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In this work developing e-learning are adopting the replica approaches used by online higher education user. E-learning can provide the numerous benefits to the institutions such as accessing education at any time connecting to each other’s. Also it’s provide enhances interactivity, integrity scalability improved by the student`s academic performance in cloud service. However, sound studies and show majority of these benefits are enjoyed on institutions in the e-learning. The fundamental new idea to keep service data into mind is that the strength of online educational platform comes not just from replicating things that should be possible work in other ways, however when it is used to do things that weren`t possible without it. In this propose on Emerging support measurement in Higher Education (ESMHE) this technique using user short on cloud data security initiatives. To implement the online higher educator to arrange document protected close time level less in quick information telling on the e-learning system. In this e-learning to be consider entire process of academic education such as design, knowledge on developing and creating by the wonderful employing e-learning arrangement in cloud. They are significant, and they include improved efficiency, effectiveness, and enjoyment of the learning experience system colossal idea measurement online higher education user.

E-learning, Higher education, measurement, services, emerging

[1]. T.A.Hryhorova, O.S. Moskalenko, B. Enhanced Access to Training Information in E-Learning Systems, (2018)
[2]. Yonas Hagos and Monica J. Garfield. A Conceptual Model of E-learning Systems Success and Its Implication for Future Research. IEEE (2018).
[3]. Amir Mohamed Talib., Fahad Omar Alomary. An Interactive Distance Education and E-Learning System Based on Multi-Agent System Architecture, 978-1-5386-4427-0/18/ 2018 IEEE
[4]. Karim Aтто, Elena Е.Kotova. Communication Models in the E-Learning Environment Based on Intelligent Agents, 978-1-5386-5532-0/17/. 2018 IEEE
[5]. Karim Aтто, Elena Е.Kotova LETI" St. Petersburg, Russia St. Petersburg, Russia Communication Models in the E-Learning Environment Based on Intelligent Agents, 978-1-5386-5532-0, 2018 IEEE
[6]. Kotchakorn, JetinaiUbon Ratchathani, Thailand Rule-Based Reasoning for Resource Recommendation in Personalized e-Learning, 978-1-5386-5384-5 2018 IEEE
[7]. Eberhard Heuel, Birgit Feldman Hagen, Germany New Standardisation and Certification Initiative in E-Learning – The Qualification Standard “Certified European E-Tutor, 978-1-4673-5094-5 13 2013 IEEE
[8]. Ahmed Farouk Mohamed Saleh, Al Farouk Ahmed Farouk “Students with Disabilities Attitudes towards E-learning courses in developing countries” 978-0-7695-5036-72013 IEEE
[9]. YANG Ping, WANG Yanni, LI Jinping KONG Bo “Study on personality learning in E-learning” 978-0-7695-3907-2 2009 IEEE
[10]. Zendi Asma, Bouhadada Tahar, “Hybrid Approach for Modeling Units of Learning Using a Prototype Learning Design Model and IMS Learning Design Standard”, 978-1-4673-2225-6/12 2012 IEEE
[11]. Sofie Bitter, Gabriele Frankl, “Evaluation of blended learning courses the assessment of the e-tutors”, 978-1-4673-2427-4/12 2012 IEEE
[12]. Gloria C. Alaneme, Peter O. Olayiwola2, Comfort O. Reju3, “Combining Traditional Learning and the E-Learning Methods in Higher Distance Education”, 978-1-4244-8752-31101 2010 IEEE
[13]. Chi-Un Lei, Elizabeth Oh, Emily Leung, Donn Gonda, Xinyu Qi, Ruby Leung, “Scale Up Learning: Professional Development For E-Teaching/Learning”, 978-1-5090-5598-2/16 2016 IEEE
[14]. Dr. Sarmad Mohammad “SWOT Analysis of E-Learning System in Bahraini Universities”, 978-0-7695-3948-5 2010 IEEE
[15]. Muhammad Arshad, Muhammad Noman Saeed, “Emerging Technologies for E-Learning and Distance Learning: A Survey’, 978-1-4799-5739-2/14 2014 IEEE
[16]. Neurol Hamiza Zamzuri, Erne Suzila Kassim, Melissa Shahrom, “The Role of Cognitive Styles in Investigating E-Learning Usability”, 978-0-7695-3948-5 2010 IEEE
[17]. Nabil M. Hasasneh, Mohammad M. Moreb, “E-learning at Hebron University”, 978-0-7695-5036-7 2013 IEEE
[18]. Zsolt Csapo, Laszlo Karpati, Laszlo Kozar, “Andras Nabradi MBA in Agribusiness and Commerce (AGRIMBA) – a tool for life-long and e-learning”, 978-0-7695-3948-5 2010 IEEE
[19]. Faezeh Eftekhar, Saiedeh Mansouri, "the Application of Quality Management in e-Learning, by QFD Technique and Based on Customers` Needs (A Case Study in an Iranian University)", 978-1-4673-0957-8 12 2012 IEEE
[20]. Jianfeng Zhu Study On E-learning Education Model Based on Web Intelligence 978-0-7695-3948-5 2010 IEEE

M. Senthilkumar, M. Prabakaran, "EMERGING SUPPORT MEASUREMENT BASED E-LEARNING TO HIGHER EDUCATION SYSTEM," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.863-872, 2018.

