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Nowadays phishing become a major threat on internet. Phishing is a kind of attack for defacement of website in which attacker can access sensitive information of users. Phishers are one who create website same as the trusted website with the same content and designs of the trusted website. Phishing can be done through email, websites and malicious software to get intellectual information, business secrets or military secrets etc. This paper is explored the various researches for avoiding phishing and detecting phishing symptoms. Many researchers have been proposed various methods for algorithms for avoiding all conditions with the detection of phishing using data mining techniques so that any user can use internet effectively. This paper is based on Associative Classification methods of data mining for avoidance of phishing attack.

Phishing, Associative Classification, Data Mining, Avoidance methods of phishing

[1] Mitesh Dedakia, Khushali Mistry, “Phishing Detection using Content Based Associative Classification Data Mining”, Journal of Engineering Computers and Applied Sciences, Vol.4, No.7, pp.1-11, 2015.
[2] Jaswant Meena, Ashish Mandloi , “Classification of Data Mining Techniques for Weather Prediction”, International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.1, pp.21-24, 2016.
[3] Suzan Wedyan, Fadi Wedyan, “An Associative Classification Data Mining Approach for Detecting Phishing Websites”, Journal of Emerging Trends in Computing and Information Science, Vol. 4, No. 12, pp.23-37, 2013.
[4] N.M. Shekokar, Chaitali Shah, Mrunal Mahajan, Shruti Rachh, “An Ideal Approach for Detection and Prevention of Phishing Attacks”, Procedia Computer Science, Vol.4, Issue.9, pp.82-91, 2015.
[5] Rajendra Gupta, Piyush Shukla, “Performance Analysis of Anti-Phishing Tools and Study of Classification Data Mining Algorithms for a Novel Anti-Phishing System”, I. J. Computer Network and Information Security,Vol.7, No.12, 70-77, 2015,.
[6] R.V. Patil, S.S. Sannakki, V.S. Rajpurohit, “A Survey on Classification of Liver Diseases using Image Processing and Data Mining Techniques”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.29-34, 2017.
[7] M. Aburrous, M. A. Hossain, K. Dahal, F. Thabtah, “Predicting Phishing Websites Using Classification Mining Techniques with Experimental Case Studies”, 2010 Seventh International Conference on Information Technology: New Generations, Las Vegas, pp. 176-181, 2010.
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[14] M. Dadkhah, M. Jazi, V. Lyashenko, “Prediction of phishing websites using classification algorithms based on weight of web pages characteristics”, J. Math. Technol., Vol.5, Issue.2, pp.24-35, 2014.
[15] R. Jabri, B. Ibrahim, “Phishing websites detection using data mining classification model”, Trans. Mach. Learn. Artif. Intell., Vol.3, Issue.4, pp.42-51, 2015.
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[17] S. Taware, C. Ghorpade, P. Shah, N. Lonkar, “Phish detect: detection of phishing websites based on associative classification”, Int. J.Adv. Res. Comput. Sci. Eng. Inf. Technol., Vol.4, Issue.3, pp.384-395, 2015.
[18] M. Antonelli, P. Ducange, F. Marcelloni, A. Segatori, “A novel associative classification model based on a fuzzy frequent pattern mining algorithm”, Expert Syst. Appl., Vol.42, Issue.4, pp.2086-2097, 2015.
[19] IRA. Hamid, Jemal Abawajy, Taihoon Kim, “Using Feature Selection and Classification Scheme for Automating Phishing Email Detection”, Studies in Informatics and Control, Vol.22, Issue.1, pp.61-70, 2013.

M. Shukla, S. Sharma, "A Comparative Study of Existing Data Mining Techniques for Phishing Detection," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.182-187, 2017.

