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Glaucoma is a non-curable eye disease hence early detection is required to prevent the further progression of it. This disease leads to total blindness. To deal with the disease various automated systems have been designed. In this paper we are giving explanation for Cup to Disc ratio (CDR) measurement using fuzzy algorithm. The Cup to Disc ratio (CDR) is one of the important factor in glaucoma detection. In the proposed method, the optic disc and optic cup is segmented using fuzzy c-means clustering and thresholding. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier to confirm glaucoma for a patient. It classifies the given input image as normal or diseased and if it is recognized as diseased then classify the stage of glaucoma affected patient whether Moderate, Severe or normal based on the CDR ratio. In this paper we are giving a number of screenshots which shows results of different image processing techniques applied on input images.

Fundus image, Optic disc, Optic cup, Cup-to-disc ratio, Fuzzy c-means clustering

[1] S. Kavitha,, S. Karthikeyan &, K. Duraiswamy “Early detection of glaucoma in retinal images using cup to disc ratio.” In the proceedings of International Conference on Computing Communication and Networking Technologies (ICCCNT), India, pp. 1-5, 2010.
[2] J. Cheng,, J. Liu,, Y. Xu, , F. Yin, D. W. K. Wong,, N. M. Tan &, T. Y. Wong “Super pixel classification based optic disc and optic cup segmentation for glaucoma screening.” In the IEEE transactions on Medical Imaging, Vol: 32, Issue: 6, pp:1019-1032, 2013.
[3] N. M. Noor,, N. E. A. Khalid & N. M. Ariff “Optic cup and disc color channel multi-thresholding segmentation”. In the proceedings of International Conference on Control System, Computing and Engineering, pp. 530-534., 2013.
[4] C. Burana-Anusorn, W. Kongprawechnon, T. Kondo, S. Sintuwong & K. Tungpimolrut. “Image processing techniques for glaucoma detection using the cup-to-disc ratio.” Thammasat International Journal of Science and Technology, Vol. 18, Issue. 1, 2013.
[5] Hafsah Ahmad, Abubakar Yamin, Aqsa Shakeel, Syed Omer Gillani, Umer Ansari, “Detection of glaucoma using Retinal Fundus images”, in the proceedings of International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE) Islamabad, 2014
[6] M. Kankanala, & S. Kubakaddi, “Automatic segmentation of optic disc using modified multi-level thresholding.”, in the proceedings of IEEE International Symposium Signal Processing and Information Technology (ISSPIT), pp: 000125-000130, 2014.
[7] F. Khan, S. A. Khan, U. U. Yasin, I. ul Haq & U. Qamar, “Detection of glaucoma using retinal fundus images.” Biomedical Engineering International Conference (BMEiCON), 6th, pp. 1-5, 2013
[8] A. Yadav , V.K. Harit “Fault Identification in Sub-Station by Using Neuro-Fuzzy Technique”. International Journal of Scientific Research in Computer Science and Engineering Vol.4, Issue.6, pp.1-7, 2016.
[9] D. Dey “Image Encryption using Linear Cryptanalysis and different Fuzzy operations” , International Journal of Scientific Research in Computer Science and Engineering Vol. 6, Issue. 4, pp: 01-08, 2018
[10] N. E. A.Khalid, , N. M. Noor, & N. M. Ariff. “Fuzzy c-means (FCM) for optic cup and disc segmentation with morphological operation.”, Procedia Computer Science, 42, pp: 255-262, 2014.

Karabi Barman, Parismita Sarma, "Glaucoma Detection Using Fuzzy-C Means Clustering Algorithm and Thresholding," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.859-864, 2019.

Big Data is playing a very significant role to take any industry forward. In the fraud detection, automated fraud detection tries to collect all information to reduce in aadhar card frauds by doing analysis and data mining of Big Data. This paper investigates the benefits of Big Data technology and main methods of analysis that can be applied to the particular case of fraud detection in aadhar card. This paper hereby addresses aadhar card fraud detection via the use of data-mining techniques in classification of, Naive Bayesian (NB), c4.5, and Back Propagation (BP) analyze the customer data. In order to identify the patterns that can lead to frauds. Upon identify all sectors, adding a Aadhaar card include Name, Age, Date of Birth, Aadhaar Number ,Gender ,Photograph ,Residential Address, that are stored in data base according to biometric data are Fingerprints and Iris scan. Representing the Aadhaar number Details stored in the database in Fingerprints, Iris scan. Finally Aadhar card frauds are identified and detected using data mining algorithms.

