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共 11827 条记录，以下是141-150 订阅

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ProctorGuard: AI-Powered Examination Monitoring with Face and Device Detection 3

ProctorGuard: AI-Powered Examination Monitoring with Face an...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Harshitha, G.M. Kotian, Rahul V. Shetty, Rakshith Nitte Karkala India

ISBN: (纸本)9798331527549

This study addresses the challenges of cheating and illegal access in online exams, maintaining academic integrity with an AI-powered web-based monitoring *** we uses COCO-SSD to detect misconduct such as many faces, no face, and phone usage, notifying the user and recording instances for teachers. It is built with *** and *** to deliver a safe, scalable, and user-friendly platform. Future additions will incorporate advanced models such as Vision Transformers, super-resolution techniques, and language assistance to improve detection and accessibility. This solution establishes a standard for secure, inclusive, and dependable online exams. © 2025 IEEE.

关键词： Academic honesty AI-powered surveillance anti-plagiarism tools automated exam grading digital exam platform distance learning internet-based assessments live monitoring Node.js server-side

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Unsupervised Vehicle Re-Identification with Clustering and Appearance Style Exploration 24

Unsupervised Vehicle Re-Identification with Clustering and A...

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3rd International conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Taleb, Hamdan Zhu, Xiaobo Wang, Chao College of Electronic and Information Engineering Tongji University Shanghai China School of Informatics and Engineering Suzhou University Jiangsu Suzhou China

ISBN: (纸本)9798400718120

Vehicle re-identification (Re-ID) has emerged as a critical challenge in the fields of computer vision and intelligent transportation systems, driven by the need to accurately identify vehicles across multiple camera views in diverse urban environments. Traditional methods relying on appearance-based features such as color and resemblance often fall short due to the variability in vehicle appearances caused by factors like illumination changes, camera angles, and occlusions. To address these limitations, this paper proposes a novel approach that leverages unsupervised learning, integrating agglomerative clustering techniques and style transfer via the StarGAN2 model. By treating each camera as a unique style domain, the proposed method generates new training pictures with diverse styles, reducing camera-induced variability and enhancing cross-camera vehicle identification. The repeated learning framework further refines vehicle identities by clustering similar pictures, thereby improving the system’s ability to distinguish between vehicles in real-world scenarios. This approach not only minimizes the need for manual annotation but also enhances the robustness and adaptability of vehicle Re-ID systems, making it a valuable contribution to urban surveillance and intelligent transportation systems. © 2024 Copyright held by the owner/author(s).

关键词： Unsupervised learning

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Predicting Sector Indices Using Machine learning: A Comprehensive Analysis Incorporating Macroeconomic Indicators 3

Predicting Sector Indices Using Machine Learning: A Comprehe...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Murugan, Vasundra Jangale, Sumit Gangawane, Prasenjeet Gupta, Nidhi Sonawane, Prasanna Jadhav, Anil Symbiosis Centre for Information Technology Pune India

ISBN: (纸本)9798331515911

The influence of macroeconomic indicators on stock market movement continues to attract attention, particularly in emerging markets, especially countries such as India. The objective of this study is to predict NIFTY 50 sectoral indices namely Automobile, Energy, Banking, IT, FMCG, Metal, and Realty using macroeconomic indicators such as exports, imports, exchange rates, GDP, interest discount rates, and inflation rates. Traditional regression models like Lasso, Linear Regression, Elastic Net, and advanced machine learning models like XGBoost, Decision Tree, and Random Forest Regressor, Support Vector Regressor have been used for the prediction. The experiments show that the Random Forest Regressor came out with the best performance, providing a lower Mean Squared Error (MSE) and the highest R-squared values in almost all the sectors, except the Realty sector. It proves that advanced machine learning techniques go beyond ordinary regression methods in capturing nonlinear relationships. This study highlights the importance of macroeconomic factors as drivers of sector-specific stock performances in emerging markets and the potential of using machine learning models to understand them. © 2025 IEEE.

