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Plants are affected by a disease which leads to the variation in the growth stages of it’s and finally affects the throughput from it. Identification of the plant leaves diseases id the key role in preventing the losses in farming, where it’s a challenging to detect multi plant diseases. Here four major diseases affected by the plant are selected like Alternaria Alternata, Anthracnose, Bacterial Blight and Cercospora Leaf Spot and also addition with the healthy leaves using image processing technologies. The algorithm consist of a image pre-processing, image segmentation, feature extraction and finally with classification method.

Image processing, Segmentation, Feature Extraction, Support Vector Machine, Gray-Level Cooccurrence Matrix (GLCM).

[1]. Naikwadi, NiketAmoda, “Advances in image processing for detection of plant diseases”, International journal of application or innovation in engineering and managrment(IJAIEM) Volume 2,Issue11,November 2013
[2]. Arti N. Rathod, Bhavesh A. Tanawala, Vatsal H. Shah, International Journal of Advance Engineer ing and Research Development (IJAERD) Volume 1,Issue 6,June 2014, e-ISSN: 2348 - 4470
[3]. Zulkifli Bin Husin, Abdul Hallis Bin Abdul Aziz, Ali Yeon Bin Md Shakaff Rohani Binti S Mohamed Farook, “Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques”, 2012 Third International Conference on Intelligent Systems Modelling and Simulation.
[4]. Mrunalini R. Badnakhe, Prashant R. Deshmukh, “Infected Leaf Analysis and Comparison by Otsu Threshold and k-Means Clustering”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue, March 2012.
[5]. Prakash and K Thangadurai., 2016. Implementation of RGB and Grayscale Images in Plant Leaves Disease Detection – Comparative Study. Indian Journal of Science and Technology, Vol. 9, pp. 1-6.

Bharath Kumar R, Balakrishna K, Shreyas M S, Sonu S, Anirudh H, Abhishek B J, "SVM Based Plant Diseases Detection using Image Processing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1263-1266, 2019.

Throughput of Cooperative network can be enhanced by using different relay selection scheme ,therefore interest in relay selection is going upwards .It an open research topic these days.We proposed a relay selection algorithm named as chase algorithm which select the best relay on the basis of some parameters like SNR, channel allocation, power allocation ,interference constraint .Further at every relay node, Decode & Forward protocol is used for removing the noise in further stages .Finally with the help of fuzzy controller in which the SNR,channel allocation ,power allocation,interference constraint are the fuzzy parameters ,we propose an expected model that reduce some computational load and enhance the channel rate.

CooperativeCommunication , ChaseAlgo, Decode and forward , FuzzyLogic

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Vasundhara, Avtar Singh Buttar, "A Novel Approach of Cooperative Sharing Based On Hybrid Relaying Scheme of Chase Algo and Decode & Forward Using Fuzzy Logic in Cognitive Radio," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1267-1270, 2019.

Security in the Wireless Sensor Network plays an important role and can be achieved by cryptographic algorithms. The cryptography is the science and art of transforming messages to make them secure and immune to attacks by authenticating the sender and receiver within the network. The Proposed methodology, hybrid cryptographic technique has combined Blowfish algorithm for symmetric and Elliptic Curve Diffie-Hellman algorithm for asymmetric. Blowfish algorithm provides high speed encryption process when compared with the other symmetric algorithms. The Elliptic Curve Diffie-Hellman algorithm combines the concept of elliptic curve and Diffie-Hellman key exchange algorithm. Hence, it provides more security compared to other asymmetric algorithm and the key exchange mechanism. Cluster based trust Management approaches are used to identify unauthorized user in WSN. It first identifies the trusted nodes in networks, then send packets through that trusted nodes. Trusted nodes are identified based on trust values to identify the neighboring nodes during verification process for improving the Packet Delivery Ratio and Energy Consumption.

