iosr-JCE   Volume-22 ~ Issue-3 ~May - Jun. 2020

Abstract: Now's days, a vast number of data is swiftly produced in cyberspace. Data science is a composite number of pretexting disciplines. It refers to the study of extracting, collecting, gathering and representation of data to be used for business purpose or in technical issue. Data Science contributes a novel search way for natural and social science and goes by computer science in reasserting data. With a big amount of data now available in cyberspace, the organization in most of the field are series on data for their competitive advantages. This paper presents the challenges present in data and will discuss the differences in data science and other technologies like big data, the life cycle of data science, various data science technologies that are trending with its application. Also, the paper will describe the roles and responsibilities of a data scientist

Key Word: Data Nature, cyberspace, data management

[1]. Dhar, V (2013) Data Science and Prediction. Google scholar.
[2]. Zhu, Zhong and Xiong(2009)., Zhu &Xiong(2009), Data Science and Methodology,
[3]. Data Science in action,F.Provost , T.Fovcett, Big data(2013), Google scholar.
[4]. Hayashi, C. (2010) What is Data Science? Fundamental concepts and Heuristic examples.
[5]. Loikides, M.(2010) What is Data science ? An O'Reilly Radar Report.

Abstract: PT.Panasonic Manufacturing Indonesia needs to utilize information technology to be able to run business processes faster and can process data more accurately.To help with this problem, PT.Panasonic Manufacturing Indonesia requires a system that can meet the needs of information and business that runs and integrated so that can provide information in real time to its users. SAP (System, Product and Application) answered the need. With the SAP system, the company also needs a reliable local SAP vendor to assist its implementation.This research will select the best local SAP vendor to assist the implementation of SAP system in PT.Panasonic Manufacturing Indonesia, with AHP (Analytical Hierarchy Process) method. It starts from defining the problem and determining the desired solution, then creating a hierarchical structure, sub-goals, possible alternatives, and a comparison matrix based on the judgment of the decision maker by assessing the importance of an element over other elements.

Key words: SAP; Vendor; AHP;Manufacturing;

[1]. Forman, E. H., T. L. Saaty., M. A. Selly, R. Whitaker, J. Saaty dan F. Ruffing., (1986). Expert Choice; User Manual. Expert Choice, Inc., Pittsburgh.
[2]. Saaty T.L. (1990), "The Analytic Hierarchy Process", New York: McGraw Hill.
[3]. Brunelli, M. (2015). Introduction to the Analytic Hierarchy Proces.New York: Springer.
[4]. O'Brien, J. A. (2000). Introduction to Information Systems (edisi 11). New York: McGraw-Hill Irwin.
[5]. Brady, J., Monk, E., Wagner, B. (2001). Concept in Enterprise Resource Planning. Canada: Thomson Learning.

Abstract: In the age of information security, user detection methods are highly recommended for technological innovations. Continuous detecting a real user is a challenging issue. For this reason, researchers are giving importance on mouse gesture pattern to detect user.Different classifier algorithms are using in this technology. This paper triumphs a comparative study on classifier algorithms, Support Vector Machine, K-Nearest Neighbor and Naive Bayes based on mouse gesture for user detection process. Benchmark data of mouse gesture are collected and for our own testing more dataare captured by using Jitbit Macro Reader. Mouse gesture features are generated from both benchmark data and our own data. Classifier algorithms are applied for these features to detect user mouse gesture pattern. It is found that K-Nearest Neighbor classifier shows the best performance to detect user.

