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Weak passwords, default passwords in combination with bruteforce attack and dictionary attack are one of the most dangerous combination for security of systems across the globe. False Positive and False Negative Password Authentication System is an attempt to decrease the efficiency of Dictionary, Bruteforce Attack which can be implemented in any authentication system without any significant changes.

Bruteforce, Dictionary, Attacks, Botnets, Authentication System

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[2] “‘BrickerBot’ Results in PDoS Attack,” Radware, 5 Apr. 2017; security.radware.com/ddos-threats-attacks/brickerbot-pdos-permanent-denial-of-Service.
[3] S. Edwards and I. Profetis, “Hajime: Analysis of a Decentralized Internet Worm for IoT Devices,” Rapidity Networks; 16 Oct. 2016; security.rapiditynetworks.com/publications/2016-10-16/hajime.pdf.
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[7] Pinkas, Benny, and Tomas Sander. "Securing passwords against dictionary attacks." Proceedings of the 9th ACM conference on Computer and communications security, ACM, 2002.
[8] John the Ripper password cracker, http://www.openwall.com/john/

Arun Malik, Suman Sangwan, Devender Rathee, Vineet Nandal, Payal, "False Positive and False Negative Authentication System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.922-924, 2019.

The inflaming use of current digital technology has transformed the life of people. Unlike the current electoral system, there are various applications as such. Security is an outmost priority which is predominant in the election with the offline system. The existing electoral system still uses a Centralized system in which an organization has full access grant. The problems encountered are mostly due to outdated electoral systems with the organization that has total control over the system as well as the database. Database tampering is a major reason which causes huge problems in the system. Our answer to this problem is Block Chain Technology. Block chain Technology is an ideal solution as it holds a decentralized system and the database is owned by various users. Examples like Bit coin can be taken as a good example of Block Chain Technology application as it uses a decentralized bank system. By applying the concept of block chain in the existing electoral system, it can reduce the deceitful sources of database action. Our project aims to apply voting results using block chain algorithms from all place of election. Unlike Bitcoin, this process based on a pre-set turn on the system for each node in the built of block chain.

Block Chain, Crypto Currency, E-Voting, Decentralized, Consensus, Marklee Tree.

[1] Yash G. Gupta, Arun kushwaha, Amar S. Rajeevan, Govind Mhala and Bhagyashree Dhakulka, "Survey On E-Voting using Block Chain Technology",CiiT International Journal of Software Engineering and Technology, Vol 11, No 1, January 2019.
[2] Ahmed Ben Ayed,”A Conceptual Secure Block Chain-Based Electronic Voting System”,2017 IEEE International Journal of network &Its Applications(IJNSA),03 May 2017.
[3] RifaHanifatunnisa, Budi Rahardjo,” Blockchain Based E-Voting Recording System Design”,IEEE 2017.
[4] KejiaoLi, HuiLi,HanxuHou,KedanLi,YongleChen,” Proof of Vote:A High-Performance Consensus Protocol Based on Vote Mechanism & Consortium Blockchain”, 2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems.
[5] AliKaanKoç, EmreYavuz, UmutCanÇabuk, GökhanDalkilic,” abcccTowards Secure E-Voting Using Ethereum Blockchain”, 2018 cccccIEEE.
[6] Supriya Thakur Aras, Vrushali Kulkarni,” Blockchain andItsApplications– A Detailed Survey”, International Journal of Computer Applications (0975 – 8887) Volume 180 – No.3, December 2017.
[7] Freya Sheer Hardwick, ApostolosGioulis, RajaNaeemAkram,KonstantinosMarkantonakis,” E-Voting with Blockchain: An E-Voting Protocolwith Decentralisation and Voter Privacy”,IEEE 2018,03 July 2018.
[8] KashifMehboob Khan, Junaid Arshad, Muhammad Mubashir Khan,” Secure Digital Voting System based on BlockchainTechnology”, IEEE 2017.
[9] Huaiqing Wang, Kun Chen and DongmingXu. 2016. A maturity abcccmodel for blockchain adoption. Financial nnovation,Springer,OpenAccess,DOI10.1186/s40854-016-0031-z
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[13] Cachin etal. 2017. Blockchain, cryptography, and consensus,IBMResearch,Jun2017,https://www.itu.int/en/ITU- T/Workshop and seminars.
[14] Gareth W. Peters, Efstathios Panayi, 2015. Trends in crypto-currencies and blockchain technologies: A monetary theory and regulation perspective, Aug 2015
[15] Trevor Kiviat. 2015. Beyond Bitcoin: Issues in Regulating Blockchain Transactions, HeinOnline.org.
[16] CNBCNews,2017,https://www.cnbc.com/2017/10/10/bitcoin-price-fallsafter-russia-proposes-ban-on-exchanges.html.
[17] AustralianTaxationOffice,https://www.ato.gov.au/General/Gen/Tax-treatment-ofcrypto-currencies-in-Australia---specifically-bitcoin/
[18] The magic of mining, 8 January 2015, https://www.economist.com/news/business/21638124- minting-digital-currency-has-become-big-ruthlesslycompetitive-business-magic.
[19] Wei Xin, et.al. 2017. On Scaling and Accelerating Decentralized Private Blockchains, 2017 IEEE 3rd International Conference on Big Data Security on Cloud,https://doi.org/10.1109/BigDataSecurity.2017.5
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Yash G. Gupta, Arun kushwaha, Amar S. Rajeevan, Bhagyashree Dhakulkar, "E-Voting using Block Chain Technology," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.925-931, 2019.

