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Epilepsy is a stable neurological disorder of the brain, described by regular seizures, i.e., irregular activities. Seizure is the most imperative signal of epilepsy, which is solitary of the most expected neurological disorders. An electroencephalogram (EEG) is a test out used to weigh up the electrical activity in the brain, and is widely used in the recognition and study of epileptic seizures. Hence, it is decisive to develop a quantitative technique to automatically clustering the normal and epileptic brain activities. Several techniques have been developed for unbending out the important features of seizures present in EEGs. The proposed approach is evaluated an extracting the features of EEG signals using wavelet transform coefficients and unsupervised learning technique like clustering the data using Fuzzy C- Means with Modified Particle Swarm Optimization (PSO) and K- Mode Clustering. The recital of the Clusters are analyzed and examined that Fuzzy C-Means with PSO less error rate and out performs than K-Mode Clustering in accuracy.

Fuzzy C-means, K-mode, EEG, Seizures, Wavelet, PSO, Clustering

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[9] Varsavsky, I., Mareels and M. Cook, “Epileptic seizures and the EEG”, Boca Raton: CRC Press, 2011.
[10] Zhexue Huang, A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining.
[11] Guo Tao, Ding Xingu, Li Yefeng, Parallel k-modes Algorithm based on MapReduce.
[12] S. Agrawal, R. Panda, L. Dora, "A study on fuzzy clustering for magnetic resonance brain image segmentation using soft computing approaches", Applied Soft Computing, 2014.
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C.V. Banupriya, D. Deviaruna, "K-modes and Fuzzy C-means with modified Particle Swam Optimization Clustering Algorithm for Epilepsy Seizure Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.73-77, 2019.

Smart Agriculture helps to reduce wastage, effective usage of fertilizer and thereby increase the crop yield. In this work, a system is developed to monitor crop-field using sensors (soil moisture, temperature, humidity) and automate the irrigation system. Our main objective of this work is to for Farming where various new technologies to yield higher growth of the crops and their water supply. Automated control features with latest electronic technology using microcontroller which turns the pumping motor ON and OFF on detecting the dampness content of the earth and GSM phone line is proposed after measuring the temperature, humidity, and soil moisture. The irrigation is automated if the moisture and temperature of the field falls below the brink. The notifications are sent to farmers’ mobile periodically. The farmers’ can able to monitor the field conditions from anywhere. This system will be more useful in areas where water is in scarce. This system is 92% more efficient than the conventional approach.

Sonsors, Irrigation

[1] Manish Giri and DnyaneshwarNathaWavhal “Automated Intelligent Wireless Drip Irrigation Using Linear Programming”, in International Journal of Advanced Research in Computer Engineering & Technology, Vol. 2, No. 1, 2013, pp. 1-5.
[2] DeeptiBansal and S.R.N Reddy "WSN Based Closed Loop Automatic Irrigation System", in International Journal of Engineering Science and Innovative Technology (IJESIT), Vol. 2, No. 05, 2013, pp. 229-237.
[3] M.Ramu and CH. Rajendraprasad “Cost effective atomization of Indian agricultural system using 8051 microcontroller”, in International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, No. 7, 2013, p. 2563-2566.
[4] PrakashgoudPatil and B.L.Desai “Intelligent Irrigation Control System by Employing Wireless Sensor Networks”, International Journal of Computer Applications, Vol. 79, No. 11, 2013, pp. 33-40.
[5] N. Dinesh Kumar, S. Pramod and ChSravani “Intelligent Irrigation System”, in International Journal of Agricultural Science and Research, Vol. 3, No. 3, Trans Stellar-2013, pp. 23-30.
[6] R. Suresh, S.Gopinath, K. Govindaraju, T. Devika and N. SuthanthiraVanitha “GSM based Automated Irrigation Control using Raingun Irrigation System”, in International Journal of AdvancedResearch in Computer and Communication Engineering, Vol. 3, No. 2, 2014, pp. 5654-5657.
[7] Ms. JyotsnaRaut and Prof. V. B. Shere "Automatic Drip Irrigation System using Wireless Sensor Network and Data Mining Algorithm", in International Journal of Electronics Communication and Computer Engineering, Vol. 5, No. 07, 2014, pp. 195-198.
[8] HarithaTummala and SwathiNallapati "Intelligent Irrigation Control System", in SSRG International Journal of Electronics and Communication Engineering, Vol. 1, No. 10, 2014, pp. 25-31.
[9] Prashant S. Patil, Shubham R. Alai, Ashish C. Malpure and PrashantL.Patil “An Intelligent and Automated Drip Irrigation System Using Sensors Network Control System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, No. 12, 2014, pp. 7557-7559.
[10] Veera Samson Janga and RaghawaYathiraju "Fabrication of an Intelligent Irrigation Monitoring System Using Wireless Network", in International Journal & Magazine of Engineering,Technology, Management and Research, Vol. 2, No. 01, 2015, pp. 277-282.
[11] PrashantB,Yahide, Prof. S.A.Jain and Prof. Manish Giri "Survey On Web Based Intelligent Irrigation System In Wireless Sesnsor Network", in Multidisciplinary Journal of Research in Engineering and Technology, Vol. 2, No. 01, 2015, pp. 375 -385.
[12] K Nilson, G Sharmila and P Praveen Kumar "Intelligent Auto Irrigation System Using ARM Processor and GSM", in International Conference on Innovative Trends in Electronics Communication and Applications 2015 (ICIECA 2015), ISBN 978-81-929742-6-2, 2015, pp. 36-40.
[13] TupeAlok R, GaikwadApurva A and KambleSonali U "Intelligent Drip Irrigation System", in International Journal of Innovative Research in Advanced Engineering, Vol. 2, No. 2, 2015, pp. 120-125.
[14] Suraj S. Avatade and P.Dhanure “Irrigation System Using a Wireless Sensor Network and GPRS”, in International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, No. 5, 2015, pp. 521-524.

