Volume-6 , Issue-11 , Nov - 2018 Go Back

In case of ad hoc network, there is no provision for centralized control over network resources and the routing operations. Each node manages the communication links itself, updates the routing information to adopt the variations in topology. Ad hoc networks uses the shared channel and any node can transmit the data at any time over the network using shared channel but during the data transmission, any other node can also try for the data transmission which can lead to the congestion and contention over the shared channel and finally results in collision. For the better channel utilization, there are many solutions has been developed that can control the shared channel utilization in order to reduce collision and to enhance network performance. This survey paper contains a study of various kind of routing protocols used in communication systems whether it may be wired communication or may be wireless or sensor network system but still main focus is towards those routing protocols which are mostly suitable to Mobile Ad-hoc Networks.

Ad-hoc, Centralized, Channel, Communication ,Collision, Transmission, Topology, Node, Link, performance, Routing, Utilization, etc

[1] Martin Matis ; Lubomir Dobos ; Jan Papaj, A brief comparision of fundamental routing methods for Mobile Multihop Networks(MANET), 2016 International Symposium ELMAR, 2016
[2] Vikash ; R K Singh, Hybrid routing protocol for mobile-ad hoc network in rural environments, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016
[3] Debaditya Choudhury ; Debanjana Kar ; Katha Roy Biswas ; Himadri Nath Saha, Energy efficient routing in mobile ad-hoc networks, 2015 International Conference and Workshop on Computing and Communication (IEMCON), 2015
[4] Beijar Nicklas, "Zone Routing Protocol",FIN, 2015
[5] Nabil Nissar, Najib Naja, Abdellah Jamali, “A review and a new approach to reduce routing overhead in MANETs,” Springer Science and Business Media New York - 2014
[6] Ze Li, Haiying Shen, “A QoS-Oriented Distributed Routing Protocol for Hybrid Wireless Networks”, IEEE Transactions on mobile computing, Vol. 13,(3),IEEE-2014
[7] Resources Optimization Design for Ubiquitous Wireless Networks”, Wireless Pers Commun ,2014
[8] Farooq, M.O, Kunz, T, "BEAR: Bandwidth estimation-based admission control and routing for IEEE 802.15.4-based networks", WMNC-IEEE, 2013
[9] Zheng Li., Kai Liu, "A multiple-recipient-based cooperative MAC protocol for wireless ad hoc networks", MAPE, 2013
[10] Surendra H. Raut, Hemant P. Ambulgekar, "Proactive and Reactive Routing Protocols in Multihop Mobile Ad hoc Network", ISSN 2277128X, vol. 3, no. 4, 2013
[11] Avni Khatkar ; Yudhvir Singh, Performance Evaluation of Hybrid Routing Protocols in Mobile Ad Hoc Networks, 2012 Second International Conference on Advanced Computing & Communication Technologies, 2012
[12] S Gopinath, N Sureshkumar, G Vijayalakshmi, N. A. Natraj, T Senthil, P Prabu, "Energy Efficient Routing Protocol for MANET", IJCSI International Journal of Computer Science Issues, vol. 9, no. 2, 2012
[13] Singh Ajit, Kumar Mukesh, Rishi Rahul, and Madan D.K., “A Relative Study of MANET and VANET: Its Applications, Broadcasting Approaches and Challenging Issues”, CCIS , Springer, pp. 627–632, 2011.
[14] Yuki Sato, Akio Koyama, Leonard Barolli “A Zone Based Routing Protocol for Ad Hoc Networks and Its Performance Improvement by Reduction of Control Packets”, International Conference on Broadband, Wireless Computing, Communication and Applications, IEEE - 2010
[15] J. Lakkakorpi, M. Pitkanen, J. Ott, "Adaptive routing in mobile opportunistic networks", ACM MSWIM’ 10, pp. 101-109, October 2010. "Routing protocol for Delay Tolerant Network: A survey and comparison Communication Control and Computing Technologies (ICCCCT)", 2010 IEEE International Conference on Date of Conference, 2010
[16] Yogesh Chaba, Yudhvir Singh, Manish, "Performance Evaluation and Analysis of Cluster Based Routing Protocols in MANETs" Proc. IEEE/ACEEE ACT 2009, India [Online : IEEE Xplore Digital Library, Digital Object Identifier: 10.1109/ACT.2009.26], pp. 64-66, Available: http://ieeexplore.ieee.org/ , 2009

A. Bano, R. K. Yadav, V. Namdeo, "An Overview Of Routing Protocols For Mobile Ad-Hoc Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.699-702, 2018.

Non-destructive quality assessment of Fruits is essential and exceptionally fundamental for the sustenance and rural industry. The Fruits in the market ought to fulfill the buyer inclinations. Generally reviewing of Orange fruit is performed basically by visual examination utilizing size as a specific quality characteristic. Picture preparing offers answer for computerized Orange Fruits estimate reviewing to give exact, solid, predictable and quantitative data separated from dealing with extensive volumes, which may not be accomplished by utilizing human graders. This Research shows an Orange size and Bacteria Spot Defect distinguishing and reviewing framework dependent on picture preparing. The early appraisal of Orange quality requires new apparatuses for size, color and texture estimation. Subsequent to catching the Orange side view picture, some fruits characters is removed by utilizing identifying calculations. As indicated by these characters, reviewing is figured it out. The benefit of high precision of evaluating, rapid and ease. It will have a decent prospect of use in OrangeFruit quality distinguishing and evaluating zones. In this paper we will elaborate different types of features and classification methods using advantages and disadvantages.

Image Processing, K-Means clustering, Color features, Texture features, Shape feature, Random forest classifier, SVM, ANN

[1] Bhavini J. Samajpati And Sheshang D. Degadwala “Hybrid Approach for Apple Fruit Diseases Detection and Classification Using Random Forest Classifier” IEEE-2016
[2] Manali R. Satpute And Sumati M. Jagdale ”Automatic Fruit Quality Inspection System” IEEE-2016
[3] Nashat M. Hussain Hassan And Ahmed A. Nashat “New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques” Springer 2018
[4] Tasneem Abass Najeeb And Maytham Safar “Dates Maturity Status and Classification using Image Processing” IEEE-2018
[5] Yogesh, And Ashwani Kumar Dubey “Fruit Defect Detection Based on Speeded Up Robust Feature Technique” IEEE-2016
[6] Naeem Sattar, Sheikh Ziauddin, Sajida Kalsoom, Ahmad R. Shahid, Rafi Ullah, Amir H. Dar “An Orange Sorting Technique based on Size and External Defects”
[7] Ahmed M. Abdelsalam 1 and Mohammed S. Sayed, “Real-Time Defects Detection System for Orange Citrus Fruits Using Multi-Spectral Imaging”
[8] United States Department of Agriculture, “Citrus: World markets and trade,” ap.ps.fas.usda.gov/psdonline/circulars/citrus.pdf
[9] P.Mohanaiah, P. Sathyanarayana, L. GuruKumar, “Image Texture Feature Extraction Using GLCM Approach”
[10] K. Srinivasa Reddy , V. Vijaya Kumar , B. Eswara Reddy ,“Face Recognition Based on Texture Features using Local Ternary Patterns”
[11] Giacomo Capizzi, Grazia Lo Sciuto, Christian Napoli, Emiliano Tramontana, Marcin Woz´niak “Automatic Classification of Fruit Defects based on Co-Occurrence Matrix and Neural Networks”
[12] Dayanand Savakar “Identification and Classification of Bulk Fruits Images using Artificial Neural Networks”

Hardik Patel, Rashmin B. Prajapati, "A Review on Methodology for Fruit Defect Identification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.703-707, 2018.

