检索结果-内蒙古大学图书馆

Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

引用

computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

来源：评论

学校读者我要写书评

暂无评论

A Dynamic Characteristic Aware Index Structure Optimized for Real-world Datasets

引用

ACM Transactions on Storage 2025年第2期21卷 1-32页

作者： Yang, Jin Yoon, Heejin Yun, Gyeongchan Noh, Sam H. Choi, Young-Ri Computer Science and Engineering Unist Ulsan Korea Republic of Department of Computer Science Virginia Tech Blacksburg United States

Many datasets in real life are complex and dynamic, that is, their key densities are varied over the whole key space and their key distributions change over time. It is challenging for an index structure to efficiently support all key operations for data management, in particular, search, insert, and scan, for such dynamic datasets. In this article, we present DyTIS (Dynamic dataset Targeted Index Structure), an index that targets dynamic datasets. DyTIS, although based on the structure of Extendible hashing, leverages the CDF of the key distribution of a dataset, and learns and adjusts its structure as the dataset grows. The key novelty behind DyTIS is to group keys by the natural key order and maintain keys in sorted order in each bucket to support scan operations within a hash index. We also define what we refer to as a dynamic dataset and propose a means to quantify its dynamic characteristics. Our experimental results show that DyTIS provides higher performance than the state-of-the-art learned index for the dynamic datasets considered. We also analyze the effects of the dynamic characteristics of datasets, including sequential datasets, as well as the effect of multiple threads on the performance of the indexes. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Dynamic datasets index structure key distribution

来源：评论

学校读者我要写书评

暂无评论

A Global-Local Parallel Dual-Branch Deep Learning Model with Attention-Enhanced Feature Fusion for Brain Tumor MRI Classification

引用

computers, Materials & Continua 2025年第4期83卷 739-760页

作者： Zhiyong Li Xinlian Zhou School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411100China

Brain tumor classification is crucial for personalized treatment *** deep learning-based Artificial Intelligence(AI)models can automatically analyze tumor images,fine details of small tumor regions may be overlooked during global feature ***,we propose a brain tumor Magnetic Resonance Imaging(MRI)classification model based on a global-local parallel dual-branch *** global branch employs ResNet50 with a Multi-Head Self-Attention(MHSA)to capture global contextual information from whole brain images,while the local branch utilizes VGG16 to extract fine-grained features from segmented brain tumor *** features from both branches are processed through designed attention-enhanced feature fusion module to filter and integrate important ***,to address sample imbalance in the dataset,we introduce a category attention block to improve the recognition of minority *** results indicate that our method achieved a classification accuracy of 98.04%and a micro-average Area Under the Curve(AUC)of 0.989 in the classification of three types of brain tumors,surpassing several existing pre-trained Convolutional Neural Network(CNN)***,feature interpretability analysis validated the effectiveness of the proposed *** suggests that the method holds significant potential for brain tumor image classification.

关键词： Deep learning attention mechanism feature fusion dual-branch structure brain tumor MRI classification

来源：评论

学校读者我要写书评

暂无评论

Visually enhanced multi-secret sharing scheme with arithmetic modulo, DWT, and particle swarm optimization for Meaningful Shares

引用

Cluster Computing 2025年第5期28卷 1-28页

作者： Rawat, Arjun Singh Deshmukh, Maroti Singh, Maheep Department of Computer Science and Engineering National Institute of Technology Uttarakhand Srinagar Garhwal India Department of Computer Science Doon University Dehradun India Department of Computer Science and Engineering National Institute of Technology Delhi India

Traditional multi-secret sharing (MSS) schemes generate random shares to secure secrets, but their noisy appearance can raise suspicion. To address this, we present an advanced (n+1,n+1) MSS scheme that generates meaningful, high-quality visual shares, reducing the risk of detection by attackers. Our scheme combines arithmetic Modulo, discrete wavelet transform (DWT), and particle swarm optimization (PSO), balancing security and visual appeal. The scheme operates through two main processes: the first involves the generation of randomized shares and the embedding of watermarks in the meaningful share generation process, while the second focuses on the extraction of randomized shares or watermark images from meaningful shares and the reconstruction of the secret during the secret reconstruction process. PSO optimizes embedding factors, achieving an ideal balance between imperceptibility and visual quality, often exceeding that of the meaningful cover images. Quantitative assessments with metrics like correlation, mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) show marked improvements in both robustness and visual quality of the shared images. This solution addresses the limitations of traditional schemes, providing a secure and visually appealing approach to multi-secret image sharing. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Weighted rank aggregation based on ranker accuracies for feature selection

