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A fuzzy logic based decision support system (DSS) for agriculture support system is presented. The primary focus is on the algorithm used to correctly predict how much water should be poured to the agriculture for the optimal growth of the crops. Over-watering as well as under-watering has always been a big problem in farming. The proposed system uses three input parameters; namely field moisture, field humidity and field temperature. However, for predicting the proper amount of water so as to get the optimized best growth of the crop, few more parameters also play a vital role but in this work for simplicity purpose we have taken these three parameters as input. Through decision support system, the meaning of transferred data is translated into linguistic variables that can be understood by non-experts. Mamdani inference engine is used to deduce from the input parameters. Design of the proposed system is given with the fuzzy logic controller and simulation is being done using MATLAB (Matrix Laboratory) for solving the water irrigation issue

Fuzzy logic, Decision Support Systems, Agriculture support, Rule base

[1]. S. J. Yelapure, Dr. R. V. Kulkarni, “Literature Review on Expert System in Agriculture,” International Journal of Computer Science and Information Technologies (IJCSIT), vol. 3, no. 5, 2012.
[2]. Patterson, D.W., “Introduction to Artificial Intelligence and Expert Systems,” Prentice-Hall, New Delhi, 2004.
[3]. S.Saini, Harvinder, Kamal Raj and Sharma A.N., ”Web Based Fuzzy Expert System for Integrated Pest Management in Soybean”, Inter. Journal of Information Technology, vol. 8, no.1, 2002.
[4]. Prasad, G.N.R. and Babu, A.V., “A study on various expert systems in agriculture,” Georgian Electronic Scientific Journal: Computer Science and Telecommunications, vol. 5, no. 4, pp. 81-86, 2006.
[5]. Anna Perini and Angelo Susu, “Developing a Decision Support System for Integrated Production in Agriculture,” Preprint submitted to Environmental Modelling and Software, 10 January 2003.
[6]. D.J. Power, R. Sharda, and F. Burstein, “Decision Support Systems,” Management Information Systems, Published Online, vol. 7, 21 Jan. 2015 DOI: 10.1002/9781118785317.weom070211
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[8]. P.P. Mumba, and E. Kambwiri, “Water Quality of Irrigation Water into and out of an Irrigated Sugar Cane Plantation,” Asian Journal of Water, Environment and Pollution, IOS Press. 2013.
[9]. E Ostrom, WF Lam, and M Lee, “The Performance of Self Governing Irrigation Systems in Nepal,” Human Systems Management, IOS Press, 1994.
Mirschel W, Wenkel K-O, Berg M, Wieland R, Nendel C, Köstner B, Topazh AG, Terleev VV, and Badenko VL, (2016) “A spatial model-based decision support system for evaluating agricultural landscapes under the aspect of climate change,” L. Mueller et al. (Eds), Springer, Cham, pp 519–540 (Chapter 23 of this book).

Monika Varshney, Azad Kumar Shrivastava, Alok Aggarwal, "A Fuzzy based Decision Support System for Agriculture Support System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.788-793, 2018.

Whenever a person travelling from one place to another place. There are different routes between different regions. A person travelling from source to destination chooses a path based on different factors. Route choices from source to destination play an important role. It helps the user to choose the best route from many routes present based on different considerations. The traveller chooses the route with best factors like less time and distance. Such types of route factors are the main reasons to choose the route. We here develop a visual analytic system to display few more route choices. Here, based on the route factors the route with best factors is chosen as the best path and viewed to the user. We study and analyse route factors based on dataset. We analyse the dataset and a system with best and multiple route factors is developed using hadoop Framework.