Cognitive radio networks (CRNs) are presently gaining massive recognition as the most-likely next-generation wireless communication paradigm. Naturally it has characteristic like attractive aptitude of modifying insufficient spectrum usage and/or underutilisation challenge. CRN is an excellent intellectual wireless radio communication system and it has great awareness of its environment. Here, heterogeneous hops spectrum sensing is great problem by means of fluctuating computing energy, range of sensing, and distribution of spectrum dynamically in CRN where there exist multiple primary users and secondary users have been considered. The scheduling of dynamic spectrum access methods provides a lot of challenges. Due to its demand many researchers were developed different system for spectrum allocation in dynamic fashion. The secondary users (SUs) likely exploit the spectrum when primary user’s absences, this technique would be improved the spectrum usage effectively. In this article we studied the different techniques were used to improve the spectrum usages and present the comprehensive analysis and comparison of each method which are used to improve the effective utilization of unused spectrum in CRN.

CRN, OFDM, MIMO, Femtocel, SINR

[1] Hui Zhao, Youyu Tan , Gaofeng Pan , Yunfei Chen, “Secrecy Outage on Transmit Antenna Selection/Maximal Ratio Combining in MIMO Cognitive Radio Networks”, IEEE Transactions On Vehicular Technology, Vol. 65, NO. 12, pp.10236-10242, 2016.
[2] Wenhao Xiong, Amitav Mukherjee, Hyuck M. Kwon, “MIMO Cognitive Radio User Selection With and Without Primary Channel State Information”, IEEE Transactions On Vehicular Technology, Vol. 65, NO. 2, pp. 985-991, 2016.
[3] M.G. Adian, Hassan Aghaeinia, “Optimal resource allocation for opportunistic spectrum access in multiple-input multiple-output –orthogonal frequency division multiplexing based cooperative cognitive radio networks”, IET Signal Process, India, pp.549-557, 2013.
[4] Cunhao Gao, Shan Chu, Xin Wang, “Distributed Scheduling in MIMO Empowered Cognitive Radio Ad Hoc Networks”, IEEE transactions on mobile computing, vol. 13, no. 7, pp.1456-1468. , 2014
[5] Renchao Xie, F. Richard Yu, Hong Ji, “Spectrum Sharing and Resource Allocation for Energy-Efficient Heterogeneous Cognitive Radio Networks with Femtocells”, IEEE ICC 2012 - Cognitive Radio and Networks Symposium, US, pp.1661-1665, 2012.
[6] Xingyu Zhou, BoBai, Wei Chen, “Greedy Relay Antenna Selection for Sum Rate Maximization in Amplify-and-Forward MIMO Two-Way Relay Channels Under a Holistic Power Model”, IEEE Communications Letters, Vol. 19, NO. 9, pp.1648-1651, 2015.
[7] Ye Wang and Yuhan Dong, “A Genetic Antenna Selection Algorithm for Massive MIMO Systems with Channel Estimation Error”, 2015 Advances in Wireless and Optical Communications (RTUWO), CA, PP: 1-4, 2015.
[8] Yajun Zhang, Tao Liang, Aiwei Sun, “Joint Transmit Antenna Selection and Jamming for Security Enhancement in MIMO Wiretap Channels”, IEEE/CIC ICCC 2015 Symposium on Privacy and Security in Communications, China, pp.1-6, 2015.
[9] Utpal Pandey, Sharad Kumar, Abhishek Bajpai, Rajat Kumar Singh, “Channel Selection Algorithm for MIMO in Multipath Environment”, 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON), India, pp.1-4, 2015.
[10] Hongjiang Lei, Chao Gao, IS. Ansari, Yongcai Guo, Yulong Zou, Gaofeng Pan, KA. Qaraqe, “Selection for MIMO Underlay Cognitive Radio Systems Over Nakagami-m Channels”, IEEE Transactions On Vehicular Technology, Vol. 66, NO. 3, pp.2237-2250, 2017.
[11] D. Lee, "SER of TAS-MRC with Relay and User Selection in MIMO-Relay Systems over Non-Identical Nakagami Fading Channels", in IEEE Communications Letters , vol.PP, no.99, pp.1-1, 2017.
[12] Maliheh Soleimani, Mahmood Mazrouei-Sebdani, WA. Krzymien, Jordan Melzer, “A Path Selection Algorithm for Sparse Massive MIMO Channels”, 2016 50th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, pp. 208-212, 2016.
