Volume-7 , Issue-7 , Jul - 2019 Go Back

Recursion is a programming methodology used to write efficient computer programs. A recursion based solution to a given programming problem can be translated to an iteration based solution or by using some other programming constructs which do not accord with the methodology of recursion. Computer programmers and software engineers usually face this kind of confusion that which methodology to implement in a particular case or to some specific problem class as there are no candid guidelines regarding the implementation of recursive solution, leaving the developers in a state of taking decisions upon their choices and preferences. This paper explores the issue of recursion in various aspects of developing a computer program and discovers the seven fundamental principles behind the use and implementation of recursion in generating a solution for a problem of specific nature and class. These seven principles can be referred as pillars of foundation on which recursion is employed by many programmers as a tool to generate an effective solution which adheres to the quality standards of software engineering.

Recursion, Principles, Programming, Functions, Methods

[1] Ronald L. Graham, Donald E. Knuth, Oren Patashnik, "Concrete mathematics", Addison-Wesley Publishing Company, United States of America, pp. 1-20, 1990, ISBN-13: 978-0201558029.
[2] John Hudson Tiner, "Exploring the World of Mathematics: From Ancient Record Keeping to the Latest Advances in Computers", New Leaf Publishing Group, United States of America, pp. 81-83, 2004, ISBN 978-1-61458-155-0.
[3] Nguyen, V., Deeds-Rubin, S., Tan, T., & Boehm, B. (2007, October). A SLOC counting standard. In Cocomo ii forum (Vol. 2007, pp. 1-16). Citeseer.
[4] Seymore Lipschutz and Marc Lipson, “Schaum`s Outline of Discrete Mathematics”, McGraw-Hill Education, United States of America, pp. 164-263, 2007, ISBN-13: 978-1259062537
[5] H. Schildt, “Java The Complete Reference Ninth Edition”, McGraw-Hill Education, United States of America, pp. 178-179, 2014, ISBN: 978-0-07-180856-9
[6] E. Rich, K Knight, S. B. Nair, “Artificial Intelligence”, McGraw-Hill Education, United States of America, pp. 25-30, 2009, ISBN: 978-0-07-008770-5
[7 ] U. Ray, T.K. Hazra, U.K. Ray, "Matrix Multiplication using Strassen’s Algorithm on CPU & GPU", International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.98-105, 2016.
[8] N. Karthikeyan, "Shortest Route Algorithm Using Dynamic Programming by Forward Recursion", International Journal of Computer Sciences and Engineering, Vol.2, Issue.2, pp.44-48, 2014.

Rishi Saxena, "Seven Fundamental Principles in the Effectuation of Recursion," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.63-67, 2019.

Nowadays people uses Google Maps (digital Navigator) during travelling to get exact road map with proper direction including distance in km and total time remaining to reach out destination. For this purpose people need to have facility like, inbuilt GPS system in vehicle or cell phone with internet connectivity but there are certain limitations. GPS (Global Positioning System) basically works with satellite. It is used to determine location of user. Navigator (Google Map) always gives shortest path during travelling that is the biggest limitation. Many times travellers face problems with width of road and create traffic problems as well on smaller roads. So, it is suggested that navigator should consider road width and based on findings it should provide alternate shortest route. For that in this paper an algorithm is provided for solving this problem.

Navigator, Search Engine, GPS

[1] Vincent, Luc. "Taking online maps down to street level." Computer Vol. 40, Issue. 12 ,pp. 118-120, 2007
[2] Ramaswamy, Ashok B., and Randall T. Brunts. "Mapless GPS navigation system in vehicle entertainment system." U.S. Patent 5,627,547, issued May 6, 1997.
[3] Chen, Hsuan-Eng, Yi-Ying Lin, Chien-Hsing Chen, and I. Wang. "BlindNavi: A navigation app for the visually impaired smartphone user." In Proceedings of the 33rd annual ACM conference extended abstracts on human factors in computing systems, pp. 19-24. ACM, 2015.
[4] Hawkins, Bradford A., Marta Rueda, and Miguel Á. Rodríguez. "What do range maps and surveys tell us about diversity patterns?." Folia Geobotanica 43, no. 3, pp.345, (2008).
[5] Wang, Fahui, and Yanqing Xu. "Estimating O–D travel time matrix by Google Maps API: implementation, advantages, and implications." Annals of GIS 17, no. 4 ,pp.199-209, (2011).

Raina D. Gaharwar, Birajkumar V. Patel, "Navigation Based on Distance and Width of Road," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.68-70, 2019.

Liver diseases averts the normal function of the liver. Mainly due to the large amount of alcohol consumption liver disease arises. Early prediction of liver disease using classification algorithms is an efficacious task that can help the doctors to diagnose the disease within a short duration of time. Discovering the existence of liver disease at an early stage is a complex task for the doctors. The main objective of this paper is to analyse the parameters of various classification algorithms and compare their predictive accuracies so as to find out the best classifier for determining the liver disease. This paper focuses on the related works of various authors on liver disease such that algorithms were implemented using Weka tool that is a machine learning software written in Java. Various attributes that are essential in the prediction of liver disease were examined and the dataset of liver patients were also evaluated. This paper compares various classification algorithms such as Random Forest, Logistic Regression and Separation Algorithm with an aim to identify the best technique. Based on this study, Random Forest with the highest accuracy outperformed the other algorithms and can be further utilised in the prediction of liver disease.

