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Digital image processing techniques enable ophthalmologists to detect and treat several eye diseases like diabetic retinopathy and glaucoma. Glaucoma is an eye problem that affects the retina and weakens the nerve cells that assist in visual recognition. Glaucoma, the most common cause of blindness is the disease of the optic nerve of the eye and can lead to ultimate blindness if not treated at an early stage. Raised intraocular pressure, increase in cup to disk ratio and visual field test are some of the measures for such a disease. This paper presents a succinct of different types of image processing methods employed for the detection of Glaucoma The main objective of this project is to find an automated tool to detect glaucoma at an early stage and to classify this disease based on its severity and damage of the optic fibre. In this paper different existing methods are reviewed and their performance are evaluated so that it can help the researchers in their work.

Glaucoma, Cup-Disc Ratio, Image Processing, Glaucoma Stages

[1] H. A. Quigley, “Number of people with glaucoma worldwide,” The British Journal of Opthalmology, 80(5), pp. 389–393, 1996
[2] Erik linner, “The early detection of glaucoma” Springer, Chapter Public Health Opthalmology Volume 5 of the series Documenta Ophthalmologica pp 23-24.
[3] http://www.glaucoma.org/glaucoma/types-of-glaucoma.php
[4]http://www.claruseye.com/portfoliopost/glaucoma/
[5] Tehmina Khalil, Samina Khalid and Adeel M. Syed ‘Review of Machine Learning Techniques for Glaucoma Detection and Prediction’ Science and Information Conference 2014 August 27-29, 2014 | London, UK
[6] R.N. Weinreb, and P.T. Khaw, “Primary open-angle glaucoma,” The Lancet, vol. 363, pp. 1711–1720, 2004.
[7] A.Murthi & 2M.Madheswaran ‘Enhancement Of Optic Cup To Disc Ratio Detection In Glaucoma Diagnosis’ 978-14577-1583-9/12/$26.00 © 2012 IEEE
[8] Li Xiong, Huiqi Li and Yan Zheng ‘Automatic Detection of Glaucoma in Retinal Images’ 978-14799- 43159/14/$31.00_c 2014 IEEE.
[9] Hafsah Ahmad, Abubakar Yamin, Aqsa Shakeel, Syed Omer Gillani, Umer Ansari ‘Detection of Glaucoma Using Retinal Fundus Images’ 978-14799-5132-1/14/$31.00 ©2014 IEEE.
[10] Tangelder, G.J.M., Reus, N.J., and Lemij, H.G., “Estimating the clinical usefulness of optic disc biometry for detecting glaucomatous change over time,” Eye, vol. 20, pp. 755–763, 2006.
[11] Kevin Noronha, Jagadish Nayak, S.N. Bhat, “Enhancement of retinal fundus Image to highlight the features for detection of abnormal eyes”.
[12] J. Xu, O. Chutatape, E. Sung, C. Zheng, and P. C. T. Kuan, “Optic Disk Feature Extraction Via Modified Deformable Model Technique for Glaucoma Analysis”, Pattern Recognition, 2007, Vol. 40, pp. 2063–2076
[13] Mei-Ling Huang, Hsin-Yi Chen, Jian-Jun Huang: “Glaucoma detection using adaptive neuro-fuzzy inference system”, Expert Systems with Applications 32 (2007) 458–468.
[14] Jyotika Pruthi, Dr.Saurabh Mukherjee: “Computer Based Early Diagnosis of Glaucoma in Biomedical Data Using Image Processing and Automated Early Nerve Fiber Layer Defects Detection using Feature Extraction in Retinal Colored Stereo Fundus Images”, International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April 2013.
[15] U. Rajendra Acharya, Sumeet Dua, Xian Du, “Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features”, IEEE Transactions On Information Technology In Biomedicine, May 2011, pp 449-455.
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[17] Inoue, Kenji Yanashima, Kazushige Magatani, Takuro Kurihara, Naoto, “Development Of A Simple Diagnostic Method For The Glaucoma Using Ocular Fundus Pictures”, in the Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005
[18] R¨udiger Bock, J¨org Meier, Georg Michelson, L´aszl´o G. Ny´ul, and Joachim Hornegger, “Classifying Glaucoma with Image-Based Features from Fundus Photographs”, DAGM 2007, LNCS 4713, pp. 355–364, 2007. Springer-Verlag Berlin Heidelberg 2007.
[19] S. Sekhar, W. Al-Nuaimy, and A.Nandi, “Automated localisation of retinal optic disk using hough transform,” in: Proceedings of 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro., pp. 1577–1580, 2008.
[20] J. Cheng, J. Liu, Y. Xu, F. Yin, D. Wing, K. Wong, N. Tan, and D. Tao, “Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening”, IEEE Transactions On Medical Imaging, Vol. 32, no. 6, 2013, pp. 1019– 1032.
[21] Hafsah Ahmad, Abubakar Yamin, Aqsa Shakeel et al. ,”Detection of glaucoma using retinal fundus images”, IEEE 2014.
[22] Sobia Naz, Sheela Rao, “Glaucoma detection in color fundus images using cup to disc ratio”, IJES 2014.
[23] Anindita Septiarini, Agus Harjoko, “Automatia glaucoma detection based on the type of features used: A Review”, JATIT 2015.

