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The “Home Automation” concept is an approach to wirelessly control the home appliances. The terms “Smart Homes”, “Intelligent Homes” are associated with this kind of system and has been used to introduce the networking concepts based appliances and devices in the house. Smart homes and home control system ideas have grown along with the enhancement in technology and the constant improvement of the living standards of humans. Home automation Systems represents a great research opportunity in developing new ideas in the fields of engineering, architecture and computing. The people using home automation can access the use of appliances anytime and from anywhere, making our houses become more and more automated and intelligent. The Smart Energy initiative fulfils these abundant needs by providing an adoptable and sustainable experience by linking new and useful digital technologies to the requirements of consumers.

Home Automation System, Sensor, User Fiendly, Microcontroller

[1] C. Perera et al.,” Context Aware Computing for The Internet of Things: A Survey,” IEEE communications surveys & tutorials, vol. 16, no. 1, first quarter 2014, pp 414 – 417.
[2] L.Tan et al.,”Future Internet: The Internet of Things” 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), pp 376 – 380.
[3] S. D`Oro, L. Galluccio, G. Morabito and S. Palazzo, "Exploiting Object Group Localization in the Internet of Things: Performance Analysis," in IEEE Transactions on Vehicular Technology, vol. 64, no. 8, pp. 3645-3656, Aug. 2015.
[4] J. Huang, Y. Meng, X. Gong, Y. Liu and Q. Duan, "A Novel Deployment Scheme for Green Internet of Things," in IEEE Internet of Things Journal, vol. 1, no. 2, pp. 196-205, April 2014.
[5] A. P. Castellani et al., “Architecture and protocols for the Internet of Things: A case study,” in Proc. 8th IEEE Int. Conf. Pervasive Comput.Commun. Workshops (PERCOM), 2010, pp. 678–683.
[7] Rozita. T et al., “Smart GSM Based Home Automation System,” 2013 IEEE Conference on Systems, Process & Control (ICSPC2013), December 2013, Kuala Lumpur, Malaysia, pp 306 – 309
[8] Bhupinder Singh, Neha Kapur, Puneet Kaur, “Speech Recognition with Hidden Markov Model: A Review”, International Journal of Advance Research In Computer Science And Software Engineering, vol. 2, issue 3, 2012.
[9] S. Kim, P. Georgiou, S. Lee, S. Narayana, “Real Time Emotion Detection System Using Speech: Multi Modal Fusion of Different Time Scale Features”, Proceedings of IEEE Multimedia Signal Processing Workshop, Chania,
[10] Poorna Banerjee Dasgupta, "Detection and analysis of human emotions through voice and speech pattern processing", An International Journal of Computer Trends and Technology (IJCTT), Vol. 52, No.1, 2017.

Anoop Kumar, Shubham Goswami, Swati Goel, "Smart Home Automation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.824-827, 2019.

The Rice is a very useful fool for human .Basically, it is cropped the all human consumes in the overall world, mostly in Asian countries. Basically, it is classified based on the grain shapes; color, etc.This paper presents the use of machine vision system for the grain classification. Different features of the rice grain are depending on different rice grains. An automated system is introduced which can be used for rice grain type identification and classification where the digital image is classified. Images are acquired for the rice using camera. In that , Image processing techniques, segmentations, feature extractions are performed on the acquire images. That can be extracted features from the rice grain in non – contact manner. In this we also discuss and suggesting methods classify seven varieties of rice and it also find the percentage of purity of rice using the grain the techniques of image processing based on the several features such as major axis length, minor axis length, Area etc.

Grain classification, Image processing, MATLAB techniques, Segmentation, Neural network

