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Location-based mobile applications are steadily gaining popularity across the world. These applications require information about user’s current location to access different kind of services. This paper discover aggregated mobility and place visiting patterns of people in developing countries using one CDR (Call Detail Records) dataset collected in Ivory Coast and two finegrained location information datasets collected in India and Switzerland. We have compared these mobility patterns with existing studies for developed countries and found several differences. One of the difference is that people in developing countries are less likely to travel long distance on weekends as compared to developed countries. This paper tries to fill that gap and provide practical and promising solutions to enable location-based services on both feature phones and smartphones using low energy location interfaces.

Aggregated, Call Detail Records, Clustering, WIFI

[1] Latest Mobile Statistics. MobiThinking (online), 2011. http://mobithinking. com/mobile-marketing-tools/latest-mobile-stats.
[2] China 3G Mobile Subscribers Statistics, 2012. http://in.news.yahoo.com/ billion-mobile-phones-china-3g-penetration-low-042833846.html.
[3] Featurephones to smartphones ratio, 2012. http://mobithinking.com/ mobile-marketing-tools/latest-mobile-stats/.
[4] India 3G Mobile Subscribers Statistics, 2012. http://www.zdnet.com/in/ 3g-subscribers-form-2-percent-of-indias-mobile-users-7000004883/.
[5] MIT Technology Review Big Data Articlef, 2012. http: //www.technologyreview.com/featuredstory/513721/ big-data-from-cheap-phones/.
[6] Mobile and PC based Internet Users in China Statistics, 2012. http://www.reuters.com/article/2012/07/19/ us-china-internet-idUSBRE86I0FC20120719.
[7] Mobile only Internet User Statistics, 2012. http://mobithinking.com/ mobile-marketing-tools/latest-mobile-stats/b#mobile-only.
[8] Nokia Nearby Hyperlocal Search, 2012. http://www.youtube.com/watch?v= gsELVWr9v3E. [9] Opera Mobile Web Report, 2012. http://business.opera.com/smw/2012/ 09/.
[10] PlaceMap Mobile Application, 2012. https://play.google.com/store/ apps/details?id=com.iiitd.muc.placemap.
[11] WiFiShare, 2012. https://play.google.com/store/apps/details?id=com. iiitd.muc.wifishare. [12] WiFiShare Client, 2012. https://play.google.com/store/apps/details? id=com.iiitd.muc.wifi_shr.

S. John Grasias, "Mobile Computing: Aggregated Human Mobility Patterns," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1271-1273, 2018.

High utility itemsets (HUIs) mining is a developing topic in information mining, which alludes to finding all itemsets having a utility meeting a user-specified minimum utility threshold min_util. However, setting min_util appropriately is a difficult problem for users. Finding an appropriate minimum utility threshold by trial and error is a tedious process for users. If min_util is set too low, too many HUIs will be generated, which may cause the mining process to be very inefficient. On the other hand, if min_util is set too high, it is likely that no HUIs will be found. In this paper, we address the above issues by proposing a new framework for top-k high utility itemset mining, where k is the desired number of HUIs to be mined. Two types of efficient algorithms named TKU (mining Top-K Utility itemset) and TKO (mining Top-K utility itemset in One phase) are proposed for mining such itemset without the need to set min_util. We provide a structural comparison of the two algorithms with discussions on their advantages and limitations. Empirical evaluations on both real and synthetic datasets show that the performance of the proposed algorithms is close to that of the optimal case of state-of-the-art utility mining algorithms.

ItemSets, Mining, High Utility, TKO, HUIs

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[2] C. Ahmed, S. Tanbeer, B. Jeong, and Y. Lee, “Efficient tree structures for high-utility pattern mining in incremental databases,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 12, pp. 1708–1721, Dec. 2009.
[3] K. Chuang, J. Huang, and M. Chen, “Mining top-k frequent patterns in the presence of the memory constraint,” VLDB J., vol. 17, pp. 1321–1344, 2008.
[4] R. Chan, Q. Yang, and Y. Shen, “Mining high-utility itemsets,” in Proc. IEEE Int. Conf. Data Mining, 2003, pp. 19–26.
[5] P. Fournier-Viger and V. S. Tseng, “Mining top-k sequential rules,” in Proc. Int. Conf. Adv. Data Mining Appl., 2011, pp. 180–194.
[6] P. Fournier-Viger, C.Wu, and V. S. Tseng, “Mining top-k association rules,” in Proc. Int. Conf. Can. Conf. Adv. Artif. Intell., 2012, pp. 61–73.
[7] P. Fournier-Viger, C. Wu, and V. S. Tseng, “Novel concise representations of high utility itemsets using generator patterns,” in Proc. Int. Conf. Adv. Data Mining Appl. Lecture Notes Comput. Sci., 2014, vol. 8933, pp. 30–43.
[8] J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” in Proc. ACM SIGMOD Int. Conf. Manag. Data, 2000, pp. 1–12.
[9] J. Han, J. Wang, Y. Lu, and P. Tzvetkov, “Mining top-k frequent closed patterns without minimum support,” in Proc. IEEE Int. Conf. Data Mining, 2002, pp. 211–218.
[10] S. Krishnamoorthy, “Pruning strategies for mining high utility itemsets,” Expert Syst. Appl., vol. 42, no. 5, pp. 2371–2381, 2015.
[11] C. Lin, T. Hong, G. Lan, J. Wong, and W. Lin, “Efficient updating of discovered high-utility itemsets for transaction deletion in dynamic databases,” Adv. Eng. Informat., vol. 29, no. 1, pp. 16–27, 2015.
[12] G. Lan, T. Hong, V. S. Tseng, and S. Wang, “Applying the maximum utility measure in high utility sequential pattern mining,” Expert Syst. Appl., vol. 41, no. 11, pp. 5071–5081, 2014.
[13] Y. Liu, W. Liao, and A. Choudhary, “A fast high utility itemsets mining algorithm,” in Proc. Utility-Based Data Mining Workshop, 2005, pp. 90–99.
[14] M. Liu and J. Qu, “Mining high utility itemsets without candidate generation,” in Proc. ACM Int. Conf. Inf. Knowl. Manag., 2012, pp. 55–64.
[15] J. Liu, K. Wang, and B. Fung, “Direct discovery of high utility itemsets without candidate generation,” in Proc. IEEE Int. Conf. Data Mining, 2012, pp. 984–989.

