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A Knowledge Graph(KG) is a graph that contains information about anything and everything in the world, these graphs can be represented by plain-simple nodes and links. Knowledge Graph Embedding refers to attaining valuable information about every node present in the graph, essentially it could be defined as representing a node as a low-dimensional vector. Knowledge graph Embedding techniques have become an increasingly popular research topics. Despite all the efforts invested in creation and maintenance of knowledge graphs only contain a part of what it contains is true and it also still consists of missing facts. Prediction of these missing facts in scientific term can be known as Link Prediction. Several recent works use deep learning approaches to generate richer and more expressive embedding. However, observations show that the following methods are very expensive computationally. This paper proposes a novel approach for generating Graph Embeddings which can be used to perform the task of Link Prediction i.e. interpreting missing data. ?CompNet? uses a modified version of the complEx and Cluster-Graph Convolution Network (Cluster-GCN) algorithms. Laying out certain constraints on the ComplEx algorithm i.e. discussed in detail through this paper, can improve the time complexity drastically. ?CompNet? is tested on the large-scale dataset Freebase, wherein it out-performs the traditional approaches (translation based approaches and semantic search approaches) in terms of Mean Reciprocal Rank (MRR) and also utilizes low storage space

Convolutional Neural Networks, ComplEx, Knowledge Graphs, Link Prediction, Graph Embedding

[1] Schlichtkrull, Michael & Kipf, Thomas & Bloem, Peter & Berg, Rianne & Titov, Ivan & Welling, Max. Modeling Relational Data with Graph Convolutional Networks. 10.1007/978-3-319-93417-4_38, 2018.
[2] Dai, Shaozhi & Liang, Yanchun & Liu, Shuyan & Wang, Ying & Shao, Wenle & Lin, Xixun & Feng, Xiaoyue. Learning Entity and Relation Embeddings with Entity Description for Knowledge Graph Completion. 10.2991/icaita-18.2018.49,2018.
[3] Antoine Bordes, Nicolas Usunier, Alberto GarciaDuran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multirelational data. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, Curran Associates, Inc., pages 2787? 2795, 2013.
[4] Feng, Jianlin. Knowledge Graph Embedding by Translating on Hyperplanes, 2014.
[5] TransH: Knowledge Graph Embedding by Translating on Hyperplanes. Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen. AAAI , 2014.
[6] Nickel, Maximilian & Tresp, Volker & Kriegel, Hans-Peter. A Three-Way Model for Collective Learning on Multi-Relational Data.. Proceedings of the 28th International Conference on Machine Learning, ICML 2011. 809-816, 2011.
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[8] ICML`16: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 Pages 2071?2080, June 2016
[9] KDD `19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Pages 257?266 , July 2019
[10] George Karypis and Vipin Kumar. 1998. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20, 1, 359?392, 1998.
[11] Improving Knowledge Graph Embedding Using Simple Constraints, Boyang Ding and Quan Wang and Bin Wang and Li Guo, year 2018.
[12] German Kruszewski, Denis Paperno, and Marco Baroni. 2015. Deriving Boolean structures from distributional vectors. Transactions of the Association for Computational Linguistics 3:375?388.
[13] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, ?Translating embeddings for modeling multirelational data,? in NIPS, pp. 2787?2795. 2013.

Kohsheen Tiku, Jayshree Maloo, R. Indra, "CompNet : A novel Knowledge Graph Embedding Technique for Link Prediction," International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.1-4, 2020.

