Volume-07 , Special Issue-15 , May - 2019 Go Back

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to con dentiality issues. Nowadays digitalization gaining popularity because of seamless, easy and convenience use of e-commerce. It became very rampant and easy mode of payment. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards. Inspired by the recent novel idea of Trerngad [1], we also quantize the released gradients to ternary levels {−B, 0, B}, where B is the bound of gradient clipping. Voting based prediction aggregation provides the final predictions. A hybrid technique of under-sampling and oversampling is carried out on the skewed data. Capsule Network (CapsNet) is adopted to further dig some deep features on the base of the expanded features, and then a fraud detection model is trained to identify if a transaction is legal or fraud.

AdaBoost, classification, credit card, fraud detection, predictive modelling, voting

[1] N. Mahmoodi, E. Duman, “Detecting credit card fraud by Modified Fisher Discriminant Analysis”, Elsevier Expert System with Application, 2015, pp. 2510-2516.
[2] Y. Sachin, E. Duman, “Detecting Credit Card Fraud by Decision Tree and Support Vector Machine”, In Proceedings of the international multi Conference of Engineers and Computer Scientists, Hong Kong, 2011, pp. 1-6.
[3] V. Van Vlasselaer et al., “APATE : A novel approach for automated credit card transaction fraud detection using network-based extensions[J],”Decision Support Systems, 2015, 75:38-48.
[4] Mafarja M , Mirjalili S. Hybrid Whale Optimization Algorithm with simulated annealing for feature selection[J]. Neurocomputing, 2017, 260:302-312.
[5] Kecman, Vojislav; Learning and Soft Computing Support Vector Machines, Neural Networks, Fuzzy Logic Systems, The MIT Press, Cambridge, MA, 2001.
[6] Fadaei Noghani, F., and M. Moattar. ”Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection.” Journal of AI and Data Mining 5.2 (2017): 235-243.
[7] The Nilson Report. (2015). U.S. Credit & Debit Cards 2015. David Robertson.
[8] Stolfo, S., Fan, D. W., Lee, W., Prodromidis, A., & Chan, P. (1997). Credit card fraud detection using meta-learning: Issues and initial results. In AAAI-97 Workshop on Fraud Detection and Risk Management.

S Sharath, Sherwin Sampath Doddamani, Lakshmikantha S, "Using Adaboost AND Majority Voting Techniques For Detecting Fradulent Transcations In Credit Card", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.217-219, 2019.

The model is about making cars more intelligent and interactive which may notify or resist user under unacceptable conditions, they may provide critical information of real time situations to rescue or police or owner himself. Driver fatigue resulting from sleep deprivation or Drunken driving is prevented is an important factor in the increasing number of accidents on today`s roads. In this paper, we describe a real -time safety prototype that stop vehicle ignition. The purpose of such a model is to advance a system to detect fatigue symptoms in drivers and control the vehicle to avoid accidents. In this paper, we propose a driver drowsiness detection and alcohol detection system in which alcohol detection sensor and image processing are used for detecting the driver. If the driver is found to have sleep, or driver in an alcoholic condition then buzzer will start and then turns the vehicle ignition off.

Component, Formatting, Style, Styling, Insert (key words)

[1]. 1.Yuichi Saito, Makoto Itoh, Toshiyuki Inagaki, “Driver Assistance System With a Dual Control Scheme: Effectiveness of Identifying Driver Drowsiness and Preventing Lane Departure Accidents“ IEEE Transactions on Human-Machine Systems March 21, 2016.
[2]. 2. D. Tran, E. Tadesse and W. Sheng, Y. Sun, M. Liu and S. Zhang, “A Driver Assistance Framework Based on Driver Drowsiness Detection“ The 6th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems June 19-22, 2016.
[3]. 3. A. Mittal, K. Kumar, S. Dhamija, M. Kaur “Head Movement-Based Driver Drowsiness Detection: A Review of State-of-Art Techniques” 2
[4]. Nd IEEE International Conference on Engineering and Technology (ICETECH) March 17-18, 2016.
[5]. 4. Alesandar, Oge Marques and Borko Furht “Design and Implementation of a Driver Drowsiness Detection System A Practical Approach”.
[6]. 5. Anjali K U, Athiramol K Thampi, Athira Vijayaraman, Franiya Francis M, Jeffy James N, Bindhu K Rajan “ Real-Time Nonintrusive Monitoring and Detection of Eye Blinking in View of Accident Prevention Due to Drowsiness” 2016 International Conference on Circuit , Power and Computing Technologies[ICCPCT].
[7]. 6. J. Ahmed, Jain–Ping Li, S. Ahmed Khan, R.Ahmed Shaikh “Eye Behavior Based Drowsiness Detection System”.
[8]. 7. A. Rahman, M. Sirshar, A. Khan ”Real Time Drowsiness Detection Using Eye Blink Monitoring” 2015 National Software Engineering Conference(NSEC 2015).