Ameena Aiman, Raafiya Gulmeher, "Efficient Algorithms for Mining Top-K High Utility Itemsets," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1274-1280, 2018.

A collection of wireless mobile hosts forming a temporary network without the aid of any centralized administration or standard support services. Network topology is dynamic, nodes enters and leaves the network dynamically. No centralized control or fixed infrastructure to support network configuration and reconfiguration. The main objectives of this paper are to address the characteristics of MANET, Various Routing Protocols, Applications and Issues and Challenges of MANET.

Mobile Networks, Routing Protocol, Applications, Challenges of MANET

[1] Komala CR, Srinivas Shetty, Padmashree S., Elevarasi E., “Wireless Ad hoc Mobile Networks”, National Conference On Computing Communication and Technology, pp.168-174, 2010
[2] Samir R. Das, Charles E.Perkins and Elizabeth M. Royer, “Performance Comparison of Two On-demand Routing Protocols for Ad Hoc Networks”
[3] Ramanarayana Kandikattu, and Lillykutty Jacob, “Secure Internet Connectivity for Dynamic Source Routing(DSR) based Mobile Ad hoc Networks”, International Journal of Electronics, Circuits and Systems, pp. 40-45, 2007.
[4] Hui Li and Dan Yu, “Comparison of Ad Hoc and Centralized Multihop Routing”.
[5] S.Corson and J. Macker, Mobile Ad hoc Networking (MANET) : Routing Protocols Performance Issues and Evaluation Considerations, RFC: 2501, January 1999.
[6] Carlo Kopp, “Ad Hoc Networking”, Systems Journal, pp 33-40, 1999.
[7] “Mobile Ad hoc” http://www.eursecom.eu/message/messageMar 2005/images/Daidols-figure.jpg`
[8] Rajiv Misra and C.R. Mandal, “Perfoemance Comparison of ADOV/DSR, On-demand Routing Protocols for Ad Hoc Networks in Constrained Situation”`
[9] Study of Secure Reactive Routing Protocols in Mobile Ad Http://www.docstoc`com/docs/30136052/Study-of Secure-Reactive-Routing-Protocols-in-Mobile-Ad
[10] Charles E.Perkins and Elizebth M.Royer, ”Ad-hoc on-Demand Distance Vector Routing”`

K.Sharmila, G.Umarani, "A Survey on MANETs: Mobile Ad-hoc Networks And Its Routing Protocols," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1281-1284, 2018.

Sentiment Analysis is application that is changing the ecommerce and many other businesses around the world. It is mainly an application related with text mining and works with the integration of machine learning algorithms(ML) and deep learning algorithms. It is used to increase the business productivity and also to better the customer experience by providing meaningful data out of unstructured data. This paper explains different ways and levels to do sentiment analysis and also explains Natural Language Processing(NLP) and its different approaches. Therefore, this paper brings an overview on sentiment analysis and different techniques and approaches integrated with it.

Lexicon, Machine Learning, NLP,Semantic Analysis, Keyword Spotting

[1] Anna Baj-Rogowska(2017),” Sentiment Analysis of Facebook Posts:the Uber case”, IEEE International Conference on Intelligent Computing and Information Systems (ICICIS).
[2] Antonio Teixeira and Raul M.S. Laureano(2013),”Data extraction and preparation to perform the sentiment analysis using open source tools”.
[3] Sanjida Akter and Muhammad Tareq Aziz(2016),” Sentiment Analysis On Facebook Group Using Lexicon Based Approach”,IEEE.
[4] Saud Alashri, Srinivasa Srivatsav Kandala, Vikash Bajaj, Roopek Ravi, Kendra L. Smith and Kevin C. Desouza(2016),” An Analysis of Sentiments on Facebook during the
2016 U.S. Presidential Election”, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).
[5] N. AZMINA M. ZAMANI, SITI Z. Z. ABIDIN, NASIROH OMAR, M. Z. Z.ABIDEN(2013),”Sentiment Analysis: Determining People`s Emotions in Facebook”, Universiti Teknologi MARA, Malaysia.
[6]Vishal A. Kharde and S.S. Sonawane(2016),” Sentiment Analysis of Twitter Data: A Survey of Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 139 – No.11.
[7] Shufeng Xiong, Hailian Lv , Weiting Zhao, Donghong Ji(2017),” Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings”, Elsevier.
[8] Nehal Mamgain, Ekta Mehta, Ankush Mittal and Gaurav Bhatt(2016),” Sentiment Analysis of Top Colleges in India Using Twitter Data”, International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT).
[9] Aliaksei Severyn abd Aliaksei Severyn(2015),” Twitter Sentiment Analysis with Deep Convolutional Neural Networks”, ACM. ISBN 978-1-4503-3621-5/15/08.
[10] Jˆonatas Wehrmann, Willian Becker, Henry E. L. Cagnini, and Rodrigo C. Barros(2017),” A Character-based Convolutional Neural Network for Language-Agnostic Twitter Sentiment Analysis”,IEEE.
[11] Nhan Cach Dang, Fernando De la Prieta, Juan Manuel Corchado and María N. Moreno(2016),” Framework for Retrieving Relevant Contents Related to Fashion from Online Social Network Data, Springer.