Wireless Sensor Network consists of large number of Sensor nodes, which firstly sense the data and then transmit the sensed data to the base station which is known as the Sink. Network lifetime is one of the key challenges for Wireless Sensor networks because of the limited battery life of the nodes. As we know sensor nodes are energy constraint devices and to increase the lifetime of the network it is very necessary to minimize the consumption of energy of nodes while sensing and transmitting the data. Clustering in Wireless networks is one of the pre-eminent ways to improve the lifetime of the network. In EDEEC clustering-based hierarchical model is used where data is aggregated in the cluster and sent to a higher-level cluster head where the cluster hear is selected randomly on the basis of residual energy of the network. EDEEC works on 3-level heterogeneous wireless sensor networks in which there are three type of sensor nodes named as normal nodes, advanced nodes and super node, which still have scope of improvement because if the levels of heterogeneous wireless sensor networks are to be increased then more complexity will be there in the network and then a more stable behaviour of network is required. In this work it is proposed that performance of the network can enhanced if the level of heterogeneity in the network increased because in real world there can exist more than three types of nodes in the network

Clustering, Energy, Stability period, Heterogeneous, Wireless Sensor Networks

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[8] P. Saini and A. K. Sharma, ?E-DEEC - Enhanced distributed energy efficient clustering scheme for heterogeneous WSN?, 2010 1st Int. Conf. Parallel, Distrib. Grid Comput. PDGC - 2010, pp.205?210, 2010.
[9] J. Yick, B. Mukherjee, and D. Ghosal, ?Wireless sensor network survey?, Computer Networks, Vol.52, Issue.12, pp.2292?2330, 2008.
[10] J. Yan, M. Zhou, and Z. Ding, ?Recent Advances in Energy-Efficient Routing Protocols for Wireless Sensor Networks?, Middle-East J. Sci. Res., Vol.4, pp.5673?5686, 2016.

H. Kaur, S. Sharma, "Behaviour Analysis of EDEEC for 4-Level Heterogeneous Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.5-12, 2020.

In recent years video surveillance system has been broadly established and nowadays we can see the billions of cameras are embedded in smart phones and other devices. Face investigation from videos and images is most predominant task for various government agencies and industry alike. The process in which unlabeled facial images are assembled according to individual characteristics is called face clustering. Clustering is required in numerous applications for example, law enforcement, disruptive design, construction, social media, health care and surveillance applications etc. Here, in this paper we reviewed some of the face clustering works done by different researchers using different methods for different applications and also gives the brief knowledge about the work done by the previous researcher

