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In present society, busy life has influenced individuals to overlook numerous things in everyday life. The elderly individuals need to take their medicines on time without missing. This project depends on IOT. The project is a smart medication system for elderly individual that reminds to take their solution. It additionally recognizes to the guardian whether medicine is taken or not. At a specific time set by the guardian through the application, the medication box reminds elderly by a sound yield. Moreover, to recognize the tablet to be taken at a specific time, LED lights will flicker in the right rack of medication box. These activities gets rehashed thrice a day i.e., morning, evening and night. The guardian gets notification from the application about the status of elderly that is whether elderly took medicine or not. The guardian can change the alert timings of pill box through the application. The guardian can likewise set the updates about any appointments of hospital which is appropriate for elderly. He can likewise observe the medicine history of elderly in the application.

LED lights, Prompt, medication box, guardian mobile

[1] International Conference on Information Security & Privacy (ICISP2015), 11-12 December 2015,Nagpur, INDIA .Medicine Reminder and Monitoring System for Secure Health Using IOT. Samir V. Zanjal, Girish. R. Talmale b.
[2] Smart Phone Based Medicine In-take Scheduler, Reminder and Monitor.John K. Zao (SMIEEE), Mei-Ying Wang Peihsuan Tsai, Jane W.S. Liu (FIEEE).
[3] Design and Evaluation of a Multimodal mHealth based Medication Management System for Patient Self Administration. Günter Schreier, Senior Member, IEEE, Mark Schwarz, Robert Modre-Osprian, Peter Kastner, Daniel Scherr, and Friedrich Fruhwald.
[4] Pill Dispenser with Alarm via Smart Phone Notification. Nurmiza Binti Othman1 and Ong Pek Ek2.
[5] RFID Technology for IoT-Based Personal Healthcare in Smart Spaces Sara Amendola, Rossella Lodato, Sabina Manzari, Cecilia Occhiuzzi, and Gaetano Marrocco.
[6] Smart Drug Kit Development under Pharmaceutical Services. Daih-Huang Kuo1.
[7] A Health-IoT Platform Based on the Integration of Intelligent Packaging, Unobtrusive Bio-Sensor and Intelligent Medicine Box. Geng Yang, Li Xie, Matti Mäntysalo, Xiaolin Zhou, Member, IEEE, Zhibo Pang, Li Da Xu, Sharon Kao-Walter, Qiang Chen, Lirong Zheng, Senior Member, IEEE.
[8] A Touchscreen-Equipped Medicine Case as a Medical Interface for Assisting an Elderly Person in Medication Management. Takuo Suzuki, Student Member, IEEE, Yuta Jose, and Yasushi Nakauchi, Member, IEEE
[9] iCare: A Mobile Health Monitoring System for the Elderly Ziyu Lv, Feng Xia, Guowei Wu, Lin Yao, Zhikui Chen.
[10] A Smart Pill Box with Remind and Consumption Confirmation Functions Huai-Kuei Wu1, Member, IEEE, Chi-Ming Wong2, Pang-Hsing Liu1, Sheng-Po Peng1, Xun-Cong Wang1, Chih-Hi Lin1 and Kuan-Hui tul.
[11] A Study on Architectures for Embedded Devices
Arun Radhakrishnan and T.Muralikrishna International Journal of Computer Science and Engineering

M. A. Waheed, Pooja P.G., Hashmathunnisa Begum, "Smart Medication System For Elderly Person," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.637-641, 2018.

Agriculture is the backbone of Indian economy. Sunflower is one of the most important oil seed crops grown in temperate countries and India is one of the largest producers of oil seeds in the world. The farmers can determine which type of crops to be cultivated in a particular place with the help of the soil condition analysis. The valuable knowledge is extracted from the agricultural data set with the help of data mining techniques. The farmers can make use of the technology and the right techniques; they can make agriculture a profitable enterprise. Salem district is one of the largest districts in Tamil Nadu, India and it is famous for mango cultivation This paper analyzes whether the Salem district soil is suitable for the cultivation of sunflower crop with the help of data mining classification techniques.

Agriculture Soil, Bayes Net, Random Forest, J48

[1] Mucherino.A, Petraq Papajorgji and P.M.Pardalos, “A survey of data mining techniques applied to agriculture”. Published online 2009 © Springer-verlag.
[2] G.M. Nasira , N. Hemageetha, “Vegetable price prediction using data mining classification technique” Preceedings of the International Conference on pattern Recognition, Informatics and Medical Engineering (PRIME 2012), PP. 99-102 ISBN No:978-1-4673-1038-3. © 2012 IEEE.
[3] N.Hemageetha, Dr. G.M. Nasira ,” Vegetable Price Prediction using Adaptive Neuro-Fuzzy Inference System”, International Journal of Computer Sciences and Engineering IJCSE E- ISSN: 2347-2693 Vol-4 Issue -3 June 2017, pp 75-79.
[4] R.Santhi et at (2014) ,GIS based Soil map for salem district of Tamilnadu. TechInical Folder, TNAU,Coimbatore.
[5] Natesan et at(2007),. Technical Bulletinon “Soil test crop response based fertilizer prescription for different soils and crops in tamil nadu” ,AICRP-STCR TamilNadu Agricultural University, Coimbatore.
[6] N. Hemageetha, Dr. G.M. Nasira Analysis of Soil condition Based on pH value Using Classification Techniques IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 6, Ver. III (Nov.-Dec. 2016), PP 50-54
[7] N. Hemageetha ,Dr.G.M. Nasira , “Availability of Macro Nutrients Status in Salem District Soil using DataMining Classification Techniques “International Journal Of Control Theory And Applications ISSN: 0974-5572 9(40),2016, pp:57-66
[8] Durga karthik , K.vijayarekha, Simple and quick classification of soil for sunflower cultivation using data mining algorithm , International journal of chemtech research. Vol .7,No.6.,PP 2601-2605 2014-15.
[9] N.Hemageetha, Dr. G.M. Nasira , “Analysis of the Soil data Using Classification Techniques for Agricultural Purpose, International Journal of Computer Sciences and Engineering IJCSE E-ISSN: 2347-2693 Vol-4 Issue -6 June 2016
[10] N. Hemageetha ,Dr.G.M. Nasira , “Classification of Soil Type in Salem District using J48 Algorithm, “International Journal Of Control Theory And Applications ISSN: 0974-5572 9(40),2016 pp 33-41

N. Hemageetha, N. Nagalakshmi, "Classification Techniques in Analysis of Salem District Soil condition for Cultivation of Sunflower," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.642-646, 2018.

