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IDS is used to detect any kinds of attacks that may harm the safety of systems. A capable IDS system needs low FAR, and high accuracy. In this paper, we have used fully distinct DM approaches on IDS with the KDD data set. Here the BGA which offers a new method used for fixing normal & DOS.

Intrusion Detection System, Binary Genetic Algorithm(BGA), Classifiers, Anomaly Detection

[1] R. Kaur and M. Bansal, "Multidimensional attacks classification based on genetic algorithm and SVM," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 561-565.
[2] R. Samrin and D. Vasumathi, "Review on anomaly based network intrusion detection system," 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp. 141-147
[3] A. R. Jakhale, "Design of anomaly packet detection framework by data mining algorithm for network flow," 2017 International Conference on Computational Intelligence in Data Science (ICC IDS), Chennai, 2017, pp. 1-6.
[4] A. Sultana, and M.A. Jabbar, “Intelligent network intrusion detection system using data mining techniques,” In the Proceedings of 2ndInternational Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 329-333, 2016.
[5] H. Om and A. Kundu, "A hybrid system for reducing the false alarm rate of anomaly intrusion detection system," 2012 1st International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, 2012, pp. 131-136.
[6] L. Li, D. Z. Yang and F. C. Shen,” A novel rule-based Intrusion Detection System using data mining", Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on. Vol. 6. IEEE, pp. 169-172, July 2010.
[7] K. Goeschel,” Reducing false positives in intrusion detection systems using data-mining techniques utilizing support vector 2016 2nd International Conference on Next Generation Computing Technologies (NGCT-2016) Dehradun, India 14-16 October 2016 564 machines, decision trees, and naive Bayes for off-line analysis", SoutheastCon 2016 IEEE, pp. 1-6, Mar 2016
[8] N. Shahadat, I. Hossain, A. Rohman and N. Matin, "Experimental analysis of data mining application for intrusion detection with feature reduction," 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox`s Bazar, 2017, pp. 209-216.
[9] I. S. Thaseen and C. A. Kumar, "Intrusion detection model using a fusion of PCA and optimized SVM," 2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysore, 2014, pp. 879-884.
[10] W. Lee and S. J. Stolfo. “Datamining approaches for intrusion detection,” In Proceedings of the 7th USENIX Security Symposium, San Antonio, TX, January 1998.
[11] http://kdd.ics.uci.edu/databases/kddcup99/task.html

[12] M. Ektefa, S. Memar, F. Sidi and L. S. Affendey, "Intrusion detection using data mining techniques," 2010 International Conference on Information Retrieval & Knowledge Management (CAMP), Shah Alam, Selangor, 2010, pp. 200-203
[13] D.Srivastava, L.Bhambu.Data Classification using support vector machine.Journal of Theoretical Applied Information Technology, Vol.12 (1), pp.1-7, 2010.

S. Rani, "Classification of Attack for IDS Using Binary Genetic Algorithm Based Feature Selection," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.203-208, 2018.

Twitter is one of the most used social networking sites where millions of people give their opinions about various subjects. Thus it can be treated as one of the largest and most updated psychological database. Analysis can be performed on this data to gain valuable insights. The goal of this paper is to study the correlation between public opinion about the Cryptocurrency, Bitcoin and the trajectory of its price graph. The results can later be used to develop a system for algorithmic trading of Bitcoin. This is done by collecting tweets on bitcoin and performing sentimental analysis on it. The tweets are labelled positive or negative. Supervised machine learning algorithms are used to see how sentiments of tweets play a role in bitcoin market movement. A positive sentiment and an increase in the price of bitcoin at the same time will indicate selling as favourable and vice versa in case of a negative polarity of tweets.

