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Software applications developed in recent times are written in lakhs of lines of code and have become increasingly complex in terms of structure, behaviour and functionality. At the same time, development life cycles of such applications reveal a tendency of becoming increasingly shorter, due to factors such as rapid evolution of supporting and enabling technologies. As a consequence, an increasing portion of software development cost shifts from the creation of new artefacts to the adaptation of existing ones. A key component of this activity is the understanding of the design, operation, and behaviour of existing artefacts of the code. For instance, in the software industry, it is estimated that maintenance costs exceed 80% of the total costs of a software product’s lifecycle, and software understanding accounts for as much as half of these maintenance costs. Software Comprehension is a key subtask of software maintenance and evolution phase, which is driven by the need to change software. This paper will help in enhancing the ability of the developers to read and comprehend large pieces of software in an organized manner, even in the absence of adequate documentation by using existing open source tools. It highlights the program elements, components, its analytical solutions for understanding, comprehensions and extension.

beautifiers, profilers, slicers, top-down, version control systems

[1] G. M. Weinberg, Editor, “The Psychology of Computer Programming”, vol. 932633420, 1971. New York: Van Nostrand Reinhold.
[2] M. A. Storey, “Theories, Tools and Research Methods in Program Comprehension: Past, Present and Future”, Software Quality Journal, vol.14 (3), pp. 187-208, 2006.
[3] T. J. Biggerstaff, B. G. Mitbander, and D. E. Webster,”Program Understanding and the Concept Assignment Problem”, Communications of the ACM, vol. 37(5), pp. 72-82, 1994.
[4] R. P. Gabriel, Editor, “Patterns of Software”, vol. 62, 1996, Newyork, Oxford University Press.
[5] S. Rugaber, “Program Comprehension for Reverse Engineering”, In AAAI Workshop on AI and Automated Program Understanding, San Jose, California, pp. 106-110, 1992.
[6] D. L. Parnas, “On the Criteria to be used in Decomposing Systems into Modules”, Coomunications of the ACM, vol. 15(12), pp. 1053-1058, 1972.
[7] V. Rajlich and N. Wilde, “The Role of Concepts in Program Comprehension”, In Program Comprehension, Proceedings, 10th International Workshop on IEEE, pp. 271-278, 2002.
[8] J. Siegmund, C. Kastner, S. Apel, A. Brechmann and G. Saake,” Experience from Measuring Program Comprehension-Toward a General Framework, 2013.
[9] B. Di Martino, C. W. Kebler, “Two Program Comprehension Tools for Automatic Parallelization,” IEEE Concurrency, vol. 8(1), pp. 37-47, 2000.
[10] X. Xia, L. Bao, D. Lo, Z. Xing, A. E. Hassan ans S. Li,” Measuring Program Comprehension: A Large-Scale Field Study with Professionals”, IEEE Transactions on Software Engineering, 2017.
[11] R. Schauer and R. K. Keller, “ Integrative Levels of Program Comprehension”, In Reverse Engineering, WCRE`08, 15th Working Conference on IEEE, pp. 145-154, 2008.
[12] N. Saroni, S. A. Aljunid, S. M. Shuhidan, and A. Shargabi, “An Empirical Study on Program Comprehension Task Classification of Novices”, In e-Learning, e-Management and e-Services (IC3e), 2015 IEEE Conference on IEEE, pp. 15-20, 2015.
[13] R. Wettel and M. Lanza, “Program Comprehension through Software Habitability”, in Program Comprehension, ICPC`07,15th IEEE International Conference on IEEE, pp. 231-240, 2007.
[14] N. Sasirekha, a. E. Robert and D. M. Hemlata, “Program Slicing Techniques and its Applications”, arXiv preprint arXiv: pp. 1108-1352, 2011.
[15] N. Carvalho, C. da Silva Sousa, J. S. Pinto and A. Tomb, “Formal Verification of kLIBC with the WP Frama-C Plug-in”, in NASA Formal Methods, pp. 343-358, 2014.
[16] M. A. Storey, K. Wong and H. A. Muller,” How do Program Understanding Tools Affect How Programmers Understand Programs?.”, In Reverse Engineering, Proceedings of the 4th Working Conference on IEEE, pp. 12-21, 1997.
[17] Y. Liu, X. Sun, X. Liu and Y. Li, ”Supporting Program Comprehension with Program Summarization”, In Computer and Information Science (ICIS), IEEE/ACIS 13th International Conference on IEEE, pp. 363-368, 2014.
[18] A. Von Mayrhauser and A. M. Vans, “Program Comprehension During Software Maintenance and Evolution,” Computer, vol. 28(8), pp. 44-55, 1995.
[19] B. Cornelissen, A. Zaidman, A. Van Deursen, L. Moonen and R. Koschke, “ Systematic Survey of Program Comprehension through Dynamic Analysis,” IEEE Transactions on Software Engineering, vol. 35(5), pp. 684-702, 2009.
[20] E. Soloway, K. Ehrlich, “Empirical Studies of Programming Knowledge,”IEEE Transactions on Software Engineering, vol. 5, pp. 595-609, 1984.
[21] F. Détienne,”Expert Programming Knowledge: A Schema-Based Approach”, Psychology of Programming, pp. 205-222, 1990.

Jyoti Yadav, "Software Comprehension Using Open Source Tools: A Study," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.657-668, 2019.

IOT is the trending technology which may affect the networking, communication and business. Among the various applications of Internet of Things, healthcare is one of the important one. Heart disease is the leading cause of death worldwide, therefore in order to reduce this there is a need for efficient heart disease detection system. Remote health monitoring system is emerging as an essential part in one’s life. Various wearable sensors either worn or attached to the body of the patients helps in the collection of various health metrics. These sensor devices generate the data at a very high speed and it is difficult to manage and store the huge amount of data. In this paper the review on an IOT framework is given for the prediction of the heart disease. The first part focuses on the acquisition of the data using various sensors, second part focus on the data storage using cloud technologies, and third part is about the analysis of the data using various machine learning algorithms.

