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11th International Conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2023

作者： Biswas, Chandan Mallick, Rahul Paul, Subrata Mukherjee, Dipta Master of Computer Application University of Engineering and Management Jaipur Jaipur India University of Engineering and Management Jaipur Dept of Computer Science & Engineering Jaipur India

ISBN: (纸本)9781665475129

Data retrieval from a website, frequently automatically and without the owner's consent, is known as scraping. This information can also be utilized however the scraper sees fit. The action is considered criminal, but the legality has changed because hasn't stopped others from following suit. anti-scraping instruments are not used though. Anti-scraping Solutions are offered as fairly expensive services that are both slow, and effective services. This essay list offers suggestions for reducing impediments. the development of an anti-scraping System as a Product app for small- to medium-sized websites. are expensive. © 2023 IEEE.

关键词： Websites

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A Comparison of Machine Learning Methods for Forecasting Solar Energy Production

A Comparison of Machine Learning Methods for Forecasting Sol...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Prajapati, Dharmistha K. Joshi, Ritesh A M Patel Institute of Computer Studies Ganpat University Master of Computer Application Mehsana India Prestige Institute of Engineering Management and Research Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Fundamentally, the study's findings show how important machine learning algorithms are to improving solar power forecasts and system optimization. The study illustrates how well the ANN algorithm predicts solar energy generation by utilizing large datasets and advanced computational methods. This insight opens the door for proactive maintenance plans and increased PV installation reliability in addition to helping energy firms and decision-makers make well-informed judgments. Looking ahead, the conclusions of this study provide insightful advice for industrial solar energy forecasting procedures and further research. Through the identification of ideal model configurations and performance measures, interested parties can enhance their forecasting abilities and optimize the solar resource usage. Additionally, the incorporation of machine learning algorithms into real-time monitoring systems facilitates quick decisions, enabling communities to maximize energy production and adjust to shifting environmental conditions. The coming together of data-driven insights, technical innovation, and sustainability imperatives offers a once-in-a-lifetime chance to quicken the shift to renewable energy. We can fully utilize solar energy and lessen the negative consequences of climate change by utilizing machine learning and predictive analytics. In addition to fostering economic growth and energy security, this revolutionary change also builds a more resilient, just, and sustainable society for coming generations. In conclusion, a major advancement in the global search for clean and sustainable energy solutions is the use of machine learning algorithms into solar energy forecasts. We can take advantage of these developments and work together to create a more innovative and environmentally friendly future for everybody. © 2024 IEEE.

关键词： Clean energy

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Advancing Phishing Detection with Random Forest: A Machine Learning Approach for Enhanced Cybersecurity 3

Advancing Phishing Detection with Random Forest: A Machine L...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Chandra, Sucheta Das, Priyanka Mandal, Ankita Chakroborty, Sriparno Chowdhury, Ishika Ghosh, Kathamrita University of Engineering & Management Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798331523893

Phishing attacks remain a critical threat to cybersecurity, leveraging deception to steal sensitive information from individuals and organizations. Traditional detection methods often fail to keep up with evolving phishing tactics, highlighting the need for advanced solutions. This paper investigates the use of the Random Forest algorithm, a powerful machine learning technique, to enhance phishing detection systems. By leveraging the ensemble learning approach of Random Forest, we improve accuracy and efficiency in classifying phishing attempts, while reducing false positives. The methodology includes data preprocessing, feature extraction, and model implementation, showcasing its effectiveness in real-time detection. The study also emphasizes the importance of continuous learning, allowing the model to adapt to new phishing strategies, thereby offering a robust defense against emerging threats. This research contributes to advancing cybersecurity by providing a dynamic, scalable approach to phishing detection. © 2025 IEEE.