Route factors , Hadoop Framework, Big data

[1] Y. Zheng, L. Capra, O. Wolfson, and H. Yang, “Urban computing: concepts, methodologies, and applications,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 5, no. 3, p. 38, 2014.
[2] Vaishali Y. Chandurkar “A Survey on Evaluation of Traffic Mobility Using Clustering” International Journal of Innovative Research in Computer and Communication Engineering.
[3] Alessandro Vacca a, Italo Meloni “Understanding route switch behavior: an analysis using GPS based data” Transportation Research Procedia 5 ( 2015 ) 56 – 65
[4] W. Liu, Y. Zheng, S. Chawla, J. Yuan, and X. Xing, “Discovering spatiotemporal causal interactions in traffic data streams,” in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011, pp. 1010–1018
[5] J. Yuan, Y. Zheng, X. Xie, and G. Sun, “T-Drive: enhancing driving directions with taxi drivers’ intelligence,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 1, pp. 220–232, 2013. Carlo Giacomo Prato, Victoria Gitelman and Shlomo, “Mapping patterns of pedestrian fatal accidents in Israel”, Accident Analysis and Prevention, pp.54–62, Jan 2012.
[6] H. Li, R. Guensler, and J. Ogle, “Analysis of morning commute route choice patterns using global positioning system-based vehicle activity data,” Transportation Research Record: Journal of the Transportation Research Board, vol. 1926.1, pp. 162–170, 2005.
[7] Shlomo Bekhor, Moshe E. Ben-Akiva, M.Scott Ramming, “Evaluation of choice set generation algorithms,” Springer science, 2006.

D. Swetha Priya M. Humera khanam, "An Approach To Analyze Different Route Factors Using Hadoop Framework," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.794-798, 2018.

In high voltage applications, the liquid insulating oils are used as the insulating medium as well as cooling medium. For the past several decades, the mineral based transformer oil which are extracted from petroleum crude oil is used traditionally for the purpose of liquid insulations. In the environmental aspect, there are several disadvantages of the mineral oil even though it has better insulating properties. By considering the environmental aspect and insulating properties, the researchers tend to find the alternate insulating fluids for the high voltage applications. Increasing power demand forces the development of the high-rated power devices such as Transformers Circuit Breakers etc. In a transformer, petroleum-based mineral oil is used as insulation, currently Transformer oil produces environmental and health issues because it is non-biodegradable. Thus it has been thought that why not to use vegetable oils if found suitable. The present work investigates breakdown voltage, flash point & fire point of Three different vegetable oils namely Rice Bran (Hareli Brand), Mustered (Fortune Brand) and Blended (75%Rice Bran+25%Mustered) oils. Results obtained from experiments are validated with benchmark results and are found to be in good agreement as per IS-335:1993. The results are reported in dimensional form and presented graphically. The results provide a substantial insight in understanding the behavior of vegetable oil for high voltage applications. The Cost comparison of these oils with standard mineral oil is also tabulated

Breakdown voltage(BDV); Breakdown Trials(BDT); Flash point; Fire point

[1]. Matharage B. S. H. M. S. Y.. Fernando M. A. R. M.. Bandara M. A. A. P.. Jayantha G. A.. Kalpage C. S.. 2013. “Performance of Coconut Oil as an Alternative Transformer Liquid Insulation”, IEEE Transactions on Dielectrics and Electrical Insulation. 20( 3), Page No-887
[2]. Abderrazzaq M. H.. Hijazi F.. 2012, “Impact of Multi-filtration Process on the Properties of Olive Oil as a Liquid Dielectric”,IEEE Transactions on Dielectrics and Electrical Insulation .19.(5)Page No-1673.
[3]. IEC Publication 296:1982, “Specification for unused mineral insulating oil for transformers and switchgear” (incorporating Amendment 1:1986).
[4]. Choi C., Yoo H. S. and Oh J. M. 2008,”Preparation and heat transfer properties of nanoparticle-in-transformer oil dispersions as advanced energy-efficient coolants”, Current Appl. Physics. 8(6), Page No-710-712.
[5]. Fofana I., Borsi H. and Gockenbach E. 2001, “Fundamental investigations on some transformer liquids under various outdoor conditions”, IEEE Trans.Dielectr. Electr. Insul. 8, Page No-1040-1047.
[6]. Hallerberg D.A. 1999, “Less-flammable liquids used intransformers”, IEEE Ind. Applicat. Mag.5, Page No. 50-55.
[7]. HilaireM., Marteau C. and Tobazeon R. 1988, “Apparatus developed for measurement of the resistivity of highly insulating liquids”,IEEE Electr. Insul. 23, Page No-779-787.
[8]. Hosier L., Guushaa A., Vaughan A.S. and SwinglerS.G. 2009. “Selection of a Suitable Vegetable Oil for High Voltage Insulation Application”, Phys J. Conf. Series 183 012014.
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[10]. Jian. L., Zhaotao Z. , Ping Z., Stanislaw G. and Markus Z. 2012.” Preparation of a Vegetable Oil-Based Nano fluid and Investigation of its Breakdown and Dielectric Properties” ,IEEE Electrical Insulation Magazine.28(5), Page No-0883-7554.
[11]. Li X., Li J. and Sun C. 2006. “Properties of transgenicrapeseed oil based dielectric liquid”, IEEE Southeast Conference, Memphis, TN, Page No- 81–84.

