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This article provides an overview of the various recommender systems, their classifications and comparative study. A recommender system is a software tool used for making suggestions about the items which are of interest to the user and the word “item” refers to the products or services that the system recommends to the individuals. With the emergence of internet, the amount of information available to the users is immense which may lead to confusion while making the final decision of selecting an item. Therefore, it becomes highly imperative to assist the users in selecting the final item. The recommender system attempts to solve the problem by exploring large amount of information and bring personalized content for the users. Such systems are being used for making decisions in different contexts ranging from movies recommendation to news feed.

Recommender System, Collaborative, Content-based Filtering, Hybrid Recommendation

[1] J. Ben. Schafer, D. Frankowski, J. Herlocker, S. Sen, “Collaborative filtering recommender systems”, The adaptive web, Springer, Vol. 4321, pp. 291-324, 2007.
[2] B. Pathak, R. Garfinkel, R. D. Gopal, R. Venkatesan, F. Yin, “Empirical analysis of the impact of recommender systems on sales”, Journal of Management Information Systems, Taylor & Francis, Vol. 27(2), pp. 159-188, 2010.
[3] J. Bobadilla, F. Ortega, A. Hernando, A. Gutierrez, “Recommender systems survey” Knowledge-based systems, Elsevier, Vol. 46, pp. 109-132, 2013.
[4] F. O. Isinkaye, Y. O. Folajimi, B. A. Ojokoh, “Recommendation systems: Principles, methods and evaluation”, Egyptian Informatics Journal, Elsevier, Vol. 16(3), pp. 261-273, 2015.
[5] J. Jooa, S. Bangb, G. Parka, “Implementation of a recommendation system using association rules and collaborative filtering”, Procedia Computer Science. Elsevier, Vol. 91, pp. 944-952, 2016.
[6] J. Aguilar, P.Valdiviezo-Diaz, G. Riofrio, “A general framework for intelligent recommender systems”, Applied Computing and Informatics, Elsevier, Vol. 13(2), pp. 147-160, 2017.
[7] R. Katarya, O. P. Verma, “An effective collaborative movie recommender system with cuckoo search”, Egyptian Informatics Journal, Elsevier, Vol. 18(2), pp. 105-112, 2017.
[8] J. Rathnavel, K. Kelkar, “Personalized Book Recommendation System”, International Journal Of Engineering And Computer Science, Vol. 6, Issue.,4, pp. 149-153, 2017.
[9] S.N. Patil, S.M. Deshpande, A. D. Potgantwar, “Product Recommendation using Multiple Filtering Mechanisms on Apache Spark”, International Jouranl of Scientific Research in Network Security and Communication, Vol. 5, Issue. 3, pp. 76-83, 2017.
[10] N. Sujatha, K. Prakash, “An Efficient and Scalable Auto Recommender System Based on Users Behavior”, International Journal of Scientific Research in Computer Science and Engineering , Vol. 6, Issue. 6, pp. 35-40, 2018.

Sanjay, Yogesh Kumar, Rahul Rishi, "Study and Comparative Analysis of Existing Recommender Systems," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.262-266, 2019.

Ant colony optimization algorithm is a popular meta-heuristic optimization algorithm that has been proven successful for solving travelling salesman problem. In this paper, modified version of ant colony optimization for solving travelling salesman problem has been proposed. In this modified version, update pheromone phase of ant colony optimization algorithm is updated. Here, best distance is calculated by comparing all the nodes distance and taken the best distance for find next node instead of taking ants one by one and keep updating later on. This modified version improves the total cost as well as total time of travelling salesman problem. Proposed algorithm is performed on 51 cities, 61 cities, 70 cities and 76 cities problem. Comparative study shows that proposed algorithm is better than standard ant colony optimization algorithm.

