitative trust between contracting parties as blockchain establish instead a quantitative notion of trust. However, currently existing smart-contract solutions that equip the protocol layer on top of blockchains with Turing-complete programming languages, lead to the false claim by industry practitioners they can manage smart contracts successfully. Instead, it is important to start considering the currently missing application layer for smart contracts.

Alex Norta
Digitization of Ancient Manuscripts and Inscriptions - A Review

The article describes the most recent developments in the field of enhancement and digitization of ancient manuscripts and inscriptions. Digitization of ancient sources of information is essential to have an insight of the rich culture of previous civilizations, which in turn requires the high rate of accuracy in word and character recognition. To enhance the accuracy of the Optical Character Recognition system, the degraded images need to be made compatible for the OCR system. So, the image has to be pre-processed by filtering techniques and segmented by thresholding methods followed by post processing operations. The need for digitization of ancient artefacts is to preserve information that lies in the ancient manuscripts and improve the tourism of our country by attracting more and more tourists. This article gives an analysis of the different methods used for the enhancement of degraded ancient images in terms of low resolution, minimal intensity difference between the text and background, show through effects and uneven background. The techniques reviewed include ICA, NGFICA, Cumulants Based ICA and a novel thresholding technique for text extraction.

N. Jayanthi, S. Indu, Snigdhaa Hasija, Prateek Tripathi
Heart Disease Prediction System Using Random Forest

The scope of Machine Learning algorithms are increasing in predicting various diseases. The nature of machine learning algorithm to think like a human being is making this concept so important and versatile. Here the challenge of increasing the accuracy of Heart disease prediction is taken upon. The non-linear tendency of the Cleveland heart disease dataset was exploited for applying Random Forest to get an accuracy of 85.81%. The method of predicting heart diseases using Random Forest with well-set attributes fetches us more accuracy. Random Forest was built by training 303 instances of data and authentication of accuracy was done using 10-fold cross validation. By the proposed algorithm for heart disease prediction, many lives could be saved in the future.

Yeshvendra K. Singh, Nikhil Sinha, Sanjay K. Singh
MapReduce Based Multilevel Association Rule Mining from Concept Hierarchical Sales Data

Multilevel association rule mining is one of the important techniques of data mining to analyze the sales data. Multilevel association rules provide detailed information as compare to single level association rules. Today’s era of e-commerce and e-business, various online marketing sites and social networking sites are generating tremendous amount of data in the form of sales, tweets, text mails, web usages and many more. The data generated from these sources is really too large so that it becomes tedious task to process and analyze using traditional approaches. This paper overcomes the drawback of single node computing by distributing the task to cluster of nodes. The performance of this system is analyzed using reduced minimum support threshold at different levels of concept hierarchy and by varying the database size. In this experiment, the transactional dataset is generated from big sales dataset then the distributed multilevel frequent pattern mining algorithm (DMFPM) is implemented to generate level-crossing frequent itemset using hadoop mapreduce framework. The multilevel association rules are generated from frequent itemset. The hierarchical redundant rule affects the efficiency of the system, so hierarchical redundancy is removed from it. Finally, the time efficiency of proposed algorithms is compared with existing Multilevel Frequent Pattern Mining Algorithm (MFPM).

Dinesh J. Prajapati, Sanjay Garg
Backmatter
Metadaten
Titel
Advances in Computing and Data Sciences
herausgegeben von
Prof. Mayank Singh
P.K. Gupta
Prof. Dr. Vipin Tyagi
Dr. Arun Sharma
Dr. Tuncer Ören
William Grosky
Copyright-Jahr
2017
Verlag
Springer Singapore
Electronic ISBN
978-981-10-5427-3
Print ISBN
978-981-10-5426-6
DOI
https://doi.org/10.1007/978-981-10-5427-3