检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Electrical and Computer Engineering Department Western University LondonON Canada Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students’ progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students’ performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Another Two-Timescale Duplex Neurodynamic Approach to Portfolio Selection 11

Another Two-Timescale Duplex Neurodynamic Approach to Portfo...

引用

11th International Conference on Intelligent Control and Information Processing, ICICIP 2021

作者： Leung, Man-Fai Wang, Jun Che, Hangjun Hong Kong Metropolitan University School of Science and Technology Hong Kong City University of Hong Kong Department of Computer Science School of Data Science Hong Kong Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing China

ISBN: (纸本)9781665425155

This paper is concerned with portfolio selection based on the Markowitz mean-variance framework using neurodynamic optimization. The portfolio optimization problem is formulated as a biconvex optimization problem. A two-timescale duplex neurodynamic approach is then applied for solving the profolio selection problem. The approach makes use of two recurrent neural networks (RNNs) which operate at different timescales for local search. A particle swarm optimization algorithm is employed to update the neuronal states of the two RNNs for global optima. Experimental results on four stock market datasets show the superior performance of the neurodynamic approach in terms of long-term expected returns. © 2021 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Investigating the Use of Virtual Private Networks on ATM Network Security

Investigating the Use of Virtual Private Networks on ATM Net...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Mohan Garg S Senthurya Jagtej Singh Anila Bajpai Nakul S. Sharma K. Yuvaraj Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement – UG Management JAIN (Deemed to be University) Bangalore Karnataka India Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Digital personal Networks have been increasingly utilized in ATM networks for ease of communication. This paper studies the use of VPNs over ATM networks, providing a synopsis of their features and advantages and discussing bible safety threats. The paper also explains VPNs, their sub-types, and how they may be implemented in ATM surroundings; observing takes place once they provide additional protection capabilities while used on ATM networks; we also talk about the fate of VPNs throughout ATM networks, precisely due to the risk posed by using Internet-based providers. These novel results are encouraging and indicate that VPNs may also offer a solution for securing ATM transactions and protecting network data from threat actors.

关键词： Telecommunication traffic Network security Virtual private networks Cryptography Protection

来源：评论

学校读者我要写书评

暂无评论

SVM-based Predictive Modeling of Drowsiness in Hospital Staff for Occupational Safety Solution via IoT Infrastructure

SVM-based Predictive Modeling of Drowsiness in Hospital Staf...

引用

computer, Communication and Control (IC4), International Conference on

作者： B. Jai Ganesh P. Vijayan V. Vaidehi S. Murugan R. Meenakshi M. Rajmohan Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu India Department of Computer Science Vinayaka Mission’s Kirupananda Variyar Arts and Science College Vinayaka Mission’s Research Foundation (DU) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

This research proposes an efficient approach to improve hospital worker safety on the job by detecting and preventing drowsiness. The system incorporates data from hospital-deployed Internet of Things (IoT) infrastructure and using Support Vector Machines (SVM) as its primary predictive modeling tool. data pertaining to ambient factors, work habits, and physiological indications of employees are collected in real-time by the IoT infrastructure. To identify patterns linked to sleepiness, the SVM model is trained on an extensive dataset. The next step is to implement the model in a healthcare setting so that it can detect signs of sleepiness and act accordingly in a timely manner. With the help of the IoT the predictive model can be dynamically adjusted and monitored in real-time, making it useful in a wide range of situations. The recommended approach is to enhance workplace safety by reducing the likelihood of accidents caused by sleepiness among healthcare workers. The SVM-based prediction model can correctly detect and forecast cases of sleepiness, the research subjected it to extensive testing and validation. This system's adoption helps with the creation of data-driven, proactive methods to improve hospital occupational safety.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Style Content Decomposition-based data Augmentation for Domain Generalizable Medical Image Segmentation

arXiv

引用

arXiv 2025年

作者： Shen, Zhiqiang Cao, Peng Yang, Jinzhu Zaiane, Osmar R. Chen, Zhaolin School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada Monash Biomedical Imaging Monash University Australia Department of Data Science and AI Monash University Australia