Ant colony optimization, Travelling salesman problem, ACO, TSP, Update Pheromone Phase

[1] L. Shufen, L. Huang and H. Lu,” Pheromone Model Selection in Ant Colony Optimization for the Travelling Salesman Problem”, Chinese Journal of Electronics, Vol.26, No.2, Mar. 2017.
[2] D. M. Chitty,” Applying ACO to Large Scale TSP Instances,” UK Workshop on Computational Intelligence, pp. 104-118. Springer, Cham, 2017.
[3] N. Xiong, W. Wu and C. Wu,” An Improved Routing Optimization Algorithm Based on Travelling Salesman Problem for Social Networks” Sustainability 2017, 9, 985; doi:10.3390/su9060985.
[4] Z. A. Aziz,” Ant Colony Hyper-heuristics for Travelling Salesman Problem”, IEEE International Symposium on Robotics and Intelligent Sensors (IRIS 2015), Procedia Computer Science 76 ( 2015 ) 534 – 538.
[5] Jiang, Y. ,”The Application of an Improved Ant Colony Optimization for TSP”, South-central University for Nationality: Wuhan, China, 2009.
[6] Chen, W.; Jiang, Y.,” Improving ant colony algorithm and particle swarm algorithm to solve TSP problem”, Inf. Technol. 2016, 2016, 162–165.
[7] Wang, Z.; Bai, Y.; Yue, L.,” An Improved Ant Colony Algorithm for Solving TSP Problems”, Math. Pract. Theory 2012, 42, 133–140.
[8] Sun, J.,” Research on Ant Colony Algorithm for Solving Travelling Salesman Problem”, Wuhan University of Technology: Wuhan, China, 2005.

Ranjeet Savita, Pankaj Sharma, Manish Gupta, "An Improved Version of Update Pheromone Rule of ACO algorithm for TSP," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.267-270, 2019.

Presence of multiple networks in a particular area is a very common real time scenario, due to this there is a presence of multiple service providers which can serve a particular node`s communication needs. Taking a handoff decision in such a complicated heterogeneous network scenario is tricky at both node and network level, as the nodes demand higher QoS while the network demands optimum number of nodes for service. To solve this complex problem, we propose a deep learning based approach which minimizes the false handoffs in the network, and reduces the delay needed during the handoff procedure. Our observations show that there is a 10% reduction of number of unnecessary handoffs in the network, and more than 8% reduction in handoff decision delay as compared to the existing game theory based algorithms.