Face Recognition, Face Clustering, Video segmentation, Video Organization etc

[1] Zhang, Ling-Fei, et al. "Research On Face Image Clustering Based On Integrating Som And Spectral Clustering Algorithm." 2018 International Conference on Machine Learning and Cybernetics (ICMLC). Vol. 2. IEEE, 2018.
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[3] Xia, Siyu, Hong Pan, and A. Kai Qin. "Face clustering in photo album." 2014 22nd International Conference on Pattern Recognition. IEEE, 2014.
[4] Shi, Yichun, Charles Otto, and Anil K. Jain. "Face clustering: representation and pairwise constraints." IEEE Transactions on Information Forensics and Security 13.7: 1626-1640, 2018
[5] Chang, Leonardo, Airel P?rez-Su?rez, and Miguel Gonz?lez-Mendoza. "Effective and Generalizable Graph-Based Clustering for Faces in the Wild." Computational Intelligence and Neuroscience 2019 (2019).
[6] Xiao, Shijie, Mingkui Tan, and Dong Xu. "Weighted block-sparse low rank representation for face clustering in videos." European Conference on Computer Vision. Springer, Cham, 2014.
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[8] Antonopoulos, Panagiotis, Nikos Nikolaidis, and Ioannis Pitas. "Hierarchical face clustering using sift image features." 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing. IEEE, 2007.
[9] Cao, Xiaochun, et al. "Robust face clustering via tensor decomposition." IEEE transactions on cybernetics 45.11: 2546-2557, 2014
[10] Cao, Xiaochun, et al. "Constrained multi-view video face clustering." IEEE Transactions on Image Processing 24.11: 4381-4393, 2015.
[11] Wu, Baoyuan, et al. "Constrained clustering and its application to face clustering in videos." Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 2013.
[12] Sheffer, Alla. "Model simplification for meshing using face clustering." Computer-Aided Design 33.13: 925-934, 2001.
[13] Vretos, Nicholas, Vassilios Solachidis, and Ioannis Pitas. "A mutual information based face clustering algorithm for movie content analysis." Image and Vision Computing 29.10: 693-705, 2011.
[14] Li, Zhifeng, and Xiaoou Tang. "Bayesian face recognition using support vector machine and face clustering." Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.. Vol. 2. IEEE, 2004.
[15] Vretos, Nicholas, Vassilios Solachidis, and Ioannis Pitas. "A mutual information based face clustering algorithm for movies." 2006 IEEE International Conference on Multimedia and Expo. IEEE, 2006.
[16] Pei, Xiaobing, et al. "Manifold adaptive label propagation for face clustering." IEEE transactions on cybernetics 45.8: 1681-1691, 2014.
[17] Tao, Ji, and Yap-Peng Tan. "Face clustering in videos using constraint propagation." 2008 IEEE International Symposium on Circuits and Systems. IEEE, 2008.
[18] Sharma, Vivek, M. Saquib Sarfraz, and Rainer Stiefelhagen. "A simple and effective technique for face clustering in tv series." 2017.
[19] Tapaswi, Makarand, Marc T. Law, and Sanja Fidler. "Video face clustering with unknown number of clusters." Proceedings of the IEEE International Conference on Computer Vision. 2019.
[20] Tang, Zhiqiang, et al. "Face clustering in videos with proportion prior." Twenty-Fourth International Joint Conference on Artificial Intelligence. 2015.
[21] Otto, Charles, Brendan Klare, and Anil K. Jain. "An efficient approach for clustering face images." 2015 International Conference on Biometrics (ICB). IEEE, 2015.
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[23] Kulshreshtha, Prakhar, and Tanaya Guha. "An Online Algorithm for Constrained Face Clustering in Videos." 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018.
[24] Asad, Mahim-Ul, Rashed Mustafa, and Mohammad Shahadat Hossain. "An Efficient Strategy for Face Clustering use in Video Surveillance System." 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR). IEEE, 2019.
[25] Chu, Wei-Ta, Ya-Lin Lee, and Jen-Yu Yu. "Visual language model for face clustering in consumer photos." Proceedings of the 17th ACM international conference on Multimedia. 2009.
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Rakhi, Suman, "Review Paper on Face Clustering," International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.13-16, 2020.

For a wide variety of applications, image contrast enhancement is very essential. Thus, it is imperative to have a low-cost, highly efficient and quick image contrast enhancement tools. In the past image contrast enhancement has always been performed sequentially on one core. With the advent of multi-core architecture in computers, it is now possible to perform tasks concurrently which do not have task dependencies. Parallel MATLAB Toolbox is an additional functionality provided by MATLAB which utilizes the parallel capabilities of modern day computers by allowing us to resolve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. Image processing and contrast enhancement are fields which could greatly benefit from parallelization to improve performance and achieve speedup. These applications have the potential of high degree of parallelism and thus are a exceptional source for multi-core platform. Parallelization aims at taking less time and making better use of available resources for time intensive and real time jobs. This paper aims to use Parallel MATLAB Toolbox to parallelize the process of Discrete Cosine Transform and coefficient Scaling