Anil Brahmin, D.D.Neema, Arpan Dwivedi, Devanand Bhonsel, "Suitability Analysis and Comparison of Rice Bran, Mustered and Blended Oils for High Voltage Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.799-802, 2018.

Web page recommender systems play a major role in web searches by retrieving most relevant results. The goal of personalized recommendation is to tailor the search results to a particular user based on his/her interest. Traditional retrieval systems are not adaptive enough to satisfy the user’s individual needs and interests. A collaborative filtering approach, called Normal Recovery Collaborative Filtering (NRCF) is used to increase the accuracy of webpage recommendation. As an enhancement, this work applies Case Based Reasoning (CBR) in web searches to optimize the retrieval strategy and Weighted Association Rule Mining (WARM) algorithm to predict more accurate webpages using association rules generated specifically for individual user profiles. For any active user, the system retrieves most similar user profiles matching the current user. Weighted rules are generated based on the frequency of visit and duration spent on the page. WARM is based on the profile similarity between the active user and the computed weighted rules. Based on these rules, new pages that visited by similar users are recommended to the active user. Experiment results show that the proposed algorithm combining CBR and WARM outperforms well with more accuracy by providing more efficient and appropriate recommendation.

Normal Recovery Collaborative Filtering, Case Based Reasoning (CBR), Weighted Association Rule Mining (WARM), Hypertext Induced Topic Search (HITS)

