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Predictive Modeling of Fire Hazard: Integrating Environmental Factors with Machine Learning

Predictive Modeling of Fire Hazard: Integrating Environmenta...

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International Conference on Electrical, Computer and Communication engineering (ECCE)

作者： Tanjim Taharat Aurpa Samiha Maisha Jeba Khaza Minhaz Bin Monib Maherun Nessa Ela Md Shoaib Ahmed Md Musfique Anwar Dept. of Data Science and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur Bangladesh Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Bangladesh Boise State University Boise Idaho USA Dept. of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In today’s fast-paced world, where technology is advancing rapidly, the tragic loss of lives due to fire accidents is not only heart-wrenching but also poses a significant risk to public safety. Early prediction of such risks can play a crucial role in educating people about fire hazards, enabling them to be better prepared in case they find themselves in such unfortunate circumstances. Our study employed various machine learning (ML) techniques and evaluated their effectiveness by analyzing different factors. These factors are derived from the diverse living conditions in Bangladesh and were gathered through surveys. To refine and identify the critical features for classification, we utilized feature selection methods like ANOVA-f and the chi-square test. Our approaches have achieved a success rate of up to 97.98%.

关键词： Surveys Accuracy Feature extraction Hazards Encoding Vectors Public security Bayes methods Analysis of variance Random forests

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Boothless Toll Collection System

Boothless Toll Collection System

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Swagata Sarkar Selvabharathi M Sarveswaran D Arun Raja M Aswin Raju Dept. of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

As a developing country, it is critical that we prioritize the construction and improvement of our road infrastructure in order to stimulate economic growth and promote easier logistics operations. However, the current toll collection system is hampered by difficulties such as overcharging and traffic congestion, reducing its effectiveness. To solve these issues, we propose implementing an advanced toll collection system that includes GPS technology, an ANPR camera, and a variety of machine learning algorithms. This new method will transform toll collecting by properly calculating toll charges based on the actual distance traveled by vehicles on the highway, removing the need for drivers to stop at toll booths. The system uses GPS technology to precisely compute the distance traveled by each car, while ANPR cameras guarantee the journey’s validity. As a result, vehicles will only be paid for what distance they have traveled, reducing overcharging concerns and congestion at toll gates. Our major goal is to create a smooth and efficient tolling mechanism that overcomes the limitations of traditional tollgate systems. We hope to improve the toll collection process, shorten wait times, and reduce the consumption of fuel associated with long lines at toll booths by utilizing cutting-edge technologies. By applying this innovative strategy, we hope to improve not only toll collecting efficiency but also commuters' overall driving experience.

关键词： Machine learning algorithms Roads Transforms Pricing Logic gates Cameras Traffic congestion Global Positioning System Vehicles Logistics

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An Al Powered Exam Proctoring: Comprehensive Monitoring for Integrity

An Al Powered Exam Proctoring: Comprehensive Monitoring for ...

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knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： Dhruv Mewada Shlok Gaikwad Bhavika Gharat Priyanshu Kamble Dept. of Artificial Intelligence&Data science Vidyavardhini’s College of Engineering and Technology Mumbai India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

In recent times, remote learning has experienced significant growth, yet the challenge of conducting academic examinations in a secure manner persists. Various approaches have been adopted by universities, such as collecting assignments susceptible to plagiarism or implementing remote proctoring with manual supervision. As our current lifestyle appears to be transitioning towards a new normal, it becomes imperative to explore effective solutions. This paper introduces a proposed remedy: the development of an AI-based integrated system designed to prevent cheating during examinations. The system not only detects fraudulent activities but also records evidence, ensuring both security and cost-effectiveness.

关键词： knowledge engineering Technological innovation Distance learning Face recognition Plagiarism Speech recognition Manuals

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Live Streaming Surveillance Video Upscaling Using Generative AI With Anomaly Identification

Live Streaming Surveillance Video Upscaling Using Generative...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Deepika Ajalkar Abhishek Mahindrakar Akshay Popale Ashish Meshram Prabal Khillarkar Dept. of Data Science G H Raisoni College of Engineering and Management (Affiliated to SPPU) Pune India

