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Stream Data is now more than ever highly distributed, loosely structured, increasingly large in volume and changing over time. Broadly speaking, firstly the volume of data increasing exponentially each year and secondly the speed at which the new data is being generated of distinct concept and changes over time. Stream Data is generated by number of sources. Data streaming applications are typically dealing with large amounts of data over an extended period of time. However, in most cases the user is only interested in recent data instead of the whole data set. Furthermore, stream data tends to express features of a concept drift, i.e. the data is evolving over time. This would cause algorithms which consider the whole data set with the same importance to produce distorted results. In such cases the majority of processed data would not be valid anymore. Sometimes the nature of a data stream itself requires giving up a certain amount of precision because its high volume couldn’t be processed otherwise and one would end up with no information at all. If the data distribution is stable, mining a data stream is largely the same as mining a large data set, since statistically it is easily to mine a sufficient sample. The expectations of mining data streams are finding and understanding changes, maintaining an updated model. For evolving data, two classes of problems are of particular interest: model maintenance and change detection. The goal of model maintenance is to maintain a data mining model under inserts and deletes of blocks of data. In this model, older data is available if necessary. Change detection is related to quantify the difference between two sets of data and determine when the change has statistical significance. Data streams can be seen as stochastic processes in which events occur continuously and independently from each another [1]. Querying data streams is quite different from querying in the conventional relational model. A key idea is that operating on the data stream model does not preclude the use of data in conventional stored relation, data might be transient. In this paper proposed methods are addressing Classification of balanced and unbalanced data streams by considering concept drift and data skewness. The classification accuracy depends on the selection of learning model. In data streams at the time of classification ,concept drift plays the vital role .Comparing to traditional classification data stream classification needs more accurate methods .Because traditional methods always follows the training model which may not predict the novel classes. In data streams by considering the concept drift with unsupervised learning model can predict the novel class. In the proposed methodology classification of data streams are addressed by ensemble methods with supervised learning, unsupervised learning for novel class detection to increases the accuracy of the system. A scalable and adaptable online genetic algorithm is proposed to mine classification rules for the largest data streams with concept drifts. The data skewness is addressed by considering the data level, the algorithmic level to favor the positive class.

Data Mining

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[4]Pedro Domingos, Geoff Hulten, “ Mining High Speed Data Streams”,KDD-00 in proceeding of sixth ACM SIGKDD international conference on knowledge discovery and data mining, USA, 2000, pp 71-80.
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[7] N. Japkowicz and S. Stephen. The class imbalance problem: A systematic study. Intelligent data analysis,6(5):429{449, 2002.
[8] W. Nick Street and Yong Seog Kim. A Streaming Ensemble Algorithm (SEA) for Large- Scale Classification. KDD – 01. San Francisco, CA.
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[10]. Dougherty, J., R. Kohavi, and M. Sahami. Supervised and unsupervised discretization of continuous features. In Proceedings of International Conference on Machine Learning (ICML-1995), 1995.
[11] Pedro Domingos, Geoff Hulten, “ Mining High Speed Data Streams”,KDD-00 in proceeding of sixth ACM SIGKDD international conference on knowledge discovery and data mining, USA, 2000, pp 71-80.
[12] A. Tsymbal. “The problem of concept drift: definitions and related work”, Technical Report TCD-CS-2004-15, Computer Science Department, Trinity College Dublin, Ireland. 2004.
[13] W. Nick Street and Yong Seog Kim. A Streaming Ensemble Algorithm (SEA) for Large- Scale Classification. KDD – 01. San Francisco, CA.
[14] E Padmalatha, C R K Reddy and Padmaja B Rani. Article: Ensemble Classification for Drifting Concept. International Journal of Computer Applications 80(11):33-36, October 2013.
[15] E.Padmalatha,C.R.K.Reddy, B.Padmaja Rani ”Classification of Concept Drift Data Streams”In the proceedings of the Fifth International Conference on Information Science and Applications .ICISA 2014.IEEE PP291-295, 2014.
[16] Periasamy Vivekanandan and Raju Nedunchezhian, “Mining data streams with concept drifts using genetic algorithm”, Artificial Intelligence Review, Vol. 36, Issue 3, pp 163-178, Springer, October 2011.
[17] Basheer M. Al-Maqaleh and Hamid Shahbazkia, “A Genetic Algorithm for Discovering Classification Rules in Data Mining”, International Journal of Computer Applications (0975-8887), Vol. 41-No. 18, March 2012.
[18] Syed Shaheena and Shaik Habeeb, “Classification Rule Discovery Using Genetic Algorithm-Based Approach”, NIMRA Institute, Department of CSE, IJCTT Journal, Vol. 4, Issue 8, pp 2710-2715, August 2013.
[19] E Padmalatha, C R K Reddy and Padmaja B Rani. Article: Classification of Concept-Drifting Data Streams using Optimized Genetic Algorithm. International Journal of Computer Applications 125(15):1-6, September 2015.
[20]Wei Liu, Sanjay Chawla, David A. Cieslak, Nitesh V. Chawla, ― A Robust Decision Tree Algorithm for Imbalanced Data Sets‖, 2010.
[21]Xu-Ying Liu, Jianxin Wu, Zhi-Hua Zhou” Exploratory Undersampling for Class-Imbalance Learning”, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 39, NO. 2, APRIL 2009, pp.no:539 – 550.
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[23] Junfeng Pan and Qiang Yang, Yiming Yang and Lei Li, Frances Tianyi Li and George Wenmin Li “Cost-SensitiveData Preprocessing for Mining Customer Relationship Management Databases”, JANUARY/FEBRUARY 2007, A Technical Report.
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E.Padmalatha, S.Sailekya, "Significance of learning methods for mining of real time data streams," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.188-209, 2018.