Kajal Patel, Yogesh Kumar Rathore, "“Glaucoma Detection and Classification: A Review”," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.543-547, 2019.

The problems of missing values in the field of data mining have become emerging areas of research in recent years. It has been a great challenge in research for quite a long time. The missing values may occur due to several reasons. The missing values in the data set can affect accuracy and performance of result when any algorithm is implemented on it. Presence of missing values leads to less efficiency and difficulty in extracting meaningful information. As we go through the literature we can find there are various imputation techniques basing on type of missing value. Since the amount of data is increasing day by day, there is a need for an appropriate technique to handle the missing values in the data set. In this paper a brief year wise study of existing methods are being done so that it would be a great help while formulating and implementing a new one towards solving the problem of missing values.

Data Mining, Data Set, Missing Values, Algorithm, Information, Imputation

[1] Qian Ma, Yu Gu, Wang-Chien Lee and Ge Yu, ”Order-Sensitive Imputation for Clustered Missing Values”, IEEE Transactions on Knowledge and Data Engineering, 1041-4347 ©2018.
[2] Teresa Pamuła, “Impact of Data Loss for Prediction of Traffic Flow on an Urban Road Using Neural Networks”, IEEE Transactions On Intelligent Transportation Systems 1524-9050 © 2018.
[3] Siamak Zamani Dadaneh , Edward R. Dougherty and Xiaoning Qian , “Optimal Bayesian Classification With Missing Values”, IEEE Transactions On Signal Processing, Vol. 66, No. 16, August 15, 2018.
[4] Aiguo Wang, Ye Chen, Ning An, Jing Yang, Lian Li, and Lili Jiang, “Microarray Missing Value Imputation: A Regularized Local Learning Method”, IEEE, 1545-5963 ©2018.
[5] Wujun Si, Qingyu Yang , Leslie Monplaisir and Yong Chen, “Reliability Analysis of Repairable Systems With Incomplete Failure Time Data”, IEEE , 0018-9529 © 2018.
[6] Nur Afiqah Zakaria, Norazian Mohamed Noor,” Imputation Methods For Filling Missing Data In Urban Air Pollution Data For Malaysia”, Urbanism. Arhitectură. Construcţii, Vol. 9 , No. 2 , 2018.
[7] Xiaolong Xu, Weizhi Chong, Shancang Li, Abdullahi Arabo, “Missing Data Imputation Based On The Evidence Chain”, IEEE Access, Vol. 6, 2169-3536, 2018.
[8] Zeng Yu, Tianrui Li, Shi-Jinn Horng, Yi Pan, Hongjun Wang and Yunge Jing, “An Iterative Locally Auto-Weighted Least Squares Method for Microarray Missing Value Estimation”, IEEE Transactions On Nanobioscience, Vol. 16, No. 1, January 2017.
[9] Ivan Markovsky,” A Missing Data Approach to Data-Driven Filtering and Control”, IEEE Transactions On Automatic Control, Vol. 62, No. 4, April 2017.
[10] Weiwei Shi, Yongxin Zhu, Philip S. Yu, Jiawei Zhang, Tian Huang, Chang Wang, and Yufeng Chen, “Effective Prediction of Missing Data on Apache Spark over Multivariable Time Series”, IEEE Transactions on Big Data ,DOI 10.1109/TBDATA.2017.2719703.