Due to the domain shifts between training and testing medical images, learned segmentation models often experience significant performance degradation during deployment. In this paper, we first decompose an image into its style code and content map and reveal that domain shifts in medical images involve: style shifts (i.e., differences in image appearance) and content shifts (i.e., variations in anatomical structures), the latter of which has been largely overlooked. To this end, we propose StyCona, a style content decomposition-based data augmentation method that innovatively augments both image style and content within the rank-one space, for domain generalizable medical image segmentation. StyCona is a simple yet effective plug-and-play module that substantially improves model generalization without requiring additional training parameters or modifications to the segmentation model architecture. Experiments on cross-sequence, cross-center, and cross-modality medical image segmentation settings with increasingly severe domain shifts, demonstrate the effectiveness of StyCona and its superiority over state-of-the-arts. The code is available at https://***/Senyh/StyCona. Copyright © 2025, The Authors. All rights reserved.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

A Motion Flow Guided MicroNet Framework for Micro Expression Recognition

TechRxiv

引用

TechRxiv 2022年

作者： Verma, Monu Vipparthi, Santosh Kumar Singh, Girdhari The Electrical and Computer Engineering University of Miami FL33155 United States The Mehta Family School of Data Science and Artificial Intelligence IIT Guwahati 781039 India The Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur302017 India

The micro-expression recognition (MER) has gained high attention in real-world applications: human-computer interaction, depression estimation, virtual reality, etc. However, MER systems still face difficulty in capturing the subtle information with spatial appearance of micro expressions. In this paper, an efficient and robust motion flow guided MicroNet framework is proposed for MER. The proposed MicroNet consists: motion flow generator (MFGen) and avalanche feature (AFeat) block. The MFGen is introduced to extract the temporal changes at expressive regions by analyzing the motion intensity of pixels among the frames. Further, the AFeat block is designed to capture the spatiotemporal feature using a multi-lateral complementary feature (MCFeat) block. The MCFeat block is proposed to elicit the coarse and deep edge responses from multi-scale receptive fields. Thus, the proposed MicroNet estimates not only momentary variations but also learns affective appearance features of micro expressions. Furthermore, the efficacy of the proposed MicroNet is examined by conducting experiments on two experimental setups using six comprehensive datasets: CASME-I, CASME-II, CAS(ME)2, SAMM, SMIC and COMPOSITE, with two validation schemes: leave-one-subject-out and cross-domain to demonstrate the generalization capacity and robustness of the proposed framework. Also, eight ablation experiments have been executed to validate each module’s role in the MicroNet framework. © 2022, CC BY.

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

Multi-objective Aerial IRS-assisted ISAC Optimization via Generative AI-enhanced Deep Reinforcement Learning

arXiv

引用

arXiv 2025年

作者： Xie, Wenwen Sun, Geng Wang, Jiacheng Du, Hongyang Kang, Jiawen Huang, Kaibin Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Automation Guangdong University of Technology Guangzhou510006 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless networks. However, achieving a performance balance between communication and sensing in the dual-function radar communication (DFRC)-based ISAC system remains a significant challenge. In this paper, an aerial intelligent reflecting surface (IRS)-assisted ISAC system is explored, where a base station (BS) supports dual-functional operations, enabling both data transmission for multiple users and sensing for a blocked target, with the channel quality enhanced by an IRS mounted on the unmanned aerial vehicle (UAV). Moreover, we formulate an integrated communication, sensing, and energy efficiency multi-objective optimization problem (CSEMOP), which aims to maximize the communication rate of the users and the echo rate of the target, while minimizing UAV propulsion energy consumption by jointly optimizing the BS beamforming matrix, IRS phase shifts, the flight velocity and angle of the UAV. Considering the non-convexity, trade-off, and dynamic nature of the formulated CSEMOP, we propose a generative diffusion model-based deep deterministic policy gradient (GDMDDPG) method to solve the problem. Specifically, the diffusion model is incorporated into the actor network of DDPG to improve the action quality, with noise perturbation mechanism for better exploration and recent prioritized experience replay (RPER) sampling mechanism for enhanced training efficiency. Simulation results indicate that the GDMDDPG method delivers superior performance compared to the existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Consistent and Asymptotically Statistically-Efficient Solution to Camera Motion Estimation