Agriculture plays very important role in the development of agricultural country. In India about 75% of population depends upon farming and one third of the nation’s capital comes from farming. Issues concerning agriculture have been always hindering the development of the country. The only solution to this problem is smart agriculture by modernizing the current conventional methods of agriculture. To improve efficiency, productivity, global market and to reduce human intervention, time and cost there is a need to divert towards new technology named Internet of Things. It is the network of devices to transmit the information without human involvement. IoT works in synergy with agriculture to obtain smart farming. This paper focuses on major role of IoT in agriculture that leads to smart farming industry.

Internet of Things, Smart Farming, Efficiency, Productivity

[1] Vinayak N. Malavade1, Pooja K. Akulwar2 OSR Journal of Computer Engineering (IOSR-JCE)e-ISSN: 2278-0661,p-ISSN: 2278-8727 PP 56-57
[2] http://avancer.in/internet-of-things-is-important/
[3] Jim Chase, The Evolution of the Internet of Things . White Paper, Texas Instruments, September, 2013.
[4] https://www.embitel.com/blog/embedded-blog/how-iot-works-an-overview-of-the-technology-architecture-2
[5] Xiaohui Wang and Nannan Liu, “The application of internet of things in agricultural means of production supply chain management”, Journal of Chemical and Pharmaceutical Research, 2014, 6(7):2304-2310, ISSN : 0975-7384,2014
[6] https://www.mouser.in/applications/smart-agriculture-sensors/
[7] https://www.sciencedaily.com/terms/agriculture.htm
[8] https://data-flair.training/blogs/iot-applications-in-agriculture/
[9] https://www.c2m.net/blog/10-benefits-of-a-smart-agriculture-solution.aspx
[10] https://data-flair.training/blogs/iot-applications-in-agriculture/

R. Shankar, S. Duraisamy, "IoT Technology is Benefiting Today’s Modern Farming Industry," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.708-713, 2018.

The input to handheld devices using a traditional keyboard is not a user-friendly process for Indian scripts due to large and complex character sets. Handwritten character recognition can be a best possible solution. Handwritten character recognition is gaining noteworthy attention in the area of pattern matching and machine learning. Named categories Recognition is a method to find for a particular Named field from a file or an image, recognize it and classify it into specified Entity Classes like Name, Location, Organization, Numbers and Other Categories. The main purpose of using Hybrid Feature is that it provides better performance and can be easily implemented for any languages. A remarkable amount of work has been carried out for many languages like English, Greek, and Chinese etc. But still a wide scope is open for Indian Origin Languages like Hindi, Gujarati, and Devanagari etc. As Gujarati is not only the Indian Language, but a language that is most spoken in Gujarat. Thus, in this research paper different type of features and classification techniques are compare with advantages and disadvantages. After review all methods give idea for future direction research for Guajarati OCR recognition.

Gujarati Language, Named Entity Recognition, Invariant feature Extraction, Character classification

[1] Parita R. Paneri, Ronit Narang, Mukesh M.Goswami, “Offline Handwritten Gujarati Word Recognition”, 2017 Fourth International Journal on Image Information Processing (ICIIP).
[2] Vishal A. Naik, Apurva A. Desai “ Online Handwritten Gujarati Character Recognition Using SVM, MLP, And K-NN ” , 8th ICCCN IIT Delhi,(2017).
[3] Komil Vora, Dr. Avani Vasant, Rachit Adhvaryu, “Named Entity Recognition And Classification For Gujarati Language” Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI)(2016).
[4] Chhaya C Gohell, Mukesh M Goswam, Yishal K Prajapate, “On-line Handwritten Gujarati Character Recognition Using Low Level Stroke” Third International Conference on Image Information Processing(2015).
[5] Apurva A. Desai “Support vector machine for identification of handwritten Gujarati alphabets using hybrid feature space” CSI Publications, (2015).
[6] S. S. Magare, Y. K. Gedam, D. S. Randhave, R. R. Deshmukh “Character Recognition of Gujarati and Devanagari Script” International Journal of Engineering Research & Technology (IJERT), (2014).
[7] Mukesh M. Goswami, Suman K. Mitra “High-Level Shape Representation in Printed Gujarati Characters” Sixth International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017).
[8] Swital J. Macwan, Archana N. Vyas “Classification of Offline Gujarati Handwritten Characters” IEEE(2015).
[9] Ami Mehta, Ashish Gor “Multi font Multi size Gujarati OCR with Style Identification” International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017)

Amitkumar T Solanki, Sheshang D Degadwala, Kishori Shekokar, "A Review On Offline Gujarati Word Categories Using Hybrid Features," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.714-718, 2018.

In the past few years, sentiment analysis received a great attention as the mining of opinions or reviews from the customer about the product or object. Mining of opinions is defined as a computational task for reviewing the emotions, attitudes and opinions of an individual about a particular product. It evaluates the results as positive, negative or neutral. Before buying a product, the customers want to know the reviews of other users about that product. To make the product more profitable and other future determinations, customer feedback about the product are analyzed by the companies. So the sentiment analysis is decisive for both individuals and companies for making certain decisions. Now a day’s opinion mining is most prominent branch of research in the area of text mining. This survey deals with a detail study of sentiment analysis, opinion mining and natural language processing techniques used for sentiment analysis. Some existing work that has been done in past few years has also been discussed in this survey.