This research addresses the challenge of low-resolution CCTV footage in criminal investigations through the application of generative artificial intelligence (AI). Leveraging Generative Adversarial Networks (GANs) and Super-Resolution Convolutional Neural Networks (SRCNNs), our approach focuses on real-time upscaling to improve the quality of surveillance video. The proposed system involves preprocessing low-resolution frames and utilizing generative AI models to produce high-resolution images, enhancing crucial visual details for effective crime scene analysis. Extensive experiments on real-world CCTV datasets demonstrate the superiority of our method in terms of accuracy, visual fidelity, and real-time processing compared to conventional upscaling techniques. Furthermore, our research explores the novel use of generative AI for crime scene identification. By automatically analyzing the enhanced footage, the system detects and identifies potential evidence such as objects, vehicles, and individuals. This capability contributes to more efficient criminal investigations, aiding law enforcement agencies in solving cases effectively. While advancing the field of computer vision and AI-assisted law enforcement, our work provides a practical and effective solution for real-time CCTV footage upscaling and crime scene identification. The deployment of this technology has the potential to significantly improve the speed and accuracy of investigations, thereby enhancing public safety and security. However, ethical considerations, including privacy and potential biases, are crucial aspects that require careful attention during implementation.

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3D-CAN: A 3D Convolution Attention Network for Feature Extraction and Classification of Hyperspectral Images

3D-CAN: A 3D Convolution Attention Network for Feature Extra...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Murali Kanthi K. Venkateshwara Rao L. Chandra Sekhar Reddy T. Hitendra Sarma Nuthanakanti Bhaskar Nam Vasundhara Dept. of Computer Science & Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Dept. of Computer Science & Engineering CMR College of Engineering & Technology Hyderabad Telangana India Dept. of Computer Science & Engineering (Data Science) CMR College of Engineering & Technology Engineering Hyderabad Telangana Dept. of Information Technology Vasavi College of Engineering Hyderabad Telangana India Dept. of Computer Science & Engineering CMR Technical Campus Hyderabad Telangana India

In recent years, the deep learning method has gained widespread popularity in hyperspectral image classification (HSI) tasks because of remarkable feature extraction capability. However, conventional deep learning approaches often yield suboptimal classification performance due to the limited discrimination ability of the extracted features. Moreover, the challenge of obtaining high classification performance with a limited number of samples in hyperspectral images (HSIs) has emerged as a prominent research area of interest. To address this concern, this letter provides a novel 3D convolution attention network (3D-CAN) for feature extraction and HSIs classification. The attention mechanism employed in this context enables capturing both the long-range spatial dependencies of HSIs in the horizontal and vertical directions and the varying significance levels across distinct spectral channels. The experiments are executed on both the new data sets and the publicly accessible benchmark data sets to expose the efficiency and durability of the proposed model. Empirically, it has been demonstrated that the presented model succeeds over the remaining state-of-the-art approaches in terms of classification accuracy.

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Exploration and Aspects on Augmented Reality 3

Exploration and Aspects on Augmented Reality

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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Sarkar, Swagata Adhish, S. Adarsh, V. Divakar, K. Sri Sai Ram Engineering College Dept. Artificial Intelligence and Data Science Tamil Nadu Chennai India Sri Sai Ram Engineering College B.Tech Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9781665462754

Augmented Reality (AR) is a collaborative experience of a physical world environment along with 3D virtual objects. Augmented Reality allows the user to interact with the virtual objects created in the real world. A device must be capable of calculating the position and orientation of virtual objects with respect to other physical objects for displaying AR content. This is one of the emerging technologies in today's world. Augmented Reality has become popular in various domains. Various applications of AR include prototyping, Sales and marketing, Gaming, health care, etc. A device that generates AR will combine the real world and virtual objects and provide concluded meanings for the user. The combination of AR with other technologies such as Computer Vision, Artificial Intelligence, IoT, Natural Language Processing takes the technology to another level. The evolution of mobile apps helped in the spread of AR among people. The main objective of this paper is to review various aspects of Augmented Reality, its applications along with advantages and challenges faced by it. © 2022 IEEE.

关键词： Augmented reality

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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Enhancing Early Detection of Pancreatic Cancer: A Machine Learning Approach with Explainable AI Insights

Enhancing Early Detection of Pancreatic Cancer: A Machine Le...

