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检索条件"任意字段=IEEE/ACS International Conference of Computer Systems and Applications"

共 62005 条记录，以下是41-50 订阅

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Semi-Supervised Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning 6

Semi-Supervised Generalized Adaptive Weighted Recursive Leas...

引用

6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Yousefi, Mohadeseh Naderahmadian, Yashar Beheshti, Soosan University of Guilan Department of Electrical and Computer Engineering Rasht Iran Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9798331506520

This work presents a novel semi-supervised dictionary learning framework that updates the dictionary by online learning and is efficient in utilizing the training data. The method employs a two-stage process to train the dictionary: initial training with limited labeled data, followed by online refinement using abundant unlabeled data. We introduce an adaptive correction weight to control the influence of new unlabeled data on the dictionary update based on its consistency with the current model estimate. This approach enables efficient use of the training data set. Moreover, results in faster dictionary convergence and improves data representation accuracy, especially in scenarios with limited training data. Experimental results demonstrate significant enhancement in the classification accuracy of the proposed method compared to the state-of-the-art semi-supervised dictionary learning methods, particularly when dealing with a limited number of training samples. © 2025 ieee.

关键词： Self-supervised learning

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Enhancing Fatty Liver Disease Diagnosis Using Deep from Ultrasound Images 6

Enhancing Fatty Liver Disease Diagnosis Using Deep from Ultr...

引用

6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Naserameri, Mohammad Amirmozafarisabet, Kavian Namazi, Meisam Concordia University Department Of Computer Science Montreal Canada University Of Tehran Department Of Computer Science Tehran Iran

ISBN: (纸本)9798331506520

The increasing prevalence of fatty liver disease necessitates accurate and efficient diagnostic methods. This study investigates the integration of deep learning techniques to enhance the diagnosis of fatty liver disease using ultrasound images. A dataset was utilized to train a deep learning model. The model achieved an impressive accuracy of 96% on the training dataset for distinguishing patients with fatty liver disease from healthy individuals, while maintaining a commendable accuracy of 90% on a completely new dataset. Furthermore, the model demonstrated a sensitivity of 92% in classifying different levels of liver fat in the training dataset, with an accuracy of 83% on the new dataset. These results underscore the effectiveness of combining deep learning in medical imaging, providing a robust framework for the early detection and classification of fatty liver disease. The findings suggest that this hybrid approach can significantly improve diagnostic accuracy, ultimately contributing to better patient management and outcomes in clinical practice. Further research is warranted to validate these results across diverse populations and enhance model generalizability. © 2025 ieee.

关键词： Ultrasonic imaging

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Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management 6

Waste Sorting System Using AI: Development of An Embedded So...

引用

6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Remili, Amal Mahdi, Mouna Zouari Elleuch, Jihen Frikha Fakhfakh, Ahmed Sellami, Dorra National Engineering School of Sfax Electrical Engineering Department Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (纸本)9798331506520

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. This paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. The data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. The system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge. © 2025 ieee.

关键词： AI models computer vision Waste detection Waste management Waste sorting

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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

引用

5th ieee international conference on Power, Electronics and computer applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, Tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-Time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 ieee.

关键词： Temperature sensors

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Hierarchical ArUco Marker Array for Coarse-to-Fine Localization in XR applications 7

Hierarchical ArUco Marker Array for Coarse-to-Fine Localizat...

引用

7th ieee international conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Miyashita, Leo Tabata, Satoshi Ishikawa, Masatoshi Research Institute for Science and Technology Tokyo University of Science Tokyo Japan

ISBN: (纸本)9798331521578

Some markers have been proposed to determine the positional relationship between an object and a camera for XR applications. Most of markers consider only a single scale, but there are many cases where the distance to the object varies greatly, and therefore, multi-scale recognition is required in XR systems. In this study, we propose a hierarchical marker array, which is composed of toned ArUco markers and forming fractal structure. The proposed marker array realizes handling multi-scale and coarse-to-fine localization while using the conventional ArUco marker detection and identification algorithm without any modification. © 2025 ieee.

关键词： Fractals

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning 6

A Machine Learning Framework for Skin Cancer Detection using...

引用

6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Augustine, Mary Linda Panakal Spanogianopoulos, Sotirios Sharif, Mhd Saeed University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom University of Portsmouth School of Electrical and Mechanical Engineering Portsmouth United Kingdom

ISBN: (纸本)9798331506520

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes. © 2025 ieee.

关键词： Adversarial machine learning

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Research on Speech Recognition Model Optimization and Real-Time Speech Interaction System Based on Deep Learning 5

Research on Speech Recognition Model Optimization and Real-T...