Data mining, Web mining, Opinion mining, Sentiment analysis, NLP

[1] Nidhi R.Sharma, Prof. Vidya D. Chitre, “Opinion Mining, Analysis and it’s challenges”, International Journal of Innovations and Advancement in Computer Scicence (IJIACS), ISSN 2347-8616, Vol.6, Issue.1, 2014.
[2] Bakhtawar Seerat, Farouque Azam, “Opinion Mining: Issues and Challenges (A Survey)”, International Journal of Computer Applications (IJCA), Vol.49- No.9, 2012.
[3] Shiliang Sun, Chen Luo, Junyu Chen, “A Review of Natural Language Processing Techniques for Opinion Mining Systems” Department of Computer Science and Technology, East China Normal University, P.R. China, 2016.
[4] G.Vinodhini, RM.Chandrasekaran, “Sentiment Analysis and Opinion Mining: A Survey. International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), ISSN: 2277 128X, Vol.2, Issue.6, 2012.
[5] Walaa Medhat, Ahmed Hassan and Hoda Korashy. 2014. Sentiment analysis algorithms and applications: A Survey. Ain Shams Engineering Journal (ASEJ).
[6] Bin Lin, Fiorella Zampetti, Gabriele Bavota, Massimiliano Di Penta, Michele Lanza, Rocco Oliveto, “Sentiment Analysis for Software Engineering: How Far Can We Go ?”, ICSE’18: 40th International Conference on Software Engineering (ICSE), Sweden, 2018.
[7] Arti Buche, Dr. M.B. Chandak, Akshay Zadgaonkar, “Opinion Mining And Analysis: A Surevey”, International Journal on Natural Language Computing (IJNLC), Vol.2, No.3, 2013.
[8] Khairullah Khan, Baharum Baharudin, Aurnagzeb Khan, Ashraf Ullah, “Mining opinion components from unstructured reviews: A review”, Journal of King Saud University- Computer and Information Sciences (2014) 26, 258-275.
[9] S.Bird, E. Loper, E.Klein, “Natural Language Processing with Python”, Journal of Data Analysis and Information Processing, Vol.3, No.4, 2015.
[10] C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, D. Mc-Closky, “The Stanford CoreNLP natural language processing toolkit” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2014, pp. 55–60.
[11] Z. M. Ma. 2005, “Engineering information modeling in databases: needs and constructions”, Vol.105, Issue.7, pp. 900-918, 2005.
[12] Amandeep Kaur, Vishal Gupta, “A Survey on Sentiment Analysis and Opinion Mining Techniques”, Journal of Emerging Technologies in Web Intelligence, Vol.5, No.4, 2013.
[13] R. ˇReh°uˇrek, P. Sojka, “Software framework for topic modelling with large corpora, In Proceedings of LREC 2010 workshop New Challenges for NLP Frameworks, Valletta, Malta: University of Malta, p. 46--50, 5 pp. ISBN 2-9517408-6-7.
[14] X. Qiu, Q. Zhang, X. Huang, “Fudannlp: A toolkit for Chinese natural language processing”, In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 49–54, 2013.
[15] W. Che, Z. Li, T. Liu, “LTP: A Chinese language technology platform”, In Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, pp. 13–16, 2010.
[16] S.R. Chheda, A.K Singh, P.S. Singh, A.S Bhole, “Automated Trading of Cryptocurrency Using Twitter Sentiment Analysis”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.209-214, 2018.
[17] J. Zhu, M. Zhu, Q. Wang, T. Xiao, “Niuparser: A Chinese syntactic and semantic parsing toolkit”, In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: System Demonstrations, pp. 145–150, 2015.
[18] Bing Liu, “Sentiment Analysis and Opinion Mining”, Morgan & Claypool Publishers, May 2012.
[19] Andreas Auinger, Martin Fischer, “Mining consumers’ opinions on the web”, Protègè Ontology Editor, Stanford University, CA/USA, 2008.
[20] Qingyu Zhang, Richard S. Segall, “Web Mining: a Survey of Current Research, Techniques and Software”, Vol.7, No.4, 2008.
[21] Biu Liu, Minqing Hu, J. Cheng, “Opinion Observer: Analyzing and Comparing Opinions on the Web” International World Wide Web Conference Committee (IW3C2), ACM 1-59593-046-9/05/0005, 2005.
[22] Bing Liu, “Sentiment Analysis: A Multi-Faceted Problem”, Vol.25, Issue.3, 2010.
[23] Hyun Duk Kim, K. Ganesan, P Sondhi, C Zhai, “Comprehensive Review of Opinion Summarization”, 2018.
[24] J. A. Balazs, J. D. Vel´asquez, “Opinion mining and information fusion: A survey”, Information Fusion 27 (2016) 95–110.
[25] M. Ganapathibhotla, Bing Liu, “Mining Opinions in Comparative Sentences”, Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 241–248 Manchester, 2008.
[26] Anna Stavrianou and Jean-Hugues Chauchat, “Opinion Mining Issues and Agreement Identification in Forum Texts”, 5 avenue Pierre Mendès-France, 69676 Bron Cedex 2008.
[27] Andrea Esuli, “Automatic Generation of Lexical Resources for Opinion Mining. Models, Algorithms and Applications”, ACM SIGIR Forum, Vol.42, No.2, 2008.
[28] B. Ohana, B. Tierney, “Opinion Mining with SentiWordNet”, 9th. IT&T Conference, Dublin Institute of Technology, Dublin, Ireland, doi:10.21427/D77S56, 2011.
[29] M. Sadegh, R. Ibrahim, Zulaiha Ali Othman, “Opinion Mining and Sentiment Analysis: A Survey”, International Journal of Computer & Technology, Vol.2, No.3, 2012.
[30] S.S. Ansari, T. Diwan, “Survey on Tweet Segmentation and Sentiment Analysis”, International Journal of Computer Sciences and Engineering (IJCSE), ISSN: 2277-3061, Vol.6, Issue.1, 2018

Amanpreet Kaur, Prabhpreet Kaur, "A Survey on Sentiment Analysis and Mining of Opinions using Natural Language Processing Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.719-726, 2018.

Recommendation System predicts or recommends a set of products or items based upon the preference of the user. Recommender systems are utilized in variety of areas including movies, music, news, books search queries in general. This paper focuses on the design and development of a movie recommendation system using the SVD (Singular Value Decomposition) algorithm where we see that how sparse data are in real life situation and thereby predefined strategies such as collaborative or content-based filtering cannot be applied to these data for better results. Our objective is to reduce the sparsity of the data using dimensionality reduction by the SVD algorithm and hence recommend a list of movies based on the given input parameters.

Recommendation System, SVD Decomposition, Netflix, Dimensionality reduction

[1] F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI=
[2] B.M. Sarwar, et, "Application of Dimensionality Reduction in Recommender System—A Case Study," Proc. KDD Workshop on Web Mining for e-Commerce: Challenges and Opportunities (WebKDD), ACM Press, 2000.
[4] Bobadilla, J., Ortega, F., Hernando, A., Gutierrez, A.: Recommender systems survey. Knowledge-Based Systems 46(0), 109–132 (2013)
[5]To view full code visit the following link:
https://github.com/lucifermorningstar1305/machine_learning/blob/master/Codelogicx/Codelogicx/RecommenderSystemFinal. ipynb

Asoke Nath, Adityam Ghosh, Arion Mitra, "Building a Movie Recommendation System using SVD algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.727-729, 2018.

Nowadays Data is one of the important assets for industries in almost all fields. Advances in information technology and its widespread growth in several areas such as business, engineering, medical, and scientific studies are resulting in data explosion. This data explosion created a new problem that, data cannot be handled by traditional techniques. This problem was, therefore, solved through the creation of a new paradigm: Big Data. Big-data refers to massive volume of structured, semi structured and unstructured data that conventional data management methods are incapable of handling. One of the finest and most popular technology available for handling and processing that enormous amount of data is the Hadoop ecosystem[4]. Enterprises are increasingly relying on Hadoop for storing their valuable data and processing it. However, Hadoop is still evolving. Many researchers have found vulnerabilities in Hadoop, which can question the security of the sensitive information that enterprises are storing on it. In order to prevent these concerns from becoming real harms, effective policy and technological measures are required on the part of organizations that uses “Big Data”, as well as for individuals to whom the data relates. It is almost impossible to carry out detailed research into the entire topic of security. In this paper we try to present a big picture of the main problems related to security in a Hadoop ecosystem, along with the possible solutions to those problems proposed by the research community.