Whole the planet is international village currently on a daily basis, this is often a bit like a dream return true. In nineteenth and up to mid-20th century communication was primarily based mostly based on postal and telegram. Invention of radio, TV and telephone was developing within the field of communication. Globalization is just attainable because of satellite communication. Little or no to take advantage of total population is computer literate. Since the age of Sh. Rajiv Gandhi, numerous makes an attempt are created to market computer acquisition. It`s up to some. We tend to square measure largest hardware producer in world. Bangalore is understood as Silicon Valley. Aloof from lucent image their square measure some areas wherever computer is simply sort of a TV. I belong to Haryana one in every of the superior state of the nation in per capita financial gain, acquisition rate, human right index. However, computer literacy isn`t shows there. This paper is especially centered on computer and digital literacy among the gram panchayat representatives. It had been not as shining at we tend to thought call centers out sourcing centers at NCR. Most of the present time schemes of central and state government are operated the data communication. I attempted my uttermost in assortment and interpretation of information and genuineness. There`s giant scope of improvement in implementation of digital acquisition to a layperson i .e. Manage labor, farmers, panchayat Representative, dairy farm farmers.

ICT, Gram Panchayats, Literacy, Computer, Globalization, Rural, Smart Village

[1]. Atul Kumar Sinha; Abhay Kumar Singh, eds. (2007), Udayana New Horizons in History, Classics and Inter-Cultural studies, Anamika Publishers, ISBN 81-7975-168-6
[2]. "District Census 2011". Census2011.co.in. 2011. Retrieved 2011-09-30.
[3]. http://esaharyana.gov.in/Data/StateStatisticalAbstract/StatisticalAbstract(2011-12).pdf
[4]. NIDM, National Disaster Risk Reduction Portal - Haryana (PDF), National Institute of Disaster Management (MHA, GOI) "Digital India campaign: Panchkula comes out on top among all districts of Haryana",The Indian Express, 26 December 2015
[5]. "Digital India Week: Digital Locker, MyGov.in, and other projects that were unveiled", The Indian Express, 5 July 2015
[6]. Saugata, B., and Masud, R.R. (2007). Implementing E-Governance Using OECD Model(Modified) and Gartner Model (Modified) Upon Agriculture of Bangladesh. IEEE. 1-4244-1551-9/07.
[7]. Garson, D.G. (2006). Public Information Technology and E-Governance. Sudbury, MA: Jones and Bartlett Publishers.
[8]. Rossel, Pierre, and Matthias Finger. “Conceptualizing e-Governance.” Management (2007) : 399-407.
[9]. Miller, W & Walling, J, (2013). "Government in the twenty-first century: New Avenues of Study". Taking Sides. New York, NY: McGraw Hill.
[10]. https://en.wikipedia.org/wiki/Gram_panchayat
[11]. Sharma, Suresh K (2006). Haryana: Past and Present. New Delhi: Mittal Publications. p. 763. ISBN 81-8324-046-1. Retrieved July 11, 2012.
[12]. Khanna, C. L. (2008). Haryana General Knowledge. Agra: Upkar Prakashan. p. 75. ISBN 81-7482-383-2. Retrieved July 11, 2012.
[13]. Yadav, Ram B. (2008). Folk Tales & Legends of Haryana. Gurgaon: Pinnacle Technology. p. 305. ISBN 81-7871-162-1. Retrieved July 11, 2012.
[14]. Mittal, Satish Chandra (1986). Haryana, a Historical Perspective. New Delhi: Atlantic Publishers & Distributors. p. 183. Retrieved July 11, 2012.
[15]. Singh, Mandeep; Kaur, Harvinder (2004). Economic Development Of Haryana. New Delhi: Deep and Deep Publications. p. 234. ISBN 81-7629-558-2. Retrieved July 11,2012.
[16]. Gandhi, Mahatma (1977). Gandhiji and Haryana: A collection of his speeches and writings pertaining to Haryana. Usha Publications. p. 158. Retrieved July 11, 2012.
[17]. Phadke, H. A. (1990). Haryana, ancient and medieval. Harman Publishing House. p. 256. ISBN 81-85151-34-2. Retrieved July 11, 2012.
[18]. Singh, Chattar (2004). Social and economic change in Haryana. National Book Organisation. p. 252. ISBN 81-87521-10-4. Retrieved July 11, 2012.
[19]. Yadav, Kripal Chandra (2002). Modern Haryana: History and culture, 1803–1966. Manohar Publishers & Distributors. p. 320. ISBN 81-7304-371-X. Retrieved July 11, 2012.
[20]. Rai, Gulshan (1987). Formation of Haryana. B.R. Publishing Corporation. p. 223. ISBN 81-7018-412-6. Retrieved July 11, 2012.

Suresh Kumar, Vijay Athavale, "Role of ICT in Grassroots: A Review on Gram Panchayats of District Hisar in Haryana," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.647-651, 2018.

Mobile Ad-hoc Networks (MANETs) is an autonomous system of mobile nodes connected by wireless links; the mobile nodes are available to run in any direction. MANETs are usually formed without any significant infrastructure also its depended on opened queuing network. As a result, they are almost exposed to the traffic observer that target damage, who try to block the data packets by compromising nodes and trace the data transmission direction. Therefore, detecting the edge node is an essential part in MANETs. It is easy to observe a node that induces edge node activity in other nodes, but very difficult to identify a node which is passively observing and misusing network data. In this work, we propose stack optimization based on Edge Node Detecting through Piping Queue (ENDPQ) for Secure Data Transmission in MANETs. Also, a novel approach using node duplication method and two hop neighbor discovery method using which the location of the node can be verified. The source node performs two-hop neighbor discovery to collect the neighbor nodes and perform node duplication method to ascertain the location of the node being selected to route the packet. The proposed plan reduces the overhead introduced by verification procedure and increases the network performance.