Amardeep Kaur, Jagroop Kaur, "Sentiment Analysis: Approaches and Methods," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1285-1287, 2018.

Cloud computing is a popular paradigm in today’s world that exposes it to various threats and attacks. Security is one of the major challenges as basic administrations are regularly outsourced to the cloud vendors. The major concern in cloud environment is how to make the environment safe and secured. This paper investigates some of the security attacks and the existing solutions for cloud security threat.

Security, cloud computing, side channel attacks

[1] William, S “Cryptography and network security: principles and practice”, pp. 23-50, 1999 Prentice- Hall,
[2] Carlos Moreno, Anwar Hasan and Sebastian Fischmeister “A Family of Fast Exponentiation Algorithms Resistant to SPA, Fault, and Combined Attacks”, pages 157- 166, 2015 IEEE
[3] Paul Kocher “Timing attacks on implementations of diffie-hellman, RSA, DSS, and other systems.
Advances in Cryptology”, pages 104-113, 1996.
[4] Chen C., Wang T., and Tian J., “Improving Timing Attack on RSA-CRT via Error Detection and Correction Strategy,” Information Sciences, vol.232, pp. 464-474, 2013 for paper
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[6] Berk Gulmezoglu, Mehmet Sinan Inci, Gorka Irazoqui, Thomas Eisenbarth and Berk Sunar “Cross-VM cache attacks on AES “issue No.03- July-Sept (2016 vol.2), p 211-222.
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[10] Amazon elastic compute cloud.
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Toa Bi Irie Guy-Cedric, Suchithra. R., "Review of Existing Encryptions Techniques used to Prevent Side Channel Attacks in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1288-1290, 2018.

Student performance, dropouts has become an interesting topics in engineering education. To improve student Academic performance, employability and reducing dropouts are the most vital issues in the research. As it has been observed in the literature survey, there are many existing techniques in the Data mining to predict student’s GPAs, grades, dropouts, and desertion. If the student desertion issue is underestimated then one cannot cope the student prediction optimally, which can causes significantly high error. This problem can be minimized with appropriate preventive strategies of Data mining techniques like Matrix Factorization, Rador, and Part in advance. However, the results obtained are still erroneous and to overcome this risk of failure some Machine Learning Approaches like Regression, classification and clustering methods are applied along with DMT which are highly effective. To predict the performance of the students accurately, here we considered various datasets like previous grades, study time, parent’s status, GPA, school support, higher education, internet usage, travel time etc.,. Which crucially carry out the effective performance, grades for the next term. This can help us for the satisfactory graduation and completion of education on time. The comparative study is done on different algorithms such as linear regression, K-means clustering and neural networks using Weka and Azure tools. This can yield us a better student prediction along with preventive strategies for significantly low error. Further, we can extend our study with few more datasets and it might be possible to find a particular student who can perform effectively up to the mark without any failure. This will help us to reduce the drop outs, failure percentage and increases the confidence levels in the students so that, the progression of student performance can be monitor semester by semester.

Dropouts, Academic performance, Employability, Machine learning and Data mining techniques

