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

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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

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S3GAAR: Segmented Spatiotemporal Skeleton Graph-Attention for Action Recognition

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IEEE Transactions on Multimedia 2025年 27卷 3437-3446页

作者： Ghaseb, Musrea Abdo Elhayek, Ahmed Alsolami, Fawaz Ali, Abdullah Marish King Abdulaziz University Faculty of Computing and Information Technology Jeddah Saudi Arabia University of Prince Mugrin Faculty of Computer and Cyber Sciences Madinah Saudi Arabia

Human motion recognition is extremely important for many practical applications in several disciplines, such as surveillance, medicine, sports, gait analysis, and computer graphics. Graph convolutional networks (GCNs) enhance the accuracy and performance of skeleton-based action recognition. However, this approach has difficulties in modeling long-term temporal dependencies. In Addition, the fixed topology of the skeleton graph is not sufficiently robust to extract features for skeleton motions. Although transformers that rely entirely on self-attention have demonstrated great success in modeling global correlations between inputs and outputs, they ignore the local correlations between joints. In this study, we propose a novel segmented spatiotemporal skeleton graph-attention network (S3GAAR) to effectively learn different human actions and concentrate on the most operative part of the human body for each action. The proposed S3GAAR models spatial-temporal features through spatiotemporal attention for each segment to capture short-term temporal dependencies. Owing to several human actions that focus on one or more body parts such as mutual actions, our novel method divides the human skeleton into three segments: superior, inferior, and extremity joints. Our proposed method is designed to extract the features of each segment individually because human actions focus on one or more segments. Moreover, our segmented spatiotemporal graph introduces additional edges between important distant joints in the same segment. The experimental results show that our novel method outperforms state-of-the-art methods up to 1.1% on two large-scale benchmark datasets, NTU-RGB+D 60 and NTU-RGB+D 120. © 1999-2012 IEEE.

关键词： Sports medicine

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Numerical and spatiotemporal features fusion for video summarization

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Multimedia Tools and Applications 2025年 1-16页

作者： Aboelenien, Mohamed Salem, Mohammed A.-M. Faculty of Media Engineering and Technology German University In Cairo Cairo11865 Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo11566 Egypt

In this paper, we propose a numerical spatiotemporal approach for video summarization. The current solutions leverage deep learning techniques to tackle this task. However, existing methods do not employ the video shots’ length data in their networks. We first introduce a new ground truth labelling for video summarization. This ground truth tackles multiple users’ annotations and is inclusive of video shot length information. We then propose a novel network architecture Numerical SpatioTemporal Fusion Net, NSTFNet. The proposed architecture leverages the temporal modelling ability of the self-attention mechanism to model video’s vision data to be fused with structured numerical features of video shots. We evaluate our results on TVSum and SumMe datasets. Experimental results show that the proposed model outperforms state-of-the-art performance on the SumMe dataset with 51.29% and 54.42% f-scores on the canonical and augmented configurations, demonstrating the effectiveness of our proposed model compared to state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Video analysis

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of information technology (Singapore) 2024年 1-21页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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Machine Learning Based Predictive Modelling for Fraud Detection in Digital Banking 21

Machine Learning Based Predictive Modelling for Fraud Detect...

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21st IEEE International Conference on computer Applications, ICCA 2024

作者： Thar, Kaung Wai Wai, Thinn Thinn University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9789998601673

Banking stands out as a pioneering industry, having amassed vast amounts of structured data over its history and being among the earliest adopters of data science technologies. The world is witnessing rapid growth in banking sector and hence payment transaction method is changing into the use of cashless transaction instead of the use of cash money. Digital banking gives customers a better service quality in the areas of prepaid card, credit card, cashless payment, money transfer, etc. However, the security of digital banking has received attention due to the fraudulent behavior of fraudsters as the absence of sufficient digital banking security has kept many customers away from the service till today. Although fraud is not a new issue, activities related with fraud always cost the global economy billions of dollars each year. Fraudulent activities involve inclusively significant financial risks which may threaten profitability, and the image of the whole of an economic sector. This study aims to use machine learning based predictive modelling to implement to improve fraud detection and prevention, inside of a public or private economic entity. In this paper, this problem is focused on the propose an autonomous and effective intelligent new ensemble method for fraud transaction detection. Firstly, Hidden Markov Model (HMM) is used for observing the hidden states of financial transaction, that is based on the idea of the probabilistic models designed for sequences of observations where the underlying process is assumed to be a Markov process with hidden states. And then, machine learning model called Gradient Boosting Classifier (GBC) is applied to classify the fraud. Finally, our hybrid method integrates GBC and HMM. Experiments are conducted to ensure the effectiveness of HMM and GBC. © 2024 IEEE.