arXiv

引用

arXiv 2024年

作者： Zeng, Guangyang Zeng, Qingcheng Li, Xinghan Mu, Biqiang Chen, Jiming Shi, Ling Wu, Junfeng The School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen518172 China The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Given 2D point correspondences between an image pair, inferring the camera motion is a fundamental issue in the computer vision community. The existing works generally set out from the epipolar constraint and estimate the essential matrix, which is not optimal in the maximum likelihood (ML) sense. In this paper, we dive into the original measurement model with respect to the rotation matrix and normalized translation vector and formulate the ML problem. We then propose a two-step algorithm to solve it: In the first step, we estimate the variance of measurement noises and devise a consistent estimator based on bias elimination;In the second step, we execute a one-step Gauss-Newton iteration on manifold to refine the consistent estimate. We prove that the proposed estimate owns the same asymptotic statistical properties as the ML estimate: The first is consistency, i.e., the estimate converges to the ground truth as the point number increases;The second is asymptotic efficiency, i.e., the mean squared error of the estimate converges to the theoretical lower bound - Cramer-Rao bound. In addition, we show that our algorithm has linear time complexity. These appealing characteristics endow our estimator with a great advantage in the case of dense point correspondences. Experiments on both synthetic data and real images demonstrate that when the point number reaches the order of hundreds, our estimator outperforms the state-of-the-art ones in terms of estimation accuracy and CPU time. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Image-Based Soil Health Analysis Using Deep Learning for Precision Agriculture

Image-Based Soil Health Analysis Using Deep Learning for Pre...

引用

International Conference on Communication and Electronics Systems (ICCES)

作者： V. S. Saranya Saikiran Mangali K Srinija Galeiah Medabalimi Meena Devi R Venkata Ramana N Ajanthaa Lakkshmanan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Department of CSBS B V RAJU INSTITUTE OF TECHNOLOGY Medak Telangana Department: Data science MLRIT DUNDIGAL Sagi Ramakrishnam Raju Engineering College Sagi Ramakrishnam Raju Marg Bhimavaram Andhra Pradesh India Department of electrical and electronics engineering KCG college of technology Chennai Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Soil health is a critical component of agricultural productivity and sustainability. Traditional methods of assessing soil health, including physical sampling and laboratory testing, are labor-intensive, time-consuming, and costly. This paper proposes a novel image-based approach for soil health analysis using deep learning techniques. By utilizing aerial images captured by drones or satellites equipped with multispectral and thermal sensors, the system non-invasively assesses key soil health indicators such as moisture content, nutrient levels, organic matter, and erosion patterns. The proposed method integrates image processing techniques with machine learning models, particularly Convolutional Neural Networks (CNNs), to extract relevant soil features from the images. These CNNs are trained to predict soil health metrics, and the system's performance is evaluated through accuracy, mean squared error (MSE), and F1-score, showing significant improvements over traditional methods. This scalable solution offers a real-time tool for precision agriculture, aiding farmers and agronomists in making informed, data-driven decisions regarding soil health management.

关键词： Precision agriculture Deep learning Moisture Soil Thermal sensors Nutrients Feature extraction Real-time systems Convolutional neural networks Monitoring

来源：评论

学校读者我要写书评

暂无评论

SIDVis: Designing Visual Interactive System for Analyzing Suicide Ideation Detection

SIDVis: Designing Visual Interactive System for Analyzing Su...

引用

International Conference on Information Visualisation (IV)

作者： Md Rafiqul Islam Md. Kowsar Hossain Sakib Anwaar Ulhaq Shanjita Akter Jianlong Zhou David Asirvathamt Business Information Systems Australian Institute of Higher Education Sydney Australia School of Computing Mathematics and Engineering Charles Sturt University Australia Manna Institute NSW Australia School of Computer Science Taylors University Malaysia Data Science Institute (DSI) University of Technology Sydney NSW Australia

Suicide is a critical global issue that demands a comprehensive examination of factors such as mental illness, substance abuse, financial stress, and trauma. Effectively identifying individuals at risk is vital for intervention and prevention efforts. However, distinguishing suicidal ideation (SID) from non-suicidal language poses challenges. Existing research has addressed this issue, but limited attention has been given to visually interpretable and interactive systems tailored for SID. This study contributes to responsible AI by leveraging deep learning and machine learning techniques to enhance SID detection, enabling proactive interventions and support. In this paper, we introduce SIDVis, an interactive visualization system that improves performance and interpretability at the same time. The rigorous evaluation demonstrates that SIDVis not only outperforms existing methods in terms of accuracy but also provides an explanation for the responsible use of the underlying AI approach, demonstrating its potential to improve SID detection and intervention strategies.

关键词：

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：