Big data; Hadoop; security; privacy

[1] Julio Moreno, Manuel A. Serrano and Eduardo Fernandez-Medina “Article Main Issues in Big Data Security” Future Internet 2016, 8, 44; doi: 10.3390/fi8030044.
[2] B. Saraladevi, N. Pazhaniraja, P. Victer Paul, M.S. Saleem Basha, P. Dhavachelvan “Big Data and Hadoop-A Study in Security Perspective” 2nd International Symposium on Big Data and Cloud Computing (ISBCC’15) doi: 10.1016/j.procs.2015.04.091
[3] Youssef Gahi, Mouhcine Guennoun, and Hussein T. Mouftah “Big Data Analytics: Security and Privacy Challenges” IEEE Symposium on Computers and Communication (ISCC), 2016
[4] Raj R. Parmar, Sudipta Roy, Debnath Bhattacharyya,Samir Kumar Bandyopadhyay, (Senior Member, IEEE), And Tai-Hoon Kim “Large-Scale Encryption in the Hadoop Environment: Challenges and Solutions” IEEE Access, 2017. Digital Object Identifier 10.1109/ACCESS.2017.2700228
[5] YANG Mengke, ZHOU Xiaoguang, ZENG Jianqiu, XU Jianjian,
“Challenges and solutions of information Security issues in the
Age of Big Data” China communications, 2016
[6] Pradeep Adluru, Srikari Sindhoori Datla, Xiaowen Zhang, “Hadoop Eco System for Big Data Security and Privacy” 978-1-4577-1343-9/12/$26.00 ©2015 IEEE
[7] Xianqing Yu, Peng Ning, Mladen A. Vouk, “Enhancing Security of Hadoop in a Public Cloud” 6th International Conference on Information and Communication Systems (ICICS), IEEE 2015
[8] Masoumeh RezaeiJam, Leili Mohammad Khanli, Mohammad Kazem Akbari, “A Survey on Security of Hadoop” IEEE 4th international conference on computer and knowledge engineering, 2014
[9] Yannan Maa, Yu Zhoua, Yao Yua, Chenglei Penga, Ziqiang Wanga, Sidan Dua “A Novel Approach for Improving Security and Storage Efficiency on HDFS” The 6th International Conference on Ambient Systems, Networks and Technologies (ANT 2015) doi: 10.1016/j.procs.2015.05.062
[10] Zichan Ruan , Yuantian Miao, Lei Pan, Nicholas Patterson, Jun Zhang “Visualization of big data security: A Case study on the KDD99 cup data set” Digital Communications and Networks 3 (2017) 250–259 doi.org/10.1016/j.dcan.2017.07.004
[11] Chao YANG, Weiwei LIN, Mingqi LIU “A Novel Triple Encryption Scheme for Hadoop-based Cloud Data Security” Fourth International Conference on Emerging Intelligent Data and Web Technologies, IEEE 2013 DOI 10.1109/EIDWT.2013.80
[12] MATTURDI Bardi, ZHOU Xianwei, LI Shuai, LIN Fuhong “Big Data security and privacy: A review”,China Communications Supplement No.2, 2014
[13] Madhvaraj M Shetty, Manjaiah D.H “Data Security in Hadoop Distributed File System” International Conference on Emerging Technological Trends [ICETT], IEEE 2016
[14] Thu Yein Win,Member IEEE, Huaglory Tianfield, and Quentin Mair, Member IEEE “Big Data Based Security Analytics for Protecting Virtualized Infrastructures in Cloud Computing” IEEE TRANSACTIONS ON BIG DATA, VOL. 4, NO. 1, JANUARY-MARCH 2018
[15] Khairulliza Ahmad Salleha,Lech Janczewski “Technological, organizational and environmental security and privacy issues of big data: A literature review” Procedia Computer Science 100 ( 2016 ) 19 – 28, doi: 10.1016/j.procs.2016.09.119
[16] A RENCI/ National consortium for data science white paper “Security and Privacy in the Era of Big Data” November 2013
[17] C.L.Philip Chen,Chun-Yang Zhang “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data” Information Sciences 275 (2014) 314–347, dx.doi.org/10.1016/j.ins.2014.01.015
[18] Tian, Y. (2017) “Towards the Development of Best Data Security for Big Data”.Communications and Network, 9, 291-301.
[19] Priya P. Sharma, Chandrakant P. Navdeti “Securing Big Data Hadoop: A Review of Security Issues, Threats and Solution” International Journal of Computer Science and Information Technologies, Vol. 5 (2), 2014, 2126-2131
[20] Alfredo Cuzzocrea “Privacy and Security of Big Data: Current Challenges and Future Research Perspectives” Copyright © 2014 ACM 978-1-4503-1583- 8/14/11
[21] Poonam R. Wagh, Amol D. Potgantwar “Providing Security to Data Stored on HDFS Using Security Protocol” International Journal of Scientific Research in Network Security and Communication Volume-5, Issue-4, August 2017, ISSN: 2321-3256
[22] Anitya Kumar Gupta, Srishti Gupta “Security Issues in Big Data with Cloud Computing” International Journal of Scientific Research in Computer Sciences and Engineering, Vol.5, Issue.6, pp.27-32, December (2017) E-ISSN: 2320-7639
[23] Masoumeh RezaeiJam, Leili Mohammad Khanli, Mohammad Kazem Akbari “A Survey on Security of Hadoop”,4th international conference on computer and knowledge engineering (ICCKE) IEEE 2014.

Sathisha M S, K C Ravishankar, "Security and Privacy Issues in Big-data Hadoop: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.730-738, 2018.

In today’s world scenario where security and privacy are the primary concern, the systems that are developed must employ accurate techniques to achieve this. The biometric recognition provides automated verification of individuals based on unique characteristics processed by an individual. The commercial biometric systems are popular and are used extensively, but not restricted to, in the fields of banking services, access secured database, airport surveillance, access control in the boarders etc. Biometric systems are developed based on the physical or behavioural unique characteristics of the individuals. Iris recognition system is the most reliable and accurate, which is grabbing the attention of the researchers now a day. The iris epigenetic patterns are unique, stable and accurate when compared with the other biometric traits. The iris recognition system is a very good research topic in the areas digital image processing, computer vision & pattern recognition. The segmentation or localization is a very crucial stage, because the system’s accuracy highly relies on segmentation. In this paper, detailed state-of-the-art segmentation techniques have been presented.