Healthcare, Prediction, Liver Disease, Classification Algorithms, Random Forest, Logistic Regression and Separation Algorithm

[1] Hoon Jin, Seoungcheon Kim, Jinhong Kim, “Decision Factors on Effective Liver Patient Data Prediction”, International Journal of Bio-Science and Bio-Technology, Vol. 6, Issue.4, pp. 167-178, 2014.
[2] Ayesha Pathan, Diksha Mhaske2, Shrutika Jadhav, Rupali Bhondave, Dr.K.Rajeswari, “Comparative Study of Different Classification Algorithms on ILPD Dataset to Predict Liver Disorder”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Vol. 6, Issue.2, pp. 388-394, 2018.
[3] Tapas Ranjan Baitharu, Subhendu Kumar Pani, “Analysis of Data Mining Techniques For Healthcare Decision Support System Using Liver Disorder Dataset”, International Conference on Computational Modeling and Security, India, pp. 862-870, 2016.
[4] Dr. S. Vijayarani, Mr.S.Dhayanand, “Liver Disease Prediction using SVM and Naïve Bayes Algorithms”, International Journal of Science, Engineering and Technology Research (IJSETR), Vol. 4, Issue.4 pp. 816-820, 2015.
[5] Rakhi Ray, “Advances in Data Mining: Healthcare Applications”, International Research Journal of Engineering and Technology (IRJET), Vol. 5, Issue.3, pp. 3738-3742, 2018.
[6] B.Saritha, S.V. Ramana, Narra Manaswini, RamaPriyanka, D.Hiranmayi, K.Eswaran, “Classification of liver data using a new algorithm”, 4th International Conference on New Frontiers of Engineering, Science, Management and Humanities, Hyderabad, 2017.
[7] HanMa, Cheng-fu Xu, Zhe Shen, Chao-hui Yu, You-ming Li, “Application of Machine Learning Techniques for Clinical Predictive Modeling: A Cross-Sectional Study on Nonalcoholic Fatty Liver Disease in China”, BioMed Research International, pp. 1-9, 2018.
[8] Nazmun Nahar, Ferdous Ara, “Liver Disease Prediction using different decision tree techniques”, International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol. 8, Issue.2, pp. 1-9, 2018.
[9] Shapla Rani Ghosh and Sajjad Waheed, “Analysis of classification algorithms for liver disease diagnosis”, Journal of Science,Technology&Environment Informatics, Vol. 5, Issue.1, pp. 360-370, 2017.
[10] Insha Arshad, Chiranjit Dutta, Tanupriya Choudhury, Abha Thakra, “Liver Disease detection due to excessive alcoholism using Data Mining Techniques”, International Conference on Advances in Computing and Communication Engineering, Paris, France, pp. 163-168, 2018.
[11] V.V. Ramalingam, A.Pandian, R. Ragavendran, “Machine Learning Techniques on Liver Disease – A Survey”, International Journal of Engineering & Technology, Vol. 7, Issue.4, pp. 493-495, 2018.
[12] Shambel Kefelegn, Pooja Kamat, “Prediction and Analysis of Liver Disorder Diseases by using Data Mining Technique: Survey, International Journal of Pure and Applied Mathematics, Vol. 118, Issue.9, pp. 765-769, 2018.
[13] R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra, “A Review: Design and Development of Novel Techniques for Clustering and Classification of Data”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol.6, Special Issue.01, pp. 19-22, 2018.
[14] Pawan S. Wasnik, S.D.Khamitkar, Parag Bhalchandra, S. N. Lokhande, Ajit S. Adte, “An Observation of Different Algorithmic Technique of Association Rule and Clustering”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol.6, Special Issue.01, pp. 28-30, 2018.

Binish Khan, Piyush Kumar Shukla, Manish Kumar Ahirwar, "Strategic Analysis in Prediction of Liver Disease Using Different Classification Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.71-76, 2019.

The basic concept in Vehicular Ad hoc Network is to integrate and fuse the mobile vehicles on road into an ad hoc network to enable communication among the vehicles on the move. The ad hoc network is infrastructure less and do not have any centralized administration. As a result, every individual node in VANETs act as a router in transmitting the packets from source towards the respective destinations. But, the delivery of packets is not always assured. Sometimes there is a possibility of high loss of packets due to adversarial conditions. Because of the high mobile nature of the nodes in VANETs, there could be frequent link failures resulting in high packet drops. In this paper the survey of different kinds of clustering algorithm have done and find best algorithm for VANET and studied different kinds of parameters.