Cloud computing plays an indispensable role in the present day scenario. The concept of load balancing elevates this emerging technology to reach pivotal position in providing services to the end users. This challenging phenomenon applies different logic to handle the users efficiently. However several challenges like heterogeneity, management, storage, response time, processing and resource utilization and analysis of load balancing hinder their efficient and real-time applications. All these challenges call for well-adapted distributed framework for LB management that can efficiently handle request in cloud. In this paper, we present a novel, distributed, scalable and efficient framework for LB in cloud computing. The proposed cloud computing based framework can answer technical challenges for efficient resource utilization and overall response time of request in cloud computing. Further, this framework achieves considerable performance and efficiency in load balancing system.

Cloud Computing, Load Balancing, Virtual Machine

[1] Sachin Soni1, Praveen Yadav, “A Load Balancing Approach to Minimize the Resource Wastage in Cloud Computing”, International Advanced Research Journal in Science, Engineering and Technology,Vol. 3, Issue 3, pp.no 94-98 ,ISSN (Online) 2393-8021, ISSN (Print) 2394-1588, March 2016.
[2] Reena Panwar ,Bhawna Mallick, “A Comparative Study of Load Balancing Algorithms in Cloud Computing”, International Journal of Computer Applications (0975 – 8887) Volume 117 – No. 24, pp.no 33-37, May 2015 .
[3] Sonia Sindhu, “Task Scheduling In Cloud Computing International”, Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 Issue 6, pp.no 3019-3023, ISSN: 2278 – 1323, June 2015.
[4] Er. Rajeev Mangla, Er. Harpreet Singh,“ Recovery and user priority based load balancing in cloud computing”, International Journal of Engineering and Science and Research, pp.no. 407-412, ISSN: 2277-9655, Mangla, 4(2): February 2015.
[5] Harish Chandra, Pradeep Semwal, Sandeep Chopra,” load balancing in cloud computing using a novel minimum makespan algorithm”, International Journal of Advanced Research in Computer Engineering & Technology ,Volume 5, Issue 4,pp.no.1160-1164, ISSN: 2278 – 1323,April 2016.
[6] D.Suresh Kumar, Dr. E. George Dharma Prakash Raj, VMARLB – Virtual Machine Based Algorithm for Random Load Balancing in Cloud Computing, 2017 International Conference on Advanced Computing and Communication Systems (ICACCS – 2017), Coimbatore, INDIA, pp.no. 482-485, IEEE ISBN No. 978-1-5090-4558-7, Jan. 06 – 07, 2017.
[7] D. Suresh Kumar, Dr. E. George Prakash College, “PBVMLBA-Priority Based Virtual Machine Load Balancing algorithm in Cloud Computing”, International Journal of Computer Science and Software Engineering, Volume 6, Issue 11, ISSN (Online): 2409-4285, Page: 233-238, November 2017.
[8] D. Suresh Kumar, Dr. E. George Prakash College, “EVMLBA-Enhance Virtual Machine Load Balancing algorithm in Cloud Computing”, International Journal of Advanced Studies in Computer Science and Engineering, Volume 6 issue 11, page: 8-15, 30 /11/2017.

D. Suresh Kumar, E. George Dharma Prakash Raj, "A Novel Frame Work for Load Balancing in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.210-213, 2018.

Cross media retrieval is a propelled technique created in the domain of multimedia retrieval that aides in interfacing the different substance with each other and makes a retrieval system. The evaluations of correlation and the projection of the correct matches are the two noteworthy properties found in cross media retrieval. The low-level element writes were customarily utilized strategy and it neglects to beat different issues. Abnormal state highlights are acquainted as an answer with deal with the projection of the substance. Semantic relationship is worked at a more raised measure of reflection which is closer to the human comprehension than content correlation. In this investigation, a crossover model of solidified correlation techniques is used for perceiving the interactive media pictures and their likenesses. The consideration of different methods and algorithms identified with CMR is upgraded in the examination alongside the assurance of the conceivable result of those methods.

