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Genetic testing and precision medicine have changed how a disease like cancer is treated. It`s a very time- consuming task where a clinical pathologist has to manually review and classify every single genetic mutation based on evidence from text-based clinical literature takes up a considerable amount of human efforts and time. In this paper, we survey different machine learning models with an intent to automate the mutation classification. Additionally, to speed up the learning process while maintaining accuracy, Jeffreys-Multi-Hypothesis (JMH) divergence method is used to select words with large discriminative capacity for classification of text. Text Encoding Schemes like BoW (Bag-of-Words), TF-IDF (Term Frequency-Inverse Document Frequency, and Graph-based TW-IDF (Term Weight - Inverse Document Frequency) is used to encode text to numerical form. Macro-based F1-score is used to score performance during feature selection and model evaluation. This paper surveys the specified methods based on comparisons and tries to conclude which turns out to be better.

BoW, TF-IDF,TW-IDF, JMH Divergence, Precision, Recall, F1-Score

[1] Chakravarty et.al, “OncoKB: A Precision Oncology Knowledge Base”, JCO Precision Oncology, pp 1-16, 2017
[2] Zheng, “Feature Engineering for Machine Learning”, O’REILLY Publisher, USA, pp 43-45, 2018
[3] M. Liu, “An improvement of TFIDF weighting in text categorization”, In the Proceedings of the 2012 International Conference on Computer Technology and Science, Hong Kong, pp 44-45, 2012
[4] F.D. Malliaros, “Graph-Based Term Weighting for Text Categorization”, In the Proceedings of the 2015 Advances in Social Networks Analysis and Mining, Canada, pp 1473-1479, 2015
[5] Tang, “Toward Optimal Feature Selection in Naive Bayes for Text Categorization”, IEEE Transactions on Knowledge and Data Engineering, Vol.28, Issue.9, pp 2508-2521, 2016
[6] Y. Xu, “A Study on Mutual Information-based Feature Selection for Text Categorization”, Journal of Computational Information Systems, Vol.3, pp 1007-1012, 2007
[7] S.D. Jadhav, “Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques”, International Journal of Science and Research, Vol.5, Issue.1, pp 1842-1845, 2016
[8] Zhang et.al, “Multi-view Ensemble Classification for Clinically Actionable Genetic Mutations”, Springer International Publishing, pp 79-99, 2018
[9] R. Nair, “An Efficient Approach for Sentiment Analysis Using Regression Analysis Technique”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp 161-165, 2019
[10] Sharma, “Evaluation of Stemming and Stop Word Techniques on Text Classification Problem”, International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.2, pp 1-4, 2015
[11] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques”, International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.6, pp 5-8, 2017.

Varun Saproo, Rujuta Upadhyay, Manisha Valera, "Survey of Feature Selection and Text Classification Methods for Genetic Mutation Classification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.933-937, 2019.

Nowadays frauds have become common in many countries. These frauds are started affecting many organizations as per their businesses. Smaller organizations think that fraud won’t affect their business and fail to take the necessary steps to prevent their money and assets. Preventing fraud is important to every organization, it prevents from financial condition of any business, as well as the name and fame of the organization. Larger frauds in any organization may affect their employees by losing their jobs, or even the organization collapses. To prevent fraud blockchain technology is the best example and nowadays it’s gaining in the market. This paper analyzes how to prevent fraud by using blockchain technology.

Nirav Modi, Scam, Bitcoin, Blockchain,LoU, Smart Contract

[1] Marco A. Santori, Craig A. DeRidder and James M. Grosser. Blockchain Basics: A Primer. Blockchain, the technology underlying the cryptocurrency Bitcoin, is poised to revolutionize how all commercial transactions are conducted, pages 2-3, May 2016
[2] BLOCKCHAIN AND THE LAW: Practical implications of a Revolutionary technology for Financial Markets and beyond.
[3] Using blockchain technology for government auditing Tatiana Antipova2018 13th Iberian Conference on Information Systems and Technologies(CISTI)
[4] Danson S. Fraud, and how to avoid it. NZB, August 2015, pp 42-43.
[5] Lewis A., Neiberline C., Steinhoff J. Digital Auditing: Modernizing the Government Financial Statement Audit Approach. The Journal of Government Financial Management; 2014. 63, 1, pp. 32-37.
[6] https://www.thegazette.co.uk/all-notices/content/100990
[7] https://medium.com/regen-network/building-a-network-of-trust-using-blockchain-technology-1745b295c6c7
[8]https://economictimes.indiatimes.com/markets/stocks/news/what-is-bitcoin-and-how-does-it-work/articleshow/60701874.cms
[9] https://blockgeeks.com/guides/blockchain-developer/

Poonam Popat Magar, Sudeshna Roy, "A Study to Investigate the reasons for Nirav Modi scam in Banking Sector," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.938-940, 2019.

Cloud computing be a model for enabling on-demand network access to a collective pool of configurable computing resources that can be rapidly released. Cloud attempt adjustable and cost effective repository for big data but the major problem is to deal with big data access control. classical encryption techniques are used for confidentiality and integrity over transmitted data. ABE is a fascinating explication for data access control in cloud. ABE methods encrypt attributes to a certain extent than the whole data. This paper comparative surveys the possibility of different ABE methods.

