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共 71423 条记录，以下是21-30 订阅

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Enhanced Realtime Sign Language Detection and Multilingual Translation System

Enhanced Realtime Sign Language Detection and Multilingual T...

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Kakkar, Madhur Daniel, John Sudha, D. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331538538

Sign language is the communication medium that is utilized by the hearing-impaired community. The medium is visual and it uses hand signs and symbols for communication. Over 300 different types of sign languages present in the world today. Sign language uses gestures and hand movements for the majority of the words and finger spellings for the rest. Nonverbal speakers face a lot of challenges while interacting with verbal speakers. Even non-verbal speakers who speak different sign languages face difficulties communicating with each other. This project proposes an enhanced real-time system which detects gestures and finger spellings of multiple sign languages, and translates them into multiple spoken languages. The proposed system aims to utilize CNN network for finger spelling recognition and Long Short-Term Memory (LSTM) network for gesture interpretation. The system will use computer vision for accurate gesture detection and Google Translator API for recognizing signs which will be translated into multiple spoken languages. Initial tests with isolated datasets indicate promising accuracy for both finger spelling and gesture recognition. The goal of the completed system is to help aid communication between verbal and non-verbal speakers and to provide an efficient mode of communication between the two communities. © 2025 IEEE.

关键词： Interactive computer systems

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data Complexity and Stability Induced Predictive Distributed Learning Approach 19th

Data Complexity and Stability Induced Predictive Distributed...

引用

19th International Middle Eastern Simulation and Modelling Conference, MESM 2024

作者： Igodan, E.C. Ukaoha, K.C. Al-Akaidi, M. Dept. of Computer Science University of Benin Benin City Nigeria Dept. of Computer Science Wigwe University Isiokpo Nigeria College of Science & Computing Wigwe University Isiokpo Nigeria

ISBN: (纸本)9789492859341

The performance of most classifiers degrades because the majority of feature selection algorithms do not scale well, primarily due to the inherent characteristics in high-dimensional microarray datasets, making them eventually inapplicable. This research study proposed a novel predictive distributed FS model by sample partitioning while maintaining class distribution using data complexity and stability measures. For implementation, brain tumor microarray datasets were adopted. Six feature selection algorithms and classifiers, along with their ensemble learning methodology, were implemented. The dataset was preprocessed and the SMOTE technique applied, so as to balance the class distribution and the feature/sample size ratio. The study showed significant improvement in the proposed distributed model with an accuracy of 99.67% and demonstrated a sharp reduction both in the runtime complexity and the number of selected features respectively. © 2025 EUROSIS-ETI.

关键词： Contrastive Learning

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Enhancing Academic Feedback Systems with Deep Learning and Ensemble Methods 1

Enhancing Academic Feedback Systems with Deep Learning and E...

引用

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sharma, Hemant Joshi, Hiren Gujarat University Dept. Computer Science Ahmedabad India

ISBN: (纸本)9798331518578

This paper attempts to improve the academic feedback systems via complex deep learning and ensemble methods. Leveraging the NIRF Rankings 2022 dataset that provides radical performance metrics of Indian academic institutions, the project would attempt to develop trustworthy models to monitor and facilitate improvement in the academic feedback system. The techniques used in data preparation prevent missing values, outliers, and categorical data in using advanced machine learning models. In order to construct the model and capture fine-grained patterns linked to academic achievement, 1D Convolutional Neural Networks (CNNs) along with Multi-Layer Perceptrons (MLPs), both individually and in combination, are employed extrapolated from the National Institutional Ranking Framework to obtain data. Precision, accuracy, recall, along with the F1-score will be used to evaluate these models' performance. By the time these findings are finished, they should be able to provide practical advice on how to improve academic evaluations' effectiveness and advance educational institutions' more flexible feedback systems. © 2025 IEEE.

关键词： Contrastive Learning

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Forecasting Traffic Air Pollution Increases from Urban Transportation Using Machine Learning in Metropolitan Cities-A Review 4

Forecasting Traffic Air Pollution Increases from Urban Trans...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Bhaskar, Hari Naga Vijaya Mayil, V. Valli KL University Dept. of Computer Science and Applications India

ISBN: (纸本)9798331523923

Rapid urban growth and industrial expansion in Indian cities have led to a 30% increase in vehicles over the past 5-10 years, causing a rise in air pollution and health risks. Existing methods to predict air quality face challenges like limited data, inaccurate models, slow predictions, and changing environmental conditions. This review explores how machine learning, including deep learning and hybrid models, can improve pollution forecasting. It also highlights issues such as unreliable sensors, high computing costs, and difficulty in understanding model results. Finally, the study suggests future directions for better air quality management using AI-driven predictions and policy improvements. © 2025 IEEE.