Cross media retrieval(CMR), Image Retrieval, Pattern graph, Image acquisition, Correlation

[1] Yan, Jihong, et al. "Joint graph regularization based modality-dependent cross-media retrieval." Multimedia Tools and Applications (2017): 1-19.
[2] Xie, Liang, Peng Pan, and Yansheng Lu. "Analyzing semantic correlation for cross-modal retrieval." Multimedia Systems 21.6 (2015): 525-539.
[3] Lu, Tong, et al. "Content-oriented multimedia document understanding through cross-media correlation." Multimedia Tools and Applications 74.18 (2015): 8105-8135.
[4] Allani, Olfa, et al. "Pattern graph-based image retrieval system combining semantic and visual features." Multimedia Tools and Applications (2017): 1-30.
[5] Huang, Lei, and YuxinPeng. "Cross-media retrieval by exploiting fine-grained correlation at entity level."Neurocomputing 236 (2017): 123-133.
[6] N. Rasiwasia, J. Costa Pereira, E. Coviello, G. Doyle, G. Lanckriet, R. Levy, and N. Vasconcelos. A new approach to cross-modal multimedia retrieval. In Proceedings of the 18th ACM International Conference on Multimedia, pages 251–260. ACM, 2010.
[7] H. Hotelling, “Relations between two sets of variates”, Journal of Biometrika, Vol.28, pp.321–377, 1936.
[8] D. Li, N. Dimitrova, M. Li, I.K. Sethi, Multimedia content processing through cross modal association, in: Proceedings of the 11th ACM International Conference on Multimedia (ACM-MM), 2003, pp. 604–611.
[9] D.W. Jacobs, A. Kumar, A. Sharma, H. Daume III, Generalized multi view analysis: a discriminative latent space, in: Proceedings of IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 2160–2167
[10] J. Pereira, E. Coviello, G. Doyle, N. Rasiwasia, G. Lanckriet, R. LevyN. Vasconcelos, On the role of correlation and abstraction in cross-modal multimedia retrieval, IEEE Trans. Pattern Anal. Mach. Intell. 36 (3) (2014) 521–535.
[11] R. Li, X. Wang, Cross-modal retrieval with correspondence auto encoder, in: Proceedings of the 22nd ACM International Conference on Multimedia (ACMMM), 2014, pp. 7–16.
[12] Liang, Jian, et al. "Self-paced cross-modal subspace matching." Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval.ACM, 2016.

Monelli Ayyavaraiah, "A Review on Correlation Maximized Similarity Measurement in Cross Media Retrieval Method," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.214-218, 2018.

Ad-hoc wireless sensor networks (AWSN) have become the most standard technical development in commercial and industrial applications. The use of AWSN along with Zigbee standards in Wireless Personal Area Networks (WPAN) has paved the way for effective data collections with optimum use of network resources. Zigbee Technology is designed for low cost of deployment, low complexity and low power consumption. This paper presents a comprehensive review on AWSN and its routing protocols. This paper also presents a detailed description of Zigbee technology, its various standards and enabling technologies.

Medium access control (MAC), Personal Area Network (PAN), ZigBee Trust Center (ZTC), Zone Routing Protocol (ZRP), Optimized Link State Routing Protocol (OLSR), Temporally Ordered Routing Algorithm (TORA)

