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This paper provides a comprehensive study of vulnerabilities and various possible attacks associated with LTE. According to reports released by GSMA, association of mobile operators, the number of cell phone users globally will surpass five billion by the middle of this year. With this rapid increase in the number of users, security of cellular network is of utmost importance. In order to ruggedize the security mechanism of cellular networks it is first essential to deeply analyse the vulnerabilities and threats. This paper surveys the attacks and vulnerabilities and provide classification and categorization of attacks in LTE.

UMTS (Universal Mobile Terrestrial System), LTE (Long Term Evolution), DoS (Denial of Service), MITM (Man In The Middle), IP address (Internet Protocol), MAC address (Medium Access Control), AKA (Authentication and Key Agreement), ICMP (Internet Control Message Protocol), EPS (Evolved Packet System), 3GPP (3rd Generation Partnership Projects), WLA

[1] A. Ahlawat and S. Kumar “Analysis of Different Security and Vulnerability in Cellular Networks”, Journal of Engineering and Applied Sciences, vol 12, no.22, pp. 6252-6259, 2017.
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[24] J. Cao, H. Li, M. Ma, Y. Zhang, and C. Lai “ A Simple and Robust Handover Authentication between HeNB and eNB in LTE Networks”, Computer Networks, Vol. 56, No. 8, pp. 2119-2131, 2012.
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[29] B. Matt and C. Li “A survey of the security and threats of the IMT advanced requirements for 4G standards”, in Proc. IEEE Conf. Anthol., pp. 1–5, 2013.
[30] M. Habib and M. Ahmad “A review of some security aspects of WiMAX and converged network” In Proc.2ndInt.Conf.Commun.Softw. Netw. (ICCSN), pp. 372–376, 2010.
[31] N. Qachri and JM. Dricot “On the security of WLAN access points integrated /LTE architectures”, in Proc.19thIEEE Workshop Local Metropolitan Area Netw. (LANMAN), pp. 1–6, 2013.
[32] TQ. Thanh, Y. Rebahi, and TA. Magedanz “DIAMETER based security framework for mobile networks”, in Proc. Int. Conf. Telecommun. Multimedia (TEMU), pp. 7–12, 2014.
[33] X. Li and Y. Wang “Security enhanced authentication and key agreement protocol for LTE/SAE network”, in Proc. 7th Int. Conf. Wireless Commun., Netw. Mobile Comput. (WiCOM), pp. 1–4, 2011.
[34] M. Purkhiabani and A. Salahi “Enhanced authentication and key agreement procedure of next generation evolved mobile networks”, in Proc. IEEE 3rd Int. Conf. Commun. Softw. Netw. (ICCSN), pp. 557–563, 2011.
[35] H. Choudhury, B. Roychoudhury, and DK. Saikia “Enhancing user identity privacy in LTE”, in Proc. IEEE 11th Int. Conf. Trust, Secur. Privacy Comput. Commun., pp. 949–957, 2012.

A. Ahlawat, S. Kumar, "Investigating Various Possible Attacks and Vulnerabilties in LTE," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.389-395, 2018.

Pattern can be learned from existing data to provide sense to the previously unknown data, this is possible only with the help of Machine learning. As part of both artificial intelligence and statistics, machine learning algorithms process large amounts of information. Machine learning refers to usage of computers to infer data based on knowledge. In the big data analysis huge amount of update data should be deal with the highly sophisticated machine learning algorithms . Quantum computing may improve classical machine learning algorithms if we deal with big data. The quantum computer works on the principle of translating stochastic methods into the language of quantum theory. This paper is focusing on the brief review on quantum computers and machine learning.

Machine learning ,page ranking artificial intelligence

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E. Padmalatha, S. Sailekya, "A Review on Quantum Computers and Machine Learning," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.396-399, 2018.

In Human Computer Interaction (HCI) area speech emotion recognition is one of the popular topic in the world. Many researchers are engaged in developing systems to recognize different emotions from human speech. This is done to make HCI and human interface more effective and develop systems like humans. In this paper we have stated the basics of speech emotion recognition system and reviewed different feature extraction and classification technique for the system. Features are classified as Elicited features, Prosodic features and Spectral features. Different classifying techniques are used to classify different emotions from human speech like Hidden Markov Model (HMM), Gaussian Mixtures Model (GMM), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-nearest neighbor (KNN). Performance of classifiers are also discussed shortly. Different applications where speech emotion recognition systems are used are also discussed in last section of the paper.

Emotion, Speech, Emotional Speech database, Elicited featues, HMM, GMM, SVM, ANN, KNN, Application

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[13] A. Joshi, "Speech Emotion Recognition Using Combined Features of HMM & SVM Algorithm," International Journal of Advanced Research in Computer Science and Software Engineering, pp. 387-392, 2013.
[14] A. B Ingale and D. S. Chaudhari, “Speech Emotion Recognition”, International Journal of Soft Computing and Engineering (IJSCE), March 2012, pp. 235-238.
[15] A. S. Utane and S.L. Nalbalwar, “Emotion Recognition through Speech”, International Journal of Applied Information Systems (IJAIS), 2013, pp. 5-8.