[11] R. Misir and R.K. Samanta,”A Study on performance of UCI Hungarian dataset using missing value management techniques”, IJCSE, Volume-5, Issue-3, 2017.
[12] Malay Mitra and R. K. Samanta,”
A Study on Missing Data Management”, IJCSE, Volume-5, Issue-2, E-ISSN: 2347-2693, 2017.
[13] Yelipe UshaRani, Dr.P.Sammulal, “An Innovative Imputation and Classification Approach for Accurate Disease Prediction”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 14 S1, February 2016.
[14] Darryl ND, Rahman MM, “Missing Value Imputation Using Stratified Supervised Learning for Cardiovascular Data”, Global J Technol Optim 7:6 DOI: 10.4172/2229-8711. S1:113,2016.
[15] Tejal Patil, “Systematic Mapping Study of Missing ValuesTechniques using Naive Bayes”, IRJET, e-ISSN: 2395 -0056, Volume: 03, Issue: 03 , Mar-2016.
[16] Y.Usha Rani1, P. Sammulal, “A Novel Approach for Imputation of Missing Attribute Values for Efficient Mining of Medical Datasets – Class Based Cluster Approach”, Rev. Téc. Ing. Univ. Zulia. Vol. 39, No 2, 184 - 195, 2016.
[17] R. Naveen Kumar, M. Anand Kumar, “Enhanced Fuzzy K-NN Approach for Handling Missing Values in Medical Data Mining”, Indian Journal of Science and Technology, Vol 9(S1), DOI: 10.17485/ijst/2016/v9iS1/94094 , December 2016.
[18] Jocelyn T. Chi, Eric C. Chi, and Richard G. Baraniuk, “k-POD A Method for k-Means Clustering of Missing Data”, arXiv:1411.7013v3 [stat.CO] 27 Jan 2016.
[19] Swati Jain & Mrs. Kalpana Jain, “Estimation of Missing Attribute Value in Time Series Database in Data Mining”, Global Journals Inc. (USA), Volume 16, Issue 5, Version 1.0, Year 2016.
[20] P.Saravanan,P.Sailakshmi, “Missing Value Imputation Using Fuzzy Possibilistic C Means Optimized With Support Vector Regression And Genetic Algorithm”, JATIT & LLS, Vol.72, No.1, 2015.
[21] Elsiddig Elsadig Mohamed Koko, Amin Ibrahim Adam Mohamed, “Missing Data Treatment Method On Cluster Analysis”, International Journal of Advanced Statistics and Probability, Vol.3,No.2 ,191-209, 2015.
[22] Huseyin Ozkan, Ozgun Soner Pelvan, and Suleyman S. Kozat, “Data Imputation Through the Identification of Local Anomalies”, IEEE Transactions On Neural Networks And Learning Systems, Vol. 26, NO. 10, October 2015.
[23] Edgar Acuna ,Caroline Rodriguez, “The treatment of missing values and its effect in the classifier accuracy”, Research Gate, DOI: 10.1007/978-3-642-17103-1_60, 2015 .
[24] Artur Matyja, “Comparison of Algorithms for Clustering Incomplete Data, Foundations Of Computing And Decision Sciences”, Vol.39, No.2, DOI: 10.2478/fcds-2014-0007, ISSN 0867-6356, 2014.
[25] Minakshi, Dr. Rajan Vohra, Gimpy, “Missing Value Imputation in Multi Attribute Data Set”, IJCSIT, Vol. 5 (4) , 5315-5321, 2014,.
[26] Xiaoping Zhu, “Comparison of Four Methods for Handling Missing Data in Longitudinal Data Analysis Through a Simulation Study”, Open Journal of Statistics, 4, 933-944, 2014.
[27] Jiri Kaiser, “Dealing with Missing Values in Data, Journal Of Systems Integration”, 2014/1.