Piping, Traffic Analysis, Edge node, Neighbor Node, Queuing Network

[1]. Adams, ‘Active Queue Management: A Survey,` IEEE Communications Surveys & Tutorials, third Quarter, vol. 15, no. 3, pp. 1425-76, 2013.
[2]. Ahammed & Banu, R, ‘Analyzing the Performance of Active Queue Management Algorithm,` International Journal of Computer Networks and Communications (IJCNC), vol. 2, no. 2, 2010.
[3]. Dana & Malekloo, A, ‘Performance comparison between active and passive queue management,` International Journal of Computer Science Issues, vol. 7, issue. 3, no. 5, pp. 13 – 17, 2010.
[4]. Ganesh Sally, McClean, Gaurav & Raina, ‘Drop Tail and RED Queue Management with Small Buffers: Stability and HOPF Bifurcation,` ICTACT Journal on Communication Technology, vol. 2, no. 2, pp. 339-344, 2011.
[5]. Wang, Wang, X. Chen, S. Ci, "Stochastic queue modeling and key design metrics analysis for solar energy powered cellular networks," Proc. ICNC, pp. 472-477, Feb. 2014.
[6]. Yoshigoe, "Threshold-based exhaustive round-robin for the cicq switch with virtual crosspoint queues," IEEE International Conference on Communications, pp. 6325-6329, June 2007.
[7]. Kiruthiga, Raj & EGDP, ‘Survey on AQM Congestion Control Algorithm,` International Journal of Computer Science and Mobile Applications, vol. 2, no. 2, pp. 38-44, 2014.
[8]. Tamir, Frazier, "Dynamically-Allocated Multi-Queue Buffers for VLSI Communication Switches," IEEE Trans. Computers, vol. 41, pp. 725-737, June 1992.
[9]. Li, Wang, H, ‘Study of Active Queue Management Algorithms Towards stabilize and high link utilization,` communication magazine IEEE, 2002.
[10]. Saleh, Dong, "Comparing FCFS & EDF scheduling algorithms for real-time packet switching networks," International Conference on Networking Sensing and Control IEEE, pp. 698-703, 2010.
[11]. Aggeliki Sgora, Student Member, IEEE, and Dimitrios D. Vergados, Member, ‘Handoff Prioritization and Decision Schemes in Wireless Cellular Networks’: Quarter 2009.
[12]. Fei Yu; Leung, V.C.M., "A framework of combining mobility management and connection admission control in wireless cellular networks," in the Proc. of IEEE International Conference on Communications, (ICC 2001), Vol.7, pp.lI-14, June 2017.
[13]. Huang, Generalized Pollaczek-chinchin Formula for Queueing Systems with Markov Modulated Services Rates: diss, The Chinese University of Hong Kong, 2013.
[14]. Geoffrey, Hoefler, "Adaptive routing strategies for modern high-performance networks," 16th Annual IEEE Symposium on High Performance Interconnects, pp. 165-172, 26-28 August 2008.
[15]. Indurkar, Kulkarni, "Congestion control in wireless sensor networks: A survey," International Journal of Engineering Research and Applications, vol. 1, no. 4, pp. 109-113, 2014.
[16]. Ramos, "Stochastic Models in Queueing Theory: J. Medhi Academic Press New York 1991", Applied Mathematical Modelling, vol. 17, no. 5, pp. 280, 1993.
[17]. Huang, Lee, "Generalized Pollaczek-Khinchin formula for Markov channels," IEEE Transactions on Communications, vol. 61, no. 8, pp. 3530-3540, 2013.
[18]. QoS Scheduling and Queueing on Catalyst 3550 Switches, [online] Available: http: //www.cisco.com/c/enlus/support/docs/lan-switching/lan-quality-of-service/24057- 2006.
[19]. Guay, Bogdanski, “vFtree - A Fat-Tree Routing Algorithm Using Virtual Lanes to Alleviate Congestion," IEEE International Symposium on Parallel and Distributed Processing, pp. 197-208, 16-20 May 2011.
[20]. Dhillon, Ganti, F. Baccelli, Andrews, "Modeling and analysis of K-tier downlink heterogeneous cellular networks," IEEE J. Sel. Areas Commun., vol. 30, no. 3, pp. 550-560, 2012.

G.K. Vijaya, S. Bharathidass, "Edge Node Detection For Better Data Transmission Under The Queuing Network Using Pipelining In Mobile AD HOC Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.652-659, 2018.

The Wireless sensor is an improved technology for gathering highly sensitive information. Wireless sensor devices are used to gather this information with the help of sensing nodes. These devices are used in various fields but the rapid consumption of energy in wireless network and its usage is still a challenge for researchers. This paper has been designed to provide the security to user credentials as well as private key distribution for the data transmission of secure. We are proposing a new algorithm in this research paper to trim down the consumption of energy as well as to provide security in the network. It will improve the lifespan of the network.

Wireless sensor network (WSN), cluster head, authentication, Homomorphic algorithm, energy consumption, LEACH