G.N. Peerzade, R.R. Deshmukh, S.D. Waghmare, "A Review: Speech Emotion Recognition," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.400-402, 2018.

In the technically advanced world image fusion attracts as a considerable assistant for image processing experts. The role of image fusion in processing of images is robust by extracting the best and complementary features from two or more images and integrating that information by using appropriate algorithm in order to provide better recognition characteristics. Image fusion experts have been using images for a long time with machine learning algorithms. It requires very intensive pre-processing steps. Recently experts are very much interested in using long existing deep learning algorithms in processing the image data. This paper presents the deep convolutional neural network based image fusion using Laplacian pyramid method. Firstly the paper concentrates on the existing image fusion techniques and related work. Secondly on convolutional neural networks, deep learning and their features. Thirdly it presented the similarities among Convolutional Neural Network, Gaussian pyramid, Laplacian pyramid models. Lastly our proposed method and discussion on experimental results. It was observed that Deep Convolutional Neural Network and Laplacian pyramid based image fusion method gave better PSNR Values than the existing Laplacian Pyramid fusion methods for various images.

Image Fusion, Deep Learning, Convolutional Neural Network, Laplacian Pyramid

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B. Asha Latha, M.Babu Reddy, "Image Fusion through Deep Convolutional Neural Network and Laplacian Pyramid," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.403-407, 2018.

Cloud computing offers services with much flexibility and very less infrastructure because of the distributed structure of cloud computing, this technology is used by an increasing number of end users. On the other hand, cloud computing presents an added level of risk because clouds are distributed in nature, so it becomes an easy targets for the intruders to exploit the vulnerabilities of the network. Therefore in this paper we have discussed the need of security mentioning different types of attacks which affect the availability, confidentiality and integrity of resources and services in cloud computing environment and suggested the cloud providers to protect the user data and information from inside or outside attacks by installing an intrusion detection and prevention system.

Cloud Computing, Attacks, Intrusion Detection System, Intrusion Prevention System

[1] Zhao G, Liu J, Tang Y, Sun W, Zhang F, Ye X, Tang N (2009) Cloud Computing: A Statistics Aspect of Users. First International Conference on Cloud Computing (CloudCom), Beijing, China. Springer Berlin, Heidelberg, pp. 347-358.


[2] Zhang S, Zhang S, Chen X, Huo X(2010) Cloud Computing Research and Development Trend in Second International Conference on Future Networks, Sanya, Henan, China, IEEE Computer Society, Washington, DC, USA, pp. 93-97

[3] B R. Kandukuri, R. Paturi V.and A. Rakshit, “Cloud Security Issues,” 2009 IEEE International Conference on Services Computing, Sep 21-25,2009, Bangalore, India, pp. 517-520.


[4] E. H. Spafford and D. Zamboni, “Intrusion Detection Using Autonomous Agent,” Computer Networks, Vol. 34, Issue 4, 2000, pp. 547-570.

[5] Ms. Parag K. Shelke, Ms. Sneha Sontakke, Dr. A. D. Gawande,”Intrusion Detection System for Cloud Computing”, International Journal of Scientific & Technology Research, Vol. 1, Issue 4,May 2012, pp. 67-71.

[6] Chi-Chun Lo, Chun-Chieh Huang, Joy Ku, “A Cooperative Intrusion Detection System Framework for Clouding Computing Networks,” 2010, 39th International Conference on Parallel Processing Workshops, pp. 280-284.
[7] Anna Lenart, “ERP in the Cloud- Benefits and Challenges,”Springer Verlag Berlin Heidelberg, 2011, pp. 39-50.
[8] C. Modi, D. Patel, B. Borisaniya, H. Patel, M. Rajarajan, “A Survey OF Intrusion Detection Techniques in Cloud”, Journal of Network and Computer Applications 36(2013), pp. 42-57.

Naveen Chandra, Parag Rastogi, Amit Asthana, "An analysis On Security Concerns and Their Possible Solutions in Cloud Computing Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.408-413, 2018.

Vehicular adhoc networks (VANETs) play a significant role to design an intelligent transportation system. Secured routing in VANETs is still an ill-posed problem. Because, in VANETs communication of packets between vehicles and Road side units (RSUs) is achieved by using public networks. Therefore, VANETs are prone to various routing attacks such as Blackhole, Grayhole, Wormhole etc. The overall objective of this paper is to study various routing attacks, then compare them with each other. The main goal behind this is to determine best secure routing protocol. Also, to evaluate various research gaps in existing research on VANETs. From, extensive review it has been observed that no technique is efficient for every circumstance.

