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In this present approach, some efficient computing techniques of Ancient Indian Vedic Mathematics for elliptic curve cryptography (ECC) over the Ring A4 has been studied, in which, it has been observed that the applications of AIVM Techniques or Sutras decrease the number of multiplications and squares which occur in point doubling and point addition in ECC over the Ring A4. This paper described the use of AIVM Sutras, Urdhva-Tiryagbhyam for multiplication and Dvandva-Yoga for the square of any number in the ECC over the Ring A4. The results proved that AIVM based scheme shows better performance in speed, processing time and power consumption of multipliers compared to conventional method. The effect of some AIVM techniques over ECC was investigated and the obtained results are explained in the form of tables and graphs.

Cryptography, Dvandva-Yoga, Elliptic Curve, Finite Field, Point Addition, Point Doubling, Ring A4, Scalar Multiplication, Urdhva-Tiryagbhyam, Vedic Mathematics

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Manoj Kumar, Ankur Kumar, "Some Techniques of Ancient Indian Vedic Mathematics for Elliptic Curve Cryptography over the Ring A4," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1330-1337, 2019.

This research study focuses on a comparative study of various clustering algorithms for the performance evaluation of large datasets. Analysis of large datasets is required for effective knowledge discovery. Use of data mining, machine learning techniques are often being used to refine of larger datasets. Traditional approach of processing of large datasets is inefficient and needs to consider the fast processing parallel environment to enhance the performance. This study has emphasis on four clustering algorithms, K-Means, Wards, PAM and CLARA to study performance on larger dataset of GeoJson format and CSV formats. Statistical techniques Medoid and Centroid are used for experimental work with different sample sizes to measure the performance of algorithms. Experimental work is carried out using R programming on Azure cloud for parallel computing with HDInsight Cluster. This research study provide evidence that the algorithm CLARA shows constant Medoid computations for different sample sizes compare to algorithm PAM and K-,Means. Silhouette widths of the algorithms CLARA (0.41) and Silhouette width of PAM (0.36) indicates well defined clusters are present in CLARA. Performance of these algorithms is effectively enhanced by reducing the time of DBSCAN by 45.72%, K-means by 99.95% and CLARA by 99.96% in comparison with Ward’s Algorithm for larger datasets using parallel processing environment.

Azure, CLARA, Clustering Algorithms, GeoJson dataset, PAM, R Studio, Ward’s Method

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Anupama Jawale, Ganesh Magar, "Survey of Clustering Methods for Large Scale Dataset," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1338-1344, 2019.

As open source software systems are becoming bigger and more complex, the bug detection task and fixing it to improve the performance of the software is also getting complex, time taking, and inefficient. Users are permitted by the developers to report bugs that are found by them using a bug tracking system such as Bugzilla to improve the quality and efficiency of the software. In Bugzilla, users identify clearly the details of the bug, such as the description, the component, the version, the product, and the severity. Depending on this information, the priority levels to the reported bugs are assigned by the developers according to their severity. In this research, the model is proposed that is a customized version of a classification technique called “Customized Cascading Randomized Weighted Majority Voting”. This technique will include an ensemble of two base classifiers: Naïve Bayes classifier and Random Forest classifier with different proposed weights in case of textual datasets.

Eclipse, Priority Prediction, Severity Prediction, Machine Learning, Textual Analysis, Bugzilla, Jupyter Notebook

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Prachi Pundir, Satwinder Singh, Gurpreet Kaur, "A Machine Learning Based Bug Severity Prediction using Customized Cascading Weighted Majority Voting," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1345-1350, 2019.

In cloud computing the datacenters are utilized to coordinates the distinct tasks where tasks may require more resources and source of these cloudlets could be from thousands of users. These datacenters aims to deliver reliable services. But the equal reliability to all the users at the same time according to their requirements may be a difficult task and may vary. So in this paper we purpose a technique that considers optimized elastic reliability in cloud computing using distance based server selection policy. In our scheme reliability enhancement through distance dependent check pointing with resource maximization mechanism is distance aware checkpoint. Resource maximization mechanism uses division policy which divides the jobs by looking at the capacity of virtual machine on which load is to be dispersed. This operation can efficiently solve the problem of reliability. it improves the resource usage in the datacenters and also gives optimized reliability to the user.

Reliability, Checkpointing, datacenters, cloudcomputing

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Preet Kawal Kaur, Kamaljit Kaur, "Reliability Optimization Using Distance Check pointing Through Dynamic Request And Requirement Aware Mechanism," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1351-1359, 2019.

This paper describes about data mining and its techniques , the main focus for this paper is the clustering techniques. There are many different tools available for performing the clustering techniques , for this paper the clustering technique which is chosen is the Kmeans clustering and the tool which is used to perform this clustering technique is R studio. R studio is the tool which is used for both data mining as well as for the visual analytics also . The Kmeans clustering is the clustering technique which is used to cluster the data in which data items are categorised into the k groups of similarity. This tool not only provide interface to use data mining but it also give us a visual representation of the result generated by that data mining algorithm . The data set which has been used for performing Kmeans clustering consist of nine attributes and 583 observations.

Data Mining. Clustering , classification ,clustering , Kmeans , R studio

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Shivani Chauhan, Bharti Nagpal, "Kmeans Clustering in R Studio," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1360-1362, 2019.

