Volume-07 , Special Issue-14 , May - 2019 Go Back

The term Phishing refers to a fraudulent technique of stealing people’s private data .The main aim of phishing attack is to steal consumer private data by provoking them to enter their details in the portals which are sent by the attacker .These portals resembles with the original website portal and provokes the users to enter their data like username and password ,bank account details and other sensible information .The main goal of this paper is to perform research on the machine learning data techniques to detect the phishing attacks and to assist the police in handling the complicated phishing activities

Phishing, criminal technique, associative classification algorithms

[1] Abdulghani Ali Ahmed, Nurul Amirah Abdullah, “Real Time detection of phishing websites”.In the proceedings of the 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON 2016),USA,pp. 2-5,2016.
[2] Stuart McClure, “Hacking Exposed”, McGraw-Hill Education,pp. 28-45, 2012.
[3] Christopher Atikins,”Phishing attacks:Advanced Attack Techniques”, CreateSpace Independent Publishing Platform,pp. 55-75,2018.
[4] Dr.Radha D,”Study on Phishing attacks and antiphishing tools” International Research Journal of Engineering and Technology (IRJET),Vol 3,Issuse.1,pp.1-4,2016.
[5] Himani T,”A survey paper on Phishing detection” International Journal of Advanced Research in Computer Science,Vol 5,pp 1-4,2016.

N. Saivikas Reddy, Vinay Kumar M, "Detection of E-Banking Phishing Websites", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.49-52, 2019.

Communitarian sifting (CF) calculations have been generally used to manufacture recommender frameworks since they have recognizing ability of sharing aggregate wisdoms and encounters. Notwithstanding, they may effortlessly fall into the snare of the Matthew impact, which will in general prescribe prevalent things and consequently less famous things become progressively less well known. Under this situation, a large portion of the things in the proposal list are now well-known to clients and in this way the execution would truly deteriorate in discovering cold things, i.e., new things and specialty things. To address this issue, a client overview is first directed on the internet shopping propensities in China, in light of which a novel suggestion calculation named trend-setter based CF is recommended that can prescribe cold things to clients by presenting the idea of pioneers. In particular, trend-setters are an extraordinary subset of clients who can find cold things without the assistance of recommender framework. In this way, chilly things can be caught in the suggestion list through trailblazers, accomplishing the harmony among good fortune and precision

Recommendation System; E-Commerce Applications; Machine Learning; Sentiment Analysis

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[15]. Manish Raka, Prof. Sachin Godse, “Implementing Product Recommendation System using Neural Network by Connection Social Networking to E-Commerce”, IJIRST –International Journal for Innovative Research in Science & Technology, Volume 3, Issue 08, January 2017 ISSN (online): 2349-6010.

G Kiran Prabhu, D Sai Kumar, Ayasya V Bulusu, E Poojitha, Raghavendra Reddy, "New Approach to Product Recommendation System by Using Blog Data for E-Commerce Applications", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.53-58, 2019.

Study includes information about the recommendation system using Machine Leaning. The Recommendation system could recommend the whole thing from songs, movies, jokes, restaurants with rankings. That may collect the relevant data from the web. And give a relevant outcome to the user. The author using a Collaborative Filtering technique is a basic path of any recommendation System. But only Collaborative Filtering cannot give sufficient result about scalability and accuracy and also provide a computation of sample value of the evaluation prediction and measures for evaluating the algorithm. The major consciousness of this paper, the author provides the methodology of Data Pre-processing, Singular Value Decomposition (SVD), Content-based Collaborative filtering algorithm based on the recommendation system. The similarity is determined using for a Collaborative Filtering (CF) set of rules based totally on person similarity, behaviour and personalized movie recommendation system. And this consists of an analysis of the outcomes and conclusions based totally at the simulations executed on the computer to assess how the algorithms work

Data Preprocessing, Singular Value Decomposition(SVD), Content based Collaborative Filtering Algorithm