Heterogeneous, handoff, deep learning, QoS, delay

[1] S. Althunibat, M. Al-Hasanat and A. Al-Hasanat, "To handover or not to handover (as a secondary user): An energy efficiency perspective," 2017 IEEE 22nd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Lund, 2017, pp. 1-6.
[2] R. Agrawal, A. Bedekar, S. Kalyanasundaram, T. Kolding, H. Kroener, and V. Ram, “Architecture principles for cloud RAN,” in IEEE 83rd Vehicular Technology Conference (VTC Spring), May 2016, pp. 1–5.
[3] P. Lescuyer, T. Lucidarme "Evolved packet system-the LTE and SAE evolution of 3G UMTS", John Wiley & Sons, 2008.
[4] Li Danyang, Zhang Zhizhong, GaoYiyi, "Modular handover algorithm for 5G HetNets with comprehensive load index", The Journal of China Universities of Posts and Telecommunications, Volume 24, Issue 2.
[5] X. Xu, Z. Sun, X. Dai, T. Svensson and X. Tao, "Modeling and Analyzing the Cross-Tier Handover in Heterogeneous Networks," in IEEE Transactions on Wireless Communications, vol. 16, no. 12, pp. 7859-7869, Dec. 2017.
[6]. M. C. Bhatt, H. S. Ahluwalia and Oshin, "Travelling distance prediction based handoff optimization in wireless networks," 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, 2017, pp. 947-952.
[7] E. Baccarelli, et al., “Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study,” in IEEE Network, vol. 30, no. 2, pp. 54–61, March–April 2016.
[8] Zhu, F., & McNair, J. (2004, March). Optimizations for vertical handoff decision algorithms. In Wireless Communications and Networking Conference, 2004. WCNC. 2004 IEEE (Vol. 2, pp. 867-872). IEEE.
[9] A. Checko, H. L. Christiansen, Y. Yan, L. Scolari, G. Kardaras, M. S. Berger, and L. Dittmann, “Cloud RAN for mobile networks; a technology overview,” IEEE Communications Surveys Tutorials, vol. 17, no. 1, pp. 405–426, Q1 2015.
[10] M. Lahby and A. Sekkaki, "Optimal vertical handover based on TOPSIS algorithm and utility function in heterogeneous wireless networks," 2017 International Symposium on Networks, Computers and Communications (ISNCC), Marrakech, 2017, pp. 1-6.
[11] S. Barbera, K. I. Pedersen, C. Rosa, P. H. Michaelsen, F. Frederiksen, E. Shah, and A. Baumgartner, “Synchronized RACH-less handover solution for LTE heterogeneous networks,” in 2015 International Symposium on Wireless Communication Systems (ISWCS), Aug 2015, pp. 755–759.
[12] L. L. Vy, L. P. Tung and B. S. P. Lin, "Big data and machine learning driven handover management and forecasting," 2017 IEEE Conference on Standards for Communications and Networking (CSCN), Helsinki, 2017, pp. 214-219.
[13] J. H. Yun, M. Lee, “Comparison of Handover Schemes for 3GPP Long Term Evolution and 3GPP2 Ultra Mobile Broadband”, in IEEE PIMRC, 2008.
[14] P. Singh, "Enhancement of handover decision in heterogeneous networks," 2017 International Conference on Signal Processing and Communication (ICSPC), Coimbatore, 2017, pp. 436-441.
[15] X. Sun and N. Ansari,“PRIMAL: PRofIt Maximization Avatar pLacement for Mobile Edge Computing,” in Proceedings of IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, May 23–27, 2016, pp. 1–6.
[16] Inderscience Publishers, "Vertical handover decision algorithm in heterogeneous wireless networks", International Journal of Internet Protocol Technology, Volume 10 Issue 4, January 2017

Piyush K.Ingole, M. V. Sarode, Menakshi S. Arya, "Improving Handoff Decisions for Heterogeneous High Speed Networks Using Deep Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.271-276, 2019.

Image reranking is effective for improving the performance of a text-based image search. However, existing reranking algorithms are limited for two main reasons: 1) the textual meta-data associated with images is often mismatched with their actual visual content and 2) the extracted visual features do not accurately describe the semantic similarities between images. Recently, user click information has been used in image reranking, because clicks have been shown to more accurately describe the relevance of retrieved images to search queries. However, a critical problem for click-based methods is the lack of click data, since only a small number of web images have actually been clicked on by users. Therefore, we aim to solve this problem by predicting image clicks. We propose a multimodal hypergraph learning-based sparse coding method for image click prediction, and apply the obtained click data to the reranking of images. We adopt a hypergraph to build a group of manifolds, which explore the complementarily of different features through a group of weights. Unlike a graph that has an edge between two vertices, a hyperedge in a hypergraph connects a set of vertices, and helps preserve the local smoothness of the constructed sparse codes. An alternating optimization procedure is then performed, and the weights of different modalities and the sparse codes are simultaneously obtained. Finally, a voting strategy is used to describe the predicted click as a binary event (click or no click), from the images’ corresponding sparse codes. Thorough empirical studies on a large-scale database including nearly 330K images demonstrate the effectiveness of our approach for click prediction when compared with several other methods. Additional image reranking experiments on real world data show the use of click prediction is beneficial to improving the performance of prominent graph-based image reranking algorithms.

