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

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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Video captioning – a survey

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Multimedia Tools and Applications 2025年第2期84卷 947-978页

作者： Vaishnavi, J. Narmatha, V. Department of Computer Applications SRM Institute of Science and Technology Trichy India Department of Computer and Information Science Annamalai University Annamalai Nagar Tamilnadu 608002 India

The explosion of the novel phenomenon of the combination of computer vision and Natural language processing is playing a vital role in converting the ordinary world into a more technological pool. Natural language processing and computer vision are the vanguard of Artificial intelligence with the enormous potential currently ruling more fields. The trending Accelerated research of the NLP and CV combo are cancer screening, surgical simulation, visual properties description, visual retrieval, visual description, etc. Video captioning is a task of localizing the event and various features are extracted from the particular event and then the description of the entire event is performed with Natural languages using various techniques. The task of video captioning has some evolution of various techniques from Traditional methods to Deep learning. Currently, Deep learning techniques are ruling the field. Several models are being proposed for the better enhancement of the video captioning task with the default Encoder-Decoder framework and some models with the variance of Attention mechanism and Transfer learning etc., The Attainment of every technique and the accuracy of the generated results are highly dependent on the nature of the problem, diversity, performance of chosen dataset and construction of the technique with various layers, and the amount of data split for train, validate, and test. This survey discusses the captioning types, real-time applications of video captioning, evolution of various methods in video captioning, advanced techniques, different datasets, metrics that are used to evaluate the results, and analysis of the results of the existing models for video captioning. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computer vision

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A Novel Framework for Gauging information Extracted from Smartphones Using Neutrosophic Logic

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Neutrosophic Sets and Systems 2025年 76卷 154-171页

作者： Abou Alzahab, R.M. Ismail, Amr Abd Elkhalik, S.H. Shams, M.Y. El-Bakry, H.M. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Department. of Communication and Computer Engineering Air Defense University Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Information Systems. Faculty of Computer and Information Science Mansoura University Egypt

Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and use the most relevant information. Neutrosophic logic is a generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool for dealing with the ambiguity of real-world data. The proposed framework aims to identify and utilize the most relevant information for forensic investigators, making it easier to solve criminal cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, or Indeterminacy of this information. Additionally, this study analyzes conversations between individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in each conversation and which can be used to determine its admissibility as evidence and which not. © 2025, University of New Mexico. All rights reserved.

关键词： Smartphones

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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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Dynamic Spatial Focus in Alzheimer’s Disease Diagnosis via Multiple CNN Architectures and Dynamic GradNet

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computers, Materials & Continua 2025年第5期83卷 2109-2142页

作者： Jasem Almotiri Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The evolving field of Alzheimer’s disease(AD)diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance(MR)*** study introduces Dynamic GradNet,a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD ***,four state-of-the-art convolutional neural network(CNN)architectures,the self-regulated network(RegNet),residual network(ResNet),densely connected convolutional network(DenseNet),and efficient network(EfficientNet),were comprehensively compared via a unified preprocessing pipeline to ensure a fair *** these models,EfficientNet consistently demonstrated superior performance in terms of accuracy,precision,recall,and F1 *** a result,EfficientNetwas selected as the foundation for implementing Dynamic *** GradNet incorporates gradient weighted class activation mapping(GradCAM)into the training process,facilitating dynamic adjustments that focus on critical brain regions associated with early dementia *** adjustments are particularly effective in identifying subtle changes associated with very mild dementia,enabling early diagnosis and *** model was evaluated with the OASIS dataset,which contains greater than 80,000 brain MR images categorized into four distinct stages of AD *** proposed model outperformed the baseline architectures,achieving remarkable generalizability across all *** findingwas especially evident in early-stage dementia detection,where Dynamic GradNet significantly reduced false positives and enhanced classification *** findings highlight the potential of Dynamic GradNet as a robust and scalable approach for AD diagnosis,providing a promising alternative to traditional attention-based *** model’s ability to dynamically adjust spatial focus offers a powerful tool in artificial intelligence(AI)assisted precisionmedicine,particularly in the early det