关键词： Air Pollution Forecasting Environmental Impact Machine Learning Metropolitan Cities Public Health Urban Transportation

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Quantum Network Routing with Heterogeneous Nodes 17

Quantum Network Routing with Heterogeneous Nodes

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Pandey, Anoop Kumar Tamma, Bheemarjuna Reddy Rao, M.V. Panduranga IIT Hyderabad Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331531195

Deciding a route for the transfer of quantum information (namely, qubits) on a quantum network is a significant problem that is being studied extensively. In this work, we investigate the problem of routing on a quantum network of heterogeneous nodes both with and without fidelity and timing *** propose a Path Request Packet (PRP) based routing scheme for dynamically selecting a path that maximizes the overall probability of transmission success while maintaining fidelity above a threshold and adhering to time constraints. While the proposed scheme can perform suboptimally under fidelity and timing constraints, we discuss a straightforward proof of optimality in the absence of these constraints. Simulations on random graphs demonstrate that the PRP-based scheme performs better, both in terms of reliability and fidelity, than a non-trivial baseline routing algorithm. © 2025 IEEE.

关键词： Quantum electronics

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Wormhole Attack Detection in the Internet of Things using a Hybrid Deep Learning Model 3

Wormhole Attack Detection in the Internet of Things using a ...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Geo Francis, E. Sheeja, S. Antony John, E.F. Joseph, Jismy Dept. of Computer Science Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Dept. of Data Science Sri Krishna Adithya College of Arts and Science Tamil Nadu Coimbatore India Kerala Thrissur India Kerala Mala India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Internet growth has led to widespread adoption and transmission of the IoT in numerous areas. From personal networks to industrial applications, healthcare to transportation, IoT is everywhere. Due to their widespread usage, attackers deploy sophisticated methods and software to infiltrate IoT networks. IoT devices' low processing power and resources can threaten security and privacy. This vulnerability has inspired attackers to undertake a variety of attacks against IoT networks. Wormhole attacks (WHA) are routing attacks which can interrupt IoT network packet routing and flow. To address the security issues related to IoT devices, a novel hybrid deep Learning (DL) method is proposed to classify potential security risks in IoT circumstances. The proposed module combines the Bidirectional Long Short-Term Memory with attention mechanism (ABS-BiLSTM) and AlexNet CNN to achieve higher detection accuracy. The security service provided by the proposed monitoring solution ensures compatibility among various IoT network communication protocols. The results clearly show that the effectiveness and excellence of the schemes in terms of network performance, accuracy, and detection speed enhancement. © 2025 IEEE.

关键词： Network intrusion

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Improving Deep Detector Robustness via Detection-Related Discriminant Maximization and Reorganization

Improving Deep Detector Robustness via Detection-Related Dis...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Choi, Jung Im Lan, Qizhen Tian, Qing Bowling Green State University Dept. of Computer Science OH United States University of Alabama at Birmingham Dept. of Computer Science AL United States

ISBN: (纸本)9798331510831

Deep visual detectors are known to be vulnerable to adversarial attacks, raising concerns about their real-world applications (e.g., self-driving perception). We argue that this vulnerability arises from the spurious dependency of final detections on irrelevant/loophole latent dimensions. The greater the number of such dimensions, the higher the likelihood of the detector being compromised by adversarial attacks, making it more susceptible to input perturbations. To enhance detection robustness, we propose Detection-related Discriminant Maximization and Reorga-nization (DDMR), condensing the detection utility to a compressed number of relevant dimensions while deactivating the influence of irrelevant ones. This approach also alleviates the misalignment issue between the two task domains in visual detection and, consequently, their gradients. This enables the generation of more potent adversarial attacks and defenses for visual detectors within the adversarial training framework. Extensive experiments conducted with four cutting-edge visual detectors on the KITTI and COCO datasets showcase the efficacy of the proposed approach in improving the adversarial robustness of deep visual detectors against both white-box and black-box attacks. For example, on the KITTI dataset, our method demonstrates an increase in robustness of up to 12.4% and 28.0% without and with adversarial training, respectively. © 2025 IEEE.