To test the modified code, we employ regression testing procedures with an aim to provide assurance that modified code behaves correctly and those modifications have not adversely affected the existing behavior or functionality of the code. Retest-all regression testing is the basic approach in which all the test cases in the initial test suite are re-executed to validate the changes. But re-running all the test cases from an existing test suite in order to test the code that is undergone minor change may be expensive as it requires an unacceptable amount of time and resources to perform it. An important problem found during regression testing is how to select a subset of test cases from an existing test suite in order to retest the modified code. Therefore, in this study we propose an efficient test suite management technique that utilizes data clustering approach for regression testing in order to effectively partition an initially random and large test suite to re-test the modified section of the code that has been modified within resource and time constraints.

Software testing, Regression testing, Test Case Selection, Data Clustering, K-Means

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Fayaz Ahmad Khan, "Regression Test Suite Management using Data Clustering Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.873-879, 2018.

In the present scenario the optimization of engine parameters is very much necessary for the smooth operation of automotive engines. Switching among various strcutures is an essential feature in many engineering applications such as power systems, aircraft systems, etc. This paper presents the intelligent switching technique for the two optimized feedback controllers of the exhaust gas recirculation (EGR) system. The proposed switching control is investigated for the linearized state space model of turbocharged diesel engine with an EGR system. The results are compared with individual feedback structure as well as switching techniques to select the best controller for the future direction.

EGR, LQR, Reference input and Optimal Control Theory

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[3] Chris Criensy, Frank Willemsyz, Maarten Steinbuchy, Eindhoven University of Technology, TNO Automotive “A Systematic Approach Towards Automated Control Design for Heavy-Duty EGR Diesel Engines” .
[4] Felipe Castillo Buenaventura, Emmanuel Witrant, Vincent Talon, Luc Dugard “Air Fraction and EGR Proportion Control for Dual Loop EGR Diesel Engines”, Ingenieria y Universidad, Pontifica Universidad Javeriana, Faculty of Science, 2015, 19 (1), pp.115 - 133. <10.11144/Javeriana.iyu19-1.aegr>. .
[5] Jakob Mahler Hansen, Mogens Blanke, Hans Henrik Niemann, Morten Vejlgaard, Laursen “Exhaust Gas Recirculation Control for Large Diesel Engines, Achievable Performance with SISO Design”.
[6] Benjamin Haber, B.S.A “Robust Control Approach on Diesel Engines with Dual-Loop Exhaust Gas Recirculation Systems”, Thesis, The Ohio State University, 2010. .
[7] Yathisha L and S Patil Kulkarni. “Optimum LQR Switching Approach for the Improvement of STATCOM Performance”, Springer LNEE, Vol 150, E-ISSN: 1876-1100, 2013, pp. 259-266, DOI: 10.1007/978-1-4614-3363-7_28.
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[14] Hussain N. Al-Duwaisha and Zakariya M. Al-Hamouz. “A Neural Network Based Adaptive Sliding Mode Controller: Application to a Power System Stabilizer”, Elsevier Proceedings, Energy Conversion and Management, Volume 52, Issue 2, February 2011, Pp. 1533-1538.
[15] Yathisha L, Kourosh Davoodi and S Patil Kulkarni. “Optimal switching control strategy for UPFC for wide range of operating conditions in power system”, 3rd Indian Control Conference, IEEE Xplore, Jan 2017, Indian Institute of Technology (IIT), Guwhati, pp 225-232. DOI:10.1109/INDIANCC.2017.7846479.
[16] Yathisha L and S Patil Kulkarni. “Application and comparison of switching control algorithms for power system stabilizer”, IEEE International Conference on Industrial Instrumentation and Control (ICIC), IEEE Xplore, May 2015, Pune, pp 1300-1305. DOI:10.1109/IIC.2015.7150949.

Shashidhar S Gokhale, S. Patil Kulkarni, Yathisha L, "Optimal Feedback Switching Control Design for the Turbocharged Diesel Engine with an EGR System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.880-894, 2018.