Image reranking, click, manifolds, sparse codes

[1] Xiaogang Wang, Ke Liu et.al, “Web Image Re-Ranking UsingQuery-Specific Semantic Signatures”, IEEE Transactions on Pattern Analysis and Machine Intelligence Volume: 36 , Issue: 4 April 2014.
[2] Xinmei Tian, Dacheng Tao et.al, “Active Re-ranking for Web Image Search”, IEEE Transactions on Image Processing, Vol. 19, No. 3, March 2010.
[3] J.Cui, F. Wen, et.al, “Real time Google and live image search reranking”, The 16th ACM international conference on Multimedia, Pages 729-732, 2008.
[4] X. Tang, K. Liu, J. Cui, et. a, “Intent Search: Capturing User Intention for One-Click Internet Image Search”, IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 34, No.7 pages 1342 – 1353, July 2012.
[5] Y. Rui, T. S. Huang, M. Ortega, et.al, “Relevance feedback: a power tool for interactive Content-based image retrieval”, IEEE Transactions on Circuits and Systems for Video Technology, 1998.
[6] N. Rasiwasia, P. J. Moreno, et.al, “Bridging the gap: Query by semantic example”, IEEE Transactions. On Multimedia, vol. 9, no. 5, pages.923 -938, August 2007.
[7] Xin Jin, JieboLuo,Jie Yu et. al, “Reinforce Similarity Integration in Image Rich Information Network”, IEEE Transactions on Knowledge & Data Engineering, vol.25, Issue No.02, Feb 2013.
[8] E. Bart and S. Ullman. Single-example learning of novel classes using representation by similarity. In Proc. BMVC, 2005.
[9] D. Tao, X. Tang, X. Li, and X. Wu. “Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2006.
[10] A.W.M. Smeulders, M. Worring, S. Santini, et. al, “Content-Based Image Retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, Dec. 2000.

Mohammadi Aiman, Ruksar Fatima, "Image Reranking Using Multimodal Sparse Coding," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.277-282, 2019.

The unique and special features of Dielectric Resonator Antenna (DRA) which has mainly focus on good quality communication with low profile Antenna. Cylindrical Dielectric Resonator optical Antenna (CDROA) is compact in structure, light in weight conformable to surface planar some natural carbon atom form hollow cylinder with out side diameter of only one nano meter. Dielectric Resonator Antenna is well suited for microwave devices integration and feeding Technique and, especially with the microwave integrated circuit technology. In addition, technological applications for such as direct broadcast Technique for satellite system on satellite communication at global positioning system (GPS) and high frequency navigation system and good accuracy and a large variety of radar systems demand for good quality Antenna for the best performance of antenna system.

DRA, Global positioning system (GPS) Antenna, satellite, Cylindrical dielectric resonator antennas (CDRA)

[1]. Anand Mohan, The advanced generation mobile broadband technology for wireless communication system and its applications, International, Journal of Applied Research 2015; 1(12): 383-385
[2]. Gaurav Varshney, Anoop Kumar Verma, Shailesh Kr. Singh,”Ultra Wide Band Dielectric Resonator Antenna for Wireless Communication” International Journal of Imerging Technology and Advanced Engineering (ICADET-14) Volume 4, Special Issue 1, February 2014)
[3]. Raghvendra Kumar Chaudhary, Kumar Vaibhav Srivastava and Animesh Biswas, “Four Element Multilayer Cylindrical Dielectric Resonator Antenna Excited by a Coaxial Probe for Wideband Applications” IEEE Communications (NCC), 2011 National Conference on, pp. 1-5, ISBN-978-1-61284-090-1, 2011.
[4]. J. Q. Howell, “Microstrip antennas,” in Dig. IEEE Int. Symp. Antennas Propagation, Dec. 1972, pp. 177–180.
[5]. R. E. Munson, “Conformal microstrip antennas and microstrip phased arrays,” IEEE Trans. Antennas Propagat., vol. 22, no. 1, pp. 74–78, 1974.
[6] R. Garg, P. Bhartia, I. Bahl, and A. Ittipiboon, Microstrip Antenna Design Handbook. Norwood, MA: Artech House, 2001.
[7]. C. Kumar and D. Guha, “Nature of cross-polarized radiations from probefed circular microstrip antennas and their suppression using different geometries of defected ground structure (DGS),” IEEE Trans. Antennas Propagat., vol. 60, no. 1, pp. 92–101, Jan. 2012
[8]. Makwana, G. D. and K. J. Vinoy, “A microstrip line fed rectangular dielectric resonator antenna for WLAN Application,“Proceeding of IEEE Internation symposium on microwave, 299-303, Dec. 2008
[9]. Petosa, A. and A. Ittipiboon, Dielectric resonator antennas: A historical review and the current state of the art," IEEE Antennas and Propag. Mag., Vol. 52, 2010.
[10]. Wong, H., K. B. Ng, C. H. Chan, and K. M. Luk, Printed antennas for millimeter wave application," International Workshop on Antenna Tech., 411{414, 2013.
[11]. Chin, K. S., H. T. Chang, J. A. Liu, et al., 28-GHz patch antenna arrays with PCB and LTCC substrates," Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, Vol. 1,355358, 2011.
[12]. Mohan, Study of Plasmonic Nano Antennas and Their Optimization; International Journal of Emerging Research in Management &Technology; ISSN: 2278-9359 (Volume-5, Issue-5, May 2016.