Sensor node, Elliptic Curve, Blowfish, Diffie-Hellman, Trust Node Calculation, WSN

[1]. Asha Rani Mishra, Mahesh Singh “Elliptic Curve Cryptography (ECC) For Security in Wireless sensor Network” International Journal of Engineering Research and Technology (IJERT), vol-1,Issue-3,may-2012.
[2]. Madhumita Panda “Security in Wireless Sensor Networks using Cryptographic Techniques” American Journal of Engineering Research (AJER) , Volume-03, Issue-01, pp(50-56)-2014.
[3]. kamulu Deepthi, Krishnachaitanya,.Katkam “Wireless Sensor Networks Security Survey Using Cryptography” International Journal Of Emerging Trends & Technology In Computer Science (IJETTCS), Volume 5, Issue 5, September - October 2016.
[4]. Mohamed Elhoseny , Hamdy Elminir , Alaa Riad , Xiaohui Yuan “A secure data routing schema for WSN using Elliptic Curve Cryptography and homomorphic encryption” Journal of King Saud University – Computer and Information Sciences, pp( 262–275), ELSEVIER-2016.
[5]. Abdullah Smadi, Hesham Enshasy, Qasem Abu Al-Haija “Estimating Energy Consumption of Diffie-Hellman Encrypted Key Exchange (DH-EKE) for Wireless Sensor Network” International Conference On Intelligent Techniques In Control, Optimization And Signal Processing, IEEE-2017.
[6]. Preetika Joshi, Manju verma, Pushpendra R Verma “Secure Authentication Approach Using DiffieHellman Key
Exchange Algorithm for WSN” International Conference on Control,Instrumentation, Communication and
Computational Technologies (lCCICCT), IEEE-2015.
[7].Chaitali Haldankar, Sonia Kuwelkar “IMPLEMENTATION OF AES AND BLOWFISH ALGORITHM” International Journal of Research in Engineering and Technology (IJRET) , Volume-3 Special Issue-3,May- 2014 .
[8].Shamina Ross.B, Josephraj.V “ Performance Enhancement of Blowfish Encryption Using RK-Blowfish Technique” International Journal of Applied Engineering Research(IJAER) , Volume 12, pp( 9236-9244) – 2017.
[9].Rajat Soni, Deepak Sethi, Partha Pratim Bhattacharaya “ ANALYSIS OF VARIOUS CRYPTOGRAPHIC ALGORITHMS FOR SECURITY IN WIRELESS SENSOR NETWORKS” International Journal For Advance Research In Engineering And Technology(IJARE), Volume-4, Issue-V, May-2016.
[10]. Juan Li “A Symmetric Cryptography Algorithm in Wireless Sensor Network Security” International journal of
online Engineering ((iJOE) ‒ Vol 13,2017.
[11]. Huang Lu, Jie Li, Mohsen Guizani “Secure and Efficient Data Transmission for Cluster-based Wireless Sensor Networks” IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEM - 2013.
[12]. Thiruppathy Kesavan Venkatasam, Radhakrishnan Shanmugasundaram “ Authentication in Wireless Sensor Networks Using Dynamic Keying Technique” International Journal of Intelligent Engineering and Systems (IJIES), Vol.9, No.3, 2016.
[13]. Usha.A, Dr.Subramani.A “Performance Study of Key Developer Data Encryption and Decryption Algorithm (KDDEDA) with AES, DES and BLOWFISH” International Journal Of Engineering And Computer Science (IJEC), Volume 5, Issue 12, Pp( 19596-19611), Dec- 2016

V. Perumal, K. Meenakshi Sundaram, "Cluster Based Secured Data Transmission using Hybrid Cryptography Techniques in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1271-1276, 2019.

Crop forecasting or prediction is the art of predicting crop yields and production before the harvest actually takes place, typically a couple of months in advance. Crop forecasting relies on computer programs that describe the plant-environment interactions in quantitative terms. The soil testing program starts with the collection of a soil sample from a field. The first basic principle of soil testing is that a field can be sampled in such a way that chemical analysis of the soil sample will accurately reflect the field’s true nutrient status.

Precision Agriculture, Recommendation system, Ensemble model, Majority Voting technique, K-Nearest Neighbour.