Vehicular Ad hoc Networks (VANET), Ad-Hoc, Vehicles, Routing

[1] Parmar, Prof. S. K. Hadia and Prof. A. M. Shah, “Implementing and analyzing routing protocols for self- organized vehicular adhoc network”, 2013 Nirma University International Conference on Engineering (NUiCONE).
[2] Emad Alizadeh, Khalilollah Raeisi Lejjy and Esmaeil Amiri, “Improving Routing in Vehicular Ad-hoc Network with VIKOR Algorithm”, 2018 9th International Symposium on Telecommunications (IST`2018).
[3] H. Bello-Salau, A.M. Aibinu, Z. Wang, A.J. Onumanyi, E.N. Onwuka, J.J. Dukiya, “An optimized routing algorithm for vehicle ad-hoc networks”, Engineering Science and Technology, an International Journal 2019.
[4] Ms. Divya Rathi and Mrs. R. R. Welekar, “Performance Evaluation of AODV Routing Protocol in VANET with NS2”, Special Issue on Advances and Applications in the Internet of Things and Cloud Computing 2017.
[5] Alexandros Ladas, Nikolaos Pavlatos, Nuwan Weerasinghe and Christos Politis, “Multipath Routing Approach to Enhance Resiliency and Scalability in Ad-hoc Networks”, IEEE ICC 2016 Ad-hoc and Sensor Networking Symposium.
[6] Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks”, Proceedings of the 33rd Hawaii International Conference on System Sciences – 2000
[7] Nejla Ghaboosi, Abolfazl T. Haghighat, “Tabu search based algorithms for bandwidth-delay-constrained least- cost multicast routing”, Telecommun System, Vol. 34 pp. No. 147–166, 2007.
[8] Abdelmorhit El Rhazi and Samuel Pierre, “A Tabu Search Algorithm for Cluster Building in Wireless Sensor Networks”, IEEE Transactions on Mobile Computing, Vol. 8, No. 4, April 2009.
[9] Hamed Orojloo, Abolfazl T. Haghighat, “A Tabu search based routing algorithm for wireless sensor networks”, Springer 2015.
[10] Dhanush yadav M and Flory Francis, “Delay and hop sensitive routing protocol for Vehicular Ad Hoc Networks”, International Conference On Recent Trends in Electronics Information & Communication Technology (RTEICT), IEEE 2017.
[11] Shrikant Mane and R. Ramanathan, “Investigation of Greedy Forwarding Strategies for Three Dimensional Vehicular ADHOC Networks”, IEEE WiSPNET 2017 conference.
[12] Taqwa O. Fahad and Abduladhem A. Ali, “Multi-objective Optimized Routing Protocol for VANETs”, Hindawi Advances in Fuzzy Systems, 2018.
[13] Kalupahana Liyanage Kushan Sudheera , Maode Ma and Peter Han Joo Chong, “Link Stability Based Optimized Routing Framework for Software Defined Vehicular Networks”, IEEE Transactions On Vehicular Technology, Vol. 68, No. 3, March 2019.

Aayushi More, Shikha Agrawal, Manish Ahirwar, "Vehicular Ad-Hoc Network Performance with Different Routing Protocol Approaches: A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.77-82, 2019.

There are rapidly increasing attacks on computers creates a problem for network administration for averting the computer from these attacks. There are many conventional intrusion detection systems (IDS) is present but they are unable to prevent computer system completely. These IDS finds the spiteful actions on net traffics and they find the anomalies in network system. But in numerous instances they are unable for detecting spiteful actions in the networks. There is human interaction is also required to process the network traffic or detect malicious activity. In this paper we present various data mining algorithms helps in machine learning to detect intrusion accurately.

Intrusion Detection system, Anomaly detection, deep belief network, state preserving extreme learning machine

[1] Rahul Vigneswaran K, Vinayakumar R and Prabaharan Poornachandran, "Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security" in IEEE 2018.
[2] Yaping Chang ; Wei Li ; Zhongming Yang, " Network Intrusion Detection Based on Random Forest and Support Vector Machine" in IEEE 2017.
[3] David Ahmad Effendy, Sudarmawan Sudarmawan, “Classification of Intrusion Detection System (IDS) Based on Computer Network” in 2017 IEEE.
[4] Amreen Sultana, M.A.Jabbar, “Intelligent Network Intrusion Detection System using Data Mining Techniques” in IEEE 2016.
[5] James P. Anderson, "Computer security threat monitoring and surveillance," in USA, April 1980.
[6] Nawfal Turki Obeis, Wesam Bhaya, “Review of Data Mining Techniques for Malicious Detetion”, in RJAS, 2016.
[7] Jau-Hwang WANG and Peter S. DENG, “Virus Detection Using Data Mining Techniques”, in Taiwan.
[8] Chi Zhang, Jinyuan Sun, “Privacy and Security for Online Social Networks: Challenges and Opportunity”, in University of Florida and Xidian University.
[9] Uma Salunkhe, Suresh N. Mali, “ Enrichment in Intrusion Detection System Using Ensemble”, in JECE.
[10] Q.S. Qassim, A. M. Zin and M. J. Ab Aziz, “Anomalies classification approach for network- based intrusion detection system”, in IJNS, 2016.
[11] O.Y.Al-Jarrah, P.D.Yoo, K.Taha and K. Kim, “ Data Randomization and Cluster-based Partitioning for botnet intrusion detection”, in IEEE, 2016.
[12] Solane Duque, Dr. Mohd. Nizam Bin Omar, “Using Data Mining Algorithm for Developing a Model for Intrusion Detection System(IDS)”, in procedia Computer Science, 2015.
[13] Abhaya, K. Kumar, S. Afroz, “Data Mining Techniques for Intrusion Detection: A Review,” in IJARCCE, 2014.
[14] R.J. Manish, H.T. Hadi, “A review of network traffic analysis and prediction techniques”.
[15] S. Choudhury, A.Bhowal,“Comparative Analysis of Machine Learning Algorithms along with Classifiers for Network Intrusion Detection.” in IEEE, 2015.
[16] ] S.B. Kotsiantis, P.E. Pintelas, “Machine Learning: a Review of Classification and combining Techniques,” in Artificial Intelligence Review, 2006.
[17] I. Witten, E. Frank, M. Hall, “Data mining: Practical Machine Learning Tools and Techniques.” in 2011.
[18] M. Masud, L. Khan, B. Thuraisingham, “Data mining tools for malware detection,” in 2012.
[19] R.S. Wahono, “A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks,” In 2015.
[20] M.H. Haratian, “An Architectural Design for a Hybrid Intrusion Detection System for Database,”.
[21] S. Zargari, D. Voorhris, “Feature Selection in the Corrected KDDdataset,” in 2012.