Anand Mohan, "Cylindrical Dielectric Resonator Optical Antenna (CDROA) & its Applications for Convenient Technology," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.283-286, 2019.

Wireless networks are the technology changer that has changed the modern communication system. In the field of intelligent transport system, wireless network play an important role in the form of Vehicular Adhoc Network (VANET). VANET is a special type of adhoc wireless network which is characterised with fast moving vehicle node, high vehicle speed, and moving along the road. But the VANETs have the security issues. The different malicious nodes present in the coverage decrease the efficiency of the network. Researchers have performed lot of work to secure the VANET. In this work, a new algorithm is proposed for the detection of malicious nodes in VANET. The proposed algorithm is designed and implemented. The results show that there is improvement in the VANET.

VANET, attacks, DMN, ERDV, routing protocol

[1] Geetha Jayakumar, Gopinath Ganapathi, “Reference Point Group Mobility and Random Waypoint Models in Performance Evaluation of MANET Routing Protocols”, Journal of Computer Systems, Networks, and Communications, Volume 2008
[2] Mainak Ghosh, Anitha Varghese, Arzad A. Kherani and Arobinda Gupta, “Distributed Misbehavior Detection in VANETs”, WCNC 2009 proceedings, IEEE, 2009.
[3] Wenshuang Liang, Zhuorong Li, Hongyang Zhang, Shenling Wang, and Rongfang Bie, “Vehicular Ad Hoc Networks: Architectures, Research Issues, Methodologies, Challenges, and Trends”, Hindawi Publishing Corporation International Journal of Distributed Sensor Networks, Volume 2015, Article ID 745303.
[4] Sherali Zeadally, Ray Hunt, Yuh-Shyan Chen, Angela Irwin, Aamir Hassan, “Vehicular ad hoc networks (VANETS): status, results, and challenges”, Springer Science Business Media, LLC 2010.
[5] Arun Kumar, “Enhanced Routing in Delay Tolerant Enabled Vehicular Ad Hoc Networks”, International Journal of Scientific and Research Publications, Volume 2, Issue 9, September 2012.
[6] Jani Kurhinen, Jukka Janatuinen, “Delay Tolerant Routing in Sparse Vehicular Ad Hoc Networks”, Acta Electrotechnica et Informatica, Vol. 8, No. 3, 2008, 7–13.
[7] Archana Harit, N C Barwar, “Comparative Analysis of Identification of Malicious Node in VANET using FFRDV and ERDV Routing Algorithm”, 6th International Conference on Recent Innovation in Science, Engineering and Management, IIMT College of Engineering, 20 August 2016.
[8] Hyunwoo Kang, Syed Hassan Ahmed, Dongkyun Kim, and Yun-Su Chung, “Routing Protocols for Vehicular Delay Tolerant Networks: A Survey”, Hindawi Publishing Corporation, International Journal of Distributed Sensor Networks, Volume 2015, Article ID 325027.
[9] Jochen H. Schiller, “Mobile Communications”, Second Edition, Pearson Education Limited, 2003
[10] Chaker Abdelaziz Kerrache, Carlos T. Calafate, Juan-Carlos Cano, Nasreddine Lagraa, Pietro Manzoni, “Trust Management for Vehicular Networks: An Adversary-Oriented Overview”, IEEE Access, Received December 1, 2016, accepted December 20, 2016, date of publication December 26, 2016, date of current version January 27, 2017.
[11] Amit Mane A., “Privacy Aware VANET Security: - Sybil Attack Detection in VANET”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 7, Issue 4, April 2017 ISSN: 2277 128X.
[12] Jayant Vasu, Gaurav Tejpal, Sonal Sharma, “Review on Various outing Attacks in Vehicular Adhoc Networks”, International Journal of Computer Applications (0975 – 8887), Volume 167 – No.1, June 2017.
[13] Uzma Khana, Shikha Agrawala, Sanjay Silakaria, “Detection of Malicious Nodes (DMN) in Vehicular Ad-Hoc Networks”, Procedia Computer Science, 46, Page 965 – 972, 2015.