ZigBee, Bluetooth, Sensors, Cloud, Data mining, wearable devices

[1] M.U. Farooq, Muhammad Waseem, Sadia Mazhar, Anjum Khairi, Talha Kamal “A Review on Internet of things”, International Journal of Computer Applications, Vol. 113, No.1, pp.1-7 ,2015.
[2] Zeinab Kamal Aldein Mohammed, Elmustafa Sayed AliAhmed, “Internet of Things Applications, Challenges and Related Future Technologies”, World Scientific News, pp.126-148, 2017.
[3] Vandana Sharma, Ravi Tiwari, “A Review on “IOT” & It’s Smart Applications”, International Journal of Science, Engineering and Technology Research (IJSETR), Vol. 5, Issue.2, pp.472-476, 2016.
[4] Stephanie Baker , Wei Xiang, Ian Atkinson, “Internet of Things for Smart Healthcare: Technologies, Challenges and opportunities”, IEEE Access 2017.
[5] Juan Pablo Tello P., Oscar Manjarres, Mauricio Quijano, Arcelio Ulises Blanco, ”Remote Monitoring System of RCG and Temperature Signal using Bluetooth”, International Symponium on information technology and education, IEEE 2012
[6] R.N. Kirtana, Y.V. Lokeswari, “An IoT Based HRV Monitoring System for Hypertensive Patients ”, IEEE international conference on computer, communication, and signal processing, 2017
[7] Nair Siddharth, Shivakumar, M. Sasikala, “Design of Vital Sign Monitor based on Wireless Sensor Networks and Telemedicine technology”
[8] Abdulaziz Shehab, Ahmed Ismail, Lobna Osman, Mohamed Elhoseny and I.M. El-Henawy, “ Quantified Self Using IoT Wearable Devices”, Proceedings of the international conference on advanced intelligent systems and informatics, Springer International publishing, pp.820-831, 2018
[9] Zhe Yang, Qihao Zhou, lei Lei, Kan Zheng, Wei Xiang, “An IoT-cloud based Wearable ECG Monitoring System for Smart Healthcare”, Mobile and Wireless Health , Springer publications2016
[10] Moeen Hassanalieragh, Alex Page, et al, “Health Monitoring and Management using Internet of things (IoT) sensing with clpoud based processing: opportunities and challenges”, international conference on service somputing, IEEE, 2015.
[11] Chao Li, Xiangpei hu, Lili Zhang, “The IoT based heart disease monitoring system for pervasive healthcare service”, international conference on knowledge based and intelligent information and engineering systems, Elsevier publication 2017.
[12] Jihwan Lee, Jaehyo Jung, Youn Tae Kim, ”Design and development of mobile cardiac marker monitoring system for prevention of acute cardiovascular disease”, IEEE 2011.
[13] Priyan Malarvizhi Kumar, Usha Devi Gandhi, “A Novel three tier internet of things architecture with machine learning algorithm for early detection of heart disease ”, international journal of computer and electrical engineering, pp.1-14, 2017.
[14] Meherwar Fatima, Maruf Pasha, “Survey of machine learning algorithms for disease diagnostic”, journal of intelligent learning systems and applications, Vol.9, pp.1-16, 2017.
[15] Uma N Dulhare, “Prediction system for heart disease using Naïve Bayes and particle swarm optimization”, Biomedical research, Vol.29, issue.12, pp.2646-2649, 2018.
[16] Lalitha Kumari Gaddala, Dr. N. Naga Malleswara Rao, “An analysis of heart disease prediction using swarm intelligence algorithms”, International journal of innovations in engineering and technology, Vol.9, issue.3, pp.081-087, 2018.
[17] Sanjay Kumar Sen, “Predicting and diagnosis of heart disease using machine learning algorithms”, International journal of engineering and computer science, Vol.6, issue.6, pp.21623-21631, june 2017.
[18] Youness Khourdiffi, Mohamed Bahaj, “Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization”, International journal of intelligent engineering and systems, Vol.12, no.1, pp.242-252, 2018.
[19] Hamza Turabieh, “Ahybrid ANN-GWO algorithm for prediction of heart disease”, American journal of operations research, vol.6, pp.136-146, 2016.
[20] Hlaudi Daniel Masethe, Mosima Anna Masethe, “Prediction of heart disease using classification algorithm”, Proceedings of the world congress on engineering and computer science, Vol.2, pp.22-24, 2014.

Komal Saini , Sandeep Sharma, "Review on the Heart Disease Detection Using IoT Framework," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.669-674, 2019.

Cloud security is the protection of data stored online from theft, leakage and deletion. Hierarchical attribute-based encryption scheme is first designed for a document collection. A set of documents can be encrypted together if they share an integrated access structure. Compared with the CP-ABE schemes, both the ciphertext storage space and time costs of encryption/decryption are saved. Then, an Index Structure named attribute-based retrieval features (ARF) tree is constructed for the document collection based on the TF-IDF model and the documents attributes. A depth-first search algorithm (DFS) for the ARF tree is designed. It is difficult to search the large collection of documents. To overcome the difficulties an IDDFS method is introduced.

Cloud computing, document Retrieval, file hierarchy, attribute-based encryption

[1] N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, “Privacy-preserving multi-keyword ranked search over encrypted cloud data,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, pp. 222–233, Jan. 2014.
[2] C. Chen, X. Zhu, P. Shen, J. Hu, S. Guo, Z. Tari, and A. Zomaya, “An efficient privacy-preserving ranked keyword search method,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, pp. 951–963, Apr. 2016.
[3] H. Deng, Q. Wu, B. Qin, J. Domingo-Ferrer, L. Zhang, J. Liu, and W. Shi, “Ciphertext-policy hierarchical attribute-based encryption with short ciphertext,” Information Sciences, vol. 275, pp. 370–384, Aug. 2014.
[4] Z. Fu, K. Ren, J. Shu, X. Sun, and F. Huang, “Enabling personalized search over encrypted outsourced data with efficiency improvement,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, pp. 2546–2559, Sep. 2016.
[5] Y. Guo, J. Li, Y. Zhang, and J. Shen, “Hierarchical attribute-based encryption with continuous auxiliary inputs leakage,” Security and Communication Networks, vol. 9, no. 18, 2016.
[6] J. Li, X. Lin, Y. Zhang, and J. Han, “Ksf-oabe: Outsourced attribute-based encryption with keyword search function for cloud storage,” IEEE Transactions on Services Computing, vol. 10, no. 5, pp. 715–725, 2017.
[7] E. Luo, Q. Liu, and G. Wang, “Hierarchical multi-authority and attribute based encryption friend discovery scheme in mobile social networks,” IEEE Communications Letters, vol. 20, pp. 1772–1775, Sep. 2016.
[8] Y. S. Rao, “A secure and efficient ciphertext-policy attribute-basedsigncryption for personal health records sharing in cloud computing,”Future Generation Computer Systems, vol. 67, pp. 133–151, Feb. 2017.
[9] S. Wang, J. Zhou, J. K. Liu, J. Yu, J. Chen, and W. Xie, “An efficient file hierarchy attribute-based encryption scheme in cloud computing,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 6, pp. 1265–1277, 2016.
[10] Z. Xia, X. Wang, X. Sun, and Q. Wang, “A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, pp. 340–352, Jan. 2016.