Ritu Ganeshe, Manish Kumar Ahirwar, Rajeev Pandey, "Performance Analysis of Different Machine Learning Algorithm on Intrusion Detection System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.83-86, 2019.

Communication is the only way to exchange views and feelings. If communication between two parties is strong, there must always be more trust and decisions are taken firmly. Social networking sites are the potential tools which are utilized for communication without much physical efforts. Nowadays, the Social Networking phenomenon is spread over the globe and affects every individual who uses a social medium to communicate with others. Social networking sites have revolutionized the way companies communicate with their customers. The reachability of companies to customers has drastically improved because of the revolution in mobile technology. The main goal of our work is to get the insight into the impact of social networking on customer behavior, to explain why, when, and how social media has impacted on the customer decision process. We briefly introduce various laws used in mining techniques and the concept of link analyzis used for analyzing the data gathered from social networking sites. We explain the concept of centrality in social networks and exponential growth in online shopping and the causes behind this trend are also analyzed as well.

Social Network Mining, E-commerce, Web Mining, Customer Behavior, Six Degree Separation

[1] Harris L, Rae A. Social networks: the future of marketing for small business. Journal of business strategy. 2009 Sep 4;30 (5):24-31.
[2] Watts DJ. The “new” science of networks. Annu. Rev. Sociol.. 2004 Aug 11;30:243-70.
[3] Odhiambo M, Adhiambo C. Social Media as a Tool of Marketing and Creating Brand awareness: Case study research.
[4] Irfan R, King CK, Grages D, Ewen S, Khan SU, Madani SA, Kolodziej J, Wang L, Chen D, Rayes A, Tziritas N. A survey on text mining in social networks. The Knowledge Engineering Review. 2015 Mar;30(2):157-70.
[5] Kanchan U, Kumar N, Gupta A. A study of online purchase behavior of customers in India. Ictact Journal on Management Studies. 2015;1(3).
[6] Salehan M, Kim D. Predicting the Performance of Online Consumer Reviews: A Sentiment Mining Approach.
[7] Alsubagh H. The impact of social networks on consumers` behaviors. International Journal of Business and Social Science. 2015 Jan 1;6(1).
[8] Verbeke W, Martens D, Baesens B. Social network analyzis for customer churn prediction. Applied Soft Computing. 2014 Jan 1;14:431-46.
[9] Wei GT, Kho S, Husain W, Zainol Z. A study of customer behavior through web mining. J Inform Sci Comput Technol. 2015 Feb;2(1):103-7.
[10] Watts DJ. Six degrees: The science of a connected age. WW Norton & Company; 2004 Feb 17.A
[11] Nohuddin PN, Christley R, Coenen F, Patel Y, Setzkorn C, Williams S. Social network trend analyzis using frequent pattern mining and self organizing maps. InInternational Conference on Innovative Techniques and Applications of Artificial Intelligence 2010 Dec 14 (pp. 311-324). Springer, London.
[12] Smith, P.R. and Zook, Z., 2012. Marketing communications: integrating offline and online with social media/PR Smith & Ze Zook. Philadelphia, PA: Kogan Page,.
[13] Kim, D., Kim, J.H. and Nam, Y., 2014. How does industry use social networking sites? An analyzis of corporate dialogic uses of Facebook, Twitter, YouTube, and LinkedIn by industry type. Quality & Quantity, 48(5), pp.2605-2614.
[14] Wang YY, Susarla A, Sambamurthy V. The untold story of social media on offline sales: the impact of Facebook in the US automobile industry.
[15] Ibrahim NF, Wang X, Bourne H. Exploring the effect of user engagement in online brand communities: Evidence from Twitter. Computers in Human Behavior. 2017 Jul 1;72:321-38.
[16] Wattenhofer, M., Wattenhofer, R. and Zhu, Z., 2012, May. The YouTube social network. In Sixth International AAAI Conference on Weblogs and Social Media.
[17] Koch, T., Gerber, C. and de Klerk, J.J., 2018. The impact of social media on recruitment: Are you LinkedIn?. SA Journal of Human Resource Management, 16(1), pp.1-14.
[18] http://www.smallbusinesssem.com/articles/marketing-on-flickr/2012
[19] Evans, N.J., Phua, J., Lim, J. and Jun, H., 2017. Disclosing Instagram influencer advertising: The effects of disclosure language on advertising recognition, attitudes, and behavioral intent. Journal of Interactive Advertising, 17(2), pp.138-149.
[20] Wang, R.J.H., Malthouse, E.C. and Krishnamurthi, L., 2015. On the go: How mobile shopping affects customer purchase behavior. Journal of Retailing, 91(2), pp.217-234.
[21] Kalia, P., Singh, T. and Kaur, N., 2016. An empirical study of online shoppers’ search behavior with respect to sources of information in Northern India. Productivity: A Quarterly Journal of the National Productivity Council, 56(4), pp.353-361.
[22] Grandhi S, Chugh R, Wibowo S. An Empirical Study of Customers’ Purchase Intentions from Australian Group Buying Sites.
[23] https://yourstory.com/2013/01/google-india-study-about-online-shopping
[24] Jeyashoke N, Vongterapak B, Long Y. Does Culture Matter? A Case Study on Online Retailing Stores across Three Asian Countries. InPACIS 2014 (p. 283).
[25] Khanna P, Sampat B. Factors influencing online shopping during Diwali festival 2014: Case study of Flipkart and Amazon. in. Journal of International Technology and Information Management. 2015;24(2):5.
[26] Mayfield, R., 2005. Social network dynamics and participatory politics. Extreme democracy, pp.116-132.
[27] Odlyzko, A. and Tilly, B., 2005. A refutation of Metcalfe’s Law and a better estimate for the value of networks and network interconnections. Manuscript, March, 2, p.2005.
[28] Zhang, X.Z., Liu, J.J. and Xu, Z.W., 2015. Tencent and Facebook data validate Metcalfe’s law. Journal of Computer Science and Technology, 30(2), pp.246-251.
[29] Centola D. The spread of behavior in an online social network experiment. science. 2010 Sep 3;329(5996):1194-7.
[30] Ghosh R, Lerman K. Predicting influential users in online social networks. arXiv preprint arXiv:1005.4882. 2010 May 26.