[1]. Kasun Herath, “Rice grains classification using image processing techniques”, Research gate, December 2016.
[2]. 2.Ozan aki,Aydin Gullu,Erdim Ucar, “Classification of Rice Grains using Image processing and machine larning techniques.” , International scientific conference,20-21 November.
[3]. Rexce J, Usha kigsly Devi K, “ clasificaiton of Miled Rice Using Image Processing”, International journal of Scientific and engineering research, Vol 8, Issue 2, February 2017.
[4]. Anusha Anchan,Shabari Shedthi B, “Classification and Identification using Neural Network”, International Journal of Innovative research in Computer and Communication Engineering, Vol 4 ,Issue4,April2016
[5]. Dr, prashant kumbharkar, Priyanka Upale, “Application for Rice quality Assement and classification using Image processing Technique.” ,International journal of Innovative research in Computer and Communication engineering, Vol 4, Issue 10, October 2016.
[6]. Nikhade Pratibha, More Hemlata, “Analysis and Identification of Rice Granules Using Image processing and Neural Network”, International Journal of Electronics and Communication engineering, Vol 10, Issue 1, 2017.
[7]. Jagdip singh Aulakh ,Dr ,V.K.Banga , “ Grading of Rice Grain by Image Procesing”, International Journal of Research and Technology, Vol 1 ,Issue 4, June 2012.
[8]. Wan putrid N.W.M tahir ,Nor haida Hussian , “ Rice Grading Using Image Processing”, ARPN journal of Engineering and Applied Sciences, Vol 10 ,Issue 21,November 2015.
[9]. Prof, P.M Soni, “A Review on Identification of Rice Grain Quality Using Matlab and Neural Network.” International Journal of Innovations in Engineering Research and Technology, Vol 4, Issue 1, January 2017.
[10]. Dr muhammd anzar Alam,Engr Zahida parveen, “Assement and Quality of Rice Grain Using Optical and Image processing Technique.”,International conference on computing communication and digital system 2017.

A.H. Bhensjaliya, H.D. Vasava, "Survey on Classification of Rice Grains Using Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.828-831, 2019.

Over the past decade, there has been a fast development in the advanced education system which prompts an enormous amount of data. Predicting students’ performance turns out to be all the more difficult because of this enormous measure of information in educational databases. However, this data from the educational department acts as a gold mine for institutions and also encourages the analysts and researchers to make a framework that can improve the general educating and learning process. Analysts and researchers apply Data mining techniques on educational data to explore it. Educational Data Mining helps in a big way to answer the issues of predictions and grouping of not only students but also the other stakeholders of education sectors. This paper talks about the utilization of different Data Mining techniques and tools that can be adequately utilized in noting the issues of predictions of students’ performance and their grouping.

Data mining, Data Mining Techniques, Educational Data Mining, Student Performance Prediction