Meteorological data analysis in the form of data mining is concerned to predict the knowledge of weather condition. To make an accurate prediction is one of the challenging of meteorologist to survey the weather condition efficiently. Decision tree algorithms are suitable for analyzing the data of meteorological behavior. By evaluates three algorithm of decision tree such as Random Forest, C4.5, C4.5 with Bootstrap aggregation, to analyse the time efficiency and accuracy of classification. These accuracy of algorithm when it operates on trained weather data of selected location. Those locations are selected through monsoon condition based on India country.

Randon Forest, C4.5, C4.5 with Bootstrap Algorithm, Meterological Data, Accuracy,Time efficiency

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M. Manikandan, R. Mala, "Analyzing Machine learning Algorithm for Predicting an Accuracy of Meteorological Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.895-899, 2018.

Nowadays Wireless Sensor Networks (WSN) plays vital role in different fields.WSN are more attractive because of their behavior in collecting various kinds of data from harsh environments. These Networks are more popular because these are buildup with less expensive nodes .Nodes in WSN collects the information and send to the base station or sink. Routing plays major role in improving the entire network life time by selecting optimized path. Routing is the process of reaching the destination from source node. Data can be sensed in two ways, those are flat and hierarchical .In flat routing each node in the network is given equal responsibilities. In case of hierarchical routing different nodes plays heterogonous tasks and organized into different clusters. Now in this paper a survey is taken on cluster based routing methods and comparing them with performance issues such as energy awareness, latency and scalability. Advantages and limitations of these methods are presented and conclude with open issues in cluster based routing in WSN.

Cluster based routing, hierarchical Routing, Energy efficient routing, Wireless sensor networks

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[31] Neha P . Dahihanderkar, v. v. Kimbahune. “Cluster-based protocolfor heterogeneous Wireless sensor Networks”, International Journal of science and research (IJSR), Volume 4 issue 6, june 2015, 292-295
[32] Vivekchandran K. C, Nikesh Narayan .P. “ Energy Efficiency and Latency Improving In Wireless Sensor Networks” , International Journal of science and research (IJSR), Volume 4 issue 5, May 2015, 1291-1295

Praveen kumar Rapolu, B. Srinu, "A Comprehensive Study of Routing Protocols in Cluster Based Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.900-907, 2018.

Aquaponics is an emerging region in food manufacturing technique which combines traditional hydroponics with aquaculture in a symbiotic surroundings that enables a sustainable machine with essential input as all of the water and nutrients inside are re-circulated in order to develop terrestrial plant life and aquatic lifestyles. When aquaponics gadget meets with technology it appears to produce some first rate outputs which makes it green and productive generation. In Iot Based Smart Aquaponics System with Fuzzy Logic, we take specific readings regarding the pH stage, temperature, moisture content and the extent of the water by using using distinctive sensors. Readings from every of these sensors are stored in the server for destiny use. Also these values are utilized by the bushy controller which controls the overall working of the system in drastic condition. Iot removes the gap between the physical world and digital international. In order to introduce technologies to the conventional aquaponics machine, we use of Arduino, Fuzzy controller and Internet of Things.

Aquaponics, Arduino, IOT, fuzzylogic, MachineLearning, SVM, Matlab

[1] Karen M. Buzby, Nicole L. Waterland, Kenneth J. Semmens, and Lian-Shin Lin.Evaluating aquaponics crops in a freshwater flowthrough fish culture system, Aquaculture, Volume 460, 1 July 2016, Pages 15-24, ISSN 0044-8486, http://0- dx.doi.org.wizard.umd.umich.edu/10.1016/j.aquaculture.2016.03.04 6 .
[2] D Wang, J Zhao, L Huang and D Xu, Design of a Smart Monitoring and Control System for Aquaponics Based on Openwrt, 5th International Conference on Information Engineering for Mechanics and Materials, 2015.
[3] L. Dan, C. Xin, H. Chongwei and J. Liangliang, "Intelligent Agriculture Greenhouse Environment Monitoring System Based on IOT Technology," Intelligent Transportation, Big Data and Smart City (ICITBS), 2015 International Conference on, Halong Bay, 2015, pp. 487-490.doi: 10.1109/ICITBS.2015.126
[4] M. U. Leatherbury, "VEGILAB and aquaponics indoor growing system," Technologies for Sustainability (SusTech), 2014 IEEE Conference on, Portland, OR, 2014, pp. 135-139. doi: 10.1109/SusTech.2014.7046233
[5] Juby Joseph, Vinodh P Vijayan” Misdirection Attack in WSN Due to Selfish Nodes; Detection and Suppression using Longer Path Protocol” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 7, July 2014, pp. 825-829, ISSN: 2277 128X.
[6] DC Love, JP Fry, L Genello, ES Hill and KSJ Adam Frederick and Ximin Li, An International Survey of Aquaponics Practitioners, PLoS One, 2014, 9, e102662.
[7] C Somerville, M Cohen, E Pantanella, A Stankus and A Lovatelli, Small-Scale Aquaponic Food Production, Integrated Fish and Plant Farming, FAO, Fisheries and Aquaculture Technical Paper, 2014.
[8] V P Vijayan, Biju Paul “Multi Objective Traffic Prediction Using Type-2 Fuzzy Logic and Ambient Intelligence” International Conference on Advances in Computer Engineering 2010 , Published in IEEE Computer Society Proceedings, ISBN: 978-0-7695-4058-0, Print ISBN: 978-1-4244-7154-6
[9] Vinodh P Vijayan, Deepti John, Merina Thomas, Neetha V Maliackal, Sara Sangeetha Vargheese “Multi Agent Path Planning Approach to Dynamic Free Flight Environment” International Journal of Recent Trends in Engineering (IJRTE), ISSN 1797-9617 Volume 1, Number 1, May 2009, Page(s): 41-46
[10] J. E. Racoky, "Aquaculture- Aquaponics system," Agricultural Statement Experiment, 2003.
[11] Vijayan V P, Gopinathan E “Improving Network Coverage and Llife-Time in a Cooperative Wireless mobile Senso Network ”Fourth International Conference on Advances incomputing and communications (ICACC)Aug,2014. Published in IEEE Computer Society Proceedings. Print ISBN: 978-1-4799-4364-7.