Akash Gowda BR, Dharmesh Bchavda, MD Ismail Zabi Ulla, Swetha N, "Implementation of Driver Drowsiness Alert and Automatic Vehicle Control System Using Arduino", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.220-225, 2019.

Biometrics is one of the most encouraging authentication systems in the recent years. However, spoof attack is one of the main problems with a biometric system. Spoof attack falls within a subset of what is called presentation attack. The heart is an emerging biometric modality which is getting attention for its robustness against presentation attacks. Introducing heart-signal into a fingerprint biometric system can yield promising results showing its robustness against spoof attacks with increasing the authentication accuracy. In this work, a sequential fusion method is improved for anti-spoofing capability. The idea behind the proposed system is the utilization of the natural liveness property of heart-biometrics in addition to boosting the heart-signal scores to increase the anti-spoofing of a multimodal biometric system. We have evaluated our proposed method with public databases of fingerprint biometric and heart-signal (ECG signal). The obtained results are very encouraging for the development of a robust anti-spoofing multimodal authentication system.

Component, Formatting, Style, Styling, Insert (key words)

[1] A. K. Jain and A. Kumar, "Biometric Recognition: An Overview," in Second Generation Biometrics: The Ethical, Legal and Social Context, E. Mordini and D. Tzovaras, Eds., ed: Springer Netherlands, 2012, pp. 49-79.
[2] F. Alonso-Fernandez, J. Bigun, J. Fierrez, H.Fronthaler, K. Kollreider, and J. Ortega-Garcia, "Fingerprint recognition," in Guide to biometric reference systems and performance evaluation, ed: Springer, 2009, pp. 51-88.
[3] E. Marasco and A. Ross, "A survey on antispoofing schemes for fingerprint recognition systems," ACM Computing Surveys (CSUR), vol. 47, p. 28, 2015.
[4] M. S. Islam, "Heartbeat Biometrics for Remote Authentication Using Sensor Embedded Computing Devices," International Journal of Distributed Sensor Networks, vol. 2015, p. e549134, 2015/01/29/ 2015.
[5] L. Biel, O. Pettersson, L. Philipson, and P. Wide, "ECG
analysis: a new approach in human identification," Instrumentation and Measurement, IEEE Transactions on, vol. 50, pp. 808-812, 2001 2001.
[6] S. A. Israel, J. M. Irvine, B. K. Wiederhold, and M. D.
Wiederhold, The heartbeat: the living biometric: Wiley-IEEE Press, New York, NY, USA, 2009.
[7] F. Agrafioti, D. Hatzinakos, and J. Gao, Heart biometrics: Theory, methods and applications: INTECH Open Access Publisher, 2011.
[8] M. S. Islam and N. Alajlan, "Biometric template extraction from a heartbeat signal captured from fingers," Multimedia Tools and Applications, vol. doi: 10.1007/s11042-016-3694-6, 2016.
[9] A. Lourenço, H. Silva, and A. Fred, "Unveiling the biometric potential of Finger-Based ECG signals,"Computational intelligence and neuroscience, vol. 2011, p. 5, 2011 2011.
[10] R. M. Jomaa, M. S. Islam, and H. Mathkour, "Enhancing the information content of fingerprint biometrics with heartbeat signal," in Computer Networks and Information Security (WSCNIS), 2015 World Symposium on, 2015, pp. 1-5.
[11] S. Pouryayevali, "ECG Biometrics: New Algorithm and Multimodal Biometric System," Master Thesis, Graduate Department of Electrical and Computer Engineering, University of Toronto, 2015.
[12] N. Alajlan, M. S. Islam, and N. Ammour, "Fusion of fingerprint and heartbeat biometrics using fuzzy adaptive genetic algorithm," 2013, pp. 76-81.
[13] S. A. Israel, W. T. Scruggs, W. J. Worek, and J. M. Irvine, "Fusing face and ECG for personal identifica ion," 2003, pp. 226-231.