Iris segmentation, Biometrics, Recognition system, Computer vision

[1] A. Jain, L. Hong, and S. Pankanti, “Biometric identification,” Commun. ACM, vol. 43, no. 2, pp. 90–98, Feb. 2000.
[2] Henry, F.: “On the skin-furrows of the hand”, Nature, p. 605, 1880
[3] Adler, F, H.,Doggart.: “Physiology of the eye”, chapter VI, p.143, 193
[4] Flom, L., Safir, A.: “Iris recognition system”, US patent, 4,641,349, 1987.
[5] Daugman, J, G.: “High confidential visual recognition of persons by a test of statistical indepence”, IEEE Trans. on Patterns Anal. and Machi. Intel., 1993, 15, p. 1148-1161
[6] J. Daugman, “How Iris Recognition Works,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 21–30, Jan. 2004.
[7] R. Y. F. Ng, Y. H. Tay, and K. M. Mok, “A review of iris recognition algorithms,” in 2008 International Symposium on Information Technology, Kuala Lumpur, Malaysia, 2008, pp. 1–7.
[8] M. M. Alrifaee, M. M. Abdallah, and B. G. Al Okush, “A Short Survey of IRIS Images Databases,” Int. J. Multimed. Its Appl., vol. 9, no. 2, pp. 01–14, Apr. 2017.
[9] E. M. Arvacheh, “A Study of Segmentation and Normalization for Iris Recognition Systems,” p. 81.
[10] S. Patil, S. Gudasalamani, and N. C. Iyer, “A survey on Iris recognition system,” in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, 2016, pp. 2207–2210.
[11] S. Jayalakshmi and M. Sundaresan, “A survey on Iris Segmentation methods,” in 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, Salem, 2013, pp. 418–423.
[12] S. V. Sheela and P. A. Vijaya, “Iris Recognition Methods - Survey,” Int. J. Comput. Appl., vol. 3, no. 5, pp. 19–25, Jun. 2010.
[13] A. A. Nithya and D. C. Lakshmi, “Iris Recognition Techniques: A Literature Survey,” p. 15.
[14] S. Kalsoom and S. Ziauddin, “Iris Recognition: Existing Methods and Open Issues,” p. 6, 2012.
[15] N. S. Sarode and A. M. Patil, “Review of Iris Recognition: An evolving Biometrics Identification Technology,” vol. 2, no. 10, p. 7, 2014.
[16] R. P. Wildes, “Iris recognition: an emerging biometric technology,” Proc. IEEE, vol. 85, no. 9, pp. 1348–1363, Sep. 1997.
[17] W. W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Trans. Signal Process., vol. 46, no. 4, pp. 1185–1188, Apr. 1998.
[18] Li Ma, Yunhong Wang, and Tieniu Tan, “Iris recognition using circular symmetric filters,” 2002, vol. 2, pp. 414–417.
[19] L. Ma, T. Tan, Y. Wang, and D. Zhang, “Efficient Iris Recognition by Characterizing Key Local Variations,” IEEE Trans. Image Process., vol. 13, no. 6, pp. 739–750, Jun. 2004.
[20] L. Masek, “Iris Recognition,” The University of Western Australia,2003.
[21] S. A. C. Schuckers, N. A. Schmid, A. Abhyankar, V. Dorairaj, C. K. Boyce, and L. A. Hornak, “On Techniques for Angle Compensation in Nonideal Iris Recognition,” IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 37, no. 5, pp. 1176–1190, Oct. 2007.
[22] V. Dorairaj, N. A. Schmid, and G. Fahmy, “Performance evaluation of iris-based recognition system implementing PCA and ICA encoding techniques,” 2005, p. 51.
[23] D. M. Monro, S. Rakshit, and D. Zhang, “DCT-Based Iris Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 4, pp. 586–595, Apr. 2007.
[24] K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi, and H. Nakajima, “An Effective Approach for Iris Recognition Using Phase-Based Image Matching,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 10, pp. 1741–1756, Oct. 2008.
[25] Zhaofeng He, Tieniu Tan, Zhenan Sun, and Xianchao Qiu, “Toward Accurate and Fast Iris Segmentation for Iris Biometrics,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 9, pp. 1670–1684, Sep. 2009.
[26] S. Pundlik, D. Woodard, and S. Birchfield, “Iris segmentation in non-ideal images using graph cuts,” Image Vis. Comput., vol. 28, no. 12, pp. 1671–1681, Dec. 2010.
[27] D. S. Jeong et al., “A new iris segmentation method for non-ideal iris images,” Image Vis. Comput., vol. 28, no. 2, pp. 254–260, Feb. 2010.
[28] M. Frucci, M. Nappi, D. Riccio, and G. Sanniti di Baja, “WIRE: Watershed based iris recognition,” Pattern Recognit., vol. 52, pp. 148–159, Apr. 2016.
[29] A. Ferone, M. Frucci, A. Petrosino, and G. Sanniti di Baja, “Iris Detection through Watershed Segmentation,” in Biometric Authentication, vol. 8897, V. Cantoni, D. Dimov, and M. Tistarelli, Eds. Cham: Springer International Publishing, 2014, pp. 57–65.
[30] A. Radman, N. Zainal, and K. Jumari, “Fast and reliable iris segmentation algorithm,” IET Image Process., vol. 7, no. 1, pp. 42–49, Feb. 2013.
[31] L. L. Ling and D. F. de Brito, “Fast and Efficient Iris Image Segmentation,” J Med Biol Eng, vol. 30, no. 6, p. 12, 2010.
[32] Tisse, C,L.,Martin, L.,Torres, L., Robert, M.: “Person identification technique using human iris recognition”, vision interface (VI2002), CIPPRS-2002, 15th international conf. on vision interface, p. 294-299.
[33] Y. Chen et al., “A highly accurate and computationally efficient approach for unconstrained iris segmentation,” Image Vis. Comput., vol. 28, no. 2, pp. 261–269, Feb. 2010.
[34] R. Donida Labati and F. Scotti, “Noisy iris segmentation with boundary regularization and reflections removal,” Image Vis. Comput., vol. 28, no. 2, pp. 270–277, Feb. 2010.
[35] Jinyu Zuo and N. A. Schmid, “On a Methodology for Robust Segmentation of Nonideal Iris Images,” IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 40, no. 3, pp. 703–718, Jun. 2010.
[36] V. Kumar, A. Asati, and A. Gupta, “A Novel Edge-Map Creation Approach for Highly Accurate Pupil Localization in Unconstrained Infrared Iris Images,” J. Electr. Comput. Eng., vol. 2016, pp. 1–10, 2016.
[37] F. Jan, I. Usman, and S. Agha, “Reliable iris localization using Hough transform, histogram-bisection, and eccentricity,” Signal Process., vol. 93, no. 1, pp. 230–241, Jan. 2013.
[38] P. Cai and C. Wang, “An Eyelid Detection Algorithm for the Iris Recognition,” Int. J. Secur. Its Appl., vol. 9, no. 5, pp. 105–112, May 2015.
[39] J. Chen, F. Shen, D. Z. Chen, and P. J. Flynn, “Iris Recognition Based on Human-Interpretable Features,” IEEE Trans. Inf. Forensics Secur., vol. 11, no. 7, pp. 1476–1485, Jul. 2016.
[40] W.-K. Kong and D. Zhang, “Detecting Eyelash and Reflection for Accurate Iris Segmentation,” Int. J. Pattern Recognit. Artif. Intell., vol. 17, no. 06, pp. 1025–1034, Sep. 2003.
[41] A. Bendale, A. Nigam, S. Prakash, and P. Gupta, “Iris Segmentation Using Improved Hough Transform,” in Emerging Intelligent Computing Technology and Applications, vol. 304, D.-S. Huang, P. Gupta, X. Zhang, and P. Premaratne, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 408–415.
[42] Q. Tian, Z. Liu, L. Li, and Z. Sun, “A Practical Iris Recognition Algorithm,” in 2006 IEEE International Conference on Robotics and Biomimetics, Kunming, China, 2006, pp. 392–395.
[43] K. M. Ali Alheeti, “Biometric Iris Recognition Based on Hybrid Technique,” Int. J. Soft Comput., vol. 2, no. 4, pp. 1–9, Nov. 2011.
[44] Z. Sun, Y. Wang, T. Tan, and J. Cui, “Improving Iris Recognition Accuracy Via Cascaded Classifiers,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 35, no. 3, pp. 435–441, Aug. 2005.
[45] Z. Zhao and A. Kumar, “An Accurate Iris Segmentation Framework Under Relaxed Imaging Constraints Using Total Variation Model,” in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp. 3828–3836.
[46] T. Z. Khan, P. Podder, and M. F. Hossain, “Fast and efficient iris segmentation approach based on morphology and geometry operation,” in The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014), Dhaka, Bangladesh, 2014, pp. 1–8.
[47] T. Thomas, A. George, and K. P. I. Devi, “Effective Iris Recognition System,” Procedia Technol., vol. 25, pp. 464–472, 2016.
[48] M. Nabti and A. Bouridane, “An effective and fast iris recognition system based on a combined multiscale feature extraction technique,” Pattern Recognit., vol. 41, no. 3, pp. 868–879, Mar. 2008.
[49] A. Dirami, K. Hammouche, M. Diaf, and P. Siarry, “Fast multilevel thresholding for image segmentation through a multiphase level set method,” Signal Process., vol. 93, no. 1, pp. 139–153, Jan. 2013.
[50] S. Kotte, P. Rajesh Kumar, and S. K. Injeti, “An efficient approach for optimal multilevel thresholding selection for gray scale images based on improved differential search algorithm,” Ain Shams Eng. J., Jul. 2016.
[51] S. Dey and D. Samanta, “A Novel Approach to Iris Localization for Iris Biometric Processing,” vol. 1, no. 5, p. 12, 2007.
[52] S. Dey and D. Samanta, “Fast and accurate personal identification based on iris biometric,” Int. J. Biom., vol. 2, no. 3, p. 250, 2010.
[53] T.-H. Min and R.-H. Park, “Eyelid and eyelash detection method in the normalized iris image using the parabolic Hough model and Otsu’s thresholding method,” Pattern Recognit. Lett., vol. 30, no. 12, pp. 1138–1143, Sep. 2009.
