检索结果-内蒙古大学图书馆

Advanced human activity recognition on wearables with multi-scale sparse attention

Multimedia Tools and Applications 2025年 1-30页

作者： Muniasamy, Anandhavalli Asiri, Fatima College of Computer Science Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid University Abha Saudi Arabia

Maintaining a regular daily activity routine is essential for overall health and well-being. Wearable sensors offer a convenient way to track daily activities, but accurately identifying a wide range of activities remains a challenge for existing methods. These methods relied heavily on feature selection approaches in CNNs, which were not only tedious but also struggled to accurately identify activities with similar patterns and, also fails to find the long-range dependencies. The introduction of transformers in CNNs addresses these limitations, enabling more efficient and precise recognition of activities even when patterns are similar. In this paper, a novel Human activity recognition model that combines CNN with a Residual Transformer Network (RTN) is proposed. This model incorporates two key improvements: a sparse attention technique applied at multiple scales to capture complex spatial patterns, and an adaptive fusion. Sparse attention allows the model to concentrate on the most critical parts of the spectrogram at different scales and directions. This approach aids the model in distinguishing between activities with similar overall patterns by capturing subtle differences. Additionally, it enhances the model’s ability to recognize long-range dependencies. An adaptive fusion technique is introduced to combine features from CNN and RTN, with a dynamic scoring function optimally weighting each feature stream. This ensures balanced contributions from spatial patterns and temporal dependencies for accurate activity classification. Experiments conducted on the HARTH, HU-HAR, and HuGaDB datasets demonstrate impressive results with higher accuracies of 94.82%, 98.94%, and 96.33%, respectively. These findings highlight the effectiveness of the proposed method for accurate activity detection and continuous health monitoring applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wearable sensors

来源：评论

学校读者我要写书评

暂无评论

Permissions-based Android malware detection using machine learning

引用

Neural Computing and Applications 2025年第6期37卷 5255-5270页

作者： Alomar, Atheer AlJarullah, Asma Abu-Ghazalah, Sarah Department of Information Systems King Khalid University Alfara Abha61421 Saudi Arabia Department of Informatics and Computer Systems King Khalid University Alfara Abha61421 Saudi Arabia

Android applications are evolving quickly, and both consumers and developers are growing in number. This popularity increases Android system vulnerabilities and attacks. Researchers have proposed various approaches to detect Android malware. One of these approaches is to utilize machine learning to detect Android malware based on permissions. This study developed a permissions-based Android malware detection model using machine learning. It aims to identify the significant permissions list that can be used to distinguish between benign and malware apps. A dataset containing permissions for both types of apps was collected to compare feature selection algorithms (IG, RFE, CHI, GA) with machine learning algorithms (SVM, DS, RF, GB, XG) to identify the best prediction model. The results showed that the SVM model achieved the highest accuracy of 98.88% using RFE with 13 permissions. The model was able to complete the detection process in 12.00 milliseconds. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Android malware

来源：评论

学校读者我要写书评

暂无评论

Integrating Bayesian and Convolution Neural Network for Uncertainty Estimation of Cataract from Fundus Images

引用

computer Modeling in Engineering & Sciences 2025年第4期143卷 569-592页

作者： Anandhavalli Muniasamy Ashwag Alasmari Department of Informatics and Computer Systems College of Computer ScienceKing KhalidUniversityAbha61421Saudi Arabia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of ***,the mass disease that needs attention in this context is *** deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty ***,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the *** prepares convolutional neural network(CNN)and BCNN *** proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN *** study then trains them on a dataset of cataract images filtered from the ocular disease fundus images *** deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye *** compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.

关键词： Bayesian neural networks(BNNs) convolution neural networks(CNN) Bayesian convolution neural networks(BCNNs) predictive modeling precision medicine uncertainty quantification

来源：评论

学校读者我要写书评

暂无评论

Optimizing task scheduling in cloud environments: a hybrid golden search whale optimization algorithm approach

引用

Neural Computing and Applications 2025年 1-23页

作者： Acharya, Biswaranjan Panda, Sucheta Das, Satyabrata Majhi, Santosh Kumar Gerogiannis, Vassilis C. Kanavos, Andreas Department of Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Burla India Bilaspur India Department of Digital Systems University of Thessaly Larissa Greece Department of Informatics Ionian University Corfu Greece