C. Felci, A. SenthilRajan, "Water Flow Monitoring and Automation in Agriculture Field," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.78-83, 2019.

Fuzzy C Means is one of the most popular machine learning technique for image segmentation. However, traditional Fuzzy C Means is insensitive to noise as it does not consider spatial information. To solve this issue a wide variety of modified Fuzzy C means techniques, considering spatial information of pixels, are proposed by different researchers. In this paper we propose a hierarchical Fuzzy C Means algorithm considering spatial features of image pixels. Our method aims to overcome the shortcomings of traditional Fuzzy C Means by incorporating spatial feature as well as the issue of misclassification of pixels associated with single level clustering. The proposed method divides the original image pixels into a set of clusters using a spatial fuzzy C means technique in the first level of the hierarchical model. In the second level of the hierarchy, the cluster which contains the candidate mass is further divided into sub clusters using traditional Fuzzy C Means algorithm to yield the final segmentation result. The experimental outputs show improved segmentation result by our proposed method.

Clustering, Fuzzy, Spatial, Segmentation, Hierarchical

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[14] J. Suckling et al., “The mammographic image analysis society digital mammogram database”, In the Proceeding of 2nd International Workshop on Digital Mammography, Elsevier Science, England, pp 375–378, 1994.

Manasi Hazarika, Lipi B. Mahanta, "A Hierarchical Spatial Fuzzy C Means Algorithm for Mammographic Mass Segmentation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.84-88, 2019.

Software fault detection plays a significant role in the management of software systems quality to locate the fault and to identify the cause. Few research works has been developed for detecting the cause of failure occurrence from event log files. Performance of conventional software failure prediction technique was not effective. In order to overcome such limitation, a Stochastic Bio-inspired Genetic-based Trapezoidal Fuzzy Logic (SBG-TFL) Model is proposed using event log files. The SBG-TFL Model is designed to identify the failure cause with the portfolio formation of good parameters. The SBG-TFL Model at first constructs the projects portfolio with help of optimal parameters selected from event log files with application of Stochastic Bio-inspired Gene Optimization (SBGO) Algorithm. The formation of projects portfolio assists for SBG-TFL Model to reduce the amount of time taken for analysing the failure behaviour of a systems application. SBG-TFL Model applies Trapezoidal Fuzzy Logic Model to formulated projects portfolio in order to effectively predict the failure causes of software application. SBG-TFL Model increases the accuracy and true positive rate of software failure prediction. The SBG-TFL Model conducts the experimental process on metrics such as recall precision and software failure identification time with respect to different software code size. The experimental result shows that SBG-TFL Model is able to improve the precision of software failure detection and also reduces software failure identification time when compared to state-of-the-art-works.

