iosr-JCE   Volume-23 ~ Issue-4 ~ Jul. - Aug. 2021

Abstract:This paper is about the development and operational analysis report for business user using various components of SAS (statistical Package)serving the growing needs of human capital Data Ware House in Information Technology industry. This paper is a guideline of Human Resource Management System Data Ware House. In this paper we propose a developments of Global HR data Mart – POC(Proof of concept)that appropriately source its data from the host of existing data sources its data from the Host of existing data sources and make the data available globally for viewing and analysis. It would provide single a windows for organizational HR data that cut across the multitudes of systems and repositories thus ensuing the consistency in management reports. It also support tp run adhoc quries in an efficient manner and generate analysis and operational reports in prevent report formats like PDF Excel and HTML......

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Abstract: Background: Multiple choice question (MCQ) generation from a text is a popular research area. They are widely used for a learner's knowledge assessment in the education sector. Manual generation of Mcq is hard, time- consuming and expensive. We have developed a system using various applications of Natural Language Processing Techniques. In this paper, we present a set of MCQs (Multiple Choice Questions) generated for any given input text along with a set of distractors and the correct answer to the question. In addition, The BERT(Bidirectional Encoder Representations from Transformers) model
is used to generate a summary of the input text. The RAKE (Random Automatic Keyword Extraction) technique is used for extracting the keyword from the generated summary. This keyword is used as a parameter to generate questions as well......

Key Words: Summary, Keywords, Distractor, Wordnet, Conceptnet, BERT, RAKE

[1]. Keyword Extraction Performance Analysis, 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)
[2]. An Analysis on Different Document Keyword Extraction Methods, Proceedings of the Third International Conference on Computing Methodologies and Communication (ICCMC 2019)
[3]. Extractive Text Summarization Using Sentence Ranking, J.N.Madhuri Dept. of Computer science and Engineering CHRIST (Deemed to be University) Bangalore, India, 2019.
[4]. Topic level summary generation using BERT induced Abstractive Summarization Model, Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS 2020)
[5]. Automatic Question Generation for Intelligent Tutoring Systems-A survey(2019).

Abstract: The covid pandemic has resulted in our new normal lifestyle of wearing masks in public places and following social distancing. Therefore, developing a model for identifying an individual wearing a mask had turned obligatory. In addition, in colleges, there is a need for a safe and modern technique for students' attendance system with face recognition. This system is a combination of mask detection with attendance system for students. This system can be applied in industries, offices, companies, conferences and at other public places.

Key Words: Face recognition, eigenfaces, Haar cascade, AdaBoost

[1]. Shamsul Jamel Elias, Shahirah Mohamed Hatim, Nur Anisah Hassan, Lily Marlia Abdul Latif - Face recognition attendance system using Local Binary Pattern (LBP) Vol. 8, ISSN: 2302-9285, 1, March 2019, pages- 7.
[2]. Susanto, Febri Alwan Putra, Riska Analia, Ika Karlina Laila Nur Suciningtyas - The Face Mask Detection For Preventing the Spread of COVID-19 , 16 February 2021, DOI: 10.1109/ICAE50557.2020.9350556
[3]. Real-Time Face Mask Detector with Python, OpenCV, Keras
[4]. Vivek Kumar Pandey - Artificial Intelligence Based Face Mask Detection System, Volume 5, Issue 8, August – 2020.pages- 4.

Abstract: The research studies the impact of human-computer interactions on other human interactions, particularly pertaining to the effect of user interface design (UI) on a human's social decisions. The dissemination of "fake news," especially health-related fake news, on social media has been on the rise amidst COVID19 and to counter the same, social media platforms (Twitter, Instagram, Facebook) have introduced certain design changes to their platform. While these design changes have been introduced with the belief that they would manipulate user decisions to not spread misleading news further, the platforms have not released any official data or observations on the effect of these features, drawing scepticism on their effectiveness. This paper, therefore, aims to explore the impact of specific preventive user interface features against the spreading of fake news by simulating social......