Big Data Analytics, Big Data Applications, and Aadhar card fraud detection, Classification Algorithm

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K. Ramya, A.Sumathi, "Big Data Applications in Aadhar Card Fraud Detection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.865-867, 2019.

The paper discusses the performance of the online handwritten recognition module designed for Kannada numerals. In the proposed system, there is an improvement in the recognition accuracy of the recognition module when compared to the previous ones. The difference feature has given the improved performance of the recognition module. The difference feature vector is formed by computing the difference of consecutive x- and y- coordinate values of online handwriting. The writer independent experiments are carried out by dividing the collected online handwritten Kannada numeral data into a disjoint set of training and testing data sets. Out of 1400 numeral data samples, 700 numeral data samples are used for training the recognition modules and the remaining 700 numeral data samples are used for testing. The collected online handwritten data are subjected to preprocessing and feature extraction. The difference feature is extracted from the preprocessed data. The extracted features are mapped to lower-dimensional feature space by using OLDA. The subspace features are used to train and test the recognition module. Classification of the test data is carried out by using the nearest neighbor classifier. The average recognition accuracy of 99.3% is achieved.

Online Handwriting, Kannada Numerals; Handwriting Recognition Module; Difference Feature; Nearest Neighbor Classifier

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[12] M. Mahadeva Prasad, “Writing Direction Feature based Online Handwritten Kannada Numeral Recognition,” Int. Jl. of Computational Engineering Research, Vol. 9, No. 2, pp. 65-68, 2019.

M. Mahadeva Prasad, "Improvement in the Online Handwritten Kannada Numeral Recognition with the Difference Feature," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.868-870, 2019.

Cloud Computing is performing computing using the internet facility. Computing is performed as on demand of the user. The Cloud Computing Load Balancing algorithms can be applied in Static, Dynamic and Centralized environment. The paper compares and summarizes some of the load balancing strategies in cloud computing environment. The paper discusses some existing cloud load balancing algorithms and compares them according to the usage of resources and makespan at each Node. The paper proposes new improved algorithm for cloud resource allocation in cloud computing and compares its resource allocation with the existing algorithm.

