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This paper evaluates the performance efficiency of K-means clustering, Agglomerative hierarchical clustering and Density based clustering methods for low and high dimensional data. Efficiency concerns the computational time required to build up datasets. To evaluate the performance of clustering methods extensive experiments are carried out on different datasets. The results reveal that Agglomerative hierarchical clustering method is efficient in time as compared to other methods but results may vary when dataset instances are large in number.

Clustering, K-means, Agglomerative hierarchical, Euclidean

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[3] N.Chitra Devia, V.Palanisamyb, K.Baskaranc, S.Prabeelad, “A Novel Distance for Clustering to Support Mixed Data Attributes and Promote Data Reliability and Network Lifetime in Large Scale Wireless Sensor Networks”, International Conference on Communication Technology and System Design, pp. 669-677, 2011.
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[8] L. Rokach, O. Maimon, “Clustering Methods”, Data Mining and Knowledge Discovery Handbook, Springer, 2005.
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[11] R. Remco, Bouckaert, Eibe Frank, Mark Hall, Richard Kirkby, Peter Reutemann, Alex Seewald, David Scuse, “WEKA Manual for Version 3-7-10”, July 31, 2013.
[12] M. Hassani, “Overview of Efficient Clustering Methods for High-Dimensional Big Data Streams”, Clustering Methods for Big Data Analytics, Springer Cham, pp.25-42, 2019.
[13] T. SenthilSelvi, R.Parimala, “Improving Clustering Accuracy using Feature Extraction Method”, International journal of scientific research in computer science and Engineering, vol. 6,Issue 2 ,pp. 15-19, 2018.

Smita Chormunge, Sudarson Jena, "Clustering Methods Analysis on Low and High Dimensional Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.658-661, 2019.

Agriculture is still the backbone of livelihood in India and it is primary source of income for 58 percent of population while agriculture is invented 10000 years ago. India is the 2nd, 7th and 3rd largest country in the world based on population, nominal GDP and purchasing power parity respectively. The economy of India is directly depending on agriculture because Gross Value Added by agriculture, forestry and fishing is approximately Rs 17.67 trillion in financial year 2K18. The traditional Indian farming has affected productivity because Indian farmers do not believe in modern methods of agriculture or lack of knowledge of modern agriculture technology like precision farming. That is why productivity of United States farmers is seven times more wheat per acre than Indian farmers because of adoption of precision farming. The scope of precision farming is limited due to smaller farm size per hectare i.e. less than one per hectare and crop diversity in India. But it has very bright scope in North West states like Punjab, Rajasthan, Haryana and Gujarat states with respect to rest of India because of large farm size and rest of India also for commercial crops, high value crops and fruit and vegetable crops. To fulfill the promise of present government of India to double the income of farmers, adoption of precision farming is the necessary condition. The US and Europe are the first and second position holder in terms of precision farming according to recent market study while Asian pacific region countries are in early stages. The growth rate of India and China are highest among the Asian continent. Adoption of precision farming is present time demand to solve the crisis of yield in India also because of reduction of input cost and enhancement of yield 18-20% and 30% respectively.

Precision agriculture, Precision Farming, remote sensing, Modern Technology, spatial Variability, Adoption of Technology

[1]. Singh AK (2004) Precision Farming. Water Technology Centre, I.A.R.I., New Delhi.
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Shiv kumar, Shrawan Kumar Sharma, "Precision Farming India & Abroad," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.662-666, 2019.

The Density Based Signal Management in traffic system is to clarify traffic congestion problem which is a big problem in many various modern cities, and many population face this problem. For that, In our project we designed the framework for dynamic traffic light control system, and the automatic traffic light control system, developed the model with codes to help build the system. Generally, each traffic light on Smart Signal System an intersection is assigned set signal time. This is possible to propose dynamically time-based coordination schemes where a green signal time of the traffic lights is assigned base on the present conditions of vehicle density in that traffic.