[54] R. Y. F. Ng, Yong Haur Tay, and Kai Ming Mok, “An effective segmentation method for iris recognition system,” in 5th International Conference on Visual Information Engineering (VIE 2008), Xi’an, China, 2008, pp. 548–553.
[55] A. Bouaziz, A. Draa, and S. Chikhi, “Artificial bees for multilevel thresholding of iris images,” Swarm Evol. Comput., vol. 21, pp. 32–40, Apr. 2015.
[56] L. Hanfei and C. Jiang, “Toward Multiple Features Template Matching Based on Iris Image Recognition,” presented at the Computer Science and Technology 2015, 2015, pp. 85–88.
[57] M. T. Ibrahim, T. M. Khan, M. A. Khan, and L. Guan, “Automatic segmentation of pupil using local histogram and standard deviation,” presented at the Visual Communications and Image Processing 2010, Huangshan, China, 2010, p. 77442S.
[58] N. F. Soliman, E. Mohamed, F. Magdi, F. E. A. El-Samie, and A. M, “Efficient iris localization and recognition,” Opt. - Int. J. Light Electron Opt., vol. 140, pp. 469–475, Jul. 2017.
[59] N. Zainal, A. Radman, M. Ismail, and J. Nordin, “Iris Segmentation for Non-ideal Images,” J. Teknol., p. 6, 2015.
[60] N. B. Puhan, N. Sudha, and A. Sivaraman Kaushalram, “Efficient segmentation technique for noisy frontal view iris images using Fourier spectral density,” Signal Image Video Process., vol. 5, no. 1, pp. 105–119, Mar. 2011.
[61] Y. Du, N. L. Thomas, and E. Arslanturk, “Multi-level iris video image thresholding,” in 2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, Nashville, TN, USA, 2009, pp. 38–45.
[62] Y. Guo, K. Liu, Q. Wu, Q. Hong, and H. Zhang, “A New Spatial Fuzzy C-Means for Spatial Clustering,” vol. 14, p. 13, 2015.
[63] H. Proença and L. A. Alexandre, “Iris segmentation methodology for non-cooperative recognition,” IEE Proc. - Vis. Image Signal Process., vol. 153, no. 2, p. 199, 2006.
[64] S. A. Sahmoud and I. S. Abuhaiba, “Efficient iris segmentation method in unconstrained environments,” Pattern Recognit., vol. 46, no. 12, pp. 3174–3185, 2013.
[65] P. Li, X. Liu, L. Xiao, and Q. Song, “Robust and accurate iris segmentation in very noisy iris images,” Image Vis. Comput., vol. 28, no. 2, pp. 246–253, Feb. 2010.
[66] U. Kannathasan, “A Human Iris Recognition Using Fuzzy Matching Technique,” vol. 4, no. 6, p. 5, 2013.
[67] T. Tan, Z. He, and Z. Sun, “Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition,” Image Vis. Comput., vol. 28, no. 2, pp. 223–230, Feb. 2010.
[68] N. Reddy, A. Rattani, and R. Derakhshani, “A robust scheme for iris segmentation in mobile environment,” in 2016 IEEE Symposium on Technologies for Homeland Security (HST), Waltham, MA, USA, 2016, pp. 1–6.
[69] K. Misztal, P. Spurek, E. Saeed, K. Saeed, and J. Tabor, “Cross entropy clustering approach to iris segmentation for biometrics purpose,” p. 10.
[70] Y. D. Khan, S. A. Khan, F. Ahmad, and S. Islam, “Iris Recognition Using Image Moments and k-Means Algorithm,” Sci. World J., vol. 2014, pp. 1–9, 2014.
[71] J. Shelton, K. Roy, F. Ahmad, and B. O’Connor, “Iris recognition using Level Set and hGEFE,” in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, CA, USA, 2014, pp. 1392–1395.
[72] Dr. Babasaheb Ambedkar Marathwada University, E. Chirchi, K. Digambar, and Gyanchand Hirachand Raisoni Institute of Engineering & Technology, “Modified Circular Fuzzy Segmentor and Local Circular Encoder to Iris Segmentation and Recognition,” Int. J. Intell. Eng. Syst., vol. 10, no. 3, pp. 182–192, Apr. 2017.
[73] B. H. Shekar and S. S. Bhat, “Multi-patches iris based person authentication system using particle swarm optimization and fuzzy c-means clustering,” ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLII-2/W4, pp. 243–249, May 2017.
[74] S. Rapaka, R. Pullakura, and J. Mandelli, “A New Approach for Non-Ideal Iris Segmentation Using Fuzzy C-Means Clustering Based on Particle Swarm Optimization,” Part. Swarm Optim., p. 4, 2018.
[75] M. Vatsa, R. Singh, and P. Gupta, “Comparison of iris recognition algorithms,” in International Conference on Intelligent Sensing and Information Processing, 2004. Proceedings of, Chennai, India, 2004, pp. 354–358.
[76] M. Vatsa, R. Singh, and A. Noore, “Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing,” IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 38, no. 4, pp. 1021–1035, Aug. 2008.
[77] J. Daugman, “New methods in iris recognition.,” IEEE Trans. Syst. Man Cybern. Part B Cybern. Publ. IEEE Syst. Man Cybern. Soc., vol. 37, no. 5, pp. 1167–1175, 2007.
[78] K. Roy and P. Bhattacharya, “Nonideal Iris Recognition Using Level Set Approach and Coalitional Game Theory,” in Computer Vision Systems, vol. 5815, M. Fritz, B. Schiele, and J. H. Piater, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 394–402.
[79] K. Roy, P. Bhattacharya, and C. Y. Suen, “Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs,” Eng. Appl. Artif. Intell., vol. 24, no. 3, pp. 458–475, Apr. 2011.
[80] K. Roy, P. Bhattacharya, and C. Y. Suen, “Unideal Iris Segmentation Using Region-Based Active Contour Model,” in Image Analysis and Recognition, vol. 6112, A. Campilho and M. Kamel, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 256–265.
[81] R. Chen, X. R. Lin, and T. H. Ding, “Iris segmentation for non-cooperative recognition systems,” IET Image Process., vol. 5, no. 5, p. 448, 2011.
[82] S. Shah and A. Ross, “Iris Segmentation Using Geodesic Active Contours,” IEEE Trans. Inf. Forensics Secur., vol. 4, no. 4, pp. 824–836, Dec. 2009.
[83] M. A. M. Abdullah, S. S. Dlay, and W. L. Woo, “Fast and accurate method for complete iris segmentation with active contour and morphology,” in 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings, Santorini Island, Greece, 2014, pp. 123–128.
[84] M. A. M. Abdullah, S. S. Dlay, W. L. Woo, and J. A. Chambers, “Robust Iris Segmentation Method Based on a New Active Contour Force With a Noncircular Normalization,” IEEE Trans. Syst. Man Cybern. Syst., vol. 47, no. 12, pp. 3128–3141, Dec. 2017.
[85] S. Jamaludin and N. Zainal, “Comparison of Iris Recognition between Active Contour and Hough Transform,” vol. 8, no. 4, p. 6.
[86] Y. Yan, L. An, and Q. Wang, “Heterogeneous Iris Segmentation Based on Active Contour Model and Prior Noise Characteristics,” in Proceedings of the International Conference on Internet Multimedia Computing and Service - ICIMCS’16, Xi’an, China, 2016, pp. 298–301.
[87] A. Hilal, B. Daya, and P. Beauseroy, “Hough Transform and Active Contour for Enhanced Iris Segmentation,” vol. 9, no. 6, p. 10, 2012.
[88] A. Hilal, P. Beauseroy, and B. Daya, “Elastic strips normalisation model for higher iris recognition performance,” IET Biom., vol. 3, no. 4, pp. 190–197, Dec. 2014.
[89] Z.-C. Li, J.-P. Qiao, B.-S. Li, and H.-L. Wan, “Non-ideal iris segmentation using anisotropic diffusion,” IET Image Process., vol. 7, no. 2, pp. 111–120, Mar. 2013.
[90] B. O. Connor and K. Roy, “Iris Recognition Using Level Set and Local Binary Pattern,” Int. J. Comput. Theory Eng., vol. 6, no. 5, pp. 416–420, Oct. 2014.
[91] X. Zhang, Z. Sun, and T. Tan, “Texture removal for adaptive level set based iris segmentation,” in 2010 IEEE International Conference on Image Processing, Hong Kong, Hong Kong, 2010, pp. 1729–1732.
[92] S. Rapaka and P. R. Kumar, “Efficient approach for non-ideal iris segmentation using improved particle swarm optimisation-based multilevel thresholding and geodesic active contours,” IET Image Process., Apr. 2018.
[93] R. H. Abiyev and K. Altunkaya, “Personal Iris Recognition Using Neural Network,” Int. J. Secur. Its Appl., vol. 2, no. 2, p. 10, 2008.
[94] P. Wild, H. Hofbauer, J. Ferryman, and A. Uhl, “Quality-based iris segmentation-level fusion,” EURASIP J. Inf. Secur., vol. 2016, no. 1, Dec. 2016.
[95] Muhammad Arsalan et al., “Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment,” Symmetry, vol. 9, no. 11, p. 263, Nov. 2017.
[96] D. Nguyen, K. Kim, H. Hong, J. Koo, M. Kim, and K. Park, “Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction,” Sensors, vol. 17, no. 3, p. 637, Mar. 2017.
[97] Jong Kim, Hyung Hong, and Kang Park, “Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors,” Sensors, vol. 17, no. 5, p. 1065, May 2017.
[98] N. Liu, M. Zhang, H. Li, Z. Sun, and T. Tan, “DeepIris: Learning pairwise filter bank for heterogeneous iris verification,” Pattern Recognit. Lett., vol. 82, pp. 154–161, Oct. 2016.
[99] A. Gangwar and A. Joshi, “DeepIrisNet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition,” in 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 2016, pp. 2301–2305.
[100] N. Liu, H. Li, M. Zhang, Jing Liu, Z. Sun, and T. Tan, “Accurate iris segmentation in non-cooperative environments using fully convolutional networks,” in 2016 International Conference on Biometrics (ICB), Halmstad, Sweden, 2016, pp. 1–8.