[1] Sethunya R Joseph, Hlomani Hlomani, Keletso Letsholo, “Data Mining Algorithms: An Overview”, International journal of Computers and Technology, Vol.15, Issue.6, pp.6806-6813, 2016.
[2] S. A. Kumar and M. Vijayalakshmi, “A Novel Approach in Data Mining Techniques for Educational Data” (ICMLC 2011), pp.152–154, 2011.
[3] Vandna Dahiya, “A Survey on Educational Data Mining”, International Journal of Research in Humanities, Arts and Literature, Vol. 6, Issue.5, pp. 23-30, 2018.
[4] Iti Burman, Subhranil Som, Mayank Sharma, “Enhancing Student Learning Behavior Using EDM And Psychometric Analysis” In International Conference on Reliability, Infocom Technologies and Optimization (ICRITO 2017), pp.20-22, 2017.
[5] Abdulmohsen Algarni, “Data Mining in Education” International Journal of Advanced Computer Science and Applications, Vol. 7, Issue. 6, pp. 456-461, 2016.
[6] Ryan Baker, “Mining Data for Student Models.” Advances in Intelligent Tutoring Systems, pp. 323-338, 2010.
[7] Zailani Abdullaha , Tutut Herawanb , Noraziah Ahmadb , Mustafa Mat Derisc “Mining significant association rules from educational data using critical relative support approach” Procedia - Social and Behavioral Sciences, Vol. 28 , pp.97-101, 2011.
[8] https://en.wikipedia.org/wiki/R_(programming_language)
[9] T.Thilagaraj, Dr.N Sengottaiyan “A Review of Educational Data Mining in Higher Education System” In Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, pp. 349-358, 2017.
[10] Kalpana Rangra, Dr. K. L. Bansal, “Comparative Study of Data Mining Tools” , International Journal of Advanced Research in Computer Science and Software Engineering , Vol.4, Issue.6, pp. 216-223, 2014.
[11] C. Romero and S. Ventura, "Educational data mining: A survey from 1995 to 2005," Expert Systems with Applications, vol. 33, Issue. 1, pp. 135–146, 2007.
[12] C. Romero, S. Ventura, P. G. Espejo, C. Herv “Data mining algorithms to classify students, in: Educational Data Mining 2008”, EDM 2008, 2008.
[13] G. Dekker, M. Pechenizkiy and J. Vleeshouwers. "Predicting students drop out: A case study." In Educational Data Mining 2009,pp.41-50,2009.
[14] S. Sembering, M.Zarlis, “Prediction of student academic performance by an application of data mining techniques”, International conference on management and Artificial Intelligence (IPEDR 2011), Indonesia, Vol.6, pp.110-114, 2011.
[15] Dr. Saurabh Pal, “Mining Educational Data Using Classification to Decrease Dropout Rate of Students”, International Journal Of Multidisciplinary Sciences And Engineering, Vol. 3, Issue. 5, pp.35-39, 2012.
[16] Suhem Parack, Zain Zahid, Fatima Merchant, ” Application of Data Mining in Educational Databases for Predicting Academic Trends and Patterns”, 2012 IEEE International Conference on Technology Enhanced Education (ICTEE), India ,2012.
[17] D. Kabakchieva, “Predicting Student Performance by Using Data Mining Methods for Classification”, Cybernetics and Information Technologies- The Journal of Institute of Information and Communication Technologies of Bulgarian Academy of Sciences, Vol.13, Isse.1, pp.61-72, 2013.
[18] D. Ahmed, I. Sayed, “Data Mining: A Prediction for student’s performance using classification method”, World journal of Computer Application and Technology, Vol.2, Issue.2, pp.43-47, 2014.
[19] M. Mayilvaganan, D. Kalpanadevi, “Comparison of classification techniques for predicting the performance of students academic environment”, in: Communication and Network Technologies, International Conference on, IEEE, pp. 113–118, 2014.
[20] A. M. Shahiri, W. Husain, and N. A. Rashid, "A review on predicting student’s performance using data mining techniques," Procedia Computer Science, Vol. 72, pp. 414–422, 2015.
[21] Elaf Abu Amrieh1, Thair Hamtini and Ibrahim Aljarah, “Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods”, International Journal of Database Theory and Application, Vol.9, Issue.8, 2016.
[22] Ashwin Satyanarayana, Mariusz Nuckowski, “Data Mining using Ensemble classifiers for improved Prediction of student AcademicPerformance”,Spring`2016`Mid.Atlantic`ASEE`Conference, 2016.
[23] Febrianti Widyahastuti and Viany Utami Tjhin. “Predicting Students Performance in Final Examination using Linear Regression and Multilayer Perceptron”, IEEE 10th International Conference on Human System Interactions (HSI), South Korea, PP.188-192, 2017.
[24] Xiaofeng Ma and Zhurong Zhou. “Student Pass Rates Prediction Using Optimized Support Vector Machine and Decision Tree”, IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp.209-215, 2018.
[25] Mrinal Pandey and S. Taruna “An Ensemble-Based Decision Support System for the Students’ Academic Performance Prediction” Springer, Advances in Intelligent Systems and Computing, Vol.653, pp.163-169, Singapore, 2018.
[26] Pooja Kumari, Praphula Kumar Jain, Rajendra Pamula “An Efficient use of Ensemble Methods to predict Student Academic Performance” IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), USA, 2018.
[27] Olugbenga Wilson Adejo, Thomas Connolly, "Predicting student academic performance using multi-model heterogeneous ensemble approach", Journal of Applied Research in Higher Education, Vol.10, Issue.1, PP.61-75, 2018.

K.D. Purani, M.B. Chaudhary, "Educational Data Mining: A Survey of Analyzing Student Academic Performance Methods," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.832-838, 2019.