[1] R. Gupta, H. Aggarwal, “Ad-hoc routing protocol “review paper”, Journal of Global Research in Computer Science, ISSN:2229-371X, Volume 4, No. 5, pp.26-29, 2013
[2] S.K. Kurmi, N.K. Saluja, S. Patel, “Performance Analysis of Quality of Service Evaluation in IEEE 802.15.4 Wireless Sensor Networks”, International Journal of Engineering Sciences & Research Technology, ISSN: 2277-9655, pp.3516-3522, 2013.
[3] D. Sharma, “An overview of Wireless Sensor Networks”, International Journal of All Research Education and Scientific Methods (IJARESM), Volume 3, Issue 4 ISSN: 2455-6211, pp.29-33, 2015.
[4] J. Balen, D. Zagar, G. Martinovic, “Quality of Service in Wireless Sensor Networks: A Survey and Related Patents”, Recent Patents on Computer Science, ISSN: 1874-4796, Vol. 4, No. 3, pp.188-202, 2011.
[5] Reena, S. Phulre, R. Pandit, “Wireless sensor network and its routing protocol: A survey paper”, International Journal of Computer Science and Mobile Computing (IJCSMC) , ISSN: 2320-088X, Vol. 2, Issue. 6, June 2013, pp.60 – 64, 2013.
[6] M. Abuhmida, K.R. Krishnan, “Performance Evaluation of Mobile Ad-hoc Routing Protocols on Wireless Sensor Networks for Environmental Monitoring”, 17th UKSIM-AMSS International Conference on Modeling and Simulation, 978-1-4799-8713-9/15,DOI 10.1109/UKSim.2015.60, pp.544-548, 2015.
[7] S.R. Biradar, H.H.D. Sharma, K. Shrama, S.K. Sarkar, “ Performance Comparison of Reactive Routing Protocols of MANETs using Group Mobility Model”, International Conference on Signal Processing Systems (IEEE), pp.192-195, 2009.
[8] A.A.A. Radwan, T.M. Mahmoud, E.H. Houssein E.H, “ Evaluation comparison of some ad hoc networks routing protocols”, Egyptian Informatics Journal, doi:10.1016/j.eij.2011.04.001, pp.95–106, 2011.
[9] H.D. Trung, W. Benjapolakul, P.M. Duc, “Performance evaluation and comparison of different ad hoc routing protocols”, Elsevier Comput Commun; pp.2478–96, 2007.
[10] M. Gupta, S. Kumar, “Performance Evaluation of DSR, AODV And DSDV Routing Protocol for Wireless Adhoc Network”, IEEE International Conference on Computational Intelligence & Communication Technology, DOI 10.1109/CICT.2015.95, pp.416-421, 2015.
[11] R. Kumar, P. Singh, “Performance analysis of AODV, TORA, OLSR and DSDV Routing Protocols using NS2 Simulation”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319-8753, pp.416-421, 2015.
[12] S.S. Tyagi, R.K. Chauhan, “Performance Analysis of Proactive and Reactive Routing Protocols for Ad hoc Networks”, International Journal of Computer Applications (0975 – 8887).Volume 1 – No. 14, pp.27-30, 2010.
[13] H. Fitriawan, M. Susanto, A.S. Arifin, D. Mausa, A. Trisanto, “ZigBee Based Wireless Sensor Networks and Performance Analysis in Various Environments”, 15th Intl. Conf. QiR: Intl. Symp. Elec. and Com. Eng, pp.272-275, 2017.
[14] Preeti, S. Bishnoi, “Wireless Sensor Network based AODV and DSDV Routing Protocols using Qualnet”, International Journal of Enhanced Research in Science, Technology & Engineering, ISSN: 2319-7463, Vol. 5 Issue 6, pp.194-200, 2016.
[15] Narmada, S. Rao, “Comparison of Routing Protocols for Zigbee WPANs Performance”, International journal of computer science issues (IJCSI), ISSN: 1694-0814, vol 8, pp. 394-402.
[16] M. Singh, S.N. Thakur, “Comparison of DSDV, DSR and ZRP Routing Protocols in MANETs”. International Journal of Computer Applications (0975 – 8887) Volume 108 – No. 13, 2014.
[17] K. Kaushal, T. Kaur, J. Kaur, “Zigbee based Wireless Sensor Networks”, International Journal of Computer Science and Information Technologies, ISSN: 0975-9646, Vol. 5 (6), pp 7752-7755, 2014.
[18] G. Singh, R. Bhardwaj, K. Singh, S. Mehla, “ZigBee: A Review”, International Journal of Computer Science and Information Technologies (IJCST), ISSN: 2229-4333 (Print), Vol. 3, No 1, pp.328-332, 2012.
[19] Amol B.Suryawanshi, Baljit Kaur Saini, "Survey on Various Routing Protocols in Ad-hoc Networks", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.174-178, 2017.
[20] K.M. Mohan, C. Bagavathi, K. Shalini, “A power efficient Mac protocol for quality of service evaluation in wireless sensor networks”, International Journal of Innovative Research in Electrical, Electronics, Instrumentation And Control Engineering, ISSN: 2321-2004, Vol.2, pp.2154-2159, 2014.
[21] Surender, P. Samundiswary, “Performance Analysis of Node Mobility in Beacon and Non-Beacon enabled IEEE 802.15.4 based Wireless Sensor Network”, International Journal of Computer Applications (0975 – 8887) Volume 76– No.12, pp 32-35, 2013.
[22] S.K. Guha, P.Y. Nabhiraj, T.K. Bhaumik, C. Mallik, “Design and implementation of an IEEE 802.15.4 / Zigbee based Star Network for data Acquisition and Monitoring”, Proceedings of PCaPAC2012, Kolkata, India, ISBN 978-3-95450-124-3, pp.160-162, 2012.
[23] Ishita Chakraborty, Prodipto Das, "Data Fusion in Wireless Sensor Network-A Survey", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.6, pp.9-15, 2017.
[24] sB. Fan, “Analysis on the Security Architecture of ZigBee Based on IEEE 802.15.4”, IEEE 13th International Symposium on Autonomous Decentralized Systems, DOI 10.1109/ISADS.2017.23, pp.241-246, 2017.
[25] M. Qianqian, B. Kejin, “Security Analysis for Wireless Networks Based on ZigBee”, International Forum on Information Technology and Applications, DOI 10.1109/IFITA.2009.439, pp.158-160, 2009.
[26] J. Sen, “A Survey on Wireless Sensor Network Security”, International Journal of Communication Networks and Information Security, ISSN: 2073-607X (Online) vol. 1, pp.55-78, 2009.

K. Mor, S. Kumar, D. Sharma, "Ad-Hoc Wireless Sensor Network Based on IEEE 802.15.4: Theoretical Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.219-224, 2018.

