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In the recent years, Dream 11, a fantasy sports platform has taken the Indian gaming landscape into storm by raking in a valuation of 1 million USD. One of the important aspects of participating in a Dream 11 contest is team selection. Though Dream 11 hosts fantasy Cricket, Kabbadi, Football and Basketball in its platform, Fantasy Cricket has gained more users due to its popularity in India. Moreover, Cricket is one such sport that generates large volumes of data, and therefore provides many opportunities for data analysis. A Dream 11 user needs to select the right mix of players to maximize his/her points, and thereby win some cash rewards. The paper describes a retrospective approach to team selection using the real-world data collected from Player performances in the last 10 matches, to propose a Dream 11 Fantasy team for the upcoming match. The technique used is Integer Programming, implemented using the Gurobi library in Python. The team selection problem has also been analyzed through the lens of Markowitz Optimization, which is mostly used to select stocks in a financial portfolio. The concept of risk aversion has been applied to penalize inconsistent performances, as risk taking and risk averse users might want to bet on different odds for the same match.

Integer Programming, Binary Optimisation, Team Selection, Cricket

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[9] Farhana Siddiqui, Hasan Phudinawala, Chetan Davale, Soham Pawar,” Innovative Idea for Playerelection usingSupport Vector Machine(SVM)” International Journal of Computer Sciences and Engineering, Vol. 7, Issue-4, pp.841-843, 2019
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[14] Nihar Patel, Mrudang Pandya,” IPL Player’s Performance Prediction” International Journal of Computer Sciences and Engineering, Vol. 7, Issue-5, pp.478-481, 2019
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Saurav Singla, Swapna Samir Shukla, "Integer Optimisation for Dream 11 Cricket Team Selection," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.1-6, 2020.

Cloud computing is a recent evolution in computer technology. A cloud server has to deal with a lot of workload. So, the process of distributing load over the virtual machines or among the servers in a distributed cloud system is very important to get better performance. Before launching a cloud based application or a new load balancing policy in the cloud server, it is very difficult to know whether the performance will be good or not. Because testing an application or load balancing policy’s performance in Cloud for different workload in a repeatable manner under varying system and user configurations and requirements are very complex and expensive to achieve. So simulating the performance of an application or a load balancing policy is a good alternate. This paper is about simulating some existing load balancing approaches as well as a proposed improved load balancing approach and the comparison of the performance using CloudSim simulator.

Cloud computing, workload, load balancing, simulation, CloudSim

[1] M. Armbrust, A. Fox, R. Griffith and A. Joseph, “Above the clouds: A Berkeley view of cloud computing,” Univ. California, Berkeley, Tech. Rep. UCB, pp. 07–013, 2009.
[2] Rajkumar Buyya, Rajiv Ranjan and Rodrigo N. Calheiros, “Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities”, In the Proceedings of the 2009 International Conference on High Performance Computing & Simulation, Leipzig, Germany, 2009.
[3] Soumya Ray and Ajanta De Sarkar, “Execution analysis of load balancing algorithms in cloud computing environment”, IJCCSA, Vol.2, No.5, October 2012
[4] A. K. R. Parveen Kumar, “An overview and survey of various cloud simulation tools,” in Journal of Global Research in Computer Science, vol. 5, no. 1, January 2014.
[5] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, “ CloudSim : A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms”, Software: Practice and Experience, 41(1): 2350, Wiley, January 2011
[6] D. R. M. P. Jain;, “Study and comparison of various cloud simulators available in the cloud computing,” in International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 9, September 2013.
[7] Soumya Ranjan Jena, Sudarshan Padhy, Balendra Kumar Garg, “Performance Evaluation of Load Balancing Algorithms on Cloud Data Centers”, International Journal of Scientific & Engineering Research, Volume 5, Issue 3, March-2014
[8] Jasmin James and Dr. Bhupendra Verma, “Efficient VM load balancing algorithm for a cloud computing environment”, International Journal on Computer Science and Engineering (IJCSE), 2012.
[9] Srinivas Sethi, Anupama Sahu and Suvendu Kumar Jena, “Efficient load balancing in cloud computing using Fuzzy logic “, IOSRJEN, ISSN: 2250-3021 Volume 2, Issue 7, 2012.
[10] M. Randles, D. Lamb, and A. Taleb-Bendiab, “A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing”, Proceedings of 24th IEEE International Conference on Advanced Information Networking and Applications Workshops, Perth, Australia, April 2010.
[11] Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose and Rajkumar Buyya, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms.” Published online 24 August 2010 in Wiley Online Library (wileyonlinelibrary.com).