Robots can replace worker to finish a lot of complex works in complex environment. Recently, much research work has been done in the application of robots. To which robot navigation has become one of the most popular research field. The much emphasis is on programming of a complete, deterministic algorithm which is able to generate an optimum path in real time will and allow achieving a high level of autonomy. This means, for example, that you can read the newspaper when your car takes you to work on its own. In this paper we used the plant growth optimization algorithm for route selection in obstacle environment. The plant grow optimization algorithm is multi-objective optimization technique with multiple constraints such as growing of leaf and competition of branch. The system that needs to be optimized first "grows" from the root of a plant and then continues to "grow" until you find the optimal solution

Path Planning, Robotics, Obstacles, Plant Grow Optimisation

[1] Belinda Matebese, Daniel Withey and Mapundi K. Banda "Path planning with the leapfrog method in the presence of obstacles", IEEE, 2016, Pp 613-618.
[2] B.T. Matebese, D.J. Withey and M.K. Banda “Application of the leapfrog method to robot path planning”, IEEE, 2014, Pp 710-715.
[3] Hao-dong ZHU and Bao-feng HE “Path planning of mobile robot using optimized ACA”, AITA, 2016, Pp 92-98.
[4] Prithviraj Dasgupta “coverage path planning using mobile robot team formations”, IGI Global, 2015, Pp 214-227.
[5] Andrea I. Schäfer, Gordon Hughes and Bryce S. Richards “Renewable energy powered membrane technology: a leapfrog approach to rural water treatment in developing countries”, Elsevier, 2014, Pp 1-44.
[6] S. Sekar and K. Prabhavathi “Numerical solution of first order linear fuzzy differential equations using leapfrog method”, IOSR Journal of Mathematics, 2014, Pp 7-12.
[7] Noor Hazarina Hashim, Jamie Murphy, Olaru Doina and Peter O’Connor “Bandwagon and leapfrog effects in internet implementation”, International Journal of Hospitality Management, 2014, Pp 91–98.
[8] S. Sekar and M. Vijayarakavan “Numerical investigation of first order linear singular systems using leapfrog method”, International Journal of Mathematics Trends and Technology, 2014, Pp 89-93.
[9] Ki-Baek Lee and Jong-Hwan Kim “Multi-objective particle swarm optimization with preference-based sort and its application to path following footstep optimization for humanoid robots”, IEEE, 2013, Pp 1-13.
[10] P. Raja and S. Pugazhenthi “Optimal path planning of mobile robots: a review”, International Journal of Physical Sciences, 2012, Pp 1314-1320.
[11] Jonathan Binney, Andreas Krause and Gaurav S. Sukhatme “Informative path planning for an autonomous underwater vehicle”, IEEE, 2010, Pp 4791-4796.
[12] Yogita Gigras, Kavita Choudhary, Kusum Gupta and Vandana “A hybrid ACO-PSO technique for path planning”, IEEE, 2015, Pp 1616-1621.
[13] Ahlam Ansari and Mohd Amin Sayyed, Khatija Ratlamwala and Parvin Shaikh “An Optimized Hybrid Approach for Path Finding”, International Journal in Foundations of Computer Science & Technology, 2015, Pp 47-58.
[14] Wei Cai, Weiwei Yang and Xiaoqian Chen “A Global Optimization Algorithm Based on Plant Growth Theory: Plant Growth Optimization”, Intelligent Computation Technology and Automation, 2008, Pp 1194-1199.
[15] Manoj Garg and Dinesh Kumar “Simple GA & Hybrid GA for Basis Path Testing under BDFF”, International Journal of Scientific Research in Computer Sciences and Engineering, 2016,Pp 28-35.
[16] G.P. Sunitha, B.P. Vijay Kumar, S.M. Dilip Kumar “A Nature Inspired Optimal Path Finding Algorithm to Mitigate Congestion in WSNs”, International Journal of Scientific Research in Network Security and Communication, 2018 Pp50-57.

Souresh.K, A. Saxena, K. Singh, D. S. Tomar, "Automatic Route Planning of Robot Based On Plant Grow Optimization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.839-843, 2019.

Blockchain’s integration with various domains beyond cryptocurrency has produced exciting results and innovative products. Blockchain’s capabilities of being distributed peer-to-peer network, scalability, reliability and security has potential to solve many challenges faced by AI and at the same time add exciting features to it. This article analyses and reviews many existing research where Blockchain is integrated with AI. These research works have produced many encouraging results for data privacy, security, distributed processing and trustless collaboration. Blockchain has also contributed to enhance security of the AI system and bring trust among various AI systems. Cryptocurrency has also added trading capabilities to AI and encouraged models like AI as Service. This article also discusses scope for future research work. With more maturity and feature fullness, Blockchain is poised to become one of the most suitable platforms for decentralized AI applications.

