检索结果-内蒙古大学图书馆

International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Nobuyuki Umezu Yuya Imada Dept. of Computer and Information Sciences Ibaraki University Hitachi Japan Dept. of Mechanical Systems Engineering Ibaraki University Hitachi Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

We propose a novel visualization system to enhance soccer passing practice by providing potential pass courses and their scores. The proposed system is based on a first-person video footage captured during soccer play and extracts frames for pass-course suggestions using the rate of ground-surface pixels in the frame and dept. estimation. Possible pass courses were scored according to their direction and distance to the closest player. To validate the proposed method's effectiveness, we conducted a series of experiments involving 17 participants, comprising 12 museum visitors and five university students. The estimated score of visualized pass courses using the proposed method was generally successful, whereas some pass courses received lower scores from participants owing to limitations in the system's ability to accurately detect the color of players' uniforms, which affected the overall evaluation of certain passing options.

关键词： Visualization Color Museums Sports

来源：评论

学校读者我要写书评

暂无评论

Cluster Aware Personalized News Recommendation System Using a Global News Graph

Cluster Aware Personalized News Recommendation System Using ...

引用

International CSI computer Conference, CSICC

作者： Melika Sadat Araghi Hadi Shakibian Dept. of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

With the increasing use of online news services and the production of a large amount of news daily, finding news content that matches user interests has become an important challenge. Personalized news recommendation systems, by mod-eling users' behavior and interests, can help us to respond to this challenge. In this study, a new method for personalized news recommendations based on clustering and graph enhancement has been introduced, consisting of two primary phases. The first phase involves dividing the news graph into multiple clusters. In the second phase, recommendations are generated within each cluster. The findings indicate that this proposed approach yields more precise recommendations while also reducing computational demands.

关键词： Training Analytical models Accuracy Clustering methods Computational modeling Scalability Production Parallel processing Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Disease Detection And Severity Prediction Of Pomegranate Fruit

Disease Detection And Severity Prediction Of Pomegranate Fru...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Poulin Paul S Manishankar R Satheesh Kumar Dept. of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur Kerala Dept. of Computer Science and Engineering Jawaharlal College of Engineering and Technology Palakkad Kerala

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper presents an integrated framework for the comprehensive analysis of diseases affecting pomegranate fruit. The suggested system, which makes use of deep learning techniques, includes semantic segmentation for diseased region localization, multiclass illness classification, and image processing-based severity assessment. Initially, four common diseases—Alternaria, Anthracnose, Bacterial Blight, and Cer-cospora—are successfully identified through the use of a Convolutional Neural Network (CNN) for multiclass classification. Then, annotated photos are used as training data for Semantic Segmentation, which uses the UNet architecture to identify the unhealthy spots within the fruit. The area and percentage of the segmented region are determined, and thresholds for high, medium, and low severity levels are then defined. The segmentation results are then used as inputs for severity estimate. Early identification and intervention tactics are made easier by the severity level prediction, which is based on the proportion of the segmented region. In order to improve pomegranate fruit management methods, the suggested framework provides a thorough method of disease analysis by combining categorization, semantic segmentation, and severity prediction.

关键词： Location awareness Deep learning Microorganisms Semantic segmentation Training data computer architecture Convolutional neural networks Diseases

来源：评论

学校读者我要写书评

暂无评论

Advanced Decision-Making for Autonomous Vehicles Utilizing Deep Q Networks

Advanced Decision-Making for Autonomous Vehicles Utilizing D...