Rafat Khan, "A Simulative Study on the Performance of Load Balancing Techniques Over Varying Cloud Infrastructure Using Cloudsim," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.7-13, 2020.

With the rapid growth of usage of online social media platforms in daily life there has also been an increase in opinion mining or sentiment analysis to extract the user’s sentiments or views towards any topic. Twitter’s data or tweets has been the focus point among the researchers as it provides abundant data and in a wide variety of fields. While most of the study in this field has been in the extraction of polarity scores of the sentiments namely positive negative and neutral in a tweet, this paper focuses on extracting the real sentiments such as love, hate, worry, sadness and more out of the tweets. This paper proposes different machine learning and deep learning techniques such as Random Forest, Bi-directional LSTM, BERT and more to present a comparative analysis of the performance of different techniques and extract the sentiments with high accuracy. Tweets have been collected from the Crowdflower dataset and experimental findings reveal that the methodology comprising BERT produces the maximum accuracy followed by the methodology that comprises bi-directional LSTM and then the rest of the model follows.

Sentiment Analysis, BERT, Bi-directional LSTM, Multi-class Classification, Random Forest, GloVe

[1] M. Bouazizi, T. Ohtsuki, “A pattern-based approach for multi-class sentiment analysis in Twitter”, IEEE Access, vol. 5, pp. 20617-20639, 2017.
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[3] M.M. Madbouly, S.M. Darwish, R. Essameldin, “Modified fuzzy sentiment analysis approach based on user ranking suitable for online social networks”, IET software, Vol. 14, Issue 3, pp. 300-307, 2020.
[4] M. Bibi, W. Aziz, M. Almaraashi, I.H. Khan, M.S.A. Nadeem, N. Habib, “A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis”, IEEE Access, Vol. 8, pp. 68580-68592, 2020.
[5] S.E. Saad, J. Yang, “Twitter sentiment analysis based on ordinal regression”, IEEE Access, Vol. 7, pp. 163677-163685, 2019.
[6] H. Rehioui, A. Idrissi, "New Clustering Algorithms for Twitter Sentiment Analysis", IEEE Systems Journal, Vol. 14, Issue 1, pp. 530-537, 2019.
[7] L. Wang, J. Niu, S. Yu, "SentiDiff: Combining textual information and sentiment diffusion patterns for Twitter sentiment analysis", IEEE Transactions on Knowledge and Data Engineering, Vol. 32, Issue 10, pp. 2026-2039, 2020.
[8] Z. Jianqiang, G. Xiaolin, Z. Xuejun, “Deep convolution neural networks for twitter sentiment analysis”, IEEE Access, Vol. 6, pp. 23253 - 23260, 2018.
[9] A.C. Pandey, D.S. Rajpoot, M. Saraswat, “Twitter sentiment analysis using hybrid cuckoo search method”, Information Processing & Management, Vol. 53, Issue 4, pp.764-779, 2017.
[10] U. Naseem, I. Razzak, K. Musial, M. Imran, "Transformer based deep intelligent contextual embedding for twitter sentiment analysis", Future Generation Computer Systems, Vol. 113, pp. 58-69, 2020.
[11] Y. AI. Amrani, M. Lazaar, KE. EI. Kadiri, “Random forest and support vector machine based hybrid approach to sentiment analysis", Procedia Computer Science, Vol. 127, pp. 511-520.
[12] J. Wang, L.C. Yu, K.R. Lai, X. Zhang, “Dimensional sentiment analysis using a regional CNN-LSTM model”, In the Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Vol. 2: short papers, pp. 225-230, 2016.
[13] Y. Zhang, J. Wang, X. Zhang, “YNU-HPCC at SemEval-2018 Task 1: BiLSTM with attention based sentiment analysis for affect in tweets", In the Proceedings of the 12th International Workshop on Semantic Evaluation, China, pp. 273-278, 2018.
[14] A.A. Sharfuddin, M.N. Tihami, M.S. Islam, “A deep recurrent neural network with bilstm model for sentiment classification”, In the Proceedings of the 2018 IEEE International Conference on Bangla Speech and Language Processing (ICBSLP), Sylhet, Bangladesh, pp. 1-4, 2018.
[15] M. Jabreel, A. Moreno, “Target-dependent Sentiment Analysis of Tweets using a Bi-directional Gated Recurrent Unit”, In the 13th International Conference on Web Information Systems and Technologies (WEBIST), pp. 80-87, 2017.
[16] M. Munikar, S. Shakya, and A. Shrestha. "Fine-grained sentiment classification using bert." In the Proceedings of the 2019 IEEE International Conference on Artificial Intelligence for Transforming Business and Society (AITB), Kathmandu, Nepal, pp. 1-5, Vol. 1, 2019.
[17] D. G. Aggarwal, “Sentiment Analysis: An insight into Techniques, Application and Challenges”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp. 697-703, 2018.
[18] M.G. Huddar, S.S. Sannakki, V.S. Rajpurohit, “A Survey of Computational Approaches and Challenges in Multimodal Sentiment Analysis”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp. 876-883, 2019.