RFID Tag, Sensor, time, Traffic Detector

[1] Cullen Rhodes and Soufiene Djahel School of Computing, Mathematics and Digital Technologies, Manchester Metropolitan University, UK TRADER: Traffic Light Phases Aware Driving for Reduced Traffic Congestion in Smart Cities.
[2] Y M Jagadeesh, G. Merlin Suba, S Karthik, and K Yokesh Smart Autonomous Traffic Light Switching by Traffic Density Measurement through Sensors. International Conference on Computers, Communications and Systems 2015.
[3] Bilal Ghazal Faculty of Sciences IV Lebanese University (UL) Zahle, Lebanon Smart Traffic Light Control System.
[4] Shubhangi M. Deshmukh Bhairavi, N. Savant Traffic Congestion Alerting System.
[5] Zuzana Bělinová, Tomáš Tichý, Jan Přikryl, Kristýna Cikhardtová Smarter traffic control for middle-sized cities using adaptive algorithm.
[6] Design of an Intelligent Auto Traffic Signal Controller with Emergency Override by Geetha.E, V.Viswanadha, Kavitha.G. International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 3, Issue 4, July 2014
[7] Density Based Intelligent Traffic Signal System Using PIC Microcontroller by G.Kavya, B.Sarany. International Journal for Research in Applied Science & Engineering Technology (IJRASET).
[8] Agent- Based Traffic Simulator for Autonomous Vehicles by K. Saha1* , S. Rathee2 Vol.5, Issue.2, pp.42-45, April (2017)

Jayashree Jadhav, Rupali Bhagat, Trupti Chatale, Sandhya Jamdade, Priti Sarnaik, "Smart Traffic System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.667-670, 2019.

Computer vision is a field of computer science that works on enabling computers to see, identify and process data in the same way that human vision does, and then provide appropriate output. It is like imparting human intelligence and instincts to a computer. It includes methods for acquiring, processing, analyzing and understanding Videos or Images. The main goal is not only to see, but also process and provide useful results based on the observation. Age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. This research report represents information regarding Age & Gender Detection of a person by using Deep Learning Models and Transfer Learning.

Age & Gender Detection, Convolutional Neural Network, Deep Learning, Transfer Learning