[14] Uzma Khan, Shikha Agrawal and Sanjay Silakari, “A Detailed Survey on Misbehavior Node Detection Techniques in Vehicular Ad Hoc Networks”, Information Systems Design and Intelligent Applications, Advances in Intelligent Systems and Computing 339, Springer India, 2015
[15] Philippe Golle, Dan Greene, Jessica Staddon, “Detecting and Correcting Malicious Data in VANETs”, VANET’04, October 1, 2004, Philadelphia, Pennsylvania, USA. ACM, 2004
[16] Gurpreet Singh, Seema, “Malicious Data Detection in VANET”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 1, Issue 7, September 2012
[17] V. Lakshmi Praba, A. Ranichitra, “Detecting Malicious Vehicle in a VANET scenario by Incoporating Security in AODV Protocol”, ICTACT Journal on Communication Technology, Vol: 03, Issue: 03, Sept. 2012
[18] S. RoselinMary, M. Maheshwari, M. Thamaraiselvan, “Early Detection Of DOS Attacks In VANET Using Attacked Packet Detection Algorithm(APDA)”, International Conference on Information Communication and Embedded Systems, ICICES, 2013
[19] Omar Abdel Wahab, Hadi Otrok, Azzam Mourad, “A cooperative watchdog model based on Dempster – Shafer for detecting misbehaving vehicles”, Elsevier, Computer Communications 41, (2014), 43–54.
[20] Miss S.A. Ghorsad, Dr. V. M. Thakare Dr. R.V Dharaskar, “DoS Attack Detection in Vehicular Ad-Hoc Network Using Malicious Node Detection Algorithm”, International Conference on “Advances in Computing, Communication And Intelligence” ICACC 2014 Special Issue of International Journal of Electronics, Communication & Soft Computing Science and Engineering, 2014.
[21] Ravneet Kaur, Nitika Chowdhary, Jyoteesh Malhotra, “Sybil Attacks Detection in Vehicular Ad Hoc Networks”, International Journal of Advanced Research, Volume 3, Issue 6, 2015, pp.1085-1096.
[22] Harsimrat Kaur,Preeti Bansal, “Efficient Detection & Prevention of Sybil Attack in VANET”, IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 2 Issue 9, September 2015.
[23] Adity, Dalveer Kaur, “Detection and Prevention of Malacious Node using Data Centric Technique”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 5, Issue 2, March - April 2016.
[24] J.Nethravathy, Dr.G. Maragatham, “Malicious Node detection in Vehicle to Vehicle Communication”, International Journal of Engineering Trends and Technology (IJETT), Volume 33 Number 5- March 2016.
[25] Zaid Abdulkader, Azizol Abdullah, Mohd Taufik Abdullah, Zuriati Ahmad Zukarnain, “Malicious Node Identification Routing and Protection Mechanism for VANET against Various Attacks”, Journal of Information Security Research, Volume 8, Number 4, December 2017.
[26] John Tobin, Christina Thorpe, Damien Magoni, Liam Murphy. “An Approach to Mitigate Multiple Malicious Node Black Hole Attacks on VANETs”, 16th European Conference on Cyber Warfare and Security, Dublin, Ireland. Proceedings of the 16th European Conference on Cyber Warfare and Security. , Jun 2017.
[27] Vishal Shrivastava, Ajay Samota, “A Framework for Detecting Malicious Node in VANET”, International Journal on Future Revolution in Computer Science & Communication Engineering, ISSN: 2454-4248, Volume: 4, Issue: 6, June 2018.
[28] Kanwalprit Singh, Harmanpreet kaur, “Evaluation of proposed technique for detection of Sybil attack in VANET”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.5, pp.11-15, October (2018)
[29] R. Kumari, P. Nand, “Performance Analysis for MANETs using certain realistic mobility models: NS-2”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.70-77, February (2018).
[30] P. Chouksey, “Introduction to MANET”, IJSRNSC, Volume-4, Issue-2, April 2016