Saurav Singla, Vikash Kumar, "Multi-Class Sentiment Classification using Machine Learning and Deep Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.14-20, 2020.

Due to the rapid growth in technology, the development and use of cryptosystems has become plays an important role in networked and distributed applications. To get the benefits of such applications, the principals will cooperate by exchanging information over an open networks such as the internet. A key distribution protocol is an essential component of any cryptosystem to generation and sharing of cryptographic keys between the principals involved in the network securely. In the current days, there are a number of key distribution protocols that have been developed and implemented. However, the most of such protocols were found to be prone to several attacks a long time after deployment. In this paper, the key distribution protocol is designed to improve the Nomaskd protocol. The two protocols are analyzed and verified by a formal verification tool called Scyther, the verification results show that the Nomaskd protocol does not fulfill the strong authentication goals, whereas the improved protocol fulfill these goals.

Key distribution, Formal verification, Symmetric key cryptography, Nomaskd protocol, Scyther tool

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[2] A. Aasarmya and S. Agarwal, "Improving Security for Data Migration in Cloud Computing using Randomized Encryption Technique", International Journal of Computer Sciences and Engineering,Vol.7, Issue.8, pp.39-43, 2019.
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Yasser Ali Alahmadi, Saleh Noman Alassali, "An Improved Key Distribution Protocol Using Symmetric Key Cryptography," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.21-26, 2020.

The Human Activity Recognition using the Signal produced by the Sensors have a number of applications in field of the fitness and health. The Human activities are recorded with the help of the various types of the sensor embedded in a wearable device or in a smartphone. There are many research works have been done for the Human Activity Recognition using the machine-learning as well as deep-learning models, but there is requirement to find out that which model is more efficient for a specific dataset, for which the comparative study of the model comes in mind. In this research paper the comparative study of three most efficient Deep Learning models LSTM-RNN, GRU-RNN and CNN has been performed on the most famous dataset ‘Human Activity Recognition Using Smartphones Data Set’ present at UCI machine-learning repository. ‘LSTM-RNN’ is abbreviated for ‘Long Short-Term Memory-Recurrent Neural Network’ is an updated version of the recurrent neural network based on the concept of back-propagation, is capable of remembering the dependencies for comparatively longer time-span. ‘GRU-RNN’ is abbreviated for ‘Gated Recurrent Units-Recurrent Neural Network’ is also an updated version of the recurrent neural network based on the concept of back-propagation, with fewer parameters than LSTM-RNN. ‘CNN’ is abbreviated for ‘Convolutional Neural Network’ is a feed forward Neural network using Convolutional layers for feature-extraction and fully-connected layer for classification.