Event Logs, Fuzzy Rule, Paremeter, Projects Portfolio Software Failure, Trapezoidal Membership Function

[1] Marcello Cinque, Domenico Cotroneo, and Antonio Pecchia, “Event Logs for the Analysis of Software Failures: a Rule-Based Approach”, IEEE Transactions on Software Engineering, Volume 39, Issue 6, Pages 806 – 821, 2013
[2] De-Qing Zou, Hao Qin, Hai Jin, “UiLog: Improving Log-Based Fault Diagnosis by Log Analysis”, Journal of Computer Science and Technology, Springer, Volume 31, Issue 5, Pages 1038–1052, September 2016
[3] Michael Grottke, Dong Seong Kim, Rajesh Mansharamani, Manoj Nambiar, Roberto Natella, and Kishor S. Trivedi, “Recovery From Software Failures Caused by Mandelbugs”, IEEE Transactions On Reliability, Volume 65 , Issue 1, Pages 70 – 87, July 2015
[4] Subhashis Chatterjee, Bappa Maji, “A New Fuzzy Rule Based Algorithm for Estimating Software Faults in Early Phase of Development”, Soft Computing, Springer, Volume 20, Issue 10, Pages 4023–4035, June 2015
[5] Ilenia Fronza, Alberto Sillitti, Giancarlo Succi, Mikko Terho, Jelena Vlasenko, “Failure prediction based on log files using Random Indexing and Support Vector Machines”, Journal of Systems and Software, Volume 86, Issue 1, Pages 2-11, January 2013
[6] Yuan Yuan, Shiyu Zhou, Crispian Sievenpiper, Kamal Mannar, Yibin Zheng, “Event log modeling and analysis for system failure prediction”, IIE Transactions Journal, Volume 43, Issue 9, Pages 647-660, 2011
[7] Maggie Hamill, Katerina Goseva-Popstojanova, “Analyzing and predicting effort associated with finding and fixing software faults”, Information and Software Technology, Elsevier, Volume 87, Pages 1-18, July 2017
[8] Ruchika Malhotra, “A systematic review of machine learning techniques for software fault prediction”, Applied Soft Computing, Elsevier, Volume 27, Pages 504-518, February 2015
[9] Partha S. Bishnu, Vandana Bhattacherjee, “Software Fault Prediction Using Quad Tree-Based K-Means Clustering Algorithm”, IEEE Transactions on Knowledge and Data Engineering, Volume 24, Issue 6, Pages 1146 – 1150, June 2012
[10] Santosh S. Rathore, Sandeep Kumar, “An empirical study of some software fault prediction techniques for the number of faults prediction”, Soft Computing, Springer, Volume 21, Issue 24, Pages 7417–7434, 2016
[11] Karel Dejaeger, Thomas Verbraken, Bart Baesens, “Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers”, IEEE Transactions on Software Engineering, Volume 39, Pages 237-257, 2013
[12] Subhashis Chatterjee, Arunava Roy, “Novel Algorithms for Web Software Fault Prediction”, Quality and Reliability Engineering International, Wiley Publications, Volume 31, Pages 1517–1535, 2015
[13] Ezgi Erturk, Ebru Akcapinar Sezer, “Software fault prediction using Mamdani type fuzzy inference system”, International Journal of Data Analysis Techniques and Strategies, Volume 8, Issue 1, Pages 14 – 28, 2016
[14] Chubato Wondaferaw Yohannese, Tianrui Li, “A Combined-Learning Based Framework for Improved Software Fault Prediction”, International Journal of Computational Intelligence Systems, Volume 10, Issue 1, Pages 647 – 662, 2017
[15] Momotaz Begum, Tadashi Dohi, “A Neuro-Based Software Fault Prediction with Box-Cox Power Transformation”, Journal of Software Engineering and Applications, Volume 10, Pages 288- 309, 2017
[16] Pradeep Singh and Shrish Verma, “An Efficient Software Fault Prediction Model using Cluster based Classification”, International Journal of Applied Information Systems, Volume 7, Issue 3, Pages 35-41, May 2014
[17] Feras A. Batarseh, Avelino J. Gonzalez, “Predicting failures in agile software development through data analytics”, Software Quality Journal, Springer, Pages 1–18, 2015
[18] Manjula Gandhi Selvaraj, Devi Shree Jayabal, Thenmozhi Srinivasan, and Palanisamy Balasubramanie, “Predicting Defects Using Information Intelligence Process Models in the Software Technology Project”, The Scientific World Journal, Hindawi Publishing Corporation, Volume 2015 (2015), Article ID 598645, Pages 1-6, 2015
[19] Jinsheng Ren, Ke Qin, Ying Ma, and Guangchun Luo, “On Software Defect Prediction Using Machine Learning”, Journal of Applied Mathematics, Hindawi Publishing Corporation, Volume 2014 (2014), Article ID 785435, Pages 1-8, 2014
[20] Ying Ma, Guangchun Luo, “Kernel Based Asymmetric Learning for Software Defect Prediction”, IEICE Transaction on Information & System, Issue 1, Pages 1-4, January 2010