[1]. Shashichhikara and Purushottam Sharma , “Weighted Association Rule Mining: A Survey’, International Journal of Research in Applied Science and Engineering Technology”, Vol. 2, pp. 84-88,2014.
[2]. Wesley Chu and Tsau Young Lin, “Foundations and Advances in Data Mining (Studies in Fuzziness and Soft Computing”, Springer Verlag, Vol. 180, 2005.
[3]. Şule Gunduz-Oguducu,“Web Page Recommendation Models: Theory and Algorithms”, Synthesis Lectures on Data Management, Vol. 2, pp. 1-85, 2010.
[4]. Zibin Zheng, Hao Ma, Michael Lyu, R. and Irwin King, “Wsrec: A Collaborative Filtering Based Web Service Recommender System”, IEEE International Conference on Web Services, pp. 437 – 444, 2009
[5]. Zibin Zheng, Hao Ma, Michael Lyu, R. and Irwin Kin, “QoS-aware Web Service Recommendation by Collaborative Filtering”, IEEE Transactions on Services Computing, Vol. 4, pp. 140-152, 2012.
[6]. Huifeng Sun, Zibin Zheng, Junliang Chen and Michael Lyu, R., “Personalized Web Service Recommendation via Normal Recovery Collaborative Filtering”, IEEE Transactions on Services Computing, Vol. 6, pp. 573 – 579, 2012.
[7]. Yong-Bin Kang, Shonali Krishnaswamy and Arkady Zaslavsky, “A Retrieval Strategy for Case-Based Reasoning Using Similarity and Association Knowledge”, IEEE Transactions on Cybernetics, Vol. 44, pp. 473 – 487, 2014.
[8]. Liang Yan and Chunping Li, “Incorporating Page View Weight into an Association-Rule-Based Web Recommendation System”, In proceedings of 19th Australian Conference on Advances in Artificial Intelligence, pp. 577-586, 2006
[9]. Barry Smyth, “The Adaptive Web”, Springer Berlin Heidelberg, LNCS.4321, 2007.
[10]. Pooja Devi, Ashlesha Gupta and Ashutosh Dixit, “Comparative Study of HITS and PageRank Link Based Ranking Algorithms”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, pp. 5749-5754, 2014.
[11]. Bhavithra Janakiraman, Saradha Arumugam, Aiswarya Jayaprakash, “An Improved Mechanism For User Profiling And Recommendation Using Case-Based Reasoning”, Emerging trends in Computer Engineering and Research (ECER),Vol 8, no.2, pp.319-327, 2017.
[12]. Huifeng Sun, Yong Peng, Junliang Chen and Chuanchang Liu and Yuzhuo Sun, “A New Similarity Measure Based on Adjusted Euclidean Distance for Memory-Based Collaborative Filtering”, Journal of Software, Vol. 6, pp. 993-1000, 2011.
[13]. Forsati, R., Meybodi, M. R. and Ghari Neiat, A., “Web Page Personalization based on Weighted Association Rules”, IEEE, International Conference on Electronic Computer Technology, Macau, China, pp. 130 – 135,2009.
[14]. Ujwala H. Wanaskar, Sheetal R. Vij and Debajyoti Mukhopadhyay , “A Hybrid Web Recommendation System Based on the Improved Association Rule Mining Algorithm”, Journal of Software Engineering and Applications, Vol. 6, pp. 396 -404, 2013.
[15]. YiBo Chen, ChanLe Wu, Ming Xie and Xiaojun Guo , “Solving the Sparsity Problem in Recommender Systems Using Association Retrieval”, Journal of Computers, Vol. 6, no. 9, pp. 1896-1902, 2011
[16]. R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra, “A Review: Design and Development of Novel Techniques for Clustering and Classification of Data”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol.06 , Special Issue.01 , pp.19-22, 2018
[17]. M. Patel, A. Hasan , S.Kumar, “A Survey: Preventing Discovering Association Rules for Large Data Base”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol.1 , Issue.2 , pp.30-32, 2013

Aiswarya Jayaprakash, Bhavithra Janakiraman, "An Improved Mechanism for User Profile Generation Using Case-Based Reasoning and Weighted Association Rule Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.803-809, 2018.

Data mining is the process of sorting through large datasets to identify patterns and establish the relationship to solve problems through data analysis. Data mining tools allows enterprises to predict future trends. One of the prominent applications of data mining tools is in the field of education. Educational data mining, also known as EDM is an emerging, multidisciplinary area of research, concerned with studying and analyzing data from various educational databases. It helps in extracting meaningful information from large repositories using various data mining methods to enable data-driven decision making for improving the current educational practice. One of the biggest challenges faced by higher education today is predicting the paths of students. Data mining is a better tool to predict the results of the student. Educational Data Mining (EDM) helps in a big way to answer the issues of predictions and profiling of not only the students but other stake holders of education sectors. Thispaper focus on using various data mining tools like Association, Clustering, and Decision tree to analyze the large volume of data stored in the University databases to track the academic results of the students.

Data Mining, EDM,Analyze, Decision, Clustering

[1]. Brijesh Kumar Baradway and saurabhPal,“Mining Educational Data to Analyze Students Performance”, Int. Journal of Advances in Computer Science and Applications, Vol. 2(6) ,pp. 63- 69, 2011.
[2]. Ajith P,,Tejaswi B, Sai MSS. Rule Mining Framework for Students Performance Evaluation. International Journal of Soft Computing and Engineering, Vol.2(6), 2013.
[3]. GalitShmueli, Nitin R. Patel, Peter C. Bruce, Data Mining In Excel.
[4]. Amritpal Kaur, Use of Data Mining in Education Sector.
[5]. Dr. MohdMaqsood Ali, Roleof Data Mining in Educatin Sector.
[6]. Vasile Paul Bresfelean, Data Mining Applications in Higher Education and Academic Intelligence Management.
[7]. B.Namratha, Nitesh Sharma. Educational Data Mining – Applications and Techniques.
[8]. Himanshi, Komal Kumar Bhatia, Prediction Model for Under-Graduating Student’s Salary Using Data Mining Techniques. IJSRNSC Vol.6, Issue-2, April.2018.
[9]. Ajay Vrma, Y.S. Chouhan. Recent Methodologies for Improving and Evaluating Academic Performance. ISROSET Vol.3, Issue.2

B. Venkataratnam, G. Sravanthi, C. Deepa, "Data Mining is used in Education System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.810-812, 2018.

