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The paper depicts the various natural disasters occurring in different parts of the globe. With the help of information technology along with other branches of science it has become possible to study the basic environmental conditions before and after the events; even it becomes possible to predict the manifestations and indications before a disastrous occurrence. After the brief study of the different predictive models, it is easier to view the scenario of a country like India, where the major cities frequently faces the problem of urban flood which occurs due to continuous downpour. Along with the rain water the debris, sand particles and plastic solid waste (PSW) flowing from the nearby hilly or high level areas cause blockage to the major sewer drainage basins; resulting in different types of epidemic diseases. Urban flood can be avoided by keeping the flow of drainage basin clean, running and free of blockages. The study further helps to improvise a mathematical calculation of urban flood where the Lyapunov’s stability criterion is discussed and a relation between the parameters of the artificial flood is implemented using the theory.

Mike, OpenQuake, Artificial Flood, Sediment, Lyapunov stability

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A. Noorie, N. Deb, "The Role of Information Technology in the Predictive, Risk and Loss Estimate Models for various Natural Disasters," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.972-979, 2018.

This paper reviews the techniques for one of the unsolved problem of tackling channel management and deciding which one is most efficient in proper channel management technique and employs better usage of wireless Spectrum (which includes assigning APs, Detecting interference among clients/APs etc.).The main reason for the interference is improper channel assignment with access points and clients in WLAN. The common solution to this complication is assigning different channels to the access points which are having common areas across the range. In this paper we study CACAO: Distributed Client-Assisted Channel Assignment Optimization for Uncoordinated WLANs and Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network. In this we are going to study multiple interfaces with multiple channels having interference in them and problems associated with the channels like less bandwidth, throughput and channels reusability. One approach is LCCS (Least Congested Channel Search) based approach which is AP-centric in nature and involves an AP monitoring its channel for interference from other APs sharing the same physical space. Another approach which has a better chance for channel management over LCCS is based on “Conflict Set Coloring” formulation. This approach breaks down channel management into two different sets of tasks, Load balancing and channel assignment. CACAO (Client Assisted Channel Assignment Optimization) is another efficient algorithm since it is distributed as well as it enables the APs to automatically configure their channels depending on their local traffic log. MICA (Minimum Interference Channel Assignment Algorithm) algorithm is also reviewed which is having better efficiency than others.

channel interference, congestion, channel assignment

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[3] Xiaonan Yue, Chi-Fai Michael Wong, and Shueng-Han Gary Chan, “CACAO: Distributed Client-Assisted Channel Assignment Optimization for Uncoordinated WLANs”, 2011.
[4] Kyasanur, Pradeep, and Nitin H. Vaidya. "Routing and interface assignment in multi-channel multi-interface wireless networks." Wireless Communications and Networking Conference, 2005 IEEE. Vol. 4. IEEE, 2005.
[5] Ramachandran, Krishna N., et al. "Interference-aware channel assignment in multi-radio wireless mesh networks." INFOCOM 2006. 25th IEEE International Conference on Computer Communications. Proceedings. IEEE, 2006.
[6] Das, Arindam K., Rajiv Vijayakumar, and Sumit Roy. "WLC30-4: static channel assignment in multi-radio multi-channel 802.11 wireless mesh networks: issues, metrics and algorithms." Global Telecommunications Conference, 2006. GLOBECOM`06. IEEE. IEEE, 2006.
[7] Sangil Choi and Jong Hyuk Park 2, Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network, 2016.
[8] Arunesh Mishra ,Vladimir Brik, Suman Banerjee, Aravind Srinivasan, William Arbaugh, “A Client-driven Approach for Channel Management in Wireless LANs”,2006.
[9] Mishra, Arunesh, Suman Banerjee, and William Arbaugh. "Weighted coloring based channel assignment for WLANs." ACM SIGMOBILE Mobile Computing and Communications Review 9.3 (2005): 19-31.
[10] Das, Arindam K., Rajiv Vijayakumar, and Sumit Roy. "WLC30-4: static channel assignment in multi-radio multi-channel 802.11 wireless mesh networks: issues, metrics and algorithms." Global Telecommunications Conference, 2006. GLOBECOM`06. IEEE. IEEE, 2006.
[11] Peters, Steven W., and Robert W. Heath. "Cooperative algorithms for MIMO interference channels." IEEE Transactions on Vehicular Technology 60.1 (2011): 206-218.
[12] Marina, Mahesh K., Samir R. Das, and Anand Prabhu Subramanian. "A topology control approach for utilizing multiple channels in multi-radio wireless mesh networks." Computer networks 54.2 (2010): 241-256.
[13] Gomadam, Krishna, Viveck R. Cadambe, and Syed A. Jafar. "A distributed numerical approach to interference alignment and applications to wireless interference networks." IEEE Transactions on Information Theory 57.6 (2011): 3309-3322.
[14] Tingjuan Yao, Xiaodong Guo, Yihong Qiu, and Liansheng Ge. "An integral optimization framework for WLAN design", 2013 15th IEEE International Conference on Communication Technology, 2013.