引用

Soft Computing 2025年第4期29卷 1981-2001页

作者： Abdolrazzagh-Nezhad, Majid Kherad, Mahdi Faculty of Engineering Department of Computer Engineering Bozorgmehr University of Qaenat Qaen Iran Department of Computer Science Faculty of Computer and Industrial Engineering Birjand University of Technology Birjand Iran Faculty of Engineering Department of Computer Engineering University of Qom Qom Iran

Rank aggregation is the combination of several ranked lists from a set of candidates to achieve a better ranking by combining information from different sources. In feature selection problem, due to the heterogeneity of methods, there are some base rankers (Filter-based methods) that are of diverse quality and usually the ground truth of ratings is not available. Existing rank aggregation methods that take the diverse quality of base rankers into account do not have any explicit approach for appropriate weighting, require prior assumptions, and suffers from high computational complexity. In this paper, to overcome these challenges, an efficient unsupervised method is introduced for estimating the base rankers’ qualities and aggregating the rankers based on the estimated weights. We first compute the ratio of disagreement between base rankers in ordering different element pairs and then estimate the accuracies in a way that to minimize the discrepancy between these computed ratios and their analytical counterparts. We use the weighted majority voting method for obtaining the aggregated results. To resolve the probable inconsistencies in the final aggregation, the result is formed as a graph, and a greedy algorithm is used to find an acyclic subgraph with the highest weigh. To demonstrate the performance of the proposed method, nine standard UCI datasets are used. The obtained results by the proposed method have higher values of classifier measures than the existing baseline Feature Selection methods and rank aggregation-based multi-filter methods in the most datasets. The experiments show that rank aggregation-based Feature Selection methods outperform individual methods. The proposed method also shows the weight of each Filter-based Feature Selection method, in which the MRMR method has a higher weight than other methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wiener filtering

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Mental Health Sentiment Analysis With BERT-Fuse: A Hybrid Deep Learning Model

引用

IEEE Access 2025年 13卷 85428-85446页

作者： Hossain, Md. Mithun Sanjara Hossain, Md. Shakil Chaki, Sudipto Rahman, Md. Saifur Ali, A. B. M. Shawkat Department of Computer Science and Engineering Dhaka1216 Bangladesh

Mental health disorders profoundly affect intellectual ability, emotional resilience, and social stability, leading to lasting effects on both individuals and society. Increasing global concern over mental health calls for innovative solutions, with sentiment analysis emerging as a promising approach for early diagnosis and intervention. Traditional methods are limited by the complexity of mental health-related texts, which contain specialized terminology and domain-specific nuances. To address these challenges, we propose BERT-Fuse, a hybrid deep learning model combining traditional feature extraction techniques (TF-IDF and Bag-of-Words) with advanced neural architectures, including Bidirectional Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Transformer blocks, and Convolutional Neural Networks (CNN). We evaluated three word embedding techniques-BERT, GloVe, and FastText within BERT-Fuse and found that BERT embeddings achieve the highest performance in terms of accuracy, precision, recall, and F1-score. The model was trained on a balanced dataset of text samples representing seven mental health conditions. An ablation study highlights the contributions of key model components to its performance. BERT-Fuse shows promise as a scalable, high-precision system for mental health detection with an impressive average test accuracy of 97.05%. Our model bridges the gap between deep learning and psychological text analysis, paving the way for future mental health assessment tools that are more intelligent and easily accessible. © 2013 IEEE.

关键词： mHealth

来源：评论

学校读者我要写书评

暂无评论

Safe Reinforcement Learning for Grid-forming Inverter Based Frequency Regulation with Stability Guarantee

引用

Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 79-86页

作者： Hang Shuai Buxin She Jinning Wang Fangxing Li Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN37996USA

This study investigates a safe reinforcement learning algorithm for grid-forming(GFM)inverter based frequency *** guarantee the stability of the inverter-based resource(IBR)system under the learned control policy,a modelbased reinforcement learning(MBRL)algorithm is combined with Lyapunov approach,which determines the safe region of states and *** obtain near optimal control policy,the control performance is safely improved by approximate dynamic programming(ADP)using data sampled from the region of attraction(ROA).Moreover,to enhance the control robustness against parameter uncertainty in the inverter,a Gaussian process(GP)model is adopted by the proposed algorithm to effectively learn system dynamics from *** simulations validate the effectiveness of the proposed algorithm.