引用

Intelligent systems (ETIS), Emerging Technologies for

作者： Akash IJ Britto Abhishek S Dhanush S Soja Salim Soniya B Dept. of Computer Science Engineering SCT College of Engineering Thiruvananthapuram India

ISBN: (数字)9798331507541

ISBN: (纸本)9798331507558

Autonomous vehicles are poised to revolutionize the transportation industry by offering safer and more efficient navigation in dynamic environments. A critical challenge is managing interactions with other vehicles, pedestrians, and road obstacles, which complicates framing autonomous driving as a supervised learning problem. This study leverages Deep Q-Networks (DQN), a reinforcement learning technique, to control steering, throttle, and braking in autonomous vehicles. The DQN approach enables vehicles to autonomously learn and improve driving strategies, thereby enhancing their ability to navigate complex traffic scenarios effectively. Our objective is to train a model using a Deep Reinforcement Learning (DRL) algorithm, guiding the vehicle to adhere to a predetermined itinerary while adapting to real-time traffic conditions. This work emphasizes the potential of reinforcement learning for enabling continuous learning and adaptation in complex traffic scenarios, laying the groundwork for more reliable and adaptive autonomous driving systems.

关键词： Training Adaptation models Navigation Transportation industry Decision making Supervised learning Real-time systems Vehicle dynamics Driver behavior Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

An Interpretable Framework for Predicting Type 2 Diabetes using ML and Explainable AI

An Interpretable Framework for Predicting Type 2 Diabetes us...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Kazi Fardeen Ahmed Md Shahid Uz Zaman Hasibul Islam Peyal Anika Hossain Md. Toyaha Rahman Ratul Md. Nazmul Abdal Md. Injamul Islam Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Uttara University Dept. of Computer Science & Engineering Discipline Khulna University

Diabetes Mellitus is an incurable disease and stands as a major universe cause of destruction. With the universal prevalence of diabetes increasing rapidly, accurate detection and identification of the disease have become crucial. In this paper, we present an explainable AI-based diabetes indicative methodology that is carefully constructed, effective, and, most importantly, interpretable. Using two real-world diabetes datasets, the three most well-known machine learning classifiers in the literature—Random Forest (RF), Decision Tree (DT), and XGBoost—were subjected with quantitative assessments. After training and assessment of all classification models, the suggested method achieved the best results in the XGBoost classifier for the Sylhet dataset with 99.4% accuracy and the Pima Indian dataset with 92.59% accuracy. The datasets were normalized across observations to provide standardized values for improved analytical applicability and comparability. Explainable AI has been implemented for the XGBoost machine learning model by generating both global and local explanations using Shapley additive explanations (SHAP) and Local interpretable model-agnostic explanations (LIME). The elements that contribute to diabetes are explained and shown in graphs to help medical professionals make decisions about clinical diagnosis and treatment options.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Performance Analysis Modeling Framework for Extended Reality Applications in Edge-Assisted Wireless Networks

A Performance Analysis Modeling Framework for Extended Reali...

引用

International Conference on Distributed Computing systems

作者： Anik Mallik Jiang Xie Zhu Ran The Dept. of Electrical and Computer Engineering The University of North Carolina Charlotte NC USA The Dept. of Electrical and Computer Engineering University of Houston TX USA Dept. of Computer Science and Engineering Kyung Hee University Seoul South Korea

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Extended reality (XR) is at the center of attraction in the research community due to the emergence of augmented, mixed, and virtual reality applications. The performance of such applications needs to be uptight to maintain the requirements of latency, energy consumption, and freshness of data. Therefore, a comprehensive performance analysis model is required to assess the effectiveness of an XR application but is challenging to design due to the dependence of the performance metrics on several difficult-to-model parameters, such as computing resources and hardware utilization of XR and edge devices, which are controlled by both their operating systems and the application itself. Moreover, the heterogeneity in devices and wireless access networks brings additional challenges in modeling. In this paper, we propose a novel modeling framework for performance analysis of XR applications considering edge-assisted wireless networks and validate the model with experimental data collected from testbeds designed specifically for XR applications. In addition, we present the challenges associated with performance analysis modeling and present methods to overcome them in detail. Finally, the performance evaluation shows that the proposed analytical model can analyze XR applications' performance with high accuracy compared to the state-of-the-art analytical models.