Managing fluctuating workloads and optimizing resource utilization in cloud environments pose significant challenges, particularly in fields requiring real-time data processing, such as healthcare. This paper introduces a novel hybrid metaheuristic technique, the Golden Search Whale Optimization Algorithm (GSWOA), specifically designed for scheduling independent dynamic biomedical data. GSWOA merges the strengths of Golden Search Optimization (GSO) and Whale Optimization Algorithm (WOA), optimizing numerical function optimization and achieving a balance between exploration and exploitation. The algorithm’s effectiveness was assessed using MATLAB by applying standard benchmark functions and further evaluated on a real-world biomedical dataset within the CloudSim environment. The performance evaluations demonstrate that GSWOA significantly outperforms existing metaheuristic and traditional scheduling techniques, achieving a 42.71% increase in resource utilization and a 14.17% reduction in makespan compared to conventional methods. These results highlight GSWOA’s potential to enhance scheduling efficiency substantially in cloud computing infrastructures, suggesting it is a powerful tool for complex task allocations. Future research will explore the scalability of GSWOA and its applicability across other data-intensive sectors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

Attention-Driven Emotion Recognition in EEG: A Transformer-Based Approach With Cross-Dataset Fine-Tuning

引用

IEEE Access 2025年 13卷 69369-69394页

作者： Ghous, Ghulam Najam, Shaheryar Alshehri, Mohammed Alshahrani, Abdulmonem AlQahtani, Yahya Jalal, Ahmad Liu, Hui Riphah International University Department of Electrical and Computer Engineering Islamabad44000 Pakistan Bahria University Department of Electrical Engineering Islamabad44000 Pakistan King Khalid University Department of Computer Science Abha61421 Saudi Arabia King Khalid University Department of Informatics and Computer Systems Abha61421 Saudi Arabia Air University Faculty of Computer Science and AI Islamabad44000 Pakistan Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of University of Bremen Cognitive Systems Laboratory Bremen28359 Germany Guodian Nanjing Automation Company Ltd. Nanjing210032 China

Emotion recognition from EEG (electroencephalogram) signals is crucial in mental health diagnostics and human-computer interaction but is often hindered by high dimensionality, noise, and complex temporal dependencies in the data. This paper presents a novel approach that integrates transformer models, attention mechanisms, and transfer learning to enhance emotion recognition accuracy from EEG signals. The proposed methodology consists of two phases: Attention Enhanced Base Model Development (AE-BMD) and Cross-Dataset Fine Tuning Adaptation (CD-FTA). In the AE-BMD phase, the base model is developed and trained on the SEED-IV dataset (15 participants, 62 EEG channels), achieving an accuracy of 84%, with an average precision of 84.75%, recall of 84% and F1-score of 84%. This phase employs optimized feature extraction from key EEG frequency bands (Delta, Theta, Alpha, Beta, Gamma) using techniques such as MFCC, GFCC, power spectral density, and Hjorth parameters. A transformer encoder with integrated spectral and temporal attention mechanisms captures intricate patterns and long-range dependencies within the EEG signals. In the CD-FTA phase, the model undergoes fine-tuning on the SEED-V dataset (20 participants, 62 channels) leading to an improved accuracy of 90%, with an average precision of 90.6%, recall of 90.6%, and F1-score of 90.6%. The model’s generalization is further validated on the MPED dataset (23 participants, 62 channels, seven emotion classes), achieving 79%, with an average precision of 79.3%, recall of 79.3% and F1-score of 79.1% across diverse emotional states. This cross-dataset adaptation leverages transfer learning to enhance the model’s generalization across different emotional states and EEG datasets. Experimental results show that the proposed approach outperforms traditional methods, achieving superior accuracy and robustness in emotion recognition tasks. This work advances emotion recognition systems by addressing challenges in EEG signal proc

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A Social Robot Companion for Individuals Eating Alone 25

A Social Robot Companion for Individuals Eating Alone

引用

30th International Conference on Intelligent User Interfaces Companion, IUI 2025

作者： Soufargi, Selim Niewiadomski, Radoslaw Fong, Hunter Mancini, Maurizio Department of Computer Science Sapienza University of Rome Rome Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy

ISBN: (纸本)9798400714092

In this paper, we present the first implementation of a social robot acting as a companion for individuals eating alone. Our system can engage in multimodal interactions with the user during meals. It conducts conversations on various topics, complemented by gestures and other nonverbal behaviors. The system comprises a NAO robot, human activity recognition modules, a speech recognition module, and a dialog management system integrated with an LLM model. In the paper, we present a technical description of the system and the interaction flow, as well as discuss the possible applications and planned validation. © 2025 Copyright held by the owner/author(s).