S.K. Mishra, A. Agarwal, "A Brief Review on Impact of Social Network Mining on Online Shopping for Classifying Customers," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.87-92, 2019.

In the field of image processing one of the important process is image enhancement. In many image processing applications, image enhancement techniques are used. Many research works have been done for image enhancement. In this paper, different techniques and algorithms using machine learning approach such as genetic algorithm, neural networks, fuzzy logic enhancement and optimization techniques are studied and discussed. The aim of this study is to determine the application of machine learning approaches that have been used for image enhancement. The review given in this paper is quite efficient for future researchers to overcome problems related to machine learning approach as well as helps in designing efficient algorithm which enhances quality of the image.

Image enhancement, Image quality, Machine learning approaches, Digital image processing

[1] Dong-liang, P., An-Ke, X.: “Degraded image enhancement with applications in robot vision”, published in IEEE International Conference on Systems, Man and Cybernetics, Vol. 2, pp. 1837–1842, IEEE, 2005.
[2] Xianghong, W., Shi, Y., Xinsheng, X.: “An effective method to colour medical image enhancement”, published in IEEE/ICME International Conference on Complex Medical Engineering, pp. 874–877, IEEE, 2007.
[3] Benala, T.R., Jampala, S.D., Villa, S.H., Konathala, B.: “A novel approach to image edge enhancement using artificial bee colony optimization algorithm for hybridized smoothening filters”, published in IEEE, pp. 1071–1076, 2009.
[4] Wang, L.J., Huang, Y.C.: “Non-linear image enhancement using opportunity costs”, published in Second International Conference on Computational Intelligence Communication Systems and Networks (CICSyN), IEEE, pp. 256–261, 2010.
[5] Gorai, A.,Ghosh, A.: “Hue-Preserving Color Image Enhancement Using Particle Swarm Optimization”, published in IEEE, pp. 563–568, 2011.
[6] Hanumantharaju, M.C., Aradhya, V.N.M., Ravishankar, M., Mamatha, A.: “A Particle Swarm Optimization Method for Tuning the Parameters of Multiscale Retinex Based Color Image Enhancement”, published in ICACCI’12, Chennai, T Nadu, India, ACM, pp. 721–727, August 3–5, 2012.
[7] Zhou, X., Sun, G., Zhao, D., Wang, Z., Gao, L., Wang, X., Jin, Y.: “A Fuzzy Enhancement Method for Transmission Line Image Based on Genetic Algorithm”, published in Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 223–226, 2013.
[8] Singh, P.K., Sangwan, O.P., Sharma, A.: “A Systematic Review on Fault Based Mutation Testing Techniques and Tools for Aspect-J Programs”, published in 3rd IEEE International Advance Computing Conference, IACC-2013 at AKGEC Ghaziabad, IEEE Xplore, pp. 1455–1461, 2013.
[9] Verma, A., Goel, S., Kumar, N.: “Gray level enhancement to emphasize less dynamic region within image using genetic algorithm”, published in 3rd International conference on Advance Computing Conference (IACC), pp. 1171–1176. IEEE, 2013.
[10] Khan, T.M., Khan, M.A., Kong, Y.: “Fingerprint image enhancement using multi-scale DDFB based diffusion filters and modified Hong filters”, published in Optik-International Journal for Light and Electron Optics Vol. 125, No. 16, pp. 4206–4214, 2014.
[11] Raju, G., Nair, M.S.: “A fast and efficient color image enhancement method based on fuzzy-logic and histogram”, published in AEU-International Journal of electronics and communications, Vol. 68, No. 3, pp. 237–243, 2014.
[12] Negi, S.S., Bhandari, Y.S.: “A hybrid approach to Image Enhancement using Contrast Stretching on Image Sharpening and the analysis of various cases arising using histogram”, published in Recent Advances and Innovations in Engineering (ICRAIE), pp. 1–6, 2014.
[13] Wu, C., Liu, Z., Jiang, H.: “Catenary image enhancement using wavelet-based contourlet transform with cycle translation”, published in Optik-International Journal for Light and Electron Optics, Vol. 125, No. 15, pp. 3922–3925, 2014.