[1] Mariel F. Musso, Eva Kyndt, Eduardo C. Cascallar, Filip Dochy, “Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks,” Frontline Learning Research 1,pp.42 – 71, 2013.
[2] A. Dinesh Kumar, Dr.V.Radhika, “A Survey on Predicting Student Performance,” International Journal of Computer Science and Information Technologies (IJCSIT), pp. 6147-6149, 2014.
[3] Edin Osmanbegović, Mirza Suljić,“data mining approach for predicting student performance,” Economic Review – Journal of Economics and Business, Vol. X, Issue 1, May 2012.
[4] Ahmet TEKIN, “Early Prediction of Students’ Grade Point Averages at Graduation: A Data Mining Approach,” Eurasian Journal of Educational Research, Issue 54, pp.207-226, 2014.
[5] Mashael A. Al-Barrak and Muna Al-Razgan, “Predicting Students Final GPA Using Decision Trees: A Case Study,” International Journal of Information and Education Technology, Vol. 6, No. 7, July 2016.
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[7] Young-Jin Lee, “Predicting Students’ Problem Solving Performance using Support Vector Machine,” Journal of Data Science, pp. 231-244, Issue 14, 2016.
[8] Long Bing Cao, “Data science and analytics: a new era,” Int J Data Sci Anal, Springer, Issue 1, pp.1–2, 2106.
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[10] Ikmal Hisyam Mohamad Paris, Lilly Suriani Affendey, and Norwati Mustapha, “Improving Academic Performance Prediction using Voting Technique in Data Mining,” International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol.4, Issue 2, 2010.
[11] Nguyen Thai-Nghe, Lucas Drumond, Artus Krohn-Grimberghe, Lars Schmidt-Thieme, “Recommender System for Predicting Student Performance,” Elsevier, pp. 1-9, 2010.
[12] Brijesh Kumar Bhardwaj, Saurabh Pal, “Data Mining: A prediction for performance improvement using classification,” International Journal of Computer Science and Information Security (IJCSIS), Vol. 9, Issue. 4, April 2011.
[13] V.Ramesh, P.Parkavi, P.Yasodha, “Performance Analysis of Data Mining Techniques for Placement Chance Prediction,” International Journal of Scientific & Engineering Research, Vol. 2, Issue 8, August-2011.
[14] P. Usha, “predicting student performance using Genetic and Svm classifier,” international journal of computer engineering (IJCE), Vol. 3, Issue 2, pp. 97–102, July-December 2011.
[15] Diego Garc_a-Saiz, Marta Zorrilla,” Comparing classi_cation methods for predicting distance
[16] Students` performance,”JMLR: Workshop and Conference Proceedings 17, 2nd Workshop on Applications of Pattern Analysis, pp. 26-32, 2011.
[17] Sajadin Sembiring, M. Zarlis, Dedy Hartama, Ramliana S, Elvi Wani, “prediction of student academic performance By an application of data mining techniques,” International Conference on Management and Artificial Intelligence IPEDR vol.6, 2011.
[18] Erkan Er, “Identifying At-Risk Students Using Machine Learning Techniques: A Case Study with IS 100,” International Journal of Machine Learning and Computing, Vol. 2, Issue 4, August 2012.
[19] Bangsuk Jantawan, Cheng-Fa Tsai, “The Application of Data Mining to Build Classification Model for Predicting Graduate Employment, “International Journal of Computer Science and Information Security (IJCSIS), Vol. 11, Issues. 10, October 2013.
[20] Aranuwa Felix Ola, Prof. Sellapan Pallaniappan, “A data mining model for evaluation of instructors’ performance in higher institutions of learning using machine learning algorithms,” International Journal of Conceptions on Computing and Information Technology, Vol. 1, Issue 2, Dec’ 2013.
[21] D. Magdalene Delighta Angeline, “ Association Rule Generation for Student Performance Analysis using Apriori Algorithm,” The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), Vol. 1, Issue 1, March-April 2013.
[22] Ayinde A.Q, Odeniyi O.A and Sarumi O.A, “Mining Parent Socio-Economic Factors to Predict Students’ Academic Performance in Osun State College of Technology, Esa Oke,” International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 12, December – 2013.
[23] Ayinde A.Q, Dr Adetunji A.B, Bello M and Odeniyi O.A, “Performance Evaluation of Naive Bayes and Decision Stump Algorithms in Mining Students’ Educational Data,” International Journal of Computer Science Issues (IJCSI), Vol. 10, Issue 4, pp. 1, July 2013.
[24] A.Dinesh Kumar, Dr.V.Radhika, “A Survey on Predicting Student Performance,” International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 5 (5), pp. 6147-6149, 2014.
[25] David L. la Red Martinez, Carlos E. Podestá Gomez, “Contributions from Data Mining to Study Academic Performance of Students of a Tertiary Institute, “American Journal of Educational Research, Vol. 2, No. 9, pp.713-726, 2014.
[26] Devikala.D M.phil and Kamalraj.N MCA, M.phil “Data Mining Approaches on Detection of Students’ Academic Failure and Dropout: A Brief Survey,” International Journal of Computer Trends and Technology (IJCTT) – volume 14 Issue 3 – Aug 2014.
[27] N.Magesh M.E., DR.P.Thangaraj Ph.D., S.Sivagobika, S.Praba, R.Mohana Priya, “Employee performance evaluation using machine learning algorithm,” International Journal of Computer Communications and Networks (IJCCN), Vol.4, No.2, April 2014.
[28] Tripti Mishra, Dharminder Kumar, Sangeeta Gupta, “Students’ Employability Prediction Model through Data Mining,” International Journal of Applied Engineering Research, Vol. 11, Issue 4, pp. 2275-2282, 2016.
[29] Jeng-Fung Chen, Ho-Nien Hsieh and Quang Hung Do, “Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks,” Issue 7, pp. 538-553, 2014.