[28] Tapas Ranjan Baitharu and Subhendu Kumar Pani, “Effect of Missing Values on Data Classification, JETEAS”, 4(2): 311-316, (ISSN: 2141-7016), 2013.
[29] Luciano C. Blomberg, Duncan Dubugras A. Ruiz, “Evaluating the Influence of Missing Data on Classification Algorithms in Data Mining Applications”,SBSI,2013.
[30] Jing Tian, Bing Yu, Dan Yu, and Shilong Ma, “Clustering-Based Multiple Imputation via Gray Relational Analysis for Missing Data and Its Application to Aerospace Field”, The ScientificWorld Journal, Article ID 720392, 10 pages, 2013.
[31] Sujatha.R, “Enhancing Iterative Non-Parametric Algorithm for Calculating Missing Values of Heterogeneous Datasets by Clustering”, IJSR Publications, Volume 3, Issue 3, March 2013.
[32] Aasha.M, “Imputation in Mixed Attribute Datasets using Higher Order Kernel Functions”, IJIET, Vol. 2 Issue 3, ISSN: 2319-1058, June 2013.
[33] Santosh Dane, Dr. R. C. Thool, “Imputation Method for Missing Value Estimation of Mixed-Attribute Data Sets”, IJARCSSE, Volume 3, Issue 5, ISSN: 2277 128X, May 2013.
[34] Ji Liu, Przemyslaw Musialski, Peter Wonka, and Jieping Ye, “Tensor Completion for Estimating Missing Values in Visual Data”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 35, No. 1, January 2013.
[35] Bhavisha Suthar, Hemant Patel, Ankur Goswami,”A Survey: Classification of Imputation Methods in Data Mining”,IJETAE, ISSN 2250-2459, Volume 2, Issue 1, January 2012.
[36] R.Devi Priya, S.Kuppuswami, “A Genetic Algorithm Based Approach for Imputing Missing Discrete Attribute Values in Databases”, WSEAS Transactions On Information Science And Applications, E-ISSN: 2224-3402, Volume 9, Issue 6, June 2012.
[37] Noel Lopes, Bernardete Ribeiro, “Handling Missing Values Via A Neural Selective Input Model” Neural Network World 4/12, 357-370, ICS AS CR 2012.
[38] Julian Luengo, Jose A. Saez, Francisco Herrera,”Missing data imputation for fuzzy rule-based classification systems”, 16:863–881 DOI 10.1007/s00500-011-0774-4, 2012.
[39] Satish Gajawada, Durga Toshniwal, “Missing Value Imputation Method Based on Clustering and Nearest Neighbours”, International Journal of Future Computer and Communication, Vol. 1, No. 2, August 2012.
[40] K. Raja , G. Tholkappia Arasu ,Chitra. S. Nair, “Imputation Framework for Missing Values, International Journal of Computer Trends and Technology”, volume3,Issue2,2012.
[41] Ganga.A.R, B.Lakshmipathi, “Higher Order Kernel Function Algorithm for Imputing Missing Values”,IJARCS, Volume 3, No. 3, ISSN No. 0976-5697, May-June 2012,.
[42] Ibrahim Berkan Aydilek and Ahmet Arslan, “A Novel Hybrid Approach To Estimating Missing Values In Databases Using K-Nearest Neighbors And Neural Networks”, International Journal of Innovative Computing, Information and Control, ISSN 1349-4198,Volume 8, Number 7(A), pp. 4705-4717, July 2012,.
[43] R.S. Somasundaram, R. Nedunchezhian, “Missing Value Imputation using Refined Mean Substitution”, IJCSI, Vol. 9, Issue 4, No 3, July 2012.

