Volume-6 , Issue-12 , Dec - 2018 Go Back

Fraud detection in credit card transactions have become mandatory for the financial services industry due to the huge levels of automations observed in the industry. This work presents a Feature Augmentation based Boosted Ensemble (FABE) for credit card fraud detection on huge data. The proposed model integrates two major components; feature augmentation and ensemble creation. Feature augmentation phase performs feature reduction, feature transformation and feature engineering. Feature reduction aids in effective elimination of unnecessary features, while feature transformation and feature engineering aids in creation of new features that can aid in better predictions. The ensemble creation phase models a boosted ensemble using Decision Trees. Multiple training data bags are created, and multiple base learners are created. The learner with highest weight and lowest error levels is iteratively modelled and used as the final learner. Experiments were performed and comparisons with existing models in literature exhibit the high-performance levels of the proposed FABE model.

Credit card fraud detection; Ensemble model; Feature Augmentation; Feature Reduction; Feature Engineering; Boosting

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[9] A. Somaundaram., U.S. Reddy, "Risk based bagged ensemble (RBE) for credit card fraud detection." In Proceedings of International Conference on Inventive Computing and Informatics (ICICI-17), pp. 670-674, 2017
[10] G. Vaughan, “Efficient big data model selection with applications to fraud detection”, International Journal of Forecasting, 2018
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[13] A.Somasundaram, and U.S. Reddy, "Cost Sensitive Risk Induced Bayesian Inference Bagging (RIBIB) for Credit Card Fraud Detection", Journal of Computational Science, 2018
[14] A. Somasundaram, U. S. Reddy, "Parallel and incremental credit card fraud detection model to handle concept drift and data imbalance", Neural Computing and Applications, pp. 1-12, 2018
[15] A. Somasundaram., U.S. Reddy, "Credit Card Fraud Detection Using Non-Overlapped Risk Based Bagging Ensemble (NRBE)" In Proceedings of IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-4, 2017
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V. Sobanadevi, G. Ravi, "Credit Card Fraud Detection using Feature Augmentation based Boosted Ensemble (FABE)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.841-846, 2018.

— An unmanned ground vehicle (UGV) is a vehicle that operates on the ground without an onboard human presence. UGVs can be used for many applications where it may be extremely dangerous, impossible, inconvenient, and unreachable by human operators. The UGV is equipped with a set of sensors to monitor the environment, and then it can take an autonomous decision or pass the sensory information to a human operator at a different location who will control the vehicle through wireless or wired communication channel. Study and implementation of various controlling techniques for Unmanned Ground Vehicle are our primary objective. Most of the techniques presently under use are extremely complex and costly as they require high bandwidth communication channel and complex hardware. We have used Short Messaging Service (SMS) to control the UGV, which eliminates the use of high bandwidth channels, costly mobile devices and internet or data connection network. The UGV will be continuously under the supervisory control of an operator till it is under the area covered by the GSM/GPRS mobile network. SMS would be sent through the mobile phone to the UGV housing a GSM module and an Arduino UNO. The SMS will be decoded and a set of signals corresponding to those SMS will be generated and sent to the controlling circuit of the vehicle. Entire experiment was carried out using Arduino UNO, GSM module SIM900A, Motor control driver unit based on L293D, geared DC motors, Aluminium based vehicle chassis, 9V DC batteries and a valid SIM card with enabled SMS facility.

SMS, GSM Module SIM 900A, Arduino Uno, Mobile phone, Near far communication (NFC).

[1] Raul Ionel, Gabriel Vasiu, Septimiu Mischie, `` GPRS based data acquisition and analysis system with mobile phone control``, Measurement 45 (2012) 1462–1470, Elsevier.
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[6] B.A.M. Wakileh, K.F. Gill, `` Robot Control Using Self-Organizing Fuzzy Logic”, 0166-3615/90, 1990 - Elsevier Science Publishers B.V.
[7] Alexander A S Gunawan*, Williama, Boby Hartanto, Aditya Mili, Widodo Budiharto, Afan G Salman, Natalia Chandra, “Development of Affordable and powerful swarm mobile robot based on smartphone android and IOIO board”, Elsevier, Procedia Computer Science, 2nd International Conference on Computer Science and Computational Intelligence 2017.
[8] Ru Nie,``Design and realization of intelligent vehicle based on Android mobile phone Bluetooth control``, Advanced Materials Research Vols 926-930 (2014) pp 2466-2469, Trans Tech Publications, Switzerland.

Tanmoy Biswas, Arup Kr. Goswami, Manas Pal, Saptarshi Naskar, "SMS Controlled Unmanned Ground Vehicle," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.847-854, 2018.