关键词： Random forests

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Graph Based Keywords Extraction from Crime Data 16

Graph Based Keywords Extraction from Crime Data

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Das, Priyanka Munshi, Anuvab Naha, Annwesha Dey, Adrija Chakraborty, Sriparno Dept. of Computer Application and Science Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata India

ISBN: (纸本)9798331505264

It takes a lot of work to gather a large amount of data to help in criminal investigation and prevention of crimes. The most effective methods for reporting crimes are insufficient for both civilians and law enforcement. The police and witness narrative summaries are frequently deemed to be too lengthy to read. Hence, there exists many research works that represents summary of those descriptive reports. But to make it easier, there should be certain phrases or words so that Police investigators can quickly understand criminal scenarios saving them from having to read the entire report. This can be accomplished with the use of criminal information extraction. Here, the proposed study uses a graph-based methodology and natural language processing techniques to extract crimerelated keywords from in-depth police and witness narrative descriptions. Initially, the suggested work selected nouns, verbs, and adjectives from the crime reports and evaluated their importance scores. Based on the words' importance scores, a graph partitioning technique has been applied;following repeated runs of the algorithm, the words are classified into clusters. Each cluster in this instance, together with the words that reside within it, represent a different aspect of crime. These clustered words are considered as the keywords for representing the crime. © 2024 IEEE.

关键词： Crime

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Sentiment Analysis for Movie Reviews on Social Media based on Sentiment Lexicon and Deep Learning 15

Sentiment Analysis for Movie Reviews on Social Media based o...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Jakhar, Yogesh Kumar Mishra, Nidhi Department of Computer Application University of Engineering & Management Jaipur India Amity University Noida India

ISBN: (纸本)9798331300579

Now days, with the fast improvement in internet technology and social media network, user give his/her feedback on many things and companies also asked to give feedback about their services as product or service delivered to the customers in the form of review. User's review about product or service playing major role in improving services as user pleasure. In this paper we present a sentiment analysis model, which is constructed with the help of sentiment lexicon with association of attention-based Bidirectional Gated Recurrent Unit (BiGRU) and CNN (Convolutional Neural Network). For experimental work we used movie review dataset and matched the sentiment analysis results of the proposed method with the mutual sentiment analysis models (SVM, Naïve Bayes, Decision Tree, Random Forest, CNN, and BiGRU) on the dataset. The Experiment result shows that proposed model perform better than other classifiers. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Role of cloud computing in management and education

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Materials Today: Proceedings 2023年 80卷 3726-3729页

作者： Gupta, Awaneesh Mazumdar, Bireshwar Dass Mishra, Manmohan Shinde, Priyanka P. Srivastava, Surabhi Deepak, A. United Institute of Management Prayagraj India Department of Computer Science &Engineering IERT Prayagraj211002 India Department of Computer Application United Institute of Management Prayagraj211010 India Master of Computer Application Department Government College of Engineering Maharashtra Karad415124 India Koneru Lakshmaiah Education Foundation India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

In the current day, information and communication technologies are tightly linked to provide ultimate business solutions. Cloud computing is one of these topics that has recently been hotly contested and carefully explored, but still has a lot of room for further research and growth. Technology-assisted learning is becoming more common, with most educational institutions across the globe using learning management systems, content management systems, virtual networks, and virtual machines to enhance student learning. In this day and age, educational institutions are even using private clouds to improve the student experience. Despite the fact that many educational institutions, colleges, and universities have effectively implemented and incorporated instructional technology, many of them still fall short in a variety of areas. The purpose of this research is to investigate the influence of cloud computing technology on educational institutions and corporate management. © 2021

关键词： Cloud computing

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Self-Converged Ensemble Deep RNN Classifier for Student Performance Prediction in Academics

Self-Converged Ensemble Deep RNN Classifier for Student Perf...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Sharma, Vaibhav Pandey, Vivek Patil, Manoj Jeswani, Ravi Shinde, Pratiksha Agrawal, Shital Mumbai University Computer Engineering Mumbai India Mumbai University Master of Management Studies Mumbai India

ISBN: (纸本)9798350388800

Education institutes have been gaining major improvisation due to the establishment of virtual learning techniques which provide the pupils and staff with enormous information and knowledge that accessed from anywhere at any time virtually, which eventually. The virtual learning environment provides datasets for examining the learning methods of pupils and their evolution as well as their performance in academics. This paper proposed a Self-converged Ensemble Deep RNN Classifier that included an activation ensemble for the prediction and analysis of student performance and explores the moment to establish the relevant methods for student performance prediction by utilizing deep learning techniques. The proposed model self-converged ensemble deep learning RNN classifier, that enhances the MAE is 2.82, MSE is 12.07 and RMSE is 3.47 based on the mathematical dataset. © 2024 IEEE.