Cloud Computing, Load Balancing, Static Load Balancing

[1] Peter Mell and Timothy Grance (September 2011). The NIST Definition of Cloud Computing (Technical report). National Institute of Standards and Technology: U.S. Department of Commerce. doi:10.6028/NIST.SP.800-145. Special publication 800-145.
[2] Deepti Sharma, Vijay B. Aggarwal, "Dynamic Load Balancing Algorithms for Heterogeneous Web Server Clusters", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.56-59, 2017
[3] Raza Abbas Haidri, C.P. Katti, P.C. Saxena, “A Load Balancing Strategy for Cloud Computing Environment”, 2014 IEEE International Conference on Signal Propagation and Computer Technology (ICSPCT), Ajmer, India, pp. 636-641.
[4] A. Kumar and M. Kalra, "Load balancing in cloud data center using modified active monitoring load balancer," 2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Spring), Dehradun, 2016, pp.1-5
[5] Ren, X., R. Lin and H. Zou, "A dynamic load balancing strategy for cloud computing platform based on exponential smoothing forecast", International Conference on. Cloud Computing and Intelligent Systems (CCIS), IEEE sept. 2011, pp: 220-224
[6] A. Beloglazov, Jemal Abawajy, Rajkumar Buyya, “Energy Aware Resource Allocation heuristics for efficient management of data centers for Cloud Computing”, Future Generation Computer Systems 28 (2012) 755–768
[7] Mohammadreza Mesbahi, Amir Masoud Rahmani, “Load Balancing in Cloud Computing: State of art Survey, MECS, pp. 64-78, March 2016
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[10] S.B. Shaw, A.K. Singh, “A Survey on Scheduling and Load Balancing Technique in Cloud Computing Environment”, 2014 International Conference on Computer and Communication Technology (ICCCT), Allahabad, 2014, pp. 87-95.
[11] Geethu Gopinath P P, Shriram K Vasudevan “An in depth analysis of load balancing technique in cloud computing environment”, 2nd International Symposium on Big Data and Cloud Computing Challenges, VIT University, Chennai, India, ISBCC-2015, pp. 427-432, 2015.
[12] S.Domanal, G. Reddy, “Optimal Load Balancing in Cloud Computing by Efficient Utilization of Virtual Machines”, IJATES, Vol 3, issue 2, 2014, pp.122-129
[13] Sanyogita Manhas, Jawahar Thakur, “Comparison of Load Balancing Algorithm in Cloud Computing”, IJCST Vol. 3, issue 4, Dec. 2012
[14] S. Wang, K. Yan, W. Liao, and S. Wang, “Towards a Load Balancing in a Three-level Cloud Computing Network”, Proceedings of the 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), Chengdu, China, September 2010, pages 108-113.
[15] Jasmin James, Dr. Bhupendra Verma, “Efficient VM Load Balancing Algorithm for a Cloud Computing Environment”, International Journal on Computer Science and Engineering (IJCSE) , Vol. 4 No. 09 Sep 2012 , pp. 1658-1663.
[16] Boutaba, R., Zhang, Q., & Zhani, M. F. (n.d.). “Virtual Machine Migration in Cloud Computing Environments.” In Communication Infrastructures for Cloud Computing (pp. 383–408). IGI Global. https://doi.org/10.4018/978-1-4666-4522-6.ch017
[17] Harish C. Sharma, Himanshu Bahuguna, “A Survey of Load Balancing Algorithm in Cloud Computing”, International Journal of Computer Engineering and Applications, issn: 2321-3469, Vol. XI, Issue XII, December 2017, pp. 150-160
[18] Harish C. Sharma, Himanshu Bahuguna, “Comparative Study of Load Balancing in Cloud Computing”, International Journal of Scientific & Engineering Research, Vol. 7, Issue 12, Dec. 2016, pp. 12-17, ISSN 2229-5518

Harish C. Sharma, Meenakshi Bisht, "Best Fit Resource Allocation in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.871-875, 2019.

Emotions play a crucial role in human communication and the Brain-Computer Interfaces (BCI) aid in emotion-based communication, similar to the way a mobile device aids in text-based communication. Emotions are expressed in myriad ways, including verbal, non-verbal and physiological signals. Most BCI systems accomplish this by using electroencephalography (EEG) signals. Before BCI systems are employed to practical use, efficient algorithms need to be developed in order to maximize efficiency. This paper proposes a method to map several emotional states using EEG signals collected from the publicly available Dataset for Emotion Analysis using Physiological signals (DEAP). It presents a comparative analysis of two different classification algorithms along with two different dimensionality reduction techniques. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are used in tandem with Support Vector Machine (SVM) and K-Nearest Neighbours (KNN). The objective of this paper is to weigh the results to find the most promising classifier and dimensionality reduction technique. The average accuracy of binary classification using KNN-LDA for each valence, arousal, dominance and liking was 97.98%, 96.21%, 98.24% and 96.19% respectively.

Emotion classification, EEG, Physiological signals, Signal processing, Pattern classification, Affective computing, DEAP Dataset, Machine Learning, Brain-Computer Interfaces (BCI), Electroencephalogram (EEG), Linear Discriminant Analysis (LDA), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Principal Component Analysis (PCA).

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[9] Afzal Ahmad, Mohammad Asif, Shaikh Rohan Ali, "Review Paper on Predicting Mood Disorder Risk Using Machine Learning", International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.1, pp.16-22, 2019
[10] S. Koelstra et al., “DEAP: A Database for Emotion Analysis; Using Physiological Signals,” IEEE Transactions on Affective Computing, vol. 3, no. 1. pp. 18–31, 2012.
[11] Russell, J. A. (1980), “A circumplex model of affect,” Journal of Personality and Social Psychology, 39, December, 1161-1178.