Poly (ethylene oxide) (PEO) based Nano-Composite Polymer Electrolyte (NPE) membranes (70PEO:30AgNO3)(100-x)(TiO2)x, where x = 0≤x≤10 wt% have been casted by hot-press/solution free technique. Solid Polymer Electrolyte (SPE) composition 70PEO:30AgNO3 (wt. %), has highest conducting film with room temperature conductivity σrt ~ 3.6 x 10-6 Scm-1, has been used as the first phase host matrix and TiO2 filler particles of nano-dimension (< 100 nm) as second phase dispersion. The fractional dispersal of TiO2 filler (viz. x = 3 wt. %) in SPE host results increase in room temperature conductivity. This NPE film (70PEO:30AgNO3)97(TiO2)3 referred as Optimum Conducting Composition NPE(OCC) film. The morphological analysis performed by Scanning Electron Microscopy (SEM) techniques. The ionic transport properties characterized by basic ionic parameters viz. conductivity (σ), mobility(µ), mobile ion concentration (n), ionic transference number (tion) and cationic transport number (t+). Using these electrolytes a thin symmetric capacitor has been prepared which shows capacitance about 5 F/g of in the cycling in the range of 0−1.5 V at 0.5 A g−1 .

Solid polymer electrolyte, Energy Storage devices, Supercapacitor, Hybrid electric Vehicle

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Manish Kurrey, Satpreet Singh Gill, Nirbhay K Singh, Ggandeep Singh Gill, O. P. Verma, "Electrical Characterization of (70PEO:30AgNO3)(1-x)(TiO2)x Nanocomposite Polymer Electrolyte for Energy Storage Devices used in HEV," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.932-936, 2019.

A fuzzy expert system is a form of artificial intelligence that uses a collection of membership functions as fuzzy logic and rules to reason about data. India is being the largest democracy in the world; therefore rule based fuzzy expert system for prediction of Indian General Election results has applicable importance. For proper growth and development of any country there is need of democratic governance. Election is very famous in same way the prediction of election results is also has great importance. Authors has designed & developed the fuzzy expert system which predicts election results in India. Authors studied the voting behavior of voters of India by survey. System accepts input as behavioral parameter those are linguistic variables considered for prediction of chances of winning candidate or party. Fuzzy logic toolbox from MATLAB is used for designing and development of fuzzy expert system. The Fuzzy Expert System “FuzzyExitPoll” also works like exit poll as well as opinion poll. This research help to predict the election results so respective action should done by that particular party or candidate to win the elections.