[30] Mosima Anna Masethe, Hlaudi Daniel Masethe, “Prediction of Work Integrated Learning Placement Using Data Mining Algorithms,” Proceedings of the World Congress on Engineering and Computer Science, Vol. 1, pp. 22-24, October 2014.
[31] Camilo E. Lopez G.,Elizabeth León Guzman, Fabio A. Gonzalez, “Data Mining Model to Predict Academic Performance at the Universidad National de Colombia,” Twelfth LACCEI Latin American and Caribbean Conference for Engineering and Technology (LACCEI’2014) Excellence in Engineering to Enhance a Country’s Productivity pp. 22 - 24, 2014.
[32] S.Rukkumani, Mr.G.Suresh, “Student performance on academic with relative survey of classification and regression algorithms,” International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) Vol. 17, Issue 1,AUGUST 2015.
[33] Pooja Thakar, Anil Mehta, Ph.D., Manisha, Ph.D., “Performance Analysis and Prediction in Educational Data Mining: A Research Travelogue,” International Journal of Computer Applications, Vol. 110, Issue 15, pp. 0975 – 8887, January 2015.
[34] Dr. B. Uma Devi, R. Dhanalakshmi, "A Comprehensive Survey of Students Performance Using Various Data Mining Techniques,” International Journal of Science and Research (IJSR),pp. 78-96, 2015.
[35] C.R. Durga Devi, “A Survey on forecasting students’ performance using EDM,” International Journal of Science Technology & Engineering (IJSTE), Vol. 2, Issue 01, 2015.
[36] David L.la Red Martinez, Marcelo Karanik, Mirtha Giovannini, Noelia Pinto,” academic performance profiles: A descriptive model based on data mining, “European Scientific Journal, vol.11, Issue.9, March 2015.
[37] Jai Ruby, Dr. K. David, “Analysis of Influencing Factors in Predicting Students Performance Using MLP – A Comparative Study,” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 2, February 2015.
[38] Ashish Dutt, Saeed Aghabozrgi, Maizatul Akmal Binti Ismail, and Hamidreza Mahroeian, “Clustering Algorithms Applied in Educational Data Mining,” International Journal of Information and Electronics Engineering, Vol. 5, Issue. 2, March 2015.
[39] Revathy P., Kalaiarasi P., Kavitha J., Madhumita D. A., “Data Mining Approach for Suggesting Higher Education Courses Based on Student`s Performance,” International Journal of Science & Techno ledge, Vol. 3, Issue 3, 2015.
[40] Ms. Ashna Sethi, Mr. Charanjit Singh, “Data Mining for Prediction and Classification of Engineering Students achievements using Improved Naïve Bayes,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 6, Issue 7, July 2017.
[41] G. Jayanthi and Dr.V.Ramesh, “Design of Academic Performance Prediction System Using Multi-Layer Perceptron,” International Journal of Computer Science and Software Engineering Volume 1, Issue 1, pp. 9-15, 2015.
[42] Solankar Punam Anil, Jagatap Trupti Baban, Rupnawar Sachin Hanumant, Shitole Vibhavari Jayvant, Prof. Kumbhar S. L., “Student Performance Evaluation in Education Sector using Prediction and Clustering Algorithms,” IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015.
[43] Pooja Thakar, Research Scholar, Prof. Dr. Anil Mehta, Dr. Manisha,” Role of Secondary Attributes to Boost the Prediction Accuracy of Students’ Employability Via Data Mining,” International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 6, No. 11, 2015.
[44] Sadaf Fatima Salim Attar, Prof. Y.C Kulkarni, “Precognition of Students Academic Failure Using Data Mining Techniques,” International Journal of Engineering Research and General Science Vol. 3, Issue 3, May-June 2015.
[45] Lumbini p. Khobragade, Prof. Pravin Mahadik, “Predicting Students’ Academic Failure Using Data Mining Techniques,” International Journal of Advance Research in Computer Science and Management Studies, Vol.3, Issue. 5, May 2015.
[46] Mingjie Tan, Peiji Shao, “Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method,” IJET, Vol.10, Issue 1, 2015.
[47] Humera Shaziya, Raniah Zaheer, G.Kavitha, “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, October 2015.
[48] Mohammadreza Zahedifard, Iman Attarzadeh, Hadi Pazhokhzadeh, Javad Malekzadeh, “Prediction of students’ performance in high school by data mining classification techniques,” International Academic Journal of Science and Engineering, Vol. 2, Issue. 7, pp. 25-33, 2015.
[49] Havan Agrawal, Harshil Mavani, “Student Performance Prediction using Machine Learning,” International Journal of Engineering Research & Technology (IJERT), Vol. 4, Issue 03, March-2015.
[50] P V V Satya Eswara Rao, S K Sankar, “Survey on Educational Data Mining Techniques,” International Journal of Engineering and Computer Science, Vol. 6, Issue 4, pp. 21034-21041, April 2017.
[51] Glyn Hughes and Chelsea Dobbins, “The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs),” Hughes and Dobbins Research and Practice in Technology Enhanced Learning, 2015.
[52] Susan Bergin, Aidan Mooney, John Ghent and Keith Quille, “Using Machine Learning Techniques to Predict Introductory Programming Performance,” International Journal of Computer Science and Software Engineering (IJCSSE), Volume 4, Issue 12, Page: 323-328, December 2015.
[53] Ms. Priti S. Patel1, Dr. S.G.Desai, “Various Data Mining Techniques used to Study Student’s Academic Performance,” International Journal of Computer Science and Mobile Applications, Vol.3 Issue. 6, pg. 55-58, June- 2015.