[1]. Geetha D. Devanagavi, N. Nalini .”Trusted Neighbors Based Secured Routing Scheme Using Agents.” Springer transactions on routing algorithm,014-1704-4. 2014.
[2]. Monia , Sushma Jain ,Sukhchandan randhava .” An Efficient Trust Management Algorithm in Wireless Sensor Network .”Springer 0287-8_26. 2016
[3]. Mukesh Kumar and Kamlesh Dutta.“.A Survey of Security Concerns in Various Data Aggregation Techniques in Wireless Sensor Networks.” Springer India . 2009.
[4]. Vinod Kumar, Surinder Singh, N.P. Pathak .”Various trusted and reputation models wireless sensor networks.” Springer Science ,1144-4. 2015
[5]. Weidong Fang , Chuanlei Zhang , Shi Qing Zhao. “Evaluation system and reputation Beta-based Trust in WSN” 1084-8045 Published by Elsevier Ltd. 2015
[6]. X Anita , M.A. Bhagyaveni. “Collaborative Lightweight Trust Management Scheme.” Published in Springer Science ,014-1998-2. 2014
[7]. Yun Liu, Qing-An Zeng. “.Improved trust management scheme for secure data aggregation” .Springer publications 015-0078-6. 2015
[8]. Yannis Stelios, Sitiris Maniatis, Helen C. Leligoug. “.Routing based Distributed Energy-Aware Trust Management System in Wireless Sensor Networks”. Institute for Computer Sciences and Telecommunication 85-92. 2016
[9]. Rong Fan, Ling-Di Ping, Jian-Qing Fu , Xue-Zeng.” Two tired architecture of WSN.” Pan College of Computer Science and Technology Zhejiang University 5626600.2010
[10]. Gun-Wook Choi . “paired base key distribution scheme in wireless sensor network.”IEEE publication 10.1109. 2017
[11]. Karim ZKIK Ghizlane ORHANOU Said EL HAJJI .” Secure framework using ECC”.10.1109/ICEngTechnol.2017.8308144 .2017
[12]. longzhe Han , Xin Song, Yi Buz .” province key laboratory of water information cooperative sensing processing” 330099. 2015
[13]. Kamal, L.K. W- LEACH, abdulsalam .” Weighted low energy adaptive clustering hierarchy aggregation algorithm for data stream in WSN”. IEEE 1-8. 2010
[14]. Younis, M A, Akkaya. “ A survey on routing protocols for WSNs”. 325-349. 2005
[15]. .” A survey on sensor networks.” IEEE communication. 102 - 14.2016
[16]. Kamal, A.E. “Routing techniques in wireless sensor networks.” IEEE, 6-28.2012
[17]. Maja J Matari c, Andrew Howard and Gaurav S Sukhatme .”Mobile sensor network deployment using potential fields”. DARS02 425-6788.2010
[18]. Holliday, J,Ding..”A distributed energy efficient hierarchical clustering for wireless sensor network”. IEEE conference on distributed computing in sensor networks. 322-339. Fan, X.; Song, Y. Improvement on LEACH Protocol of Wireless Sensor Network. In Proceedings of International Conference on Sensor Technologies and Applications, Valencia, Spain, 14–20 October 2007, pp. 260–264. 2015
[19]. Fereris , Kowshik,N.M. H Kumar. “Fundamentals of large sensor networks.” IEEE, 98, 1828-1846. 2010
[20]. A. bestavros, G. Smaragdakis, I. Matta,.” A stable selection protocol for herogenous wireless sensor network.”1245-1267. 2017
[21]. Halpern, Haas, Z,J, Li, L . “Gossip based adhoc routing in WSN.” INFOCOM, New York, pp. 1707-1716. 2016

Jatinder Kaur, Er. Navroz Kahlon , "A Problem Evaluation of Algorithm for Secure Node Authentication in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.660-666, 2018.

This Review portrays the significance of NoSQL databases in the field of Big Data and distributed computing. Because a number of clients added to cloud computing and since they produce the huge volume of organized, unstructured and semi-structured information, to deal with these kinds of information NoSQL databases are preferred. This survey helps to analysis the data models of NoSql and its advantages and limitations of NoSql and relational databases. In the end, the Review concludes with various reasons to adopt NoSQL in Big Data and Cloud computing.

Not Only SQL, BigData, GridFS, JSON

[1] Y. Gu, X. Wang, S. Shen, J. Wang and J. Kim, "Analysis of data storage mechanism in NoSQL database MongoDB," 2015 IEEE International Conference on Consumer Electronics - Taiwan, Taipei, 2015, pp. 70-71.
[2] J. Klein, I. Gorton, N. Ernst, P. Donohoe, K. Pham and C. Matser, "Application-Specific Evaluation of No SQL Databases," 2015 IEEE International Congress on Big Data, New York, NY, 2015, pp. 526-534.
[3] M. J. Mior, K. Salem, A. Aboulnaga and R. Liu, "NoSE: Schema Design for NoSQL Applications," in IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 10, pp. 2275-2289, 1 Oct. 2017.
[4] M. Ghosh, W. Wang, G. Holla and I. Gupta, "Morphus: Supporting Online Reconfigurations in Sharded NoSQL Systems," in IEEE Transactions on Emerging Topics in Computing, vol. 5, no. 4, pp. 466-479, 2017.
[5] A. Maraj, E. Rogova, G. Jakupi and X. Grajqevci, "Testing techniques and analysis of SQL injection attacks," 2017 2nd International Conference on Knowledge Engineering and Applications (ICKEA), London, 2017, pp. 55-59.
[6] A. Antoniadis, Y. Gerbessiotis, M. Roussopoulos and A. Delis, "Tossing NoSQL-Databases Out to Public Clouds," 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, London, 2014, pp. 223-232.
[7] Dayne Hammes ,Hiram Medero,”Comparison of NoSQL and SQL Databases in the Cloud”, Georgia Southern University Hiram_Medero@georgiasouthern.edu, Hammes, Medero, et al. Proceedings of the Southern Association for Information Systems Conference, Macon, GA, USA March 21st –22nd , 2014
[8] R. Pasumarti, R. Barot, S. Xia, A. Xu and H. Ramachandra, "Capacity Measurement and Planning for NoSQL Databases," 2017 IEEE 11th International Conference on Semantic Computing (ICSC), San Diego, CA, 2017, pp. 390-394.
[9] V. Bhatia and A. Jangra, "SETiNS: Storage efficiency techniques in No-SQL database for Cloud based design," 2014 International Conference on Advances in Engineering & Technology Research (ICAETR - 2014), Unnao, 2014, pp. 1-5.
[10] S. Malhotra, M. N. Doja, B. Alam and M. Alam, "Bigdata analysis and comparison of bigdata analytic approches," 2017 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, 2017, pp. 309-314.
[11] L. Ho, M. Hsieh, J. Wu and P. Liu, "Data Partition Optimization for Column-Family NoSQL Databases," 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, 2015, pp. 668-675
[12] B. Jose and S. Abraham, "Exploring the merits of nosql: A study based on mongodb," 2017 International Conference on Networks & Advances in Computational Technologies (NetACT), Thiruvanthapuram, 2017, pp. 266-271
[13] Raju Sharma1, Yatendra kashyap,”A Study Of Nosql Databases And Working Overviews”, International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 02; February – 2016[ISSN:2455-1457].
[14] D. C. M. Maia, B. D. C. Camargos and M. Holanda, "Voluntary geographic information systems with document-based NoSQL databases," 2016 11th Iberian Conference on Information Systems and Technologies (CISTI), Las Palmas, 2016, pp. 1-6.
[15] Nikhil Dasharath Karande, “A Survey Paper on NoSQL Databases: Key-Value Data Stores and Document Stores”, International Journal of Research in Advent Technology, Vol.6, No.2, February 2018 E-ISSN: 2321-9637.