Blockchain, Artificial Intelligence, Machine Learning, Deep Learning, Robotics, Decentralized Applications

[1] M. Swan, "Blockchain Thinking: The Brain as a DAC(Decentralized Autonomous Organization)," in Texas Bitcoin Conference, 2015.
[2] "Proof of Work," Wikipedia, 16 01 2019. [Online]. Available: https://en.wikipedia.org/wiki/Proof-of-work_system. [Accessed 21 01 2019].
[3] "Proof of Stake," wikipedia, 18 01 2019. [Online]. Available: https://en.wikipedia.org/wiki/Proof-of-stake. [Accessed 21 01 2019].
[4] G. J. Mendis, M. Sabounchi, J. Wei and R. Roche, "Blockchain as a Service: An Autonomous, Privacy Preserving, Decentralized Architecture for Deep Learning," 05 07 2018. [Online]. Available: https://arxiv.org/abs/1807.02515. [Accessed 21 01 2019].
[5] M. Galtier and C. Marini, "Morpheo Traceable Machine Learning on Hidden data," [Online]. Available: https://arxiv.org/abs/1704.05017.
[6] N. K. Bore, R. K. Raman, I. M. Markus, S. L. Remy, O. Bent, M. Hind, E. K. Pissadaki, B. Srivastava, R. Vaculin, K. R. Varshney and K. Weldemariam, "Promoting Distributed Trust in Machine Learning and Computational Simulation via a Blockchain Network," [Online]. Available: https://arxiv.org/abs/1810.11126. [Accessed 20 01 2019].
[7] M. Shayan, C. Fung, C. J. Yoon and I. Beschastnikh, "Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning," 27 11 2018. [Online]. Available: https://arxiv.org/abs/1811.09904. [Accessed 21 01 2019].
[8] A. B. Kurtulmus and K. Daniel, "Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain," 2018.
[9] T.-T. Kuo and L. Ohno-Machado, "ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private Blockchain Networks," [Online]. Available: https://arxiv.org/abs/1802.01746. [Accessed 20 01 2019].
[10] E. C. Ferrer, "The blockchain: a new framework for robotic swarm systems," in FTC 2018: Proceedings of the Future Technologies Conference (FTC) 2018, 2018.
[11] SingularityNET Foundation, "SingularityNET: A decentralized, open market and inter-network for AIs," [Online]. Available: https://public.singularitynet.io/whitepaper.pdf. [Accessed 20 01 2019].
[12] DeepBrain Chain Foundation, "DeepBrain Chain Whitepaper," [Online]. Available: https://www.deepbrainchain.org/assets/pdf/DeepBrainChainWhitepaper_en.pdf. [Accessed 20 01 2019].
[13] "Blockchain," Wikipedia, 02 12 2018. [Online]. Available: https://en.wikipedia.org/wiki/Blockchain. [Accessed 03 12 2018].
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[15] Wikipedia, "Ethereum," Wikipedia, 19 01 2019. [Online]. Available: https://en.wikipedia.org/wiki/Ethereum. [Accessed 21 01 2019].

Yatrik Buch, Mosin Hasan, Prashant Swadas, "Decentralized Artificial Intelligence on Blockchain," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.844-848, 2019.

Hand gestures are powerful means of communication among humans and sign language is the most natural and expressive way of communication for dumb and deaf people. Thus, it provides a replacement for speech among deaf and mute people, but no one form of sign language is universal. American Sign Language (ASL) is one of the sign languages which is the most widely used sign language among the English-speaking community. ASL is also widely learned language, serving as lingua franca. The communication between the deaf /dumb and normal people is extremely poor due to the fact that there is no single standalone platform that can bridge the communication gap. Sign language is a way to bridge this gap still cannot a success since the normal people ever learned such sign languages. One of the ways to eradicate this gap is by identifying the sign language’s hand gestures thus predicting the language for the normal people. In this way, the normal person will be aware of the dumb and deaf’s speech and can respond appropriately. Such a solution will eliminate the level of dependencies which a deaf/dumb person has on a translator for communicating which in turn will provide job opportunities and eradicate many more problems faced by such people in their everyday life.