关键词： Social robots

来源：评论

学校读者我要写书评

暂无评论

Aspect-based sentiment analysis in MOOCs: a systematic literature review introducing the MASC-MEF framework

引用

Journal of King Saud University - computer and Information Sciences 2025年第1期37卷 1-33页

作者： Awadh, Wed Akeel Sulaiman, Rosnafisah Bte Mahmoud, Moamin A. Institute of Informatics and Computing in Energy University Tenaga Nasional Kajang Malaysia Department of Computer Information Systems University of Basrah Basrah Iraq Institute of Informatics and Computing in Energy Department of Informatics Universiti Tenaga Nasional Jalan IKRAM UNITEN Selangor Kajang Malaysia Institute of Informatics and Computing in Energy Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Selangor Kajang Malaysia

Aspect-based sentiment analysis (ABSA) in massive open online courses (MOOCs) offers a powerful tool to understand student experiences and improve the quality of online education. As MOOCs grow in popularity, there is an increasing need to analyze student feedback at a granular level, focusing on specific course aspects rather than just overall sentiment. Despite ABSA’s potential in MOOCs, research in this specific domain remains limited. Thus, this review of the literature seeks to fill this gap by systematically examining ABSA within the realm of MOOCs. We utilized a PRISMA framework to guide the search process. Consequently, this review covered 57 studies conducted between 2019 and 2024, retrieved from eight electronic research databases. The studies included in this analysis were examined on the basis of their datasets, data preprocessing, ABSA tasks in MOOCs, evaluation aspects in online courses, fusion approach in ABSA, techniques of word representations, and evaluation criteria utilized to evaluate the proposed models. Additionally, this review introduces the multiaspect sentiment classification using multiembedding and fusion (MASC-MEF) framework, a novel approach that uniquely incorporates multiword embeddings and multifeature extraction along with advanced classification techniques. Results shed light on several critical issues and various limitations, pointing towards future directions for ABSA research within the realm of MOOCs. This review underscores significant contributions to improving ABSA in educational technology, with a focus on course effectiveness and advancements in online learning, guiding researchers towards innovative techniques to tackle this challenge in the future. © The Author(s) 2025.

关键词： Aspect-based sentiment analysis Massive open online courses Multiaspect sentiment classification Online education technology Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Sobol Sequence Optimization for Hardware-Efficient Vector Symbolic Architectures

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 937-950页

作者： Aygun, Sercan Najafi, Hassan University of Louisiana at Lafayette School of Computing and Informatics LafayetteLA70503 United States Case Western Reserve University Electrical Computer and Systems Engineering Department ClevelandOH44106 United States

Hyperdimensional computing (HDC) is an emerging computing paradigm with significant promise for efficient and robust learning. In HDC, objects are encoded with high-dimensional vector symbolic sequences called hypervectors. The quality of hypervectors, defined by their distribution and independence, directly impacts the performance of HDC systems. Despite a large body of work on the processing parts of HDC systems, little to no attention has been paid to data encoding and the quality of hypervectors. Most prior studies have generated hypervectors using inherent random functions, such as MATLAB’s or Python’s random function. This work introduces an optimization technique for generating hypervectors by employing quasi-random sequences. These sequences have recently demonstrated their effectiveness in achieving accurate and low-discrepancy data encoding in stochastic computing systems. The study outlines the optimization steps for utilizing Sobol sequences to produce high-quality hypervectors in HDC systems. An optimization algorithm is proposed to select the most suitable Sobol sequences via indexes for generating minimally correlated hypervectors, particularly in applications related to symbol-oriented architectures. The performance of the proposed technique is evaluated in comparison to two traditional approaches of generating hypervectors based on linear-feedback shift registers and MATLAB random functions. The evaluation is conducted for three applications: 1) language;2) headline;and 3) medical image classification. Our experimental results demonstrate accuracy improvements of up to 10.79%, depending on the vector size. Additionally, the proposed encoding hardware exhibits reduced energy consumption and a superior area-delay product. © 2024 IEEE.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Human Pose Estimation and Event Recognition via Feature Extraction and Neuro-Fuzzy Classifier