Dipalika Das, Maya Nayak, Subhendu Kumar Pani, "Missing Value Imputation-A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.548-558, 2019.

In this technology bound era, data analytics is a decisive way to deal with this enormous amount of data that is getting collected from various sources such as social media, banking, healthcare etc. With the growing volume of this data, it has been getting more and more difficult to analyze the same with the existing techniques. This is where the concept of Machine Learning (ML) has turned out to be an indispensable way for giving this data an intelligent structure i.e. by sorting the clusters of data into data sets and drawing associations from previous information. However, the traditional machine learning methods are not helpful in manipulating the data in a way that we require as we are advancing in these various fields involving big data. In our research we have reviewed the various ML algorithms and learning paradigms for handling the big data problems by associating them with the challenges of the 6 big data dimensions- Volume, Veracity, Velocity, Variety, Visualization and Value. We have studied the similar approach of research given by Alexandera et al. and Gandomi and Haider. Adding on to their findings and methods we have considered two more V’s – Visualization and Value and associated their characteristic challenges with the ML methods. We have mentioned the use of ML in preserving the privacy and security of the data as securing the data being generated is also a significant problem that needs to be addressed.

Big data analytics, Machine Learning, V’s of big data, algorithms, learning paradigms

[1] L. Sweeney, “k-anonymity: A model for protecting privacy,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 05, 2002
[2] A.Vinothini and Dr.S.Baghavathi Priya “Survey of Machine Learning Methods for Big Data Applications” International Conference on Computational Intelligence in Data Science, 2017.
[3] Xi Fang and Juanjuan Wang, “The Application of Big Data Technology and Method in Moral Education in Colleges and a Universities”, International conference on Intelligent Transportation, Big data and Smart City (ICITBS)
[4] S. R. Sukumar, “Machine Learning in the Big Data Era: Are We There Yet?” in Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining: Workshop on Data Science for Social Good (KDD 2014), 2014.
[5] M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, “Deep Learning Applications and Challenges in Big Data Analytics,” Journal of Big Data, vol. 2, Feb. 2015.
[6] J. Qiu, Q. Wu, G. Ding, Y. Xu, and S. Feng, “A Survey of Machine Learning for Big Data Processing,” EURASIP Journal on Advances in Signal Processing, vol. 67, 2016.
[7] J.Qiu et al., “A survey of machine learning for big data processing,” Signal Processing for Big Data, 28 May,2016
[8] O. Y. Al-Jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis, and K. Taha, “Efficient Machine Learning for Big Data: A Review,” Big Data Research, vol. 2, no. 3, Apr. 2015.
[9] A. L`Heureux, K. Grolinger, H. F. Elyamany, and M. A. M. Capretz, “Machine Learning with Big Data: Challenges and Approaches,” IEEE Access, vol. 5.
[10] A. Gandomi and M. Haider, “Beyond the Hype: Big data Concepts, Methods, and Analytics,” International Journal of Information Management, vol. 35, Apr. 2015.
[11] S.Athmaja, M.Hanumanthapa and V.Kavitha, “A Survey Of Machine Learning Algorithms For Big Data Analytics,” International Confrence on Innovations in Information, Embedded Communication Systems(ICIIECS),2017
[12] H. Liu and H. Motoda, Instance Selection and Construction for Data Mining, vol. 608. Springer Science & Business Media, 2013.
[13] A. Buades, B. Coll, and J. Morel, “A Review of Image Denoising Algorithms, with a New One,” Multiscale Modeling & Simulation, vol. 4, no. 2, 2005.
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[15] R. Agrawal and R. Srikant, “Privacy-preserving data mining,” in ACM Sigmod Record, vol. 29, no. 2. ACM, 2000.
[16] K. Liu, H. Kargupta, and J. Ryan, “Random projection-based multiplicative data perturbation for privacy preserving distributed data mining,” IEEE Transactions on knowledge and Data Engineering, vol. 18, no. 1, 2006.
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[18] A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, “l-diversity: Privacy beyond k-anonymity,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 1, 2007.
[19] R. Salakhutdinov and G. E. Hinton, “Deep Boltzmann Machines,” in Proceedings of the International Conference on Artificial Intelligence and Statistics, 2009.
[20] G. Hinton, “Deep Belief Nets,” in Encyclopedia of Machine Learning, Springer, 2010, pp. 267–269.
[21] CW Tsai, CF Lai, MC Chiang, LT Yang, “Data mining for the internet of things: a survey”. IEEE Commun Surv Tut 16(1), 77–97 (2014)
[22] X Wu, X Zhu, G Wu, W Ding, “Data mining with big data”. IEEE Trans Knowl Data Eng 26(1), 97–107 (2014)
[23] U Fayyad, G Piatetsky-Shapiro, P Smyth, “From data mining to knowledge discovery in databases”. AI Mag 17(3), 37–54 (1996)
[24] Mohammed Z. Omer and Hui Gao, “Privacy Preserving in Distributed SVM Data Mining on Vertical Partitioned Data”, 3rd International Conference on Soft Computing & Machine Intelligence, 2016.
[25] M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, “Deep Learning Applications and Challenges in Big Data Analytics,” Journal of Big Data, Feb. 2015.
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[29] Long Xu and Yihua Yan, “Machine Learning for Astronomical Big Data Processing”, IEEE,2017
[30] A.Vinothini, S.Baghavati, “Survey of Machine Learning for Big Data Applications,” International Confrence on Computational Intelligence in Data Science(ICCIDS),2017
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M. Sharma, I.S. Sohal, R.M.Singh, A. Wadhwa, D. Garg, "Review of Machine Learning Method for Resolving Issues of Big Data Analytics," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.559-563, 2019.