Kiran Kumar B G, Prakash H.Unki, Suvarna L.Kattimani, "NO SQL (NOT ONLY SQL) DataBases: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.667-670, 2018.

Gene expression analysis has been vital in cancer detection across the world. Genes regulating cell growth in cancer, suffer altered expressions. This leads to various phenotypic traits. Gene expression profiling has been extensively used by researchers to accurately identify tumours and has thus enabled better understanding of tumour biology. However, feature extraction and classification of gene expression datasets is challenging due to the high dimension of gene expression datasets and the non-linear relationships among the data. In this article, we have developed a deep learning-based dimension reduction and multi-class classification model using deep auto-encoder and multi-layer perceptron (MLP). We have trained the auto-encoder to extract meaningful features from the RNA-Seq data. These features are then used for supervised classification of tumour samples using a multilayer perceptron. Our (deepAE-MLP) model showed better feature extraction and disease classification capabilities when compared to benchmark methods.

Gene expression, Deep Learning, Auto-encoder, Multi-layer perceptron, Dimension Reduction, Multi-class Classification

[1] Creighton CJ et al., "Comprehensive molecular characterization of clear cell renal cell carcinoma", Nature 499(7456):43-9 (2013)
[2] H. Li, B. Yu, J. Li, L. Su, M. Yan, J. Zhang, C. Li, Z. Zhu and B. Liu, "Characterization of differentially expressed genes involved in pathways associated with gastric cancer", PloS one 10, p. e0125013
[3] (2015).
[4] T. Zhou, Y. Du and T. Wei, "Transcriptomic analysis of human breast cancer cells reveals differentially expressed genes and related cellular functions and pathways in response to gold nanorods", Biophysics Reports 1, 106 (2015)
[5] J. S. Myers, A. K. von Lersner, C. J. Robbins and Q.-X. A. Sang, "Differentially Expressed Genes and Signature Pathways of Human Prostate Cancer", PloS one 10, p. e0145322 (2015)
[6] M. Maienschein-Cline, J. Zhou, K. P. White, R. Sciammas and A. R. Dinner, "Discovering transcription factor regulatory targets using gene expression and binding data", Bioinformatics 28, 206 (2012)
[7] K. Shabana, K. A. Nazeer, M. Pradhan and M. Palakal, “A computational method for drug repositioning using publicly available gene expression data", BMC bioinformatics 16, p. 1 (2015)
[8] Yoo, C.K., Leeb, I. and Vanrolleghema, P.A. (2005) "Interpreting patterns and analysis of acute leukemia gene expression data by multivariate fuzzy statistical analysis", Computers & Chemical Engineering, Vol. 29, No. 6, pp.1345–1356.
[9] Liao, C., Li, S. and Luo, Z. (2006) "Gene selection using Wilcoxon rank sum test and support vector machine for cancer classification", Proceedings of the International Conference on Computational Intelligence and Security, 3–6 November, Guangzhou, China, pp.57–66.
[10] Peterson, L.E. and Coleman, M.A. (2005) "Comparison of gene identification based on artificial neural network pre-processing with k-means cluster and principal component analysis", Proceedings of the 6th Conference Workshop on Fuzzy Logic and Applications, 15–17 September, Crema, Italy, 267–276.
[11] Huerta, E.B., Duval, B. and Hao, J.K. (2006) "A hybrid GA/SVM approach for gene selection and classification of microarray data", Proceedings of the EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, 10–12 April, Budapest, Hungary, pp.34–44.
[12] Baena, R.M.L., Urda, D., Subirats, J.L., Franco, L. and Jerez, J.M. (2013) "Analysis of cancer microarray data using constructive neural networks and genetic algorithms", Proceedings of the 1st International Work-Conference on Bioinformatics and Biomedical Engineering, 18–20 March, Granada, Spain, pp.55–63.
[13] Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast
learning algorithm for deep belief nets.". Neural computation 18, no. 7 (2006): 1527-1554.
[14] Wang, Haohan, and Bhiksha Raj. "A Survey: Time Travel in Deep
Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas." arXiv preprint arXiv:1510.04781 (2015).
[15] Vincent, Pascal, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. "Extracting and composing robust features with de-noising autoencoders." In Proceedin7,s of the 25th international conference on Machine learning, pp. 1096-1103. ACM, 2008.
[16] Vincent, Pascal, Hugo Larochelle, Isabelle Lajoie, Y oshua Bengio, and Pierre-Antoine Manzagol. "Stacked de-noising autoencoders: Learning useful representations in a deep network with a local de-noising criterion." The Journal of Machine Learning Research II (20 I 0): 3371- 3408.
[17] T. S. Furey, N. Cristianini, N. Du
y, D. W. Bednarski, M. Schummer and D. Haussler, "Support vector machine classification and validation of cancer tissue samples using microarray expression data", Bioinformatics 16, 906 (2000).
[18] S. Reddy, K. T. Reddy, V. V. Kumari and K. V. Varma, "An SVM Based Approach to Breast Cancer Classification using RBF and Polynomial Kernel Functions with Varying Arguments", International Journal of Computer Science and Information Technologies 5, 5901 (2014).
[19] S. Wold, K. Esbensen and P. Geladi, “Chemometrics and intelligent laboratory systems”, 2, 37 (1987).
[20] Fakoor R, Ladhak F, Nazi A, Huber M, editors, "Using deep learning to enhance cancer diagnosis and classification", Proceedings of the ICML Workshop on the Role of Machine Learning in Transforming Healthcare Atlanta, Georgia: JMLR: W&CP; 2013.
[21] A. Gupta, H. Wang and M. Ganapathiraju, “Learning structure in gene expression data using deep architectures, with an application to gene clustering”, in Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on, 2015.
[22] Jie Tan, MAtthew Ung, Chao Cheng and Casey S Greene, "Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with de-noising autoencoders", Pac Symp Biocomput. 2015; 20: 132–143.
[23] Padideh Danaee, Reza Ghaeini, and David A. Hendrix, "A deep learning approach for cancer detection and relevant gene identification", Pac Symp Biocomput. 2016; 22: 219–229.
[24] Rui Xie, JiaWen, Andrew Quitadamo, Jianlin Cheng and Xinghua Shi, "A deep auto-encoder model for gene expression prediction", BMC Genomics 2017, 18(Suppl 9):845 DOI 10.1186/s12864-017-4226-0
[25] Ayse Dincer, Safiye Celik, Naozumi Hiranuma, and Su-In Lee, "DeepProfile: Deep learning of patient molecular profiles for precision medicine in acute myeloid leukemia", bioRxiv preprint first posted online Mar. 8, 2018; doi: http://dx.doi.org/10.1101/278739.
[26] Kumardeep Chaudhary, Olivier B Poirion, Liangqun Lu, Lana X Garmire, "Deep Learning based multi-omics integration robustly predicts survival in liver cancer", Clinical Cancer Research, Pages
clincanres. 0853.2017
[27] Fadhl M. Alkawaa, Kumardeep Chaudhary, and Lana X. Garmire, "Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data", J. Proteome Res., DOI: 10.1021/acs.jproteome.7b00595, Publication Date (Web): 07 Nov 2017
[28] Maryam M Najafabadi, Flavio Villanustre, Taghi M Khoshgoftaar, Naeem Seliya, Randall WaldEmail author and Edin Muharemagic, "Deep learning applications and challenges in big data analytics", Journal of Big Data20152:1 https://doi.org/10.1186/s40537-014-0007-7
[29] S Min, B Lee, S Yoon, "Deep learning in bioinformatics", Briefings in bioinformatics 18 (5), 851-869
[30] Angermueller C, Pärnamaa T, Parts L, Stegle O, "Deep learning for computational biology", Mol Syst Biol. 2016 Jul 29;12(7):878. doi: 10.15252/msb.20156651.
[31] Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ, "Deep Learning for Health Informatics", IEEE J Biomed Health Inform. 2017 Jan;21(1):4-21. doi: 10.1109/JBHI.2016.2636665. Epub 2016 Dec 29.
[32] Mamoshina P, Vieira A, Putin E, Zhavoronkov A, "Applications of Deep Learning in Biomedicine", Mol Pharm. 2016 May 2;13(5):1445-54. doi: 10.1021/acs.molpharmaceut.5b00982. Epub 2016 Mar 29.
[33] Imad, Hafidi & Rochd, Yassir. (2018). An Enhanced Apriori Algorithm Using Hybrid Data Layout Based on Hadoop for Big Data Processing. International Journal of Network Security. 18. 161.
[34] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825{2830, 2011.
[35] S. Hochreiter, “The vanishing gradient problem during learning recurrent neural nets and problem solutions," Int. J. Uncertain. Fuzziness Knowl.-Based Syst., vol. 6, pp. 107{116, Apr. 1998.
[36] B. Scholkopf, A. Smola and K.-R. Muller, "Kernel principal component analysis", in International Conference on Articial Neural Networks, 1997.
[37] T. SenthilSelvi1 and R. Parimala, “Improving Clustering Accuracy using Feature Extraction Method”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issue. 2, pp.15-19, 2018.