Key Words: social media, human-computer interaction, misinformation, fake news

[1]. Allcot, H., & Gentzkow, M. (2017). Social Media and Fake News in the 2016 Election. National Bureau of Economic Research.
[2]. Apuke, O. D., & Bahiyah, O. (2020). Fake news and COVID-19: modelling the predictors of fake news sharing among social media users. Telematics and Informatics.
[3]. Biros, D. P., George, J. F., & Zmud, R. W. (2002). Inducing Sensitivity to Deception in Order to Improve Decision Making Performance: A Field Study. MIS Quarterly, 119-144.
[4]. Bloomberg. (2021, March 15). Facebook to label, add Information to posts on Covid-19 vaccine. Retrieved from Hindustan Times: https://www.hindustantimes.com/world-news/facebook-to-label-add-information-to-posts-on-covid-19-vaccine-101615814186680.html
[5]. Boyd, D., Golder, S., & Lotan, G. (2010). Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter. 2010 43rd Hawaii International Conference on System Sciences. Honolulu: IEEE.

Abstract: Background: Machine learning has taken the technological world by storm in recent years. Every expert that needs to cope with a specific problem, demands to develop a model that will be able to cover all the problem's needs in combination with all the available resources that come by. Along the way of this process, a lot of challenges may come up. One of these challenges refers to the selection of the most appropriate hyperparameters that should be used for the construction of the most efficient model. This process, called hyperparameter optimization, is considered to be extremely vital. Even though the created models are proved to be effective in both performance and execution time, the same models can be rendered rather than useless without the appropriate hyperparameters.....

Key Words: Hyperparameters, Hyperparameter optimization, Hyperparameter tuning, Supervised learning, Classification, Regression, Machine learning, HyperOpt, Scikit-learn

[1]. The world's most valuable resource is no longer oil, but data. Available online: https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data
[2]. Data is everywhere and it powers everything we do!. Available online: https://www.kdnuggets.com/2020/08/data-everywhere-powers-everything.html
[3]. Machine learning. Available online: https://searchenterpriseai.techtarget.com/definition/machine-learning-ML
[4]. Sala, R., Zambetti, M., Pirola, F., & Pinto, R. (2018). How to select a suitable machine learning algorithm: a feature-based, scope-oriented selection framework. In 23rd Summer School" Francesco Turco"-Industrial Systems Engineering 2018 (Vol. 2018, pp. 87-93). AIDI-Italian Association of Industrial Operations Professors.
[5]. Wistuba, M., Schilling, N., & Schmidt-Thieme, L. (2016, October). Hyperparameter optimization machines. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 41-50). IEEE.

Abstract: In step with the researches finished until date millions of students graduate every yr. The problem of consumer recruitment with accept as true with is a big problem. Automatic consumer Recruitment machine is a product which can be exceptional ideal for any agency's recruitment procedure. The device could be strong enough for you to automatically extract the resume content and keep it in a shape form inside the Database. Class algorithm (Naïve Bayes) will be run at the profiles to perceive profile classes or lessons. Also the company can specify his criteria and also decide the importance level. Because the internet grows, amount of electronic text increases swiftly. This brings the benefit of attaining the records sources in a reasonably-priced and quick way. Textual content mining is useful

Key Words: User Recruitment, Trust Evolution, Classification Algorithm, Keyword Extraction, Naïve Bayesian Classifier, Supervised Learning, Text Mining, Resume

[1] Jongwoo Kim, Daniel X. Le, and George R. "Naïve Bayes Classifier for Extracting Bibliographic Information from Biomedical Online Articles", National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.
[2] Deepak A. Vidhate and Parag Kulkarni, "A Framework for Improved Cooperative Learning Algorithms with Expertness (ICLAE)", International Conference on Advanced Computing and Communication Technologies Advances in Intelligent Systems and Computing, Springer Singapore, volume 562, pp. 149-160, 2017
[3] Ajay S. Patil, B. V. Pawar "Automated Classification of Web Sites using Naive Bayesian Algorithm", Proceedings of the International Multi-Conference of Engineers and Computer Scientists 2012 vol. I,IMECS 2012,March 14-16,2012,Hong Kong
[4] Deepak A. Vidhate and Parag Kulkarni, "Expertise Based Cooperative Reinforcement Learning Methods (ECRLM)", International Conference on Information & Communication Technology for Intelligent System, Springer book series Smart Innovation, Systems and Technologies (SIST, volume 84), Springer Cham, pp 350-360, 2017.