Keywords—Classifier Algorithm, Support Vector Machines (SVM), KNN, Mouse Gesture, Naïve Bayes, Behavioral Authentication

[1]. Chinmayee.KS, Vanishree C; ―Continuous Authentication for Mouse Gesture Recognition using Hidden Markov Model‖; InternatIonal Journal of electronIcs & communIcatIon technology; Vol- 8, Issue 4 OCT - DEC 2017.
[2]. Ahmed Awad E. Ahmed and IssaTraore, Member, IEEE; ―A New Biometric Technology Basedon Mouse Dynamics‖; IEEE Transactions on dependable and secure computing, Vol.4, No.3, July-September 2007.
[3]. Anam Khan, Suhail Javed Quraishi, Sarabjeet Singh Bedi; ―Mouse Dynamics as Continuous User Authentication Tool‖; International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume-8 Issue-4, November 2019.
[4]. S. Suganya, G. Muthumari, and C. Balasubramanian; ―Improving the Performance of Mouse Dynamics Based Authentication Using Machine Learning Algorithm‖; International Journal of Innovation and Scientific Research; ISSN 2351-8014 Vol. 24 No. 1 Jun. 2016, pp. 202-209.
[5]. Mr. Vishal S. Patil; Prof. P.R.Devale; ―Biometric Validation by Storing different Patterns using Mouse Gesture Signatures‖; International Journal on Recent and Innovation Trends in Computing and Communication; Volume: 3 Issue: 4; April 2015.

Abstract: In today's modern world surveillance system has become essential for our daily life, due to ever-increasing crime rates security authorities rely on this surveillance system for catching the suspects. Trailing of suspects can be done through a surveillance system but it is a very long and tiring process as each camera produces several hours of feed which is to be processed by the operator. To ease the effort of searching through hours of camera feed a robust method is been proposed to track a person in a video feed and then connecting his tracks further in the network using a trajectory prediction algorithm which saves time and speeds up the process. It aids in dipping the time invested for searching an entity in a lengthy video stream and finding its track through multiple camera feeds

Keywords—Surveillance System, Tracking, Trajectory, Lstm.

[1]. Rui Li a ,Baopeng Zhang a , Dong-Joong Kang b , Zhu Teng a,"Deep attention network for person re-identification with multi-loss", Computer and Electrical Engineering, vol 79, 2019, https://doi.org/10.1016/j.compeleceng.2019.106455
[2]. IssamElaÞ , Mohamed Jedra , Noureddine Zahid," Unsupervised detection and tracking of moving objects for video surveillance applications", Pattern Recognition Letters, vol. 84, pp. 70-77, 2016, https://doi.org/10.1016/j.patrec.2016.08.008
[3]. K. Shi, Y. Zhu and H. Pan, "A novel model based on deep learning for Pedestrian detection and Trajectory prediction," 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, 2019, pp. 592-598.
[4]. Kumar S. Ray, Soma Chakraborty, "Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background", Journal of Visual Communication and Image Representation, vol. 58, pp. 662-674, 2018, https://doi.org/10.1016/j.jvcir.2018.12.002
[5]. T. Mahalingam, M. Subramoniam, "A robust single and multiple moving object detection, tracking and classification", Applied Computing and Informatics,2018, https://doi.org/10.1016/j.aci.2018.01.001

Abstract: Since the beginning of human life, we have been progressing mentally in an exceptional manner and it took millions of years to produce an intellect and mind, which processes information in such an exceptional and amazing way that today our species is considered most advanced and intellectual around the globe. Now, we leap towards the next step of evolution by trying to make systems that process information and produce results naturally like human beings. The process of making such systems has eventually started and we, humans have already built the first phase of Artificial Intelligence, i.e., Narrow Artificial Intelligence Systems that are capable of performing tasks, which fall under their domain easily and efficiently than humans, but we still have further stages to reach in terms of development and research. For General AI systems, development is still ongoing and we hope to achieve it someday. Then comes the concept of Super AI Systems, which is still a hypothetical concept for us, humans.