关键词： Inverter-based resource(IBR) virtual synchronous generator(VSG) safe reinforcement learning Lyapunov function frequency regulation grid-forming inverter

来源：评论

学校读者我要写书评

暂无评论

QoS-Aware Application Assignment and Resource Utilisation Maximization Using AHP in Edge Computing

引用

IEEE Internet of Things Journal 2025年第11期12卷 17717-17728页

作者： Koganti, Yasasvitha Sridhar, Vidhyuth Yadav, Ram Narayan Pratap, Ajay Iitram Department of Electrical and Computer Science Engineering Ahmedabad380026 India Indiana University Department of Computer Science and Engineering Bloomington United States Banaras Hindu University Indian Institute of Technology Department of Computer Science and Engineering Varanasi India

Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can be done in the proximity of IoT users. Thus, the required constraints related to tasks such as latency requirements, and the Quality Of Service (QoS) can be guaranteed. However, the question that how to determine the task offloading strategy under various constraints of resources, distance, and cost is still an open issue. In this paper, we study the task offloading problem from a matching perspective and propose an Edge-User Assignment Algorithm (EUAA) that aims to maximize the resource utilization of edge servers and maximize the number of assigned IoT users. The main concern, in any matching algorithm, is how to generate the preference order of either side. To generate preference orders for edge servers, we use the concept of the Analytical Hierarchy Process (AHP). We have considered the following criteria: distance from users to the server, latency, available resources, and pricing. This generates the priority of the users for matching to edge servers. From IoT users' perspective, we use cost, and QoS parameters to improve their satisfaction. We have used state-of-the-art schemes to compare the performance of the proposed algorithm under different simulation scenarios. We compare the performance using the number of assigned users, servers' profit, number of satisfied users, edge server resources used, and execution time. The simulation results confirm that significant profit and satisfied user can be achieved by the proposed algorithm. © 2014 IEEE.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

Integration of data science with the intelligent IoT(IIoT): Current challenges and future perspectives

引用

Digital Communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon University University of Electronic Science and Technology of China College of Information Science and Engineering Hohai University Department of Computer Science University of Salerno Graduate School of Informatics and Engineering The University of Electro-Communications

The Intelligent Internet of Things(IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence(AI) and machine learning, to make accurate decisions. Data science is the science of dealing with data and its relationships through intelligent approaches. Most state-of-the-art research focuses independently on either data science or IIoT, rather than exploring their integration. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions.

关键词： Data science Internet of things (IoT) Big data Communication systems Networks Security Data science analytics

来源：评论

学校读者我要写书评

暂无评论

MF-CGAN: Multifeature Conditional GAN for Synthetic Data Generation in Internet of Medical Things

引用

IEEE Internet of Things Journal 2025年第10期12卷 13469-13476页

作者： Pandey, Chandrasen Tiwari, Vaibhav Francis, Sharmila A. J. Pal, Vipin Sinha Roy, Diptendu Manipal University Jaipur Department of Computer Science and Engineering Jaipur303007 India Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India King Khalid University Department of Computer Science and Engineering Abha61421 Saudi Arabia Netaji Subhash University of Technology Department of Computer Science and Engineering New Delhi110078 India National Institute of Technology Meghalaya Department of Computer Science and Engineering Sohra 793108 India

Synthetic data generation via generative artificial intelligence (GenAI) is essential for enhancing cybersecurity and safeguarding privacy in the Internet of Medical Things (IoMT) and healthcare. We introduce multifeature conditional generative adversarial network (GAN) (MF-CGAN), a Conditional GAN with multifeature integration, that generates realistic synthetic data using the WUSTL EHMS 2020 dataset of network traffic and health metrics. MF-CGAN’s architecture integrates conditional variables such as cyberattack types, traffic direction, and status flags, preserving their intricate interdependencies. The generator employs dense layers with batch normalization and Leaky ReLU activations to produce conditional synthetic data;the discriminator evaluates authenticity using the same conditional inputs. Experimental results show that MF-CGAN produces high-fidelity synthetic data, closely mirroring real data characteristics. In addition, we quantitatively demonstrate (MAE = 0.012 , RMSE = 0.035 , and classification accuracy up to 99%) that MF-CGAN-generated data effectively supports cybersecurity tasks, indicating its potential to significantly enhance data availability for IoMT. © 2014 IEEE.

关键词： Generative adversarial networks

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：