Image segmentation aims at investigating and the Images with respect to the pattern of the pixels. Many Models are addressed in this direction. Among the various focused areas of segmentation, medical segmentation has gained importance; this is due to the fact that proper categorization of the pixels helps in the identification of the diseases. However, for successful segmentation one require to judge suitable features. In this paper, mainly we are proposing a Bivariate Beta Mixture Model (BMM) for segmenting the medical images by considering the Bivariate features. In order to implement the model, Berkley Bench Mark dataset is considered. This proposed model Performance is evaluated by using PSNR, MSE, IF, Average difference (AD).

Beta Mixture Model, Berkley Images, IF, PSNR, MSE, Image Segmentation

[1] Adelino R. Ferreira da Silva: “Bayesian mixture models of variable dimension for image segmentation”, Computer methods and programs in biomedicine, pp.1-14, 2009.
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[5] Dr.Samir Kumar Bandhyopadhyay, TuhinUtsab Paul: “Segmentation of Brain MRI Image – A Review”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, Issue 3, pp.409-413, 2012.
[6] G. Dugas-Phocion, M. Á. González Ballester, G. Malandain, C. Lebrunand N. Ayache: “Improved EM-based tissue segmentation andpartial volume effect quantification in multi-sequence brain MRI”, International Conference Medical Image Computing and Computer-Assisted Intervention, Vol. 3216, pp. 26–33, 2004.
[7] GuangJianTian, Yong Xia, Yanning Zhang, Dagan Feng:“Hybrid Genetic and Variational Expectation-Maximization Algorithm for Gaussian-Mixture-Model-Based Brain MR Image Segmentation”, IEEE Transactions on Information Technology in Biomedicine, Vol. 15, No. 3, 2011.
[8] Herng-Hua Chang, Daniel J. Valentino, Gary R. Duckwiler and Arthur W. Toga: “Segmentation of Brain MR Images Using a Charged Fluid Model”, IEEE Transactions on Biomedical Engineering, Vol.54, No.10, pp. 1798-1813, 2007.
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[11] Shubhangi D.C., Sabahat Fatima, "Privacy-Preserving Outsourcing of Medical Image Data using SIFT Descriptor", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.141-145, 2017.
[12] P. Umorya, R. Singh, "A Comparative Based Review on Image Segmentation of Medical Image and its Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.71-76, 2017
[13] Nagesh Vadaparthi, “Unsupervised Medical Image Segmentation on Brain MRI images using Skew Gaussian Distribution”, IEEE – International Conference on Recent Trends in Information Technology, pp.1293 – 1297, 2011.
[14] Sonam S. Gavhande, S.B.Jadhav, “Image Segmentation and Identification of Brain Tumor from MRI Image”, International Research Journal of Engineering and Technology (IRJET), Volume: 02 Issue: 02 . PP.167-170, 2015.
[15] Vrushali Borase, Gayatri Naik, Vaishali Londhe, “Brain MR Image Segmentation for Tumor Detection using Artificial Neural”, International Journal Of Engineering And Computer Science, Volume 6 Issue 1, pp. 20160-20163,2017.
[16] K Van Leemput, F. Maes, D. Vandeurmeulen, and P. Suetens: “Automated model-based tissue classification of MR images of the brain”, IEEE Transactions Medical Imaging, Vol. 18, no. 10, pp. 897–908, 1999.
[17] Nookala Venu, B. Anuradha: “Medical Image Segmentation Using Kernal Based Fuzzy”, International Journal of Engineering Innovation & Research, Vol. 4, Issue 1, pp-207-212, 2015.
[18] M. Prastawa, E. Bullitt, S. Ho, and G. Gerig: “Robust estimation forbrain tumor segmentation”, in International Conference Medical Image Computing and Computer-Assisted Intervention, LNCS 2879, pp. 530–537, 2003.
[19] Yazid Cherfa, Assia Cherfa, Yacine Kabir, Sammyh Kassous, Assia Jaillard, Michel Dojat, and Catherine Garbay: “Segmentation of Magnetic Resonance Brain Images Using Edge and Region Cooperation Characterization of Stroke Lesions”, International Arab Journal of Information Technology, Vol. 4, No 3. pp. 281-288, 2007.
[20] Ruchi,” Study of Different Brain Tumor MRI Image Segmentation Techniques”, International Journal of Computer Science Engineering and Technology (IJCSET), Vol 4, Issue 4, PP.133-136, 2014.
[21] Azzeddine Riahi, “Image Segmentation Techniques Based on Fuzzy C-Means and Otsu, Applied to the Brain MRI in Tumor Detection”, International Journal of Computer Sciences and Engineering (IJCSE), Vol 3, Issue 12, PP. 89-101, 2015.

K.Vanitha, M. Suresh Kumar, Sk. Althaf Rahaman, "An Effective Segmentation Approach of Medical Images Based on Beta Mixture Model," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.225-229, 2018.