[54] Pimpa Cheewaprakobkit, “Predicting Student Academic Achievement by Using the Decision Tree and Neural Network Techniques,” Volume 12, Issue. 2, 2015.
[55] Amirah Mohamed Shahiri, Wahidah Husain, Nur’aini Abdul Rashid, “A Review on Predicting Student’s Performance using Data Mining Techniques,” The Third Information Systems International Conference,pp.414 – 422, 2015.
[56] Fullgence Mwachoo Mwakondo, Lawrence Muchemi, Elijah Isanda Omwenga, “Automatic Mapping of Graduates’ Skills to Industry Roles
[57] Using Machine Learning Techniques: A Case Study of Software Engineering,” IJCST Vol. 7, Issue 4, Oct - Dec 2016.
[58] Mojisola G. Asogbon, Oluwarotimi W. Samuel, Mumini O. Omisore, and Bolanle A. Ojokoh, “A Multi-class Support Vector Machine Approach for Students Academic Performance Prediction,” International Journal of Multidisciplinary and Current Research, Vol.4, 2016.
[59] Ramanathan l., Angelina geetha, Khalid m., swarnalatha p., “A novel genetic nand paft model for enhancing the student grade performance system in higher educational institutions,” IIOAE journal, vol. 7, issue. 5, pp. 1-11, 2016.
[60] P. Kavipriya, “A Review on Predicting Students’ Academic Performance Earlier, Using Data Mining Techniques,” International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 6, Issue 12, December 2016.
[61] Richa Shambhulal Agrawal, Mitula H. Pandya, “Data Mining With Neural Networks to Predict Students Academic Achievements,” IJCST Vol. 7, Issue 2, April - June 2016.
[62] I.A Ganiyu, “Data Mining: A Prediction for Academic Performance Improvement of Science Students using Classification,” International Journal of Information and Communication Technology Research, Vol. 6, Issue. 4, April 2016.
[63] Lavannya Varghese, Ms. Christina Joseph, Dr. Vince Paul, “Recommendation System Using Machine Learning and Data Mining Techniques,” Vol. 6, Issue. 6, 2016.
[64] Ahmed Mueen, Bassam Zafar, Umar Manzoor, “Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques,”I.J. Modern Education and Computer Science, Issue 11, pp. 36-42, 2016.
[65] Neelam peters, Aakanksha s. Choubey, “a survey on data classification and machine learning for forecasting of student performance,” international journal of engineering sciences & research technology, december-2016.
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[93] Karan Manchandia, Shweta Kondla, Vasudev Lambhate, “review paper on student performance evaluation through Supervised learning using neural network,” international journal of engineering sciences & research Technology (IJESRT), March-2017.
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[98] Barnabas Ndlovu Gatsheni, Olga Ngala Katambwa, “The design of predictive model for the academic performance of students at University based on machine learning,” Int`l Conf. Artificial Intelligence,2017.
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[103] Zlatko J. Kovacic, “Predicting student success by mining enrolment data,” Research in Higher Education Journal, pp. 1-20.
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[106] Jay Bainbridge, James Melitski, Anne Zahradnik, Eitel J. M. Lauría, Sandeep Jaya prakash, and Josh Baron, “Using Learning Analytics to Predict At-Risk Students in Online Graduate Public Affairs and Administration Education, “Journal of Public Affairs Education, pp. 247-262.
[107] Jaya Srivastava, Dr Abhay Kumar Srivastava, “Data Mining in Education Sector: A Review,” National Conference on Cloud Computing & Big Data, pp. 184-190.
[108] Wenjun Zeng, Si-Chi Chin, Brenda Zeimet, Rui Kuang, Chih-Lin Chi, “Dropout Prediction in Home Care Training,” 10th International Conference on Educational Data Mining, pp. 442-447.
[109] Norman Poh, Ian Smythe, “To What Extend Can We Predict Students’ Performance? A Case Study in Colleges in South Africa”.
[110] Dorina Kabakchieva, “Predicting Student Performance by Using Data Mining Methods for Classification,” cybernetics and information technologies, Vol. 13, Issue 1, 2013.
[111] Cristobel Romero, Sebastian Ventura, Pedro G. Espejo and César Hervás, “Data Mining Algorithms to Classify Students”.
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[113] Emmanuel N. Ogor, “Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques,” IEEE, pp. 354-359.
[114] Tiffany Y. Tang and Gordon McCall, “Student Modeling for a Web-based Learning Environment: a Data Mining Approach,” pp. 967-968.
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[116] Emily H. Thomas, and Nora Gal ambos, “WHAT SATISFIES STUDENTS? Mining Student-Opinion Data with Regression and Decision Tree Analysis,” Research in Higher Education, Vol. 45, Issue. 3, May 2004.
[117] Shradha Shet1, Gayathri, “Approach for Predicting Student Performance Using Ensemble Model Method,” International Journal of Innovative Research in Computer and Communication Engineering, Vol.2, Issue 5, pp. 161-169, October 2014.
[118] Yi-Chun Chang a,*, Wen-Yan Kao a, Chih-Ping Chu a, Chiung-Hui Chiu, “A learning style classification mechanism for e-learning,” Elsevier, pp. 273-285, 2009.
[119] Driyani Rajeshinigo 1, J. Patricia Annie Jebamalar, “Educational Mining: A Comparative Study of Classification Algorithms Using WEKA,” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 3, pp. 5583-5589,March 2017.
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C. Jayasree, K.K. Baseer, "Predicting Student Performance to Improve their Employability by Applying Data Mining and Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1292-1308, 2018.