Floods are rare and dangerous disaster in minimum duration, which have the most destructive impact within urban and rural areas. The research on the advancement of flood prediction models contributed to risk reduction, to prevent the loss of human life, and reduction the property damage in floods. This paper structure the machine learning models, two separate models based on including and excluding the river flow were developed for each variable to quantify the importance of the river flow on the accuracy of the flood forecasting models. In this paper, aims to discovering more accurate and efficient prediction models. In recent research, two main approaches are developed in hydrological forecasting. The first approach is based on mathematical modeling. It models the physical dynamics between the principal components of the hydrological system. The second approach is based on modeling the statistical relationship between the hydrologic input and output, without explicitly considering the relationships that exist among the involved physical processes. The water level flow is deducted in this research, river flow proves the most and least improvement on the efficiency of the models applied for flood forecasting. As a result, this paper describes the most promising prediction methods for both long-term and short-term floods. This paper can be used as a predicting the flood by choosing the proper Machine Learning (ML) algorithm such as Support Vector Machine(SVM) and Artificial Neural Network(ANN) algorithm for showing higher accuracy.

flood prediction, hydrological model, machine learning, flood prediction, artificial intelligence, time series prediction

[1] H. Shahraiyni, M. Ghafouri, S. Shouraki, B. Saghafian, and M. Nasseri, “Comparison between active learning method and support vector machine for runoff modeling,” J. Hydrol. Hydromechanics, vol. 60, no. 1, pp. 16–32, 2012.
[2] S. H. Elsafi, “Artificial Neural Networks (ANNs) for flood forecasting at Dongola Station in the River Nile, Sudan,” Alexandria Eng. J., vol. 53, no. 3, pp. 655–662, 2014.
[3] J. Noymanee, N. O. Nikitin, and A. V. Kalyuzhnaya, “Urban Pluvial Flood Forecasting using Open Data with Machine Learning Techniques in Pattani Basin,” Procedia Comput. Sci., vol. 119, no. 2017, pp. 288–297, 2017.
[4] O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. E. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, vol. 4, no. 11, p. e00938, 2018.
[5] R. UshaRani, T. K. R. Krishna Rao, and R. Kiran Kumar Reddy, “An Efficient Machine Learning Regression Model for Rainfall Prediction,” Int. J. Comput. Appl., vol. 115, no. 23, pp. 24–30, 2015.
[6] J. Abbot and J. Marohasy, “Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies from Eastern Australia,” Eng. Math. Top. Rainfall, 2018.
[7] F. Liu, F. Xu, and S. Yang, “A Flood Forecasting Model Based on Deep Learning Algorithm via Integrating Stacked Autoencoders with BP Neural Network,” Proc. - 2017 IEEE 3rd Int. Conf. Multimed. Big Data, BigMM 2017, pp. 58–61, 2017.
[8] M. Hitokoto and M. Sakuraba, “Applicability of the Deep Learning Flood Forecast Model Against the Inexperienced Magnitude of Flood,” vol. 3, pp. 901–893, 2018.
[9] P. Misra and S. Shukla, “Implementation of Neuro-Fuzzy and Statistical Technique for Flood Forecasting in Cauvery Basin , India,” no. 6, 2018.
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D. Suganya, V. Savithri, "Eyeopen for Predicting of Flood Flow by Statistical and Machine Learning Model," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.564-568, 2019.

Virtual Reality is based on the notion of immersion i.e. a new technological advancement in the field of human machine interaction bringing it closer to real life. It is a technology for simulation of a real or virtual world in which one can immerse, touch, & sense the objects with the virtual presence in that 3-D world. Virtual Reality (VR) is a well-known concept and has been proven to be beneficial in various areas such as entertainment, research, military training, medical training, etc. Also, many applications using VR technology in education have been reported. This paper reviews the ideas & concepts behind the architectural representation, supporting software & hardware implementations, various categorized languages & modelling tools etc. This paper also studies current research objectives, comparison with other virtualized environments, development trends of VR, & modeling methods. Based on the analysis of structure & functioning of virtual reality environment, this paper presents various applications of virtual reality & different categories of issues related to it.