From the viewpoint of humans and computers, images could be interpreted in different ways. In case of humans, an image could be simply some description or scene of an action or environment etc.; while with respect to computers, it is just some combination of pixels or digital numbers. The process of Image Captioning deals with assigning internal data in the form of captions or keywords to a digital image. This paper is a comprehensive survey of different methodologies to generate appropriate image captions. Here, we have compared various approaches available for implementation of image captioning. We have also described the evaluation metrics that could be used by such systems. Appropriate captions will assist the users to search images with long queries. Automatic image captioning could also be useful for visually impaired people in understanding pictures.

Automatic image captioning, Deep CNN, Hidden Markov Model, LSTM, Neural Network, RNN

[1] Moses Soh, "Learning CNN-LSTM Architectures for Image Caption Generation ", 2016.
[2] Mathews, Alexander & Xie, Lexing & He, Xuming, " SentiCap: Generating Image Descriptions with Sentiments", 2015.
[3] Jianhui Chen, Wenqiang Dong, Minchen Li, "Image Caption Generator Based On Deep Neural Networks".
[4] X. Chen and C. L. Zitnick, "Mind`s eye: A recurrent visual representation for image caption generation," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 2422-2431.
[5] J. Donahue et al., "Long-Term Recurrent Convolutional Networks for Visual Recognition and Description," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, Issue 4, pp. 677-691, April 1 2017.
[6] H. Fang et al., "From captions to visual concepts and back," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 1473-1482.
[7] A. Karpathy and L. Fei-Fei, "Deep Visual-Semantic Alignments for Generating Image Descriptions," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 4, pp. 664-676, April 1, 2017.
[8] V. B. Kumar, T. R. Baadkar, and V. Joshi, "CRYPTANITE: A New Look to the World of Social Networks Using Deep Learning," 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Naples, 2016, pp. 358-364.
[9] O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, "Show and tell: A neural image caption generator," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 3156-3164.
[10] Geetika, Tulsi Jain, “Discriminatory Image Caption Generation Based on Recurrent Neural Networks and Ranking Objective”, International Journal of Computer Sciences and Engineering, Vol. 5, Issue.10, pp.260-265, 2017.
[11] Arnab Ghoshal, Pavel Ircing, Sanjeev Khudanpur "Hidden Markov Models for Automatic Annotation and Content-Based Retrieval of Images and Video".
[12] Zajic R. Schwartz, D & Door, B & Schwartz, Richard "Automatic Headline Generation for Newspaper Stories", 2018.
[13] PHILO SUMI , ANU.T.P " A Systematic Approach for News Caption Generation", International Journal of Advanced Research in Computer Science & Technology (IJARCST 2014), Vol. 2, Issue 2, Ver. 1 (April - June 2014)
[14] K. Ramnath et al., "AutoCaption: Automatic caption generation for personal photos," IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, 2014, pp. 1050-1057.
[15] K. Shivdikar, A. Kak, and K. Marwah, "Automatic image annotation using a hybrid engine," 2015 Annual IEEE India Conference (INDICON), New Delhi, 2015, pp. 1-6.
[16] D. J. Kim, D. Yoo, B. Sim and I. S. Kweon, "Sentence learning on deep convolutional networks for image Caption Generation," 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Xi`an, 2016, pp. 246-247.

Sailee P. Pawaskar, J. A. Laxminarayana, "Image Caption Generation: A Comprehensive Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.230-234, 2018.

This present study focused, full factorial design modeling to achieve the optimum treatment conditions on removal of zinc from the industrial wash water. After experimentation, mathematical model for zinc removal was developed to correlate the influences of process parameters and output response. The main & interactive effects of three different experimentally controlled factors were investigated. The contribution percentage of each factor were obtained in the ascending order as Initial Concentration (15.49%) < Current Density (22.19%) < Time (25.29%). The experimental result analysis showed that the combination of higher level of initial concentration and lower levels of both current density and time is essential to achieve maximization of zinc removal rate. ANOVA was used to find out the most significant parameters which affect the response characteristics. Results also show that about 100% of zinc removal can be obtained at optimum conditions.

Electro coagulation, Wash water, FFD, Removal of zinc, ANOVA

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S.Kalaivani, S.Ananthalakshmi, "Application of 3³ Ffd Modelling for Removal of Zinc from the Industrial Wash Water," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.235-240, 2018.

Smart Home is an approach to control the home appliances using sensors with a significant reduction in human efforts. This project represents a design of monitoring and controlling home automation from an android application based on Raspberry Pi (model 3B). The system uses Wi-Fi technology as a communication protocol to connect system components. Our home automation system can be split into 2 main constituents; the first part is android application that can give orders to the device that one wishes to control locally or remotely, and second part is Raspberry Pi (model 3B) that has a fitting hook up with all the Sensors and Appliances of a home automation system. It is possible to communicate with the Appliances via an android application through wireless technology. The home automation system plays a significant hand in lowering the total power utilized by home devices and gadgets.