representation from predicting 10,000 classes. In Proc. Conf. Comput. Vision Pattern Recognition, pages 1891–1898. IEEE, 2014.
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[3] E. Eidinger, R. Enbar, and T. Hassner. Age and gender estimation of unfiltered faces. Trans. on Inform. Forensics and Security, 9(12), 2014
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[5] C. Liu and H. Wechsler. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. Trans. Image Processing, 11(4):467–476, 2002.
[6] W.-L. Chao, J.-Z. Liu, and J.-J. Ding. Facial age estimation based on label-sensitive learning and age-oriented regression. Pattern Recognition, 46(3):628–641, 2013.
[7] https://www.kaggle.com/jessicali9530/celeba-dataset
[8]https://www.kaggle.com/ttungl/adience-benchmark-gender-and-age-classification
[9] Felix Anda, David Lillis, Nhien-An Le-Khac, Mark Scanlon, Evaluating Automated Facial Age Estimation Techniques for Digital Forensics, 2018 IEEE Symposium on Security and Privacy Workshops.
[10] Y. Fu, G. Guo, and T. S. Huang. Age synthesis and estimation via faces: A survey. Trans. Pattern Anal. Mach. Intell., 32(11):1955–1976, 2010.
[11] H. Han, C. Otto, and A. K. Jain. Age estimation from face images: Human vs. machine performance. In Biometrics (ICB), 2013 International Conference on. IEEE, 2013.
[12] Y. H. Kwon and N. da Vitoria Lobo. Age classification from facial images. In Proc. Conf. Comput. Vision Pattern Recognition, pages 762–767. IEEE, 1994.
[13] N. Ramanathan and R. Chellappa. Modeling age progression in young faces. In Proc. Conf. Comput. Vision Pattern Recognition, volume 1, pages 387–394. IEEE, 2006.
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[15] G. Guo, Y. Fu, C. R. Dyer, and T. S. Huang. Imagebased human age estimation by manifold learning and locally adjusted robust regression. Trans. Image Processing, 17(7):1178–1188, 2008.
[16] Y. Fu and T. S. Huang. Human age estimation with regression on discriminative aging manifold. Int. Conf. Multimedia, 10(4):578–584, 2008.
[17] A. Lanitis. The FG-NET aging database, 2002. Available: www-prima.inrialpes.fr/FGnet/html/ FERET.html
[18] K. Ricanek and T. Tesafaye. Morph: A longitudinal image database of normal adult age-progression. In Int. Conf. on Automatic Face and Gesture Recognition, pages 341–345. IEEE, 2006.
[19] S. Yan, X. Zhou, M. Liu, M. Hasegawa-Johnson, and T. S. Huang. Regression from patch-kernel. In Proc. Conf. Comput. Vision Pattern Recognition. IEEE, 2008.
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[21] X. Zhuang, X. Zhou, M. Hasegawa-Johnson, and T. Huang. Face age estimation using patch-based hidden markov model supervectors. In Int. Conf. Pattern Recognition. IEEE, 2008.
[22] C. Perez, J. Tapia, P. Est´evez, and C. Held. Gender classification from face images using mutual information and feature fusion. International Journal of Optomechatronics, 6(1):92– 119, 2012.
[23] S. Baluja and H. A. Rowley. Boosting sex identification performance. Int. J. Comput. Vision, 71(1):111–119, 2007.
[24] M. Toews and T. Arbel. Detection, localization, and sex classification of faces from arbitrary viewpoints and under occlusion. Trans. Pattern Anal. Mach. Intell., 31(9):1567–1581, 2009.
[25] G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical report, Technical Report 07-49, University of Massachusetts, Amherst, 2007.
[26] Flavio H. de B. Zavan, Nathaly Gasparin, Julio C. Batista, Luan P. e Silva, Vıtor Albiero, Olga R. P. Bellon and Luciano Silva, Face Analysis in the Wild, 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials.
[027] Bartłomiej Hebda and Tomasz Kryjak, A compact deep convolutional neural network architecture for video based age and gender estimation, Proceedings of the Federated Conference on Computer Science and Information Systems 2016 .
[28] Vladimir Khryashchev, Andrey Priorov and Alexander Ganin, Gender and age recognition for video analytics solution, Image Processing Laboratory, P.G. Demidov Yaroslavl State University Yarodlavl, Russia, 2014 IEEE.
[29] Gil Levi and Tal Hassner, Age and Gender Classification using Convolutional Neural Networks,Department of Mathematics and Computer Science The Open University of Israel, 2015 IEEE [30] Vladimir Khryashchev, Lev Shmaglit, Andrey Shemyakov, Anton Lebedev Yaroslavl State University, Gender Classification for Real-Time Audience Analysis System, PROCEEDING OF THE 15TH CONFERENCE OF FRUCT ASSOCIATION 2015, Russia.
[31] Lijia Lu, Weiyang Liu, Yandong Wen, and Yuexian Zou, Automatical Gender Detection for Unconstrained Video Sequences based on Collaborative Representation, ICSP2014 Proceedings.
[32] Ke Zhang, Ce Gao, Liru Guo, Miao Sun, Xingfang Yuan, Tony X. Han, Zhenbing Zhao and Baogang Li, Age Group and Gender Estimation in the Wild with Deep RoR Architecture, Age Group and Gender Estimation in the Wild with Deep RoR Architecture, IEEE TRANSACTIONS ON L ATEX CLASS FILES, VOL. XX, NO. X, AUGUST 2017.
[33] Meltem Demirkus, Matthew Toews, James J. Clark, Combining Motion and Appearance for Gender Classification from Video Sequences, IEEE 2016.
[34] Hu Han, Member, Anil K. Jain, Shiguang Shan and Xilin Chen, Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Arsala Kadri, Kirti Sharma, Narendrasinh Chauhan, "Age and Gender Detection using Deep Learning Models," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.671-676, 2019.

Segmentation of a trajectory is the problem of subdividing a trajectory into subparts where each part is homogeneous and expresses similar movement characteristics. We formalize trajectory segmentation problem using likelihood of the distance and bearing parameters. Section of a trajectory is considered homogeneous when distance between trajectory points and angular movement between them are within the user specified threshold value. We developed a framework for trajectory segmentation based on calculating distance and bearing between two trajectory points. An algorithmic framework is presented to segment the trajectory into a minimum number of segments based on the distance and bearing parameters. The algorithm has been tested on real data set.

Trajectory segmentation, Bearing based segmentation, Trajectory analysis.