[1]Satish Babu (2013), ‘A Software Model for Precision Agriculture for Small and Marginal Farmers’,at the International Centre for Free and Open Source Software (ICFOSS) Trivandrum, India.
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[3]Aakunuri Manjula, Dr.G .Narsimha (2015), ‘XCYPF: A Flexible and ExtensibleFramework for Agricultural Crop Yield Prediction’ , Conference on Intelligent Systems and Control (ISCO)
[4]Yash Sanghvi, Harsh Gupta, Harmish Doshi, DivyaKoli, AmoghAnshDivyaKoli, Umang Gupta (2015), ‘Comparison of Self Organizing Maps and Sammon’s Mapping on agricultural datasets for precision agriculture’, International Conference on Innovations in Information,Embedded and Communication systems (ICIIECS).
[5]Rakesh Kumar, M.P. Singh, Prabhat Kumar and J.P. Singh (2015), ’Crop Selection Method to Maximize Crop Yield Rate using Machine Learning Technique’, International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM).
[6]A.T.M Shakil Ahamed, NavidTanzeem Mahmood, Nazmul Hossain, Mohammad Tanzir Kabir, Kallal Das, Faridur Rahman, Rashedur M Rahman (2015) , ‘Applying Data Mining Techniques to Predict Annual Yield of Major Crops and Recommend Planting Different Crops in Different Districts in Bangladesh’ , (SNPD) IEEE/ACIS International Conference.
[7]Liying Yang (2011), ‘Classifiers selection for ensemble learning based on accuracy and diversity’ Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS].
[8]Tapas Ranjan Baitharua, Subhendu Kumar Panib (2016), ‘Analysis of Data Mining Techniques for Healthcare Decision Support System Using Liver Disorder Dataset’ International Conference on Computational Modeling and Security (CMS).
[9]Aymen E Khedr, Mona Kadry, Ghada Walid (2015), ‘Proposed Framework for Implementing Data Mining Techniques to Enhance Decisions in Agriculture Sector Applied Case on Food Security Information Center Ministry of Agriculture, Egypt’, International Conference on Communications, management, and Information technology (ICCMIT`) .
[10]Monali Paul, Santosh K. Vishwakarma, Ashok Verma (2015), ‘Analysis of Soil Behaviour and Prediction of Crop Yield using Data Mining Approach’, International Conference on Computational Intelligence and Communication Networks.

Bharath Kumar R, Balakrishna K, Bency Celso A, Siddesha M, Sushmitha R, "Crop Recommendation System for Precision Agriculture," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1277-1282, 2019.

Development of speech recognition systems in local languages will help anyone to make use of the technological advancement of the speech recognition . In India, speech recognition systems have been developed for many indigenous languages, however very less work has been done in Malayalam Language. Malayalam language is famous for its unique phonemes. Hence one of the main objectives of this work is to explore the Alveolar and Retroflex phonemes of Malayalam language which has unique phonetic realizations.

Automatic Speech Recognition , Malayalam , Phonome

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Cini Kurian, "Automatic Speech Recognition of Alveolar Rhotic and Retroflex Rhotic Phonemes of Malayalam Language," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1283-1286, 2019.

Data analysis and data visualization is the requirement of today’s organization. Data science is a field that relates to data cleansing, preparation and analysis. Data science algorithms are used in many industries like Internet searches, Digital Advertisements, Travelling, Healthcare, Gaming, Financial services etc. There are various applications in today’s world where data analysis and data visualization is required. Data science can solve the problems like classification, identifying anomalies, to quantify, finding way of organization, decision making issues etc. In this paper, we have shown how python is useful and acts as a key to solve such problems. In addition to python, there are also some other platforms which are used to solve a task completely based on data science. Here we have focused on python and it’s packages that are highly useful for data science based problems. We have shown how python can be used for data analysis and data visualization.

Data Science, Python, Data analysis, Data Visualization

[1]. Javin D. West, “The science of data science”,
Journal of Integrated creative studies, No. 2016-010-e, May 2016.
[2]. Wes McKinney, “pandas: a Foundational Python Library for DataAnalysis and Statistics”, DLR Portal, www.dlr.de/sc/Portaldata/15/Resources/dokument e/.../pyhpc2011_submission_9.pdf.
[3]. Gaurav, Zunaid Alam, “Road Safety in india using Data Mining Approach”, International Conference, REDSET 2017, CCIS 799, https://doi.org/10.1007/978-981-10-8527-7_17
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[6]. Fabien Dubosson , Stefano Bromuri, and Michael Schumacher, “A Python Framework for Exhaustive Machine Learning Algorithms and Features Evaluations “, IEEE Conference, 1550-445X/16, March-2016.
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[8]. Nurul Afiqah Mat Zaib , Nor Erne Nazira Bazin, Noorfa Haszlinna Mustaffa , Roselina Sallehuddin, “Integration of System Dynamics with Big Data Using Python: An Overview”, IEEE Conference, 978-1-5090-6255-3/17, May 2017.