: Access Control, Key Policy Attribute Based Encryption, Ciphertext Policy Attribute Based Encryption, Hidden Policy Attribute Based Encryption

[1] Ms. Yogita S. Gunjal, Mr. Mahesh S. Gunjal, Mr. Avinash R. Tambe, “Hybrid Attribute Based Encryption and Customizable Authorization in Cloud Computing”, IEEE 2018 International Conference On Advances in Communication and Computing Technology (ICACCT)
[2] Sucharita Khuntia, P. Syam Kumar, “New Hidden Policy CP-ABE for Big Data Access Control with Privacy-preserving Policy in Cloud Computing”, IEEE – 43488.
[3] Parmar Vipul Kumar, RajaniKanth Aluvalu, “Key Policy Attribute Based Encryption (KP-ABE):A Review”, International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 2, 2015
[4] Shweta Kaushik, Charu Gandhi, “Cloud data security with hybrid symmetric encryption”, IEEE 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT)
[5] Umesh Chandra Yadav and Syed Taqi Ali, “Ciphertext Policy-Hiding Attribute-Based Encryption”, IEEE 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
[6] Runhua Xu, Yang Wang, Bo Lang, “A Tree-Based CP-ABE Scheme with Hidden Policy Supporting Secure Data Sharing in Cloud Computin”, 2013 International Conference on Advanced Cloud and Big Data (CBD)
[7] Ho Hui Chung, Peter Shaojui Wang, Te-Wei Ho, Hsu-Chun Hsiao, Feipei Lai, “A Secure Authorization System in PHR based on CP-ABE”, The 5th IEEE International Conference on E-Health and Bioengineering - EHB 2015.

Ruchika Katariya, Amit Dangi, "A Review on CP-ABE for Big Data Access Control in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.941-943, 2019.

The focus of the paper is to recommend crops to farmers based on their geographical location, soil and weather conditions. India is a country where agriculture employs a significant amount of the country’s population. Regardless of that agriculture contributes only about 15-17% of the country’s total Gross Domestic Product. Also the suicide rates of farmers are increasing in India due to lack of information or less produce or no produce. Some reasons for this are growing crops that are incompatible with the type of soil, the weather conditions or the water content. Another reason is the inexperience of novice farmers in the field of agriculture. One solution to solve this dwindling produce is to use a crop recommendation system that recommends crops to farmers using filtering techniques like collaborative filtering and content-based filtering and machine learning algorithms. By doing this the farmers would be recommended crops that would maximize their crop yield and grow healthier crops.

Farming, Agriculture, Machine Learning, Recommendation System, Filtering Techniques.

[1] Lee, C., Lee, M., Han, D., Jung, S., & Cho, J., “A framework for personalized Healthcare Service Recommendation”, HealthCom 2008 - 10th International Conference on e-health Networking, Applications and Services, pp. 90-95, 2008.
[2] Simon, Annina & Singh Deo, Mahima & Selvam, Venkatesan & Babu, Ramesh, “An Overview of Machine Learning and its Applications”, International Journal of Electrical Sciences & Engineering. Vol. 22-24, 2016.
[3] Ricci, F., Rokach, L., & Shapira, B., “Introduction to recommender systems handbook”, In Recommender systems handbook , Springer Publication, Boston, MA,pp. 1-35, 2011.
[4] Akhil, P. V., and Shelbi Joseph. "A SURVEY OF RECOMMENDER SYSTEM TYPES AND ITS CLASSIFICATION", International Journal of Advanced Research in Computer Science, Vol. 8, No. 9, 2017.
[5] Lops, P., De Gemmis, M., & Semeraro, G., “Content-based recommender systems: State of the art and trends”, In Recommender systems handbook, Springer Publication, Boston, MA, pp. 73-105, 2011.
[6] Lacasta, Javier & Lopez-Pellicer, Francisco & García, Borja A. & Nogueras-Iso, Javier & Zarazaga, F.J., “Agricultural recommendation system for crop protection”, Computers and Electronics in Agriculture. Vol. 152, pp. 82-89, 2018.
[7] Kumar, Vikas, Vishal Dave, Rahul Bhadauriya, and Sanjay Chaudhary, "Krishimantra: agricultural recommendation system", In Proceedings of the 3rd ACM Symposium on Computing for Development, pp. 45. ACM, 2013.
[8] Z. Laliwala, V. Sorathia and S. Chaudhary, "Semantic and Rule Based Event-driven Services-Oriented Agricultural Recommendation System", 26th IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW`06), Lisboa, Portugal, pp. 24-24, 2006.
[9] Jankowski, P., “Integrating geographical information systems and multiple criteria decision-making methods”, International journal of geographical information systems, Vol. 9, No.3, pp. 251-273, 1995.
[10] S. Pudumalar, E. Ramanujam, R. H. Rajashree, C. Kavya, T. Kiruthika and J. Nisha, "Crop recommendation system for precision agriculture," 2016 Eighth International Conference on Advanced Computing (ICoAC), Chennai, pp. 32-36, 2017.
[11] S.N. Patil, S.M. Deshpande, Amol D. Potgantwar, "Product Recommendation using Multiple Filtering Mechanisms on Apache Spark", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.76-83, 2017.
[12] Kumar R., "Candidate Job Recommendation System", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.6, pp.12-15, 2018.
[13] Aggarwal, P., Tomar, V., & Kathuria, A., “Comparing content based and collaborative filtering in recommender systems”, International Journal of New Technology and Research, Vol. 3, No.4, 2017.

