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10th International Conference on information Management, ICIM 2024

作者： Odeh, Yousra Software Engineering Department Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman11733 Jordan

ISBN: (纸本)9783031643583

The emergence of the metaverse has ushered in a new era of possibilities in healthcare, offering solutions that promise to enhance patient care, improve access to medical services, and revolutionize healthcare practices. In critical systems such as healthcare systems, the non-functional requirements are more significant than the functional requirements of the healthcare services. However, the classification of non-functional requirements of healthcare services in the metaverse has not been identified yet. In the virtual landscape of the healthcare in metaverse, the need for a comprehensive and well-defined classification of non-functional requirements that specifies desired quality requirements and associated constraints becomes paramount. This paper presents non-functional requirements classification for healthcare applications in the promising metaverse. This work is anticipated to contribute to facilitating further identification and specification of non-functional requirements in the domain and acting as basis for understanding these requirements for both healthcare and metaverse domain experts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Health care

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Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

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Neural Computing and Applications 2024年第30期36卷 18727-18759页

作者： Kassaymeh, Sofian Al-Betar, Mohammed Azmi Rjoubd, Gaith Fraihat, Salam Abdullah, Salwani Almasri, Ammar Software Engineering Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Artificial Intelligence Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Data Mining and Optimization Research Group Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia Bangi Malaysia Management Information Systems Department Al-Balqa Applied University Amman Jordan

This work presents a new version of the salp swarm optimizer (SSA), called "mSSA," that uses complex mathematical expressions to dynamically manipulate the crucial control parameter (c1) during optimization. These expressions are carefully designed to modulate the shift in search strategy from exploratory to exploitative, improving the flexibility and speed of convergence of the algorithm. To evaluate the performance of the developed mSSA variants, a thorough examination is carried out on twenty-three benchmark test functions alongside their application to the complex task of software module classification. The process of classifying defective software modules involves developing a multilayer perceptron (MLP) classifier that is suited to the particular complexity and heterogeneity of the task. Selecting the best optimizer is made easier by systematically evaluating the different mSSA versions as MLP classifier trainers. Based on metrics like classification accuracy, convergence speed, and avoidance of local minima, a comparative analysis in opposition to six previously published metaheuristic optimizers shows that mSSA3, when combined with the developed MLP classifier, outperforms both other mSSA variations and state-of-the-art metaheuristic optimizers in terms of overall performance. The excellent classification accuracy, swift convergence, and ability to avoid local minima of mSSA3 highlight its superiority and establish it as a cutting-edge method in the application of metaheuristic algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Global optimization

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based software Systems

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Computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Adaptive Data Forwarding Scheme for Diverse Infotainment Services in Vehicular Named Data Networking

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IEEE Transactions on Vehicular technology 2024年第5期74卷 8012-8022页

作者： Khan, Sajjad Ahmad Lim, Huhnkuk Chaudhry, Shehzad Ashraf Kocaeli University Software Engineering Department Izmit41000 Turkey Hoseo University Computer Engineering Department Korea Republic of Abu Dhabi University Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering & Architecture Nisantasi University Istanbul Turkey

Providing diverse infotainment services is crucial for enhancing road safety and improving the overall driving experience in vehicular networks. However, delivering these services within Vehicular Named Data Networking (VNDN) environments presents significant challenges, primarily due to the data broadcast phenomenon and the varying Quality of Service (QoS) requirements of different applications. This paper introduces an adaptive data forwarding scheme designed to effectively manage these challenges and optimize the delivery of infotainment services in VNDN. Our proposed scheme enhances the data packet format by incorporating additional attributes that prioritize data based on its criticality, specifically categorizing it into critical, non-critical, and entertainment data. Furthermore, we introduce an Enhanced Weighted Fair Queuing (EWFQ) policy to optimize the scheduling of data packets according to their importance. Two innovative data forwarding schemes, namely Advanced Push-based Data Forwarding (APDF) and Enhanced Pull-based Data Forwarding (EPDF), are developed to address the needs of critical and non-critical information and entertainment data, respectively. Simulation results demonstrate that our adaptive data forwarding scheme significantly outperforms legacy fragment schemes in delivering infotainment data within dynamic VNDN environments, ensuring reliability and efficiency. This paper contributes to advancing data dissemination strategies in vehicular networks and lays the groundwork for future research in adaptive QoS management. © 1967-2012 IEEE.