Twitter, opinions, sentimental analysis, bitcoin, machine learning algorithms

S.Colianni, S.Rosales, M.Signorotti , “Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis”, Department of Computer Science Project, Stanford University, 2015.
[2] E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne. "Finding high-quality content in social media". in Proceedings of the international conference on Web search and web data mining. 2008. ACM.
[3] H.K. Sul, A.R. Dennis, and L.I. Yuan, “Trading on twitter: Using social media sentiment to predict stock returns” Decision Sciences, 2016.
[4] C. Nanda , M. Dua, “A Survey on Sentiment Analysis”, International Journal of Scientific Research in Computer Science and Engineering (0975 – 8887) Vol.5(2), April(2018), E-ISSN: 2320-7639
[5] S .Bird, E. Klein,E. Loper.”Natural Language Processing with Python– Analyzing Text with the Natural Language Toolkit” ,O’Reilly Media Inc, USA,pp 39-250,2009.
[6] Mamatha M, Thriveni J , K.R.Venugopal, “Techniques of Sentiment Classification, Emotion Detection, Feature Extraction and Sentiment Analysis ”, International Journal of Computer Sciences and Engineering Vol.6(1), Jan 2018, E-ISSN: 2347-2693
[7] B.M. Jadav ,V.B. Vaghela, “Sentiment Analysis using Support Vector Machine based on Feature Selection and Semantic Analysis”, International Journal of Computer Applications (0975 – 8887) Volume 146 – No.13, July 2016.
[8] R.Garreta , G.Moncecchi , “Learning Scikit-learn: Machine Learning in Python” PACKT publishing , India, pp 24-96 , 2013 [9] E.Stenqvist, J. Lonno, “Predicting Bitcoin price fluctuation with Twitter sentiment analysis”, DEGREE PROJECT IN TECHNOLOGY, FIRST CYCLE

S.R. Chheda, A.K.Singh, P.S. Singh, A.S. Bhole, "Automated Trading of Cryptocurrency Using Twitter Sentimental Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.209-214, 2018.

Cloud computing is a technology where user can store their data and any application software on it. With the use of this technology user need not to worry about the cost of hardware installation and their maintenance cost. Hence, the cloud computing security becomes the current research focus. To secure the cloud data, Encryption Scheme is used. Different encryption techniques used for security purpose like Fully Disk Encryption and Fully Homomorphic Encryption. FDE encrypts the entire disk and FHE encrypts the particular functions. FHE is used to secure the cloud data from exploitation during the computation. In this research paper, FHE encryption scheme is optimized in which the Encryption Time, Probability of Attacks and Space Used by encrypted data is reduced using RSA and Diffie- Hellman Algorithm. RSA Algorithm is applied to improve performance of FHE schemes. The Diffie- Hellman algorithm is also applied for the secure channel establishment in the fully homomorphic encryption It has been analyzed that RSA algorithm based FHE performs well as compared to Diffie- Hellman algorithm.

Cloud Computing, Diffie Hellman Algorithm, RSA Algorithm, Fully Homomorphic Encryption, Security