in big data”,9th International Conference on, Intelligent Systems and Control (ISCO), Sept 2015, pp 1-7.
[2] Swati Mittal Singal, Tejal, Bhawna Juneja “AdaBoosting for Case-Based Recommendation System” ,IEEE 2016.
[3] Sonali R. Gandhi, Prof. Jaydeep Gheewala “A Survey on Recommendation System with Collaborative Filtering using Big Data” ,ICIMIA ICIMIA 2017 International Conference Innovative Mechanism IndustryApplications.
[4] Sasmita Panigrahi, Rakesh Ku. Lenka, Ananya Stitipragyan, “A Hybrid Distributed Collaborative Filtering Recommender Engine Using Apache Spark”, Procedia Computer Science, 2016
[5] Karan Patel, Yash Sakaria and Chetashri Bhadane, “Real Time Data processing Framework”, International Journal of Data Mining and Knowledge Management Process (IJDKP) Vol.5, No.5, September 2015.
[6] Michael Stonebraker, Uur etintemel, Stan Zdonik, “The 8 Requirements of Real-Time Stream Processing”, IEEE 2017.
[7] Ms. Nishigandha Karbhari, Prof. Asmita Deshmukh ,Prof. Asmita Deshmukh “Recommendation System using Content Filtering” ,International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017) .
[8] Ibrahim Hussein Mwinyi, Husnu S. Narman, Kuo-Chi Fang, and Wook-Sung Yoo “Predictive Self-Learning Content Recommendation System for Multimedia Contents”, WTS 2018 1570426345.
[9] Gopi Krishna Durbhaka, Barani Selvaraj “Predictive Maintenance for Wind Turbine Diagnostics using Vibration Signal Analysis based on Collaborative Recommendation”, Approach 2016 Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept. 21-24, 2016, Jaipur, India.
[10] De˘ger Ayata, Yusuf Yaslan and Mustafa E.Kamasak “Emotion Based the Music Recommendation System Using Wearable Physiological Sensors” ,IEEE 2018.
[11] Ruchika1, Ajay Vikram Singh2, Mayank Sharma3. “Building an Effective Recommender System Using Machine Learning Based Framework”,@IEEE 2018.
[12] Wisuwat Sunhem Kitsuchart Pasupa “An Approach to Face Shape Classification for Hairstyle Recommendation”, 8th International Conference on Advanced Computational Intelligence Chiang Mai, Thailand; February 14- 16, 2016
[13] Fabiano A. Dorc¸a1, Vitor C. Carvalho1, Miller M. Mendes1, Rafael D. Ara´ujo1, Hiran N. Ferreira2, Renan G. Catellan1 “An Approach for Automatic and Dynamic Analysis of Learning Objects Repositories Through Ontologies and Data Mining Techniques for Supporting Personalized Recommendation of Content in Adaptive and Intelligent Educational Systems”, 2017 IEEE 17th International Conference on Advanced Learning Technologies
[14] Anurag Gulati Anish Batra Rohit Khurana Dr. MM Tripathi “Cognitive Learning Recommendation System in Indian Context” ,978-1- 5386-1922-3/17/$31.00 © 2017 IEEE
[15] Joseph Coelho, Paromita Nitu, and Praveen Madiraju “A Personalized Travel RecommendationSystem Using social Media Analysis” ,2018 IEEE International Congress on Big Data
[16] Sasmita Panigrahi, Rakesh Ku. Lenka, Ananya Stitipragyan, “A Hybrid Distributed Collaborative Filtering Recommender Engine Using Apache Spark”, Procedia Computer Science, 2016, pp 10001006.
[17] Lipi Shah, Hetal Gaudani and Prem Balani, “A Survey on Recommendation System”, International Journal of Computer Applications(IJCA),March 2016, pp. 43-49.
[18] A. D. a. V. S. N. Karbhari, “Recommender System in Machine Learning” , ICEMTE, vol. 5, no. 3, pp. 62-65, 2017. International.
[19] Ahmed, Muyeed, et al. “TV Series Recommendation Using Fuzzy Inference System, K-Means Clustering and Adaptive Neuro Fuzzy Inference System” ,2017, pp. 1512–1519.
[20] Robert J. Durkin, AakashVerma. “Experiential Learning in Engineering Technology”, A Case Study on Problem Solving in Project-Based Learning at the Undergraduate Level", Journal of Engineering Technology, 2016.

Sushmita Nageshwar, Laxmi B Rananavare, "A Survey on Machine Learning Techniques for Movie Recommendation System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.59-63, 2019.