Bushra Bankotkar, Sudeshna Roy, "Recommendation System for Crop Prediction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.944-948, 2019.

The criminal offences in the ATM kiosk are happening very commonly in recent days. A fully automated ATM surveillance system is the need of present era intended for detecting suspicious actions in the surveillance system and to trigger the proactive steps before the incident to occur. An innovative methodology proposed in this paper, which deals an automation of video surveillance in ATM kiosk and detect any type of potential criminal activities. In this system, an innovative methodology is proposed for automated ATM surveillance System using skeleton-based action recognition neural networks and IoT sensors. Multiple layers of detection techniques used to confirm the activity as suspicious. Skeleton-based action recognition by part-aware graph convolutional networks is used for detecting suspicious human action using the NTU RGB-D data set. Aadhar enabled finger print scanner which is integrated with ATM is used to fetch the demographic information from aadhar server. IoT proximity sensor is used to recognize any trial to block the vision of surveillance camera. Similarly, any physical attack made on ATM will be identified using IoT pressure/gas sensors. Suspicious sound generating during the criminal offence is also considered to confirm the activity as suspicious. Once, the activity is confirmed as suspicious, demographic information of the suspect will be fetched from aadhar server maintain by unique identification authority of India (UIDAI) and initiate the proactive steps and warning procedures.

ATM, part-aware graph, convolutional networks , NTU RGB-D data set, Surveillance System

[1] S.Shriram, S. B.Shetty, V.P. Hegde , KCR Nisha, Dharmambal.V, “Smart ATM Surveillance System”, 2016 International Conference on Circuit, Power and Computing Technologies [ICCPCT].
[2] M. Baranitharan, R. Nagarajan, G. ChandraPraba, “Automatic Human Detection in Surveillance Camera to Avoid Theft Activities in ATM Centre using Artificial Intelligence”, International Journal of Engineering Research & Technology (IJERT)-NCICCT – 2018. Volume 6, Issue 03.
[3] K. Archana, P. B. Reddy , A. Govardhan, “To Enhance the Security for ATM with the help of Sensor and Controllers”, International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017).
[4] S. Aakur, D.Sawyer,S.Sarkar, “Fine-grained Action Detection in Untrimmed Surveillance Videos”, IEEE winter applications of computer vision workshops Computer Science and Engineering, University of South Florida, Tampa, 2019, pp-38-40.
[5] K. Soomro, H. Idrees, M. Shah, “Online Localization and Prediction of Actions and Interactions”, IEEE Transactions on pattern analysis and machine intelligence.
[6] O.Ayankemi ONI , "A Framework for Verifying the Authenticity of Banknote on the Automated Teller Machine (ATM) Using Possibilistic C-Means Algorithm", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.57-63, 2018.
[7] V.K. Jain, N. Tripathi, "Speech Features Analysis and Biometric Person Identification in Multilingual Environment", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.1, pp.7-11, 2018
[8] Y. Qin, L. Mo1, C. Li1, J. Luo1, “Skeleton-based action recognition by part-aware graph convolutional networks”, Springer-Verlag GmbH Germany, part of Springer Nature 2019.
[9] X. Suna P. WuaSteven, C.H.Hoi, “Face detection using deep learning: An improved faster RCNN approach”, Neurocomputing (2018).

Jaimon Jacob, Sudeep Ilayidom, V.P.Devassia, "An innovative methodology for automated ATM surveillance system using skeleton-based action recognition neural networks and IoT," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.949-953, 2019.

A home automation system involves controlling and monitoring entertainment systems, security systems, appliances, lights and more. The Home Automation industry is growing rapidly. This is because people prefer automatic systems over manual systems at home so that their daily chores can be reduced and they can concentrate more on their area of interest. This led to the development of different methodologies to implement a home automation system. There are many home automation methodologies available, each of which has its own implementation methods, advantages, and limitations. However, it is up to the end-users to select their own choice of methodology for automating their home. The specific parameters being considered by the end-users for opting a specific methodology is relatively low. Therefore, in this paper, a comparative study of home automation methodologies such as GSM, Voice recognition, Bluetooth and IoT is presented from the end-users view to fill the empty space regarding the choice of methodology to be taken. The readers can expect to choose a methodology that suits their best after the study of this paper.