Vishnu Sharma, Ankur Goyal, "Delay Analysis of Proposed DMN Algorithm in VANET," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.287-290, 2019.

Social media accounts like blogs, Facebook, Twitter and online discussion sites provide an option for an individual to express his or her opinion. These opinions are usually unstructured data and these are huge in amount. These days a massive number of users collect these recommendations or reviews for products and services, based on which they make their choices. The process of extraction of this insight from unstructured web data can be handled by Natural Language Processing and Big Data Analytics techniques. In this paper, we propose a model to extract this unstructured data from various domains, and then convert it into structured format by using various supervised algorithms. Finally the opinions or sentiments of the users will be presented for further understanding. Based on which the organization can take the necessary step to improve the customer retention.

Sentiment Analysis, Natural Language Processing, Unstructured data, Opinion Mining

[1] Zolbanin, Hamed M., and Dursun Delen. "Processing electronic medical records to improve predictive analytics outcomes for hospital readmissions." Decision Support Systems (2018).
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[3] Peng K, Cooke J, Crockett A, Shin D, Foster A, Rue J, Williams R, Valeiras J, Scherer W, Tuttle C, Adams S. Predictive analytics for University of Virginia football recruiting. InSystems and Information Engineering Design Symposium (SIEDS), 2018 2018 Apr 27 (pp. 243-248). IEEE.
[4] Dabbas, Mohammad, Perambur S. Neelakanta, and Dolores DeGroff. "ANN-based predictive analytics of forecasting with sparse data: Applications in data mining contexts." In Recent Trends in Information Technology (ICRTIT), 2013 International Conference on, pp. 62-67. IEEE, 2013.
[5] Martens, David, Foster Provost, Jessica Clark, and Enric Junqué de Fortuny. "Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics." MIS quarterly 40, no. 4 (2016).
[6] Chennamsetty, Haritha, Suresh Chalasani, and Derek Riley. "Predictive analytics on Electronic Health Records (EHRs) using Hadoop and Hive." In Electrical, Computer and Communication Technologies (ICECCT), 2015 IEEE International Conference on, pp. 1-5. IEEE, 2015.
Riensche, Roderick M., Patrick R. Paulson, Gary Danielson, Stephen D. Unwin, Scott Butner, Sarah Miller, Lyndsey Franklin, and Nino Zuljevic. "Serious Gaming for Predictive Analytics." In AAAI Spring Symposium: Technosocial Predictive Analytics, pp. 108-113. 2009.
[8] Huang, Zhenyu, Pak Chung Wong, Patrick Mackey, Yousu Chen, Jian Ma, Kevin Schneider, and Frank L. Greitzer. "Managing Complex Network Operation with Predictive Analytics." In AAAI Spring Symposium: Technosocial Predictive Analytics, pp. 59-65. 2009.
[9] M.P.S. Bhatia and Deepika Khurana, “ Experimental Study of Data Clustering using k-means and Modified Algorithms”, International Journal of Data Mining and Knowledge Management Process(IJDKP), Volume 3, No. 3, May-2013.
[10] Srihari A.Hudli, Aditi A.Hudli, Ananad V.Hudli, “ Identifying Online Opinion Leaders Using K-means Clustering”, 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA).
[11] J.V.N. Lakshmi, Ananthi Sheshasaayee, "A Big Data Analytical Approach for Analyzing Temperature Dataset using Machine Learning Techniques", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.92-97, 2017
[12] R.Anupriya, P.Saranya, R.Deepika, "Mining Health Data in Multimodal Data Series for Disease Prediction", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.96-99, 2018

Shilpa B. L, Shambhavi B. R, "Changing Banking Business Model Using Sentiment Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.291-295, 2019.