[14] Y. N. Singh, S. K. Singh, and P. Gupta, "Fusion of electrocardiogram with unobtrusive biometrics: An efficient individual authentication system," Pattern Recognition Letters, vol. 33, pp. 1932-1941, 2012 2012.
[15] M. D. Bugdol and A. W. Mitas, "Multimodal biometric system combining ECG and sound signals," Pattern Recognition Letters, vol. 38, pp. 107-112, 2014 2014.
[16] C. Zhao, T. Wysocki, F. Agrafioti, and D. Hatzinakos, "Securing handheld devices and fingerprint readers with ECG biometrics," 2012, pp. 150-155.
[17] I. I. Standard, "Information technology – Biometric presentation attack detection -- Part 1: Framework," ed, 2016.
[18] A. K. Jain, K. Nandakumar, and A. Ross, "50 years of biometric research: Accomplishments, challenges, and opportunities," Pattern Recognition Letters, vol. 79, pp. 80-105, 2016.
[19] G. Fumera, G. L. Marcialis, B. Biggio, F. Roli, and S. C. Schuckers, "Multimodal Anti-spoofing in Biometric Recognition Systems," in Handbook of Biometric Anti-Spoofing: Trusted Biometrics under Spoofing Attacks, S. Marcel, M. S. Nixon, and S. Z. Li, Eds., ed London: Springer London, 2014, pp. 165-184.
[20] C. A. Shoniregun and S. Crosier, Securing biometrics applications: Springer, 2008.
[21] M. Tistarelli, S. Z. Li, and R. Chellappa, Handbook of remote biometrics vol. 1: Springer, 2009.
[22] A. A. Ross, K. Nandakumar, and A. K. Jain, Handbook of multibiometrics vol. 6: Springer, 2006.
[23] R. N. Rodrigues, N. Kamat, and V. Govindaraju, "Evaluation of biometric spoofing in a multimodal system," 2010, pp. 1-5.
[24] R. N. Rodrigues, L. L. Ling, and V. Govindaraju, "Robustness of multimodal biometric fusion methods against spoof attacks," Journal of Visual Languages & Computing, vol. 20, pp. 169-179, 2009 2009.
[25] P. A. Johnson, B. Tan, and S. Schuckers, "Multimodal fusion vulnerability to non-zero effort (spoof) imposters," in 2010 IEEE International Workshop on Information Forensics and Security, 2010, pp. 1-5.

Radha N, Kavya N, Harsha A C, "Improved Sequential Fusion of Heart-signal and Fingerprint for Anti-spoofing", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.226-231, 2019.

Devops is a collaboration of development and operation devised to stress on communication and integration between them. It is supported by a culture collaboration it helps an organization to grow with these help organization can produce software products and services. Organization associated themselves with devops to startup new methodology. Continuous development and innovation are required in an organization so devops training has been started in the orientation. Companies are focusing on the automation of the process this way timely deliver and quality results are achieved We also found that DevOps is supported by a culture of collaboration, automation, measurement, information sharing and web service usage. DevOps benefits IS development and operations performance. It also has positive effects on web service development and quality assurance performance. Finally, our mapping study suggests that more research is needed to quantify these effects.

Technology, Digital Enterprises

[1]. https://d1.awsstatic.com/whitepapers/DevOps/practicing-continuous-integration-continuous-delivery-on-AWS.pdf
[2]. http://possible.mindtree.com/rs/574-LHH-431/images/devops-are-we-there-yet.pdf
[3]. https://d1.awsstatic.com/whitepapers/jenkins-on-aws.pdf
[4]. https://hexaware.com/casestudies/at-ad-wp-01.pdf

Syeda Misba, Shilpa Biradar, "Devops:A Culture, Not A Technology Continuous Integration for Digital Enterprises", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.232-234, 2019.