Home Automation System (HAS); Micro Controller; Arduino Uno; GSM; Bluetooth; Voice Recognition; Internet of Things (IoT); Wi-Fi;5G; Network Slicing

[1] Shubham Magar; Varsha Saste; Ashwini Lahane; Sangram Konde; Supriya Madne, “Smart home automation by GSM using android application”, In the Proceedings of the 2017 International Conference on Information Communication and Embedded Systems (ICICES), India, pp. 1-6,2017.
[2] A. Sharma, S. Gupta, M. Mittal,”A Conceptual Review on Smart Homes”, International Journal of Computer Sciences and Engineering, Vol. 6, Issue.5, pp. 101-106,2018
[3] Kumar Mandula; Ramu Parupalli; CH.A.S. Murty; E. Magesh; Rutul Lunagariya, “Mobile based home automation using Internet of Things (IoT)”, In the Proceedings of the 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), India, pp. 340-343 ,2015.
[4] Rasika S. Ransing; Manita Rajput, “Smart home for elderly care, based on Wireless Sensor Network”, In the Proceedings of the 2015 International Conference on Nascent Technologies in the Engineering Field (ICNTE), India, pp. 1-5 ,2015.
[5] G. V. Vivek and M. P. Sunil, "Enabling IOT services using WIFI - ZigBee gateway for a home automation system” In the Proceedings of the 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), India, pp. 77-80,2015.
[6] S. Courreges, S. Oudji, V. Meghdadi, C. Brauers and R. Kays, "Performance and interoperability evaluation of radiofrequency home automation protocols and Bluetooth Low Energy for smart grid and smart home applications”, In the Proceedings of the 2016 IEEE International Conference on Consumer Electronics (ICCE), USA, pp.391-392,2016.
[7] Freddy K Santoso ; Nicholas C H Vun, "Securing IoT for smart home system”, In the Proceedings of the 2015 International Symposium on Consumer Electronics (ISCE), Spain, pp.1-2 ,2015.
[8] Abdelhakim Ahmim;Tam Le; Esther Ososanya ;Sasan Haghani, "Design and implementation of a home automation system for smart grid applications”, In the Proceedings of the 2016 IEEE International Conference on Consumer Electronics (ICCE), USA, pp.538-539,2016.
[9] H. ElKamchouchi; Ahmed ElShafee, “Design and prototype implementation of SMS based home automation system”,In the Proceedings of the 2012 IEEE International Conference on Electronics Design, Systems and Applications (ICEDSA),Malaysia,pp.162-167,2012.
[10] K. A. S. V. Rathnayake; S. I. A. P. Diddeniya; W. K. I. L Wanniarachchi; W. H. K. P. Nanayakkara; H. N. Gunasinghe, “Voice operated home automation system based on Kinect sensor”,In the Proceedings of the 2016 IEEE International Conference on Information and Automation for Sustainability (ICIAfS),Sri Lanka,pp.1-5,2016.
[11] Neeraj Chhabra, “Comparative Analysis of Different Wireless Technologies”, International Journal of Scientific Research in Network Security and Communication, Vol. 1, Issue.5, pp.14-17, 2013.
[12] Muhammad Asadullah; Khalil Ullah, “Smart home automation system using Bluetooth technology”, In the Proceedings of the 2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT), Pakistan, pp.1-6,2017.
[13] A. Sebastian, S. Sivagurunathan2, “A Survey on Load Balancing Schemes in RPL based Internet of Things”, International Journal of Scientific Research in Network Security and Communication, Vol. 6, Issue.3, pp.43-49, 2018.
[14] Waheb A. Jabbar; Mohammed Hayyan Alsibai; Nur Syaira S. Amran; Samiah K. Mahayadin, “Design and Implementation of IoT-Based Automation System for Smart Home”, In the Proceedings of the 2018 International Symposium on Networks, Computers and Communications (ISNCC), Italy, pp.1-6,2018.
[15] Priyang Bhatt, Bhasker Thaker, Neel Shah, “A Survey on Developing Secure IoT Products”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issue.5, pp.41-44, 2018.
[16] Mohini Joshi1, Kishore Kumbhare, “A Review on Efficient Mac Layer Handoff Protocol to Reduce Handoff Latency for Wi-Fi Based Wireless Network”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issue.2, pp.82-86, 2018.
[17] Ahmed ElShafee, Karim Alaa Hamed, “Design and Implementation of a WiFi Based Home Automation System”, International Journal of Computer and Information Engineering, Vol. 6, No.8, pp.1074-1080,2012.
[18] A. Gupta ; R. K. Jha ,“A Survey of 5G Network: Architecture and Emerging Technologies”, Journal of IEEE Access,Vol. 3,pp.1206-1232,2015. Doi: 10.1109/ACCESS.2015.2461602
[19] Diane J Cook, "Learning Setting-Generalized Activity Models for Smart Spaces" IEEE Intelligent Systems, Vol. 27, Issue.1, pp.32-38, 2012.
[20] K E Skouby ; P Lynggaard, “Smart home and smart city solutions enabled by 5G, IoT, AAI and CoT services”, In the Proceedings of the 2014 International Conference on Contemporary Computing and Informatics (IC3I),India,pp.874-878,2014.
[21] Bruno Dzogovic; Bernardo Santos; Josef Noll; Van Thuan Do; Boning Feng; Thanh van Do, “Enabling Smart Home with 5G Network Slicing”, In the Proceedings of the 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS 2019), Singapore, pp.1-6,2019.
[22] Tianyi Song; Ruinian Li; Bo Mei; Jiguo Yu; Xiaoshuang Xing ; Xiuzhen Cheng, “A Privacy Preserving Communication Protocol for IoT Applications in Smart Homes”, In the Proceedings of the 2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI),China,pp.519-524,2016.