R. Satish, P. Rajesh Kumar, "State-of-the art iris segmentation methods: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.739-748, 2018.

Being an ancient geographical practice agriculture can be enormously benefited by the application of GIS. For collection of spatially referenced data, to perform spatial analysis, decision making and application of variable rate treatment and specific farming systems may utilize GIS and several related technologies such as global positioning system (GPS), continuous yield sensors, remote sensing (RS) instruments, receivers, etc. These advanced technologies offer numerous advantages to generate & synthesize new information related to agriculture cheaply and quickly; document data source & methods of integration provide diagnostics for error detection and accuracy assessments, prepare maps and tables for proper and various types of cultivation. But due to our lack of knowledge of statistical methods for summarizing spatial patterns, difficulty of moving geographical data and model results between different scales, and cost and difficulty of field validation, we are unable to access the data and maps. This problem can be overcome through the use of GIS and related technologies and this can help us to improve the cultivation or agricultural system in the Darjeeling hills. The main objectives of sustainable agriculture follows: balancing the inherent land resource with crop requirements, optimization of resource use towards achievement of sustained productivity over a long time.

Geographic Information System(GIS) , Remote Sensing(RS), Geographical Positioning System(GPS), Precision Farming(PF)

[1] Auernhammer, H., “Precision farming-the environmental challenge” National Journal of Computer Electronics.,Vol.1 Issue. 30,pp. 41-33.
[2] Casady, W.W. and H. L. Palm,” Precision agriculture: Remote sensing and ground truthing”,2002
[3] Zhao, D., K.R. Reddy, V.G. Kakani and V.R. Reddy, “Nitrogen deficiency effects on plant growth, leaf photosynthesis and hyperspectral reflectance properties of sorghum”. Vol.-22,pp: 391-403.2002.
[4] Kunal Sood, Sharda Singh, Ranbir Singh Rana, Aditya Rana,Vaibhav Kalia, Arun Kaushal,” Application of GIS in precision agriculture” In the Proceedings of the2005 National Seminar, on Precision Farming technologies for high Himalayas,india,pp 542-545,2015.
[5] Singh, D., Sao, R. and Singh, K.P. ” A remote sensing assessment of pest infestation on sorghum”. Adv. Space Res., 39: pp.155-163,2007
[6] Smart, D.R., M.L. Whiting and C. Stockert. “Remote sensing of grape K deficiency symptoms using leaf level hyperspectral reflectance”. In the Proceedings of Western Nutrient Management Conference, 2007, Salt Lake City, UT., pp: 19-24.
[7] Tao, C.V.” Semi-automated object measurement using multipleimage matching from mobile mapping image sequences, Photogrammetric Engineering and Remote Sensing”,Vol.66, Issue-12,2000.
[8] J.V., G.R. Bock and J.A. Goode (Eds.).Chichester, Wiley “Modelling for Precision Weed Management. In: Precision Agriculture: Spatial and Temporal Variability of Environmental Quality”, pp: 182-204.
[9] Barnes, E.M. and M.G. Baker,” Multispectral data for mapping soil texture: Possibilities and limitations”.Vol.1 Issue. 16,pp. 731-741,2007
[10] Cooper, R.D., T. Mccarthy, and J. Raper,” Airborne videography and GPS”, Vol.-4 Issue.-11 pp:53-55

J. Loha, S. Das, "Function & Application of GIS in Precision Agriculture at Darjeeling Hill," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.749-752, 2018.

In current scenario, character recognition is the most important field of pattern recognition because of its application in numerous fields. Optical Character Recognition (OCR) and Handwritten Character Recognition (HCR) has specific domain to use. OCR system is most fitted for the applications like multi selection examinations, written communication address resolution etc. In returning days, character recognition system would possibly function a key issue to make paperless setting by digitizing and process existing paper documents. During this paper, we have planned the detail study on existing strategies for hand written character recognition based on ANN. This paper presents an in depth review within the field of handwritten Character Recognition.