M. Muthuselvi, Pemi. P, Rajasree. S, Sowmiya. C, "Secure Retrieval and Revocable Attribute-Based Encryption Scheme in Cloud Storage," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.675-679, 2019.

Tennis is one of the leading sports all around the world. In the past few years there has been tremendous growth in the use of high-end technology to follow every instance of every match in a Tennis Tournament. Unfortunately, the visually disabled players cannot completely enjoy following and playing the sport as they cannot determine the position of the ball precisely. This rises their stress and anxiety levels and thus many players eventually give up. Training the players and the instructors is difficult because the players need to be extensively trained to determine where is the ball currently, how is the ball being hit and where will the ball land on the court. Thus, there is a dire need for a system that makes the sport less stressful and more enjoyable. The existing systems for the visually disabled Tennis make use of special sound balls that rattle when they bounce. This paper presents a extensive survey of Tennis for the visually disabled, the existing technologies that are being used for analyzing the game and, finally, propose a system that would allow the visually disabled players to follow and play the sport with enthusiasm, just like everyone by following the match closely.

Computer Vision, Internet of Things (IoT), Object detection, Object tracking, Sound Generation, Visually impaired tennis

[1] Pascolini D, Mariotti SPM. “Global estimates of visual impairment: 2010”, British Journal Ophthalmology Online, 1 December 2011.
[2] X. Yu; C.-H. Sim; J.R. Wang; L.F. Cheong, "A trajectory-based ball detection and tracking algorithm in broadcast tennis video", IEEE International Conference on Image Processing, 2004. ICIP `04 on 24-27 Oct. 2004.
[3] M. Michalko; J. Onuška; A. Lavrín; D. Cymbalák; O. Kainz, “Tracking the object features in video based on OpenCV”, International Conference on Emerging eLearning Technologies and Applications (ICETA),2016 on 24-25 Nov. 2016.
[4] Nicholas J. Butko; Javier R. Movellan, "Optimal scanning for faster object detection", IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009 on 20-25 June 2009.
[5] Won Jin Kim; In-So Kweon, “Moving object detection and tracking from moving camera”, 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2011 23-26 Nov. 2011.
[6] Ivan Culjak; David Abram; Tomislav Pribanic; Hrvoje Dzapo; Mario Cifrek, “A brief introduction to OpenCV”, Proceedings of the 35th Convention International on MIPRO, 2012 21-25 May 2012.
[7] Kari Pulli; Anatoly Baksheev; Kirill Kornyakov; Victor Eruhimov, “Real-time computer vision with OpenCV”, Magazine Communications of the ACM, Volume 55 Issue 6, June 2012, pp. 61-69
[8] S. Prasad and S. Sinha, "Real-time object detection and tracking in an unknown environment," 2011 World Congress on Information and Communication Technologies, Mumbai, 2011, pp. 1056-1061.
[9] Singh, B. B. V. L. Deepak, T. Sethi and M. D. P. Murthy, "Real-time object detection and Tracking using color feature and motion," 2015 International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, 2015, pp. 1236-1241.
[10] J. Chatrath, P. Gupta, P. Ahuja, A. Goel and S. M. Arora, "Real-time human face detection and tracking," 2014 International Conference on Signal Processing and Integrated Networks (SPIN), Noida, 2014, pp. 705-710. [16] K. Bhure and J. Dhande, "Object Detection Methodologies for Blind People", International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 5, No. 1, pp. 194-198, 2017.
[11] K. Bhure and J. Dhande, "Object Detection Methodologies for Blind People", International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 5, No. 1, pp. 194-198, 2017.

Ahlam Ansari, Zainab Pirani, Shaikh Guffran, Sayyed Naziya Ajaz, Khan Junaid, "Audio Assistance in Tennis for The Visually Disabled," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.680-683, 2019.

Expiry of routing information is the main problem in the mobile network. The objective of the routing protocol is to establish a correct and efficient route to deliver data among mobile nodes in the network. The current routing protocols have many limitations so we need to improve the performance of existing routing protocols. In a dynamic environment, the protocol chosen for routing purpose should be the best in term of Overhead, Loop-Free, Complexity, Congestion Control, Reliability, Load Balancing, Throughput, and Route Maintenance etc. To solve the protocol problem and to improve network capacity we can combine the concept of IoT with MANET. Combing new standards, modern devices and advanced Technologies lead to the capability to develop more powerful MANET- IoT smart devices. In this paper, we have also summarized various Current limitations and future trends in mobility models. Finally, we come with a newly proposed design for inhancing routing protocol performance in MANET-IoT smart environment.