Parallel MATLAB Toolbox, DCT, Coefficient Scaling and Speedup

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[7] Khizar Hayat, Tanzeela Qazi. ?Forgery Detection in Digital Images Via Discrete Wavelet and Discrete Cosine Transforms?, Computers and Electrical Engineering, Vol 62, pp. 448-458, 20
[8] Zhenxing Qian, Wenwen Wang, Tong Qiao, ?An Edge Detection Method In DCT Domain?, Procedia Engineering Vol 29, pp. 344 ? 348, 2012
[9] Stewart Heitmann, Matthew J Aburn, Michael Breakspear, ?The Brain Dynamics Toolbox For Matlab?. Neurocomputing, NEUCOM, 2017
[10] Ivano Azzini, Ronal Muresano And Marco Ratto, ?Dragonfly: A Multi-Platform Parallel Toolbox For MATLAB/Octave?, Computer Languages, Systems & Structures, Vol. 52, pp. 21?42, 2018
[11] Rachna Mehta, Navneet Agarwal, ?Splicing Detection for Combined DCT, DWT and Spatial Markov features Using Ensemble Classifier?, Procedia Computer Science, Vol. 132, pp1695?1705, 2018
[12] Xinchen Guo, Mukul Dave And Mohamed Sayeed, ?Hpc matlab: A Framework For Fast Prototyping Of Parallel Applications In Matlab?, Procedia Computer Science, Vol. 80, pp 1461-1472, 2016
[13] S.M. Walunj , S.V. Gaikar , A.D. Potgantwar, ?Accelerate Image Reconstruction Using GPU?, JSRNSC, Vol. 5, Issue 3, June 2017
[14] Xuewen Wang, Lixia Chen. An Effective Histogram Modification Scheme For Image Contrast Enhancement. Signal Processing: Image Communication (2017). 2017
[15] Aref Miri, Saeed Sharifian, Shima Rashidi, Madjid Ghods, ?Medical Image Denoising Based On 2D Discrete Cosine Transform Via Ant Colony Optimization?, 0030-4026/? 2017 Elsevier GmbH.
[16] Wing-Kin Tama, Yang Zhib, ?Neural Parallel Engine: A Toolbox For Massively Parallel Neural Signal Processing?, Journal Of Neuroscience Methods, 2018 May 1;301:18-33
[17] Preeti Kaur, ?IMPLEMENTATION OF IMAGE PROCESSING ALGORITHMS ON THE PARALLEL PLATFORM USING MATLAB?, International Journal Of Computer Science & Engineering Technology (IJCSET), 2013, Vol. 4 No. pp. 696-706, 06 Jun 2013

Kunal Gupta, M. Madiajagan, Nipunn Miglani, Shrivats Agrawal, "Design and analysis of Contrast Enhancement of an image using DCTCS in Parallel computing environment using MATLAB," International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.17-22, 2020.

Data is the most valuable asset. As a strategy, integration is the first step towards transforming data into meaningful and valuable information. Data integration provides the ability to manipulate data transparently across multiple data sources. Healthcare sector in particular has been hindered by the diversity of the biomedical data. A framework to unify the sources of such diverse data can facilitate diagnosis and plan for treatment. According to Experian, 66% of companies lack a centralised approach to data resulting in data silos. The data integration market is expected to grow annually at the rate of 12.5% since 2018. This paper discusses the need for data integration, the challenges in implementing a data integration framework, various approaches for data integration, their strength and weakness. The research directions which act as additional add-on or improvements to the existing system have been discussed

Data integration, Big Data, Data Integration Methods

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R. Thirumahal, G. Sudha Sadasivam, "Data Integration Techniques For Healthcare ? A Comprehensive Survey," International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.23-29, 2020.

The time when the food ordering websites are filled with tons of review data regarding the quality and quantity of food. One can extract tons of conclusions while analyzing it. In this survey paper, we will study various techniques to analyze the data including algorithms like Naive Baye?s, SVM, etc. and their outcomes in the field of data mining and sentimental analysis. Sentimental analysis is a boon for the restaurant owners as they can restructure their unique selling points and services. Customers can indeed use the data to filter the restaurants according to the area, cuisines, dining time, etc. to make an opinion. Also, in the end, polarising the high-quality dataset into positive and negative vows to visualize it for the customers. Tableau the most majestic tool can help us to do the same. Working on emoticons as well as text will take us to a hard way to complete the study

Opinion Mining, Sentimental Analysis, SVM, Tableau

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[14] R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra, "A Review: Design and Development of Novel Techniques for Clustering and Classification of Data," International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.19-22, 2018

Bhanu Chugh, Sejal Arya, Mayank Pandita, Tanmay Jain, G.V. Bhole, "Survey on Sentimental Analysis and Visualization of Reviews," International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.30-33, 2020.