Election Prediction, Fuzzy Expert System, Membership Function, Linguistic Variable, Fuzzy logic Toolbox

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[11] Manjiri M. Mastoli, R.V.Kulkarni, A Review: Role of Fuzzy Expert System for Prediction of Election Results. Reviews of Literature, 2013
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[14]https://docs.google.com/forms/d/e/1FAIpQLSeesMLD0mSGpyzuMOXPZy6Ura_pRHk7XEeTMamemtN2r8lElg/viewform?usp=pp_url
[15] Fuzzy Logic ToolboxTM User’s Guide: MATLAB
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Manjiri M. Mastoli, R.V. Kulkarni, Urmila R. Pol, ""Fuzzy Expert System for Prediction of Indian General Election Results"," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.937-943, 2019.

E-learning stands for a form of electronically designed, distributed, and facilitated learning activities. It includes instruction delivered via all electronic media, such as the Internet, intranet, satellite broadcasts, audio/video tape and etc. E-learning is empowered in five dimensions: a new tool that incorporates equipment, hardware, and software; a facilitator of interaction; learning; a reduction in distance; and a collaborative enterprise. The primary advantage is that it enables learners to participate in learning activities from anywhere in the world and at any time provided a computer and internet connection are available. An efficient cloud computing technologies have changed the way applications are developed and accessed. They are aimed at running applications as services over the Internet on a scalable infrastructure. Now, Cloud computing that introduces efficient scale mechanism can let construction of E-learning system be entrusted to suppliers and provide a new mode for E-learning. Therefore, an E-learning system based on Cloud computing infrastructure is feasible and it can greatly improve the efficiency of investment and the power of management, which can make E-learning system empowerment.

Cloud computing, e-learning, electronically, media, empowerment

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Al-Zoube, M., El-Seoud, S.A., Wyne, M.F.: Cloud computing based e-learning system.and Behavioral Sciences 2(2), 938–942 (2010)

Mohammad Ibrar, Bharat Bhushan, "E-Learning Empowering through efficient system of Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.944-947, 2019.

Vehicle recognition finds wide-spread applications in analyzing traffic data, collecting electronic tolls and identifying unauthorized vehicles on roads, etc. Diverse methods have been developed for vehicle recognition and these methods give good results in controlled environment. However, variations of illumination, vehicle geometry and occlusion are frequent phenomena in real-world scenarios. Neural network proves effective in handling such variations. In this paper, we have investigated the effectiveness of single-layer neural network, multi-layer neural network and convolutional neural network (CNN) and deep CNN for vehicle detection using a standard Madrid dataset.

Vehicle recognition, Neural network, Convolutional neural network, Deep network

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Md. Golam Moazzam, Mohammad Reduanul Haque, Mohammad Shorif Uddin, "Image-Based Vehicle Recognition using Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.948-954, 2019.

The Recognition of handwritten digits is helpful in various domains such as Banking(For Fraudery), Writer Recognition(In Criminal Suspicion), Autonomous cars(For reading and identifying speed limits and Numeric signs), License Plate readers(For parking structures/security cameras). Deep Learning which serves as a subfield of Machine Learning is used for the task of classification of images. Deep Learning makes use of neural networks to accomplish this task. Among these, the most suitable neural network that is used for image classification is known as Convolutional Neural Network. Convolutional Neural Networks are very similar to ordinary Neural Networks, they are made up of layers of neurons that have learnable weights along with Activation Functions and biases. Activation Functions are used to control the output of each neuron at every layer. In this paper, we have studied the role of Sigmoid and Relu(Rectified Linear Unit) Activation Functions in Convolutional Neural Network, and we compare among these which one provides the highest accuracy for the image classification task.

Artificial Intelligence, convolutional neural networks, Deep learning, Activation function, Sigmoid, Relu

[1].http://www.academia.edu/28025198/Handwritten_Digit_Recognition_by_Combining_SVM_Classifiers
[2].Dr.J.Arunadevi and M.Devaki “The impact of Activation functions in Deep Neural net algorithm on Classification performance parameters”.
International Journal of Pure and Applied Mathematics.Volume 119 No. 12 2018, 16305-16312. ISSN: 1314-3395 (online version)
URL: http://www.ijpam.eu
[3].Dabal Pedamonti Comparison of non-linear activation functions for deep neural networks on MNIST classification task. arXiv:1804.02763v1 [cs.LG] 8 Apr 2018
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[6].A.Bhattacharjee Cluster-Then-Predict and Predictive Algorithms (Logistic Regression) International Journal of Computer Sciences and Engineering. Volume-6, Issue-2 E-ISSN: 2347-2693

Sandeep Gond, Gurneet Bhamra, Jyoti Kharade, "Survey on Activation Functions in Convolution Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.955-960, 2019.