Magnetic Resonance Imaging (MRI) scan are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. It is important to be able to detect when an automatic segmentation method fails to avoid inclusion of wrong measurements into subsequent analysis which could otherwise lead to incorrect conclusion. Sometimes due to absence of Ground Truth (manually labelled) images it is difficult to detect the failure of automatic segmentation methods. Before deployment, performance is quantised using different metrics for which the predicted segmentation is compared to a reference segmentation also known as Ground Truth (GT), which is manually obtained by an expert. In some exceptional cases it becomes difficult to know about its real performance after deployment when a reference is unavailable. To that end, this paper aims to develop an improved and advanced technique of Reverse Classification Accuracy (RCA) on new data which enables us to discriminate between the successful and failed cases. The ideal concept is that to rank the ‘best’ segmentation results in the database without knowing the manual label. Then ‘match’ the rank between the prediction and the truth image saved in database. Further, for correctly and accurately segmented and classified brain MRI images, diseases are being detected using Random forest algorithm and Deep Learning.

Machine learning, Deep Learning, image Segmentation, MRI images, classification, performance evaluation

[1] Vanya V. Valindria, Ioannis Lavdas, Wenjia Bai, Konstantinos Kamnitsas, Eric O. Aboagye,Andrea G. Rockall, Daniel Rueckert, and Ben Glocker,“Reverse Classificaton Accuracy: Predicting Segmentation Performance in the absence of Ground Truth,” IEEE Transactions on Medical Imaging, Vol. 36, No. 8, 2017.
[2] T. Heimann and H.-P. Meinzer, “Statistical shape models for 3D medical image segmentation: A review” Med. Image Anal., vol. 13, no. 4, pp. 543-563, 2009.
[3] J. E. Iglesias and M. R. Sabuncu, “Multi-atlas segmentation of biomedical images: A survey” Med. Image Anal., vol. 24, no. 1, pp. 205-219,2015.
[4] E. Geremia et al., “Classification forests for semantic segmentation of brain lesions in multi-channel MRI” in Decision Forests for Computer Vision and Medical Image Analysis. London, U.K.: Springer, pp. 245260, 2013.
[5] E. Zhong, W. Fan, Q. Yang, O. Verscheure, and J. Ren, “Cross validation framework to choose amongst models and datasets for transfer learning” in Proc. Joint Eur. Conf. Mach. Learn. Knowl. Discovery Databases, pp. 547-562, 2010.
[6] W. Fan and I. Davidson, “Reverse testing: An efficient framework to select amongst classifiers under sample selection bias,” in Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 147-156, 2006.
[7] F. Ge, S. Wang, and T. Liu, “New benchmark for image segmentation evaluation,” J. Electron. Image. vol. 16, no. 3, pp. 033011, 2007.
[8] L. Goldmann et al., “Towards fully automatic image segmentation evaluation,” in Proc. Int. Conf. Adv. Concepts Intell. Vis. Syst., pp. 566-577, 2008.
[9] H. Li, J. Cai, T. N. A. Nguyen, and J. Zheng, “A benchmark for semantic image segmentation”,in Proc. IEEE Int. Conf. Multimedia Expo, pp. 16,2013.
[10] K. Sikka and T. M. Deserno“Comparison of algorithms for ultrasound image segmentation without ground truth”, Proc. SPIE, vol. 7627, pp. 76271C176271C9, 2010.
[11] Leo Breimen, “Random Forest”, 2001.
[12] K.Yogeswara Rao, M.James Stephen, D.Siva Phanindra,“Classification Based Image Segmentation Approach”, IJCST, Vol 3, Issue 1, 2012.
[13] Xiaofeng Ren and Jitendra Malik,“Learning A Classification Model for Segmentation”.
[14] Dr Stefan Teipel, Michel J Grothe, , Henryk Barthel,“Multimodal imaging in Alzheimer’s disease: validity and usefulness for early detection ”,Lanclet Neurology,2015.
[15] B. van Ginnekan, T. Heimann, M.Styner,“3D Segmentation in the clinic: A Grand Challenge ”, in proc. MICCAI Workshop 3D Segmentation Clinic, pp. 7-15, 2007.
[16] C. Sudlow et al.,“UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age ”, PLoS Med., vol. 12, no. 3, p.e1001779, 2015.
[17] H. Zhang, S. Cholleti, S.A. Goldman, J.E.Fritts,“Meta-evaluation of Image Segmentation using Machine Learning ”,in Proc. IEEEE Comput. Soc. Conf. Compute. Vis. Pattern REcognit. (CVPR). vol. 1, pp. 1138-1145,2006.
[18] B.Lamiroy and T. Sun,“Computing Precision and Recall with Missing or Uncertain Ground Truth”, in Graphics Recognition. New Trends and Challenges. Berlin, Germany: Springer- Verlag, pp. 149-162,2013
[19] S. Chabrier, B. Emile, C. Rosenberger, H. Laurent,“Unsupervised Performance Evaluation of Image Segmentataion”, EURASIP J. Appl.Signal Process., vol. 2006, pp. 217-229, 2006.
[20] H. Zhang, J. E. Fritts, and S. A. Goldman, “Image Segmentation Evaluation: A survey of Unsupervised Methods”,Computer Vision and Image Understanding , vol. 110, no. 2, pp. 260-280, 2008.
[21] K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, B. Glocker, “Efficient Multi-scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation,” Medical Image Analysis, 2016.
[22] W. Bai, W. Shi, D. P. ORegan, T. Tong, H. Wang, S. Jamil- Copley, N. S. Peters, and D. Rueckert,“A Probabilistic Patch-Based Label Fusion Model for Multi-Atlas Segmentation with Registration Refinement: Application to Cardiac MR Images”, IEEE Transactions on Medical Imaging, vol. 32,no. 7, pp. 1302-1315, 2013

Varsha E. Jaware, Rajesh H. Kulkarni, "MRI Image Analysis based on Reverse Classification Accuracy Method," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1309-1314, 2018.

IoT is the technology in which the sensed information is aggregated to the base station, which is uploaded to the internet. Due to the decentralized nature of the network security, energy consumption is the major issue of the network. In the base paper technique, security to the network is provided in which uni-directional and bi-directional communication is possible. This research work is based on the misdirection attack on the network. The whole network is divided into fixed size clusters and cluster heads are selected in each cluster using LEACH protocol based on energy and distance. The technique of threshold-based is proposed in this work for the detection of malicious nodes from the network. The work is implemented using NS2.

IoT, Ns2, Malicious Nodes, LEACH

[1] Z. Zhong, J. Peng, K. Huang, and Z. Zhong, “Analysis on Physical-Layer Security for Internet of Things in Ultra-Dense Heterogeneous Networks”, in Proc. of IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 39-43, 2016.
[2] T. Charity, H. Hua, “Smart World of Internet of Things (IoT) and It`s Security Concerns”, in Proc. of IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 240-245, 2016.
[3] Mukhopadhyay, “PUFs as Promising Tools for Security in the Internet of Things”, IEEE Journal of Design and Test, vol. 33, no. 3, pp. 103-115, 2016.
[4] B. Sundaram, M. Ramnath, M. Prasanth, M. Sundaram, “Encryption and Hash-based Security in the Internet of Things”, in Proc. of IEEE International Conference on Signal Processing, Communication and Networking (ICSCN), vol. 3, pp. 1-5, 2015.
[5] V. Petrov, S. Edelev, M. Komar, and Y. Koucheryavy, “Towards the era of wireless keys: How the IoT can change authentication paradigm,” in IEEE World Forum on the Internet of Things (WF-IoT). Mar. 2014.
[6] A. Ranjan and G. Somani, “Access control and authentication in the internet of things environment,” in Computer Communications and Networks, pp. 283–305, 2016
[7] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of things: A survey on enabling technologies, protocols, and applications,” IEEE Communications Surveys Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.
[8] P. Wortman, F. Tehranipoor, N. Karimian, and J. Chandy, “Proposing a Modeling Framework for Minimizing Security Vulnerabilities in IoT Systems in the Healthcare Domain”, in Proc. of IEEEEMBS International Conference on Biomedical & Health Informatics (BHI), pp. 185-188, 2017.
[9] Z. Guo, N. Karimian, M. Tehranipoor and D. Forte, “Hardware Security Meets Biometrics for the Age of IoT”, in Proc. of IEEE International Symposium on Circuits & Systems (ISCAS), pp. 1318-1321, 2016.
[10] T. Abels, R. Khanna, K. Midkiff, “Future Proof IoT: Composable Semantics, Security, QoS and Reliability”, in Proc. of IEEE Topical Conference on Wireless Sensor & Sensor Networks (WiSNet), pp. 1-4, 2017.
[11] M. Mohsin and Z. Anwar, “Where to Kill the Cyber Kill-Chain: An Ontology-Driven Framework for IoT Security Analytics”, in Proc. of International Conference on Frontiers of Information Technology (FIT), pp.23-28, 2016.
[12] R. Kodali, V. Jain, S. Bose and L. Boppana, “IoT Based Smart Security and Home Automation System”, in Proc. of IEEE International Conference on Computing, Communication and Automation (ICCCA), pp. 1286-1289, 2016.