This venture explanation is the issue of idea for examination in twitter that is arranging tweets according to the thought verbalized in them: positive, negative or unprejudiced. The aim of this paper is to develop a functional classifier for accurate and automatic sentiment classification of an unknown tweet stream Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates. It is a rapidly developing administration with more than millions enlisted clients. Due to this large amount of usage all of us hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socio-economic phenomena like stock exchange

NLP, Sentiment Analysis, Polarity, DMT, Support Vector Machine (SVM)

[1] A.Pak and P. Paroubek. “Twitter as a Corpus for Sentiment Analysis and Opinion Mining". In Proceedings of the Seventh Conference on International Language Resources and Evaluation, 2010, pp.1320-1326
[2] R. Parikh and M. Movassate, “Sentiment Analysis of User- GeneratedTwitter Updates using Various Classification Techniques",CS224N Final Report, 2009
[3] Go, R. Bhayani, L.Huang. “Twitter Sentiment ClassificationUsing Distant Supervision". Stanford University, Technical Paper.2009
[4] L. Barbosa, J. Feng. “Robust Sentiment Detection on Twitterfrom Biased and Noisy Data". COLING 2010: Poster Volume,pp. 36-44.
[5] Bifet and E. Frank, "Sentiment Knowledge Discovery in Twitter Streaming Data", In Proceedings of the 13th International Conference on Discovery Science, Berlin, Germany: Springer,2010, pp. 1-15.
[6] Agarwal, B. Xie, I. Vovsha, O. Rambow, R. Passonneau, “Sentiment Analysis of Twitter Data", In Proceedings of the ACL 2011Workshop on Languages in Social Media,2011 , pp. 30-38
[7] Dmitry Davidov, Ari Rappaport." Enhanced Sentiment Learning Using Twitter Hashtags and Smileys". Coling 2010: Poster Volume pages 241{249, Beijing, August 2010
[8] Po-Wei Liang, Bi-Ru Dai, “Opinion Mining on Social Media Data", IEEE 14th International Conference on Mobile Data Management, Milan, Italy, June 3 - 6, 2013, pp 91-96, ISBN: 978-1-494673-6068-5,

Amit Malik, Ashish Yadav, Boby, Manju More, "Sentiment Analysis on Twitter Data: A Study of methods based on negativity or positivity", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.64-67, 2019.

Social media is flooded with data that is generated by bloggers, committee, business, health, marketing, education, etc., in large amount. Extracting the data information from various fields like social media, marketing, reviews, conference publications and advertisement is done to perform sentiment analysis. These text data have some emotions hidden in it, and data analysing is carried out by natural language processing (NLP). NLP is application of artificial intelligence that help machine to read text by simulating the human capability to know language. Sentiment analysis is type of data mining that measures the opinion of the users or the customer or the blogger through the natural language processing, which can be utilized to extricate and dissect emotional data from web for the most part web based life. The main purpose of sentiment analysis is to classify emotions into positive, negative and neutral. The applications of sentiment analysis are in the financial market, area of reviews of consumer services and products to monitor customer sentiment and catch the trending topics. Sentiment analysis has challenges like multilingual sentiment analysis, emotion detection, and data sparsity from the different data by social media, marketing, emails, advertisement, movie review etc.

Sentiment analysis, natural language processing, artificial intelligence

[1] Zhao Jianqiang1, Gui Xiaolin1- Deep Convolution Neural Networks for Twitter Sentiment Analysis IEEE 2017.
[2] Pennington, Jeffrey, Richard Socher, and Christopher D. Manning. "Glove: Global vectors for word representation." In EMNLP, vol. 14, pp. 1532-1543. 2014.
[3] Nurulhuda Zainuddin, Ali Selamat- Sentiment Analysis Using Support Vector Machine Conference Paper • September 2014
[4] Jianqiang Z. Combing Semantic and Prior Polarity Features for Boosting Twitter Sentiment Analysis Using Ensemble Learning. In Proc. Data Science in Cyberspace (DSC), IEEE International Conference on. IEEE, pp.709-714,2016
[5] Hagen, M., Potthast, M., Büchner, M., & Stein, B.. Twitter Sentiment Detection via Ensemble Classification Using Averaged Confidence Scores. In European Conference on Information Retrieval, Springer, Cham, 2015, pp. 741-754,2015
[6] Bhumika M. Jadav M.E. Scholar, L. D. College of Engineering Ahmedabad, India- Sentiment Analysis using Support Vector Machine based on Feature Selection and Semantic Analysis , International Journal of Computer Applications Volume 146 – No.13, July 2016
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[8] S. Behdenn, F. Barigo, G. Belalem- Document Level Sentiment Analysis: A survey, EAI 2018
[9] Bhumika M. Jadav Ahmedabad, Sentiment Analysis using Support Vector Machine based on Feature Selection and Semantic Analysis International Journal of Computer ApplicationsVolume 146 – No.13, July 2016
[10] Alena Neviarouskaya, Helmut Prendinger, and Mitsuru Ishizuka- SentiFul: A Lexicon for Sentiment Analysis, IEEE Transactions on affective computing, vol. 2, no. 1, January- 2011
[11] S.M.Shamimul Hasan, Donald A. Adjeroh- Proximity-Based Sentiment Analysis, 2011 IEEE
[12] Montejo-Ráez, A., Martníez-Cámara, E., Martní-Valdivia, M. T., &Ureña-López, L. A..A knowledge-based approach for polarity classification in Twitter.Journal of the Association for Information Science and Technology, 65(2), pp.414-425, 2014