引用

IEEE Access 2025年 13卷 37328-37343页

作者： Hanzla, Muhammad Alshammari, Naif S. Alharbi, Shuaa S. Wahid, Wasim Almujally, Nouf Abdullah Jalal, Ahmad Liu, Hui Air University Department of Computer Science Islamabad44000 Pakistan Majmaah University College of Computer and Information Sciences Department of Computer Science Al Majma’ah11952 Saudi Arabia Qassim University College of Computer Department of Information Technology Buraydah52571 Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems P.O. Box 84428 Riyadh11671 Saudi Arabia Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of University of Bremen Cognitive Systems Lab Bremen28359 Germany

This paper introduces an advanced framework for Human Pose Estimation (HPE) and Semantic Event Classification (SEC), addressing the growing demand for sophisticated human skeleton modeling, context-aware feature extraction, and machine learning techniques for accurate event recognition in real-world scenarios. HPE, a fundamental task in applications such as sports analytics and surveillance systems, involves predicting human joint locations from visual data. Despite substantial progress driven by deep learning advancements, particularly in handling occlusions and crowded environments, a comprehensive integration of these innovations remains limited in current literature. To bridge this gap, we propose a novel HPE and SEC system featuring a robust six-stage pipeline. The workflow begins with preprocessing steps, including video-to-image sequence conversion, floor removal to reduce noise, grayscale transformation, and human silhouette extraction via binary masks. Human detection is performed using the GrabCut algorithm. Full-body feature extraction incorporates 1-D Fourier Transform, 2-D energy-based motion analysis, 0–180° intensity mapping via the Radon Transform algorithm, and movable body part detection using Fourier-Based Correlation. Key point features are characterized through the degree of freedom, human landmark detection via the HSV algorithm, and angular point analysis using the media pipe algorithm. Features are then optimized using the Ray Optimizer and classified through a Neuro-Fuzzy classifier. The proposed system achieves classification accuracies of 94.82%, 92.33%, and 93.63% on the UCF Sports Actions, Human Action Recognition (HAR), and MPII Human Pose datasets, respectively, outperforming state-of-the-art methods and demonstrating its effectiveness in tackling real-world challenges. © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://***/licenses/by/4.0/

关键词： Sports

来源：评论

学校读者我要写书评

暂无评论

Autonomous vehicle surveillance through fuzzy C-means segmentation and DeepSORT on aerial images

引用

PeerJ computer Science 2025年 11卷 1-23页

作者： Qureshi, Asifa Mehmood Alotaibi, Moneerah Alotaibi, Sultan Refa AlHammadi, Dina Abdulaziz Jamal, Muhammad Asif Jalal, Ahmad Lee, Bumshik Department of Computer Science Air University Islamabad Pakistan Department of Computer Science College of Science and Humanities Shaqra University Dawadmi Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Information and Communication Engineering Chosun University Gwangju Korea Republic of Department of Computer Science and Engineering College of Informatics Korea University Seoul Korea Republic of

The high mobility of uncrewed aerial vehicles (UAVs) has led to their usage in various computer vision applications, notably in intelligent traffic surveillance, where it enhances productivity and simplifies the process. Yet, there are still several challenges that must be resolved to automate these systems. One significant challenge is the accurate extraction of vehicle foregrounds in complex traffic scenarios. As a result, this article proposes a novel vehicle detection and tracking system for autonomous vehicle surveillance, which employs Fuzzy C-mean clustering to segment the aerial images. After segmentation, we employed the YOLOv4 deep learning algorithm, which is efficient in detecting small-sized objects in vehicle detection. Furthermore, an ID assignment and recovery algorithm based on Speed-Up Robust Feature (SURF) is used for multi-vehicle tracking across image frames. Vehicles are determined by counting in each image to estimate the traffic density at different time intervals. Finally, these vehicles were tracked using DeepSORT, which combines the Kalman filter with deep learning to produce accurate results. Furthermore, to understand the traffic flow direction, the path trajectories of each tracked vehicle is projected. Our proposed model demonstrates a noteworthy vehicle detection and tracking rate during experimental validation, attaining precision scores of 0.82 and 0.80 over UAVDT and KIT-AIS datasets for vehicle detection. For vehicle tracking, the precision is 0.87 over the UAVDT dataset and 0.83 for the KIT-AIS dataset. © 2025 Qureshi et al.

关键词： Image segmentation

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：