Sangeetha S.P, Snigdha Sen, "Comparative Study of Methodologies for Home Automation System A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.954-958, 2019.

Automated Short Answer Grading (ASAG) has been an area of active research for quite some time now. Several theories and implementation have come up, but a stable method, suitable for all genres of answers is yet to be standardized. The most accurate results for short answer grading have been found for substantially longer texts which have scope for information retrieval. Smaller answers however suffer on this front and have been a bottleneck of sorts. This paper presents a simple method to evaluate very short answers, using cosine similarity method between students’ answers and model answers prepared by subject experts. The proposed method is simple, fast and easy to implement and returns scores having fair correlation with human evaluated scores.

ASAG, Students Answer, Model Answer, Cosine Similarity

[1]. Madhumita Chakraborty, 2018, ‘Here’s why DU teachers are not evaluating answer papers since May 24’, Hindustan Times, June 15, 2018.
[2]. K. A. Gafoor, T.K. Umer Farooque, “Incongruence in Scoring Practices of Answer Scripts and Their Implications: Need for Urgent Examination Reforms in Secondary Pre-Service Teacher Education”, Proceedings of UGC sponsored national seminar on Fostering 21st Century Skills: Challenges to Teacher quality, August 22-23, 2014, Kerala, pp. 2-5.
[3]. Ritu Sharma, 2017, ‘’Model Rules’: Board to train teachers how to evaluate answer-sheets’, The Indian Express, September 8, 2017.
[4]. Priyanka Dhondi, 2015, ‘Different Types of Questions in E-learning Assessments’, ElearningDesign, CommLabIndia, January 20, 2015.
[5]. Komi Reddy Deepika, 2014, ‘Different Types of Assessments Used in E-learning’, ElearningDesign, CommLabIndia, June 27, 2014.
[6]. S. Ramesh,”Exploring the potential of Multiple Choice Questions in Computer Based Assessment of Student Learning”, Malaysian Online Journal of Instructional Science, 2005, Vol. 2.
[7]. M. Bush, “Alternative Marking Schemes Fof On-line Multiple-choice Tests”, Proceedings of 7th Annual Conference on the Teaching of Computing, Belfast, 1999.
[8]. Megan Clendenon, Hannah Holley, Mauro Schimf ‘Responding to Short Answer and Essay Questions’, StudentCaffe, Updated on April 2018.
[9]. Allen Grove, ‘What is the ideal word count for the short answer on the common application?’, ThoughtCo, Updated on 22 November, 2018.
[10]. S. Burrows, I. Gurevych, B. Stein, “The Eras and Trends of Automatic Short Answer Grading”, International Journal of Artificial Intelligence in Education , 25, IOS Press, pp. 60-117, 2015.
[11]. Y. Li, A. Tripathi, A. Srinivasan, “Challenges in Short Text Classification: The Case of Online Auction Disclosure”, Tenth Mediterranean Conference on Information Systems (MCIS), Paphos, Cyprus, September 2016.
[12]. M. Hermet, S. Szpakowicz , L. Duquette and S. N. Leuven, “Automated Analysis of Students` Free-text Answers for Computer-Assisted Assessment”, Proceedings of TAL and ALAO Workshop, pp. 835--845, 2006.
[13]. A. Adam, A. Ismail, A. Rafiu, A. Mohamed, G. Shafeeu, M. Ashir, “Pedagogy and Assessment Guide”, National Institute of Education, Male, Maldives, 2014. Accessed on: 02nd September 2018.
[14]. J. Burstein, R. Kaplan, S. Wolff, & C. Lu, Using Lexical Semantic Techniques to Classify Free-Responses. In E. Viegas, editor, Proceedings of the ACL SIGLEX Workshop on Breadth and Depth of Semantic Lexicons, pages 20–29, Santa Cruz, California. Association for Computational Linguistics, 1996.
[15]. J. Cowie, Y. Wilks, Information Extraction. In R. Dale, H. Moisl, and H. Somers, editors, Handbook of Natural Language Processing, chapter 10, pages 241–260. Marcel Dekker, New York City, New York, First Edition, 2000.
[16]. C. Y. Lin, ROUGE: A Package for Automatic Evaluation of Summaries. In M.-F. Moens and S. Szpakowicz, editors, Proceedings of the First Text Summarization Branches Out Workshop at ACL, pages 74–81, Barcelona, Spain. Association for Computational Linguistics, 2004.
[17]. M. Mohler, R. Mihalcea, Text-to-text Semantic Similarity for Automatic Short Answer Grading. In A. Lascarides, C. Gardent, and J. Nivre, editors, Proceedings of the Twelfth Conference of the European Chapter of the Association for Computational Linguistics, pages 567–575, Athens, Greece. Association for Computational Linguistics, 2009.
[18]. Automated Short Answer Grading – Dataset 1 [https://sites.google.com/site/uditkc/home/reading-stuff]

Chandralika Chakraborty, Udit Kr. Chakraborty, Bhairab Sarma, "Vector Similarity Measure for ASAG," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.959-963, 2019.