ASL, Gestures, deaf/dumb, OpenCV, Android app, Communication

[1] Rung-Huei Liang, Ming Ouhyoung, “A REAL-TIME CONTINUOUS GESTURE RECOGNITION SYSTEM FOR SIGN LANGUAGE”, 3rdIEEE International Conference on Automatic Face and Gesture Recognition.
[2] T. Starner, “REAL-TIME AMERICAN SIGN LANGUAGE RECOGNITION USING DESK AND WEARABLE COMPUTER BASED VIDEO”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 20, 12, Dec 1998.
[3] CelalSavur, FeratSahin, “REAL-TIME AMERICAN SIGN LANGUAGE RECOGNITION SYSTEM USING SURFACE EMG SIGNAL”, IEEE 14thInternational Conference on Machine Learning and Applications (ICMLA), DOI:10.1109/ICMLA.2015.212.
[4] Qutaishat Munib, Moussa Habeeb, Bayan Takruri, Hiba Abed Al-Malik, “AMERICAN SIGN LANGUAGE (ASL) RECOGNITION BASED ON HOUGH TRANSFORM AND NEURAL NETWORKS”, Expert Systems with Applications, Volume 32, Issue 1, January 2007, Pages 24-37.
[5] Sigberto Alarcon, Brandon Garcia, “REAL-TIME AMERICAN SIGN LANGUAGE RECOGNITION WITH CONVOLUTIONAL NEURAL NETWORKS”, Stanford University, Stanford, CA.
[6] Dominique Uebersax, Juergen Gall, Michael Van den Bergh, Luc Van Gool, “REAL-TIME SIGN LANGUAGE LETTER AND WORD RECOGNITION FROM DEPTH DATA”, IEEE International Conference on Computer Vision Workshops, ICCV 2011 Workshops, Barcelona, Spain, November 6-13, 2011.
[7] R. Lockton, A.W. Fitzgibbon, “REAL-TIME GESTURE RECOGNITION USING DETERMINISTIC BOOSTING”, British Machine Vision Conference, 2002.
[8] J. Marnik, “THE POLISH FINGER ALPHABET HAND POSTURES RECOGNITION USING ELASTIC GRAPH MATCHING”, Computer Recognition Systems 2, Volume 45 of Advances in Soft Computing, pages 454–461, 2007.
[9] Z. Mo, U. Neumann, “REAL-TIME HAND POSE RECOGNITION USING LOW-RESOLUTION DEPTH IMAGES”, IEEE Conference on Computer Vision and Pattern Recognition, pages 1499– 1505, 2006.
[10] A. K. Gupta, S. Gupta, “NEURAL NETWORK THROUGH FACE RECOGNITION”, Research Paper at Isroset-Journal (IJSRCSE), Vol.6 Issue.2, pp.38-40,Apr-2018.
[11] BoyaAkhila, Burgubai Jyothi, “FACE IDENTIFICATION THROUGH LEARNED IMAGE HIGH FEATURE VIDEO FRAME WORKS”, Research Paper at Isroset-Journal (IJSRCSE), Vol.6, Issue.4, pp.24-29, Aug-2018.
[12] T. Jaya, Rajendran V., “HAND-TALK ASSISTIVE TECHNOLOGY FOR THE DUMB”, Research Paper at Journal (IJSRNSC), Vol.6, Issue.5,pp.27-31Oct-2018.

Fatima Ansari, Anwar Hussain Mistry, Yusuf Mirkar, Alim Merchant, "Real Time ASL (American Sign Language) Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.848-851, 2019.

To increase garbage processing and recycling, the Government implemented a Solid Waste Management Rule but it is not followed by some societies properly. And despite plastic ban plastic is used by some societies. To overcome above issues an app will be developed which take the image of garbage at garbage point and send it to the server for computation. In python, we incorporate the Machine Learning Module. Then by using convolutional a neural network technique, it will identify the garbage whether it is properly segregated or not and also how much amount of plastic is there in the garbage. Based on results we various communities will be rated. and notification will be sent to those communities who do not segregate their waste properly.