关键词： Entertainment industry Quality of service Adaptive systems Delays Data dissemination Vehicle dynamics Scalability Road safety Real-time systems Vehicles

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DeepOCL:A deep neural network for Object Constraint Language generation from unrestricted nature language

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CAAI Transactions on Intelligence technology 2024年第1期9卷 250-263页

作者： Yilong Yang Yibo Liu Tianshu Bao Weiru Wang Nan Niu Yongfeng Yin School of Software Beihang UniversityBeijingChina College of Computer Science and Technology Guizhou UniversityGuiyangGuizhouChina Faculty of Information Technology Beijing University of TechnologyBeijingChina Department of Electrical Engineering and Computer Sciences University of CincinnatiCincinnatiOhioUSA

Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.

关键词： deep learning OCL software engineering

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A Decision Support System for Enhancing Transportation Services Using Aspect-Based Sentiment Analysis

A Decision Support System for Enhancing Transportation Servi...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Benaddi, Lamya Ouaddi, Charaf Naimi, Lahbib Souha, Adnane Jakimi, Abdeslam Ouchao, Brahim Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurately identifies and classifies customer opinions on key transportation service aspects such as comfort, driver behavior, punctuality, and vehicle condition. By integrating the BERT model, known for its advanced natural language processing capabilities, our system provides an in-depth and reliable analysis of customer sentiments. This analysis enables managers and decision-makers to better understand the strengths and weaknesses of the provided transportation services and to precisely target areas that require improvement. By offering actionable insights, our approach aims to enhance the user experience, improve service quality, and increase overall customer satisfaction. © 2024 IEEE.

关键词： Customer satisfaction

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A Metamodel for a DSL to Design Decision Support Systems in Smart Tourism

A Metamodel for a DSL to Design Decision Support Systems in ...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ouaddi, Charaf Benaddi, Lamya Souha, Adnane Naimi, Lahbib Bouziane, El Mahi Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

The development of Decision Support Systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architecture. A typical DSS consists of a data component, a model component incorporating machine learning and deep learning techniques, and a user interface component, each one requires diverse technologies. This complexity increases development time and effort, hindering the efficient design of DSS tailored to specific domains like tourism, where real-time decision-making is essential. One promising approach to overcome these challenges is the use of Domain-Specific Languages (DSLs). DSLs allow developers to model and generate code more efficiently by providing high-level abstractions suited to the domain. By leveraging DSLs, it is possible to simplify the design process and automate the generation of code or parts of the DSS, thereby reducing the overall complexity and time required for development. This paper proposes a metamodel for designing DSS, specifically for the Smart Tourism sector. The metamodel serves as a foundation for the creation of DSL. The goal is to provide a solution to facilitate the development of DSS for the demands of tourism. The paper outlines the key concepts, approach, and results, presenting the first element of the proposed DSL which is the metamodel capturing the essential components and relationships for DSS in Smart Tourism. © 2024 IEEE.

关键词： Digital subscriber lines

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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

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

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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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

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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An Approach for Early Detection of Schizophrenia 15

An Approach for Early Detection of Schizophrenia

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Paranagama, Sachin Uditha, Isiwara Daundasekara, Pamodya Hettiarachchi, Shehani Weerasinghe, Lokesha Kasthurirathna, Dharshana Sri Lanka Institue Of Information Technology Faculty Of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350370249

Schizophrenia is a chronic mental disorder with distorted thinking, hallucinations, and social difficulties. Early diagnosis is vital, but current methods are limited. This research proposes a web-based application integrating machine learning, emotion recognition, and game-based elements to track symptoms and aid in early diagnosis. Interactive exercises focus on specific symptoms like cognitive evaluation, emotional analysis, and memory assessment. By combining computer science, psychology, and machine learning, the project aims to improve early detection and ultimately enhance the quality of life for affected individuals. Key components include using Deep Convolutional Neural Network (DCNN) architecture for recognizing facial and physical expressions associated with schizophrenia with an 87% accuracy rate, and utilizing Vector Regressor (SVR) with a 93% accuracy rate, along with Vision Transformer (ViT) and Custom CNN models for cognitive assessment, achieving accuracy rates of 99% and 98%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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