[1] http://www.google.com/sans.org/rr/encryption/algorithm.php the Diffie-Hellman Algorithm Riley Lochridge April 11, 2003
[2] Craig Gentry, 2009,”full homomorphic encryption scheme”
[3] John Harauz ,Lori M. Kaufman,Bruce Potter,” Data Security in the World of Cloud Computing ” IEEE Security and Privacy July 2009. pp. 61-64
[4] van Dijk, M., Gentry, C., Halevi, S., Vaikuntanathan, V (2010) Fully homomorphic encryption over the integers. In Gilbert, H., ed.: EUROCRYPT. Volume 6110 of Lecture Notes in Computer Science., Springer
[5] Geethu Thomas, 2010,"Cloud Computing security using Encryption Technique"
[6] Sean Carlin, Kevin Curran, 2011 “Cloud Computing Security” International Journal of Ambient Computing and Intelligence, pp 14-19
[7] Shui Han, Jianchuan Xing, 2011 “Ensuring Data Storage Through A Novel Third Party Auditor Scheme in Cloud Computing” IEEE computer science & Technology, pp 264-268
[8] Dawn Song, Elaine Shi, 2012 “Cloud Data Protection for the Masses” IEEE Computer Society, pp 39-45
[9] Deyan Chen, Hong Zhao, 2012” Data Security and Privacy Protection Issues in Cloud Computing” International Conference on Computer Science and Electronics Engineering, pp 647-651
[10] Deepanchakaravarthi Purushothaman1 and Dr.Sunitha Abburu2 ,2012” An Approach for Data Storage Security in Cloud Computing” IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 1.
[11] Dian-Yuan Han, Feng-qing Zhang, 2012 “Applying Agents to the Data Security in Cloud Computing” International Conference on Computer Science and Information Processing(CSIP), pp 1126-1128
[12] Dr Nashaat el-Khameesy,Hossam Abdel Rahman, 2012 “A Proposed Model for Enhancing Data Storage Security in Cloud Computing Systems” vol-3
[13] Simarjeet Kaur, 2012 “VSRD-IJCSIT, Vol. 2 (3), 2012, 242-249. Cryptography and Encryption In Cloud Computing, pp 242-249
[14] Young-Gi Min, Hyo-Jin Shin and Young-Hwan Bang, 2012 “Cloud Computing Security Issues and Access Control Solutions” Journal of Security Engineering, pp 135-140
[15] Yu, Z., Wang, C., Thomborson, C., Wang, J., Lian, S., & Vasilakos, A. V. (2012). A novel watermarking method for software protection in the cloud. Software: Practice and Experience, 42(4), 409-430.
[16] Ankur Mishra, Ruchita Mathur, Shishir Jain, Jitendra Singh Rathore, 2013 “Cloud Computing Security” International Journal on Recent and Innovation Trends in Computing and Computation, pp 36-39
[17] Bhavna Makhija, Vinit Kumar Gupta, 2013 “Enhanced Data Security in Cloud Computing with Third Party Auditor”, International Journal of Advanced Research in Computer Science and Software Engineering, pp 341-345
[18] Barron, C., Yu, H., & Zhan, J., 2013 “Cloud Computing Security Case Studies and Research”. Proceedings of the World Congress on Engineering 2013 Vol II
[19] Sanjoli Singla, Jasmeet Singh, 2013 “Cloud Data Security using Authentication and Encryption Technique” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 7, July 2013, pp 2232-2235
[20] Punithasurya K, Esther Daniel, Dr. N. A. Vasanthi, 2013 “A Novel Role Based Cross Domain Access Control Scheme for Cloud Storage” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 3, March 2013, pp 942-946
[21] Vimmi Pandey, 2013 “Securing the Cloud Environment Using OTP” International Journal of Scientific Research in Computer Science and Engineering vol-1, Issue-4
[22] Anjana Chaudhary, Ravinder Thakur, Manish Mann, 2014 "Security in Cloud Computing by using Homomorphic Encryption Scheme with Diffie-Hellman Algorithm "International Journal of Advanced Computational Engineering and Networking Volume-2, Issue-10, Oct-2014, pp 2320-2106
[23] Peidong Sha, Zhixiang Zhu, 2016 "The Modification of RSA algorithm to Adapt Fully Homomorphic Encryption Algorithm in Cloud Computing" IEEE CCIS2016
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Aparna Negi, Anshika Goyal, "Optimizing Fully Homomorphic Encryption Algorithm using RSA and Diffie- Hellman Approach in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.215-220, 2018.

Software cost estimation is very important in software project management.a major cause of failure of many software projects is the lack of accurate and early estimation. However, irrespective of great deal of importance estimating the time and development cost accurately is still a challenge in software industry. It is used to predict the effort and time need to complete the project. The need of optimization comes in various approaches like genetic algorithm of COCOMO MODEL II for providing better effort estimates and reliability.

Genetic Algorithm, Optimization, Evolutionary Algorithms

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[3] S. A. Alaa F. Sheta, “Software effort estimation inspired by cocomo and fp models: A fuzzy logic approach,” International Journal of Advanced Computer Science and Applications, vol. 4, no. 11, pp. 192–197, 2013.
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[27] S. H. Yakhchali and S. H. Ghodsypour. (2008). A Hybrid Genetic Algorithms for Computing the Float of an Activity in Networks withImprecise Durations, Proceedings of the IEEE International Conference on Fuzzy Systems, pp.1789-1794.

Arfiha Khatoon, Rupinder Kaur, "Optimization Estimation Parameters of COCOMO Model II Through Genetic Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.221-226, 2018.

Fingerprint is a popular biometric trait which is used extensively in several applications for person authentication, providing high uniqueness and acceptable performance. The goal of any fingerprint representation is to capture as much of the unique information available in a fingerprint while discarding the variations between multiple impressions of the same finger. In this paper, we propose a new alignment-free fingerprint representation using the quadrilateral structure. We construct the feature set from the quadrilateral structure. The constructed feature set is quantised and mapped to a pre-defined 3D array. By sequentially visiting the cells of the 3D array, a fixed length 1D bit string is generated. This bit string is applied with DFT to generate a complex vector and the complex vector is multiplied with a random matrix generated by user’s key, to generate a final cancellable template. The proposed method FS_QUAD is tested on FVC 2002 databases and results show satisfactory performance.