P. Saravanan, V. Sangeetha, "Stochastic Bio-Inspired Gene Optimization Based Trapezoidal Fuzzy Logic for Software Failure Prediction Based On Event Log Files," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.89-98, 2019.

Software testing is an essential process during the software development process. Test suite generation process is employed to detect test cases with sources. Recently, many research works have been developed for automatically generate the software test suites. However, software testing is a time consuming and unable to obtain high coverage rate. In this paper, Gradient Advanced Genetic Parameter Control Based Test Suite Generation (GAGPC-TSG) technique is proposed. Based on the fitness value, the best test case is selected using roulette wheel selection. Later, the gradient approach is applied to obtain the optimal test case to generate the test suites for increasing the software quality. This enhances the better performance in terms of optimal test suite generation with minimum time and maximum fault coverage rate.

Software testing, test cases, roulette wheel selection, gradient approach

[1] Bestoun S. Ahmed, “Test case minimization approach using fault detection and combinatorial optimization techniques for configuration-aware structural testing”, Engineering Science and Technology, an International Journal, Elsevier, Vol.19, pp. 737–753, 2016.
[2] Muthusamy Boopathi, Ramalingam Sujatha, Chandran Senthil Kumar, Srinivasan Narasimman, “Quantification of Software Code Coverage Using Artificial Bee Colony Optimization Based on Markov Approach”, Arabian Journal for Science and Engineering, Springer, Vol.42, Issue. 8, pp. 3503–3519, 2017.
[3] Shunkun Yang, Tianlong Man, Jiaqi Xu, Fuping Zeng, Ke Li, "RGA: A lightweight and effective regeneration genetic algorithm for coverage-oriented software test data generation", Information and Software Technology, Elsevier, Vol.76, pp. 19–30, 2016.
[4] Kamal Z. Zamlia, Basem Y. Alkazemib, Graham Kendall, “A Tabu Search hyper-heuristic strategy for t-way test suite generation”, Applied Soft Computing, Elsevier, Vol.44, pp. 57–74, 2016.
[5] Shunkun Yang, Tianlong Man, and Jiaqi Xu, “Improved Ant Algorithms for Software Testing Cases Generation”, The Scientific World Journal, Hindawi Publishing Corporation, Vol.2014, May pp. 1-9, 2014.
[6] José Miguel Rojas, Mattia Vivanti, Andrea Arcuri, Gordon Fraser, “A detailed investigation of the effectiveness of whole test suite generation”, Empirical Software Engineering, Springer, Vol.22, Issue. 2, pp. 852–893, 2017.
[7] Thair Mahmoud and Bestoun S.Ahmed, “An efficient strategy for covering array construction with fuzzy logic-based adaptive swarm optimization for software testing use”, Expert Systems with Applications, Elsevier, Vol.42, Issue. 22, pp. 8753-8765, 2015.
[8] Robert M. Hierons, “Generating Complete Controllable Test Suites for Distributed Testing”, IEEE Transactions on Software Engineering, Vol.41, Issue. 3, pp. 279 – 293, 2015.
[9] Komal Agarwal, Manish Goyal, and Praveen Ranjan Srivastava, "Code coverage using intelligent water drop (IWD)", International Journal of Bio-Inspired Computation, Vol.4, Issue. 6, pp. 392-402, 2012.
[10] Manju Khari and Prabhat Kumar, “An Effective Meta-Heuristic Cuckoo Search Algorithm for Test Suite Optimization”, Vol.41, Issue. 3, pp. 363–377, 2017.
[11] G. Fraser and A. Arcuri, “Whole Test Suite Generation", IEEE Transactions on Software Engineering, Vol.39, Issue. 2, pp. 276 – 291, 2013.
[12] Ying Xing, Yun-Zhan Gong, Ya-Wen Wang, and Xu-Zhou Zhang, “A Hybrid Intelligent Search Algorithm for Automatic Test Data Generation”, Mathematical Problems in Engineering, Hindawi Publishing Corporation, Vol.2015, pp. 1-15, 2014.