Nitin Kumar, Gaurav, "Data Analysis and Data Visualization using Python," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1287-1291, 2019.

The momentous concerns is to discard irrelevant features to boost up the detection rate. There are major problems associated with the feasibility and tolerance with the inception of recent technologies. To realize this objective, we tend to illustrate our model manipulating recursive feature elimination mechanism to reject inutile attributes that are operated on Decision Tree Classifier (DTC) and random forest algorithm (RFA). The experiment is carried on New Subset Labeled version of the KDD`99 dataset (NSL-KDD) dataset that is associated degree updated version of Knowledge Discovery and Data Mining 1999 (KDD’99) dataset. The proposed methodology is discriminated with other strategies illustrated by the previous researchers. It is classified into four distinct categories illustrates the attack classes and one as normal traffic. The model’s capability has been increased to thirteen category classification to compare the tolerance when the number of attack categories will increase. It offers excellent performance analysis metrics to assess the exploitation of our model.

Intrusion Detection System, DTC, RFA, KDD, Machine Learning

[1] N. Shone, T. N. Ngoc, Vu Dinh Phai, and Qi Shi, “A Deep Learning Approach to Network Intrusion Detection”, IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 2, NO. 1, pp. 41-50, Feb. 2018.
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Safura A. Mashayak, Balaji R. Bombade, "Network Intrusion Detection Exploitation Machine Learning Strategies with the Utilization of Feature Elimination Mechanism," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1292-1300, 2019.

CPU scheduling defines the policy for deciding which of the available process in ready queue will be dispatched next to CPU by the scheduler; so that the resource utilization and overall performance of the system could be improved. Many traditional CPU scheduling algorithms have been proposed by several authors. Lottery scheduling is one of the well organized random based scheduling algorithms. It has random based ticket allocation algorithm in which one or more tickets are randomly assigned to each Process and when CPU becomes available the winner process is selected next for assignment. In this paper, we calculated the performance of the deadlock condition. The state transition from one process to another process is done by using Markov chain model and also data set based approach is used to study different transition states. The overall performances in terms of unequal and equal numerical data set are analyzed and then comparative analysis is performed to justify the results.

Multiprocessing Environment ,Markov chain, CPU- scheduling, lottery scheduling, Process ,Deadlock Condition.

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Pradeep kumarJatav, Rahul Singhai, Saurabh Jain, "Deadlock Analysis of Hybrid Lottery scheduling algorithm using Markov Chain model," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1301-1318, 2019.

In the present analysis, an analytical approach is used to construct an exact solution of a problem of one-dimensional unsteady adiabatic flow of a blast wave propagation with generalized geometries at stellar surface in a plasma whose density ahead of the shock front is assumed to vary as a power law of the distance from the source of the point of explosion. The plasma is assumed to be an ideal gas. An analytical solution of the problem is find out in terms of flow parameters velocity, density and the pressure, which exhibits space-time dependence. In addition, an analytical expression has been derived for the total energy of the blast wave propagation at the stellar surface.

Blast wave, Ideal gas-dynamics, Rankine-Hugoniot conditions, Stellar surface

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Syed Aftab Haider, Akmal Husain, V. K. Singh, "An Exact Analytical Solution of Blast Wave Problem in Gas-Dynamics at Stellar Surface," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1319-1322, 2019.

The main objective of this research predicted the number of blood request in each month of the organization. In this research, we compared 3 statistical estimation methods (simple moving average, exponential moving average, and linear regression) to predict the number of requested blood in each month using historical time series data. Reducing the error of the expected requested blood would be beneficial for planning and decision making to prepare for the target of blood donation. Data used in this experiment, were the number of requested blood components (red blood cells) from 5 years which divided into two periods. The first period, from January 2014 to December 2017, was used as training set for building linear regression models. The second period, from January 2018 to December 2018, was used as test set to evaluate the regression models and to compare with simple moving average and exponential moving average. The results presented that the exponential moving average method had significantly lowered errors than both the simple moving average method and the linear regression method.

Requested blood, Simple moving average, Exponential moving average, Linear regression

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P. Limsaiprom, S. Rattanachan, N. Phuangphairoj, M. Kaenchuwongk, "Predictive Analytic for Blood Request by Using Moving Average and Linear Regression," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1323-1329, 2019.