Fingerprint, representation, quadrilateral, cancellable template

[1] A.K. Jain, A. Ross, S. Prabhakar, “An introduction to biometric recognition”, IEEE Transactions on Circuits System for Video Technology, Vol.14, Issue.1, pp. 4–20, 2004.
[2] D. Maltoni, D. Maio, A.K. Jain, S.Prabhakar, “Handbook of Fingerprint Recognition”, Springer, 2009.
[3] N. Ratha, S. Chikkerur, J. Connell, R.M. Bolle, “Generating cancelable fingerprint templates”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.29, Issue.4, pp.561-572, 2007.
[4] A.K. Jain, K. Nandakumar, A. Nagar, “Biometric template security”, EURASIP Journal on Advances in Signal Processing archive, Vol.2008, pp. 1 –17, 2008.
[5] U. Uludag, S. Pankanti, S. Prabhakar, A.K. Jain, “Biometric Cryptosystems: Issues and Challenges,” Vol.92, Issue.6, pp.948–960, 2004.
[6] C. Moujahdi, G. Bebis, S. Ghouzali, M. Rziza, “Fingerprint shell: secure representation of fingerprint template”, Pattern Recognition Letters, Vol.45, pp. 189 –196, 2014.
[7] S. Wang, J. Hu, “Alignment-free cancelable fingerprint template design: a densely infinite-to-one mapping (DITOM) approach”, Pattern Recognition, Vol.45, Issue.12, pp. 4129 –4137, 2012
[8] M.V.N.K. Prasad, C. Santhosh Kumar, “Fingerprint template protection using multiline neighboring relation”, Expert Systems with Applications, Vol.41, Issue.14, pp. 6114 –6122, 2014.
[9] C. Lee, J. Kim, “Cancelable fingerprint templates using minutiae-based bit-strings”, Journal of Network and Computer Applications, Vol.33, Issue.3, pp. 236 –246, 2010.
[10] Z. Jin, A.B.J. Teoh, T.S. Ong, C, Tee, “Fingerprint template protection with minutiae-based bit-string for security and privacy preserving”, Expert Systems with Applications, Vol.39, Issue.6, pp.6157–6167, 2012.
[11] W. Yang, J. Hu, S. Wang, “A Delaunay quadrangle-based fingerprint authentication system with template protection using topology code for local registration and security enhancement”, IEEE Transactions on Information Forensics and Security, Vol.9, Issue.7, pp. 1179 –1192, 2014.
[12] M. Sandhya, M.V.N.K. Prasad, “k-Nearest Neighborhood Structure (k-NNS) based alignment-free method for fingerprint template protection”. In the Proceedings of the 2015 International Conference On Biometrics (ICB), Thailand, pp.386–393, 2015.
[13] W. Yang, J. Hu, S. Wang, M. Stojmenovic, “An alignment-free finger-print bio-cryptosystem based on modified Voronoi neighbor structures”, Pattern Recognition, Vol.47, Issue.3, pp.1309–1320, 2014.
[14] J. Zhe, A.T.B. Jin, “Fingerprint template protection with minutia vicinity decomposition”, In the Proceedings of 2011 International Joint Conference on Biometrics (IJCB 2011), USA, pp.1-7, 2011.
[15] M. Sandhya, M.V.N.K. Prasad, R.R. Chillarige, “Generating cancellable Fingerprint templates based on Delaunay triangle feature set construction”, IET Biometrics, Vol.5, Issue.2, pp. 131–139, 2016.
[16] A. Vij, A. Namboodiri,“Learning Minutiae Neighborhoods: A New Binary Representation for Matching Fingerprints”, In the Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, USA, pp.64-69, 2014.
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[18] “VeriFinger SDK”, Neurotechnology, 2017.

L. Menaria, K. Jain, "A Fingerprint Representation Technique based on Minutiae Quadrilateral Structure," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.227-233, 2018.

The cloud computing is practice of using a network of remote servers hosted on the internet to store, manage, and process data, rather than a local server or personal computer, it builds on the idea of using a remote server or infrastructure as a software service. The data that is stored in centralized virtual machine known as cloud and it can be managed by an end user using services provided. Cloud Service Provider (CSP) are designed for giving users the services based on their demands and are to be paid for being served. As there is large number of requests so the load increases, hence a Load balancing is done to reduce the energy consumption and maximize resource utilization. In this paper, the work is based on the task migration when virtual machine gets overloaded at the time of cloudlet execution. The CSPs are responsible to assign tasks to the most appropriate virtual machine for the execution. When any of the virtual machine gets overloaded, the task is migrated from one virtual machine to another or it could be queued which can be decided by the threshold and check pointing algorithm by minimizing the processing time, energy and resource consumption.