In health care system environment use of IOT technologies bring convenience to both doctors and patients. This work proposes a method for drip infusion monitoring system. The system is designed to work in two modes viz Manual mode and Auto mode. The patient’s heart beat and body temperature are obtained using sensors. Based on these readings the flow rate of the glucose is determined by the doctor or nurse. While the glucose is being given to t he patient, it should be carefully monitored so that the glucose bag does not become empty. This is very much essential to prevent reverse blood flow. In this project the reverse blood flow is prevented using solenoid valve with quantitative control. The salient features of this project includes: Drip bottle weight monitoring using a load cell , Flow control using a electronic valve, Patient Monitoring using Sensors and Automation of Drip Process.

Drip infusion, Health monitoring, Quantitative control, IOT

[1]. Fan Yang, Yu Wang “Research of the New Electronic Va lve System with Quantitative Control” IEEE Third Global Congress on Intelligent Systems, 2012.
[2]. Shu xi ng Guo, Jian Wang, Qin xue Pan and Jian Guo “Solenoid Actuator based Novel Type of Micropump” IEEE International Conference on Robotics and Biomimetics,2006.
[3]. Takalkar Atul S, Lenin Babu M C “ Characterization of Va lveless Micro pump for Drug Delivery by Using Piezoelectric Effect” IEEE International Conference on Advances in Computing, Communications and Informa- tics 2016.
[4]. Lu Quan, Sen Bao, Hong Li Jun “ Research on Embedded Electro-hydraulic Proportional Valve Controller” IEEE Third International Symposium on Intelligent Information Technology Application,2009.
[5]. Jingguo Wen, Zisheng Lian ” Electro-Hydraulic Control System for Hydraulic Supports About the Study on Solenoid Valve Driver” IEEE International Conference on Computing, Measurement, Control and Sensor Network, 2012.
[6] Rani, K. R., Shabana, N., Tanmayee, P., Loganathan, S., & Ve lmathi, G. (2017). Smart drip infusion monitoring system for instant alert-through nRF24L01. 2017 International Conference on Ne xtgen Electronic Technologies : Silicon to Software. (ICNETS2).doi:10.1109/icnets2.2017.8067976
[7] T. Mizuno, K. Iida, "A study on the dripping speed of infusion", Bulletin of Chiba College of Health Science, pp. 55-60, 1986.
[8] Ying Chen, Huqiu Liu, Longbin Liu, & Li Gao. (2011). Intelligent liquid drip rate monitoring and early warning systems. 2011 International Conference on Electric Information and Control Engineering.doi:10.1109/iceice.2011.5777571
[9] Jeyapriya, S., & Ramalakshmi, R. (2017). Glucose monitoring and control in diabetes using GSM and automatic insulin injector system for human bodies. 2017 IEEE International Conference on Intelligent Techniques

Akash Das Gupta, Rakshitha J, Lavanya L.D, Meenakshi. M, Vijayalaxmi R. Patil, "Smart Drip Infusion Monitoring System and Electronic Valve System with Quantitative Control Using IoT", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.235-238, 2019.

Android based applications are currently in trend. Several business use this android application to make their work more efficient. The applications are used in the fields like ordering food, medicine, booking tickets and also can be used to book appointment between student and lecturer which reduces their some work load. In this study presents an android application for booking appointment through mobiles where both students and lectures as to be in their respective mentioned at particular time. This application allows students and lectures to access the system by connecting through internet. It also have a feature like dropping a message which indicates the purpose of the meeting and also the student can set a time during the appointment booking. The application is developed using Java as scripting language and used My Structured Query Language (MySQL) for database. This application is cheap and capable of operating on various mobile devices and also user-friendly, very effective and efficient application which can be used by the institutions.