Human Activity Recognition (HAR), LSTM-RNN, GRU-RNN, CNN

[1] S. S. Anju, K. V. Kavitha, “Performance Evolution of Varoius Machine Learning Technique for Human Activity Recognition using Smartphone”, Vol.7, Issue.8, pp.316-319, 2019.
[2] S. R. Ramammurthy, N. Roy, “Recent trends in machine learning for human activity recognition- A Survey”, Wiley Interdisciplinary Reviews: Data Minining and Knowledge Discovery, 2018, doi: 10. 1002/widm. 1254.
[3] S. Wan, Q. Lianyong, X. Xiolong, T. Chao, G. Zonhua, “Deep Learning Models for Real-time Human Recognition with Smartphones”, Mobile Networks and applications, 2019, doi: 10. 1007/S11036. 019- 01445- x.
[4] G.A. Kani, P. Geetha, A. Gomathi, “Human Activity recognition using deep learning with Ggradient Fused Handcrafted Features and Categorization based on Machine Learning Technique”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1-7, 2018.
[5] N. Geeta, E.S. Samundeeswari, “A Review on Human Activity Recognition System”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.825-829, 2018.
[6] M. Badshah, “Sensor-based Human Activity Recognition using Smartphones”, Sensors, 2019, doi: 10.31979/etd.8fjc-drpn.
[7] R. Dey, F.M. Salem, “Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks”, journal of IEEE Acess,2017, doi: 10.1109/MWSAS.2017.8053243.
[8] W. Jiang, Z. Yin, and., “ Human activity recognition using wearable sensors by deep convolutional neural networks.”, In Proceedings of the 23rd ACM international conference on Multimedia (pp. 1307-1310), 2015.
[9] H. Cho, S.M. Yoon, “Divide and Conquer based 1D CNN Human Activity Recognition using test data Sharpening”, Sensors, 2018, 1055, doi:10.3390/s 18041005.

Saurav Singla, Anjali Patel, "Comparative Study of the Deep Learning Neural Networks on the basis of the Human Activity Recognition," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.27-32, 2020.

Today sensing resources are widely increased in terms of nodes and it affects the Grid computing systems. This technology is used for predicting traffic and for road safety. These systems usually share resources and collaborate with sensing devices for processing data and propagate results. In this paper we proposed WSN based delay tolerance mechanism that considers cost matrix and dynamic delay tolerance. The allocation of resources depends critically on the cost associated with virtual machine. It considers exponential residency of VC and execution time along with bandwidth utilization. Bandwidth consumption and cost of execution is reduced greatly by the effect of proposed mechanism.

WSN, Grid computing, delay tolerance, resource scheduling

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[8] W. Zhang, Z. Zhang, and H. Chao, “Cooperative Fog Computing for Dealing with Big Data in the Internet of Nodes?: Architecture and Hierarchical Resource Management,” IEEE Access, no. December, pp. 60–67, 2017.
[9] J. Fan, R. Li, and X. Zhang, “Research on delay tolerance strategy based on two level checkpoint server in autonomous WSN Grid,” Proc. 2017 IEEE 7th Int. Conf. Electron. Inf. Emerg. Commun. ICEIEC 2017, no. 61363079, pp. 381–384, 2017.
[10] Y. Sharma, B. Javadi, W. Si, and D. Sun, “Reliability and energy efficiency in Grid computing systems: Survey and taxonomy,” J. Netw. Comput. Appl., vol. 74, pp. 66–85, 2016.
[11] H. S. Y. Lin, “EAFR: An Energy-Ef?cient Adaptive File Replication System in Data-Intensive Clusters,” IEEE Trans. Parallel Distrib. Syst., pp. 1017–1030, 2017.
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[14] H. M.-R. Mahdi Ghamkhari, “Energy and Performance Management of Green Data Centers: A Pro?t Maximization Approach,” IEEE Trans. Smart Grid, pp. 1017–1025, 2017.
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DazyKohli, Deepak, "WSN Grid Formation to Tackle Packet Drop Ratio and Increase Energy Efficiency during Packet Transmission," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.33-39, 2020.