VANETS, Classification of Routing Protocols, Attacks in VANETS Component

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[21] Jin, Li, Guoan Zhang, and Xiaojun Zhu. "Formal analysis and evaluation of the back-off procedure in IEEE802. 11P VANET." Modern Physics Letters B 31, no. 19-21 (2017): 1740063.
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[27] Zhang, Geng, and Hui Liu. "Effect of current vehicle’s interruption on traffic stability in cooperative car-following theory." Modern Physics Letters B 31.34 (2017): 1750317.
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[31] Xiao, Hong, Hai-Jun Huang, and Tie-Qiao Tang. "Impacts of road conditions on the energy consumption of electric vehicular flow." Modern Physics Letters B 31, no. 11 (2017): 1750121.
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Tamanna Gandotra, Gurpadam Singh, "Comparative Study of Various Routing Attacks Detection in VANETS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.414-419, 2018.

Parallel computing gives us an environment through which we can execute numerous assignments at the same time. It enables us to take care of enormous problem by separating it into multiple small problems. As energy utilization while satisfy deadline constraint by PCs has become a concern in recent years. This paper has exhibited a comprehensive review on the different swarm intelligence based energy efficient scheduling techniques. It has been observed that the scheduling in parallel condition is NP-hard in nature. The research on meta-heuristic based job scheduling methods have demonstrated that the utilization of Quick energy aware processor merging has low convergence rate overall world wide minimum even at high numbers of dimensions. Gravitational Search optimization algorithm has been generally acknowledged as a global optimization algorithm of current enthusiasm for disseminated advancement and control. Particle swarm optimization is constrained to beginning arrangement of particles, wrongly chosen particles tends to poor outcomes. Moreover, comparison among different job scheduling methods have displayed that no strategy is ideal for each case. At last, a few considerations about future challenges have been exhibited.

Scheduling,Energy, Deadline, Power

[1] Guoqi Xie,“Energy-aware Processor Merging Algorithms for Deadline Constraint Parallel Application in Heterogeneous Cloud Computing.”IEEE Transactions on Sustainable Computing,Vol.2 Issue2,pp.62-75, April 2017.
[2] Aeshah Alsughayyir, “Energy Aware Scheduling of HPC Tasks in Decentralised Cloud Systems”IEEE24th Euromicro International Conference on Parallel,Distributed, and Network-Based Processing,pp.617-621,2016.
[3] M. Zotkiewicz,”Minimum dependencies energy-efficient scheduling in data centers," IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 12, pp. 3561-3574, 2016.
[4] Adam Gregory,“A Configurable Energy Aware Resource Management Technique for Optimization of Performance and Energy Consumption on Clouds”IEEE 8th International Conference on Cloud Computing Technology and Science,pp.184-192,2016.
[5] Hao Li,“Energy-aware Scheduling of Workflow in Cloud Center with Deadline Constraint”IEEE 12th International Conference on Computational Intelligence and Security,pp.415-418,2016.
[6] Vahid Arabnejad,“Cost Effective and Deadline Constrained Scientific Workflow Scheduling for Commercial Clouds”IEEE 14th International Symposium on Network Computing and Applications,pp.106-113, 2015.
[7] Zhongjin Li, “Cost and Energy Aware Scheduling Algorithm for Scientific Workflows with Deadline Constraint in Clouds” IEEE Transactions on services computing,vol.pp,Issue.99,pp.1-15,2015.
[8] K. Li, “Energy-e_cient stochastic task scheduling on heterogeneous computing systems," IEEE Transactions on Parallel and Distributed Systems,vol. 25, no. 11, pp. 2867-2876, 2014.
[9] Yue Gao,“An Energy and Deadline Aware Resource Provisioning, Scheduling and Optimization Framework for Cloud Systems” IEEE, 2013 .
[10] Hamid Mohammadi Fard,“A Multi-Objective Approach for Workflow Scheduling in Heterogeneous Environments” 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp.300-309,2012.
[11] Qingjia Huang,“Enhanced Energy-efficient Scheduling for Parallel Applications in Cloud” 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing,pp.781-786, 2012.
[12] X. Wang,”An energy-aware bi-level optimization model formulti-job scheduling problems under cloud computing," Springer Soft Computing, vol. 20,no. 1, pp. 303-317, 2016.
[13] Stein Keijzers, “Energy Consumption Analysis of Practical Programming Languages” Master thesis August ,2014.
[14] Saurabh Bilgaiyan,“Workflow Scheduling in Cloud Computing Environment Using Cat Swarm Optimization” IEEE International Advance Computing Conference (IACC), pp.680-685,2014.
[15] Xinghui Zhao,“Energy-aware resource allocation for multicores with per-core frequency scaling” Springer,Journal of Internet Services and Applications, pp.1-15,2014
[16] Santiago Pagani,“Energy Efficiency for Clustered Heterogeneous Multicores” IEEE Transactions on parallel and distributed systems, vol. 28, no. 5, May, 2017
[17] Xin Li,“Slack-time-aware Energy Efficient Scheduling for Multiprocessor SoCs” IEEE International Conference on High Performance Computing and Communications,pp.278-285,2013.
[18] Kenli Li,“Energy-Aware Scheduling Algorithm for Task Execution Cycles with Normal Distribution on Heterogeneous Computing Systems” IEEE 41st International Conference on Parallel Processing, pp.40-47,2012.
[19] Hamid Reza Naji,“A High-Speed, Performance-Optimization Algorithm Basedon a Gravitational Approach” IEEE,pp.56-62, 2012.
[20] Li Shi, “Energy-aware Scheduling of Embarrassingly Parallel Jobs and Resource Allocation in Cloud” IEEE Transactions on Parallel and Distributed Systems,vol.28,Issue 6,pp.1-14,2016.
[21] Anita Choudhary, “Workflow Scheduling algorithms in cloud environment:a Review, Taxonomy, and Challenges”IEEE International Conference on Parallel, Distributied and Grid Computing,pp.617-624,2016
[22] Dhananjay Thriuvady, “Parallel ant colony optimization for resource constrained job scheduling”,Springer,Volume 242, Issue 2, pp 355–372 ,2014.
[23] Piotr Swistalski,“Scheduling parallel batch jobs in grids with evolutionary Metaheuristics”. Springer , Volume 18, Issue 4, pp 345–357,2014.
[24] Khadija Bousselmi “Energy efficient partitioning and scheduling approach for Scientific Workflows in the Cloud” IEEE International Conference on Services Computing,pp.146-154,2016.
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S. Rana, A. Kumar, "Comprehensive study about different scheduling techniques for parallel applications in cloud computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.420-426, 2018.