With the development of E-commerce, Recommendation Systems are applied more widely to guide the customers to search for their interested products. A recommendation system includes a user model, a recommended model and a recommendation algorithm. Limited resource, data valid time and cold start problems are not well considered in existing E-commerce recommendation system. This paper proposes a limited resource based algorithm to provide an improvement to the existing product recommendation algorithm and also provides a solution to cold start problem.

Limited resource, cold start, recommendation system.

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Thejaswini N, Aditya C R, "Smart E-Commerce Recommendation System for Handling Limited Resource and Cold Start Problem," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.961-964, 2019.

Routing in vehicular ad hoc networks (VANETs) where the group of vehicles is moved randomly in any direction without any central coordination. The movements of the vehicle nodes are highly dynamic, therefore ensuring data delivery with small overhead and less delay is a challenging issue. In order to improve the routing, Geographical Zone clustered Multi-objective Glowworm swarm optimized Routing (GZMGR) technique is introduced. Initially, ‘n’ numbers of glowworms (i.e., vehicle nodes) are arbitrarily distributed in search space (i.e. network). In this proposed technique, the source vehicle node sends the data packet to the master node within the zone and it sends the data packet to a master node in another zone. Then the master node in another zone transmits to the destination (i.e. cluster member). Therefore, the routing of the data packet is performed via the master node since it collects the network status and location, direction, information of its cluster members. As a result, the master node reduces the overall traffic in the network and minimizes the delay. The master node is selected based on distance, high signal strength and direction of the node. After that, the source node initiates the data packets to transmit to the destination through the neighboring node. Initially, each glowworm has luminescence quantity called luciferin (i.e., objective function). The objective functions used for neighboring node selection are nodes speed, distance and link lifetime. Then the fitness is computed based on the objective functions to find the nodes for the optimization process. Due to the mobility, the luciferin value of the node is updated and finds the neighbor node through the probability. Finally, the source node takes a local decision for selecting the optimal neighboring node with minimum distance and high link lifetime. By this way, the optimal neighboring nodes are selected to forward the data packets. Followed by, a stable routing path from source to destination is established by considering the optimal one-hop vehicle. After that, the data packets are transmitted along the route path to the destination node. The simulation is conducted with different parameters such as collision rate, packet delivery ratio, normalized routing load and average end to end delay with respect to a number of vehicle nodes.

VANET, Geographical Zone, cluster-based routing, master node selection, Multi-objective Glowworm Swarm optimization