引用

5th ieee international conference on Power, Electronics and computer applications, ICPECA 2025

作者： Jiang, Dongyu Yu, Xiaoting Harbin Huade University Harbin China

ISBN: (纸本)9798331533694

Voice recognition has been widely used in intelligent human-computer interaction, especially in the areas of voice assistant, intelligent house and autonomous driving. Due to the rapid development of deep learning, the precision and applicability of speech recognition have been greatly improved. However, in the complicated noise environment, different accents and high speed of speech, there are some problems in traditional speech recognition systems, such as poor recognition precision and slow response. In order to solve these problems, a novel voice recognition model is proposed, which combines CNN with self-attention. The model uses CNN to extract the local characteristics of voice signals, and then makes use of Transformer's self-attention mechanism to model the long-term dependency of voice data, so as to improve the precision and robustness of voice recognition. Experiments show that the proposed model achieves over 95% recognition accuracy over HMM-based speech data sets, which is 15% higher than the conventional one. Moreover, the proposed model has a good performance in real time speech interaction systems, and it can react rapidly and achieve high efficiency in speech recognition. Compared with the traditional methods, the proposed system is more adaptable in complicated environment, and it can deal with various kinds of background noise, speed difference and accent variation. © 2025 ieee.

关键词： Real time systems

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Event-based Sensor Fusion and Application on Odometry: A Survey 6

Event-based Sensor Fusion and Application on Odometry: A Sur...

引用

6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Zhang, Jiaqiang Yu, Xianjia Sier, Ha Zhang, Haizhou Westerlund, Tomi University of Turku TIERS Lab Turku Finland

ISBN: (纸本)9798331506520

Event cameras, inspired by biological vision, are asynchronous sensors that detect changes in brightness. They offer notable advantages in environments characterized by high-speed motion, low lighting, or wide dynamic range. These distinctive properties render event cameras particularly effective for sensor fusion in robotics and computer vision, especially in enhancing traditional visual or LiDAR-inertial odometry. Conventional frame-based cameras suffer from limitations such as motion blur and drift, which can be mitigated by the continuous, low-latency data provided by event cameras. Similarly, LiDAR-based odometry encounters challenges related to the loss of geometric information in environments such as corridors. To address these limitations, unlike the existing event camera-related surveys, this survey presents a comprehensive overview of recent advancements in event-based sensor fusion for odometry applications particularly investigating fusion strategies that incorporate frame-based cameras, inertial measurement units, and LiDAR. The survey critically assesses the contributions of these fusion methods to improving odometry performance in complex environments, while highlighting key applications, and discussing the strengths, limitations, and unresolved challenges. Additionally, it offers insights into potential future research directions to advance event-based sensor fusion for next-generation odometry applications. © 2025 ieee.

关键词： computer vision

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From classical techniques to convolution-based models: A review of object detection algorithms 6

From classical techniques to convolution-based models: A rev...

引用

6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Neha, Fnu Bhati, Deepshikha Shukla, Deepak Kumar Amiruzzaman, Md Kent State University Dept. of Computer Science KentOH United States Rutgers University Rutgers Business School NewarkNJ United States West Chester University Dept. of Computer Science West ChesterPA United States

ISBN: (纸本)9798331506520

Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them corresponding class labels. Traditional methods, which relied on handcrafted features and shallow models, struggled with complex visual data and showed limited performance. These methods combined low-level features with contextual information and lacked the ability to capture high-level semantics. Deep learning, especially Convolutional Neural Networks (CNNs), addressed these limitations by automatically learning rich, hierarchical features directly from data. These features include both semantic and high-level representations essential for accurate object detection. This paper reviews object detection frameworks, starting with classical computer vision methods. We categorize object detection approaches into two groups: (1) classical computer vision techniques and (2) CNN-based detectors. We compare major CNN models, discussing their strengths and limitations. In conclusion, this review highlights the significant advancements in object detection through deep learning and identifies key areas for further research to improve performance. © 2025 ieee.

关键词： Convolutional neural networks

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AI-Driven Solutions for Predictive Maintenance in Smart Transportation systems

AI-Driven Solutions for Predictive Maintenance in Smart Tran...

引用

2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Hundekari, Sheela Mittal, Tanusha Dutt, Amit Bhavana, Munugapati Kumar, Rakesh Nijhawan, Ginni School of Computer Applications Pimpri Chinchwad University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798331512965

Predictive maintenance: A game changer in infrastructure and vehicle management, and able to cut maintenance expenses and downtime of smart transportation systems. This study explores ways in which smart transportation systems might use AI in order to plan maintenance schedules and forecast equipment breakdowns. Predictive algorithms, that can detect potential problems before raising to expensive repair or system breakdown, use real time sensor data from infrastructure and vehicles. Decision trees and neural networks are two of these models. By allowing AI driven solutions, transportation systems may transition from a reactive to a proactive maintenance practice which may support efficiency, safety, and reliability. Unsupervised learning methods are used to do anomaly detection for real time operations, and we examine the patterns in previous maintenance data with supervised learning approaches. The use of AI for predictive maintenance is also being studied for use in smart cities, where transportation networks are becoming more interconnected by IoT. The paper also considers challenges on data integration as well as the importance of scaling the algorithms and guaranteeing cybersecurity in these systems. Based on our research, smart transportation systems could benefit from AI to increase operational efficiency, reduce the cost of material and labor to maintain and repair equipment systems, and decrease the rate of disruptions. © 2025 ieee.

关键词： Predictive maintenance

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