关键词： adversarial robustness deep visual detectors detection-related discriminant optimization

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ML-JET: A Benchmark dataset for Classifying Heavy Ion Collisions

ML-JET: A Benchmark Dataset for Classifying Heavy Ion Collis...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision Workshops, WACVW 2025

作者： Mehryar, Haydar Mao, Chengzhi Schwiebert, Loren Wayne State University Dept. of Computer Science United States Columbia University Dept. of Computer Science United States

ISBN: (纸本)9798331536626

Understanding relativistic heavy ion collisions is important for studying the evolution of the universe. Models of these collisions require hyperparameter tuning to accurately reproduce experimental results. Traditionally, Bayesian analysis, which is costly and non-scalable, has been used for this tuning. Deep learning is being explored as a potential alternative, although it is still in the early stages. In this work, we propose a novel benchmark dataset for anomaly detection in visual data generated from relativistic heavy ion collisions using the JETSCAPE framework. This dataset consists of 10.8 million jet event images, enabling the application of computer vision techniques to this domain. Our dataset converts complex physics simulations into event images, which are compatible with standard vision classifiers. Using the standard Convolutional Neural Networks (CNN), our initial results attain a 92% accuracy in energy loss module classification, while concurrently accelerating the tuning process by an order of magni-tude and saving millions of CPU/GPU hours over Bayesian analysis. This work demonstrates the potential of leveraging AI-based anomaly detection techniques to advance both physics and computer vision, offering a scalable solution for identifying anomalies in particle collision data. © 2025 IEEE.

关键词： Deep learning

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Smart Barcode Scanning using X-ray Technology with computer Vision and Machine Learning 4

Smart Barcode Scanning using X-ray Technology with Computer ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vijayaraj, J. Hari Yogith, S. Rithikravivarma, V. Siddharth, S. Easwari Engineering College Dept. of Artificial Intelligence and Data Science Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

Barcode scanning errors in retail environments contribute to inventory discrepancies, financial losses, and inefficiencies in stock management. Traditional optical barcode scanning methods are prone to failures due to occlusion, poor lighting conditions, and barcode damage. While recent advancements such as computer vision, deep learning-based error detection, and RFID integration have improved scanning accuracy, these approaches still face challenges related to high computational requirements, environmental dependencies, and implementation costs. This paper proposes a novel method utilizing X-ray imaging technology and advanced machine learning algorithms to detect improperly scanned or missing barcodes in real time. The system captures detailed X-ray images of items as they enter retail stores or warehouses and applies deep learning models to analyze barcode visibility. Real-time alerts are generated when scanning anomalies are detected, enabling prompt corrective action. Experimental evaluations demonstrate that the proposed approach significantly improves barcode detection accuracy, enhances inventory management, and reduces operational costs. The findings highlight the potential for widespread deployment in retail and logistics environments, offering a robust solution to barcode scanning challenges. © 2025 IEEE.

关键词： Sales

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Deep Learning for Automated Image Captioning: A CNN and Transformer Model Analysis 3

Deep Learning for Automated Image Captioning: A CNN and Tran...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saarika, Kathula Varsha, Vasaraju Sri Harsha, P. Sheikh, Anjum Nabi Dept. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology India

ISBN: (纸本)9798331527549

Automatic image captioning is a multidisciplinary effort that combines machine learning, natural language processing, and computer vision in creating meaningful and context-rich captions for images. Within a short time, this research problem has garnered a lot of attention and new techniques are continuously being proposed. However, there are many dismaying factors as well. For instance, the current models still have a long way to go in attaining human accuracy. This paper seeks to conduct a systematic review of the recent trends and developments in the field of image captioning techniques and models with a focus on 2023 and 2024 by systematically analyzing the 13 publications located in Springer and IEEEXplore libraries. According to our research, Convolutional Neural Networks allow the identification of image content at an early stage, and Recurrent Neural Networks or Long Short-Term Memory networks usually appear at the text generation stage. The most common image datasets are MS COCO and Flickr8k. Almost all studies utilized the BLEU scores (1, 2, 3, and 4) mechanism. It is alleviated that CNN with LSTM models tends to perform better when compared to RNN-modeled networks. Attention mechanisms and Encoder-Decoder architecture are aggressively becoming the two major solutions to strengthening image captioning. This review will help and provide direction to those who are interested in practicing research that aims to promote the techniques and processes of automatic image captioning. © 2025 IEEE.

关键词： Convolutional neural networks

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