关键词： Spatial focus GradCAM medical image classification deep learning early dementia detection neurodegenerative disease MRI analysis Alzheimer’s attention CNN

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An Efficient Two-State Feature Attention-Based GRU for Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第15期49卷 95-112页

作者： Mohammed, Dhurgham Ali Patel, Kalyani A. Faculty of Computer Science Gujarat University Ahmedabad India Department of Computer Science Faculty of Education for Girl University of Kufa Iraq K.S. School of Business Management and Information Technology Gujarat University India

Text classification has become crucial for mechanically sorting documents into specific categories. The goal of classification is to assign a predefined group or class to an instance based on its characteristics. To attain precise text categorization, a feature selection scheme is employed to categorize significant features and eliminate irrelevant, undesirable, and noisy ones, thereby reducing the dimensionality of the feature space. Many advanced deep learning algorithms have been developed to handle text classification drawbacks. Recurrent neural networks (RNNs) are broadly employed in text classification tasks. In this paper, we referred to a novel Two-state GRU based on a Feature Attention strategy, known as Two-State Feature Attention GRU (TS-FA-GRU). The proposed framework identifies and categorizes word polarity through consecutive mechanisms and word-feature capture. Furthermore, the developed study incorporates a pre-feature attention TS-FA-GRU to capture essential features at an early stage, followed by a post-feature attention GRU that mimics the decoder’s function to refine the extracted features. To enhance computational performance, the reset gate in the ordinary GRU is replaced with an update gate, which helps to reduce redundancy and complexity. The effectiveness of the developed model was tested on five benchmark text datasets and compared with five well-established traditional text classification methods. The proposed TS-FA-GRU model demonstrated superior performance over several traditional approaches regarding convergence rate and accuracy. Experimental outcomes revealed that the TS-FA-GRU model achieved excellent text classification accuracies of 93.86%, 92.69%, 94.73%, 92.46%, and 88.23 on the 20NG, R21578, AG News, IMDB, and Amazon review dataset respectively. Moreover, the results indicated that the proposed model effectively minimized the loss function and captured long-term dependencies, leading to exceptional outcomes when compared to the

关键词： Recurrent neural networks

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Ensemble Deep Learning Approaches in Health Care:A Review

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computers, Materials & Continua 2025年第3期82卷 3741-3771页

作者： Aziz Alotaibi Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21974Saudi Arabia

Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of ***,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/*** the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big *** deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning *** ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble *** deep learning has been successfully used in several areas,such as bioinformatics,finance,and health *** this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug *** cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also ***,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and ***,future directions and opportunities for enhancing healthcare model performance are discussed.

关键词： Deep learning ensemble learning deep ensemble learning deep learning approaches for health care health care

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Optimizing Broadcast Utilization for Efficient Disaster Management Using Wireless Ad Hoc Networks and Novel Energy-Saving Algorithms

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第20期18卷 142-156页

作者： Alzyoud, Faisal Y. Tarawneh, Monther Almaghthawi, Ahmed Altalidi, Askar Asiri, Layla Alrehaili, Meaad Department of Computer Science Faculty of Information Technology Isra University Amman Jordan Department of Information Technology College of Information Technology and Communications Tafila TechnicaUniversity Tafila Jordan Department of Computer Science College of Science & Art at Mahayil King Khalid University Abha Saudi Arabia Department of Information Science College of Science & Art at Mahayil King Khalid University Abha Saudi Arabia Departement of Computer Information System Applied college at Mahayil King Khalid University Abha Saudi Arabia Department of Computer Sciences and Artificial Intelligence Collège of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