appointment, android application, booking, academic institutions

[1] Bello RidwanOluwaseun , OlugbebiMuyiwa , BabatundeAbdulrauphOlanrewaju , Bello Bashir Omolaran , Bello ShakiratIyabo, “Student-Teacher Online Booking Appointment System in Academic Institutions”, Vol.9, No. 2, October 2016.
[2] Shubhankar Mukherjee, Prof.Jyoti Prakash, Deepak Kumar,“Android Application Development and Its Security”,International Journal of Computer Sciences and Mobile Computing, Vol.4,Iss. 3,March-2015.
[3] N.D. Oye, S. Mazleena, and N.A. Iahad, “Challenges of E-learning in Nigerian University Education Based on the Experience of Developed Countries’’. International Journal of Managing Information Technology, Vol. 3, No. 2, pp. 39–48, 2011.
[4] M. Landry, “There Are Good and Bad Ways To Set Up An Appointment System”. CMA Journal Vol. 115, No. 2, pp. 160- 168, 1976.
[5] O. L. Yekini, “Education as an Instrument for Effective National Development: Which Way Nigeria”. Business and Entrepreneurship Journal, Vol. 2, No. 2, pp. 27– 8, 2013.
[6] X. Dai Online Clinic Appointment Scheduling M.Sc. Thesis in Industrial and Systems Engineering, The Lehigh University, pp. 1467, 2013.
[7] C. Alex. ‘‘What is a web application (or "webapp")’’? http://www.jguru.com /faq/view.jsp?EID=129328
[8] Z. Stern, ‘‘How to Set Up Your Network for PCs and Macs’’. http://www.pcworld.com/article/230943/crossplatform.html

Bobby Shree G, Ambika V, Divya B, Bindu Madhavi T, Aditya Pai H, "V-MEET Android Application", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.239-242, 2019.

Hybrid Document Summarization is the technique by which the huge parts of content are retrieved. The Hybrid Document Summarization plays out the summarization task by unsupervised learning system. The significance of a sentence in info content is assessed by the assistance of 3 algorithms. As an online semantic lexicon WordNet is utilized. Word Sense Disambiguation (WSD) is a critical and testing system in the territory of characteristic dialect handling (NLP). A specific word may have distinctive significance in various setting. So, the principle task of word sense disambiguation is to decide the right feeling of a word utilized as a part of a specific setting. To begin with, Document Summarization assesses the weights, keyword and parts of speech of the considerable number of sentences of a content independently utilizing the algorithms and orchestrates them in diminishing request as indicated by their weights. Next, as indicated by the given level of rundown, a specific number of sentences are chosen from that requested rundown.