Abstract: An intrusion detection system is software that monitors a single or a network of computers for malicious activities that are aimed at stealing or censoring information or corrupting network protocols. Most technique used in today's intrusion detection system are not able to deal with the dynamic and complex nature of cyber-attacks on computer networks. Even though efficient adaptive methods like various techniques of Deep learning can result in higher detection rates, lower false alarm rates and reasonable computation and communication cost. With the use of data mining can result in frequent pattern mining, classification, clustering and mini data stream. An advanced method for intrusion detection system based on Data mining and Deep Learning is proposed in this proposal......

Key Words: Cyber Crime, Deep Learning, Data mining, Intrusion Detection System

[1] Songnian Li, Suzana Dragicevic ,Francesc Anton Castro , Monika ester, Stephan ´ Winter, Arzu Coltekin, Christopher Pettit, "Geospatial big data handling theory and methods: A review and research challenges".
[2] Deepak A Vidhate, Parag Kulkarni, 2019, "Performance comparison of multiagent cooperative reinforcement learning algorithms for dynamic decision making in retail shop application", International Journal of Computational Systems Engineering, Inderscience Publishers (IEL), Volume 5, Issue 3, pp 169-178.
[3] Yang C, Goodchild M, Huang Q, Nebert D, Raskin R, "Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?", International Journal of Digital Earth, pp. 305-329,Vol. 4, No. 4, July 2011.
[4] Deepak A Vidhate, Parag Kulkarni, 2019, "A Framework for Dynamic Decision Making by Multi-agent Cooperative Fault Pair Algorithm (MCFPA) in Retail Shop Application", Information and Communication Technology for Intelligent Systems, Springer, pp 693-703.
[5] Duffy DQ, Schnase JL, Thompson JH, Freeman SM, Clune TL, "Preliminary Evaluation of Map Reduce for High- Performance Climate Data Analysis", NASA new technology report white paper, 2012..

Abstract: This research explores the integration of satellite and drone imagery for enhanced cotton production forecasting. Traditional methods of crop monitoring and yield prediction often rely on ground-based data collection, which can be time-consuming and limited in spatial coverage. The advent of satellite and drone technology offers a transformative approach to agricultural monitoring, providing high-resolution, real-time data over large agricultural landscapes. In this study, we leverage satellite imagery with various spectral bands and resolutions to monitor cotton crops throughout their growth stages. Additionally, drone-based imagery is employed to obtain detailed, on-the-ground insights, allowing for a more accurate assessment of crop health, pest infestations, and other factors influencing cotton production.....

Key Words: Satellite, Imagery, Cotton Production

[1]. Jiang, Pingan & Zhou, Xuelin & Liu, Tonglai & Guo, Xiaohu & Ma, Deying & Zhang, Cong & Li, Yan & Liu, Shuangyin. (2023). Prediction Dynamics in Cotton Aphid Using Unmanned Aerial Vehicle Multispectral Images and Vegetation Indices. IEEE Access. PP. 1-1. 10.1109/ACCESS.2023.3235912.
[2]. Wang, Le & Liu, Yang & Wen, Ming & Li, Minghua & Dong, Zhiqiang & He, Zheng & Cui, Jing & Ma, Fuyu. (2021). Prediction of cotton yield reduction after hail damage using a UAV‐based digital camera. Agronomy Journal. 113. 10.1002/agj2.20880.
[3]. Chu, Tianxing & Chen, Ruizhi & Landivar, Juan & Maeda, Murilo & Yang, Chenghai & Starek, Michael. (2016). Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery. Journal of Applied Remote Sensing. 10. 10.1117/1.JRS.10.036018.
[4]. Feng, Aijing & Zhou, Jianfeng & Vories, Earl & Sudduth, Kenneth & Zhang, Meina. (2020). Yield estimation in cotton using UAVbased multi-sensor imagery. Biosystems Engineering. 193. 101-114. 10.1016/j.biosystemseng.2020.02.014.
[5]. Feng, Aijing & Zhou, Jianfeng & Vories, Earl & Sudduth, Kenneth. (2020). Evaluation of cotton emergence using UAV-based imagery and deep learning. Computers and Electronics in Agriculture. 177. 105711. 10.1016/j.compag.2020.105711