[1] Nils J. Nilsson's, "The Quest for Artificial Intelligence" Link - http://www.cambridge.org/us/0521122937

Abstract: Background: The present work is about reading comprehension task. In this system the task of answering questions is performed which is based on an inputted passage. This system understands the given passage. In the present work the passage is the text of Karaniya Metta Sutta from the corpus of 84,000 Teachings of Buddha. Materials and Methods: The first verse is chosen as the text for performing the Question-Answer system passage. Three methods are used in the study : 1) ELMo method 2) BiDAF Method 3) NAQANet method The experiments are performed......

Keywords: Passage context, Karaniya Metta Sutta, Question-Anwering System

[1]. https://demo-allennlp.org/reading-comprehension
[2]. https://www.nltk.org/
[3]. https://towardsdatascience.com/gentle-start-to-natural-language-processing-using-python-6e46c07addf3
[4]. https://www.lexalytics.com/lexablog/machine-learning-natural-language-processing
[5]. Kamal Nigam, et al. : Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 1999
[6]. Peter F Brown, et al. : Class-Based n-gram Models of Natural Language, 1992

Abstract: Financial Fraud has been around for centuries since the time internet banking has taken off and is increasing substantially with the innovation of technology and the global superhighways of communication as a consequence of losing out billions of dollars world-wide each year. Unusually, large transactions or the ones that happen in atypical locations evidently deserve additional verification which could be detected by looking at on-surface and evident signals. So as to overcome these fraudulent acts we employ a fraud detection system which not only detects fraud but further makes it essential to model past credit card transactions with the ones that are atypical or turned out to be fraud. In this research paper, my objective is to identify.........

Keywords: Financial Fraud Transactions, Supervised Learning, Binary Classifier, Linear learner, Amazon Sagemaker

[1]. ―Research on Credit Card Fraud Detection Model Based on Distance Sum – by Wen-Fang YU and Na Wang‖ published by 2009 International Joint Conference on Artificial Intelligence
[2]. C. Wang, Y. Wang, Z. Ye, L. Yan, W. Cai, S. Pan, ―Credit card fraud detection based on whale algorithm optimized BP neural network‖, 2018 13th International Conference on Computer Science & Education (ICCSE) pp. 1-4. IEEE.
[3]. ―P. M.,. S. H. Geoffrey F.Miller, ―Designing Neural networks using genetic algorithms,‖ [Online]. Available: ―neural networks-pdf‖.
[4]. A. Mishra, C. Ghorpade, ―Credit Card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques‖ 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) pp. 1-5. IEEE.
[5]. ―Credit Card Fraud Detection-by Ishu Trivedi, Monika, Mrigya, Mridushi‖ published by International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 1, January 2016

Abstract: I study the link between the adjoint action and the Hamiltonian cycles in a symmetric graph. Then by a simple algebraic resolution of a system of equations with several variables I find all the Hamiltonian cycles of
the graph. Finally I will apply the results found to give an algorithm of order O(n3) allowing to quickly give all the Hamiltonian cycles with their distance. This gives a proof of the conjecture P= NP . (voir [1]).
Résumé : J'étudie le lien entre l'action adjointe et les cycles Hamiltoniens dans un graphe symétrique. Puis par une simple résolution algébrique d'un système d'équations à plusieurs variables on trouve tous les cycles
Hamiltoniens du graphe. Enfin j'appliquerai les résultats trouvés pour donner un algorithme de l'ordre de O(n3) permettant de trouver rapidement tout les cycles Hamiltoniens avec leur distance. Ce qui donne une
preuve de la conjecture P= NP . (voir [1]).........

Keywords:Graph, Hamilton cycles, P=NP, the travelling salesman problem, TSP, Analysis of algorithms, adjoint action. Code : 68R10, 05CXX, 68R05, 05XX, 15AXX, 15B10, 68W99, 68XX, 14-XX, 14LXX

[1]. Stephen Cook, The p versus np problem, http://www.claymath.org/sites/default/files/pvsnp.pdf, pages 1-12.
[2]. L.Lovasz, Combinatorial problems and exercises, Noth-Holland, Amsterdam, 1979.
[3]. D.S.Johnson M.R.Garey, Computers and intractability:a guid to the theory of np-completeness. Freeman,San Francisco, 1979.
[4]. R.Diestel, Graph theory, Springer, New York, 2000.
[5]. M. Sghiar, Algorithmes quantiques, cycles hamiltoniens et la k-coloration des graphes. Pioneer Journal of Mathematics and
Mathematical Sciences,17-Issue 1:51-69, May 2016.