Document Summarization; Natural Language Processing; Word Net ; NLTK

[1] Çaglar˘Gulçehre˙ Bing Xiang, Ramesh Nallapati, Bowen Zhou, Cicerodos Santos - Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond, arXiv:1602.06023v5 [cs.CL]
[2] Santosh Kumar Bharti, Korra Sathya Babu, Sanjay Kumar Jena - Automatic Keyword Extraction for Text Summarization: A Survey, National Institute of Technology, Rourkela, Odisha 769008 India e-mail@nitrkl.ac.in 08-February-2017
[3] Abigail See, Peter J. Liu, Christopher D. Manning - Get To The Point: Summarization with Pointer-Generator NetworksarXiv:1704.04368v2 [cs.CL] 25 Apr 2017
[4] Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut - Text Summarization Techniques: A Brief Survey, arXiv:1707.02268v3 [cs.CL] 28 Jul 2017
[5] Shashi Narayan, Shay B. Cohen,Mirella Lapata- Ranking Sentences for Extractive Summarization with Reinforcement Learning arXiv:1802.08636v2 [cs.CL] 16 Apr 2018
[6] Qingyu Zhouy, Nan Yangz, Furu Weiz, Shaohan Huangz, Ming Zhouz, Tiejun ZhaoyyHarbin Institute of Technology, Neural Document Summarization by JointlyLearning to Score and Select SentencesarXiv:1807.02305v1 [cs.CL] 6 Jul 2018
[7] Feny Mehta - Machine Learning Techniques for Document Summarization: A Survey, 2016 IJEDR | Volume 4, Issue 2 |
[8] Ziqiang Cao, Furu Wei,Li Dong,Sujian Li,Ming Zhou- Ranking with Recursive Neural Networks and Its Application to Multi-Document Summarization, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence 2153
[9] Alexander M. Rush Sumit Chopra Jason Weston A Neural Attention Model for Sentence Summarization, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 379–389, Lisbon, Portugal, 17-21 September 2015. c 2015 Association for Computational Linguistics
[10] Deepali K. Gaikwad1 and C. Namrata Mahender A Review Paper on Text Summarization, International Journal of Advanced Research in Computer and Communication Engineering.
[11] elima Bhatia, Arunima jaiswal – Automatic text summarization and its methods- A review , 978-1-4673-8203/16/$31.00 IEEE 2016
[12] Anusha Bagalkotkar, Ashesh Khandelwal, Shivam Pandey, Sowmya Kamath S, A Novel Technique for Efficient Text Document Summarization as a Service 2013 Third International Conference on Advances in Computing and Communications, 978-0-7695-5033-6/13 $26.00 © 2013 IEEE,
[13] Pratibha Devihosur1, Naseer R - Automatic Text Summarization Using Natural Language Processing, International Research Journal of Engineering and Technology (IRJET), e-ISSN: 2395-0056,
[14] Yogan Jaya Kumar, Ong Sing Goh, Halizah Basiron, Ngo Hea Choon and Puspalata C Suppiah- A Review on Automatic Text Summarization Approaches, 2016 Yogan Jaya Kumar, Ong Sing Goh, Halizah Basiron, Ngo Hea Choon and Puspalata C Suppiah. This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license.
[15] Nenkova, Ani, and Kathleen McKeown. Automatic summarization. Now Publishers Inc, 2011.
[16] Mani, Inderjeet, and Mark T. Maybury. Advances in automatic text summarization. the MIT Press, 1999.
[17] Goldstein, Jade, Vibhu Mittal, Jaime Carbonell, and Mark Kantrowitz. "Multi-document summarization by sentence extraction." In Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic summarization-Volume 4, pp. 40-48.
[18] Lal, Partha. "Text Summarization." (2002)
[19] Yang, Guangbing, Wen, Nian-Shing, and Sutinen. "Personalized Text Content Summarizer for Mobile Learning: An Automatic Text Summarization System with Relevance Based Language Model." InTechnology for Education (T4E), 2012 IEEE Fourth International Conference on, pp. 90-97. IEEE, 2012.
[20] Aksoy, Bugdayci, Gur, Uysal, and Can. "Semantic argument frequency-based multi-document summarization." In Computer and Information Sciences, 2009. ISCIS 2009. 24th International Symposium on, pp. 460-464. IEEE, 2009.
[21] Shams, Rushdi, M. M. A. Hashem, Suraiya Rumana Akter, and Monika Gope. "Corpus-based web document summarization using statistical and linguistic approach." In Computer and Communication Engineering (ICCCE), 2010 International Conference on, IEEE, 2010.
[22] Foong, Oi-Mean, and Alan Oxley. "A hybrid PSO model in Extractive Text Summarizer." In Computers & Informatics (ISCI), 2011 IEEE Symposium on, pp. 130-134. IEEE, 2011.
[23] Resnik, Philip. "Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language." arXiv preprint arXiv:1105.5444 (2011)
[24] Salton, Gerard, and Christopher Buckley. "Term-weighting approaches in automatic text retrieval." Information processing & management (1988)

R Chandramma, N.P. Pandurangi, S.V. Jamadagni, Nikhil Chandran, Mohammed Abu Talha Ahmed, "Hybrid Document Summarization using NLP", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.243-247, 2019.

Performance of the student is evaluated and estimated using various evaluation methods and parameters. Modern evaluation methods can have a tremendous impact on the student performance in their curricula. Some courses in the University curriculum has some prerequisites for particular courses and one such course in the University is Data Structures of computer science stream. Students haven’t studied C programming as a prerequisite for this course and the test has been conducted. The results of this test are not satisfactory and hence a bridge course is introduced to overcome the problem of prerequisite and also the pre-test for C programming is also taken for future analysis. The bridge course is conducted for 30hrs in a laboratorysince C is a programming course and post-test is conducted for both the courses. The improvement in results is identified and the performance of studentsis calculated. This research has been conducted on 58 students in the University, the null hypothesis is usedand performed t-Test distribution to analyze the performance of students. This paper tells how a bridge course is useful for the students to perform better and suggests the best suited methods for capturing and analyzing data by choosing the right metrics and performance indicators.