[14] Premkumar, S., Parthasarathi, K.A.: “An efficient approach for colour image enhancement using Discrete Shearlet Transform”, published in 2nd International Conference on Current Trends in Engineering and Technology (ICCTET), IEEE, pp. 363–366, 2014.
[15] Bhattacharya, S., Gupta, S., Subramanian, V.K.: “Localized image enhancement”, published in Twentieth National Conference on Communications (NCC), IEEE, pp. 1–6, 2014.
[16] Shanmugavadivu, P., Balasubramanian, K.: “Particle swarm optimized multi-objective histogram equalization for image enhancement”, published in Optics Laser Technology, Vol. 57, pp. 243–251, 2014.
[17] Draa, A., Bouaziz, A.: “An artificial bee colony algorithm for image contrast enhancement”, published in Swarm and Evolutionary Computation, Vol. 16, pp. 69–84, 2014.
[18] Singh, P.K., Panda, R.K., Sangwan, O.P.: “A Critical Analysis on Software Fault Prediction Techniques”, published in World Applied Sciences Journal, Vol. 33, No. 3, pp. 371–379, 2015.
[19] Singh, P. K., Agarwal, D., Gupta, A.: “A Systematic Review on Software Defect Prediction, published in Computing for Sustainable Global Development (INDIACom)”, IEEE, pp. 1793– 97, 2015.
[20] Jianrui Cai, Shuhang Gu, and Lei Zhang, “Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images”, IEEE Transactions on Image Processing, Vol. 27, No. 4, April 2018.

Deepanjali Titariya, Rajeev Pandey, Shikha Agrawal, "A Brief Review on Image Contrast Enhancement Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.93-97, 2019.

Wireless correspondence innovations keep on experiencing fast headway. Wireless sensor nodes have restricted handling capacity, store-up and active minerals. The presence of sensor network rely on the life of sensor nodes i.e., eventually on the energy inhalation during its procedure. Therefore, in WSN, the effective use of energy resources is very much essential. Clustering is one of the viewpoints for energy saving in WSN. As of late, there has been a lofty development in research in the zone of Wireless sensor systems (WSNs). In WSNs, correspondence happens with the assistance of spatially dispersed, self-governing sensor hubs prepared to detect explicit data. WSNs can be found in an assortment of both military and regular citizen applications around the world. Models incorporate recognizing adversary interruption on the war zone, object following, natural surroundings checking, tolerant observing and fire recognition. Sensor systems are rising as an alluring innovation with extraordinary guarantee for what`s to come. Nonetheless, challenges stay to be tended to in issues identifying with inclusion and arrangement, versatility, nature of-administration, estimate, computational power, vitality proficiency and security. This paper exhibits an outline of the various utilizations of the wireless sensor systems and different security related issues in WSNs.

Wireless, Network, Self-governing, Explicit Data

[1] J. Hill, R. Szewczyk, A, Woo, S. Hollar, D. Culler, and K. Pister, “System Architecture Directions for Networked Sensors”, ASPLOS, November 2000.
[2] Culler, D. E and Hong, W., “Wireless Sensor Networks”, Communication of the ACM, Vol. 47, No. 6, pp. 30-33, June 2004.
[3] Undercoffer, J., Avancha, S., Joshi, A., and Pinkston, J., “Security for Sensor Networks”, CADIP Research Symposium, 2002.
[4] A.D. Wood and J.A. Stankovic, (2002) “Denial of Service in Sensor Networks,” Computer, vol. 35, no. 10, pp. 54– 62, 2002.
[5] J. R. Douceur, “The Sybil Attack,” in 1st International Workshop on Peer-to-Peer Systems (IPTPS ‟02), 2002.
[6] Zaw Tun and Aung Htein Maw, “Worm hole Attack Detection in Wireless Sensor networks”, proceedings of world Academy of Science, Engineering and Technology Volume 36, ISSN 2070-3740, December 2008.

Aishwarya Kumar, Awadhesh Kumar, "An Overview of Wireless Sensor Networks (WSN): Applications and Security," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.98-100, 2019.