Quality of Service, Mobile Ad-hoc Network, IoT, VANET, Routing Protocol

[1] Devi, M., & Gill, N. S. Study of Mobile Ad hoc Network Routing Protocols in Smart Environment. International Journal of Applied Engineering Research, 13(16), (2018),12968–12975.
[2] X. Jia, Q. Feng, T. Fan, and Q. Lei, “RFID Technology and Its Applications in the Internet of Things ( IoT ),” in Consumer Electronics, Communications, and Networks (CECNet), 2012 2nd International Conference, 2012, pp. 1282–1285. Available Online: 10.1109/CECNet.2012.6201508.
[3] S. Nazeem Basha, S. Jilani, and M. Arun, “An Intelligent Door System using Raspberry Pi and Amazon Web Services IoT,” Int. J. Eng. Trends Technol., vol. 33, no. 2, p. 5381, 2016.
[4] H. Tahir, A. Kanwer, and M. Junaid, “Internet of Things ( IoT ): An Overview of Applications and Security Issues Regarding Implementation,” Int. J. Multidiscip. Sci. Eng., vol. 7, no. 1, pp. 14–22, 2016.
[5] D. Kyriazis and T. Varvarigou, “Smart, autonomous and reliable Internet of Things,” Procedia Comput. Sci., vol. 21, pp. 442–448, 2013. Available Online: https://doi.org/10.1016/j.procs.2013.09.059.
[6] N. Gupta, H. Saeed, S. Jha, and S. Pandey, “Study and Implementation of IOT based Smart Healthcare System,” in International Conference on Trends in Electronics and Informatics, 2017, pp. 541–546. Available Online: 10.1109/ICOEI.2017.8300718.
[7] J. Saha et al., “Advanced IOT Based Combined Remote Health Monitoring , Home Automation and Alarm System,” IEEE Internet Things J., pp. 602–606, 2018. Online Available: 10.1109/CCWC.2018.8301659.
[8] Caroline Chibelushi1, Alan Eardley, Abdullahi Arabo,” Identity Management in the Internet of Things: the Role of MANETs for Healthcare Applications “Computer Science and Information Technology,Vol 1,Issue(2), Pages 73-81, 2013.
[9] Daniel G. Reina , Sergio L. Toral, “The Role of Ad Hoc Networks in the Internet of Things: A Case Scenario for Smart Environments”,Chapter, Internet of Things and Inter-cooperative Computational Technologies for Collective Intelligence, Vol 460,Pages 89-113, ISBN: 978-3-642-34951-5 (Print) 978-3-642-34952-2 (Online)-2013.
[10] E. Jacob and P. Sivraj, “Performance Analysis of MANET Routing Protocols in Smart City Message Passing,” Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, pp. 1255–1260, 2016. 10.1109/ICACCI.2016.7732218
[11] M. Souil and A. Bouabdallah, “On QoS provisioning in context-aware wireless sensor networks for healthcare BT - 2011 20th International Conference on Computer Communications and Networks, ICCCN 2011, July 31, 2011 - August 4, 2011,” p. IEEE Communications Society; U.S. National Science, 2011. DOI: 10.1109/ICCCN.2011.6005777
[12] Paolo Bellavista, Giuseppe Cardone,Antonio Corradi, Luca Foschini,” Convergence of MANET and WSN in IoT Urban Scenarios”, IEEE Sensors Journal, Vol. 13, No. 10, Oct 2013.
[13] Yan Sun Jingwen Bai , Hao Zhang , Roujia Sun, Chris Phillips ,” A Mobility-Based Routing Protocol for CR Enabled Mobile Ad Hoc Networks”, International Journal of Wireless Networks and Broadband Technologies (IJWNBT),Vol 4, Issue 1, 2015.

Munisha Devi, Nasib Singh Gill, Deepti Sehrawat, "Exploring Possibilities of MANET Protocols for IoT Enabled Smart Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.684-688, 2019.

Supervisory Control and Data Acquisition (SCADA) systems have emerged as critical systems of national importance in the recent times due to their deployments at critical infrastructures. Since SCADA systems are of critical importance and being high value targets, these systems attract large interest for being target for security fissures. SCADA systems security exemplifies a critical challenge in present world. High profile cyber security threats are the recent phenomenon, yet the systems running critical industrial processes are typically a generation older. There are many legacy systems that may be vulnerable to cyber-attack because cyber security was simply not a consideration at the time of initial design and implementation stages. The security of even recently deployed systems may also pose a challenge. This paper explores and discusses the security challenges, publication trends in terms of graphical representation, and research opportunities in the SCADA system.