Home Automation, Raspberry Pi, Android, Security, Voice Recognition

[1] Thad Starner,Jake Auxier,Daniel Ashbrook,”The Gesture Pendant:A Self-Illuminating,Wearble,Infrared Computer Vision SystemFor Home Automation Control and Medical Monitoring”,IEEE,2000.
[2] Carles Gomez,Josep Paradells,”Wireless Home Automation Networks:A Survey of Architectures and Technologies”,IEEE,2010.
[3] A.R. Al-Ali,Member,IEEE,M AL-Rousan,”Java Based Home Automation”,IEEE,May 2004.
[4] N. Sriskanthan and Tan Karand. “Bluetooth Based Home Automation System”. Journal of Microprocessors and Microsystems, Vol. 26, pp.281-289, 2002.
[5] Waqas Anwaar, Munam Ali Shah,”Energy Efficient Computing:A Comparison Of Raspberry Pi with Modern Devices”,International Journal of Computer and Information Technology(ISSN:2279-0764),Volume 4,March 2015.
[6] Ms. Sejal V. Ganwade,Dr. Prashant R.Deshmukh,”Raspberry Pi Technology”,International Journal of Advanced Research in Computer Science and Software Engineering,Volume 5,Issue 4,April 2015.
[7] R.Piyare, M.Tazil, ,“Bluetooth Based Home Automation System Using Cell Phone”, 2011 IEEE 15th International Symposium on Consumer Electronics.
[8] Nathan David, Abafor Chima, Ugochukwu Aronu, and Edoga Obinna,”Design of Home Automation Using Arduino”, IJSER, June 2015.
[9] Amruta Patil, Pooja Potnis,Karishma Katkar,”SMS Based Home Automation using Arduino ATMEG328 with GSM”,IJETT, May 2017.
[10] Prof.Prashant Rathod,Syed Khizaruddin,Rashmi Kotian,Shubham Lal,”Raspberry Pi Based Home Automation Using Wi-Fi,IOT &Android for Live Monitoring”,IJCST,March 2017.
[11] Mayur Khatpe, Bhrij Patel, Tushar Jain,and Varun Shah,”Remote Controlled Home Automation Using Android Application via Wi-Fi Connectivity”,IJCSE, Vol. 4, Issue 4, April 2016.
[12] Muhammad Izhar Ramli, Mohd Helmy Abd Wahab, Nabihah, “Towards Smart Home: Control Electrical Devices Online”, Nornabihah Ahmad International Conference on Science and Technology.
[13] E. Yavuz, B. Hasan, I. Serkan and K. Duygu. “Safe and Secure PIC Based Remote Control Application for Intelligent Home”. International Journal of Computer Science and Network Security, Vol. 7, No. 5, May 2007.
[14] Amul Jadhav, S. Anand, Nilesh Dhangare, K.S. Wagh “Universal Mobile Application Development (UMAD) On Home Automation” , ISSN 2224-610X (Paper) ISSN 2225-0603 (Online) Vol 2, No.2, 2012.
[15] “Raspberry Pi Home Automation With Wireless Sensors Using Smartphone”, IJCSMC, Vol. 4, Issue. 5, May 2015.
[16] Mayank Kothari, “Microcontroller Based Heart Beat Monitoring and Alerting System”, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) Vol. 9, Issue 1, Jan 2014

V. Mehta, B. Walia, V. Rathod, M. Kothari , "Smart and Interactive Home Using Raspberry Pi," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.241-245, 2018.

Li-Fi (Light Fidelity) is a wireless technology introduced as 5G VLC, which uses LED or LASER at transmitter and photo detector, photodiode, LDR, solar panel at the receiver. This technology is found to have a compound annual growth rate of 82% from 2013 to 2018. Li-Fi has a large bandwidth as it uses visible region, thus it does not obstruct other communication. As it uses visible light it does not overrun through the walls, which comes up with new generation of wireless communication. This technology has hiked great popularity from last decade. Such technology has brought not only new but harmless and pocket friendly future of communication. As the present radio wave spectrum is suffering from efficiency and interferences issues since most wireless devices are electromagnetic. Thus in order to estimate issues of expandability, accessibility and security, the idea of wireless data transmission is recommended. The objective of this work is to explore possibility of using a light fidelity which uses light not only to illuminate the room but also for sending and receiving information. Therefore, with the help of this technology communication can take place at higher speed.