Transformer based self-supervised pre-trained models have transformed the concept of Transfer learning in Natural language processing (NLP) using Deep learning approach. Self-attention mechanism made transformers more popular in transfer learning across a broad range of NLP tasks. Among such tasks, Sentiment analysis helps to identify people`s opinions towards a topic, product or service. In this project we analyse the performance of self-supervised models for Multi-class Sentiment analysis on a Non benchmarking dataset. We used BERT, RoBERTa, and ALBERT models for this study. These models are different in design but have the same objective of leveraging a huge amount of text data to build a general language understanding model. We fine-tuned these models on Sentiment analysis with a proposed architecture. We used f1-score and AUC (Area under ROC curve) score for evaluating model performance. We found the BERT model with proposed architecture performed well with the highest f1-score of 0.85 followed by RoBERTa (f1-score=0.80), and ALBERT (f1-score=0.78). This analysis reveals that the BERT model with proposed architecture is best for multi-class sentiment on a Non-benchmarking dataset.

NLP, Transfer learning, Sentiment analysis, BERT, RoBERTa, ALBERT

[1] Ashish Vashwani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ?ukasz Kaiser, Illia Polosukhin, “Attention is all you need”, 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA pp.5998-6008, 2017.
[2] Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding”, In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pp. 4171-4186.
[3] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov, “Roberta: A robustly optimized bert pre training approaches”, 2019, arXiv preprint arXiv:1907.11692.
[4] Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel,Piyush Sharma, Radu Soricut, “Albert: A lite bert for self-supervised learning of language representations”, 2019, arXiv preprint arXiv:1909.11942.
[5] Matthias Aßenmacher, Christian Heumann, “On the comparability of pre-trained language models”, CEUR Workshop Proceedings, Vol.2624.
[6] Cristóbal Colón-Ruiz, Isabel Segura-Bedmar, “Comparing deep learning architectures for sentiment analysis on drug reviews", Journal of Biomedical Informatics, Volume 110, 2020, 103539, ISSN 1532-0464.
[7] Thanapapas Horsuwan, Kasidis Kanwatchara, Peerapon Vateekul, Bonser Kijsirikul, "A Comparative Study of Pretrained Language Models on Thai Social Text Categorization", 2019, arXiv:1912.01580v1.
[8] Carlos Aspillaga, Andres Carvallo, Vladimir Araujo,"Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks", Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), European Language Resources Association (ELRA), Marseille, pp. 1882–1894, 2020.
[9] Vishal Shirsat, Rajkumar Jagdale, Kanchan Shende, Sachin N. Deshmukh, Sunil Kawale, “Sentence Level Sentiment Analysis from News Articles and Blogs using Machine Learning Techniques”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, 2019.
[10] Avinash Kumar1, Savita Sharma, Dinesh Singh, "Sentiment Analysis on Twitter Data using a Hybrid Approach", International Journal of Computer Sciences and Engineering, Vol.-7, Issue-5, May 2019.

Saurav Singla, Ramachandra N., "Comparative Analysis of Transformer Based Pre-Trained NLP Models," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.40-44, 2020.

Power grid is a network that delivers electricity from generating station to consumers. Grid efficiency and reliability can be increased by implementing real time monitoring of high voltage devices and transmission line. It helps in fault detection and hence avoids cascading failure initiated by a single fault. Real time monitoring is provided by low cost devices such as wireless sensor nodes. Wireless sensor nodes are battery operated devices and replacing theses batteries is one of the major drawback of it. These monitoring devices can be made self sustainable by harvesting varying magnetic field around high voltage devices and overhead transmission line. The amount of energy harvested by magnetic field harvester depends on magnetic core, field intensity, de-magnetization effect, and distance between harvester and magnetic field. This paper provides an overview of different magnetic field harvesting techniques employed in power grid.