SCADA, Smart Grid, Publication Trends, Security Challenges, Threats

[1]. Khurana, H., Hadley, M., Lu, N., & Frincke, D. A. (2010), ‘Smart-grid security issues’. IEEE Security & Privacy, vol. 8, no. 1.
[2]. Delgado, V., Martins, J. F., Lima, C., & Borza, P. N. (2015), ‘Smart grid security issues’, Proceedings of IEEE 9th International Conference on Compatibility and Power Electronics, pp. 534-538.
[3]. Sullivan, D., Luiijf, E., & Colbert, E. J. (2016), ‘Components of industrial control systems’, Advances in Information Security, Springer International Publishing, vol. 66, pp. 15-28.
[4]. Tan, S., De, D., Song, W. Z., Yang, J., & Das, S. K. (2017), ‘Survey of Security Advances in Smart Grid: A Data Driven Approach’. IEEE Communications Surveys & Tutorials, vol. 19, no. 1, pp. 397-422.
[5]. Mo, Y., Kim, T. H. J., Brancik, K., Dickinson, D., Lee, H., Perrig, A., & Sinopoli, B. (2012), ‘Cyber–physical security of a smart grid infrastructure’, Proceedings of the IEEE, vol. 100, no. 1, pp. 195-209.
[6]. Wang, W., & Lu, Z. (2013), ‘Cyber security in the Smart Grid: Survey and challenges’, Computer Networks, vol. 57, no. 5, pp. 1344-1371.
[7]. Bekara, C. (2014), ‘Security issues and challenges for the iot-based smart grid’, Procedia Computer Science, vol. 34, pp. 532-537.
[8]. Cherdantseva, Y., Burnap, P., Blyth, A., Eden, P., Jones, K., Soulsby, H., & Stoddart, K. (2016), ‘A review of cyber security risk assessment methods for SCADA systems’, Computers & Security, vol. 56, no. 1, pp. 1-27.
[9]. McBride, A. J., & McGee, A. R. (2012), ‘Assessing smart grid security’, Bell Labs Technical Journal, vol. 17, no. 3, pp. 87-103.
[10]. Safa, H. H., Souran, D. M., Ghasempour, M., & Khazaee, A. (2016), ‘Cyber security of smart grid and SCADA systems, threats and risks’, Proceedings of CIRED Workshop, pp. 245.
[11]. Korman, M., Välja, M., Björkman, G., Ekstedt, M., Vernotte, A., & Lagerström, R. (2017). ‘Analyzing the Effectiveness of Attack Countermeasures in a SCADA System’, Proceedings of ACM 2nd Workshop on Cyber-Physical Security and Resilience in Smart Grids, pp. 73-78.
[12]. Gao, J., Liu, J., Rajan, B., Nori, R., Fu, B., Xiao, Y., & Philip Chen, C. L. (2014), ‘SCADA communication and security issues’, Security and Communication Networks, vol. 7, no. 1, pp. 175-194.
[13]. Stefanov, A., Liu, C. C., Govindarasu, M., & Wu, S. S. (2015), ‘SCADA modeling for performance and vulnerability assessment of integrated cyber–physical systems’, International Transactions on Electrical Energy Systems, vol. 25, no. 3, pp. 498-519.
[14]. Kim, H. (2012), ‘Security and vulnerability of SCADA systems over IP-based wireless sensor networks’, International Journal of Distributed Sensor Networks, vol. 12, no. 268478.
[15]. Line, M. B. (2014), ‘Why securing smart grids is not just a straightforward consultancy exercise’, Security and Communication Networks, vol. 7, no. 1, pp. 160-174.
[16]. Yan, Y., Qian, Y., Sharif, H., & Tipper, D. (2013), ‘A survey on smart grid communication infrastructures: Motivations, requirements and challenges’, IEEE communications surveys & tutorials, vol. 15, no. 1, pp. 5-20.
[17]. Tawde, R., Nivangune, A., & Sankhe, M. (2015), ‘Cyber security in smart grid SCADA automation systems’, Proceedings of IEEE Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1-5.
[18]. Ashok, A., Hahn, A., & Govindarasu, M. (2014), ‘Cyber-physical security of Wide-Area Monitoring, Protection and Control in a smart grid environment’, Journal of advanced research, vol. 5, no. 4, pp. 481-489.
[19]. Stefanov, A., & Liu, C. C. (2014), ‘Cyber-physical system security and impact analysis’, Proceedings of the 19th World Congress the International Federation of Automatic Control, vol. 47, no. 3, pp. 11238-11243.
[20]. Hawk, C., & Kaushiva, A. (2014), ‘Cybersecurity and the smarter grid’, The Electricity Journal, vol. 27, no. 8, pp. 84-95.
[21]. Rice, E. B., & AlMajali, A. (2014), ‘Mitigating the risk of cyber attack on smart grid systems’, Procedia Computer Science, vol. 28, pp. 575-582.
[22]. Sajid, A., Abbas, H., & Saleem, K. (2016), ‘Cloud-assisted IoT-based SCADA systems security: A review of the state of the art and future challenges’, IEEE Access, vol. 4, pp. 1375-1384.
[23]. Ciancamerla, E., Fresilli, B., Minichino, M., Patriarca, T., & Iassinovski, S. (2014), ‘An electrical grid and its SCADA under cyber attacks: Modelling versus a Hybrid Test Bed’, Proceedings of IEEE International Carnahan Conference on Security Technology (ICCST), pp. 1-6.
[24]. Dondossola, G., & Terruggia, R. (2015), ‘Cyber security of smart grid communications: Risk analysis and experimental testing’, Springer Berlin Heidelberg, pp. 169-193.
[25]. Zhang, Y., Wang, L., Xiang, Y., & Ten, C. W. (2015), ‘Power system reliability evaluation with SCADA cybersecurity considerations’, IEEE Transactions on Smart Grid, vol. 6, no. 4, pp. 1707-1721.
[26]. Kuzlu, M., Pipattanasompom, M., & Rahman, S. (2017), ‘A comprehensive review of smart grid related standards and protocols’, Proceedings of IEEE 5th International Smart Grid and Cities Congress and Fair (ICSG), pp. 12-16.
[27]. Ajayi, A. O., Alese, B. K., Fadugba, S. E., & Owoeye, K. (2014), ‘Sensing the Nation: Smart Grid’s Risks and Vulnerabilities’, International Journal of Communications, Network and System Sciences, vol. 7, no. 05, pp. 151-163.
[28]. Asghar, M. R., & Miorandi, D. (2012), ‘A holistic view of security and privacy issues in smart grids’, Springer, Berlin, Heidelberg, pp. 58-71.
[29]. Ionica, D., Pop, F., Popescu, N., Popescu, D., & Dobre, C. (2018). SCADA Security: Concepts and Recommendations. In International Symposium on Cyberspace Safety and Security, pp. 85-98. Springer, Cham.
[30]. Igure, V. M., Laughter, S. A., & Williams, R. D. (2006), ‘Security issues in SCADA networks’, Computers & Security, vol. 25, no. 7, pp. 498-506.
[31]. Sebastio, S., Scala, A., & D’Agostino, G. (2016), ‘Availability Study of the Italian Electricity SCADA System in the Cloud’, Springer, Cham, pp. 201-212.
[32]. Leverett, E. P. (2011), ‘Quantitatively assessing and visualising industrial system attack surfaces’. University of Cambridge, Darwin College, vol. 7.
[33]. Gold, S. (2009), ‘The SCADA challenge: securing critical infrastructure’, Network Security, vol. 09, no. 8, pp. 18-20.
[34]. Igure, V. M., Laughter, S. A., & Williams, R. D. (2006), ‘Security issues in SCADA networks’, Computers & Security, vol. 25, no. 7, pp. 498-506.
[35]. Chen, T. (2010), ‘Stuxnet, the real start of cyber warfare?’, IEEE Network, vol. 24, no. 6, pp. 2-3.
[36]. Cherdantseva, Y., Burnap, P., Blyth, A., Eden, P., Jones, K., Soulsby, H., & Stoddart, K. (2016), ‘A review of cyber security risk assessment methods for SCADA systems’, Computers & Security, vol. 56, pp. 1-27.
[37]. Idaho National Laboratory, (2011). ‘Vulnerability Analysis of Energy Delivery Control Systems’. [Online] Available at: http://energy.gov/oe/downloads/ vulnerability-analysis-energy-delivery-control-systems [Accessed 12 August 2017].
[38]. Dán, G., Sandberg, H., Ekstedt, M., & Björkman, G. (2012), ‘Challenges in power system information security’, IEEE Security & Privacy, vol. 10, no. 4, pp. 62-70.
[39]. SCADA Systems Made Simple. (2019). [online] Schneider Electric, pp. 4-11. Available at: https://www.schneider-electric.com/en/download/document/998-2095-01-19-12AR0_EN/ [Accessed 23 Jan. 2019].
[40]. Lenzini, G., Oostdijk, M., Teeuw, W., Hulsebosch, B., Wegdam, M., & Enschede, N. (2009). Trust, security, and privacy for the advanced metering infrastructure.
[41]. SCADAguardian. (2019). Retrieved from https://www.nozominetworks.com/ products/ scadaguardian/
[42]. Cherdantseva, Y., Burnap, P., Blyth, A., Eden, P., Jones, K., Soulsby, H. and Stoddart, K.. (2016). A review of cyber security risk assessment methods for SCADA systems. Computers & security, Elsevier, 56, pp.1-27.
[43]. Babu, B., Ijyas, T., Muneer, P. and Varghese, J. (2017). Security issues in SCADA based industrial control systems. In Anti-Cyber Crimes (ICACC), 2nd International Conference on (pp. 47-51). IEEE.
[44]. Fillatre, L., Nikiforov, I. and Willett, P. (2017). Security of SCADA systems against cyber–physical attacks. IEEE Aerospace and Electronic Systems Magazine, 32(5), pp.28-45.
[45]. Rosa, L., Cruz, T., Simões, P., Monteiro, E. and Lev, L. (2017). Attacking SCADA systems: a practical perspective. In Integrated Network and Service Management (IM), IFIP/IEEE Symposium, pp.741-746. IEEE.
[46]. Antón, S.D., Fraunholz, D., Lipps, C., Pohl, F., Zimmermann, M. and Schotten, H.D. (2017). Two decades of SCADA exploitation: A brief history. In Application, Information and Network Security (AINS) Conference, pp. 98-104. IEEE.
[47]. Ali, S., Al Balushi, T., Nadir, Z. and Hussain, O.K. (2018). ICS/SCADA System Security for CPS. In Cyber Security for Cyber Physical Systems, pp. 89-113. Springer, Cham.
[48]. Sun, C.C., Hahn, A. and Liu, C.C. (2018). Cyber security of a power grid: State-of-the-art. International Journal of Electrical Power & Energy Systems, vol. 99, pp. 45-56.
[49]. Hahn, A., Sun, C.C. and Liu, C.C. (2016). Cybersecurity of SCADA within Substations. Smart Grid Handbook, Wiley, pp.1-17.
[50]. Korman, M., Välja, M., Björkman, G., Ekstedt, M., Vernotte, A. and Lagerström, R. (2017). Analyzing the Effectiveness of Attack Countermeasures in a SCADA System. In Proceedings of the 2nd Workshop on Cyber-Physical Security and Resilience in Smart Grids, pp. 73-78. ACM.
[51]. El Anbal, M., El Kalam, A.A., Benhadou, S., Moutaouakkil, F. and Medromi, H. (2016). Securing SCADA Critical Network Against Internal and External Threats. In International Conference on Critical Information Infrastructures Security, pp. 328-339. Springer, Cham.
[52]. Honkus, F. (2016). Responding to Attacks on Industrial Control Systems and SCADA Systems. In Cyber-security of SCADA and Other Industrial Control Systems, pp. 305-322. Springer, Cham.
[53]. Duka, A.V., Genge, B., Haller, P. and Crainicu, B. (2017). Enforcing end-to-end security in SCADA systems via application-level cryptography. In International Conference on Critical Infrastructure Protection, pp. 139-155. Springer, Cham.
[54]. Ahmed, I., Roussev, V., Johnson, W., Senthivel, S. and Sudhakaran, S. (2016). A SCADA system testbed for cybersecurity and forensic research and pedagogy. In Proceedings of the 2nd Annual Industrial Control System Security Workshop, pp. 1-9. ACM.
[55]. Kleinmann, A., Amichay, O., Wool, A., Tenenbaum, D., Bar, O. and Lev, L. (2017). Stealthy deception attacks against SCADA systems. In Computer Security, pp. 93-109. Springer, Cham.
[56].Nazir, S., Patel, S. and Patel, D. (2017). Assessing and augmenting SCADA cyber security: A survey of techniques. Computers & Security, Elsevier, vol. 70, pp. 436-454.
[57].Tesfahun, A. and Bhaskari, D.L., (2016). A SCADA testbed for investigating cyber security vulnerabilities in critical infrastructures. Automatic Control and Computer Sciences, Springer, vol. 50(1), pp.54-62.
[58].Industrial Control System Cyber Emergency Response Team (ICS-CERT). (2019). [online] Available at: https://cset.inl.gov/SitePages/Home.aspx [Accessed 10 Jan. 2019].