Aradhita Mukherjee, Dibyendu Bikash Seal, "A Deep Learning Model For Dimension Reduction And Multi-Class Classification Of Gene Expression Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.671-676, 2018.

Complex neural networks will take much time for training; we can achieve better accuracy with simpler models by tuning hyper-parameters of the model. Hyper parameter tuning is required for neural networks to improve the accuracy and to reduce the training time of neural networks. In this paper we used simple CNN model with four convolution layers, two pooling layers and two fully connected layers with hyper parameter tuning, batch normalization, learning rate decay, and normalization techniques to recognize hand written digit recognition. This model is giving 99.54% on test set.

Convolution Neural Networks, CNN, Deep Learning, Parameter Tuning, Batch Normalization

[1] James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13 (February 2012), 281-305.
[2] Y. Hou and H. Zhao, "Handwritten digit recognition based on depth neural network," 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, 2017, pp. 35-38
[3] U. Meier, D. C. Ciresan, L. M. Gambardella and J. Schmidhuber, "Better Digit Recognition with a Committee of Simple Neural Nets," 2011 International Conference on Document Analysis and Recognition, Beijing, 2011, pp. 1250-1254.
[4] L. Deng, "The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]," in IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 141-142, Nov. 2012.
[5] C. Laurent, G. Pereyra, P. Brakel, Y. Zhang and Y. Bengio, "Batch normalized recurrent neural networks," 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, 2016, pp. 2657-2661.
[6] Y. Xie, H. Jin and E. C. C. Tsang, "Improving the lenet with batch normalization and online hard example mining for digits recognition," 2017 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), Ningbo, 2017, pp. 149-153.
[7] D. Ito, T. Okamoto and S. Koakutsu, "A learning algorithm with a gradient normalization and a learning rate adaptation for the mini-batch type learning," 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Kanazawa, 2017, pp. 811-816.
[8] L. Lv, X. Cai, Y. Zeng and C. Chen, "Facial feature extraction method based on fast and effective convolutional neural network," 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, 2017, pp. 2011-2015.
[9] L. Chen, S. Wang, W. Fan, J. Sun and S. Naoi, "Cascading Training for Relaxation CNN on Handwritten Character Recognition," 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), Shenzhen, 2016, pp. 162-167.
[10] Tai-cong Chen, Da-jian Han, F. T. K. Au and L. G. Tham, "Acceleration of Levenberg-Marquardt training of neural networks with variable decay rate," Proceedings of the International Joint Conference on Neural Networks, 2003., Portland, OR, 2003, pp. 1873-1878 vol.3.
[11] T. Tanprasert and T. Kripruksawan, "An approach to control aging rate of neural networks under adaptation to gradually changing context," Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP `02., Singapore, 2002, pp. 174-178 vol.1.
[12] K. T. Islam, G. Mujtaba, R. G. Raj and H. F. Nweke, "Handwritten digits recognition with artificial neural network," 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T), Kuala Lumpur, 2017, pp. 1-4.

Laxmi Narayana Pondhu, Govardhani Pondhu, "Tuning Convolution Neural networks for Hand Written Digit Recognition," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.777-780, 2018.

The problem of output feedback control of doubly fed induction motor (DFIM) is considered in this paper. In particular the technique of multirate output feedback (MROF) has been employed. The challenge in control of DFIM is the nonlinear nature of torque and speed dynamics which prevents direct application of MROF techniques. We have proposed a novel strategy to achieve the torque and speed tracking via state reference tracking. The linear equations of doubly fed induction motor are utilized for application of MROF and the state reference commands are computed from torque and speed reference commands separately. The proposed output feedback controller is shown to be tracking given torque and speed commands.