[13] Justyna Petke, Myra B. Cohen, Mark Harman, Shin Yoo, "Practical Combinatorial Interaction Testing: Empirical Findings on Efficiency and Early Fault Detection", IEEE Transactions on Software Engineering, Vol.41, Issue. 9, pp. 901 – 924, 2015.
[14] Y. Huang and L. Lu, "Apply ant colony to event-flow model for graphical user interface test case generation", IET Software, Vol.6, Issue: 1, pp. 50 – 60, 2012.
[15] Saurabh Karsoliya, Prof.Amit Sinhal, Er.Amit Kanungo, “Combined Architecture for Early Test Case Generation and Test suit Reduction”, International Journal of Computer Science Issues, Vol. 10, Issue. 1, pp. 484-489, 2013.
[16] Zeeshan Anwar, Ali Ahsan, and Cagatay Catal, "Neuro-Fuzzy Modeling for Multi-Objective Test Suite Optimization", Journal of Intelligent Systems, Vol.25, Issue. 2, pp.1-24, 2015.
[17] Soma Sekhara Babu Lam, M L Hari Prasad Raju, Uday Kiran M, Swaraj Ch Praveen Ranjan Srivastav, “Automated Generation of Independent Paths and Test Suite Optimization Using Artificial Bee Colony”, Procedia Engineering, Elsevier, Vol.30, pp. 191-200, 2012.
[18] Luciano Soares de Souza, Ricardo Bastos Cavalcante Prudencio and Flavia A. de Barros, “A hybrid particle swarm optimization and harmony search algorithm approach for multi-objective test case selection”, Journal of the Brazilian Computer Society, Vol.21, Issue. 19, pp. 1-20, 2015.
[19] Fayaz Ahmad Khan, Anil Kumar Gupta, Dibya Jyoti Bora, “An Efficient Technique to Test Suite Minimization using Hierarchical Clustering Approach”, International Journal of Emerging Science and Engineering, Vol.3 Issue. 11, pp. 1-10, 2015.
[20] Gaurav Kumar, Pradeep Kumar Bhatia, “Software testing optimization through test suite reduction using fuzzy clustering”, CSI Transactions on ICT, Springer, Vol.1, Issue. 3, pp. 253–260, 2013.
[21] A. Sreepradha, “Measuring Software Quality Using Micro Interaction Metrics”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol.2, Issue. 5, pp.1-4, 2017.
[22] G. Rajendra, Dr. M. Babu Reddy, “Application of Adaptive Neural Fuzzy Inference System for the Prediction of Software Defects”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol.2, Issue. 3, pp.1-5, 2017.
[23] Kumari Seema Rani, “Open Source Software: A Prominent Requirement of Information Technology”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue. 2, pp.1-6, 2018.

T. Ramasundaram, V.Sangeetha, "Bio-Inspired Gradient Genetic Optimization for Test Suite Generation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.99-107, 2019.

This paper focuses on the study of grid connected solar photovoltaic energy conversion system modeling and control in MATLAB Software. In the modeling part photovoltaic systems have SPV array, boost converter, MPPT technique, inverter, filter and transformer. It also shows the detailed representation of the main components of the SPV system. The PV system performance mainly varies as per the location, surrounding condition, temperature and irradiation. That’s why prediction of irradiation is very important for the generation. There is lot of scope for the researchers in Maximum Power Point Tracking technique and how-to make it more efficient by increasing the tracking speed and accuracy for maximum power output from SPV system. The MPPT technique explained in this paper is Incremental Conductance based on hill climbing method which helps to extract maximum power from the SPV system. This paper will help the researchers to understand the method available for grid integration of solar photovoltaic system as well as to model and control such system for better results.