[1] A. Anagnostopoulos, M. Vlachos, M. Hadjieleftheriou, E. Keogh, and P. Yu, “Global distance-based segmentation of trajectories”, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 34–43, 2006.
[2] M. Buchin, A. Driemel, M. van Kreveld, and V. Sacrist´an, “Segmenting trajectories: A framework and algorithms using spatiotemporal criteria”, Journal of Spatial Information Science, volume 3 pp 33–63, 2011.
[3] M. Buchin, H. Kruckenberg, and A. K¨olzsch, “Segmenting trajectories by movement states”, Proc. 15th International Symposium Spatial Data Handling (SDH), pp 15–25. Springer-Verlag, 2012.
[4] J. Gudmundsson, P. Laube, and T. Wolle, “Computational movement analysis”, Springer Handbook of Geographic Information, pp 423–438. Springer, 2012.
[5] J. Shamoun-Baranes ,E. van Loon , R. Purves, B. Speckmann, D. Weiskopf, C. Camphuysen, Analysis and visualization of animal movement, Biol Lett 8(1):6–9, 2012 .
[6] F. Giannotti, M. Nanni, D. Pedreschi and F. Pinelli, ”Trajectory Pattern Mining”, KDD007, San Jose, California, USA, pp. 330-339, August 12-15, 2007.
[7] R. Mann, A. D. Jepson, T. El-Maraghi, “Trajectory segmentation using dynamic programming”, Pattern Recognition, Proceedings 16th International Conference Vol. 1, pp 331 - 334 2002.
[8] E. J. Keogh, S. Chu, D. Hart, M. Pazzani, ” Segmenting Time Series: A Survey and Novel Approach”, Data Mining In Time Series Databases, Vol. 57, pp. 1-22, (2004)
[9] J. Kang and H. Yong ”Mining Trajectory Patterns by Incorporating Temporal Properties”, In Proceedings of the 1st International Conference on Emerging Databases (EDB2009), 2009.
[10] H. Cheng, X. Yan, J. Han, and S. Philip, ”Direct Discriminative Pattern Mining for Effective Classification”, ICDE’ 08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, 2008.
[11] Theodoridis, Y. (2003). The R-tree-portal, a collection of moving object databases.
[12] V. Mirge, K. Verma. And S. Gupta, “A Novel Approach for Mining Trajectory Patterns of Moving Vehicles”, International Journal of Computer Applications vol. 104 issue 4 pp 4-8, October 2014.
[13] V. Mirge, K. Verma, & Gupta, S. Dense traffic flow patterns mining in bi-directional road networks using density based trajectory clustering. Advances in Data Analysis and Classification, vol. 11 issue 3 pp 1-15, 2017.
[14] R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra, “
Design and Development of Novel Techniques for Clustering and Classification of Data”, International Journal of Scientific Research in Computer Science and Engineering, Vol.06 , Special Issue.01 , pp.19-22, Jan-2018.

V. Mirge, K. Verma, "Distance and Bearing Based Vehicle Trajectory Segmentation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.677-681, 2019.

The era of digital technology, bulky data transmitted over the network. The main challenge is to maintain the quality of the data packet delivered at the receiving end at a very high speed, failing the image quality would fall down and also everything would turn into slow motion. To maintain the speed and quality, lossless compression of the image is required. Medical images are a bigger challenge as they have different formats, especially MRI images. The thesis proposes compression of medical images using improvised DCT algorithm. Since finer details are important in medical images a special masking technique has been used, a mathematical formulation has also been derived to achieve the goal of maintaining the quality with speed. Finally, DCT and inverse DCT is applied on the images to compress the images. To check the robustness of the proposed algorithm MSE, PSNR and compression has been computed. The proposed algorithm had been compared with standard DCT algorithm and it is found that the proposed algorithm improves MSE, PSNR and compression ratio by 8% percent on an average. It can be concluded that the proposed algorithm performs better than standard DCT algorithm for medical images.

ImageCompression,PSNR,MSE,DCT,InverseDCT,MRI

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[5] BivayKumar, PankajRai "A Fuzzy & K-L based reduced reference image quality", IOSER journal of computer engineering (IOSR-JCE), e-ISSN: 2278-0661, p-ISSN: 22788727, volume 17, Issue 5, pp 118-128, 2015.