The effects of wind loading on buildings due to straight line boundary layer type winds have been studied extensively in the past. Building code estimates are mainly based on these works. Little research has been, however, done to study the effect of tornado winds on built structures. Despite the destructive effects of tornadoes, limited attempts have been made to quantify tornado induced loading. This paper essentially deals with the study of the effects of tornado type wind loading on low-rise buildings. Origin of winds and wind loads on buildings as per Indian IS:875 (Part -3) have been discussed and investigated in detail. Thrusted areas of research covering the impact of tornado on low-rise buildings have been discussed.

Wind engineering, tornado, IS:875, low rise buildings

[1]. Paluch, M.J., Loredo-souza, A.M. and Blessmann, J., “Wind loads on attached canopies and their effects on the pressure distribution over arch roof industrial building,” Jour. of Wind Engg. and Industrial Aerodynamics, vol. 91, pp 975-994, 2003.
[2]. Rajesh Goyal and A.K. Ahuja, “Comparative Study of Wind Loads on Gable Roof Buildings With and Without Attached Canopies,” Proceeding of The fourth International Conference on Structural Engineering, Mechanics and Computation, 6-8 Sept. 2010, Cape Town, South Africa.
[3]. Vasanth Kumar Balaramudu, “Tornado-induced wind loads on a low-rise building,” Iowa State University 2007.
[4]. Jeremy Michael, “Case-Effect of building geometry on wind loads on low rise buildings in laboratory – simulated tornado with a high swirl ratio,” Iowa State University 2011.
[5]. S. Ahmad, K. Kumar, “Interference effects on wind loads on low-rise hip roof buildings Engineering Structures,” vol. 23, pp. 1577-1589, 2001.

Monika Varshney, Azad Kumar Shrivastava, Alok Aggarwal, Adarsh Kumar, "Wind Engineering: Impact of Tornado on Low-rise building," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.813-817, 2018.

Researchers has a big challenge to handle the unbalanced data, which is an issue found in many real-world applications in engineering. Dataset is unbalanced means at least one class has very fewer examples than another class. In such dataset, examples are taken as majority class (i.e. negative) and minority class (i.e. positive). This paper contains a survey of what is mean by imbalance data, an issue with it, its challenges, examples of applications, different approaches to rebalance the data like ensemble techniques( like boosting, bagging), sampling, feature selection, algorithmic to increase the performance of classification have been proposed.

Imbalanced data, classifiers, sampling, feature selection, ensemble methods, hybrid method