Globalization, industry, and population growth is considered the main reasons for climate change, so when some natural calamity or disaster occurs in any area, it leads to isolating this area from the rest of the region. Wireless ad hoc networks are important in disaster management to reduce losses and costs as they reduce the response, aiding time to save lives. However, they suffer from limited energy, which controls the lifetime of the sensor. Different methods are applied to save energy and increase battery life, such as sleep-awake schedules which assume similar initial energy for all nodes. Domestic partition in a unit disk graph is the production of the maximum possible number of such disjoint dominants. However, it assumes different initial energy levels for all nodes, which is another problem. In this paper, a new broadcast approach based on minimum set data broadcasting is proposed to mini-mize the number of broadcasting messages to increase the battery life during disasters and enhance rescue operations. The proposed minimum rebroadcast algorithm (MSRA) method balances coverage and utilization efficiency by dynamically adjusting node selection based on network conditions. The results show that MSRA significantly reduces power consumption and transmission latency, opening the way to find a new solution for disaster management using wireless ad hoc networks. © 2024 by the authors of this article.

关键词： Mobile ad hoc networks

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A new CNN-based watermarking method for color medical images in a fusion domain

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Neural Computing and Applications 2025年 1-19页

作者： Hosny, Khalid M. Abdel-Aziz, Mostafa M. Lashin, Nabil A. Hamza, Hanaa M. Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Department of Information Technology Faculty of Information Technology and Computer Science Sinai University Arish Campus Al-Arish45518 Egypt

The transmission of medical images via medical agencies raises security concerns, necessitating increased security measures to ensure integrity and security. However, many watermarking algorithms overlook equipoise;the relation between robustness, invisibility, and payload capacity results in a less satisfactory performance. To bridge this gap, we propose a new blind watermarking method for securing medical images based on a convolutional neural network in a fusion domain. First, we transmute the host color image using a phase-only transform (PHOT) to detect the surface pattern. We feed the detected surface pattern into a pre-trained VGG19 model to impeccably extract the stable feature vector. We encrypt the extracted image features using a hybrid Chirikov map to enhance their robustness and reliability. The synergy between the pre-trained VGG19 model and PHOT in an integrated domain effectively captures additional native and localized image features. A hybrid Fibonacci Q-matrix method scrambles the binary watermark to enhance security. Integrating double encryption into the proposed method markedly enhances its resistance against diverse countermeasures. Experimental outcomes indicate that the proposed scheme performs efficiently in robustness and invisibility. The obtained PSNR value was up to 60.15 dB, which is optimal for human perception. The embedded watermark can be retrieved without any warping. The retrieved watermark seems to be authentic, exhibiting ideal BER and NC values. In almost all attack circumstances, the BER values approached zero while the NC values got closer. The proposed method demonstrates notable enhancements in robustness and invisibility compared to prior methods. © The Author(s) 2025.

关键词： Watermarking

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An Experimental Study on Dynamic Lifelong Learning with GPT for Mitigating Catastrophic Forgetting in Aspect-Based Sentiment Analysis

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IEEE Access 2025年 13卷 90316-90332页

作者： Huang, Huang Mustafa, Mumtaz Begum Asemi, Adeleh University of Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Branch Faculty of Engineering Department of Computer Engineering Isfahan81551-39998 Iran

GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for individuals and small businesses, underscoring the need for efficient, domain-specific alternatives. In Aspect-Based Sentiment Analysis (ABSA), existing models are typically optimized for single domains, facing challenges in performing effectively across multiple domains. A key issue, known as "catastrophic forgetting,"arises when models trained on one domain lose previously learned knowledge upon exposure to new domain data. This leads to two significant problems: limited cross-domain generalization and difficulty in retaining prior knowledge while learning domain-specific information. To address these challenges, we introduce the Dynamic Lifelong Learning Aspect-Based Sentiment Analysis GPT (DllaGPT), a model designed to handle multiple domains while mitigating catastrophic forgetting. Leveraging datasets from four ABSA domains - Laptops, Restaurants, Tweets, and Finance - this study fine-tunes a pretrained GPT model from HuggingFace sequentially across domains. DllaGPT employs a mechanism to retain real data from earlier domains during new domain training, effectively preserving prior knowledge. Experimental results highlight that DllaGPT achieves an average accuracy of 0.85 and a Backward Transfer (BWT) score of -0.09 across the four domains, showcasing its high accuracy and robust lifelong learning capabilities. © 2013 IEEE.

关键词： Contrastive Learning

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