[13] Yangyang Zhang and Jianhua Ge, “Joint Antenna-and-Relay Selection in MIMO Decode-and-Forward Relaying Networks Over Nakagami-m Fading Channels”, IEEE Signal Processing Letters, Vol. 24, NO. 4, pp. 456-460, 2017.
[14] Zijian Wang, Luc Vandendorpe, “Energy Efficient Power Allocation and Relay Selection in MIMO Relay Channels”, 2016 IEEE 27th Annual IEEE International Symposium on Personal Indoor and Mobile Radio Communications - (PIMRC): Fundamentals and PHY, Spain, pp.1-7, 2016.
[15] Md. Abdul Latif Sarker, Moon Ho Lee, “Transmit Antenna Selection in the High SNR MIMO Downlink Correlated Channel”, ICTC 2016, South Korea, 220-224, 2016.
[16] Zaid Abdullah, Charalampos C. Tsimenidis, Martin Johnston, “Tabu Search vs. Bio-inspired Algorithms for Antenna Selection in Spatially Correlated Massive MIMO Uplink Channels”, 24th European Signal Processing Conference (EUSIPCO), USA, pp.41-45, 2016.
[17] Song Xue, Zuping Qian,Wei Shao, Wei Zhong, “Distributed Joint Antenna and User Selection for MIMO Broadcast Channels”, Computational Electromagnetics (ICCEM), 2016 IEEE International Conference on, pp. 265-268 2016
[18] Moonsik Min, Yo-Seb Jeon, Gi-Hong Im,, “On Achievable Rate of User Selection for MIMO Broadcast Channels With Limited Feedback”, IEEE Transactions On Communications, Vol. 65, NO. 1, pp.122-135, 2017.
[19] MV Shah, Shantanu Gaiki, Ashwani Singh, T. Shankar, “Energy Efficient Algorithm for MIMO Based CRN with Antenna Selection”, 2nd IEEE International Conference on Engineering and Technology (ICETECH), India, pp.1-9, 2017.
[20] Xiang Gao, Ove Edfors, Jianan Liu, Fredrik Tufvesson, “Antenna selection in measured massive MIMO channels using convex optimization”, Globecom 2013 Workshop - Emerging Technologies for LTE-Advanced and Beyond-4G, US, pp.129-134, 2013.
[21] Apoorva Nayak and Rahul Sharma, "Performance Evaluation of Image Transmission over Physical Layer Of IEEE 802.16d with Antenna Diversity Scheme", International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.132-136, 2014.
[22] Shihao Yan, Nan Yang, Robert Malaney, “Transmit Antenna Selection with Alamouti Coding and Power Allocation in MIMO Wiretap Channels”, IEEE Transactions On Wireless Communications, Vol. 13, NO. 3 , pp.1656-1667, 2014.
[23] Xingliang Li, Jean-Francois Frigon, “Algorithms for Pattern Selection MIMO Systems over Spatially Correlated Channels”, EEE ICC 2012 - Wireless Communications Symposium, China, pp.3969-3973, 2012.
[24] S. Tamilarasan, P. Kumar, "A Survey on Dynamic Resource Allocation in MIMO Heterogeneous Cognitive Radio Networks based on Priority Scheduling", International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.53-59, 2017.
[25] Nan Yang, Phee Lep Yeoh, Maged Elkashlan, “Transmit Antenna Selection for Security Enhancement in MIMO Wiretap Channels”, IEEE Transactions On Communications, Vol. 61, NO. 1, pp.144-154, 2013.
[26] Indika A. M. Balapuwaduge, Lei Jiao, Vicent Pla, “Channel Assembling with Priority-Based Queues in Cognitive Radio Networks: Strategies and Performance Evaluation”, IEEE Transactions On Wireless Communications, Vol. 13, NO. 2, pp.630-645, 2014.
[27] Feng Wang, Xia Liu, Marek E. Bialkowski, “BER Performance of MIMO System Employing Fast Antenna Selection Scheme Under Imperfect Channel State Information”, InSignal Processing and Communication Systems (ICSPCS), 2010 4th International Conference, China, pp.1-4, 2010.
[28] A.A. Tarkhan, F. Farzaneh, B.H. Khalaj, “Efficient Suboptimal Transmit Antenna selection for MIMO Relay Channels”, 2011 International Symposium on Computer Networks and Distributed Systems (CNDS), CA, pp. 45-48, 2011.
[29] Wei Zhong, Youyun Xu, HuagloryTianfield, “Game-Theoretic Opportunistic Spectrum Sharing Strategy Selection for Cognitive MIMO Multiple Access Channels”, IEEE Transactions On Signal Processing, Vol. 59, NO. 6, pp.2745-2759, 2011.
[30] Jianxin Dai, Ming Chen, “Improved Particle Swarm Optimization - Based Antenna Selection Scheme for Correlated MIMO Channels”, 2010 12th IEEE International Conference on Communication Technology (ICCT), China, pp. 512-515, 2010.