In late years, profound learning methods especially Convolutional Neural Networks have been utilized in different orders. CNNs have appeared fundamental capacity to naturally extricate expansive volumes of data from huge information. The utilization of CNNs has altogether turned out to be helpful particularly in arranging normal pictures. In any case, there have been noteworthy hindrances in executing the CNNs in medicinal area because of absence of legitimate preparing information. Therefore, general imaging benchmarks, for example, Image Net have been prominently utilized in the therapeutic area despite the fact that they are not all that ideal when contrasted with the CNNs. In this paper, a similar investigation of LeNet, AlexNet and GoogLeNet have been finished. From that point, the paper has proposed an improved theoretical structure for ordering restorative life structures pictures utilizing CNNs. In view of the proposed structure of the system, the CNNs engineering is required to beat the past three designs in ordering restorative pictures.

ImageNet, LeNet, AlexNet and GoogLeNet, Convolutional Neural Networks

[1] Wang, G., Li, W., Maria, A., Zuluaga, Pratt, R, Premal, A., Patel, Aertsen, M., Doel, T., Anna. L. Jan., D., Ourselin, S. &Vercauteren,T. Interactive medical image segmentation using deep learning with image-specific fine-tuning. IEEE Transactions on MedicalImaging, 1-12, 2018.
[2] Singh, S. & Singh, N. Object classification to analyze medical imaging data using deep learning. International Conference onInnovations in information Embedded and Communication Systems (ICIIECS), 1 – 4, 2018.
[3] Yigzaw, K. Y. &Bellika, J. G. Evaluation of secure multi-party computation for reuse of distributed electronic health data, IEEEEMBSInternational Conference on Biomedical and Health Informatics (BHI), 219-222, 2014.
[4] Ker, J., Wang, L., Rao, J., & Lim, T. Deep learning applications in medical image analysis. IEEE Access, 6, 9375 – 9389, 2017.
[5] Hsiao, C.J., Hing, E., & Ashman, J. Trends in electronic health record system use among office-based physicians: United States, 2007– 2012. Nat. Health Stat. Rep, 75, 1-18, 2014.
[6] Müller, H., Michoux, N., Bandon, D., &Geissbuhler, A. A review of content based image retrieval systems in medical applications–clinical benefits and future directions. International Journal of Medical Informatics, 73, 1-23, 2004.
[7] Qiu, C., Cai, Y., Gao, X., & Cui, Y. Medical image retrieval based on the deep convolution network and hash coding. International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, 1-6, 2017.
[8] Muller, H., Rosset, A., Vallee, J.P., &Geisbuhler, A. Comparing feature sets for content-based image retrieval in a medical casedatabase. SPIE Med. Image, PACS Image. 99–109, 2004.
[9] Felipe, J. C., Traina, A. J. M., &Traina, C. Retrieval by content of medical images using texture for tissue identification. IEEE Symp.Computer-Based Med. 175–180, 2003.
[10] LeCun, Y., Bengio, Y., & Hinton, G. Deep learning. Nature, vol. 521, no. 7553, pp. 436–444, 2017.
[11] Ashis Kumar Dhara, SudiptaMukhopadhyay, AnirvanDutta, MandeepGarg, and NiranjanKhandelwal. A combination of shape andtexture features for classification of pulmonary nodules in lung ct images. Journal of digital imaging, 29(4):466–475, 2016.
[12] Mohammad Reza Zare, Ahmed Mueen, and Woo Chaw Seng. Automatic medical x-ray image classification using annotation. Journalof digital imaging, 27(1):77–89, 2014.
[13] Wei Yang, Zhentai Lu, Mei Yu, Meiyan Huang, QianjinFeng, and Wufan Chen. Content-based retrieval of focal liver lesions usingbag-of-visual-words representations of single-and multiphase contrast-enhanced ct images. Journal of digital imaging, 25(6):708–719,2012.
[14] YannLeCun, L´eonBottou, YoshuaBengio, and Patrick Haffner. Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 1998.
[15] Alex Krizhevsky, IlyaSutskever, and Geoffrey E Hinton.Imagenet classification with deep convolutional neural networks.InAdvances in neural information processing systems, pages 1097–1105, 2012.
[16] C.-J. Hsiao, E. Hing, and J. Ashman, ``Trends in electronic health recordsystem use among ofce-based physicians: United states, 20072012,``Nat. Health Stat. Rep., vol. 75, pp. 118, May 2014.
[17] R. Smith-Bindman et al., ``Use of diagnostic imaging studies and associatedradiation exposure for patients enrolled in large integrated healthcare systems, 19962010,`` JAMA, vol. 307, no. 22, pp. 24002409, 2012.
[18] E. H. Shortliffe, Computer-Based Medical Consultations: MYCIN, vol. 2.New York, NY, USA: Elsevier, 1976.
[19] G. Litjens et al. (Jun. 2017). ``A survey on deep learning in medical imageanalysis.`` [Online]. Available: https://arxiv.org/abs/1702.05747
[20] W. S. McCulloch andW. Pitts, ``A logical calculus of the ideas immanentin nervous activity,`` Bull. Math. Biol., vol. 5, nos. 4, pp. 115133,1943.
[21] F. Rosenblatt, ``The perceptron: A probabilistic model for informationstorage and organization in the brain,`` Psychol. Rev., vol. 65, no. 6,pp. 365386, 1958.
[22] D. H. Hubel and T. N.Wiesel, ``Receptive elds, binocular interaction andfunctional architecture in the cat`s visual cortex,`` J. Physiol., vol. 160,no. 1, pp. 106154, 1962.
[23] K. Fukushima and S. Miyake, ``Neocognitron: A self-organizing neuralnetwork model for a mechanism of visual pattern recognition,`` in Competition and Cooperation in Neural Nets. Berlin, Germany: Springer, 1982,pp. 267285.
[24] Y. LeCun et al., ``Backpropagation applied to handwritten zip coderecognition,`` Neural Comput., vol. 1, no. 4, pp. 541551, 1989.
[25] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, ``Learning representationsby back-propagating errors,`` Nature, vol. 323, pp. 533536,Oct. 1986.
[26] A. Krizhevsky, I. Sutskever, and G. E. Hinton, ``ImageNetclassicationwith deep convolutional neural networks,`` in Proc. Adv. Neural Inf.Process. Syst., 2012, pp. 10971105.
[27] D. Shen, G. Wu, and H.-I. Suk, ``Deep learning in medicalimage analysis,`` Annu.Rev. Biomed. Eng., vol. 19, pp. 221,248,Mar. 2017.