Modern world communication is a web-based service which gives individual opportunity to create a public or semi-public profile within a boundary, in that they can add others in their own profile to share, view or create their own contents. The primary aim of the study is to examine the influence of social media on student’s academic performance, for that an Arts & Science College in Coimbatore Territory was selected and questionnaire was prepared based on past literatures and social media. The Independent variables includes: time appropriateness and the research adopted descriptive and explanatory research design. The collected data was analyzed using description means and regression via SPSS 17. The Pearson’s correlation coefficients of four independent are correlated with student’s academic performance.

Social media, Education service, Blog, Whats App, Academic performance

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[8]. Nishtha Anand1* and Renuka Mahajan2 1*,2 “A Statistical Analysis of Social Media as a New Investigative Tool in Marketing”. Amity Institute of Information Technology, Amity University, Noida (Uttar Pradesh), India. Research Paper Volume-2, Issue-4 E-ISSN: 2347-2693

P. Panbuselvan, V. Veerakumaran, "The Impact of Social Media on Students Academic Performance (With Special Reference to Arts and Science College Students in Coimbatore District - Tamil Nadu)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.427-430, 2018.

Each day we are surrounded by any number of decisions to make. Which book should I read next? Which movie to watch? Which book to read? Which blog to follow? Or which item to buy? Finding the appropriate choice is like finding a needle in a haystack. Increasingly, we use the web and online resources to help us make a decision. As our decision making is transported and conducted in the online sphere, the use of recommendation systems has become essential in daily life. Recommendation systems have been studied and developed for more than two and a half decades. Within this period, a variety of algorithms has been developed for various application domains. The major breakthrough in development of recommender system was in 2006 when Netflix announced the $1 million to whoever improved the accuracy of his existing system called Cinematch by 10%in a machine learning and data mining competition for movie rating prediction.

Recommender System, content-based, collaborative filtering, knowledge based filtering, IoT

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[2]Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems Journal, 46, 109–132.
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Samir N Ajani, Lokesh M Heda, Santosh Kumar Sahu, Manish M Motghare, "An Approach to Design and Development Recommender System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.431-433, 2018.

In the present era of massive usage of computers, an enormous set of data is being generated from different organizations each day, each hour and each second. This data would be of prodigious use to a diverse set of people based on their needs. Predictive analysis is a process of analysing data and identifying the different patterns in it, so as to predict the occurrence of these patterns in future. The predicted output can help plan a new strategy and adopt innovative solutions for the decision making. This paper attempts to analyse the various predictive models which are applied in the healthcare domain. These models are analysed in depth and will be proposed to be available on the cloud environment in future and can be accessed by those concerned for potential analysis.

Predictive modeling, predictive algorithms, predictive analytics in a cloud environment, supervised learning

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N.M. Annigeri, S. Shetty, A.P. Patil , "Analysing the supervised learning methods for prediction of healthcare data in cloud environment: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.434-438, 2018.