Breast cancer is a fatal disease causing high mortality in women. By applying data mining techniques people can work on the extraction of hidden, historical and previously unknown large databases. The development of the technique have promised towards intelligent component in medical decision support systems. Here efficient information have been mined from the machine learning. ANN has been widely used in breast cancer diagnosis. In the proposed system the desired output were chosen and applied to ANN for preprocessing, classification and so on. The breast cancer data set from UCI data sets will be used to demonstrate different activities.

ArtificialNeuralNetwork, ANN, DataMining, BreastCancer

[1] Bray F,Jemal A,Grey N,Ferlay J,Forman D,”Global cancer transitions according to the human development index(2008-2030)”,:A population case study.The Lancet oncology 2012;13(8):790-801
[2] Sulochana Wadhwani, A.K Wadhwani, Monika Saraswat, "Classification of breast cancer using artificial neural network", Current Research in Engineering, Science and Technology Journals, December 2009
[3] Dr. J. Abdul Jaleel, Sibi Salim, Aswin.R.B, "Artificial Neural Network Based Detection of Skin cancer", International Journal of Advanced Research in Electrical, Electronics and Instrmentation Engineering, ISSN 2278 – 8875 Vol. 1, Issue 3, September 2012
[4] Miller KD, Siegel RL, Lin CC, Mariotto AB, Kramer JL, Rowland JH, JA. Cancer treatment and survivorship statistics. CA: A Cancer Journal for Clinicians. 2016;66(4):271-289
[5]G. Holmes; A. Donkin and I.H. Witten "Weka: A machine learning workbench", Proc Second Australia and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia, 1994.
[6] Stephen M. Smith, “Fast Robust Automated Brain Extraction,” Human Brain Mapping, 17:143–155(2002).
[7]K. Somasundaram, T. Kalaiselvi, “Automatic Brain Extraction Methods for T1 Magnetic Resonance Images Using Region Labeling and Morphological Operations,” Computers in Biology and Medicine, 41 (2011), 716–725.
[8] Amit Tate, Bajrangsingh Rajpurohit “Comparative Analysis of Classification Algorithms Used for Disease Prediction in Data Mining”, International Journal of Engineering and Techniques, Volume 2 Issue 6, Nov –2016.
[9] S. a. E. N. Sharma, “Brain Tumor Detection and Segmentation Using Artificial Neural Network Techniques”, International Journal of Engineering Sciences & Research Technology, August 2014.
[10]Beant Kaur, Williamses Singh “Review on heart disease prediction using data mining techniques,” International Journal on recent and innovation trends in computer and communication” , Volume- 2, Issue-10,Page No( 3003-3008), October2014.
[11]E. Venkatesan, T. Velmurugan “Performance Analysis of Decision Tree Algorithms for Breast Cancer Classification” Indian Journal of Science and Technology”, Vol 8(29), November 2015.
[12]Kariuki Paul Wahome “Towards Effective Data Preprocessing for Classification Using WEKA”, International Journal of Science and Research (IJSR), 2016.
[13] Htet Thazin Tike Thein, Khin Mo Mo Tun,”An Approach For Breast Cancer Diagnosis Classification Using Neural Network”, Advanced Computing: An International Journal (ACIJ), Vol.6, No.1, January 2015.
[14] Shiv Shakti S, Sant A, Aharwal RP. “An Overview on Data Mining Approach on Breast Cancer data”, International Journal of Advanced Computer Research. 2013; 3(13):256–62.
[15] R.R.Janghel, Anupam Shukla, Ritu Tiwari, Rahul Kala,”Breast Cancer Diagnosis using Artificial Neural Network Model”,Research Gate,2010.
[16] Sanjay Agrawal et al. “A Study on Fuzzy Clustering for Magnetic Resonance Brain Image Segmentation Using Soft Computing Approaches”, Applied Soft Computing, 24(2014), 522–533.
[17]D.Jude hemanthl et al. “Effective Fuzzy Clustering Algorithm for Abnormal MR Brain Image Segmentation”, IEEE International Advance Computing Conference (IACC 2009), Patiala, India, 6-7 March 2009.
[18] Rajesh K, Anand S. “Analysis of SEER Dataset for Breast Cancer Diagnosis using C4.5 Classification Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering. 2012 Apr; 1(2):72–7.
[19]Sandeep Chaplot et al. “Classification of Magnetic Resonance Brain Images Using Wavelets as Input to Support Vector Machine and Neural Network”, Biomedical Signal Processing and Control, 1 (2006) 86–92.
[20] Dipali M. Joshi et al. “Classification of Brain Cancer Using Artificial Neural Network”, 2nd International Conference on Electronic Computer Technology (ICECT 2010).
[21] Martin Fodslette Moller, “Scaled Conjugate Gradient Algorithm for Fast Supervised Learning”, Neural Networks, Vol. 6, pp. 525-533, 1993.
[22] Amanpreet Singh, Narina Thakur, Aakanksha Sharma, “A Review of Supervised Machine Learning Algorithms”,2016 International Conference on Computing for Sustainable Global Development.
[23] Vrushali Y Kulkarni, Dr Pradeep K Sinha. “Random Forest Classifiers: A Survey and Future Research Directions”, International Journal of Advanced Computing, ISSN: 2051-0845, Vol.36, Issue.
[24] Poomani N, Porkodi. R. “A comparison of Data Mining classification algorithms using breast cancer microarray dataset: A study”, International Journal for Scientific Research and Development. 2015; 2(12):543–7.
[25] Patel Pinky S, Raksha R. Patel, Ankita J. Patel, Maitri Joshi “Review on Classification Algorithms in Data Mining”, (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 1, January 2015).
[26] Pankaj Sapra, Rupinderpal Singh, Shivani Khurana, "Brain Tumor Detection using Neural Network", International Journal of Science and Modern Engineering, ISSN: 2319-6386, Volume-1, Issue-9, August 2013
[27] Ian H.Witten and Elbe Frank, (2005) "Data mining Practical Machine Learning Tools and Techniques," Second Edition, San Fransisco.
[28[P.Arumugam,P.Jose,”Efficient Tree Based Data Selection and Support Vector Machine Classification materials today”Proceedings,Vol:5,Issue 1,2018,Pages 1679-1685.www.sciencedirect.com.