Solar Photovoltaic system, Incremental Conductance MPPT, DC-DC converter, Solar Photovoltaic (SPV), Photovoltaic(PV) cell, Inverter

[1] Hiren Patel, Vivek Agarwal, “MATLAB-Based Modeling to Study the Effects of Partial Shading on PV Array Characteristics,” IEEE Transactions on Energy Conversion, vol. 23, no. 1, pp. 302 – 310, March 2008.
[2] Syafaruddin, “Problem-Solving Mismatching Losses of Photovoltaic (PV) System under Partially Shaded Conditions,” 2014 Makassar International Conference on Electrical Engineering and Informatics (MICEEI), pp. 1 – 4, 26-30 November 2014.
[3] Nicole Foureaux, Alysson Machado, Érico Silva, Igor Pires, José Brito, Braz Cardoso F., “Central Inverter Topology Issues in Large-Scale Photovoltaic Power Plants: Shading and System Losses,” 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC), pp. 1 – 6, 2015.
[4] Mahmoud Amin, Jelani Bailey, Cesar Tapia, Vineeth Thodimeladine, “Comparison of PV Array Configuration Efficiency under Partial Shading Condition,” 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC), pp. 3704 – 3707, 2016.
[5] Rajeshwari Bhol, Ritesh Dash, Arjyadhara Pradhan, S. M Ali, “Environmental effect assessment on Performance of solar pv panel” 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015], Pages: 1 – 5, 2015.
[6] Jay patel, Vishal Seth, Gaurang Sharma,” Design and Simulation of Photovoltaic system using incremental mppt algorithm” International Journal of Advanced Research in Electrical, Electronics and Instrumention Engineering, Vol. 2, Issue 5, pp: 1-6 May 2013.
[7] S. S. Dheeban, V. Kamaraj,” Grid integration of 10kW solar panel” 2016 3rd International Conference on Electrical Energy Systems (ICEES), Pages: 257 – 266, 2016.
[8] Harshal Deopare, Amruta Deshpande,” Modeling and simulation of Incremental conductance Maximum Power Point tracking” 2015 International Conference on Energy Systems and Applications, Pages: 501 – 505, 2015.
[9] Rafia Rawoof, R. Balasubramanian, N. Moorthy Muthukrishnan,”Modeling and simulation of 100 kWp grid-connected Photovoltaic Power System”, 2015 Conference on Power, Control, Communication and Computational Technologies for Sustainable Growth (PCCCTSG)”, Pages: 15 – 20, 2015.
[10] S. Sheik Mohammed, D. Devaraj,”Simulation of Incremental Conductance MPPT based Two phase interleaved Boost Converter using MATLAB/Simulink”, 2015 Conference Paper, 2015.

Brijeshkumar. J.Patel, Manishkumar. J.Chauhan, "Simulation of Solar Photovoltaic System and Grid Integration," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.108-112, 2019.

India is one of the largest countries in the world and holly also. In India many festivals are celebrated with brotherhood. So in the festivals like ganpati utsav, durga puja the garbage is discharge to the lakes or rivers in large amount. As the garbage is present in the river and lake, the water of river and lake gets contaminated. This polluted water affects the human lives if that contaminated water is used for drinking, the resident suffer from epidermal, gastrointestinal, neurological disorders and cardiac ailments. It also affects the species resides in the water. There is no small and fuel efficient device for collecting the garbage from river and lakes. The conventional machineries available in the market have very high cost as well as are not efficient. The conventional machineries are pedal operated and human efforts are required for the operation of machine. Some machineries use petrol or diesel for the operation. It is possible to clean the surface of water reservoirs by using remote operated river surface cleaning machine. By taking this into consideration our main motive is to clean water. For that purpose the remote operated river surface cleaning machine is useful. This water garbage collector machine is made up of DC motors, RF transmitter and receiver, propeller, PVC pipes and chain drive with the conveyor attached to it for collecting garbage from the lake.

DC motors, RF transmitter and receiver, Propeller, PVC pipes, Water Garbage Collector Machine