A. W. Mir, K. R. Ram Kumar, "A Survey on Security Challenges and Research Opportunities in Smart Grid based SCADA Systems," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.689-706, 2019.

Students are the main stakeholders of institutions and their performance plays a significant role in country development. The aim of institutions is to give excellence educations to their students. It has been observed in the previous works, students of slow performance and dropouts are the most vital issues. Due to early detection of slow performers and dropouts of students, help the teachers, administrator, and management to take appropriate actions at the right time for improving the overall performance of the students. The purpose of this study is to analyze different data mining and machine learning techniques on student data and find which technique gives better accuracy. And, we also find different factors like socio-demographic, psychological factors, attendance of students, understanding level of students, previous grades, study time, parent’s status, internet usage, travel time, extracurricular activities, and also health factors affect the performance of students. Data mining is a process of analyzing data and turns it into useful information.

Students Academic Performance, Data Mining, Machine Learning

[1] A. Sethi, “Data Mining for Prediction and Classification of Engineering Students achievements using Improved Naïve Bayes”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Vol. 6, Issue 7, pp. 966-971, 2017.
[2] E. Osmanbegović, “Data Mining Approach for Predicting Student Performance”, Economic Review – Journal of Economics and Business, Vol. X, Issue 1, pp.3-12, May 2012.
[3] B. K. Baradwaj, “Mining Educational Data to Analyze Students‟ Performance”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, pp. 63-69, 2011.
[4] F. Marbouti, “Models for early prediction of at-risk students in a course using standards-based grading”, ELSEVIER, Vol. 103, pp.1-15, 2016.
[5] P. Kaur, “Classification and prediction based data mining algorithms to predict slow learners in education sector”, 3rd International Conference on Recent Trends in Computing 2015(ICRTC-2015), Ghaziabad, India, pp.500-508, 2015.
[6] M. Ramaswami, “A CHAID Based Performance Prediction Model in Educational Data Mining”, International Journal of Computer Science Issues, Vol.7, Issue.1, pp.10-18, 2010.
[7] V. P. Desai, “Classification Technique for Predicting Learning Behavior of Student in Higher Education”, International Conference on Digital Economy and its Impact on Business and Industry, Sangli, India, pp.163-166, 2018.
[8] R. Sumitha, “Prediction of Students Outcome using Data Mining Techniques”, International Journal of Scientific Engineering and Applied Science ,Vol.2, Issue.6, pp.132-139, 2016.
[9] A. Mueen, “Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques”, I.J. Modern Education and Computer Science, Vol.8, No.11, pp.36-42, 2016.
[10] A. Daud, “Predicting Student Performance using Advanced Learning Analytics”, International World Wide Web Conference Committee, Perth, Australia, pp.415-421, 2017.
[11] H. Shaziya, “Prediction of Students Performance in Semester Exams using a Naïve Bayes Classifier”, International Journal of Innovative Research in Science, Engineering and Technology, Vol.4, Issue.10, pp.9823-9829, 2015.
[12] M. Zahedifard, “Prediction of Students Performance in High School by Data Mining Classification Techniques”, International Academic Journal of Science and Engineering Vol. 2, No. 7, pp.25-33, 2015.
[13] K. P. Rao, “Predicting Learning Behavior of Students using Classification Techniques”, International Journal of Computer Applications, Vol.139, No7, pp.15-19, 2016.
[14] Swati, “Using Factor Classification for the Slow Learner Prediction over Various Class of Student Dataset”, Indian Journal of Science and Technology, Vol.9, Issue.48, pp.1-5, 2016.
[15] M. Kumar, “Recognition of Slow Learners using Classification Data Mining Techniques”, Imperial Journal of Interdisciplinary Research, Vol.2, Issue.12, pp.741-747, 2016.
[16] F. Marbouti, “Models for early prediction of at-risk students in a course using standards-based grading”, ELSEVIER, Vol.103, pp.1-15, 2016.
[17] Q. A. Al-Radaideh, “Mining Student Data using Decision Trees”, International Arab Conference on Information Technology, Jordan, Arab, 2006.
[18] V. Ramesh, “Predicting Student Performance: A statistical and Data Mining Approach”, International Journal of Computer Applications, Vol.63, No.8, 2013.