V.Ambikavathi, P.Arumugam, P.Jose, "Breast Cancer Classification Using Artificial Neural Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.964-968, 2019.

Tourist trip design problems are occur in our modern age, but with the help of mobile application and web service can solve this problem by recommendation of popularity POI sequences. In this paper proposed system represent personalized POI tourist information recommendation by using hierarchical modelling. Multiple tourist information with personalized POI prediction is very essential for users. There are differe4nt factor in trip recommendation such as location, updated information, weather prediction, image recommendation and route recommendation. This proposed system provides the online questionnaire for previously visited places. Users focus on recommend the popular image by using MHH algorithm. This personalized POI recommendation design by using multidimensional preference collection system. Existing system extended with automatic trip planning. The research demonstrates a high usability of this proposed system and recommends the popular place with multiple images according to user POI.

Tourist, Recommendation, POI(Point of Interest), Place, MHH(Motion History Histogram)

[1] S. Jiang, X. Qian, T. Mei, and Y. Fu, T. Mei, “Personalized travel sequence Recommendation on Multi-Source Big Social Media", IEEE Transaction on Big Data, Vol.2, Issue.1, pp.43-56, September 2016.
[2] M. Mazloom, R. Rietveld, S. Rudinac, M. Worring, and W. van Dolen, “Multimodal popularity prediction of brand-related social media posts,” in ACM MM, October 2016.
[3] F. Gelli, T. Uricchio, M. Bertini, A. D. Bimbo, and S. Chang, “Image popularity prediction in social media using sentiment and context features,” in ACM MM, pp.907–910, October 2015.
[4] A. Khosla, A. Das Sarma, and R. Hamid, "What makes an image popular? ", In Proceedings of WWW, 2014.
[5] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks", In Proceeding of NIPS, 2012.
[6] D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang, "Large-scale visual sentiment ontology and detectors using adjective noun pairs", In Proceeding of ACM MM, 2013.
[7] A. Martinez, S, Du, "A Model of the Perception of Facial Expression of Emotion by Humas: Research Overview and Perspective", In Journal of Machine Learning Research, pp.1589-1608, 2012.
[8] Q. Yuan, G. Cong, Z. Ma, A. Sun, N. Magnenat-Thalmann, " Time-aware Point-of-Interest Recommendation", In Proceedings of the SIGIR, pp. 363-372.
[9] E. Cho, S. A. Myers, and J. Leskovec, "Friendship and Mobility: User Movement in Location-Based Social Networks", In KDD, pp.1082–1090, 2011.
[10] Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma, "Mining Interesting Locations and Travel Sequences from gps Trajectories", In WWW, pp791–800, 2009.
[11] X. Cao, G. Cong, and C. S. Jensen, "Mining Significant Semantic Locations from gps Data", PVLDB, 3(1):1009–1020, 2010.
[12] K. W.-T. Leung, D. L. Lee, and W.-C. Lee. Clr, "A Collaborative Location Recommendation Framework Based on co-clustering", In SIGIR, pp. 305–314, 2011.
[13] C. Xu, D. Tao, C. Xu, "A Survey on Multi-view Learning", April 2013.
[14] N. Yasavarapu, R. Pitchiah, "An Efficient Approch for Personalized Travel Sequence Recommendation On Multi source Big Social Media", Vol. 3, Issue.1, pp. 2456-3307, 2018.
[15] Y. Yang, Member, IEEE Y. Duan, X. Wang, Zi Huang, Ning Xie, H.T. Shen, "Hierarchical Multi-Clue Modelling for POI Popularity Prediction with Heterogeneous Tourist Information", IEEE Transaction on Knowledge and Data Engineering, ISSN. 1041-4347, Issuie.1, August 2018.
[16] Y. Duan, X. Wange, Y. Yang, Z. Huang, N. Xie and H. T.Shen, "POI popularity prediction via hierarchical fusion of multiple social clue", In SIGIR, pp.1001-1004, 2017.
[17] H. Meng, B. Romera-Paredes, N. Bianchi-Berthouze, “Emotion Recognition by Two View SVM-2K Classifier on Dynamic Facial Expression Feature”, In NIPS, pp.355-362, 2005.
[18] M. Sarma, Y. Srinivas, L. Ullala, M. Sahithi Prasanthi, J. Rojee Rao, “Insider Threat Detection with Face Recognition and KNN User Classifier”, in IEEE International Conference on Cloud Computing in Emerging Markets, Vol.1, pp.39-44, 2017.