In recent years, due to new technologies databases are growing rapidly. This has resulted in evolution of term “Big Data”. Big Data is nothing but huge data sets that can be processed and analysed to get useful information. Big Data Analytics is the process of inspecting large datasets, extracting information from it. These meaningful information obtained from large data sets can be utilized in business. Big Data Analytics helps business to take innovative, better decisions so as to improve business output. Big Data is large in size, grows very quickly hence impossible for traditional systems to process it. In this paper we review the importance of Big Data, Big Data Analytics and we propose an approach for using uncovered patterns, information of Big Data Analytics in Business Intelligence. Also an attempt is made in listing challenges in Big Data Analytics.

Big Data, Business Intelligence, Big data Analytics, Data Clustering, Data Optimization, Classification

[1] Sun, Zhaohao & Zou, Huasheng & Strang, Kenneth David. (2015), “Big Data Analytics as a Service for Business Intelligence”, 58. 162-169. 10.1080/08874417.2016.1220239
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[3] Pekka Pääkkönen, Daniel Pakkala, “Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems, Big Data Research”, Volume 2, Issue 4, 2015, Pages 166-186, ISSN 2214-5796
[4] Alexandre Borba, Ana Akemilkeda, “Big Data Usage in the Marketing Information System”, Journal of data analysis and information processing 2014, 2, 77-85
[5] Pingale Murali Manish, Sheetal Kasale, Anit Dani Simon, “Banking & Big Data Analytics”, OSR Journal of Business and Management (IOSR-JBM), e-ISSN: 2278-487X, p-ISSN: 2319-7668 PP 55-58
[6] Hossin Hassani, Xu Huang, Emmanual Silva, “Digitalization and Big Data Mining in Banking”, MDPI, Received: 27 June 2018; Accepted: 17 July 2018; Published: 20 July 2018
[7] Srivastava, Utkarsh & Gopalkrishnan, Santosh, “Impact of Big Data Analytics on Banking Sector: Learning for Indian Banks”, Procedia Computer Science. 50. 643-652. 10.1016/j.procs.2015.04.098
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[9] Althaf Rahaman.Sk, Sai Rajesh.K, .Girija Rani K, “Challenging tools on Research Issues in Big Data Analytics”, 2018 IJEDR | Volume 6, Issue 1 | ISSN: 2321-9939
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[12] Siddu P. Algur, Prashant Bhat, “Web Video Mining: Metadata Predictive Analysis using Classification Techniques”, I.J Information Technology and Computer Science 2016 in MECS, DOI:10.5815/ijitcs.2016.02.09
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Prashant Bhat, Prajna Hegde, Pradnya Malaganve, "A Novel Framework for Big Data Analytics in Business Intelligence," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.855-859, 2018.

Education of forthcoming century is entirely based on technology. This technology enhances the power and style of learning. This leads to either achieve the desired aim or precede in the learning. The technology based education always offers dynamic adaptation to individual student. However the revolution in learning process has changed the entire traditional concept of learning. E-learning provides a personalized educational environment, which may give complexity in learning and decision making process. Researcher had attempted to focus on this complexity and endeavors to find out more appropriate method for its illustration by taking review of various published research articles. This paper throws light on fuzzy inference system and its mechanism by applying fuzzy logic soft computing tool. Researcher has taken care of measure attribute of fuzzy logic for getting minimal and uncertain data. It also revels prediction in e-learning, empowerment of individual and behavioral learner for making it ease and providing cost benefit to ratio.

Fuzzy logic, Learning Style, Learning model, visual, verbal, behavioral

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Sangita A. Jaju, Sudhir B Jagtap, "Fuzzy Logic: A method to Develop Human like Capabilities for Artificial Intelligence," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.860-865, 2018.

Estimations play a very crucial role in the efficient termination of the task in software development life cycle (SDLC). Incorrect estimations hamper the progress of the project. As software estimation plays a critical role to control the project failures rate, estimation in the beginning of the software life cycle turns out to be very important goal for the software engineering community. For the effective development of the software project, early size estimation is considered an important parameter as it is essential for estimating the cost and total development effort. Earlier estimation leads to better project management .The main objective of this article is to explore the present literature with an intention to gain familiarity with the situation to examine the software estimation model and techniques. These estimation model and techniques helps in estimating the software size during the early phase of SDLC and acknowledge the gaps in the literature for future directions.

SDLC,UML,Metrics,Early Size estimation

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Varinder Kaur Attri , Jatinder Singh, "A Survey on Early Size Estimation Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.866-874, 2018.

Machine Learning is a process which begins with observations of data to make better decisions of new data in future. Machine Learning algorithms divides as Supervised Machine Learning, Unsupervised Machine Learning, Semi- Supervised Machine Learning and Reinforcement Learning. In this paper, we focus on Supervised Machine Learning Algorithms especially its error rates. A Supervised learning algorithm analyses the training data and produces a classifier (conditional function), which can then be used for mapping test sets. We compare the various Supervised Machine Learning algorithms in terms of its error rates in this paper.