关键词： Performance evaluation Analytical models Solid modeling Energy consumption Extended reality Computational modeling Wireless networks

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Detection of Primary Open Angle Glaucoma Based on Deep CNN Using Fundus Images

Detection of Primary Open Angle Glaucoma Based on Deep CNN U...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Md. Toyaha Rahman Ratul Shyla Afroge Hasibul Islam Peyal Dia Zafrin Kazi Fardeen Ahmed Md. Nazmul Abdal Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Uttara University Dept. of Computer Science & Engineering Discipline Khulna University

The early and accurate diagnosis of glaucoma, a primary cause of permanent blindness, is critical for efficient treatment and prevention of vision loss. Although the exact causes of glaucoma are not yet fully understood, it is thought to be a result of several factors, including raised pressure inside the eye and decreased blood supply to the optic nerve. We have developed a convolutional neural network model for accurate detection of glaucoma. Methods based on deep learning have been effective at classifying diseases in retinal fundus images, facilitating in the evaluation of the growing number of images. The goal of this work is to create and train a unique deep CNN model that makes use of the connections between related eye-fundus tasks and metrics used to identify glaucoma. We have meticulously selected two distinct datasets to underpin this research endeavor: the ACRIMA dataset and the LAG dataset. Notably, our model attains a remarkable accuracy score of 99.29% on the ACRIMA dataset and an equally commendable accuracy score of 97.22% on the LAG dataset. This performance eclipses that of the majority of contemporary deep CNN models, underscoring the prowess and sophistication of our approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Open RAN Slicing with Quantum Optimization

arXiv

引用

arXiv 2025年

作者： Keyela, Patatchona Cherkaoui, Soumaya Computer Engineering and Software Engineering Dept. Polytechnique Montreal Montreal Canada

RAN slicing technology is a key aspect of the Open RAN paradigm, allowing simultaneous and independent provision of various services such as ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC) through virtual networks that share a single radio access infrastructure. Efficient resource allocation is crucial for RAN slicing, as each service has specific quality of service (QoS) requirements, and a balance between different services must be maintained. Although heuristic and reinforcement learning (RL) techniques have been explored to achieve efficient resource allocation, these approaches face notable limitations: heuristic algorithms face complexity issues that limit their effectiveness in large networks, RL solutions are constrained by their dependency on training data and struggle to adapt to new scenarios and environments. This paper proposes a framework that leverages quantum optimization techniques to optimize radio resource blocks allocation in Open RAN slicing for URLLC and eMBB services. We provide a classical problem formulation and the quantum implementation using the constrained quadratic model on Dwave quantum annealing platform, showcasing the potential of quantum optimization techniques to deliver in real-time optimal solutions for optimization problems in 5G and beyond networks. © 2025, CC BY.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach for Robust and Precise Lithium-Ion Battery Degradation Investigation Through Digital Twin Using Neural Network Modeling

A Novel Approach for Robust and Precise Lithium-Ion Battery ...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Kabir, Shaharier Hannan, Nasif Shufian, Abu Fattah, Shaikh Anowarul Emon, Md Mukter Hossain Zishan, Md. Saniat Rahman American International University-Bangladesh Dept.of Eee Dhaka Bangladesh Bangladesh University of Engineering and Technology Dept.of Eee Dhaka Bangladesh Lamar University Dept. of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798350364866

Rechargeable lithium-ion batteries play a crucial role in the widespread use of electric vehicles, energy systems in power grids, and portable technology devices. Their cost of aging, due to reduced charging capacity from repeated cycles, drives research into extending battery life. This research presents an innovative digital twin framework for assessing the degradation of lithium-ion batteries. Utilizing a neural network, the model simulates complete battery cell discharge, accurately determining actual discharge capacity and performance degradation from online voltage measurements. Experiments with accurate battery data confirm the method's high accuracy and robustness, with a low Mean Absolute Error value of around 0.004 during training and validation. The digital twin model allows simulation of the entire discharge process and can be scaled to form battery packs, improving maintenance and control efficiency. The experimental results showed promising outcomes, indicating the potential of this approach for extending battery lifespan. This paves the way for an analysis of parallel lithium-ion battery pack discharge, along with the creation of a digital twin model for the power system in the future. © 2024 IEEE.

关键词： Lithium-ion batteries

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：