Piyush Bhardwaj, Pratyaksha Jha, Nishtha Chheda, Suvarna Gosavi, "A Comparative Analysis of Emotion Recognition using DEAP Dataset," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.876-881, 2019.

The flight safety monitoring becomes critical and is core area of research focused upon by this literature. To this end, data mining mechanisms employed by existing literature are discussed. ZeroR classifiers shortcoming of handling string values are overcome by converting the attributes to nominal form. Overall process of improving classification process is divided into phases. First phase includes loading the dataset. The fetched dataset requires storage. The dataset is stored within local storage. Second phase is critical and requires additional storage for maintaining pre-processed dataset. Pre-processed dataset contains nominal data. ZeroR cannot handle string data hence pre-processing phase converts data in understandable format for ZeroR classifier. In the second phase, necessary fields required for result prediction are retained and rest of the fields are ignored using regression mechanism. Third phase is a classification phase indicating that the performance is above baseline or not. By accommodating, nominal value conversion process within ZeroR classifier, classification accuracy is improved by 15%.

ZeroR, Classification accuracy, Nominal values, String data

[1] C. Li, L. Zhu, and Z. Luo, “Big Time-frequency Domain Data Mining for Underdetermined BSS Using Density Component Analysis,” IEEE Access, 2016.
[2] B. Li, X. Ming, and G. Li, “Big Data Analytics Platform for Flight Safety Monitoring,” IEEE 2017 pp. 350–353, 2017.
[3] G. Zhu, K. Song, and P. Zhang, “A Travel Time Prediction Method for Urban Road Traffic Sensors Data,” 2015 Int. Conf. Identification, Information, Knowl. Internet Things, pp. 29–32, 2015.
[4] S. Jasra, J. Gauci, A. Muscat, and G. Valentino, “Literature review of machine learning techniques to analyse flight data,” Res. Gate, no. October, 2018.
[5] G. Li, T. Yuan, S. J. Qin, and T. Chai, “Dynamic time warping based causality analysis for root-cause diagnosis of nonstationary fault processes,” Int. J. Autom. Control, pp. 1289–1294, 2015.
[6] V. M. Janakiraman and D. Nielsen, “Anomaly Detection in Aviation Data using Extreme Learning Machines,” 2016.

Aparpreet Singh, Sandeep Sharma, "Flight Safety Detection Using Modified Zeror Approach with Nominal Data Conversion," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.882-887, 2019.

Web application is a program which utilizes web technology and web browsers to perform the task over internet. Eg:Google docs.Web service is a standard method of integrating web application using standards over an internet for sharing data and services among themselves. Eg : .NET application interacting with java.Shop management system have the following shortfalls such as rate of profit fall, difficulty in replacement of products, quality of the products are reduced,improper communication between the hierarchy of nodes. At times,Clients experience “out of stock”circumstances.To overcome this we presented “Multilocation shop management system”.It provides enmorous gain for both the client and retailer offerings and allows growing single store business to numerous plus loactions.The admin francise can manage and view their consolidated customer,inventor and sales information from any location and can sink customers data every two mins or as often as want.Data is stored on an admin franchise computer and not on a remote server used by master or sub franchise and hence they can only view their consolidated customers requirments.It is a hierachial process which not only communicates between its master or sub-franchise but also communicates with its neighbouring master-franchise where the order from the sub-franchise and forwards it to the admin franchise to process the corresponding stock which will be stored in the local host of sub-franchise by using WAMP serve and the periodic udation is also done. dynamic web application are created with Apache 2,php and MYSQL on windows. franchise. POS allows to moniitor the shops statics from anywhere in the world, when a product or a service is purchased and to complete the transaction. Multilocation shop managemnet system use AngularJS an open source frondend javascrpit framework.The efficiency of the MULTILOCATION SHOP MANAGEMENT SYSTEM USING WEB SERVICES over the existing system is the (1) Efficient communication between the master franchise,(2) Periodical report submission to the admin franchise (3) Javascript is replaced by Angular Js and Rest api (4)Third party people are not involved to maintain the originality of products(5)All datas are secured in cloud storage.