[1] S.Suguna Devi and A. Bhuvaneswari “An Ethical Survey on Non-Delay Tolerant Routing Protocols for VANET” International Journal of Computer Science and Engineering, Vol. 6, Issue 9, pp. 705-711, 2018.
[2] R. Brendha and V. Sinthu Janita Prakash, “Geographical Zone-Based Cluster Head for Routing in Sparse vehicular networks”, International Journal of Engineering & Technology, Vol. 7, Issue 3, pp. 1910-1914, 2018.
[3] R. Brendha and V. Sinthu Janita Prakash, “Geographical Zone-Based Cluster Head for Routing in Urban Vehicular Network”, Information and Communication Technology for Intelligent Systems, Springer, Vol. 2, pp. 149-160, 2018.
[4] Deepak Rewadkar and Dharmpal Doye, “Multi-objective auto-regressive whale optimization for traffic-aware routing in urban VANET”, IET Information Security ,Vol. 12, Issue 4, pp. 293 – 304, 2018.
[5] Dan Lin , Jian Kang , Anna Squicciarini, Yingjie Wu , Sashi Gurung , Ozan Tonguz, “MoZo: a moving zone-based routing protocol using pure V2V communication in VANETs,” IEEE Transactions on Mobile Computing, Vol. 16, Issue 5, pp. 1357–1370,2017.
[6] Saifullah Khan, Muhammad Alam, Martin Fränzle, Nils Müllner, Yuanfang Chen, “A Traffic-Aware Segment-based Routing protocol for VANETs in urban scenarios”, Computers & Electrical Engineering, Elsevier , Vol. 68, pp. 447-462, 2018.
[7] Tasneem Darwish and Kamalrulnizam Abu Bakar, “Lightweight intersection-based traffic-aware routing in urban vehicular networks”, Computer Communications, Elsevier, Vol. 87, pp. 60-75, 2016.
[8] Mahmoud Hashem Eiza and Qiang N, “An evolving graph-based reliable routing scheme for VANETs,” IEEE Transactions on Vehicular Technology, Vol. 62, Issue 4, pp. 1493 – 1504, 2013.
[9] Yong Xiang, Zheng Liu, Ruilin Liu, Weizhen Sun, Wei Wang, “GeoSVR: A map-based stateless VANET routing”, Ad Hoc Networks, Elsevier, Vol. 11, Issue 7, pp. 2125-2135, September 2013.
[10] Neeraj Kumar and Mayank Dave, “BIIR: A Beacon Information Independent VANET Routing Algorithm with Low Broadcast Overhead”, Wireless Personal Communications, Springer, Vol. 87, Issue 3, pp. 869–895, 2016.
[11] Huma Ghafoor, Insoo Koo, and Nasir-ud-Din Gohar, “Neighboring and Connectivity-Aware Routing in VANETs”, The Scientific World Journal, Hindawi Publishing Corporation, Vol. 2014, pp. 1-10, 2014.
[12] Kashif Naseer Qureshi, Abdul Hanan Abdullah, Jaime Lloret, and Ayman Altameem, “Road-Aware Routing Strategies for Vehicular Ad Hoc Networks: Characteristics and Comparisons”, International Journal of Distributed Sensor Networks, Hindawi Publishing Corporation, Vol. 2016, pp. 1-19, February 2016.
[13] Nori M. Al-Kharasani , Zuriati Ahmad Zulkarnain , Shamala Subramaniam andZurina Mohd Hanapi, “An Efficient Framework Model for Optimizing Routing Performance in VANETs”, Sensors, Vol. 18, Issue 2, pp. 1-21 , 2018.
[14] Yu ding, ya-zhi liu, xiang-yang gong, wen-dong wang, “Road traffic and geography topology based opportunistic routing for VANETs”, The Journal of China Universities of Posts and Telecommunications, Elsevier, Vol. 21, Issue 4, pp. 32-39, 2014.
[15] Kayhan Zrar Ghafoor , Jaime Lloret, Ali Safa Sadiq, Marwan Aziz Mohammed, “Improved Geographical Routing in Vehicular Ad Hoc Networks” Wireless Personal Communications, Springer, Vol. 80, Issue 2, pp. 785–804, 2015.
[16] Xuelian Cai, Ying He, Chunchun Zhao, Lina Zhu, Changle Li, “LSGO: Link State aware Geographic Opportunistic routing protocol for VANETs”, EURASIP Journal on Wireless Communications and Networking, Springer, Vol. 96, pp. 1-10, 2014.
[17] Yingcheng Xu, Li Wang, and Yuexiang Yang, “Dynamic Vehicle Routing Using an Improved Variable Neighborhood Search Algorithm”, Journal of Applied Mathematics, Hindawi Publishing Corporation, Vol. 2013, pp. 1-12, December 2012.
[18] Peppino Fazio, Floriano De Rango, Cesare Sottile, and Amilcare Francesco Santamaria, “Routing Optimization in Vehicular Networks: A New Approach Based on Multiobjective Metrics and Minimum Spanning Tree”, International Journal of Distributed Sensor Networks, Hindawi Publishing Corporation, Vol. 2013, pp. 1-13, September 2013.
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[21] Yusor Rafid Bahar Al-Mayouf , Mahamod Ismail, Nor Fadzilah Abdullah, Ainuddin Wahid Abdul Wahab, Omar Adil Mahdi, Suleman Khan, Kim-Kwang Raymond Choo, “Efficient and Stable Routing Algorithm Based on User Mobility and Node Density in Urban Vehicular Network”, PLoS ONE, Vol. 11, Issue 11, pp. 1-24, 2016.
[22] Haojing Huang and Shukui Zhang, “A Routing Algorithm Based on Dynamic Forecast of Vehicle Speed and Position in VANET”, International Journal of Distributed Sensor Networks, Hindawi Publishing Corporation, Vol. 2013, pp. 1-9, May 2013.
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R. Brendha, V. Sinthu Janita Prakash, "Geographical Zone Clustered Multi-Objective Glowworm Swarm Optimization for Routing In VANET," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.965-975, 2019.