[6] Kegu, Guangtaozhi, Weisilin, “No- reference image sharpness assessment in high autoregressive parameter space” IEEE, volume: 24, Issue: 10, Oct. 2015.
[7] Z. Xu, J. Bartrina-Rapesta, I. Blanes, V. Sanchez, J. Serra-Sagrist, M. Garca-Bach, and J. F. Muoz, “Diagnostically lossless coding of x-ray angiography images based on background suppression,” Computers & Electrical Engineering, 2016.
[8] H. B. Kekrea, Prachi Natub, and Tanuja Sarode, "Color Image Compression using Vector Quantization and Hybrid Wavelet Transform ",Procedia Computer Science 89, 2016.
[9] ShabilaBeevi, Mariya Thomas, Madhu S. Nair, M. Wilscy,"Lossless color image compression using double level RCT in BBWCA", Procedia Computer Science, 2016.
[10] Maria Georgea, Manju Thomasa, Jayadas C.K,"A Methodology for Spatial Domain Image Compression Based on Hops Encoding”, Procedia Technology, 2016.
[11] Azza Ouled Zaid and Amira Houimli, “HDR Image Compression with Optimized JPEG Coding,” 25th European Signal Processing Conference (EUSIPCO), 2017.
[12] Himani, Pawan Kumar Mishra, "Medical image compression using block processing with DCT", international journal of advanced research in computer and Communication Engineering (IJARCCE), vol. 7, issue 3, March 2018.

[13] Akshay Kekre, Dr. Sanjay Pokle, "Improved Image Compression Using Wavelet Transform and Differential Pulse Code Modulation Technique", International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064, Issue.2, Vol.4, 2015
[14] Ganesh Vilas Madhikar; S. S. Lokhande, "Detection and Classification of Brain Tumour Using Modified Region Growing and Neural Network in MRI Images", International Journal of Science and Research (IJSR), Volume 3 Issue 12, December 2014, 2362 - 2365

Hema Joshi, Pawan Kumar Mishra, "Medical Image Lossless Compression Using Improvised DCT," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.682-685, 2019.

Many times Relational Databases fails due to lack of support for handling the vast unstructured semantics of Big Data .Applications generating Big data are not efficient with relational databases in term of storage Space, database load time , query run time and application flexibility . When to use NOSQL data bases and which NOSQL database is good choice among the available NOSQL DB like Key-Value store, document store, column oriented DB, Graph DB or time Series DB needs to be evaluated carefully to get optimal performance in term of storage and query execution. This paper is about the Cloud based NOSQL –columnar oriented Database performance evaluation along with other NOSQL services like DynamoDB. Many prominent cloud providers like Google Cloud, AWS cloud and Microsoft azure Cloud they all provide NOSQL data bases as a service. These Databases are schema less, ACID free and flexible with full governance support for specific application need. This paper mainly focuses on cloud based column oriented data base performance with AWS Redshift.

NOSQL, AWS Redshift, AWS Athena, Columnar Databases, Key value DB

[1] Jagdev Bhogal and Imran Choksi “ Handling Big Data using NOSQL” ,IEEE 29th International confrence on Advanced Information Networking and applications workshop,2015
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[5] Suryawanshi Harshal, Rokade Chakrapani, Ambhore Ajay, Rathod Sharad, “Compiler as Service over Cloud”, International Journal of Computer Applications,Volume 70– No.1, May 2013.
[6] Uday Chauhan, Mayuri Patole, Yogita Mokashi, SaurabhPadalikar “Online Cloud Based Compilers System”, Computer Society Of India ,February 2016.
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[8] TeachMeHANA, “Difference between Row Store and Column Store”, May, Available at: http://teachmehana.com/row-store-vs-column-store/
[9] Ansari Mohd. Arshad, Arshiya Khan, Shaikh Sana, Er. Zainab Mirza, “Compilers on Cloud”, IJERT, 2013.
[10] Pat Research, “Top 9 Column-Oriented Databases”, 2019, Available at https://www.predictiveanalyticstoday.com/top-wide-
[11] columnar-store-d atabases/ Jon Tong, “Demystifying Columnar Databases”, April2012, Available at: https://www.youtube.com/watch?v=wYl_YxqTof4
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[13] Jyoti Kharade, Anil Rama Kale, Dhanaji S. Kharade, “NOSQL Database: Opportunities and Applications”, International Journal of Computer Sciences and Engineering, Vol.-6, Issue-5, May 2018.
[14] Jared Hilam,“What is a Columnar Database?”, Available at https://www.youtube.com/watch?v=8KGVFB3kVHQ
[15] https://docs.aws.amazon.com/redshift/latest/mgmt/welcome.ht ml
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[17] V. S. Dhaka and M. Zainab, “Big Data Management as a Service ( BDMaaS ) using NOSQL model from Cloud,” no. 3, pp. 18–22, 201
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[19] Academind, “SQL vs NoSQL or MySQL vs MongoDB“,2018, Available at: https://www.youtube.com/watch?v=ZS_kXvOeQ5Y&t=11
[20] Suryawanshi Harshal, Rokade Chakrapani, Ambhore Ajay, Rathod Sharad, “Compiler as Service over Cloud”, International Journal of Computer Applications,Volume 70– No.1, May 2013.