S. Tamilarasan, P. Kumar, "Dynamic Resource Allocation based on priority approach in MIMO Cognitive Radio Networks – A Literature Survey," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.188-193, 2017.

Software maintenance is an important phase of life cycle process. It is a transforming of a software process after receiver receives and if fault occurs then to modify software products and remove extra bugs. The phase is much important phase which starts with customer end. Therefore predicting the efforts like-cost, size has become one of an important issues which is to be analyzed for effective resource allocation. In view of these issues ,we have developed text mining techniques using machine learning method name BPA(Back Propagation Algorithm).The intended model ratified using ‘browser ‘application pack of android operated system. ROC (Receiver Operating Characteristics) curve is a graphical representation that describes the working of a binary classified system. The performance of model rely on the words count taken for classification which shows best result as the word number increases which describes its accuracy. More the words count more the accuracy.

Software maintenance,Machine learning,BPA,Software Prediction,Neural network

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N. Chaudhary, A. Kumar, "A Neural Network Approach for Anticipating Maintenance Effort using Back Propagation Algorithm," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.194-200, 2017.

Information is spread all through the city requiring little to no effort and ready to divert vehicles in development in the city to at long last accomplish shorter travel times and less congested driving conditions in smart communities. The Wi-Fi network is empowered for the driver terminals, because of that they can easily find the routes. This application proposes a constant framework for recommending appropriated customized courses in keen smart areas. The application need to collect the traffic data of past and the present data of the city. The driver hubs will get the briefest courses to the goal, if there is no immediate course to the goal then the calculation will get all the info roads which are specifically reachable from the present area to the goal. Shortest path algorithm like Warshall Floyd is used to find the optimal path to destination. The main aim of this approach is to increase the citizens’ life.

Vehicular Ad-hoc Network (VANET), Mobile Ad-hoc Network (MANET, Ant Colony Optimization Algorithm (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Warshall Floyd

[1] Tsu-Wei Chen, M. Gerla, "Global state routing: a new routing scheme for ad-hoc wireless networks", Communications, 1998. ICC Conference Record 1998 IEEE International Conference on, Atlanta, , pp. 171-175, 1998.
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[6] Krajzewicz, Daniel, "Traffic simulation with SUMO–simulation of urban mobility", Fundamentals of traffic simulation, New York, pp. 269-293, 2010.
[7] T.A. Sebaey, "Ant colony optimization for dispersed laminated composite panels under biaxial loading", Composite Structures, Vol.94, Issue.1, pp. 31-36, 2011.
[8] H. Hemmatian, "Optimization of laminate stacking sequence for minimizing weight and cost using elitist ant system optimization", Advances in engineering Software, Vol..57, Issue.8, pp.8-18, 2013.
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B.V.V.S. Padmasripriya, Chandra Mouli P.V.S.S.R., "Reducing Traffic in Smart Cities by using Shortest Path Algorithms," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.201-208, 2017.