Data analysis and Visualization is a new and trending field in computer science. Visualizations represent a means to communicate data and analysis results. It uses graphic effects to reveal patterns, trends and relationships out of data. It involves in representation of data in pictorial or graphical form which makes the information easy to understand. Visualization and data analysis can be performed on many tools such as Tableau, Apache spark, Excel spread sheets. Visualization and data analysis using Power BI is affordable, quite simple and easy to develop and maintain as it supports real time data processing. Multiple data sources can be used to imported data for analysis into Power BI for developing custom visualizations that helps in-detail understanding of resources and budget used in development and management of a project. M-query and DAX are the functional languages used to extract insights, merge or append queries, create new measures and new custom columns for developing attractive and self-explanatory dashboards about resources in developing and maintaining projects. Visualizations and data analysis help in better prediction and forecasting for the future projects in the organization, these visualizations and dashboards can be shared on cloud based BI environment at managerial level and respective lower levels with row level security

Data Analysis, DAX, M-query, Power BI, Visualization

[1]. Stephen Few, ?Selecting the Right Graph for Your Message?, Perceptual Edge, 2004.
[2]. Manuela Aparicio and Carlos J. Costa "Data visualization". Communication Design Quarterly Review. 3 (1): pp 7?11. 2014.
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[14]. Antonio M. Rinaldi ?A Document Visualization Strategy based on Semantic Multimedia Big Data?, Springer Conference paper, University of Naples Federico II, The 16th International Symposium on Pervasive Systems, Algorithms and Networks, Napoli, Italy September 2019.

Syed Tameem Ansari, Azra Nasreen, "Data Analysis and Visualization by extracting insights for Efficient Project Planning," International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.34-38, 2020.

Within the last few years, an intense curiosity has been progressed by the people in the daily air quality circumstances to which they are encountered. Directed by the growing consciousness of the physical state of air pollution exposure, especially by most sensitive sub?populations such as children and the elderly, short?term air pollution forecasts are being accentuated progressively by local authorities. The Air Quality Index (AQI) is the value implemented to estimate the quality of the air at a certain location. The components are estimated with the implementation of the covariance of the input data matrix

Air Quality Index, Linear Regression, Scikits Learn, Seaborn plot, Heat Map, Mean Absolute Error (MAE),Mean Squared Error(MSE),Root Mean Squared Error (RMSE),Pickle

[1] https://seaborn.pydata.org/generated/seaborn.heatmap.html. Heat Map correlation and plotting
[2] https://seaborn.pydata.org/introduction.html. Implementation of Seaborn Library module
[3] https://en.tutiempo.net/climate/india.html. Dataset has been downloaded from this website and implemented for Research
[4]https://www.datacamp.com/community/tutorials/pickle-python-tutorial?utm_source=adwords_ppc&utm_campaignid=10267161064&utm_adgroupid=102842301792&utm_device=c&utm_keyword=&utm_matchtype=b&utm_network=g&utm_adpostion=&utm_creative=332602034358&utm_targetid=aud-299261629574:dsa-429603003980&utm_loc_interest_ms=&utm_loc_physical_ms=9061792&gclid=Cj0KCQjwo6D4BRDgARIsAA6uN1_TSGS3sO7w32Itj0gHy4FyQpjbTL54P0Qz-0rZ2y63NZnpT_PLFjIaAueoEALw_wcB. Imported Pickle module.
[5]https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html#:~:text=An%20extra-trees%20regressor.,accuracy%20and%20control%20over-fitting.&text=If%20int%2C%20then%20consider%20min_samples_split%20as%20the%20minimum%20number. Extra Regressor Classifier
[6] https://machinelearningmastery.com/feature-selection-machine-learning-python/Feature Selection
[7] https://towardsdatascience.com/interpreting-the-coefficients-of-linear-regression-cc31d4c6f235. Interpreting coefficients
[8] https://scikit-learn.org/stable/modules/model_evaluation.html. Regression evaluation Metric
[9] Github link:- https://github.com/Soumyajit567/Air-Quality-Index. This is my GitHub project link. The code is done in Jupyter Notebook and uploaded to GitHub.