In cloud computing, task scheduling plays an important key role. The tasks provided by the user are to be allocated to the resources in cloud and the users have to pay for the usage. Even though there are number of popular schedulers available for task scheduling in Grid and other distributed environments, they are not suitable for cloud. Cloud is different from other distributed environments in resource pool and encounters less failure rate. Task scheduling in cloud has to give attention to the QoS parameters such as deadline and budget. Most conventional heuristic algorithms are proposed in the literature. But the meta-heuristic algorithm like fish swarm approach for the task scheduling in cloud is expected to give way the optimal results. A new meta-heuristic technique inspired from the swarm intelligence of fish, namely Modified Artificial Fish Swarm (MAFS) Optimization for Efficient Task Scheduling in Cloud Environment, has been proposed to solve the task scheduling problem. Then the proposed algorithm is compared with existing algorithms such as Particle Swarm Optimization (PSO) and Genetic algorithm (GA). The experimental result shows that the proposed MAFS greatly reduces the makespan and execution cost.

Task scheduling, resource utilization, cost, makespan

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H. Krishnaveni, V. Sinthu Janita, "Modified Artificial Fish Swarm Algorithm for Efficient Task Scheduling in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1363-1371, 2019.

Breast cancer is increasing day by day due to life style, hereditary. So, health care need to be modernized it means that the health care data should be properly analyzed. Breast Cancer has become the common cause of death among women. Due to long hours invested in manual diagnosis and lesser diagnostic system available emphasize the development of automated diagnosis for early diagnosis of the disease. Each individual has different values for each attributes of breast cancer. Diagnosis is done by classifying the tumor. Tumors can be either benign or malignant but only latter is the cancer. Malignant are more cancerous than the benign. Unfortunately not all physicians are expert in distinguishing between the benign and malignant. So we need a proper and reliable diagnostic system that can detect the malignant. The frameworks use will provide multipurpose beneficial outputs which includes getting the healthcare data analysis into various forms. In this Smart Health Prediction Using Python, we are proposing a evaluate classification technique used for predicting the risk level of each person. The proposed system is using 13 attributes and 569 datasets to develop an accurate result. The patient risk level is classified using machine learning classification algorithm that is Logistic regression algorithm; the accuracy of the risk level is high when using a greater number of attributes. The proposed system will group together symptoms data and analyze it to provide cumulative information. After the analysis, algorithm could be applied to the resultant and grouping can be made to show a clear result. Our aim is to classify whether the breast cancer is benign or malignant for the analysis purpose of laboratories.

Logistic regression algorithm, Malignant, Benign

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Manisha M S Pillai, Rahul Gopal, Roshitha Mariam Sunny, Revathy Chandran, Akhila Balachandran, "Smart Health Prediction System Using Python," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1372-1375, 2019.

In the healthcare domain, various techniques available for early disease diagnosis. Nail image analysis is one of the techniques for early stage disease diagnosis. Human fingernail image analysis is procedure consists of image capturing, pre-processing of image, image segmentation, segmentation of image, feature extraction. This paper presents review based generalized model for human fingernail image processing system, different classification techniques for nail feature classification and nail features. The nail features such as color, shape and texture used to predict diseases. Color features discussed are Mean, Standard Deviation, Skewness, Kurtosis and average RGB color. Shape features discussed in this paper are area, perimeter, roundness and compactness. Texture features are entropy, energy, homogeneity, contrast and correlation. Different classification techniques such as SVM classifier, KNN classifier, ANN classification used to classify the nail database for disease prediction are discussed.

image processing, feature extraction, disease detection, SVM, ANN, K-Nearest Neighbor, nail image analysis

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Trupti S. Indi, Dipti D. Patil, "Nail Feature Analysis and Classification Techniques for Disease Detection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1376-1383, 2019.

The open nature of the wireless form leaves it vulnerable to intentional interference attacks, typically referred to as jamming. This intentional intrusion with wireless transmissions can be used as a launch pad for mounting Denial-of-Service attacks on wireless networks. Typically, jamming has been addressed under an external threat representation. However, adversaries with internal knowledge of protocol description and network secrets can launch low-effort jamming attacks that are difficult to recognize and counter. In this work, we address the issue of selective jamming attacks in wireless networks. In these attacks, the opponent is active only for a short period of time, selectively targeting messages of high importance. We embellish the advantages of selective jamming in terms of network staging mortification, and opponent effort by presenting two case studies; a selective pounce, on TCP and one on routing. We show that selective jamming attacks can be launched by execute real-time packet classification at the physical layer. To mitigate this pounce, we develop three schemes that intercept real-time packet classification by combining cryptography primitives with physical-layer attributes. We inspect the security of our procedure and evaluate their computational and communication overhead.

Jamming, Attack, Network, Defective

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T. Aruna, R. Anandha Jothi, V. Palanisamy, "Defeating Jamming Attack in Wireless Network Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1384-1388, 2019.

Continuous development of Information Technology in the field of computer vision and digital image processing has brought new hope to many unfold problems of our daily life. In digital image area problems are solved by representing image as set of pixels with different amplitudes. These pixels are sent as input to the computer system and the system process these values using different algorithms. In this paper, we are working on a model, which is capable of automatically recognizing sitting and standing poses of human from digital photographs. We are also able to tag the respective images with correct pose. In this paper, we have shown standing and sitting poses which are executed using Matlab machine learning technique.

Computer vision, Image processing, Human pose, HOG

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Munindra kakati, Parismita Sarma, "Human Pose Detection From a Digital Image With Machine Learning Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1389-1393, 2019.