Education Data Mining, Bridge course, Prerequisite, t-Test, Null Hypothesis, Pre-test , Post-test

[1] N G Das, “Statistical Methods”, Mcgraw Hill, Baltimore, India, 9780070083271,2017
[2] Miss. Sharayu N, “Survey on Evaluation of Student`s Performance in Educational Data Mining”,Proceedings of the 2nd InternationalConference ICICCT,978-1-5386-1974-2, 2018
[3] R.Jindal, M.D Borah, “A Survey on Educational Data Mining and Research trends”, International Journal of Database Management System (IJDMS), 5(3), pp-53–73, 2013
[4] Riasyah Novita, Mira Kania Sabariah, Veronikha Effendy, “Identifying Factors That Influence Student Failure Rate using Exhaustive CHAID (Chi-Square Automatic Interaction Detection)” IEEE International Conference on Information and Communication technology (ICoICT) ,pp-482-487, 2015,
[5] Bhardwaj, B.K. and Pal, S, “Data Mining: A prediction for performance improvement using classification”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 4, April 2011.
[6] Ahmed, A.B.E.D. and Elaraby,. “Data Mining: A prediction for Student`s Performance Using Classification Method”, World Journal of Computer Application and Technology, I.S,2(2), pp.43-47, 2014
[7] Oyerinde O. D, Chia P. A“Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression”, Volume 157– No 4,0975 – 8887, January 2017 .
[8] Ayers E., Junker B.W, “Do skills combine additively to predict task difficulty in eighth grade mathematics?”, In AAAI Workshop onEducational Data Mining: Menlo Park, 14-20, 2006.
[9] Desmarais, M.C., Gagnon, M., Meshkinfram,”P. Bayesian Student Models Based on Item to Item Knowledge Structures”, In Conference on Technology Enhanced Learning, Crete, Greece, 1-10, 2006.

Geetha N, Piyush Kumar Pareek, Suhas G K, Sandhya Soman, "Evaluation of Student Performance based on Bridge Course", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.248-253, 2019.

Big Data is an emerging data intensive computing technology to extract intrinsic information from large scale variety forms of rapidly growing data. Big Data Analytics is a data science paradigm, which employs several statistical and machine learning tools for effective and quick decision making. As Cloud computing technologies are coming into reality, several Cloud providers are offering large scale computing and storage facilities as services based on pay and consumption models to the end users. Due, to their service oriented delivery of Clouds, these are turning as back end infrastructure to address several big data mining problems in Big Data computing. As the convergence of Clouds and Big Data is turning into new area aka “Big Data Clouds”, there is a need to address several under pinning technical elements of Big Data computing in Clouds. In this paper, we discuss Service oriented scheduling mechanisms to serve Big Data Analytics in Clouds infrastructure as services. Our main focus is on scheduling aspects, which bring out the several issues, thus meeting the constraints, and Quality of Service (QoS) parameters. We initially, bring about several challenges in scheduling the Big Data problems over Clouds infrastructure, followed by offering the service oriented analytics deliver over Clouds based SLAs and the Quality of Service while considering the dead line, budget constraints.