Abstract: Information security has evolved over the years and has become increasingly important to society at large. Similarly, information security measurement and testing have evolved over the years,from the days of BS 7799 to the present day ISO 27001 and others.The measurement and testing techniques have been incredibly beneficial to the world of today. Though it has its limitations, there are opportunities for improvement and future research and development. This paperexplores these themes, highlighting the evolution of information security measurement and testing, the current trends, the benefits and the challenges, gaps, opportunities for improvement, and future research and development. With thesethemes, the discussion examines the evolution ofinformation security measurement and testing.

Keywords: Information security, Measurement, Testing, Metrics, Management, and Risk

[1]. Ahmad, R., Sahid, S., &Azuwa, M.P. (2014). Effective Measurement Requirements for Network Security Management.International Journal of Computer Science and InformationSecurity, 12(4), 37-44.
[2]. Arora, A., Hall, D., Piato, C. A., Ramsey, D., &Telang, R. (2004). Measuring the risk-based value of IT security solutions. IT Professional, 6(6), 35–42. doi:10.1109/MITP.2004.89.
[3]. Atyam, S. B. (2010). Effectiveness of security control risk assessments for enterprises: Assess on the business perspective of security risks. Information Security Journal, 19(6), 343–350. doi:10.1080/19393555.2010.514892.
[4]. Barabanov, R., Kowalski, S., &Yngström, L. (2011). Information security metrics: Research directions. 2011 2nd European Security Conference (pp.1-16), Örebro, Sweden, June 13-14, 2011

Abstract: With development of Internet and Natural Language processing, use of regional languages is also grown for communication. In India people express their views by using mother tongue such as Hindi, Bengali, Kannada, Marathi etc. As Hindi is fourth most spoken language in the world therefore many researchers are working on Hindi Sentiment Analysis. Sentiment Analysis is natural language processing task that mine information from various text forms such as blogs, reviews and classify them on basis of polarity as positive, negative or neutral. A speech is combination of variety of topics. So there is requirement for classifying given Hindi speech document in to different classes and then extract sentiments in terms of positive, negative and neutral such as joy, love, surprise, anger, disappointment and worry. In this paper a study is presented on classification of Hindi text documents into multiple classes with the help of machine learning. Further study is given for sentiment analysis which may be carried out by using machine learning to determine the polarity of individual class.

Keywords: Hindi Text; Emotions; Feature Extraction; Classification; Emotion recognition

[1]. Amitava Das, Sivaji Bandopadaya, "SentiWordnet for Bangla", Knowledge Sharing Event -4: Task, Volume 2,2010.
[2]. Amitava Das, Sivaji Bandopadaya, "SentiWordnet for indian language", Workshop on Asian Language Resources, pp. 56-63, Beijing, China, 21-22 August 2010.
[3]. Aditya Joshi, Balamurali AR, Pushpak Bhattacharya, "A fall back strategy for sentiment analysis in hindi", International Conference on Natural Language Processing, 2010.
[4]. Piyush Arora, Akshat Bakliwal, Vasudev Verma, "Hindi Subjective Lexicon Generation using WordNet Graph Traversal", IJCLA vol. 3, no. 1, pp. 2539, 2012.
[5]. Namita Mittal, Basant Aggarwal, Garvit Chouhan, Nitin Bania, Prateek Pareek, "Sentiment Analysis of Hindi Review based on based on Negation and Discourse Relation", International Joint Conference on Natural Language Processing, pp 45-50, 2013.