Garbage Processing; Waste segregation; machine learning; convolutional neural network

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Mohammed Ahmed, Nurulhaque Usmani, Javed Khan, Shahnawaz Khan, Imran Shaikh, "Garbage Profiling – A Proposed System to rank localities based on waste segregation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.852-855, 2019.

Mobile Ad Hoc Network is able to communicate with different nodes. In this research paper we are mainly in view of the study of routing protocols in mobile ad hoc networks. As we know, still there are numbers of diverse metrics to work on. In the MANET, we are study basically a few numbers of metrics such as delay and energy-throughput in mobile ad hoc networks. According to this, further concise the study related these metrics. This research paper includes relative with different techniques and future work is also applicable.

MANET, Artifical Neural Networks, Soft Computing Techniques, Networks, Routing Protocols

[1]. Gurdeep Kaur, Vinay Bhatia, Dushyant Gupta “Comparative Study of the performance of existing protocols of MANET with simulation and justification of an improved Routing Protocol” International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 6, Issue 6, June 2017.
[2]. M. Marimuthuand A. Kannammal"A Survey on Fuzzy Based QoS Routing in Mobile Ad Hoc Networks”, Proceedings of7th International Conference on Intelligent Systems and Control (ISCO 2013)
[3]. Yasser L. Morgan and Thomas Kunz "A Proposal for an Ad-Hoc Nenvork QoS Gateway "iEEE, 2005.
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[6]. Anuj Joshi, Pallavi Srivastava and Poonam Singh, ―Security Threats in Mobile Ad Hoc Network‖, S-JPSET : ISSN : 2229-7111, Vol. 1, Issue 2, samriddhi, 2010
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[14]. Ting Lu and Jie Zhu “Genetic Algorithm for Energy Efficient QoS Multicast Routing”, IEEE, Vol 17, 2013
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Amarjit Singh, Tripatdeep Singh, "Comparative Study on Routing Protocol in Mobile Ad Hoc Networks using Soft Computing Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.856-863, 2019.

Liver is an important organ which plays major role in digesting food, removing poisons and stocking energy. One major challenge is to identify the Liver disorder using its ambiguous symptoms due to this many people’s are suffering like anything. So to overcome the challenges we have proposed a method to identify the disorder which in turn will help medical field and society. Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Technically the data mining can be considered as the sequence of steps followed for searching patterns or identifying correlations between large numbers of fields within a huge relational database. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Data mining techniques are applied to different medical domains to improve the medical diagnosis. Improving the accuracy of the classification and improving the prediction rate of medical datasets are the main tasks/challenges of medical data mining. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the performances of machine learning techniques like Logistic Regression (LR), Artificial Neural Networks (ANN), and ANN with k-fold Cross Validation Sample (CVS) with Feature Selection Methods (FSMs) using Percentage Split (PS) as test option on Liver Disorder Datasets. The performance of the proposed model is measured in the form of classification accuracy. Performance of proposed work is assessed as classification accuracy. The work deliver the better accuracy for reduced set of attributes compared with full set attribute and we state that those are the very important tests compared to all tests to identify the disorder.

artificial neural Networks; ANN; classification accuracy; CA; backward elimination; BE; classification accuracy; CA; entropy evaluation (EE): feature subset selection methods; FSM’s; forward selection; FS; logistic regression; LR

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S. Raghavendra, J. Santosh Kumar, B. K. Raghavendra, S. K. Shivashankar, "Performance Evaluation of Machine Learning Techniques for the Classification of BUPA Liver Disorder," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.864-869, 2019.

Plasma is one of the fastest emerging technologies conquering the world. It is a state of matter where atoms and molecules are electrically charged. Cold plasma is playing a very important role in almost all the fields of technology. It has witnessing the growth in scientific and industrial areas. It has extraordinary potential because of its rare characteristics such as flexibility capability and formation of new product. In addition to it plasma technology is environment friendly and saves allot of energy, due to which it finds its application in various fields. Plasma technology has emerged out as a great way of managing the waste. In this paper a detailed study is being done on plasma technology and its applications. This paper also gives an overview of various discharge sources and application. In this paper a deep analysis on cold plasma (non thermal plasma) is provided.

Plasma, Non thermal, Dielectric Barrier Discharge, Source, etc

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Rahul Ohlan, "Plasma- An Environment Friendly Technology," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.870-873, 2019.