Varun Deshmukh, Parv Singh, Tanishka Jodha, Mahima Bhatia, Ayush Agarwal, "Review of Algorithms minimizing channel interference in WLAN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.980-985, 2018.

Massive amount of information in form of text is available on internet. To get useful or important information from this massive amount of information is tough and tedious task. One can get important information by creating summary. Manual creation of summary is again a tough task. Hence research community is developing new approaches for creating automatic summaries; these approaches are called automatic text summarization. There are number of text summarization techniques available, some of them lack with quality of summary and some of them lacks in user specific needs of summary. This paper discusses the architecture for extractive type of text summarization that uses hybrid genetic fuzzy system. The goal of this paper is to give idea about effectiveness of Genetic algorithm and fuzzy logic system together to create good summary.

Text Summarization, Extractive summarization, Hybrid Genetic algorithm & Fuzzy system for text summarization

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[15] Radev, D. R., Allison, T., Blair-Goldensohn, S., Blitzer, J., Celebi, A., Dimitrov, S., . & Otterbacher, J. (2004, May). MEAD-A Platform for Multidocument Multilingual Text Summarization. In LREC.

S. Saiyed, P. Sajja, "Architecture for Hybrid genetic fuzzy system for text summarization," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.986-989, 2018.

In this paper, various data mining techniques are analyzed and their proficiencies have been evolved. Medical professionals need a reliable prediction methodology to diagnose factors influencing diabetes. There are large quantities of information about patients and their medical conditions. Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. The main aim of this thesis is to show the comparison of different classification algorithms such as Multilayer perceptron neural network (MLPNN), Zero R, K-Star based on computing time, Correctly Classified Instances, Incorrectly Classified Instances, Mean absolute error, Root mean squared error, Relative absolute error, Root relative squared error, precision value , Recall , F-measure, the data evaluated using 25 fold Cross Validation error rate, error rate focuses True Positive, True Negative, False Positive and False Negative and the clustering algorithm such as K-means algorithm based on varied number of clusters and Sum of squared error. Classification is an important data mining technique with broad applications. It classifies data of various kinds. Classification is used in every field of our life. Classification is used to classify each item in a set of data into one of predefined set of classes or groups. Clustering analysis method is one of the main analytical methods in data mining; in which k-means clustering algorithm is most popularly/widely used for many applications. K-means algorithm has higher efficiency and scalability and converges fast when dealing with large data sets. Clustering is an adaptive procedure in which objects are clustered or grouped together, based on the principle of maximizing the intra-class similarity and minimizing the inter-class similarity. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Waikato Environment for Knowledge Analysis or in short, WEKA is used to obtain the accuracy of algorithms and find out which algorithm is most suitable for user working on data of diabetic patients. Weka is a data mining tool. It contains many machine leaning algorithms. It provides the facility to classify our data through various algorithms.