Rashmi A. Wahurwagh, P. M. Chouragade, "Popular Place Prediction and Image Recommendation Using Hierarchical Multi-Clue Modeling for Tourist," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.969-972, 2019.

Cloud computing comes with higher potential in improving the healthcare services provided to patients and also promises increase in the access of qualitative healthcare services and reduction in the healthcare expenses. Even though cloud computing puts an end to the concerns regarding investment in hardware infrastructure and its maintenance by hospitals, expenses by patients and faster access of health records by both patients and doctors without interruption in service, it is still discerned as unsafe because of the security threats it faces. The patient’s health information is prone to loss, unauthorized access, misuse, coercing and altering. This can be avoided by encrypting the data before handing it over for cloud storage. This paper comprises the study of various encryption schemes which can be put to use for securing the patient’s sensitive health information on cloud along with the implementation and performance analysis of a mobile healthcare application which encrypts the health records of patients before outsourcing it for storage over cloud and ensures effective access control, secrecy and integrity of health information.

Cloud computing, healthcare, encryption, mobile healthcare application, integrity

[1] D. Boneh and M. Franklin, “Identity-based Encryption from the Weil Pairing”, In. Kilian J. (eds) Advances in Cryptology, CRYPTO 2001, Lecture Notes in Computer Science, Vol.2139, pp.213-229, 2001.
[2] R. Lakshmi, R. Lavanya, M. Meenakshi, Dr. C. Dhas, “Analysis of Attribute Based Encryption Schemes”, International Journal of Computer Science and Engineering Communications, Vol.3, Issue.3, pp.1076-1081, 2015.
[3] Abbas and S. U. Khan, “A Review on the State-of-the-Art Privacy Preserving Approaches in the e-Health Clouds”, IEEE Journal of Biomedical and Health Informatics, Vol.18, Issue.4, pp.1431-1441, 2014.
[4] A. Fiat and M. Naor, “Broadcast Encryption”, In. Stinson D. R. (eds) Advances in Cryptology, CRYPTO 1993, Vol.773, pp.480-491, 1993.
[5] C. Delerablee, “Identity-Based Broadcast Encryption with Constant Size Ciphertexts and Private Keys”, In. Kurosawa K. (eds) Advances in Cryptology, ASIACRYPT 2007, Lecture Notes in Computer Science, Vol.4833, pp.200-215, 2007.
[6] D. Lubicz and T. Sirvent, “Attribute-Based Broadcast Encryption Scheme Made Efficient”, In. Vaudenay S. (eds) Progress in Cryptology, AFRICACRYPT 2008, Lecture Notes in Computer Science, Vol.5023, pp.325-342, 2008.
[7] Q. Huang, W. Yeu, Y. He and Y. Yang, “Secure Identity-Based Data Sharing and Profile Matching for Mobile Healthcare Social Networks in Cloud Computing”, IEEE Access Special Section on Cyber-Threats and Countermeasurs in the Healthcare Sector, Vol.6, pp.36584-36594, 2018.
[8] M. Blaze, G. Bleumer and M. Strauss, “Divertible Protocols and Atomic Proxy Cryptography”, In. Nyberg K. (eds) Advances in Cryptology, EUROCRYPT’98, Lecture Notes in Computer Science, Vol.1403, pp.127-144, 2006.
[9] M. Green, G. Anteniese, “Identity-based Proxy re-encryption”, In. Katz J., Yung M. (eds) Applied Cryptography and Network Security, ACNS 2007, Vol.4521, pp.288-306, 2007.
[10] K. Liang, M. H. Au, J. K. Liu, W. Susilo, D. S. Wong, G. Yang, y. Yu and A. Yang, “A secure and efficient Ciphertext-Policy Attribute-Based Proxy Re-Encryption for cloud data sharing”, Future Generation Computer System, Vol.52, pp.95-108, 2014.
[11] J. Weng, R. H. Deng, X. Ding, C. Chu and J. Lai, “Conditional Proxy Re-Encryption Secure against Chosen-Ciphertext Attack”, Proceedings of the 4th International Symposium on Information, Computer, and Communication Security,ASIACCS’09, pp.322-332, 2009.
[12] D. Boneh, G. D. Crescenzo, R. Ostrovsky and G. Persiano, “Public Key Encryption with Keyword Search”, Proceedings of EUROCRYPT, Interlaken, pp.542-545, 2004.
[13] G. Yang, C. Tan, Q. Huang and D. Wong, “Probabilistic Public Key Encryption with Equality Test”, In. Pieprzyk J. (eds) Topics in Cryptology-CT-RSA 2010, Lecture Notes in Computer Science, Vol.5985, pp.119-131, 2010.
[14] S. Ma, “Identity-based encryption with outsourced equality test in cloud computing”, Information Sciences, Vol.328, pp.389-402, 2016.

Praneeta K. Maganti, Pushpanjali M. Chouragade, "Facilitating Secure Cloud Based Mobile Healthcare Application using Encryption Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.973-977, 2019.