Supervised Machine Learning, Classifier, Error Rate

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D.Sathiya, S. V. Evangelin Sonia, "A Comparative Study of Supervised Machine Learning Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.875-878, 2018.

Malware is the acronym of Malicious Software. It has become a big threat in today’s computing world. The threat is increasing with a greater pace as the use of Internet in our day to day activities is growing extensively. The number of malware creators and websites distributing malware is increasing at an alarming rate which attracts researchers and developers to develop a better security solution for it. Developing an efficient malware detection technique is still an ongoing research. Understanding malware, features of malware, analysis methods and detection techniques are the prerequisites of malware research. In this paper, we have studied a few past research works based on API calls, N-Grams, Opcodes features used in malware detection. A detailed fundamental concept of malware detection is also presented in this paper. Use of Data mining algorithms in malware detection, different types of malware detection and analysis methods along with their pros and cons are also presented here. Aim of this paper is to gain prerequisite knowledge of malware research and concepts of malware detection techniques.

Malware detection, API call Sequence, Malware feature, Opcode sequence, n-grams, Data mining

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Om Prakash Samantray, Satya Narayana Tripathy, Susant Kumar Das, "A Theoretical Feature-wise Study of Malware Detection Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.879-887, 2018.

The Big Data and Internet of Things (IoT) are two most emerging techniques in latest years. Data is created constantly & ever increasing rate. Different areas like mobile phones, social media, various medical imaging technologies & more creation of new data. This must be stored somewhere for some purpose. Big data is data whose scale, distribution, diversity, timeliness require the use of new technical architectures & analytics to enable insights that unlock new sources of business value. Internet Of Things is new revolution in abilities of the end point that are connected to the Internet. IoT is about devices, data and connectivity. The real value of Internet of Things is about creating smarter products, delivering intelligent insights and providing new business outcomes .This paper shows factors and relation between Big Data and Internet Of Things. Huge data generated by different IoT devices must be analyzed by different Big Data Analytic tools.

Big Data, Internet Of Things, Data Analytics

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Jadhav Jayshree M., Kulkarni Chandraprabha V. , "Big Data Analysis: Enables Internet of Things," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.888-891, 2018.

The research paper analyses the sexual violence against women in Tamil Nadu. India seemed to display a contradictory picture with on the one hand, a fast growing economy and progressive indicators of development, but on the other hand portrayed as a country with increasing reports on sexual violence against women and problems with gender inequality. The objective of this study was to try to gain a deeper understanding and reflect upon underlying factors of increased reporting of sexual violence in India especially in Tamil Nadu, and to understand in what way the modernization process possibly could be put in relation to the increased reporting of sexual violence, something that was analysed with help from Durkheim’s theory of anomie. The analysis above suggests that, far from contributing to the discussion and understanding on the structural conditions of violence against women and girls, traditional and new media normalise it. By doing so, media promote gender-based violence. This is why media are currently part of the problem rather than the solution to stopping violence against women. The growth of gender-based violence shows its mechanisms are more sophisticated than they were in the past, as are the forms of representing it in media content. There is a large negotiation across the world that offence against women is frequently failed to report. The report says for every twenty minutes, a woman undergoes sexual assault. There is also a belief that the reported crime data against women is twisted. According to Tamil Nadu police, the consciousness in the midst of women has improved that they confront to file complaints.

Gender and Violence, sexual harrasment, media portrayal, discrimination, women and gender inequality

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"Tamil Nadu tops in domestic violence cases". .(Retrieved 25 feb 2014) .
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Deccan Herald (Chennai). 23 April 2013. Retrieved 1 January 2014

J. Deepa, "The Role Of Mass Media In Creating Awareness On Female Sexual Assault," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.892-895, 2018.

Solid waste makes an ever growing problem at local and as well as universal levels. The disposal of solid waste is polluting all the factors of environment at regional and global levels. The world is facing a great challenge due to increasing urbanization as there is rise in the amount of generated waste and littering due to high demand for food products and other essentials. The main problem is faced in the developing countries than in developed countries was the continuous flow of garbage in all places where public people move around to create the unhygienic conditions. It may invoke several contagious diseases among the nearby people. To avoid such situations and to improve the cleaning, ‘smart waste management system using on IOT’ is proposed. The waste in the dustbins is checked with the help of sensors used in the system, the smallest version, E- nose on a chip containing both the sensors and the processing components then information is sent to the required control room through GSM/GPRS system. RL78/G11 Microcontroller is used to communicate the sensor system with GSM system. An android application is been modeled to monitor the information related to the waste for different selected locations.

solid waste collection, RL 78/G11 microcontroller, GSM, Sensor, E nose Sensor

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Venkateswarlu pynam, Roje Spandana Rajeti, Manasa Bobbadi, "Smart Solid Waste Collection System Based on Internet of Things," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.896-898, 2018.