Wireless Communication, Li-Fi (Light Fidelity) ,light emitting diode (LED), Wi-Fi, radio frequency (RF), visible light communication (VLC), line of sight (LOS)

[1] "Global Visible Light Communication (VLC)/Li-Fi Technology Market worth $6,138.02 Million by 2018". Markets and Markets. 10 January 2013. Archived from the original on 8 December 2015. Retrieved 29 November 2015.
[2] Parth H. Pathak, Xiaotao Feng, Pengfei Hu, and Prasant Mohapatra, “Visible Light Communication, Networking, and Sensing: A Survey, Potential and Challenges” IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 17, NO. 4, FOURTH QUARTER 2015
[3] S. Rajagopal, R. Roberts, S.-K. Lim, "IEEE 802.15.7 visible light communication: Modulation schemes and dimming support", IEEE Communication Magazine, vol. 50, no. 3, pp. 72-82, Mar. 2012.
[4] Latif Ullah Khan, “Visible light communication: applications, architecture, standardization and research challenges”,Digital Communication and Networks,3(2017)78-88.
[5] Sowdhaya M P, Vikas Krishna, Drashan S, Nikhil A R, “Evolution of Gi-Fi and Li-Fi in wireless network” IJCSE vol.4, issue 3,pp.1-7,special issue 2016.
[6] Kavyashree A, H.C. Srinivasaiah, “Data transmission and device control using Li-Fi”, International Journal of Industrial Electronics and Electrical Engineering ,ISSN:2347 -6982
[7] Anurag Sarkar, Shalabh Agrawal, Asoke Nath, “Li-Fi technology : Data transmission through visible light”,IJARCSMS vol.3,issue 6,pp.1-12,2015.
[8] Monica Leba, Simona Riurean, Andreea Ionica, University of Petrosani, “LiFi – the Path to a New Way of Communication”, ieeexplore.ieee.org/iel7/7966453/7975671/07975997.
[9] Jay H. Bhut, Dharamrajsinh N. Parmar, Khushbu V. Mehata, “Li-Fi technology-A Visible light communication”, International Journal Of Eng neering Development And Research 2014 , ISSN: 2321-9939
[10] Shivaji Kulkarni,Amogh Darekar, Pavan Joshi. ,“A Survey on Li-Fi Technology”, IEEE WiSPNET 2016 conference.

Niharika .R. Chavhan, Rakshanda .P. Shetey, Prasad .V. Dixit, Deepali Kotambkar, "Exploring Light Fidelity for Wireless Text and Audio Transmission," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.246-250, 2018.

With the large scale integration of Distributed Energy Resources (DERs) into the existing power system, there has been a significant impact on the operation of distribution networks; the major impacts being power quality problem along with congestion and voltage regulation issues. This demands coordinated control approaches which allow Distributed Generation (DG) units to actively participate in voltage and frequency regulation. To realize the same, hierarchical control structures constituting the primary, secondary and tertiary control structures are implemented. These controllers are classified as the centralized or the decentralized type. Thus by employing droop controls or impedance based controls desirable outcomes such as power sharing, non linear load sharing and harmonic reduction is possible thanks to coordinated operation of secondary and tertiary control layers with primary or local layer. This paper aims at establishing a basic understanding of these control layers as applied to AC and DC microgrids along with detailed explanation of modified structures from the conventional control structures in a typical microgrid.

Microgrid, Secondary Control, Tertiary Control, Distributed Generation, Droop control, Virtual Impedance Control

[1] S. Anand, B.G. Fernandes and J.M. Guerrero, “Distributed control to Ensure Proportional Load Sharing and Improve Voltage Regulation in Low-Voltage DC Microgrids”, IEEE Transactions on Power Electronics, vol. 28, no. 4, pp.1900-1913,
April 2013.
[2] J. M. Guerrero, J. C. Vasquez, J. Matas, L. G. de Vicufia, M. Castilla, "Hierarchical control of droop-controlled ac and dc microgrids-A general approach toward standardization", IEEE Trans. Ind. Electron., vol. 58, no. 1, pp. 158-172, 2011.
[3] V. Nasirian, S. Moayedi, A. Davoudi and F.L.Lewis, “Distributed
Cooperative Control for DC Microgrid”, IEEE Transactions on Power Electronics, vol. 30, no. 4, pp. 2288-2303, April 2015.
[4] J. M. Guerrero, J. C. Vasquez, J. Matas, L. G. de Vicufia, M. Castilla, "Hierarchical control of droop-controlled ac and dc microgrids-A general approach toward standardization", IEEE Trans. Ind. Electron., vol. 58, no. 1, pp. 158-172, 2011.
[5] S. Anand, B.G. Fernandes and J.M. Guerrero, “Distributed control to Ensure Proportional Load Sharing and Improve Voltage Regulation in Low-Voltage DC Microgrids”, IEEE Transactions on Power Electronics, vol. 28, no. 4, pp.1900-1913, April 2013
[6] P. Wang, X. Lu, X. Yang, W. Wang and D. Xu, “An Improved
Distributed Secondary Control Method for DC Microgrids With
Enhanced Dynamic Current Sharing Performance”, IEEE Transactions on Power Electronics, vol. 31, no. 9, pp. 6658-6673, Sept. 2016
[7] Q. Shafiee, J.M. Guerrero and J.C. Vasquez, “Distributed secondary control for islanded microgrids-a novel approach”, IEEE Transactions on Power Electronics, vol. 29, no. 2, pp. 1018-1031, February 2014

Tahoora Qureshi, Rizwan Alvi, "A Review on Secondary and Tertiary Control Structures for Microgrid," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.251-255, 2018.