Load Balancing, Virtual Machine Migration, Threshold Algorithm, Check Pointing Algorithm

[1] Sambit Kumar Mishra, Md Akram Khan, Bibhudatta Sahoo, Deepak Puthal and Mohammad S.Obaidat, “Time efficient dynamic Threshold-based load balancing technique for cloud computing”, 2017.
[2] N.R Rejinpaul, Maria Michael Visuwasam, “Checkpoint-based intelligent fault tolerance for cloud service providers”,2012.
[3] Vouk A.Mladen (2008), “Cloud Computing- Issues, Research and Implementations”, Journal of Computing and Information Technology, pp. 235-246.
[4] Randles, M.; Lamb, D.; Taleb-Bendiab, A., "A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing," Advanced Information Networking and Applications Workshops (WAINA), 2010 IEEE 24th International Conference on , vol., no., pp.551,556, 20-23 April 2010.
[5] Fang, Yiqiu, Fei Wang, and Junwei Ge. "A task scheduling algorithm based on load balancing in cloud computing." Web Information Systems and Mining. Springer Berlin Heidelberg, 2010. 271-277.
[6] Chaczko, Zenon, et al. "Availability and load balancing in cloud computing." International Conference on Computer and Software Modeling, Singapore. Vol. 14. 2011.
[7] Cloud Computing Bible. (2011)Wiley Publishing, Inc., Indianapolis, Indiana, p25.
[8] Shui Han, Jianchuan Xing, 2011 “Ensuring Data Storage Through A Novel Third Party Auditor Scheme in Cloud Computing” IEEE computer science & Technology, pp 264-268.
[9] Geo Jerry, Bai Xiaoying and Tsai T. Wei (2011) “Cloud Testing-Issues,challenges,Needs and Practice” ,Software engineering and international journal(SEIJ), pp. 09-23.
[10] Sean Carlin, Kevin Curran “Cloud Computing Security” International Journal of Ambient Computing and Intelligence, pp 14-19, 2011.
[11] Peter Mell, Timothy Grance , “The NIST Definition of Cloud Computing”, Recommendations of the National Institute of Standards and Technology, September 2011, Special Publication 800-145.
[12] Srivastava k.Abhinav,Yadav K.Divya (2012) “Taas:An Evolution of Testing Services using Cloud Computing”, International journal of advanced research in computer engineering& technology, pp. 42-49.
[13] Dr Nashaat el-Khameesy,Hossam Abdel Rahman, 2012 “A Proposed Model for Enhancing Data Storage Security in Cloud Computing Systems” vol-3.
[14] Arora Pankaj, Wadhawan C.Rubal, Er.Ahuja P.Satinder, 2012, “Cloud Computing Security Issue in Infrastructure as a Service”, International Journal of Advance Research in Computer Science and Software Engineering.
[15] Habib, S. M., Hauke, S., & Ries, S. (2012) “Trust as a facilitator in cloud computing: a survey”, Journal of Cloud Computing , 01-18.
[16] Dawn Song, Elaine Shi, 2012 “Cloud Data Protection for the Masses” IEEE Computer Society, pp 39-45.
[17] Deyan Chen, Hong Zhao, 2012” Data Security and Privacy Protection Issues in Cloud Computing” International Conference on Computer Science and Electronics Engineering, pp 647-651.
[18] Kaur, Rajwinder, and Pawan Luthra. "Load Balancing in Cloud Computing." Proceedings of International Conference on Recent Trends in Information, Telecommunication and Computing, ITC. 2012.
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Sheetal Karki, Anshika Goyal, "Performance Evaluation of Check Pointing and Threshold Algorithm for Load Balancing in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.234-240, 2018.

The Artificial Neural Networks (ANNs) has proved their significance to perform well in the fields of data-mining and machine learning like classification, pattern recognition, forecasting and prediction to have a few of them. This paper explores a novel approach for classification of data on four benchmark datasets from the perspective of ANNs and its intricacies. The proposed approach is successful in overcoming the drawback of over-fitting of data exists in the classification domain. Further, the proposed methodology reflects very improved and consistent results in comparison to existing techniques available in the ANNs as well as non-ANN domain.