[1] Sonak and R. A. Patankar, “A Survey on Methods to Handle Imbalance Dataset,” International Journal of Computer Science and Mobile Computing, vol. 4, no. 11, pp. 338–343, 2015. [Online].Available:http://ijcsmc.com/docs/papers/November2015/ V4I11201573.pdf
[2] Singh and A. Purohit, “A survey on methods for solving data imbalance problem for classification,” International Journal of Computer Applications, vol. 127, no. 15, pp. 37–41, 2015.
[3] N.Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Jair, vol. 16, pp.321–357, 2002.
[4] More, “Survey of resampling techniques for improving classification performance in unbalanced datasets,” vol. 10000, pp.1–7, 2016. [Online]. Available: http://arxiv.org/abs/1608.06048
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[6] M. Wasikowski and X. W. Chen, “Combating the small sample class imbalance problem using feature selection,” IEEE Transactions
[7] Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of machine learning research, vol. 3, no.Mar, pp. 1157–1182, 2003.
[8] M. Alibeigi, S. Hashemi, and A. Hamzeh, “Dbfs: An effective density based feature selection scheme for small sample size and high dimensional imbalanced data sets,” Data & Knowledge Engineering, vol. 81, pp. 67–103, 2012.
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[10] M. Alibeigi, S. Hashemi, and A. Hamzeh, “DBFS: An effective Density Based Feature Selection scheme for small sample size and high dimensional imbalanced data sets,” Data and Knowledge Engineering, vol. 81-82, pp. 67–103, 2012. [Online].Available: http://dx.doi.org/10.1016/j.datak.2012.08.001
[11] N. V. Chawla, N. Japkowicz, and A. Kotcz, “Special issue on learning from imbalanced data sets,” ACM Sigkdd Explorations Newsletter, vol. 6, no. 1, pp. 1–6, 2004.
[12] H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Transactions on Knowledge & Data Engineering, no. 9, pp.1263–1284, 2008.
[13] X.-w. Chen and M. Wasikowski, “Fast: a roc-based feature selection metric for small samples and imbalanced data classification problems,” in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2008, pp. 124–132.
[14] G. Forman, “An extensive empirical study of feature selection metrics for text classification,” Journal of machine learning research, vol. 3, no. Mar, pp. 1289–1305, 2003.
[15] H. Pant and R. Srivastava, “A Survey on Feature Selection Methods For Imbalanced Datasets,” International Journal of Computer Engineering and Applications, vol. 9, no. 2, pp. 197–204, 2015.
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[21] Rudin, I. Daubechies, and R. E. Schapire, "The dynamics of AdaBoost: Cyclic behavior and convergence of margins," Journal of Machine Learning Research, vol. 5, no. Dec, pp. 1557–1595, 2004.
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[23] N. V. Chawla, D. A. Cieslak, L. O. Hall, and A. Joshi, "Automatically countering imbalance and its empirical relationship to cost," Data Mining and Knowledge Discovery, vol. 17, no. 2, pp. 225–252, 2008.

[24] N. V. Chawla, K.W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "Smote: synthetic minority over-sampling technique,” Journal of artificial intelligence research, vol. 16, pp. 321–357, 2002.
[25] Seiffert, T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano, “Rusboost: A hybrid approach to alleviating class imbalance,”
[26] Q. Wang, “A hybrid sampling SVM approach to imbalanced data classification," in Abstract and Applied Analysis, vol. 2014.Hindawi, 2014.
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[28] M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, and F. Herrera, "A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 4, pp. 463–484, 2012.
[29] D.K. Mittal, V. Verma, R. Rastogi, "A Comparative Study and New Model for Smart Mirror", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.58-61, 2017
[30] Dharmendra Sharma and Suresh Jain, "Evaluation of Stemming and Stop Word Techniques on Text Classification Problem", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.2, pp.1-4, 2015

Pooja Yerawar, Ganesh Pakle, "A Survey of Different Techniques to Handle An Unbalanced Dataset," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.818-824, 2018.

Action recognition being one of the hot topics of many research efforts and being useful in so many commercial and scientific fields. Action recognition concerns the extraction ofactivity based knowledge, image data relationship or other patterns implicitly or explicitly stored in the images. Action in images is one of the powerful sources of high-level semantics. Recognition can be used for recognizing activities occurring in a particular scene. There is a need of effective and efficient methods to be encounter for recognizing the activities of human. The goal of this work is to study various recognition methods of common human actions represented in images. In this research, we present detailed insights on existing works and the methodologies used by researchers for recognizing the human activities. Comparison among different human activities by similarity systems is particularly challenging owing to the great variety of techniques implemented to represent likeness and the dependence that the results present of the used image dataset. This will be helpful to the researchers for their future research direction in this area.