Vinodh P Vijayan, Neena Joseph, Neema George, Simy Mary Kurian, "IOT Implemented Smart Aquaponics System Using Arduino with Fuzzy," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.908-912, 2018.

It is normal to be in the following 100 years, world`s most extreme populace will be living in urban areas. Henceforth the utilization of vehicle and its connected administrations will be vital. As the quantity of vehicles increases, parking of the vehicle in urban communities will be testing issue, particularly concerning contamination and keeping up with the eco arrangement of the area. Many individuals are not intrigued to utilize conventional or robotized stopping region as a result of the trouble they face during the utilization. Indeed, even individuals find it hard to follow the return way to their own vehicle. The bigger number of shrewd vehicle leaving frameworks is executed in different nations which typically take care of the issue of parking spot and powerful use of utilities. Yet, in a profoundly populated region security of individuals in a leaving opening and following of the vehicle will be extremely difficult because of its tendency of heterogeneous individuals and assortment of vehicles. An IOT empowered Sensor network based Advanced parking spot with camera and sound sensors will actually want to gather enormous measure of information which can be used to create intriguing example utilizing appropriate AI algorithms.

Automated Parking, Machine Learning, IoT.

[1] Shen-En Shih ; Wen-Hsiang Tsai ” A Convenient Vision- Based System for Automatic Detection of Parking Spaces in Indoor Parking Lots Using Wide-Angle Cameras” IEEE Transactions on Vehicular Technology Vol. 63 , Issue. 6, pp.2521-2532,2013.
[2] C Seo, , Yunhee Lee ; Whoi-Yul Kim “Vision-based Approach in Finding Multitype Parking Stall Entrance”2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC), pp.22-24 , 2018 .
[3] Alshamsi, Humaid & Këpuska, Veton,”Smart Car Parking System” International Journal of Science and Technology,pp. 390 – 395, 2016.
[4] Vinodh P Vijayan, E Gopinathan,”Improving Network Coverage Network”Fourth International Conference on Advances in Computing and Communications, pp.27-29, 2014.
[5] YanfengGengChristos G.Cassandras,“A new Smart Parking System Infrastructure and Implementation “Procedia - Social and Behavioral Sciences Vol.54, pp.1278-1287,2012.
[6] Vinodh P. Vijayan* and N. Kuma , “Extending Connectivity And Coverage Using Robot Initiated K Nearest Dynamic Search For WSN Communication”, International Journal of Control Theory and Applications (IJCTA), International Science Press,7 ,2017
[7] N Kumar Vinodh P Vijayan ,”Coverage and Lifetime Optimization of WSN using Evolutionary Algorithms and Collision Free Nearest Neighbour Assertion” Pertanika Journal of Science & Technology, Universiti Putra Malays, pp. 371 – 37,2016
[8] YacineAtifJianguoDingManfred A.Jeusfeld “Internet of Things Approach to Cloud-based Smart Car Parking “, Procedia Computer Science , Vol. 98, pp. 193-198
[9] N.Saritha Devi, K.S.R.Raju, A.Madhu, R.Raja Sekhar ,”Safety and Security for School children’s Vehicles using GPS and IoT Technology” International Journal of Advanced Trends in Computer Science and Engineering Vol. 7, Issue..6, 2018

Neethu Maria John, Simy Mary Kurian, Vinodh P Vijayan, Neema George, "ICT based Wireless Sensor Network for Retracing the Parked Vehicle," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.913-916, 2018.