[1] Emily Wax, “A Sacred River Endangered by Global Warming”, Washington post, June 2007.
[2] Hyde, Natalie, “Population patterns : what factors determine the location and growth of human settlements?”. New York: Crabtree Pub. p. 15. ISBN 978-0-7787-5182-3, 2010.
[3] Ketan V. Dhande, “Design and Fabrication of River Cleaning System”,International Journal of Modern trends in Engineering and Research’Volume 4, Issue 2 , ISSN (Print) : 2393-8161, February- 2017.
[4] Ganesh S. Khekare, “Design of emergency system for intelligent traffic system using VANET”, IEEE International conference on Information Communication and Embedded Systems (ICICES2014), pp. 1-7, 2014.
[5] Ganesh S. Khekare, Apeksha V. Sakhare “A smart city framework for intelligent traffic system using VANET”, IEEE International conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s2013), pp. 302-305, 2013.
[6] Ajay Dhumal, “Study of River Harvesting and Trash Cleaning Machine”, International Joiurnal of Innovative reasearch in Science and Engineering, Vol. 2, Issue 3 ISSN: 2454-9665, March-2016.
[7] N.G.Jogi, “Efficient Lake Garbage Coleector By Using Pedal Operated Boat” ‘International Journal of Modern trends in Engineering and Research’ Volume 02, Issue 04 ISSN: 2455-1457, April-2016.
[8] Girish Gaude, Samarth Borkar, “Comprehensive survey on underwater object detection and tracking”, International journal of computer science and engineering, Vol.6, Issue.11, pp.304-313, E-ISSN: 2347-2693, Nov 2018.
[9] Dinesh Lingote, Girish Katkar, Ritesh Vijay, R. B. Biniwale, “Responsive Information generation system for Kanhan River, an effective information system for river modeling”, International journal of computer science and engineering, Vol.6, Issue.12, pp.213-221, E-ISSN: 2347-2693, Dec 2018.

Ganesh S. Khekare, Urvashi T. Dhanre, Gaurav T. Dhanre, Sarika S. Yede, "Design of Optimized and Innovative Remotely Operated Machine for Water Surface Garbage Assortment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.113-117, 2019.

Color image segmentation can be treated as a process of dividing a color image into some constituent regions. This paper presents color image segmentation method using an Improved Particle Swarm Optimization (IPSO). The RGB color image taken as an input image and remove the noise using Gaussian filter. The obtained preprocessed image the components are separated and find the object regions separately. All the ungrouped pixels would be detected and put in the nearest region. The main purpose of proposed IPSO method is used to find the best values of thresholds, particles and position that can give us an appropriate partition for a target image. This method tries to treat pixels as particles and provide them search space and motivated with IPSO. It findings to better optimized region and produces more accurate segmentation results for color images. The proposed method is tested on different single and group of color images are taken as the input image and the experimental results demonstrate its effectiveness.

IPSO, Gaussian Filter, Color Component

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[2] Anita Tandan, Rohit Raja and Yamini Chouhan, “Image Segmentation based on Particle Swarm Optimization Technique”, International Journal of Science, Engineering and Technology Research (IJSETR), Vol. 3, Issue. 2, pp.257-260, 2014.
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[4] E.Boopathi Kumar and V.Thiagarasu, “Color Channel Extraction in RGB Images for Segmentation”, 2nd International Conference on Communication and Electronics Systems(ICCES), IEEE, ISBN: 978-1-5090-5013-0, pp. 234-239, 2017.
[5] Chenxue Wang and Junzo Watada, “Robust Color Image Segmentation by Karhunen-Loeve Transform based Otsu Multi-thresholding and K-means Clustering”, Fifth International Conference on Genetic and Evolutionary Computing, IEEE, pp.377-380, 2011.
[6] Chi-Yu Lee, Jin-Jang Leou and Han-Hui Hsiao, “Saliency-Directed Color Image Segmentation using Modified Particle Swarm Optimization”, Signal Processing, Elsevier, Vol. 92, pp. 1-18, 2012.
[7] Dipak Kumar Kole and Amiya Halder, “An Efficient Dynamic Image Segmentation Algorithm using A Hybrid Technique based on Particle Swarm Optimization and Genetic Algorithm”, International Conference on Advances in Computer Engineering, IEEE, Computer Society, pp. 252-255, 2010.
[8] Firas Ajil Jassim, Fawzi H. Altaani, “Hybridization of Otsu Method and Median Filter for Color Image Segmentation”, International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Vol. 3, Issue 2, 2013.
[9] Kajal Gautam and Rahul Singhai, “Color Image Segmentation using Particle Swarm Optimization in Lab Color Space”, International Journal of Engineering Development and Research (IJEDR), Vol.6, Issue. 1, pp. 373-377, ISSN: 2321-9939, 2018.
[10] Kiran Ashok Bhandari and Manthalkar Ramanchandra R, “A New Watershed Segmentation (NWS) and Particle Swarm Optimization (PSO-SVM) Techniques in Remote Sensing Image Retrieval”, Proceedings of 3rd International conference on Reliability, Infocom Technologies and Optimization, ISBN: 978-1-4799-6895-4, IEEE, 2014.
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[12] Manas Yetirajam and Pradeep Kumar Jena, “Enhanced Color Image Segmentation of Foreground Region using Particle Swarm Optimization”, International Journal of Computer Application, Vol. 57, No. 8, pp.18-23, 2012.
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[16] Saeed Mirghasemi, Ramesh Rayudu and Mengjie Zhang, “A New Image Segmentation Algorithm Based on Modified Seeded Region Growing and Particle Swarm Optimization”, 28th International Conference on Image and Vision Computing, IEEE, pp. 382-387, 2013.
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[18] Shima Afzali, Bing Xue, Harith Al-Sahaf and Mengjie Zhang, “A Supervised Feature Weighting Method for Salient Object Detection using Particle Swarm Optimization”, IEEE Symposium Series on Computational Intelligence (SSCI), ISBN: 978-1-5386-2727-3, 2017.
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V. Sheshathri, S. Sukumaran, "An Improved Particle Swarm Optimization Method for Color Image Segmentation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.118-124, 2019.