Harsha Gupta, Ekta Solanki, Anuj Kumar, "Novel Technique for Detection of Malicious nodes in IoT," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1315-1319, 2018.

There are tons of browsers in the market which are being used to surf internet. Each browser has its own DOM parser, rendering engine which has its own sense of understanding the content of a webpage pulled from the web server. It parses the html tags, images and displays the same accordingly on the screen. This is the main cause of visual xbi’s in different browsers. Companies are spending a big lump of money only for the look and feel of the sites. Numerous front-end technologies are being developed with time to enhance the web development as independent of different browsers. The main intent of this research is to identify the visual difference in how the images are being displayed by different browsers as there are different rendering engines in the market. Studying the RBG values of an image at pixel level, generating histograms and exploiting the DOM structure to extract co-ordinates of an images helps in studying that how the images in a webpage are rendered by different browsers.

Browser,CrossBrowserInconsistency,Reliability,Webapplication,DOM,RGB

[1]C.P.Patidar and Meena Sharma ,”An automated approach for cross browser inconsistencies(XBI) detection”, Ninth annual ACM India conference organized by ACM India, Oct 21-23,2016.
[2]Nepal Barskar, C.P.Patidar and Meena Sharma, “Analysis and Identification of Cross Browser Inconsistency Issues in Web Application using Automation Testing”, International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555Vol.6, No3, May-June 2016.
[3] A. Mesbah and M. R. Prasad, “Automated cross-browser compatibility testing,” in Proceeding of the 33rd International Conference on Software Engineering, ser. ICSE ’11. New York, NY, USA: ACM, 2011, pp. 561–570.
[4] Choudhary, S. R., Prasad, M. R., & Orso, A., “X-PERT: A Web Application Testing Tool for Cross-Browser Inconsistency Detection”, 2014.
[5] S. Roy Choudhary, M. R. Prasad, and A. Orso. X-PERT: Accurate Identifcation of Cross-browser Issues in Web Applications. In Proceedings of the 2013 International Conference on Software Engineering, ICSE `13, pages 702-711. IEEE Press, 2013.
[6]S. R. Choudhary, M. R. Prasad, and A. Orso. X-PERT: Accurate Identification
of Cross-Browser Issues in Web Applications. In Proceedings of the 35th IEEE and ACM SIGSOFT International Conference on Software
Engineering (ICSE 2013), pages 702–711, May 2013.
[7]S. Mahajan and W. G. Halfond. Root Cause Analysis for HTML Presentation Failures Using Search-based Techniques. In Proceedingsof the 7th International Workshop on Search-Based Software Testing(SBST), Hyderabad, India, June 2014
[8] S. Roy Choudhary and A. Orso, “Webdiff: Automated identification of cross-browser issues in web applications,” in ICSM ’10: Proceedings of the International Conference on Software Maintenance. IEEE, September 2010.
[9]Sonal Mahajan and William G. J. Halfond. 2015. Detection and Localization of
HTML Presentation Failures Using Computer Vision-Based Techniques. In Proceedings
of the 8th IEEE International Conference on So.ware Testing, Verifcation and Validation (ICST).
[10]Mesbah, A., & Prasad, M. R., “Automated cross-browser compatibility testing”. 33rd International Conference on Software Engineering, ACM Proceedings, pp. 561-570, 2011.
[11]C.P.Patidar and Meena Sharma ,”An automated approach for cross browser inconsistency(XBI) detection”, Ninth annual ACM India conference organized by ACM India, Oct 21-23,2016.
[12] Nepal Barskar, C.P.Patidar and Meena Sharma, “Analysis and Identification of Cross Browser Inconsistency Issues in Web Application using Automation Testing”, International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555Vol.6, No3, May-June 2016.
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Neha Verma, C.P. Patidar, "Implementation of Visual XBI Detector by Comparing RGB Index and Histograms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1320-1325, 2018.

— A wireless Sensor Networks and specifically Wireless Multimedia Sensor Networks (WMSN) assume a key part in numerous Internet of Things (IoT) applications, including mixed media observation, brilliant city activity shirking and control frameworks, propelled medicinal services, and so forth. In such frameworks, sensor nodes are incorporated with cameras as well as amplifiers to catch video or sound substance identified with assorted occasions. Numerous WMSN applications require novel system answers for help mixed media content conveyance at high quality of Service (QoS) levels. In any case, significantly more WMSN applications are worried about the vitality effectiveness because of the constraints of the batteries which prepare the sensor hubs. In proposed inquire about, an energy efficient and QoS flood control conspire for solid interchanges over WMSNs (EEQFC). The proposed arrangement makes utilization of QoS criticism and current battery vitality levels of sensor hubs keeping in mind the end goal to adjust sending information rate. They utilize fortification learning by defining the issue regarding a Markov Decision Process and tackle it utilizing the Q-Learning system. The proposed EEQFC is approved utilizing re-enactments and is contrasted and great MDP and UAMD, another clog control calculation for Wireless Sensor Networks. The outcomes indicate how EEQFC beats alternate arrangements under high and low system stack.

Energy efficient, Quality of service, Flood control

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P.Usharani, G.Roja, "A Quality Of Service Based Flood Control For Efficient Data Transfer In Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1326-1330, 2018.