Classification, Artificial Neural Network, Machine Learning, N-Fold Cross Validation, Transfer function.

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Kshitij Tripathi, Rajendra G. Vyas, Anil K. Gupta, "The Classification of Data: A Novel Artificial Neural Network (ANN) Approach through Exhaustive Validation and Weight Initialization," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.241-254, 2018.

The present study area is lying in the drought-prone zone of North Ahmednagar district, where the Bhandardara dam is main source of water for drinking, agriculture, and industries. Last 10 to 15 years mainly because of an extra share of water is taking by industries and urban centers, numbers of villages are facing water scarcity problem.GIS a powerful technique can be utilized to determine the water balance in watersheds. Therefore, in present study emphasis has been given to the scientific investigation of water balance within eight watersheds covering areas of three tehsils namely, Rahata, Shrirampur and Nevasa using GIS technique. The result indicates that total demand for water is 195.52 MCL, whereas water available is 1253.30 MCL consequently, 1057.77 MCL water is surplus in the entire study area. Though the 1057.77 MCL water is surplus in all eight sub-watersheds, water scarcity is increasing day by day. The result clearly shows that miss management of water in this area is the main cause of water scarcity hence, it needs to the hour that to focus on water management with storing the surplus water and its proper utilization. The modification of agricultural practices and changes in irrigation schemes are the most crucial steps should be taken to eliminate the miss management of water. With the help of GIS technique, it is possible to tackle the water scarcity problem in watersheds.

Water Scarcity, Watershed, Water balance, GIS

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S. P. Cholke, "Applications of Geographical Information System (GIS) in Assessment of Water Balance in Watersheds," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.255-259, 2018.

Considering the economics of cooking fuel used by Below Poverty Household in India, the biggest challenge in transition of such household from conventional cooking fuel to clean and modern cooking fuel like LPG is unavailability of price fragmentation and Pay-As-Use features while purchasing LPG. Recent development in the field of Internet of Things has proven stories where assets can be tracked, monitored and controlled remotely. There is huge scope as well as requirement for innovative idea that can bring the principle of mechanical engineering, electrical engineering and data analytics together to achieve the feature of price fragmentation for LPG uses. This paper presents system and solution for tracking real time LPG consumption and inventory data by using Internet of Things (IoT), connecting the different stakeholder of LPG supply chain, analysis and computation of data to automate LPG cylinder booking and delivery practice, make the LPG supply chain transparent at each level and use the data tracking and analysis system to provide “Pay-As-You-Use (PAYU) apparatus to the LPG consumer in general and financially marginal consumers in particular.

LPG, Supply-Chain, IoT, Pay As You Use, Price Fragmentation, Clean Cooking

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Vishal C. Khan, P Nitheesh Kumar, Mohinish Chatterjee, Abhishek Kumar, and S. K. Singh, "System of and Solution for Pay-As-You-Use (PAYU) and Automation of LPG Cylinder Supply Chain through Internet of Things Based Real Time Uses and Inventory Data Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.260-269, 2018.

Grid Computing is a variant of distributed system wherein the small scale computational units are aggregated to develop a large computational machine to support complex computational problems. It poses a number of challenges for dynamic load balancing due to a large number of heterogeneous resources and the size of data to be moved among them thereby causing a number of issues to handle effectively. Further, load balancing problem in heterogeneous distributed computer systems is a NP-Hard problem. Therefore, researchers are constantly devising innovative approaches to optimize the load balancing in grid environment. In this paper, two algorithms for dynamic load balancing in computational grid viz. DLBCGBH - H & DLBCGBH – D are being described. These algorithms have already been implemented by the authors using GridSim 4.0 along with the comparison of the performance with the Built-in Space Shared utility of GridSim 4.0 for various performance metrics viz. Average Consumed Time, Average Waiting Time, Average Processing Cost and Number of Tasks Migrated.

Grid Computing, Dynamic Load Balancing, Space Shared, Hierarchical Grid, Distributed Grid, Binary Heaps

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A. Kumar, H.Pathak, "Dynamic Load Balancing for Computational Grids using Binary Heaps (DLBCGBH – H / D)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.270-277, 2018.