In many of the developing countries economy greatly depend on the agricultural productivity. The most common form of detecting the plant disease infection is recognized from the leaves color and texture. The researchers with the help of information and communication enabled technology, automated the farmers traditional process of plant disease identification. To enhance the agricultural crop production, plant disease detection should be done at early stage in an automatic manner that helps in spreading the infection to other plants. This paper focus to analyze the previous studies in identifying paddy plant disease detection system. The manuscript summarizes various available paddy leaf diseases, and discusses techniques employed in the classification of healthy and infected paddy plant. The survey would help the researchers to understand the challenges involved in dataset collection and highlights several points in future research directions.

Edge detection, Image processing, Internet of Things, K-means clustering and K Nearest neighbor

[1] Pragya Lariya and Mukul Srivastava, “Review Paper on Crops Disease Diagnosing Using Image-Based Deep Learning Mechanism”,
International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.306-312, 2019.
[2] Shiddalingappa Kadakol and Jyothi B Maned, “Intelligence System for Leaf Extraction and Disease Diagnostic”,International Journal of Computer Sciences and Engineering, Vol.4, SpecialIssue.3, pp.62-66, 2016.
[3] C.Nandini and G.L. Anoop, “Classification and Grading the Level of Paddy Leaf Diseases Using Multi Classifiers”, International Journal of Advances in Science Engineering and Technology, ISSN: 2321-9009 Vol. 4, Issue.4, pp.124-128, 2016.
[4] N.Neelaveni and S.Rajeswari, “Paddy Leaf Disease Detection Classification and Remedy Finder Using K means Clustering and Image Mining”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issue.12, pp.20859-20864, 2016.
[5] M.Suresha, Shreekanth K.N, HarishaNaik, T., “Identification of Healthy and Diseased Paddy Leaves using KNN classifier”, International Journal of Advanced Research in Computer Science, Vol.8, No. 9,pp.705-710, 2017.
[6] Toran verma and Sipi Dubey, “Paddy Disease Recognition using Image Processing and Radial basis Function Network”, Indian Journal of Science and Technology, Vol.10(46), pp.1-7,2017.
[7] K. Jagan Mohan, M. Balasubramanian and S. Palanivel, “Detection and Recognition of Diseases from Paddy Plant Leaf Images”, International Journal of Computer Applications, Vol. 144, No.12, pp.34-41, 2016.
[8] R.P.Narmadha and G.Arulvadivu, “Disease Symptoms Identification In Paddy Leaf Using Image Processing”, Advances in Computational Sciences and Technology,Vol. 10, No. 8, pp. 2213-2223, 2017.
[9] G. Anthonys and N. Wickramarachchi, “An Image Recognition System for Crop Disease Identification of Paddy fields in Sri Lanka”, In the Proceedings of Fourth International Conference on Industrial and Information Systems, ICIIS 2009 pp. 28 - 31, December2009, Sri Lanka.
[10] Manoj Mukherjee, Titan Pal and Debabrata Samanta, “Damaged Paddy Leaf Detection Using Image Processing” , Journal of Global Research in Computer Science, Vol.3, No. 10, pp.7-10, 2012.
[11] K. Jagan Mohan and M. Balasubramanian, “Recognition of Paddy Plant Diseases Based on Histogram Oriented Gradient Features”, International Journal of Advanced Research in Computer and Communication Engineering, Vol.5, Issue.3, pp.1071-1074, 2016.
[12] S.Ramesh and Bharghava Rajaram, “IoT based Crop Disease Identification System using Optimization Techniques”, ARPN Journal of Engineering and Applied Sciences, Vol.13, No.4, pp.1392-1395, 2018.
[13] Sukhvir Kaur, Shreelekha Pandey and Shivani Goel, “Plants Disease Identification and Classification through Leaf Images: A Survey “, Archives of Computational Methods in Engineering, 2018.

P. Iswarya, D. Maheswari, "Paddy Leaf Disease Identification and Classification System: A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.976-979, 2019.