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Computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Neeraj V Pattanashetti R Mayur Adarsh Prakash Tryambaka B Adiga Ashwini Kodipalli Trupthi Rao Dept. of Computer Science & Engineering Global Academy of Technology Bangalore Karnataka India Dept. of Information science & Engineering Global Academy of Technology Bangalore Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Among the most deadly and complex illnesses in the world, pancreatic cancer is typified by its early asymptomatic stage and subsequent discovery of the disease when it has progressed to an advanced stage. Getting the patients in a correct treatment path at the earliest point is important for suitable intervention and patient outcomes in the long run. We utilized advanced algorithms and model prediction techniques includes, feature extraction, hyperparameter tuning and explainability methods which aims to enhance both the comprehension and predictive accuracy of models employed. The goal is to assess patient health status and diagnose pancreatic diseases (cancer vs. non-cancer) by developing a learning algorithm. We obtained a dataset containing demographic information and urine biomarkers to start the investigation. First stage of our analysis employs a single classifier and later we apply bagging and boosting algorithms to achieve ensemble models. Hyperparameter fine-tuning has got a positive outcome, which has Gradient Boosting and Cat Boosting as its top performing models. The ensemble model produced excellent results $(92.02 \%)$, demonstrating the usefulness of the suggested approach. LIME and SHAP techniques play the key role in model outcome interpretability providing an insight of algorithmic decision-making process. Such a wide range of combinations of biomarkers have a potential of pancreatic cancer detection in early stages and thus will help shape diagnostics strategies in near future.

关键词： Machine learning algorithms Shape Biological system modeling Pancreatic cancer Predictive models Biomarkers Boosting Prediction algorithms Classification algorithms Tuning

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ML-driven Emotion Identification For Feedback Analysis In E-learning Platforms

ML-driven Emotion Identification For Feedback Analysis In E-...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Chengamma Chitteti Runvika Kopparam B.V.S.S. Ganesh Sukerthi Sutraya V Kamakshi Shiridinath Jangam Dept. of Data Science School of Computing Mohan babu university (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Dept. of CSSE Mohan babu university (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Dept. of Data Science G. Narayanamma Institute of Technology and Science for women Hyderabad India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

With an emphasis on the cognitive and emotional effects of e-learning platforms, this study fills a research gap concerning user experience studies. The study suggests a nonintrusive technique to evaluate the psychological impacts of e-learning platforms by considering feedback data of users in the form of videos and analyzes emotions from video and decides the user feedback level. This process automates the understanding of feedback in a short amount of time for anyone, without requiring specific knowledge of the system. Feedback analysis for E-Learning platforms using deep learning methods for facial expression analysis is proposed in this work. Convolutional Neural Networks (CNNs), a category of deep learning, has become increasingly popular for its ability to precisely analyze and comprehend emotions from facial photos. These models may efficiently learn and extract essential features by being trained on large datasets of tagged facial expression photos. This application takes video feedback as input for various E-learning platforms and analyzes emotions and displays the predicted emotions on video. A publicly accessible dataset is used to test the proposed technique, and the results show that it achieves ${9 0. 3 \%}$ accuracy. This indicates that the approach is highly effective in accurately recognizing facial emotions.

关键词： Deep learning Training Technological innovation Electronic learning Accuracy Face recognition Feature extraction

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Comprehensive Methodologies for Breast Cancer Classification: Leveraging XAI LIME, SHAP Bagging Boosting, and Diverse Single Classifiers

Comprehensive Methodologies for Breast Cancer Classification...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Dhruvi T Avlani Abhijna MB Sai Disha GS Ashwini Kodipalli Trupthi Rao Dept. of Information Science and Engineering Global Academy of Technology Bangalore Karnataka India Dept. of Artificial Intelligence and & Data Science Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Breast cancer is one of the most common causes for deaths owed to cancer. It is very crucial and necessary to detect cancer at early phases. There are various Machine Learning techniques and computational techniques available for the aim of diagnosis of breast cancer data. This paper offers a Machine Learning model to perform automated diagnosis for the classification of breast cancer. This method employs single classifier model and bagging boosting for feature selection. Also, four algorithms SVM, Decision Tree, KNN and Logistic Regression which gives the highest accuracy of 88%. The system was trailed on BCGENES dataset. The performance of the system is measured on the basis of accuracy and precision. The incorporation of XAI methods, such as SHAP values and LIME, provides a nuanced understanding of feature importance and individual predictions, elevating model interpretability. The amalgamation of machine learning classifiers, hyperparameter tuning, and XAI techniques contributes to advanced screening tools, fostering trust among clinicians and researchers.

关键词： Support vector machines Technological innovation Machine learning algorithms Accuracy Computational modeling Boosting Breast cancer Medical diagnostic imaging Bagging Tuning

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