Multilayer perceptron neural network (MLPNN), Zero R, K-Star, K-means, WEKA

[1] P.Yasodha, M.Kannan, “Analysis of a Population of Diabetic Patients Databases in Weka Tool”, International Journal of Scientific & Engineering Research, Volume 2, Issue 5, May-2011.
[2] K. Rajesh, V. Sangeetha, “Application of Data Mining Methods and Techniques for Diabetes Diagnosis”, International Journal of Engineering and Innovative Technology (IJEIT), Volume 2, Issue 3, September 2012.
[3] Sukhjinder Singh, Kamaljit Kaur,”A Review on Diagnosis of Diabetes in Data Mining”, International Journal of Science and Research (IJSR), 2013.
[4] Veena Vijayan V, Aswathy Ravikumar, ”Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus”, International Journal of Computer Applications (0975 – 8887) Volume 95– No.17, June 2014.
[5] P.Yasodha, N.R. Ananthanarayanan, ”Comparative Study of Diabetic Patient Data’s Using Classification Algorithm in WEKA Tool”, International Journal of Computer Applications Technology and Research, Volume 3– Issue 9, 554 - 558, 2014 .
[6] Aiswarya Iyer, S. Jeyalatha and Ronak Sumbaly,”DIAGNOSIS OF DIABETES USING CLASSIFICATION MINING TECHNIQUES”, International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol.5, No.1, January 2015.
[7] Sabreena Jan, Vinod Sharma,” A Study of various data mining techniques for diabetic prognosis”, International Journal of Modern Computer Science (IJMCS), Volume 4, Issue 3, June, 2016.
[8] P. Suresh Kumar and V. Umatejaswi* , “Diagnosing Diabetes using Data Mining Techniques”, International Journal of Scientific and Research Publications, Volume 7, Issue 6, June 2017 .
[9] Saman Hina*, Anita Shaikh and Sohail Abul Sattar, “Analyzing Diabetes Datasets using Data Mining”, Journal of Basic & Applied Sciences, 2017, 13.
[10] S.Selvakumar, K.Senthamarai Kannan and S.GothaiNachiyar,”Prediction of Diabetes Diagnosis, Using Classification Based Data Mining Techniques”, International Journal of Statistics and Systems, Volume 12, Number 2 (2017).
[11] Prakash Singh, Aarohi Surya, “PERFORMANCE ANALYSIS OF CLUSTERING ALGORITHMS IN DATA MINING IN WEKA”, International Journal of Advances in Engineering & Technology, Jan., 2015.
[12] M.Mounika, S.D.Suganya, B.Vijayashanthi, S.KrishnaAnand,” Predictive Analysis of Diabetic Treatment Using Classification Algorithm”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (3), 2015, 2502-2505.
[13] Bharat Chaudhari, Manan Parikh,”A Comparative Study of clustering algorithms Using weka tools”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 1, Issue 2, October 2012.
[14] Pallavi , Sunila Godara,” A Comparative Performance Analysis of Clustering Algorithms”, International Journal of Engineering Research and Applications (IJERA), Vol. 1, Issue 3, pp.441-445.
[15] Y. S. Thakare, S. B. Bagal,” Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics, International Journal of Computer Applications (0975 – 8887), Volume 110 – No. 11, January 2015.
[16] Nidhi Singh, Divakar Singh,”Performance Evaluation of K-Means and Hierarchal Clustering in Terms of Accuracy and Running Time”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (3), 2012, 4119-4121.
[17] Arka Haldar, G.Prudhvi Raj, S.V.S.S Lakshmi, “Comparison of Different Classification Techniques Using WEKA for Diabetic Diagnosis”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 6, Issue 1, January 2018.
[18] Santosh Rani, Dr. Sandeep Kautish,” Application of Data Mining Techniques for Prediction of Diabetes - A Review, International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT | Volume 3 | Issue 3 | ISSN : 2456-3307.

Misba Reyaz, Gagan Kumar, "PROFICIENCY ANALYSIS OF VARIOUS DATA MINING TECHNIQUES FOR DIABETES PROGNOSIS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.990-997, 2018.

The pace of the monitoring of the Social media and the analysis of the social data keeps on rising high and it plays a major role in understanding the behavior of the people. Twitter, being the ninth largest social networking site in the world, is being eminent and powerful with its specialty of the short message named tweets with which people can share their opinions and also trend something worldwide with hash tags and common phrases. The Sentiment Analysis used here is to check the opinion of people related to the Government Schemes by the Central Government in the recent years with the help of Twitter Data Analysis. The tweets of the chosen schemes are classified based on the polarity and finally they are classified as positive or negative or neutral based on the opinions. This work is carried out using Digital India and Make in India tweets. Indians all over the world are sharing their ideas by tweeting and there are billions and billions of tweets tweeted every second across the world. The Sentiment Analysis is performed using R Studio. As the first step, the tweets needed for analysis are extracted with proper authentication with the help of Twitter API. The extracted tweets are cleaned by removing the stop words followed by the emotion and polarity classification. The final step is to generate the word cloud and then the comparison of the positive and the negative and the neutral tweets of the two schemes.