Mirza Zainab, Savita Shiwani, "Exploring cloud based NOSQL Services," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.686-691, 2019.

The second most common visually disabling disease after Diabetic Retinopathy is Retinal Vein Occlusion (RVO). There are three types of Retinal Vein Occlusions: Central Retinal Vein Occlusion (CRVO), Branch Retinal Vein Occlusion (BRVO) and Hemi-retinal Vein Occlusion (HRVO). Here, CRVO is the blockage of the center vein, BRVO is the blockage of smaller veins i.e. branches of the vein and HRVO is the blockage of sub-veins of the main vein. Branch Retinal Vein Occlusion (BRVO) is three times more prevalent than Central Retinal Vein Occlusion (CRVO). Vision loss or blurry vision, floaters are some of the common features of BRVO. The treatment of BRVO aims at avoiding further damage to the patient’s vision but it cannot heal or help regain the vision. Due to this reason, the detection of BRVO requires proper attention. Also, fundus machines for detection of BRVO are not available in remote areas. The symptoms of this disease cannot be easily detected due to very small variations in the early stages and also due to the absence of an ophthalmologist. To serve this purpose an Artificial Intelligence is developed with the aim of providing the first level of diagnosis of BRVO. For this, different preprocessing techniques and layers are used to build four Convolutional Neural Network models.

occlusion, Artificial Intelligence, Convolutional Neural Network

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Jecko Anto Kattampally, Koshy C Oommen, Vaibhavi Patil, Pranali Choudhari, "Artificial Intelligence Based Branch Retinal Vein Occlusion Detection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.692-698, 2019.

Cloud computing and its components make usage of resource in proper optimization manner. In order to provide better quality of services and proper cloud component scheduling many algorithms were proposed. Algorithms such as round robin, throttle, Genetic algorithm etc were proposed by the existing research work. Limitations with such algorithms are of monitoring single level of utilization. They are either concentrating on resource utilization or in energy consumption by their resources for that request process. Further the internal process does not comply with parallel process of monitoring such utilization. In this research, An Advance Algorithm named VMERRU (Virtual machine energy resource request utilization) is proposed. The approach also make use of utilizing monitoring of energy , resource usage count, input request requirement and matching requirement of assigning DC, VM to it. Thus an optimal request handling algorithm with parallel computation is proposed. An implementation is performed using CloudSim API cloud analyst simulator and further computation shows the efficiency of proposed algorithm.

Resource Optimization, Cloud Sim, Data Sharing, Virtualization, VMERRU, Parallel Computing, Request Analysis, Cloud Component Scheduling

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Mukesh Kumar, Mukesh Kumar, Devendra Singh Rathore, "AVM Aware and Energy Resource Request Utilization (VMERRU) Cloud Scheduling Algorithm Over Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.699-705, 2019.

The Network traffic arrangement is a procedure by which the large chips get away at different parameters for instance port and convention based which are utilized to identify the classes of the traffic. Thus these types of classification methods are very helpful in providing security at two levels- network as well as system. The main focus of this paper is sorting out the problem which comes while handling network traffic whereas some of the traffic classification methods are unable to find out the special requirements of individual datasets because there are massive measures of network traffic datasets and restricted quantities of resources are accessible to deliver classification examination. The paper uncovers that traffic arrangement should be refreshed normally to keep up the precision and ought to have the capacity to adjust the dynamic conduct of network stream.

Network Traffic, Network Traffic Class, Network Features, Statistical features, Classification

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Amit Kumar, Daya Shankar Pandey, Varsha Namdeo, "An Efficient NB-IWD Based Network Traffic Classification over KDD Dataset," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.706-710, 2019.