Soumyajit Chakraborty, Koustav Guha, "Air Quality Index Prediction with the Implementation of Linear Regression - A Technical Paper," International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.39-48, 2020.

The development and management of utility networks is one of the important aspects of a city planning. The water utility network is a very important factor for any developed city. Such utility networks are complex and difficult to manage due to ever-changing data, which can be difficult to store traditionally. Storing a water utility network data in the form of spatial data with the help of GIS technology is an easy way to manage, update, and analyze such complex datasets. This paper presents how Geographical Information System has been for a web-based water utility network system (WUNS) using open source technologies for making cost-effective implementation, operation, and management of spatial information through the web. The WUNS has been developed for the Navarangpura ward, Ahmedabad. This system enables users to visualize, analyze, querying, and downloading maps of water utility network for specific requirements. The WNUS provides interactive Graphical User Interface along with basic GIS functionalities over the web for users. With using WNUS users would be able to see the accurate location of water pipeline and the overall connected assets and infrastructure also. A unique tracing functionality has been developed, showing an impact analysis to display the number of properties and population affected due to damage in a water pipeline network at a particular point/stretch. The application provides a comprehensive view of spatial and nonspatial data and can act as a decision support system for organizations, decision-makers, and city planners

GIS, WebGIS, Open source technology, water utility network, OGC, FOSS4G

[1]. Kang-Tsung Chang. Introduction to Geographic Information Systems (Ninth Edition). University of Idaho, 2018.
[2]. V.V. Sai Krishna. Web Based Water Utility Management Using Geospatial Techniques ? A Case Study of Dehradun City, India. 2014.
[3]. Lucy Mulongo Mamai, Moses Gachari, Godfrey Makokha (2017). Developing a Web-Base Water Distribution Geospatial Information System for Nairobi Northern Region. Journal of Geographic Information System. Vol.9 No.1, February, 2017. DOI: 10.4236/jgis.2017.91003.
[4]. Mahdi Kalla, Abdelhalimn Bendib, Dridi Hadda (2016). Application of WebGIS in the development of interactive interface for urban management in Batna city. Journal of Engineering and Technology Research. ISSN: 2006-9790, Vol.8(2), pp. 13-20 April 2016, DOI: 10.5897/JETR2015.0579
[5]. ArcGIS Enterprise. About WebGIS. Available:https://enterprise.arcgis.com/en/server/latest/create-web-apps/windows/about-web-gis.htm, 2019.
[6]. Rajeshkumar J. Ajwaliya, Shashikant Patel, Shashikant A. Sharma (2017). Web-GIS based Application for Utility Management System. Journal of Geomatics. Vol 11 No. 1 April 2017.
[7]. Dr. Ming-Hsiang Tsou, Jennifer Smith. Free and Open Source Software for GIS solution. National Geospatial Technology Center of Excellence - Funded in part by the National Science Foundation DUE ATE #0801893, GeoTech Center, 2011.
[8]. Satya Prakash Maurya, Anurag Ohri, Sachin Mishra (2015). Open Source GIS: A Review. National Conference on Open Source GIS: Opportunities and Challenges, At Department of Civil Engineering, IIT(BHU), Varanasi. October 9-10, 2015.
[9]. Debasish Chakraborty, Debanjan Sarkar, Shubham Agarwal, Dibyendu Dutta, Jaswant R. Sharma. Web Based GIS Application using Open Source Software for Sharing Geospatial Data. International Journal of Advanced Remote Sensing and GIS. Volume 4, Issue 1, pp. 1224-1228, 2015. Article ID Tech-452. ISSN 2320-0243.
[10]. Darshini Mahadevia, Alison Brown, Michal Lyons, Suchita Vyas, Kaushal Jajoo, Aseem Mishra. Street Vendors in Ahmedabad: Status, Contribution and Challenges. CUE (Central for Urban Equity) Working Paper. CEPT University Kasturbhai Lalbhai Campus University Road, Navrangpura Ahmedabad - 380009, India, 2013.
[11]. Ahmedabad Municipal Corporation (AMC). Available: https://ahmedabadcity.gov.in/portal/jsp/Static_pages/amc_zone_list.jsp
[12]. Census 2011. Available: https://www.census2011.co.in/census/district/188-ahmadabad.html
[13]. PostgreSQL. Available: https://www.postgresql.org/about/
[14]. PostGIS. Available: https://postgis.net/
[15]. GeoServer. Available: http://geoserver.org/
[16]. OpenLayers. Available: https://openlayers.org/
[17]. QGIS. Available: https://www.qgis.org/en/site/about/index.html
[18]. Mahdi Kalla, Abdelhalimn Bendib, Dridi Hadda (2016). Application of WebGIS in the development of interactive interface for urban management in Batna city. Journal of Engineering and Technology Research. ISSN: 2006-9790, Vol.8(2), pp. 13-20 April 2016, DOI: 10.5897/JETR2015.0579
[19]. Kauri Kiiman. Introduction of Open Source Software for GIS Education. A Nordnatur Intensive Course OpenSource GIS, GPS and Crowd Sourcing in University of Copenhagen, Skovskolen, 2013
[20]. PgRouting. Available: https://docs.pgrouting.org/3.0/en/pgRouting-introduction.html