Current era is to make the connection amongst humans and their manufactured accomplices (Computers) and make communication more reliable and easier. One of the real challenges is the utilization of speech recognition. Speech recognition can be improved by visual information of human face. Visual speech recognition (Lip reading) assumes a fundamental part in automatic speech recognition and is an essential stride towards exact and robust speech recognition. In this paper, the technique is developed for visual speech recognition in detail. Optical Flow component is used to extract the feature vector and Artificial Neural Networks (ANN) for training. The effect of variation in velocity of speaking on the execution of the system is minimized by eliminating the zero energy frames and normalizing the number of frames. The efficiency of both approaches (Optical Flow and ANN) is used to evaluate words individually. Considered words are numerical numbers in Indian language (Hindi) from zero to nine, such as ek, do, theen, and so on.

Visual speech recognition, lip localization, Optical Flow, ANN, Indian Language

[1] Bor-Shing Lin, Yu-Hsien Yao, Ching-Feng Liu, Ching-Feng Lien, Bor-Shyh Lin, “Development of Novel Lip-reading Recognition Algorithm”, IEEE Access, vol. 5, no.1 , pp. 794-801, 2017.
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L.V.S. Raghuveer, Divya Deora, "Lip Localization and Visual Speech Recognition with Optical Flow in Hindi," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.209-212, 2017.

Cache memory is a small less access time semiconductor memory that sits between the processor and memory in the memory hierarchy to bridge the speed mismatch between processor and main memory. Multiprocessor System contains multiple processors working simultaneously and share memory. Multiprocessors are most widely used in computational devices due to their reliability and throughput. In multiprocessor system maintaining data consistency is an important parameter to be maintained because different processors communicate and share data. In multiprocessors caching plays a vital role because cache Coherence is a problem that should be handled very carefully. In this paper we have studied various Cache Coherence Protocols and simulate their behavior on various platforms on the basis of miss rate.

MSI, MESI, DRAGON

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S. Kumar, K. Gupta, "Comparative Study and Performance Analysis of Cache Coherence Protocols," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.213-216, 2017.

Image segmentation is one of the most significant ways to simplify complex images into human or machine readable form. The main purpose of image segmentation is to extract or segment out particular area or region of image, so that that the analysis become easier regarding its shape, size, and its boundaries. It can also be used to separate foreground image from the background image. Image segmentation has its wide utility for medical image analysis, satellite images, as well in many other fields. Segmentation methods have been applied in various computer vision fields, such as scene interpretation and representation, content based image retrieval, object tracking in videos, medical applications etc. Here various segmentation methods and Optimization techniques are being discussed with their applications in various fields. The work done in the field of image segmentation using Swarm Optimization techniques like Genetic Algorithm, Particle Swarm Optimization, Fire-Fly and many more existing techniques are been discussed in this paper.

Image Segmentation; Edge Segmentation; Region Segmentation; Data Clustering; Genetic Algorithm

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S. Pathak, V. Sejwar, "A Review on Image Segmentation Using Different Optimization Techniques," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.217-221, 2017.

Sentiment analysis is a technique to analyze peoples opinion on given topics such as political, social, and economical or review on product. An enormous amount of information and opinion is available online which is helpful for researchers to analyze in fields such as market research political issue, business intelligence, online shopping etc. This paper presents a survey which covers Sentiment Analysis, literature survey, classification, application and challenges.

Sentiment Analysis , Classification

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U. Aggarwal, G. Aggarwal, "Sentiment Analysis : A Survey," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.222-225, 2017.