Human Activities, Segmentation, Feature Extraction, Classification, Human Action Recognition

[1] Chaitra B H, Anupama H S, Cauvery N K, “Human Action Recognition using Image Processing and Artificial Neural Networks”, International Journal of Computer Applications (0975 – 8887), Volume 80 No. 9, October 2013.
[2] Christian Thurau, Vaclav Hlavac, “Pose Primitive based Human Action Recognition in Videos or Still Images”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
[3] GhazaliSulong, Ammar Mohammedali, “Recognition of Human Activities from Still Image using Novel Classifier”, Journal of Theoretical and Applied Information Technology, Vol. 71 No. 1, 10th January 2015.
[4] GuodongGuo, Alice Lai, “A Survey on Still Image based Human Action Recognition”, Journal of Pattern Recognition, Vol. 47, pp- 3343 to 3361, 9th May 2014.
[5] Md. Atiqur Rahman Ahad, J.K. Tan, H.S. Kim and S. Ishikawa, “Human Activity Recognition: Various Paradigms”, International Conference on Control, Automation and Systems, pp-1896 to 1901, Oct. 14-17, 2008 in COEX, Seoul, Korea.
[6] MoinNabi, Mohammad Rahmati, “Human Action Recognition in Still Images using Bag of Latent Poselets”, CVMP2012 (Conference on Visual Media Production), 2012.
[7] Nabil Zerrrouki, FouziHarrou, Ying Sun, AmraneHouacine, “Adaboost-based Algorithm for Human Action Recognition”, 2017.
[8] NazliIkizler, R. GokberkCinbins, SelenPehlivan and Pinar Duygulu, “Recognizing Actions from Still Images”, Indian Council of Philosophical Research, ICPR, New Delhi,2007.
[9] Suganya V, “A Survey on Image Processing and Human Action Recognition”, International Journal of Latest Trends in Engineering and Technology (IJLTET), ISSN: 2278-621X, Vol. 6, Issue 1, 1st September 2015.
[10] Sumaira Ghazal, Umar S. Khan, “Human Posture Classification using Skeleton Information”, International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2018.
[11] Weilong Yang, Yang Wang, and Greg Mori, “Recognizing Human Actions from Still Images with Latent Poses”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
[12] Zhichen Zhao, Huimin Ma, Shaodi You, “Single Image Action Recognition using Semantic Body Part Actions”, IEEE International Conference on Computer Vision (ICCV), 2017.

N. Geetha, E. S. Samundeeswari, "A Review on Human Activity Recognition System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.825-829, 2018.

Latent topics models have become a popular paradigm in many computer vision applications due to their capability to discover semantics in visual content. Various knowledge based object discovery algorithms for the classification problem in dependent images are appearing in the literature. However, these algorithms mostly suffer from the following two problems: image metadata and time measures. To overcome this kind of problem, this paper presents a Probabilistic Randomized Hough Transform (PRHT) with Deep Learning Classification Algorithm (DLC) algorithm performs the object discovery and localization used by deep learning classification algorithm. The proposed method of object regions are efficiently matched across images using a Probabilistic Randomized Hough Transform with Deep Learning Classification that evaluates the confidence for each candidate correspondence considering both appearance and spatial consistency. The achieved PRHT-DLC has high accuracy and performance increases compared to the previous method of Pipeline method and Latent Dirichlet allocation (LDA) algorithms.

Image Mining, Image Retrieval, Probabilistic Randomized Hough Transform, Deep learning, Unsupervised object discovery

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Mereena Johny, L. Haldurai, "Knowledge Based Unsupervised Object Discovery Using Probabilistic Randomized Hough Transform (PRHT) With Deep Learning Classification (DLC)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.830-836, 2018.

Cloud computing is an ocean of resources which are shared among multiple users for processing over the internet by cloud services like software, infrastructure and platform oriented. Based on the user requests, various processes such as allocation, computation, execution are performed in the cloud environment. An allocation is the most challenging process in the cloud environment. Virtualization technology is the main technology provided by the cloud that is used for that processes. Virtual machines are used for allocating the resources according to the user request. Many algorithms and techniques are used for virtual machine allocation in the cloud environment. In this paper we provide an overview of the fundamental theories and emerging techniques for cloud based virtual machine allocation process as well as several extended work in these areas. This writing provides a research on the cloud based virtual machine allocation techniques that are frequently used in the early work in cloud environment.

cloud computing, resource sharing, virtual machine allocation, virtualization, cloud environment

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N.Gomathimari, N.Geetha, "Cloud Based Virtual Machine Allocation Techniques: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.837-840, 2018.