in education domain, We can mine the hidden knowledge in the available databases for generating various analytical reports for proper decision making [10]. Grade Point Average (GPA) is commonly used as an indicator of academic performance [11]. An academic performance evaluation is a basic way to evaluate the progression of student performance, when evaluating student’s academic performance, there are occasion where the student data is grouped especially when the amounts of data is large. Thus, the pattern of data relationship within and among groups can be revealed. Grouping data can be done by using clustering methods such as K-Means, K*(STAR) and the Fuzzy C-Means algorithms. Classifying students using conventional techniques cannot give the desired level of accuracy, while doing it with the use of computing techniques may prove to be beneficial. Clustering or grouping a set of data sets is a key procedure for data processing .It is an unsupervised technique that is used to arrange pattern data into clusters. This research work deals with two of the most representative clustering algorithms namely centroid and crisp values based Fuzzy C-Means, K*(STAR) and represent object based on calculation of membership function. Fuzzy C-Means are described and analyzed for a datasets. Based on experimental results the algorithms are compared regarding their clustering quality and their performance, which depends on the time complexity between the various numbers of clusters chosen by the end user. The total elapsed time to cluster all the datasets and Clustering time for each cluster are also calculated and the results compared with one another [7].

K*(STAR) Algorithm, Fuzzy C-Means Algorithm, cluster Analysis, fuzzy logic

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[12] Shailendra Singh Raghuwanshi, PremNarayan Arya,"Comparison of K-means and Modified K-mean algorithms for Large Data-set", International Journal of Computing, Communications and Networking, Volume 1, No.3, November –December 2012

S.N.Ali Ansari, Srinivasa Rao V, "Analysis of K*(STAR) and Fuzzy C-Means Algorithm for Education Completion Performance," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.125-129, 2019.

New advancements have made it workable for an extensive variety of individuals – including humanities and sociology scholastics, advertisers, legislative associations, instructive foundations – to deliver, share, collaborate and arrange data. Monstrous informational collections that were once dark and particular are being amassed and made effectively open. The Huge volumes of heterogeneous therapeutic information these days expanding and easily obtainable from various healthcare organizations. Nowadays, the Thyroid disease is one of the common diseases found in human. The Thyroid hormones created by the thyroid organ to help the control of the body`s digestion. Because of the variations from the norm of thyroid capacity, there might be a lower production of thyroid hormone, which is known as hypothyroidism, or higher production of thyroid hormone, which is known as hyperthyroidism. In this paper, an examination of thyroid disease is carried out by performing experiment of various Machine Learning algorithms techniques such as Naïve Bayes, Support Vector Machine, Multiclass Classifier, Logistic and K Nearest Neighbour. The informational index utilized for this investigation on hypothyroid is taken from UCI information store. The experiment is also completed with WEKA and RConsole. The comparison of various parameters are done and as a result the execution and investigation of different grouping calculation is determined. In the result, it is found that Multiclass Classifier gives preferable exactness over other embraced calculations.

Machine Learning, Health Care, Thyroid Disease, Prediction

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Hetal Patel, "An Experimental Study of Applying Machine Learning in Prediction of Thyroid Disease," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.130-133, 2019.