Doubly-fed Induction Motor; Output Feedback; Multirate Output Feedback; Fast Output Sampling; Sensorless Control.

[1] M Ahmad, M R Khan, AIqbal, and AtifIqbalMukhtar Ahmad, M.Rizwan Khan. “ A Doubly Fed Induction Motor as High Torque Low Speed Drive.” In International Conference on Power Electronic, Drives and Energy Systems, pages 1–3, 2006.
[2] B. Baby Priya and AChilambuchelvan. “ Steady-state Analysis of Doubly Fed Induction Machiness for Wind Turbines using MATLAB.” International Journal of Renewable Energy Technology, 1(2):192–210, 2009.
[3] S. Drid, M. S. Nait-Said, and M. Tadjine.“ Double flux oriented control for the doubly fed induction motor.” Electric Power Components and Systems, 33(10):1081–1095, 2005.
[4] Jiabing Hu Jiabing Hu and Bin Hu Bin Hu.“ Direct Active and Reactive Power Regulation of Grid Connected Voltage Source Converters using Sliding Mode Control Approach.” IEEE Transactions on Energy Conversion, 25(4):1028–1039, 2010.
[5] Tomomichi Hagiwara and Araki Mituhiko.”Design of a Stable State Feedback Controller Based on the Multirate Sampling of the Plant Output.” IEEE Transactions on Automatic Control, 33(9):812–819, 1988.
[6] S. Janardhanan and B. Bandyopadhyay.“New Approach for Design of Fast Output Sampling Feedback Controller.” (January):5– 7, 2003.
[7] S. Lekhchine, T. Bahi, I. Abadlia, Z. Layate, and H. Bouzeria. “Speed Control of Doubly Fed Induction Motor. Energy Procedia,” 74:575–586, 2015.
[8] Willis F Long and Norbert L Schmitz. Cycloconverter“Control of the Doubly Fed Induction.Ieee Transactions on Industry and General Application”, IGA-7,(1):95– 100, 1971.
[9] T.J.E. Miller “Theory of the doubly-fed induction machine in the steady state.”The XIX International Conference on Electrical Machines - ICEM 2010, pages 1–6, 2010.
[10] R S Pena, J C Clare, and G M Asher. “Vector Control of a Variable Speed DoublyFed Induction Machine for Wind Generation Systems Vector Control of a Variable Speed Doubly-Fed Induction Machine for Wind Generation Systems.” European Power Electronics and Drives, 6(3):60–67, 2015.
[11] Sergei Peresada and Andrea Tilli.“ Dynamic Output Feedback Linearizing Control of a Doubly-Fed Induction Motor.”In International Conference on Industrial Electronics, pages 1256–1260, 1999.
[12] Adel Shaltout. “ A Control Strategy for Integration of BESS with Wind Turbine DFIG Connected to Utility Grid.”HaythamGamal. International Journal of Process Systems Engineering, 2(3):273–290, 2014.
[13] J. Soltani and A. FarrokhPayam.“ A RobustAdaptive sliding-mode controller for slip power recovery induction machine drives.”Conference Proceedings - IPEMC 2006: CES/IEEE 5th International Power Electronics and Motion Control Conference, 3:1912–1917, 2007.
[14] Vladimir V Vdovin, Denis A Kotin, and Vladimir V Pankratov. “ State Observer for Sensorless Vector Control of Doubly Fed Induction Motor.” In XIV International Conference On Micro/Nanotechnologies And Electron Devices, pages 382–388, 2013.
[15] Herbert Werner.“Multimodel Robust Control by Fast Output Sampling.”Automatica, 34(12):1625–1630, 1998.
[16] H Werner and K Furuta. “ Simultaneous stabilization based on output measurement.”Kybernetika, 31(4):395–411, 1995.
[17] Li Yuan, He Feng-you, and Ye Zong-bin.“ Study on Sliding Mode Speed Control with RBF Network Approach for Doubly-Fed Induction Motor.” 2009 IEEE International Conference on Control and Automation, 2(2):339–342, 2009.

Ravi K Biradar, R. V Sarwadnya, "Multi Rate Output Feedback control of Doubly-fed Induction Motor," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.681-686, 2018.

Image segmentation is very important in computer vision for image recovery, visual summary, image base modeling, and for many other purposes. Despite many years of research and substantial contributions, image segmentation is still a very challenging task to suit for range of applications. Brain Magnetic Resonance Image (MRI) segmentation is one of the most challenging and time consuming task in the field of medical imaging. But by nature medical images are complex and noisy. This leads to the necessity of processes that reduces difficulties in analysis and improves quality of output. Even though several methods and encouraging results are obtained in medical imaging area, reproducible segmentation and grouping of abnormalities are still a thought provoking task due to the different shapes, locations and image intensities of different types of tumors. This paper critically reviews recent brain MRI segmentation methods along with their detailed analysis, and evaluation on the basis of various parameters. The study and evaluation is useful in improving the performance of existing methods as well as helpful in the development of new methods.