Scheduling, Big data cloud , Quality of service

[1] K. Ranganathan, and I. Foster, “Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications”, Proc. 11th IEEE Symposium on High Performance Distributed Computing (HPDC). Edinburgh, UK, USA, July 2002.
[2] T. Phan, K. Ranganathan, and R.Sion, “Evolving toward the perfect schedule: Co-scheduling job assignments and data replication in wide-area systems using a genetic algorithm”, Proc. 11th Workshop on Job scheduling Strategies for Parallel Processing. Cambridge MA: Springer-Verlag, Berlin, Germany, June 2005.
[3] H. Mohamed, and D. Epema, “An evaluation of the closeto-files processor and data co-allocation policy in multiclusters”, in Proc. 2004 IEEE International Conference on Cluster Computing, San Diego, CA, USA, Sept. 2004.
[4] S. Venugopal, Scheduling Distributed Applications on Global Grids, Ph.D. Thesis, University of Melbourne, Australia, July 2006.
[5] Apache Hadoop, http://hadoop.apache.org/ (15.06.2014).
[6] Fair Scheduler , http://hadoop.apache.org/docs/r1.2.1/fair_scheduler.pdf(11. 06.2014)
[7] Capacity Scheduler, http://hadoop.apache.org/docs/r1.2.1/capacity_scheduler.pdf (11.06.2014)
[8] S. Gupta, C. Fritz, R. Price, J. D. Kleer, and C. Witteveen, Throughput Scheduler: learning to schedule on heterogeneous Hadoop clusters, Proceedings of the International Conference on Autonomic Computing, ICAC 2013, June, 2013, San Jose, CA, USA.
[9] L. Shi, X. Li , and K.L. Tan, S3: An efficient Shared Scan Scheduler On MapReduce Framework, International Conference on Parallel Processing, ICPP 2011, Taipei, Taiwan, September 2011.
[10] M. D. Assuncao, R. N. Calheiros, S. Bianchi, M. A. S. Netto, and R. Buyya, Big Data Computing and Clouds: Trends and Future Directions, Journal of Parallel and Distributed Computing, Available online 27 August 2014, DOI: 10.1016/j.jpdc.2014.08.003
[11] R. Calheiros, R. Ranjan, A. Beloglazov, C. Rose, and R. Buyya, CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms, Software: Practice and Experience, 41(1): 23-50, Wiley Press, New York, USA, January 2011S. Tamilarasan, P.K. Sharma, “A Survey on Dynamic Resource Allocation in MIMO Heterogeneous Cognitive Radio Networks based on Priority Scheduling”, International Journal of Computer Sciences and Engineering, Vol.5, No.1,pp.53-59, 2017.
[12] R. Kune, K. P. Kumar, A. Agarwal, C. R. Rao, R. Buyya, "Genetic Algorithm based Data-aware GroupScheduling for Big Data Clouds", Proc. International Symposium on Big Data Computing (BDC 2014), pp. 96-104, December 2014.

R Srinath, Arun Biradar, "A Survey on Service Oriented Scheduling for Big Data Cloud", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.254-256, 2019.

Online shopping is becoming more and more common in our daily lives. Tracking user’s interests and behaviour is essential in order to fulfil customer’s requirements. The information about user’s behaviour is stored in the web server logs. Absorbing a view of the process followed by user’s during a session can be of great interest to identify the behavioural patterns. The analysis of such information has focused on applying data mining techniques. Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. It is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs. To address this issue, in this work we proposes a linear temporal logic model checking method for the analysis of structured e-commerce web logs.

Data mining, e-commerce, web logs analysis, behavioural patterns, model checking

[1] K.-J. Kim and H. Ahn, “A recommender system using {GA} kmeans clustering in an online shopping market,” Expert Systems with Applications, vol. 34, no. 2, pp. 1200 – 1209, 2008.
[2] F. M. Facca and P. L. Lanzi, “Mining interesting knowledge from weblogs: a survey,” Data & Knowledge Engineering, vol. 53, no. 3, pp. 225–241, 2005.
[3] J. Couvreur, “On-the-fly verification of linear temporal logic,” in Proceedings of Formal Methods: World Congress on Formal Methods in the Development of Computing Systems, Toulouse (France), September, 1999, pp. 253–271.
[4] S.D. Bernhard, C.K. Leung, V.J.Reimer, and J.Westlake, “Clickstream prediction using sequential stream mining techniques with markov chains,” in Proceedings of the 20thInternational Database Engineering & Applications Symposium, ser. IDEAS ’16. New York, NY, USA: ACM, 2016, pp. 24–33.

Smriti Gupta, Komal Kumari, Latha A, "Tracking The User’s Behaviour in E- Commerce Website", International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.257-260, 2019.