Sentiment Analysis and Opinion Mining, Natural Language Processing, R - Studio

[1] A. Balahur, G. Jacquet,“Sentiment analysis meets social media – Challenges and solutions of the field in view of the current information sharing context”. Information Processing and Management, Vol.51, Issue 4, pp. 428-432, 2015.
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[4] M.Bhuvaneswari, V.Srividhya, “Enhancing The Sentiment Classification Accuracy of Twitter Data Using Preprocessing Technique”International Journal of Engineering Research, Vol. 4 , Issue 5, 2016
[5] BholaneSavitaDattu, Prof.Deipali V. Gore “A Survey on Sentiment Analysis on Twitter Data Using DifferentTechniques”,International Journal of Computer Science and Information Technologies, Vol. 6,Issue 6, pp. 5358-5362,2015.
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[7] Xin J, Gallagher A, Cao L.“The wisdom of social multimedia: Using flickr for prediction and forecast”,Proceedings of the 18th ACM international conference on Multimedia, New York, pp.1235-1244, 2010.
[8]MJ.Jensen, L.Jorba E. Anduiza “Digitalmedia and political engagement worldwide: A comparative study”,Cambridge University Press,New York, NY, pp1-15, 2012
[9] Tjong Kim Sang E, J.Bos, “Predicting the 2011 Dutch senate election results with twitter”,Proceedings ofSASN 2012, Avignon, France, pp.1-8,2012.
[10] C.Nanda, ,M.Dua, “A Survey on Sentiment Analysis “International Journmal of Scientific Research in Computer Science and Engineering, vol. 5, Issue 2, pp. 67-70, 2017.
[11] H.Yu. V.Hatzivassiloglou, “Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences” In the Proceedings of the Conference on Empirical Methods in Natural Language Processing,pp. 129-136, 2003.
[12] Sunil B. Mane, Kruti Assar, PriyankaSawant, Monika Shinde. “Product Rating using Opinion Mining”, International Journal of Computer Engineering in Research Trends, Vol.4, Issue 5, pp.161-168.
[13] S.R.Chheda, A.K.Singh, P.S.Singh, A.S.Bhole, “Automated Trading of Cryptocurrency Using Twitter SentimentalAnalysis”, International Journal of Computer Sciences and Engineering, Vol 6, Issue 5, pp.209-214

V. Srividhya, G. Raja Meenakshi, "Comparison of Sentiment Analysis of Government of India Schemes using Tweets," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.998-1001, 2018.

The use of mobile phones is increasing rapidly with varying traffic demand. This drastic increase in mobile traffic is due to video streaming, mobile TV, video conferencing etc. 4G network is unable to full fill this increasing demand of bandwidth, broader coverage and lower latency. Due to this ever increasing demand, next generation of mobile networks which uses SDN, NFV, cloud approach in mobile networks came into picture. This new generation could become more efficient if various forecasting approaches are used at the centralized controller. Various algorithms for forecasting 4G data already exists using predictable behavior of mobile data traffic. Work has already been done to deploy 5G which uses SDMN paradigm. Thereafter comes the need for some forecasting technique at SDMN controller, which could predict traffic and make decision according to that prediction at centralized controller. This paper presents existing work on traffic forecast, SDMN, traffic forecasting without SDMN and with SDMN. It also discusses data analysis for dataset collection on which traffic forecasting algorithms could be tested.