Virtual Reality , Human – Machine Interaction , Modeling and techniques

[1] https://en.wikipedia.org/wiki/Virtual_reality
[2] Namrata Singh; Sarvpal Singh, “Virtual Reality: A brief Survey”, International Conference on Information Communication and Embedded Systems (ICICES), 2017
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[4] V. Kovalcík, J. Chmelík, M. Bezdeka and J. Sochor, “Virtual Reality System as a Tool for Education”, 20th International Conference on Computer Graphics, Visualization and Computer Vision, p. 15-18, 2012.
[5] Zhenjiang Shen, Yan Ma, Kenichi Sugihara, Zhenhan Lei, Evan Shi, “Technical possibilities of cloud based Virtual Reality Implementing SaaS for online collaboration in Urban Planning”, Int. J. Communications, Network and System Sciences, 2014, 7, 463-473.
[6] Science in China Series F: Information Sciences, 2009.
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Prabhat K. Pushkar, Vishnu Nair, Himanshu Gupta, Akash Ubbu, Swati Gajbhiye, "Survey on Virtual Reality," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.569-574, 2019.

There has been a greater development of virtual technologies in the recent arena. Some of them increased the computing performances of the functioning systems. One of those highly used virtualized technology is the virtual mouse. The moments that the mouse detects are converted into the pointer movements on a display that enables the management of Graphical User Interface (GUI) on a computer platform. This paper advocates an approach for Human-Computer Interaction (HCI) where a real-time camera is used in handling the cursor movements. The Virtual mouse colour recognition program acquires real-time images continuously which will then go through a series of filtration and transformation. As the process completes the program will apply an image processing technique to capture the coordinates of the position of the targeted colours from the changed frames. Then a set of different combinations of functions are operated and then by analyzing the set of different colours thereby a program will execute the mouse function and then it is translated as an actual mouse for user’s machine.

Virtual Mouse, Graphical User Interface, Colour Recognition, Human-Computer Interaction, Calibration Phase, Recognition Phase

[1] Banerjee A, Ghosh A, Bharadwaj K,& Saikia H, “Mouse Control using a Web Camera based on color Detection”, IJCTT vol. 9 no. 1,March 2014.
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Manne Vamshi Krishna, Gopu Abhishek Reddy, B. Prasanthi, M. Sreevani, "Green Virtual Mouse Using OpenCV," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.575-580, 2019.

The Reliable service composition of web services is considered as imperative for ensuring continuous services to the users since they are accountable for consolidating varsity number of applications in spite of their opportunity. The significant enhancement in web services field in the terminal two decades facilitate the privilege of devising peculiar service composition and service selection schemes for optimal performance and success rate of the effective web service composition. Based on the analytic works proposed the effective service composition was proved to be devised using the aspects of QoS or Transactional features of workflow. This survey paper discusses the essential demand for service composition, the enforced technologies to execute service composition and also it figures out the worst candidate services from the state-of-art approaches. It further affords many different composition procedures, exhibiting early implementations of various approaches based on few currently existing service composition platforms and frameworks.

QoS constraints, Web Service Selection, Service Composition, Transactional features

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[13] Cardinale, Y., Haddad, J. E., Manouvrier, M., & Rukoz, M. (2011). CPN-TWS: A colored petri-net approach for transactional-QoS driven Web Service composition. International Journal of Web and Grid Services, 7(1), 91.
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[15] Wang, X., Xu, X., Sheng, Q. Z., Wang, Z., & Yao, L. (2016). Novel Artificial Bee Colony Algorithms for QoS-Aware Service Selection. IEEE Transactions on Services Computing, 2(1), 1-1.
[16] Dahan, F., El Hindi, K., & Ghoneim, A. (2017). Enhanced Artificial Bee Colony Algorithm for QoS-aware Web Service Selection problem. Computing, 99(5), 507-517.
[17] Gao, W., & Liu, S. (2012). A modified artificial bee colony algorithm. Computers & Operations Research, 39(3), 687–697.
[18] Dahan, F., El Hindi, K., & Ghoneim, A. (2017). Enhanced Artificial Bee Colony Algorithm for QoS-aware Web Service Selection problem. Computing, 99(5), 507-517.
[19] Zhou, J., & Yao, X. (2016). A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition. The International Journal of Advanced Manufacturing Technology, 88(9-12), 3371–3387.doi:10.1007/s00170-016-9034-1
[20] Zhu, G., & Kwong, S. (2010). Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computation, 217(7), 3166–3173.
[21] Wang, X., Xu, X., Sheng, Q. Z., Wang, Z., & Yao, L. (2016). Novel Artificial Bee Colony Algorithms for QoS-Aware Service Selection. IEEE Transactions on Services Computing, 2(1), 1-1.
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N. Arunachalam, A. Amuthan, "A Survey on QoS aware Web Service Selection for Reactive Service Composition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.581-587, 2019.