Image Segmentation, Brain MRI, Graph Cuts

[1]. Acton, S.T., Mukherjee, “Scale space classification using area morphology,” IEEE Trans Image Process 9(4):623–635, 2000.
[2]. Ahmed, M.N., Yamany, S.M., Mohamed, N. “A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data,” IEEE transaction medical imaging, Vol.21, No.3, pp.193-199, 2002.
[3]. Abdullah, N., Ngah, U.K., Aziz, “Image Classification of brain MRI using Support Vector Machine,” Imaging systems and Techniques (IST), IEEE International conference on 17-18 May 2011.
[4]. J. Tou and R. Gonzalez, Pattern Recognition Principles. Addison-Wesley, 1974.
[5]. C. C. Hung, “Competitive learning networks for unsupervised training,” Int. J. of Remote Sensing, vol. 14, no. 12, pp. 2411-2415, 1993.
[6]. S. Nagabhushana, “Computer Vision and Image Processing,” New Age International Publishers, 2005.
[7]. W. Hongzhi and D. Ying, “An Improved Image Segmentation Algorithm Based on Otsu Method,” International Symposium on Photo electronic Detection and Imaging 2007: Related Technologies and Applications, Vol. 6625, 2008.
[8]. Nobuyuki Otsu, “A Threshold Selection Method From Gray-Level Histograms,” IEEE Transactions on Systems, Man, And Cybernetics, Vol. 9, No. 1, 1979.
[9]. Tranos Zuva, Oludayo O. Olugbara, Sunday O. Ojo and Seleman M. Ngwira, “Image Segmentation, Available Techniques, Developments and Open Issues,” Canadian Journal on Image Processing and Computer Vision, Vol. 2, No. 3, 2011.
[10]. Bhattacharyya, D., Tai-hoon Kim, "Brain Tumor Detection Using MRI Image Analysis", Communications in Computer and Information Science, Vol: 151, pp. 307-314, 2011.
[11]. Bryan S. Morse, Lecture 18: Segmentation (Region Based), Brigham Young University, 1998.
[12]. H.S.Prasantha, H. Shashidhara, and Madhavi Lata, “MEDICAL IMAGE SEGMENTATION”, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 04, 2010.
[13]. Dzung L. Pham, Chenyang Xu, and Jerry L. Prince, “Current Methods in Medical Image Segmentation,” Annu. Rev. Biomed. Eng., Vol. 02 pp. 315–37. 23, 2000.
[14]. Rolf Adams and Leanne Bischof, “Seeded Region Growing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.16, No. 6, 1994.
[15]. Bo Peng, Lei Zhang, and David Zhang, “A Survey of Graph Theoretical Approaches to Image Segmentation,” IEEE Transactions on Image Processing, Vol. 7, No. 12, December 2001.
[16]. Koley, S., Majumder, "Brain MRI segmentation for tumor detection using cohesion based self-merging algorithm", Proceedings of the IEEE 3rd International Conference on Communication Software and Networks (ICCSN), Xi`an, pp: 781 – 785, 2017.
[17]. Ravi S and A M Khan, “Operators Used in Edge Detection: A Case Study,” International Journal of Applied Engineering Research, Vol. 7 No. 11, 2012.
[18]. N. Senthil kumaran and R. Rajesh, “Edge Detection Techniques for Image Segmentation – A Survey of Soft Computing Approaches,” International Journal of Recent Trends in Engineering, Vol. 1, No. 2, 2009.
[19]. http://www.cs.uu.nl/docs/vakken/ibv/reader/chapter10.pdf.
[20]. K.Duraiswamy and V. Valli Mayil “Similarity Matrix Based Session Clustering by Sequence Alignment Using Dynamic Programming,” Computer and Information Science journal, Vol. 1, No.3, 2008.
[21]. Qurat-ul Ain., Irfan Mehmood., Naqi, M. Syed., Arfan Jaffar, M "Bayesian Classification Using DCT Features for Brain Tumor Detection", Lecture Notes in Computer Science, Vol:6276, pp. 340-349, 2016.
[22]. Rogowska, J (2009): “Overview and Fundamentals of medical image segmentation,” Handbook of medical imaging: Processing and Analysis Management, I.Bankman, Ed.pp.69-85, 2009.
[23]. F. Benezit, T. Cour, and J. Shi, “Spectral segmentation with multi-scale graph decomposition,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.2, pp.1124-1131, 2005.
[24]. S. D. Kapade, S. M. Khairnar, B. S. Chaudhari, “Enhanced Graph based Normalized cut Methods for Image Segmentation,” ICTACT Journal on Image and Video Processing, vol. 5, no. 2, pp. 907 - 911, November 2014.
[25]. G. Csurka, C. Bray, and L. Fan, “Visual categorization with bags of key points,” Conf. on Statistical Learning in Computer Vision, 2004.
[26]. R. Fergus, P. Perona, and A. Zisserman, “Object class recognition by unsupervised scale - invariant learning,” in Proc. of the IEEE Conf on Computer Vision and Pattern Recognition, 2003
[27]. S. Yu and J. Shi, “Object-specific figure-ground segregation,” in Proc. IEEE Computer Society Conf on Computer Vision and Pattern Recognition, vol.2, pp. 39-45, 2003.
[28]. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.22, no.8, pp. 888-905, 2000.
[29]. I. J. Cox, S.B. Rao, and Y. Zhong, “Ratio Regions: a technique for image segmentation,” in Proc. of the Int. Conf. on Pattern Recognition, pp. 557-564, 1996.
[30]. A.X. Falcao, J.K. Udupa, and F.K. Miyazawa, “An ultra-fast user-steered Image segmentation paradigm: live wire on the fly,” IEEE Trans. on Medical Imaging, vol.19, no.1, pp. 55-62, 2000.
[31]. X. Bai and G. Sapiro, “A geodesic framework for fast interactive image and video segmentation and matting,” in Proc. of Int. Conf. on Computer Vision, pp.1-8, 2007.
[32]. L. Grady, “Minimal surfaces extend shortest path segmentation methods to 3D,” IEEE Trans. on Pattern Analysis and Machine Intelligence, pp. 321-334, vol.32, no.2, 2010.
[33]. V. Ramos V. and F. Almeida, “Artificial ant colonies in digital image habitats - a mass behaviours effect study on pattern recognition,” in Proc. Second Int. Workshop on Ant Algorithms, pp. 113-116, 2000
[34]. H. Nezamabadi, S. Saryazdi, and E. Rashedi, “Edge detection using ant algorithms,” Soft Computing, vol.10, pp. 623-628, 2006.
[35]. Aydın, “A modified ant-based approach to edge detection,” ICCCI – LNAI, vol. 5796, pp. 620 – 628, 2009.
[36]. Badran, E.F., Mahmoud, E.G., Hamdy, "An algorithm for detecting brain tumors in MRI images", Proceedings of the International Conference on Computer Engineering and Systems (ICCES), Cairo, pp: 368 – 373, 2010.
[37]. Chandra, S., Bhat, R., Singh, "A PSO based method for detection of brain tumors from MRI", Proceedings of the World Congress on Nature & Biologically Inspired Computing, Coimbatore, pp: 666 – 671, 2009.
[38]. S. D. Kapade, S. M. Khairnar, B. S. Chaudhari, “Image Segmentation using min- cut and swarm intelligence,” International Journal of mathematical Sciences and Engineering Applications, vol. 9, no. I, pp. 165 - 177, March 2015.

Naresh Ghorpade, H. R. Bhapkar, "Evaluation and Performance Analysis of Brain MRI Segmentation Methods," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.687-696, 2018.