关键词： Inflation

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Advancing Fake News Detection: From Traditional Machine learning to Transformer-based Approaches 3

Advancing Fake News Detection: From Traditional Machine Lear...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Abhinand, I.V. Ajal Krishna, K.P. Krishna, Ajay Jayadev, J. Reji Rahmath, K. Divya, T. Dept. of Computer Science and Engineering Sreepathy Institute of Management and Technology Vavannur A. P. J. Abdul Kalam Technological University kerala Thiruvananthapuram India

ISBN: (纸本)9798331527549

Fake news on online platforms is spreading at a rapid rate, posing great threats to social, political, and economic stability in linguistically diverse regions like Kerala, India. This paper reviews comprehensively the techniques of fake news detection from traditional machine learning methods to transformer-based models. Special attention is given to the detection of fake news in regional languages like Malayalam, characterized by complex morphology and syntax. The study investigates applications of ensemble-based learning as well as state-of-the-art transformer models, BERT, and XLNet, focusing on their ability to capture nuanced linguistic features and contextual relationships. Evaluation results show how significantly better the transformer-based models fare than traditional methods with respect to accuracy and robustness. This review also captures current limitations - such as resource constraints for regional languages - as well as future opportunities in developing scalable, multilingual fake news detection systems. © 2025 IEEE.

关键词： Adversarial machine learning

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Prior Prediction and Classification of Low Birth Weights (LBW) using Machine learning 3

Prior Prediction and Classification of Low Birth Weights (LB...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mishra, Priti Asha, K. Sohara Banu, A.R. Shashikala, N. Vishnu Sai Vamsi, P. Department of CSE Sai Vidya Institute of Technology Karnataka Bangalore India Department of CSE REVA University Karnataka Bangalore India

ISBN: (纸本)9798331527549

Low birth weight (LBW) is a significant global issue with various associated dangers. LBW is an important determinant of neonatal health, affecting infant mortality and long-term outcomes. There is a strong correlation between maternal well- being during pregnancy and childbirth weight. The study of machine learning techniques for classification tasks, which effectively discriminates between LBW and NOT-LBW cases. The project's primary goal is to achieve precise predictions of LBW cases through the application of various machine learning algorithms, including XGBoost, Random Forest, Decision Trees, and SVM. These models will undergo training using supervised datasets, enabling a comparative analysis to identify the most accurate predictor among them. Along with increased accuracy, this study includes creating a flexible web interface aimed at medical professionals and the public, which allows for ideal data input to predict LBW probability. The Proposed methodology aims to contributes to obstetrics and gynecology by addressing the challenge of the lack of comprehensive medical tests for LBW and promoting early intervention for LBW in the healthcare domain. © 2025 IEEE.

关键词： Adversarial machine learning

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Deep learning-based Malaria Detection: A Comparative Study of Algorithm Performance 3

Deep Learning-based Malaria Detection: A Comparative Study o...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saran, K. Vijay, M. Hariharan, M. Prema, D. Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

Malaria is caused by Plasmodium parasites and remains a serious threat to health worldwide, with high morbidity and mortality rates. However, effective treatment depends on the accurate and fast diagnosis of malaria, which traditionally has been through microscopy and rapid diagnostic tests that have limitations, including requiring expert interpretation, limited sensitivity in the early stages of infection, and variable accuracy. By automatically detecting malaria from microscopic blood smear images using deep learning models, our study has overcome the above constraints. Four sophisticated deep learning modelsDenseNet201, NasNet, EfficientNetB7, and AlexNet were tested against performance metrics of accuracy, precision, recall, F1 score, and computational efficiency. With 99% accuracy, NasNet was the most successful model among them and is hence very dependable for the diagnosis of clinical malaria. In addition, AlexNet was faster in inference speed and had moderate accuracy compared to DenseNet201 and EfficientNetB7. Such results are a significant step forward in malaria detection technology, and the potential of NasNet for automated, scalable diagnostic systems is promising. © 2025 IEEE.

关键词： Contrastive learning

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A Novel Framework Combining CNN, LSTM, and Meta-learning for Real-Time Monitoring of Anesthetic-Induced Brain States 3

A Novel Framework Combining CNN, LSTM, and Meta-Learning for...