Software Defined Network, Network Function Virtualization, Software Defined Mobile Network, Traffic Forecasting, Real time data

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[2] T. Chen, V. T. T. Technical, and N. Nikaein, “Towards Software Defined 5G Radio Access Networks,” no. March 2016, pp. 1–7, 2018.
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[5] Z. Liang and Y. Wakahara, “City traffic prediction based on real-time traffic information for intelligent transport systems,” in ITS Telecommunications (ITST), 2013 13th International Conference on IEEE, 2013, pp. 378–383.
[6] Cisco Visual Networking Index, “Global Mobile Data Traffic Forecast Update 2014-2019 White Paper”,2014.
[7] Jun Zhao, “Research on Prediction of Traffic Congestion State,” MATEC Web of Conferences, 2015,vol. 22.
[8] Shiliang Sun, Changshui Zhang, and Guoqiang Yu. A bayesian network approach to traffic flow forecasting.Intelligent Transportation Systems, IEEE Transactions on, 7(1):124 –132, march 2006.
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[11] Feng Jin and Shiliang Sun. Neural network multitask learning for traffic flow forecasting. In Neural Networks,2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, pages 1897 –1901, june 2008.
[12] Daniel B. Fambro Sangsoo Lee. Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transportation Research Record: Journal of the Transportation Research Board, pages 179–188, 2007.
[13] Goodwin, P. 2010. The Holt-Winters Approach to Exponential Smoothing: 50 Years Old and Going Strong. Foresight 19:30-33
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[15] M. Joshi and T. H. Hadi, “A Review of Network Traffic Analysis and Prediction Techniques,” p. 23, 2015.
[16] Ali Yadavar Nikravesh Samuel A. Ajila Chung-Horng Lung, Wayne Ding, “Mobile Network Traffic Prediction Using MLP, MLPWD, and SVM,” in 2016 IEEE International Congress on Big Data, 2016, pp. 402-409.
[17] R. Alvizu, S. Troia, G. Maier, and A. Pattavina, “Matheuristic With Machine-Learning-Based Prediction for Software-Defined Mobile Metro-Core Networks,” J. Opt. Commun. Netw., vol. 9, no. 9, p. D19, 2017.
[18] Z. Cao, S. Member, S. S. Panwar, M. Kodialam, and T. V Lakshman, “Networking and Cloud Computing,” vol. 25, no. 3, pp. 1–14, 2017.
[19] R. Alvizu, X. Zhao, G. Maier, Y. Xu, and A. Pattavina, “Energy aware optimization of mobile metro-core network under predictable aggregated traffic patterns,” 2016 IEEE Int. Conf. Commun. ICC 2016, 2016.
[20] R. Alvizu, X. Zhao, G. Maier, Y. Xu, and A. Pattavina, “Energy efficient dynamic optical routing for mobile metro-core networks under tidal traffic patterns,” J. Light. Technol., vol. 35, no. 2, pp. 325–333, 2017.
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[22] Z. Zhong, N. Hua, H. Liu, Y. Li, and X. Zheng, “Considerations of effective tidal traffic dispatching in software-defined metro IP over optical networks,” 2015 Opto-Electronics Commun. Conf. OECC 2015, 2015.

Anupriya, Anita Singhrova, "Review of Traffic Forecasting Approaches in Software Defined Mobile Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1002-1009, 2018.

In this research paper we are going to show how we can predict heart attack diseases and condition by applying patient data to developed software. Early techniques have not been so much efficient in finding it even medical professors are not so much efficient enough in predicting the heart disease. We have plan to solve related this concept by this paper, here some of pre-defined heart related are stored in databases, according those databases value our algorithms (K-means) detect patient condition related heart issues, including Heart Attack, Stroke, Congestive Heart Failure, Angina and Cardiovascular Disease this is very help tool which is used by all humans in any diagnostics or hospitals, here we have plan to upgrade emergency doctor contact sharing technique, if any patient want immediate response from doctors they can take some immediate suggestion form doctors by data sharing technique. Finally, we have concluded to introduce some challenging issues in the design of efficient auditing protocols for prediction heart condition.

Heart Attack, Predefined Parameters, Machine Learning, Patient Records, Doctors Records, Heart Disease, Prediction Model

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Zeinab Gazala Rafee, Gowramma G S, "Introducing K-Means Alogrithm to Predict and Detect Heart Attack Disease in Machine Learning," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1010-1013, 2018.