关键词： Contrastive Learning

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Machine Learning-Driven Economic Modeling for Enhanced Unemployment Rate Prediction and Analysis 16

Machine Learning-Driven Economic Modeling for Enhanced Unemp...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Arora, Rakhi Dixit, Nitin Sihare, Muskan Kinger, Shreshtha Shivhare, Sneha Kulshreshtha, Komal Institute of Technology & Management Computer Science and Engineering Gwalior India Masters of Computer Application Vikrant University Gwalior India

ISBN: (纸本)9798331505264

This study presents a comprehensive analysis of economic modeling using Python, specifically focusing on the prediction of unemployment rates. In response to the growing significance of data-driven decision-making in economics, our research explores the application of logistic regression and random forest classification models to comprehend and forecast unemployment status. The dataset utilized in this analysis encompasses historical unemployment rate data, serving as a valuable source for our modeling endeavors. Our methodology begins with logistic regression, establishing a baseline model to scrutinize the relationship between diverse economic indicators and the binary classification of unemployment status (above or below 10%). We evaluate the model's efficacy in capturing trends over time and identifying influential factors in determining unemployment status. Subsequently, we advance our approach by employing random forest classification, a sophisticated machine learning technique adept at capturing non-linear relationships and interactions within the data. Our investigation shows that both random forest classification and logistic regression are good at predicting jobless status. We conduct a comparative evaluation of these models, shedding light on their respective strengths and limitations. Additionally, we delve into the significance of feature selection, hyperparameter tuning, and model evaluation within the realm of economic modeling using Python. © 2024 IEEE.

关键词： Logistic regression

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Registration of CT Images of Lung Using Fireworks Algorithm 1

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2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Nayak, Somen Mondal, Subhodip Obaid, Ahmed J. Department of Computer Application University of Engineering and Management Rajasthan Jaipur India Department of Computer Science and Engineering University of Engineering and Management Rajasthan Jaipur India Department of Computer Science and Mathematics Department of Computer Science University of Kufa Najaf Iraq

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

The acquisition of medical images from various modalities can provide a number of practical challenges that can be addressed through medical image registration. Intensity-based registration methods, which are used in multi-modal registration, merge multiple images with the same content into a single representation by applying image transformation. It is essential to edit photographs as best as you can. It is highly important to improve the similarity measure between the various photographs. We introduce a new method for registering lung’s CT image affected with COVID-19 in this work since the image registration process is fundamentally an improvement problem. Lung computed tomography image registration is a technique for aligning pre- and post-contrast images for analysis the effect of COVID-19 virus in the lung. A meta-heuristics-based optimization technique called FWA-IATF was explored for registering lung CT images. Following that, the FWA-IATF algorithm successfully registers the images. The results of registration based on the FWA-IATF method and PSO approach are compared. The results indicate that the FWA-IATF-based registration methods outperform the PSO-based registration approach while registering lung CT images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Next Generation HR: Transforming Talent management with AI and ML Innovations 2

Next Generation HR: Transforming Talent Management with AI a...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Niharika Singh, Pooja Chakraborty, Sudeshna Gl Bajaj Institute of Technology & Management Dept. of Master of Computer Application U.P. Greater Noida India School of Computing Science & Engineering Galgotias University Department of Cse Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Application Greater Noida India

ISBN: (纸本)9798350371055

Artificial intelligence (AI)-based technology is now accepted as standard. Today, everything is driven by AI, which has changed our way of life. The widespread deployment of AI is helping businesses and companies by streamlining processes, increasing production, increasing effectiveness while cutting expenditures. The way businesses choose, manage, and engage their personnel is evolving as a result of the integration of artificial intelligence (AI) into HRM operations. simulated intelligence (AI) is allowing robots to make judgements that are more accurate than those made by people based on current data sets and behavioral patterns. As a result of this change, machines have taken the place of all manual labor. professionals now play more strategic roles. It is critical for businesses and professionals to comprehend how this technology operates and how it contributes to a variety of HRM activities. In order to determine how AI is bringing about change in the field a study of human resource management evaluates the work of numerous notable scholars. This review outlines the main advantages and unspoken difficulties of The use of AI in HRM and its potential in the future. This essay provides an in-depth analysis. of the utilization of AI and ML techniques in HRM, highlighting their impacts on recruitment, employee performance management, training, and overall organizational success. © 2024 IEEE.

关键词： Artificial intelligence human resource management machine learning Natural Language Processing

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