Big Data is a common term referring to a data revolution in information technology that makes it easy to collect, store and analyze user data online at relatively low costs. In simpler words, any human activity using technology leaves a ‘digital exhaust’ or a trace data, a footprint. Broadly speaking, the big pool of all these collected footprints is called Big Data. However, it’s not just a collection of these footprints but it also contains various other information like weather, train information, payments, etc. Generally these footprints may not have any apparent or obvious meaning, but they start to make sense when combined with other recorded datasets. This information could be processed using powerful analytic tools to give greater meaning and context to it while also enabling the system to ‘predict’ the unknown or missing information in the dataset. Today, we are already surrounded by a sea of ubiquitous sensors (sensors on your phones, punching access cards or swiping credit cards, etc). With each advancement, like the advent of the Internet of Things, coupled with the ‘smartphone revolution’ linking more and more information to your social media accounts, it is getting easier to gather more information and make sense of it. In this paper we discussed pseudonymization and privacy by design as the processing of personal data in such a way that the data can no longer be attributed to a specific data subject without the use of additional information.

Ubiquitous, Pseudonymization, privacy by design

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M. Suresh Babu, Mohammed Irfan, Suneetha. V, "Privacy preservation and Privacy by Design techniques in Big Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.588-593, 2019.

Small size wideband microstrip patch antenna with horizontal slot and vertical slot in patch stacked patch and multiple patch is fed through microstrip line is presented. By inserting slot on patch, stacked patch supported by wall and the multiple patch, the bandwidth can improve significantly without significant change in the frequency. The bandwidth before adding the slot and the stacked patch was 5%, whereas after adding the slot and the stacked patch the bandwidth increased ranging from 2.42 to 2.50 GHz. The radiation pattern has acceptable response at both E-plane and H-plane. The ground plane size is 56 mm by 74 mm, the antenna designed is based on FR4 substrate with dielectric constant 4.4.For all that, the design of a microstrip antenna is not always an easy problem and the antenna designer is faced with difficulties coming from a) the inherent disadvantages of a printed resonant antenna element, for example the narrow impedance bandwidth, and b)the various requirements of the specific applications, which concern the operation of the radiating element, and cannot be satisfied by a printed scheme with an ordinary configuration[3]. Moreover, the rapid development in the field of Land Mobile Telephony as well as in the field of Wireless Local Area Networks (WLANs) demands devices capable to operate in more than one frequency bands [4].

Bandwidth, gain, offset, stacking Broadband microstrip antenna

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Shubham Chouhan, Rajdeep Shrivastava, "Design and Analysis of Different Types of Modern Methods Using Line Feed of Microstrip Antenna Using CST Software," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.594-603, 2019.

From last three decades people from all around the world continuously using services provided by Information Technology industry most probably in every area, in order to fulfill the needs of business to personal activities, but from decade ago demand tremendously increased. Though as we have lots of benefits of using IT services which made our current life smoother, easier and less-hardworking as compare to past yet there is strong need arises to protect our digital world by managing Confidentiality, Integrity, Availability, Authenticity of our resources from those who unwantedly wants to look into ours privacy whether by passive or active attacks. Digital Signature is most important part from Public-key Cryptography that provides a set of security capabilities that would be difficult to implement in any other way especially for proving the authenticity of data.

Algorithm, Authenticity, Avalanche-Effect, Certificates, Confidentiality, Digest, Firewall, Integrity, Hash Function, Non-Repudiation

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Arvind K.Sharma, Satish.K.Mittal, "A Comprehensive Study on Digital-Signatures with Hash-Functions," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.604-607, 2019.