Parveen, K. Sreekanth, "Brain Tumor Diagnosis Using Convolutional Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.101-104, 2019.

Nearly 1.5 million people die from vehicles accidents per year and nearly 20 million are injured or physically disabled. we can be avoided 60 percent of accidents by using sufficient warnings system. For increasing safety in vehicles, we have Vehicular Ad hoc networks (VANETs) system. This System Designed to increase safety, driving efficiency and make the driving experience more reliable. VANETs connect vehicle into a huge mobile ad hoc network share data on a bigger scale. However, communicating in a free environment makes security and privacy issue an actual challenge, which may disrupt the VANETs system. Researchers have found a resolution to this problem. In this paper, I talked about the different techniques and security parameter which may reflect security and privacy in VANET system.

VANETs, Attacks, SECURITY AND PRIVACY REQUIREMENTS

[1]. Zing Zhu, Sumit Roy, "MAC(Media Access Control) for DSRC (Dedicated Short Range Communication) in Intelligent Transport System", IEEE Commun. Mag., vol. 41, no. 12, pp. 60-67, Dec. 2003.
[2].G. Samara, Wafaa A.H. Al-Salihy, R. Sures" study of a security review of vehicular ad hoc networks (VANET)" National Advanced IPv6 Centre, Universitiy Sains Malaysia, 2010.
[3].K. B.Sahare and DR.L.G.Malik, " Review- Method for Detection of Attacks in VANET", International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 2, February 2014.
[4]. M. Elsa Mathew and A.Raj Kumar p." threat examination and safety mechanisms in Vanet" international journal of advanced research in computer science and software engineering volume 3, issue 1, Jan 2013.
[5]. Ajmal, S., Rasheed, A., Qayyum, A., Hasan, A.: Classification of VANET MAC, Routing and approaches a detailed survey. J. UCS 20(4), 462–487 (2014).
[6]. Rasheed, A., Zia, H., Hashmi, F., Hadi, U., Naim, Warda, Ajmal, Sana: Fleet & convoy management using VANET. J. Comput. Netw. 1(1), 1–9 (2013).
[7]. Sajjad Akbar, M., Rasheed, A., Qayyum, A.: VANET architectures and protocol stacks: a survey. In: InternationalWorkshop on Communication Technologies for Vehicles, pp. 95–105. Springer, Berlin, Heidelberg (2011).
[8]. Liang, W., Li, Z., Zhang, H., Wang, S., Bie, Rongfang: Vehicular ad hoc networks: architectures, research issues, methodologies, challenges, and trends. Int. J. Distrib. Sens. Netw. 2015, 17(2015).
[9]. Da Cunha, F.D., Boukerche, A., Villas, L., Carneiro Viana, A., Loureiro, Antonio AF.: Data communication in VANETs: a survey, challenges and applications. Ph.D. diss., INRIA Saclay; INRIA (2014).
[10]. Ajmal, Sana, Jabeen, Samra, Rasheed, Asim, Hasan, Aamir: An intelligent hybrid spread spectrum MAC for interference management in mobile ad hoc networks. Comput. Commun. 72, 116–129 (2015)
[11]. Marvy B. Mansour1, Cherif Salama2, Hoda K. Mohamed3 and Sherif A. Hammad4 1British University in Egypt, Cairo, Egypt 2,3Computer and Systems Engineering Department, Ain Shams University, Cairo, Egypt 4Avelabs, Cairo, Egypt – Munich, Germany.
[12].A. Yusri Dak, "A Literature Survey on Security Difficulties in VANETs", International Journal of Computer Theory And Engineering, Volume 4, No. 6, December 2012.
[13]. You Lu, Biao Zhou, Fei Jia, and M. Gerla, "Group-based Secure Source Authentication(GSA) Protocol for VANETs", IEEE Globecom 2010 Workshop on Heterogeneous, Multi-hop Wireless and Mobile Networks.
[14]. Road Safety Facts — Association for Safe International Road Travel.

D. Kumar, V. Sindhu, "Vanet Security and Privacy – An Overview," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.105-108, 2019.