Kirtan R. Chauhan, Shital H. Shukla, Santosh Gaikwad, "Visualization of Water Utility Network and tracing in a web GIS application based on Open Source Technology," International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.49-55, 2020.

Cloud Computing is the technology, which is growing more and more in the area of information technology. It is a big platform to deliver services to user. It provides with the benefit of storage, configuration, resources and sharing and all this is possible in cloud environment. Data is outsourced by the owner to store it on cloud because they have to serve there user at every possible stage. Presented work establishes security approach to secure and enhance the security of data at every step. Purpose of proposed work is to prevent from intruders and attackers and from sniffing messages. Proposed work is implemented using hybrid system (RC6+AES) and MD5 to calculate integrity. Using essential measurements like integrity, confidentiality, encryption, authentication, and authorization security in achieved in presented work

Data security; AES; RC6; MD5; cloud computing

[1] C Akshita Bhandari, Ashutosh Gupta, Debasis D, ?A framework for Data Security and Storage in Cloud Computing?, International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), pp. 1-7, 2016
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[4] C. Y. Chen and J. F. Tu2, ?A Novel Cloud Computing Algorithm of Security and Privacy?, Hindawi Publishing Corporation: Mathematical Problems in Engineering, 2013.
[5] G. L. Prakash, M. Prateek and I. Singh, ?Data Encryption and Decryption Algorithms using Key Rotations for Data Security in Cloud System?, International Journal Of Engineering And Computer Science, vol. 3, issue 4, pp. 5215-5223, April 2014.
[6] ChorB,GilboaN,Naor M, ?Private Information Retrieval by Keywords?, Report 98-03, Theory of Cryptography Library, 1998.
[7] Arora, Rachna, Anshu Parashar, ?Secure user data in cloud computing using encryption algorithms?, International Journal of Engineering Research and Applications, Vol. 3, pp.1922-1926, 2013.
[8] Wang, Cong, ?Privacy-preserving public auditing for secure cloud storage?, Computers, IEEE Transactions on Vol 62.2, pp 362-375, 2013.
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[10] F. F. Moghaddam, M. T. Alrashdan and O. Karimi, ?A Hybrid Encryption Algorithm Based on RSA Small-e and Efficient-RSA for Cloud Computing Environments?, Journal of Advances in Computer Network, vol. 1, No. 3, Sep. 2013.

Rupal Yadav, Kaptan Singh, Amit saxena, "Hybrid Cryptographic Solution to Overcome Drawbacks of RSA in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.8, Issue.8, pp.56-60, 2020.