Authentication is the process of confirming the authenticity of a client to identify its validity. If the user is valid then server permits the accessibility of its assets. Many authentication techniques and protocols are available to protect the server’s assets from getting unauthorized access. This paper presents an overview of different factors, protocols and methods associated with authentication and their importance in real life scenarios. Extensible Authentication Protocol (EAP) is a framework which aims to provide a flexible authentication for wireless networks. The aim of this survey paper is to study the widely used authentication methods and their evaluation for advantages and disadvantages.

authentication, authentication protocols, factors, EAP ,TLS, TTLS, MD5, LEAP, PEAP

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[3] Khaled Baqer, Johann Bezuidenhoudt, Ross Anderson and Markus Kuhn, “SMAPs: Short Message Authentication Protocols”, pp. 119-132, 2017.
[4] JC Mitchell, A Roy, P Rowe and A Scedrov “Analysis of EAP-GPSK authentication protocol”, International Conference on Applied Cryptography and Network Security, pp. 309-327, 2008.
[5] Li Ping Du and Jian WeiGuo Ying Li, "Research on Micro-Certificate Based Security System for Internet of Things", Applied Mechanics and Materials, vol. 263, pp. 3125-3129, 2013.
[6] Sonal Fatangare and Archana Lomte, “SWAP: Secure Web Authentication Protocol on Windows Mobile App”, International Journal of Computer Science and Mobile Computing, vol. 3, no. 6, pp. 674–680, 2014.
[7] P. Pacyna and R. Chrabąszcz, "Evaluation of EAP re-authentication protocol”, 17th International Telecommunications Network Strategy and Planning Symposium (Networks), Montreal, pp. 45-49, 2016.
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[12] Umesh Kumar, Praveen Kumar and Sapna Gambhir, “Analysis and Literature Review of IEEE 802.1x(Authentication) Protocols”, International Journal of Engineering and Advanced Technology, vo. 3, no. 5, pp. 163-168, 2014.
[13] Umesh Kumar and Spana Gambhir, “Secured Authentication Method for Wireless Networks”, IOSR Journal of Computer Engineering, pp. 1-11, 2015.
[14] U. Kumar and S. Gambhir, "A novel approach for key distribution through fingerprint based authentication using mobile agent," 3rd International Conference on Computing for Sustainable Global Development, New Delhi, pp. 3441-3445, 2016.

Ambuj Prakash, Umesh Kumar, "Authentication Protocols and Techniques: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1014-1020, 2018.

To perform product or services’ recommendations, the Recommender System (RS) is used by most of the social media, such as Twitter, LinkedIn, Netflix, etc. and potential e-marketers, to name, Amazon, Flipkart, Alibaba, eBay, Myntra, etc. including the famous search engine Google. All of these systems uses Machine Learning (ML) algorithms claimed from the field of Artificial Intelligence (AI). However, choosing an appropriate ML algorithm to fulfil this task of Recommender System (RS) is a critical issue, if not impossible, since a considerably large number of algorithms find place in the literature. Practitioners and researchers developing Recommender System leaves a very little information about their current approaches in algorithm usage, thus it is sufficient to create further confusion to perform the task of selecting appropriate algorithm. The current paper presents a systematic insight in to the subject analysing the usage of machine learning algorithms for Recommender System (RS), and thereby identifies the research opportunities to bring further improvement into the system used. The study carried exposes that the Bayesian network and Decision Tree algorithms are widely adopted and used in the Recommender System (RS) due to their relative simplicity along with required performance. The software system requirements and the design phases adopted for the same also appears to have ample of further research opportunities. This paper presents a systematic analysis of the topic under consideration with recommendations of performance measures and evaluation procedures as per its suitability for designing an effective RS.

Machine Learning, Recommender System, Deep Learning, Artificial Intelligence, systematic study

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Shweta Sharma, D.P. Sharma, "Systematic Study and Application of Machine Learning Algorithms in Recommender System Design," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1021-1026, 2018.

As an element of IOT, IOV is a promising technology for connected vehicles.IOV involves the concept of smart driving which will be enabled only with the help of recognition of various drivers. There are various ways to predict the driver behavior. In this paper, an extensive literature review of various driver recognition mechanisms has been done. In doing so, we have classified the driving behavior according to different parameters. Some open issues and challenges have been computed so that work can be carried out on these issues.

IoT, SIoVIoV, Driving Behavior

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Sucheta Sharma, Jyoteesh Malhotra, "Investigation of the various driver recognition techniques and its behavioural characterization," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1027-1031, 2018.