Zainab Fatema, Geeta Pattun, "A Survey on Student Performance using Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.707-710, 2019.

The main objective of this paper is to provide a secure-aware routing algorithm for mobile ad hoc networks. The cooperation and communication between MANET nodes are vital. This paper discusses the new algorithm which addresses the security threat in the communication networks. The proposed algorithm has three stages: 1) the Initial bait Stage; 2) Detection Stage using Reverse Tracing, And 3) Ensuring security and protection stage. The first stage is to find the existence of a malicious node on the transfer path. The Second Stage is used to find the nonmalicious node and malicious node in the transfer path. Finally, the last stage decides the malicious node in terms of the packet delivery ratio and energy value. These three stages are designed and executed using commercial software and the outputs of the proposed method are acquired. The important parameters such as Packet Loss, Misclassification rate, Detection accuracy, Packet Delivery Ratio, Throughput, and Routing Overhead is involved to discuss the performance of the proposed method. The Simulation is executed with the given parameters and final results are used to compare with the existing methods. From the comparison, it is apparent that the proposed approach has better performance than the existing methods.

Mobile ad hoc network (MANET), malicious node, black hole attack, Cooperative bait detection scheme, Packet Modification

[1] Jin-Man Kim and Jong-Wook Jang, “AODV based Energy Efficient Routing Protocol for Maximum Lifetime in MANET”, Proceedings of the Advanced International Conference on Internet and Web Applications and Services 2006.
[2] Tripti Nema et al., “Energy Efficient Adaptive Routing Algorithm in MANET with Sleep Mode”, International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-2 Number-4 Issue-6 December-2012.
[3] Suvarna P Bhatsangave and V R Chirchi, “OAODV Routing Algorithm for Improving Energy Efficiency in MANET”, International Journal of Computer Applications 51(21):15-22, August 2012.
[4] Syed Muhammad Sajjada, Safdar Hussain Boukb, Muhammad Yousaf, “Neighbor Node Trust Based Intrusion Detection System for WSN”, Procedia Computer Science 63, pp 183 – 188,2015.
[5] Jian-Ming Chang, Po-Chun Tsou, Isaac Woungang, Han-Chieh Chao, and Chin-Feng Lai, “ Defending Against Collaborative Attacks by Malicious Nodes in MANETs: Cooperative Bait Detection Approach”, IEEE Systems Journal, Vol.9, Issue.1, 2015.
[6] Sandip Chakraborty, Sukumar Nandi, and Subhrendu Chattopadhyay, “Alleviating Hidden and Exposed Nodes in High-Throughput Wireless Mesh Networks”. IEEE Transactions on Wireless Communications, Vol.15, Issue.2, Feb 2016.
[7] Renyong Wu, Xue Deng, Rongxing Lu, and Xuemin (Sherman) Shen, “Trust-Based Anomaly Detection in Emerging Sensor Networks,” International Journal of Distributed Sensor Networks, vol. 2015, Article ID 363569, 14 pages, 2015.
[8] W. Wang, B. Bhargava, and M. Linderman, “Defending against collaborative packet drop attacks on MANETs,” in Proc. 28th IEEE Int. Symp. Reliable Distrib. Syst., New Delhi, India, Sep. 2009.
[9] W. Kozma and L. Lazos, “REAct resource-efficient accountability for node misbehavior in ad hoc networks based on random audits,” in Proc. WiSec, pp. 103–110, 2009.
[10] S. Anandukey and M. Chawla, “Detectionofpacket dropping attack using improved acknowledgment based scheme in MANET, ”International Journal of Computer Science Issues I, Vol. 7, No. 1, pp. 12-17, 2010.
[11] H. Liu, J. G. Delgado-Frias, and S. Medidi, “Using two-timer scheme to detect selfish nodes in ad-hoc networks,” in the proceedings of International Conference Communication, Internet, and Information Technology, pp.179-184, Alberta, Canada, 2007.
[12] Pham Thi Ngoc Diep, Monika Sachdeva, “Detecting Colluding Blackhole and Greyholeattack in Delay Tolerant Networks”, ICRTEDC, Vol. 1, Special Issue. 2, 2015.
[13] JaydipSen,” Detection of Cooperative Black Hole Attack in Wireless Ad Hoc Networks”, International Journal of simulations, systems, science and technology, Vol.12, No. 4, Aug 2011.
[14] Yanzhi Ren, Mooi Choo Chuah, Jie Yang, Yingying Chen “Detecting Wormhole Attacks In Delay-Tolerant Networks” IEEE Wireless Communications, Volume 17, Issue.5, October 2010.
[15] P.Santra, “An expert forensic investigation system for detecting malicious attacks and identifying attackers in cloud environment”, International Journal of Scientific Research in Network Security and Communication, Volume 6, Issue.5, Oct 2018.
[16] Afzal Ahmad, Mohammad Asif, and Shaikh Rohan Ali, “Review paper on shallow learning and deep learning methods for network security”, International Journal of Scientific Research and Computer Science and Engineering, Volume.6, Issue.5, pp. 45-54, Oct 2018.

R.Saranya, R.S.Rajesh, "Identifying Black Hole Attack Based On Energy Consumption and Packet delivery ratio in the Routing Protocol," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.711-718, 2019.

Nowadays computers are used throughout the world from any type of association, because of adaptable capacity of computer devices. We can’t even imagine the world without computer machines like number of transactions, learning, searching, entertainment and many more things from computer or to say with any technical devices becoming an important part of our daily life. As the usage of computer is more over in the world so massive amount of electricity is also required. As we know the thermodynamic rules, i.e. not achievable to deploy this electricity but the outcome is wastage of energy in the form of heat and that is not just about wastage of electricity the part of computer device will generate environmental issues extremely, because of carbon synthesized substance. As earth is also going through the global warming and the techniques of Green House effect and ozone layer because of that significantly needed to think about the environmental issues with the help of computers because usage of computers are not going to decrease but can be worked as positively. So, there are some efforts made to deal alike problems by powerful implementation of technologies and which is also named as Green computing.