The Dark Web is a part of the World Wide Web that exists on dark-net whose content is intentionally hidden, and cannot be accessed using normal search engines and require some specific browsers such as Tor browser. Dark web uses network such as Tor, I2P.While tor provides anonymity of users and make them untraceable, I2P provides anonymous hosting of websites. As the dark web hides the user’s identity and maintains secrecy, it can be used for legitimate purpose as well as illicit purpose. From smuggling of drugs and weapons to other illicit items, from hacking other’s information to using those for forging, from counterfeiting money to using crypto currency, dark web can play a number of roles in malicious activity. On the other hand, government and legitimate authority use dark web for military application such as online surveillance, sting operation and to track the malicious activities. This paper describes dark web emphasizing on how it is accessed, how the tor network (onion routing) and I2P work to hide user’s identity, how one can use TOR to access dark web, recent technological development, details on usage of dark web i.e. how different malicious activities are done using dark web, how the terrorist groups are using darkweb, how the legitimate authorities are using dark web to unveil some user’s identity and to stop the illicit activities and the future scope of dark web.

Dark Web, Tor, I2P, Tor browser, Surface Web, Deep Web, Silk Road

[1] K Finklea, "Dark Web", Congressional Research Service, 2017.
[2] https://www.torproject.org
[3] J. Kosinski, "Deepweb And Darknet- Police View", Archibald Reiss Days 2015, Belgrade, Vol.III, 2015.
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Asoke Nath, Romita Mondal, "Dark Web: The Uniluminated Side of the World Wide Web," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.296-305, 2019.

In belt conveyor system, belt is the key component contributing 25% to 50% of total cost of the system. This paper presents a simulation study of dynamic tension in the belt. Conveyor belt is a traction component which transmits power and motion, and also carrying material load. Tension developed in the belt directly governs design and strength of structural support system, pulley assembly and the drive. Traditionally, belt tension is studied by static analysis and large factor of safety (8-10) considered for design. Conveyor belt consist of fabric carcass with a protective rubber cover, thus can be represented by a viscoeleastic model. Belt conveyor system is simplified into series of lump mass parameter and dynamic characteristic of the belt studied using Lagrange’s approach. Simulation results indicates that during full load starting condition, dynamic tension suddenly rise 1.82 times more than steady state running tension value in the belt. Dynamic analysis data useful for safe and reliable running, lowering factor of safety, improve stability and reduced belt stresses during starting.

Belt Conveyor, Lump Mass, Lagrange, Dynamic Tension

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Sanjay G. Sakharwade, Shubharata Nagpal, "Analysis of Dynamic Tension in Belt During Transient Condition of Belt Conveyor System by Lagrange’s Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.306-310, 2019.

Software testing, which once was a distinct phase in software development life cycle, has now become a parallel activity. Many researchers in the past have attributed the failure of software to the lack of adequate testing. Software testing involves checking whether the actual outputs generated by the SUT matches the expected outputs. Test cases are written and executed and the results are compared with the help of a test oracle. A Test Oracle is a mechanism to determine whether a test has passed or failed. The process of finding a reliable test oracle is called the oracle problem. Software test automation has been a hot area of research for more than a decade. But, the work in the area of test oracle automation is minimal. Some of these researches have proposed solutions for test oracle automation using machine learning algorithms like Genetic Algorithms (GA) and Artificial Neural Networks (ANN). In this paper, we present a brief review and comparative analysis of the use of single-network and multi network ANNs as test oracles.

Software Testing, Artificial Neural Networks, Test Oracles, Machine Learning, SDLC

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J. Mary Catherine, S. Djodilatchoumy, "Single and Multi Network ANNs as Test Oracles – A Comparison," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.311-315, 2019.