HCR, Features, classification, Optical Character Recognition

[1]. U. Pal, B. B. Chaudhuri, ``Indian Script Character recognition: A survey``, Pattern Recognition, vol. 37, pp. 1887-1899, 2004.
[2]. Dholakia, K., A Survey on Handwritten Character Recognition Techniques for various Indian Languages, International Journal of Computer Applications, 115(1), pp 17–21, 2015.
[3]. Nafiz Arica, Fatos T. Yarman-Vural, “An Overview of Character Recognition Focused On Off-line Handwriting”, C99-06-C-203, IEEE, 2000.
[4]. R. J. Ramteke, S. C. Mehrotra, “Recognition of Handwritten Devnagari Numerals”, International Journal of Computer Processing of Oriental Languages, 2008.
[5]. Harikesh Singh, R. K. Sharma, “Moment in Online Handwritten Character Recognition”, National Conference on Challenges & Opportunities in Information Technology (COIT-2007) Gobindgarh. March 23, 2007.
[6]. Aggarwal, A., Rani, R. and Dhir, R. (2012), “Handwritten Devanagari character recognition using Gradient features”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, 5, pp. 85-90.
[7]. Shah, M. and Jethava, G. B. (2013), “A literature review on handwritten character recognition”, International Journal of Indian Streams Research Journal, Vol. 3, 2, pp. 1-19.
[8]. Das, N., Das, B., Sarkar, R., Basu, S., Kundu, M. and Nasipuri, M. (2010), “Handwritten BanglaBasic and Compound characterrecognition using MLP and SVM classifier”, Journal of Computing, Vol. 2, 2, pp. 109- 115.
[9]. Vaidya, S.A. and Bombade, B.R. (2013), “A Novel Approach of Handwritten Character Recognition using Positional Feature Extraction”, International Journal of Computer Science and Mobile Computing, Vol. 2, 6, pp. 179-186.
[10]. Bag, S., Bhowmick, P., Harit, G., 2011. Recognition of bengali handwritten characters using skeletal convexity and dynamic programming, in: Emerging Applications of Information Technology (EAIT), 2011 Second International Conference on, pp. 265–268.
[11]. Das, N., Sarkar, R., Basu, S., Saha, P.K., Kundu, M., Nasipuri, M., 2015. Handwritten bangla character recognition using a soft computing paradigm embedded in two pass approach. Pattern Recognition 48, 2054 – 2071.
[12]. Sarkhel, R., Das, N., Saha, A.K., Nasipuri, M., 2016. A multi-objective approach towards cost effective isolated handwritten bangla character and digit recognition. Pattern Recognition 58, 172–189.
[13]. Das, N., Basu, S., Sarkar, R., Kundu, M., Nasipuri, M., Basu, D., 2009. Handwritten bangla compound character recognition: Potential challenges and probable solution, in: IICAI, pp. 1901–1913.
[14]. J. Pradeepa, E. Srinivasana, S. Himavathib, "Neural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten", International journal of Engineering,Vol.25, No. 2, pp. 99-106, May 2012.
[15]. Ashutosh Aggarwal, Rajneesh Rani, RenuDhir, "Handwritten Devanagari Character Recognition Using Gradient Features", International Journal of Advanced Research in Computer Science and Software Engineering (ISSN: 2277-128X), Vol. 2, Issue 5, pp. 85- 90, May 2012.
[16]. Imaran Khan Pathan, Abdulbari Ahmed Bari Ahmed Ali, Ramteke R.J., "Recognition of offline handwritten isolated Urdu character ", International Journal on Advances in Computational Research, Vol. 4, Issue 1, pp. 117-121, 2012
[17]. Pritpal Singh, Sumit Budhi raja, “Handwritten Gurumukhi Character Recognition using Wavelet Transforms”, International Journal of Electronics, Communication & Instrumentation Engineering Research and Development, Vol. 2, Issue 3, pp. 27-37, Sept 2012.
[18]. Pal U, Roy RK, Roy K, Kimura F (2012) Multi-lingual city name recognition for Indian postal automation. In: 2012 International conference on frontiers in handwriting recognition (ICFHR), pp 169–173, 18–20 Sep 2012. ISBN: 978-1-4673-2262-1. doi:10. 1109/ICFHR.2012.238
[19]. Pal S, Alireza A, Pal U, Blumenstein M (2012) Multi-script offline signature identification. In: 12th International conference on hybrid intelligent systems (HIS), pp 236–240, 4–7 Dec 2012. ISBN 978-1-4673-5114-0. doi:10.1109/HIS.2012.6421340
[20]. Wshah S, Kumar G, Govindaraju V (2012) Multilingual word spotting in offline handwritten documents. In: 21st International conference on pattern recognition (ICPR), pp 310–313, 11–15 Nov 2012. INSPEC Accession Number: 13324552, ISSN 1051-4651
[21]. Rajput GG, Anita HB (2012) Handwritten script recognition using DCT, Gabor filter and wavelet features at line level. In: Book title: soft computing techniques in vision science, pp 33– 43. ISBN 978-3-642-25506-9. doi:10.1007/978-3-642-25507-6_4
[22]. Wu X, Tang Y, Bu W (2014) Offline text-independent writer identification based on scale invariant feature transform. IEEE Trans Inf Forensics and Secur 526–536. ISSN 1556-6013. doi:10.1109/TIFS.2014.2301274
[23]. Chaudhuri BB, Bera S (2009). Handwritten text line identification in Indian scripts. In: 10th International conference on document analysis and recognition, 2009. ICDAR ‘09, pp 636–640, 26–29 July 2009. ISBN 978-1-4244-4500-4. INSPEC Accession Number: 10904634
[24]. Fiaz Hussain, John Cowell. "Character Recognition of Arabic and Latin Scripts," iv, p. 51, Fourth International Conference on Information Visualisation, 2000.
[25]. Anbumani Subramanian and Bhadri Kubendran, “Optical Character Recognition of Printed Tamil Characters”, Department of Electrical and Computer engineering, Virginia Tech, Blacksburg. December 10, 2000.
[26]. K. Gaurav and Bhatia P. K., “Analytical Review of Preprocessing Techniques for Offline Handwritten Character Recognition”, 2nd International Conference on Emerging Trends in Engineering & Management, ICETEM, 2013.
[27]. Salvador España-Boquera, Maria J. C. B., Jorge G. M. and Francisco Z. M., “Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 4, April 2011.
[28]. Sandhya Arora, “Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition”, IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA, December 2008.
[29]. M. Hanmandlu, O.V. Ramana Murthy, “Fuzzy model based recognition of handwritten numerals”, pattern recognition, vol.40, pp.1840-1854, 2007.
[30]. Vedgupt Saraf, D.S. and Rao, 2013. “Devnagari script character recognition using genetic algorithm for better efficiency”, IJSCE, ISSN: 2231-2307, Volume-2, Issue-4, April 2013.
[31]. I.K. Sethi, "Machine Recognition of Constrained Hand Printed Devanagari", Pattern Recognition, Vol. 9, 1977, pp. 69-75.
[32]. E.Tautu & F.Leon , “Optical Character Recognition System Using Support Vector Machines ” 2012 PP -1-13 , 2012.

Rajdeep Singh, Rahul Kumar Mishra, S.S. Bedi, Sunil Kumar, Arvind Kumar Shukla, "A Literature Review on Handwritten Character Recognition based on Artificial Neural Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.753-758, 2018.