Cloud computing, Green computing, Effective environment, Carbon footprint, Parallel computing

[1] F. Alagoz and D. Cavdar. “A survey of research on greening data centers”. Proceeding of the IEEE Global communications conference. (GlobeCom) (2012)
[2] S. Kumar Peddoju, M. Mishra, A. Jain and N. Jain “Energy efficient computing green cloud computing” proceeding of the international conference of the energy efficient technologies for sustainability (ICEETS) (2013) april 9-121. Nagercoil.
[3] “Green grid metrics- describing data centre’s power efficiency”. Technical committee industry consortium (2007) February
[4] N. Rassmussen, “Electrical efficiency modelling of data centre’s American power conversion ” (APC) white paper #113, (2007) October PP-2-17.
[5] Green computing issues on the monitor of personal computers 1A. Mala. 2C. Uma Rani. 3L Ganesan Research Inventy : International journal of engineering and science volume 4, issue 3 (May 2013), PP 30-35 ISSN (P): 23196482, www.researchinventy.com.
[6] A study on green computing : the future computing and eco friendly technology S.V.S.S. Lakshmi1. MS. I Sri Lalita Sarwani2, M. Nalini Tuveera/ International journal of engineering research and applications ISSN: 2247-9623 www.ijera.com volume 3, issue4 , july- August 2012. PP- 1281-1286.
[7] S.V.S.S Lakshmi, MS. I Sri Lalita Sarwani , M. Nalini Tuveera (IJERA) ISSN: 2247-9623 www.ijera.com volume 2, Issue 4 july Aug 2012 PP 1282-1286.
[8] Green Maturity Model for virtualization by Kevin Francis and Peter Richardson. .r 2-8; Anaheim CA. Clerk Maxwell. A treatise on electricity and magnetism, volume 2 oxford clarendon 1891 PP 67-74.
[9] Otimization of energy usage for computer systems by effective implementation of green computing “Google search”. Google.co.in. N.P, 2017 web 25 feb 2017. I.S. Jacobs and C.P. Bean “Fine particles thin films and exchange anisotrop ” in magnetism volume 3 G,T. Rado and H.Suhl. NewYork : Academic 1963, PP-270-352.

Faiza Saghir, Shabina Ghafir, "Energy Saving in Green Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.719-722, 2019.

The proposed approach is performing market basket analysis using the Apriori algorithm and Classification rule Based Association algorithm (CBA) based on accuracy parameter. The motivation behind the approach is to know the customer’s interest towards the products .The objective is to enhance the sales of the business. The approach is using online retail dataset. In the market basket analysis, the frequently purchased items by the customer were analyzed by the admin, the admin keep track of the items purchased by the customers. The admin can also know the purchasing habits of the customers, which will help to improve the quality and quality of the product. This task will be time consuming if performed manually, but the proposed approach reduces the time and improves performance and efficiency of analysis. The analysis helps admin to think about the different strategy to improve the sale. The proposed approach has been implemented in R language. The accuracy is more using Classification rule Based Association algorithm (CBA).

Apriori,Classification rule Based Association algorithm, Data mining, Business Data Processing,R programming

[1] W. Yanthy, T. Sekiya, K. Yamaguchi,”Mining Interesting Rules by association and Classification Algorithms”, FCST 09.
[2] R. Agrawal and R. Srikant,” Fast Algorithms for Mining Association Rules in Large Databases”, Journal of Computer Science and Technology, vol. 15.
[3] X. Yin, J. Han, “CPAR: Classification based on Predictive Association Rules”, Proceedings of the Third SIAM International Conference on Data Mining, pp 331-335, 2003.
[4] Gourab Kundu , Sirajum Munir, Md. Faizul Bari, Md. Monirul Islam, and K. Murase, “A Novel Algorithm for Associative Classification”, 14th International Conference, ICONIP 2007, Kitakyushu, Japan, pp 453-459 , November 13-16, 2007.
[5] J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate 1-12, generation”, Proc of the ACM SIGMOD International Conference on, vol. 1, pp 2000.
[6] J. Han, J. Pei, Y. Yin, and R. Mao, ”Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach”.
[7] Phai Prasad J, Murlidher Mourya, ”A Study On Market basket Analysis Using Data”.
[8] Yen-Liang Chen, Kwei Tang, Ren-Jie Shen, Ya-Han Hu,“Market basket analysis in a multiple store environment”, SciVerse ScienceDirect, Volume 40, Issue 2, August 2005, Pages 339-354 .
[9] Raorane A.A, Kulkarni R.V, and Jitkar B.D, “Association Rule – Extracting Knowledge Using Market Basket Analysis”, Research Journal of Recent Sciences, Vol. 1(2), 19-27, Feb. (2012).
[10] Christian Borgelt, “An Implementation of the FP-growth Algorithm”.
[11] Sotiris Kotsiantis, Dimitris Kanellopoulos, “Association Rule Mining: A Recent Overview”, GESTS International Transactions on Computer Science and Engineering”, Vol. 32(1), 2006, pp. 71-82.
[12] J. Han, H. Pei, and Y. Yin. “Mining Frequent Patterns without Candidate Generation”, In Proc. Conf. on the Management of Data (SIGMOD’00, Dallas, TX). ACM Press, New York, NY, USA 2000.
[13] https://en.wikipedia.org/wiki/Association_rule_learning.
[14] Phani Prasad, MurlidherMourya, “A Study on Market Basket Analysis Using a Data Mining Algorithm”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, ISO 9001:2008 Certified Journal, Vol 3, Issue 6, June 2013.
[15] Akanksha Singh, K. K. Singh, “Data Mining and Data Warehousing”, India: Umesh Publications, 2011-2012.
[16] Harpreet Kaur, Kawaljeet Singh, “Market Basket Analysis of Sports Store using Association Rules”, International Journal of Recent Trends in Electrical & Electronics Engg.,ISSN: 22316612, Dec. 2013.
[17] Neesha Sharma, C. K. Verma, “Association Rule Mining: An Overview”, IJCSC, Volume 5, Number 1, March 2014, pp.10-15, ISSN-0973-7391.

Saroj A. Shambharkar, Ruchi Bhajipale, Neha Nagpure, Himanshu Kanoje, "Study on Market Basket Analysis using Apriori and Classification Rule Based Association Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.723-728, 2019.