store management, SOAP, POS,Rest api,cloud, Reports

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[14] E. Bendoly, J. D. Blocher, K. M. Bretthauer, S. Krishnan, and M. A.Venkataramanan, “Online/In-Store integration and customer retention,”J. Serv. Res., vol. 7, no. 4, pp. 313–327, 2005.
[15] In-Store Pickup and Returns for a Dual Channel Retailer Stephen Mahar and P. Daniel Wright
[16]Models for Cost-Benefit Analysis of RFID Implementations in Retail Stores Jayavel Sounderpandian, Rajendra V. Boppana, Senior Member, IEEE, Suresh Chalasani, Senior Member, IEEE, and Asad M. Madni, Fellow, IEEE

P.Bhavani, Preetha.S, Keerthana.D, Lokambal.V, "Multilocation Shop Management System using Web Services," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.888-893, 2019.

“Cloud Computing” a form of On-demand computing used by business peoples, organizations and institutions on pay –as-you basis. The Cloud Computing paradigm have many advantages such as availability, scalability, automated updates on software, enhanced collaboration and easily manageable, that makes it as an efficent medium for use. Security threat to its data stored in shared medium is a major concern. To ensure the authentication of the data many mechanisms were in use. Over past decades Cryptography is one most widely used technique for concealing data from third party. Symmetric key cryptography uses the similar key for both the encryption and decryption of messages. Instead, Asymmetric key cryptography uses two different types of keys. This paper discussed about the brief overview of algorithms and mechanisms done by the researchers regarding authentication and authorization issues in the asymmetric key scenario.

Cloud Computing, Authentication, Cryptography, Security

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[16]. Lakshika Singh, Anuj Kumar et.al.,” Secured Information Retrieval from cloud involving OTP and human voice”, 2017 IJSRST VoL.3 ,Issue.7 ,Print ISSN: 2395-6011,Online ISSN: 2395-602X, International Journal of Scientific Research in Science and Technology (www.ijsrst.com).
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M. Ilakiya, R. Vijithra, K. Kuppusamy, J. Mahalakshmi, "Impact of Asymmetric Encryption in Cloud Computing: A Study," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.894-897, 2019.

In this article, we made a study about the various aspects of the computing techniques such as acoustic, phonetics, phonetic-structures, language computation on linguistic perspective and so on. The study confines with the dimensions of the population ratio of languages, some phonetic structures of languages, acoustic devices and their specifications. It is important to study the languages as the spoken langauges are being lost at some rates and it is our resposibility to give stress on this topic so that human culture can be preserved and as well as the language.

Acoustic, phonetics, phonetic-structures, language-computation, linguistic

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A. Das, B. Choudhury, "A Study on Language Computations by preserving them as audio Documents," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.898-903, 2019.

Now a day due to rapid technology growth theft over transactions is emerging at higher rate. Although passwords are used as a level of authentication, the problem is solved at a certain extent. Another level of authentication may be introduced to provide secure transactions. Many biometric security provisions are employed today and selection of approach varies from application to application. Face recognition is one of popular authentication especially in the transaction areas. The major advantage of face based identification over other biometrics is uniqueness and acceptance. This system compares the image of the user with already registered image in the repository based on feature extraction, if it matches then the system proceeds to further processing. Whenever the user provides credentials in the form of password, an OTP is generated and is send to the registered mobile number. Later the system dynamically captures the facial image of the user through webcam of the system, Search in the registered repository on feature extraction approach basis then goes for comparison. If it found match then permits for e-payment procedure. Biometric applications implementation mostly use python environment due to adequate python features. Python is open source, portable, objects oriented software.

Face recognition, Biometric, Repository, Feature extraction, Pattern recognition, Webcam

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K. Nagaraju, Adepu Rajesh, S.V. Hemanth, "An Authenticated Face Recognition Application for e-Payment using Python," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.904-908, 2019.