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3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Jyothi, B.G. Arpitha, K. Adichunchanagiri University Bgs Institute of Technology Department of Computer Science Engineering B.G Nagar Mandya District India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

In order to improve our understanding of brain function, monitoring the depth of unconsciousness during anesthesia is essential for both clinical practice and neuroscience research. The electroencephalogram (EEG) is a frequently used objective instrument for monitoring the changed states of the brain caused by anesthesia in real time. The effects of various general anesthetics on brain electrical activity are not the same. However, because of the low Signal to Noise Ratio (SNR) of EEG data, traditional machine learning models have difficulty with it, particularly in office-based anesthetic settings. Applications of Brain-Computer Interface (BCI) for pattern recognition and classification have seen a rise in the use of deep learning models, which are renowned for their great generalization and resilience in the face of noise. Deep learning has shown effective in a number of BCI applications, although its use in categorizing brain states while anesthesia is still relatively new. This research presents a unique framework to characterize brain states under anesthesia that combines deep neural networks and meta-learning. The framework uses a Long Short-Term Memory (LSTM) network to capture temporal relationships, Convolutional Neural Networks (CNN) to extract power spectrum features, and a meta-learning technique to address large cross-subject variability. © 2025 IEEE.

关键词： Electroencephalography

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Cyberbullying Detection using Deep learning Algorithms 3

Cyberbullying Detection using Deep Learning Algorithms

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nirmala Devi, K. Jayanthi, P. Rajasekar, Vani Rathinasamy, Rahul Dhandapani, Pradeep Rasil Risam, F. Dept. of Computer Science Kongu Engineering College Erode India

ISBN: (纸本)9798331527549

Social media's widespread use has made it easier for people to communicate openly, but it has also fuelled the growth of cyberbullying, which has seriously harmed victims' emotional and mental health. The intricacy of human language and the variety of abusive behaviours make it difficult to detect cyberbullying. To solve this problem, numerous studies have used deep learning (DL) and machine learning (ML) algorithms over time. However, deep learning (DL) algorithms have transformed text analysis by automatically extracting features and comprehending contextual dependencies in language, while machine learning (ML) techniques are limited in their ability to handle complex and contextual linguistic patterns. Building upon this framework, this study investigates the use of transformer-based models—more especially, BERT—for the detection of cyberbullying. Out of the proposed models, DistlBERT gives the best accuracy, precision, recall, and F1-score of 98.87%,98.87%, 98.87 and 98.86% respectively. © 2025 IEEE.

关键词： Adversarial machine learning

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Research on a Bridge Health Monitoring System Based on Machine learning 24

Research on a Bridge Health Monitoring System Based on Machi...

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3rd International conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Zhang, DaYong Changjiang Survey Planning Design and Research Limited Company Chengdu China

ISBN: (纸本)9798400718120

This study designs and implements a bridge health monitoring system based on machine learning. The system adopts a four-layer architecture, including data acquisition, processing, analysis and decision-making, and user interaction layers. Core functions encompass data preprocessing, feature extraction, model training, health assessment, and early warning. It integrates algorithms such as Support Vector Machines, Random Forests, and Deep Neural Networks to enhance the accuracy of damage identification and status prediction. The development utilizes modern tools to improve efficiency. Test results show that the system is stable, responsive, and has strong processing capabilities. This system provides an intelligent solution for bridge health monitoring, improving the automation level and management efficiency of monitoring, and offers strong support for bridge safety management. © 2024 Copyright held by the owner/author(s).

关键词： Damage detection

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HL-Net: Zero-shot image denoising algorithm based on hybrid learning 24

HL-Net: Zero-shot image denoising algorithm based on hybrid ...

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3rd International conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2024

作者： Wang, Yi Luo, Xiaohui School of Computer and Software Engineering Xihua University Sichuan ChengDu China

ISBN: (纸本)9798400707308

Deep neural networks trained on large datasets have achieved good results in image denoising. However, networks trained on specific datasets often have poor generalization, which is not conducive to practical applications. In addition, most denoisers often ignore the detail differences between image texture areas and smooth areas during the denoising process, resulting in the loss of details in texture areas. In this paper, we propose a zero-shot denoising network (HL-Net) with a Texture Noise Attention module (TNA) and a hybrid learning architecture. TNA allows the network to better extract noise information from texture areas without destroying their details, while the hybrid learning architecture uses two denoising methods for image smooth areas and texture areas: content learning LC (learning non-noise information in noisy images) and noise learning LN (learning noise information in noisy images), which can better preserve the image details in complex texture areas while extracting content information from smooth areas of noisy images. Additionally, the loss function proposed for the zero-sample hybrid learning network uses residual loss, consistency loss, and mean loss, enabling the network to better perform content learning and noise learning. Experiments show that our method outperforms most zero-sample denoising methods on various types of synthetic noise datasets and real-world noise datasets. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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