关键词： Predictive analytics

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Assessing and prioritizing crucial drivers for CloudIoT-based healthcare adoption: an analytic hierarchy process approach

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International Journal of information technology (Singapore) 2024年 1-18页

The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare settings remains limited. There is a lack of comprehensive understanding of the factors that ensure successful implementation. To address this issue, we have used the Analytic Hierarchy Process to prioritize 26 factors that could potentially influence the adoption of CloudIoT-based healthcare. These factors were derived from existing literature and categorized into four main groups: personal-specific, technology-specific, environment-specific, and organization-specific factors. Our findings revealed that performance expectancy, trust, facilitating conditions, task complexity, and perceived security are the top five critical factors influencing CloudIoT-based healthcare adoption. Interestingly, our results indicated that environmental factors were less influential than those in the other categories, with personal, technological, and organizational factors dominating the top ten significant factors. The insights from this study can help policymakers, CloudIoT service providers, healthcare providers, designers, and developers better understand the factors contributing to the successful implementation of CloudIoT-based healthcare. This understanding can guide decision-making and inform the development of strategies to promote the broader adoption of this innovative technology. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Analytic hierarchy process CloudIoT Factors Healthcare industry

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DLOps: Developing Deep Learning Aided Burmese Rice Price Forecasting 5

DLOps: Developing Deep Learning Aided Burmese Rice Price For...

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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： San, Khin Pyae Phyo Thein, Nwe Nwe Myint University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

In nowadays technology areas, a pivotal concern has emerged. It's was especially concerning architecting Deep Learning enabled Systems (DLS) and sustainability. Most of DLS were failed while release to production. That's because of uncertainties in data variations, evolutionary requirements, and model inconsistencies. The one suitable solution is Deep Learning Operations (DLOps). It enhances reproducibility and sustainability in building DLS. It still has challenges and are related to environmental impact, technical maintenance, and economic concerns. The self-adaptation helps to address and overcome these challenges. In this paper proposed a sustainable modularized DLOps pipeline thorough the conceptual MAPE-K Framework to solve technical maintenance and economic concerns. The proposed system addresses the uncertainties in data handling and model training then try to proof out efficient utilization from economic concerns point of view. Further, the proposed system is implemented for a Burmese Rice Price Prediction use case to display DLOps pipeline sustainability from technical maintenance point of view. © 2024 IEEE.

关键词： Sustainable development

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Model of Secure E-Wallet Readiness 1

Model of Secure E-Wallet Readiness

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Qubati, Sana Abdulbaqi Al-Shaibany, Nagi Ali IT Department Faculty of Computer and Information Technology Sana’a University Yemen

ISBN: (纸本)9798331533557

The study's main objective is to evaluate Yemeni e-wallets' preparedness to provide safe fintech services in the rapidly developing digital economy. In general Yemen is facing several obstacles to the widespread use of digital payment services, such as a lack of understanding, incomplete regulations, inadequate infrastructure, and cybersecurity concerns. The study looks at how well regulatory bodies function, how resilient e-wallets are to cybersecurity attacks, and how prepared the architecture of digital wallets is to accommodate fintech businesses. The goal of the research is to improve e-wallet security features and guarantee the security of financial transactions by examining aspects including IT governance, communication security, and user authentication. Eleven e-wallets in Sana’a (Yemen capital) were assessed using a questionnaire based on international standards. The results showed that while there is still room for development, these e-wallets are well-equipped to offer secure fintech services. The results collectively imply that Yemeni e-wallets are capable of providing secure financial services. © 2024 IEEE.

关键词： Fintech

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