Wireless sensor node, Smart grid, magnetic field harvesters, real time monitoring

[1] A.Sen,S.Pal,R.Paul“The smart architecture of smart grid”International journal of Computer Science and Engineering vol.8,special Issue.1,pp.25-31,2020.
[2] Jue hou shaorong wang shuwei zhang “Design and Application of a CT-Based High-Reliability Energy Harvesting Circuit for Monitoring Sensors in Power System”IEEE Access vol. 7, pp.149039-149051,2019.
[3] Sheng Yuan ,Yi Huang ,Jiafeng Zhou ,Qian Xu “A High-Efficiency Helical Core for Magnetic Field Energy Harvesting” IEEETransactions on PowerElectronics Vol.32 , Iss.7,pp.5365 – 5376, 2017.
[4] Cheon-Yong Lim ,Yeonho Jeong, Keon-Woo Kim, Feel-Soon Kang“A High-Efficiency Power Supply from Magnetic Energy Harvesters” IEEE International Power Electroni Conference ,Japan, 2018
[5] Yuan Zhuang ,Chen Xu ,Chaoyun Song ,Anqi Chen ,Wei Lee “Improving Current Transformer-based Energy Extraction from AC Power Lines by Manipulating Magnetic Field” IEEE Transactions on Industrial Electronics Vol.67, issue.11, pp.9471-9479 2020 .
[6] Zheng Jun Chew , Tingwen Ruan ,Meiling Zhu “Power Management Circuit for Wireless Sensor Nodes Powered by Energy Harvesting: On the Synergy of Harvester and Load”IEEE Transactions on Power Electronics Vol.34 , Issue.9,pp.8671 – 8681, 2019.
[7] Wei Wang ,Xueliang Huang,Linlin Tan ,Junfeng Zhao “Hybrid wireless charging system for monitoring overhead 110 kV high-voltage power line equipment based on magnetoelectric conversion” IET Generation, Transmission & Distribution Vol.10 , Issue.,5 pp. 1199 – 1208,2016.
[8] Y. Zhuang C. Xu S. Yuan C. He A. Chen W. W. Lee J. Zhou Y. Huang“An Improved Energy Harvesting System on Power Transmission Lines” IEEE Wireless Power Transfer Conference (WPTC) ,Taiwan,2017.
[9] Awab A. Ali ,Syed Ahmed Ali Najafi , Okan Boler ,Yilmaz Sozer “Magnetic Field Energy Harvester and Management Algorithm for Power Tower Sensors”IEEE Energy Conversion Congress and Exposition (ECCE), USA,2018.

Pavana H., Rohini Deshpande, "Inductive Energy Harvesting for Monitoring Devices in Power Grid," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.45-47, 2020.

In these days, there are a lot of shopping websites and apps that are very good for people requirements in their daily lives. But the quality of the product is known to the customer with the help of the reviews or comments of previous users. Some product producers are doing fake actions on those comments. Hence we don’t know the right quality. Hence in this paper, I developed a shopping cart with simplicity and flexibility, user friendly. And it is incorporated with the safe comments. The admin can recognize the list of comments is of safe or unsafe. Here, multinomial naïve bayes technique is used for fake ones and python programming is used for shopping cart development. We can block the fake customer through customer credentials.

flexibility, quality, admin, multinomial NB

[1] fengXu. Mixture of latent multinomial naive Bayes classifier., p. 4, 2018
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N. Bhargavi, "Designing Shopping Cart and Determining Fake Product Comments Using Multinomial NB," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.48-52, 2020.

In the present computing era, the field of “Artificial Intelligence” play an important role in each and every sector. The major objective is to create a machine with intelligence in different level is a great challenge for researchers. Finding the solution for this issue and resultant effect is achieved in the direction of learning aspect. The most important element correlated with an “Artificial Intelligence Learning Entity” (AILE) is everlastingly entitled using a terminology “Learning Agent”. Every phases of an implementation in the learning agent applies cognitive theory in order to store as well as the knowledge representations. In this research work focus on enhancing, the learning progress or mechanism which will determine the level of intelligence in “Learning Agent”. It takes a problem statement for “Enhancing the Learning progress by using K-Mapping Mechanism in Artificial Intelligence”. The variable “K-means” various components that are linked with improve learning mechanism.

Learning, Agent, intelligence and cognitive

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Adem Ali Kabo, "Enhancing the Learning progress by using K-Mapping Mechanism in Artificial Intelligence," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.53-56, 2020.