Pooja Mahaling, P.V Bhaskar Reddy, "Sentiment Analysis Using Machine Learning: A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.68-71, 2019.

The issue of information security is one of the most important issues of the modern day. Since the internet has become hugely wide spread, it has unbelievably helped in fast and efficient access to the cloud services and information on the cloud. The issue of securing this information has become an extremely serious problem. As previously known that the hash algorithms are one-way algorithms and cannot be retrieved. These algorithms provided a solution to the problem of analyzing the frequency of characters within a particular text by using encryption for more than characters. The proposed algorithm will provide a better model for finding scattered value. This system is characterized by its ability to face the threat of dictionary attacks, making it difficult to prepare a dictionary of scattered values. In this survey, a comparison was made between the proposed model and the MD5, SHA1 systems.

hash algorithm, one-way, attack dictionary, strong collision, MD5, SHA1 systems

[1] S. Debnath, A. Chattopadhyay, and S. Dutta, “Brief review on journey of secured hash algorithms,” 2017 4th International Conference on Opto-Electronics and Applied Optics, Optronix 2017, vol. 2018–Janua, pp. 1–5, 2018.
[2] S. Gupta, N. Goyal, and K. Aggarwal, “A Review of Comparative Study of MD5 and SSH Security Algorithm,” International Journal of Computer Applications, vol. 104, no. 14, pp. 1–4, 2014.
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Abdulrahman M. Zeyad, Chandra Shekar Loganathan, Gopal K Rishna, "Comparative Study of Encryption Algorithms for Improved Security:A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.72-75, 2019.

As per a Forbes` report distributed in 2015, cloud-based security spending is required to increment by 42 percent. As per another examination, the IT security use had expanded to 79.1 percent by 2015, demonstrating an expansion of something beyond than 10 percent every year. Worldwide Data Corporation (IDC) in 2011 demonstrated that 74.6 percent of big business clients positioned security as a noteworthy test. It is a normally acknowledged actuality that since 2008, cloud is a reasonable facilitating stage; be that as it may, the recognition regarding security in the cloud is that it needs critical upgrades to acknowledge higher rates of adaption in the venture scale. As recognized by another examination, a large number of the issues standing up to the distributed computing should be settled direly. The industry has made huge advances in combatting dangers to distributed computing, yet there is something else entirely to be done to accomplish a dimension of development that at present exists with conventional/on-start facilitating. This paper outlines various companion looked into articles on security dangers in cloud figuring and the preventive techniques. The goal of my examination is to comprehend the cloud parts, security issues, and dangers, alongside developing arrangements that may possibly alleviate the vulnerabilities in the cloud.

Security Issues, Distributed Computing

[1] Qi Jiang, Jianfeng Ma, Fushan Wei, “On the Security of a Privacy-Aware Authentication Scheme for Distributed Mobile Cloud Computing Services”, 2018,
[2] Gururaj Ramachandra, Mohsin Iftikhar, Farrukh Aslam Khan, “A Comprehensive Survey on Security in Cloud Computing”, 2017.
[3] State of the Cloud Report, 2017.
[4] State of Cloud Adoption And Security, 2017.
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[6] Sharma. R. and Trivedi. R. K, “Literature review: Cloud Computing –Security Issues, Solution and Technologies”, International Journal of Engineering Research, Vol. 3, Issue 4, pp. 221-225, 2014.
[7] Chirag Modi, Dhiren Patel, Bhavesh Borisaniya, Avi Patel, Muttukrishnan Rajarajan, “A survey on security issues and solutions at different layers of Cloud computing”, 2012.
[8] Amlan Jyoti Choudhury, Pardeep Kumar, Mangal Sain, Hyotaek Lim, Hoon Jae-Lee, “A Strong User Authentication Framework for Cloud Computing”, 2011.
[9] S. Subashini N, V.Kavitha, “A survey on security issues in service delivery models of cloud computing”, 2010.
[10] Meiko Jensen, Jorg Schwenk, Nils Gruschka, Luigi Lo Iacono., “On Technical Security Issues in Cloud Computing”, 2009.

Abhishek Gaur, Sohini Bhar, Gopal Krishna Shyam, "Security in Cloud Computing: A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.76-82, 2019.

Every person is unique in its own way; every person has a different personality. Personality detection gives an idea of behaviour of the person or gives an idea of how a person will react in a particular situation. Study of the relationship between word-use and personality traits has been successful in giving insight into human behaviour. Questionnaires is the most commonly used methods in the earlier times to detect personality traits from text but is not that effective. Due to emergence in technology different new methods are now available to detect personality of a person automatically. This paper is a summarized study of various methods used to automatically predict personality of a person from its text. Beginning with various methods used in earlier times to the methods newly emerged, this paper is a detailed study of all the different types of methods which are used for personality prediction along with different personality prediction models.

Personality Prediction, Linguistics, LIWC, DISC, MBTI, Big Five Model

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Susmita S. Kunde, A. U. Bapat, "Comparative Study on Prediction of Personality of a Person Using Text", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.83-87, 2019.

In the recent years, the use of e-commerce based applications via Internet has grown rapidly, thus increasing the volume of data in the web. Therefore it necessary to have faster retrieval of required data from the web. This paper provides a comprehensive review of various image retrieval techniques with their problems. The survey presents various techniques used so far for the Image Retrieval from the Web based applications, in order to make more efficient way of retrieving the information by using image retrieval techniques. The survey describes which techniques are used for image retrieval and the problem faced during the retrieval process. Finally, based on the use of existing techniques and the demand from the real-time applications a shopping guide will be presented with enhanced features of image retrieval techniques named as Click-n-Purchase, where the input for this application is taken from the mobiles and the visual search of the related images can be extracted from web based fashion domain based applications, so that user can be able to search their favourite items in less amount of time.

Image Retrieval Techniques, Mobile Visual Search, Fashion domain, Click-n-Purchase

[1] Mehmood, Zahid and Abbas, Fakhar and Mahmood, Toqeer and Javid, Muhammad Arshad and Rehman, Amjad and Nawaz, Tabassam, Content-Based Image Retrieval Based on Visual Words Fusion Versus Features Fusion of Local and Global Features, Arabian Journal for Science and Engineering, 2018, pp. 1-20.
[2] Katrien Laenen, Susana Zoghbi, and Marie-Francine Moens, Web Search of Fashion Items with Multimodal Querying, Eleventh ACM International Conference on Web Search and Data Mining (WSDM `18), 2018, pp. 342-350.
[3] Angelo Nodari, Matteo Ghiringhelli, Alessandro Zamberletti, Marco Vanetti, Simone Albertini, Ignazio Gallo, “A mobile visual search application for content based image retrieval in the fashion domain”, 10th International Workshop on Content-Based Multimedia Indexing, 2012.
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[5] Zhou, Dibin & Hu, Baokun & Wang, Qihui & Hu, Bin & Jia, Leiping & Wu, Yingfei & Xie, Lijun. “Design of Shopping Guide System with Image Retrieval Based on Mobile Platform”. 10.2991/3ca-13.2013.37, 2013.
[6] Liu Shuguang, Qu Pingge “Fabric Texture Classification Based on Wavelet Packet”, The Eighth International Conference on Electronic Measurement and Instruments,2017.
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Nikhil. S. Tengli, Suvarna Nandyal, "Click-n-Purchase: A Shopping guide with Image Retrieval based on Mobile Visual Search in Fashion Domain: A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.88-92, 2019.

The challenge in medical breast cancer database is to differentiate the sub types of cancers in the data. Analyzing the medical breast cancer database is most important one in identifying cancer types which cause deaths. Therefore in order to analyze the types of diseases in cancer databases this paper develops fuzzy set based unsupervised effective clustering technique and implements it with breast cancer database to divide it into available subtypes. This paper introduces the objective function of unsupervised effective proposed clustering technique by incorporating kernel induced distance, kernel functions, and possibilistic memberships. Through the experimental part of this paper the efficiency of proposed method is proved.

Clustering, Fuzzy C-Means, Kernel Distance, Breast Cancer Data

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S.R. Kannan, M. Siva, R. Devi, Mark Last, Ramathilagam, "Kernel Induced Possiblistic Unsupervised Clustering Techniques in Analyzing Breast Cancer Database", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.93-98, 2019.