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IEEE Networking Letters 2023年第3期5卷 154-158页

作者： Elsayem, Mariam Abou-Zeid, Hatem Afana, Ali Givigi, Sidney Queen's University of Kingston School of Computing KingstonK7L 2N8 Canada University of Calgary Department of Electrical and Software Engineering CalgaryT2N 1N4 Canada R&d Ericsson Inc. Baseband Systems OttawaK2K 2V6 Canada

Future wireless networks will support applications demanding high data-rates, ultra-low latency, and high reliabilities. One technology for such ultra-reliable low latency communication (URLLC) is grant-free access for uplink resources, which enables user equipment (UE) to transmit data over pre-allocated resources, reducing signaling overhead and communication latency. This letter proposes a novel ensemble Deep Reinforcement Learning grant-allocator architecture combining offline and online learning providing robust performance with a wide range of dynamic network and UE scenarios. Results show enhancement of the overall latency of UEs for URLLC applications achieving less than 20 Transmission Time Interval latency for 95% of the transmissions. © 2019 IEEE.

关键词： Reinforcement learning

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Ontological Model for Cohesive Smart Health Services Management

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Computers, Materials & Continua 2023年第2期74卷 3679-3695页

作者： Muhammad Raza Naqvi Muhammad Waseem Iqbal Syed Khuram Shahzad M.Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Le Laboratoire Génie de Production de l’École Nationale d’Ingénieurs de Tarbes(LGP-INP-ENIT) Universitéde ToulouseCEDEXTarbes65016France Department of Software Engineering Superior UniversityLahore53700Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Computer Science University of Management and TechnologySialkot51310Pakistan Department of Computer Science King Abdul-Aziz UniversityJeddah21577Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21577Saudi Arabia

Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of *** massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human *** are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and *** applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service *** main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the *** ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare *** were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological *** proposed ontological model is derived from different classes,relationships,and constraints to integrate health care *** model is developed using Protégébased on each interrelated/correlated health care service having different *** querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge *** Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology *** to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.

关键词： Ontology internet of things smart health services integration

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud computing System 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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Healthcare Professionals’ Perceptions Towards Ai Integration in Jordan's Health Sector

SSRN

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SSRN 2024年

作者： Klaib, Ahmad F. Alhosanie, Tasneem N. Mahdin, Ladees W. Altatar, Enas J. Software Engineering Department King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Information Systems Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan

PurposeThe impact of AI on healthcare is widely recognized there remains a scarcity of studies examining how doctors perceive and approach its use in medicine. This study aims to gather insights from healthcare providers in Jordan concerning the advantages of integrating AI into practices, their perspectives on AI applications in healthcare, and their views on the future role of AI in replacing key tasks within health *** survey was conducted among healthcare professionals working at facilities in Jordan. An online questionnaire was used to collect data on demographics, attitudes toward using AI for tasks, and opinions on the benefits of AI adoption. Categorical variables were presented as counts and percentages, while the continuous variables were interpreted as mean and standard deviation. The associations between the determinants and the outcomes were done using one-way ANOVA. Any test with a P-value 0.05 was considered *** total of 612 healthcare professionals participated in the survey with females comprising a majority of respondents (52.8%). The majority of respondents showed optimism about AI’s potential to improve and revolutionize the field, although there were concerns about AI replacing human roles. Generally, physical therapists, medical researchers, and pharmacists displayed openness to incorporating AI into their work routines. Younger individuals aged between 18 and 40 seemed accepting of AI in the domain. A significant portion of participants believed that AI could negatively impact job opportunities and reduce the time needed for diagnosing conditions, but did not find any correlation, between responses and *** conclude, the results of this study suggest that healthcare professionals, in Jordan, hold receptive views on incorporating artificial intelligence in the medical field similar to their counterparts in developed nations. However, there is a concern about the implications of AI, on job stability a

关键词： Artificial intelligence

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Formal Timing Analysis and Verification of the Interrupt-driven Programs for ISO26262

Formal Timing Analysis and Verification of the Interrupt-dri...

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2025 2nd International Conference on Algorithms, software engineering and Network Security (ASENS)

作者： Qing She He Huang Shenglin Bao Xin Guo National Trusted Embedded Software Engineering Technology Research Center East China Normal University / Shanghai Huayuan Chuangxin Software Co. Ltd China High end Equipment Systems Department Shanghai Huayuan Chuangxin Software Co. Ltd China Basic Software Department East China Institute of Computing Technology China National Trusted Embedded Software Engineering Technology Research Center East China Normal University China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Safety-critical systems refer to software systems whose operation can affect the safety of people and facilities used in the automotive industry, aerospace, and hospital health care systems. The high level of safety of software in the aforementioned fields has often drawn attention. ISO26262 has been issued for the automotive industry, which defines standards and procedures, including software level, software development standards, and software validation standards to ensure the high level of safety of software. In high-safety automotive software, a real-time safeguard requires the analysis of interrupt-driven programming. Such analysis presents a challenging and complex task because of the uncertainty of interrupts. In ISO26262, the formalized method is suggested to verify the process of software development. To achieve the verification goals defined in ISO26262, we use a formal method instead of traditional verification methods to analyze worst-case execution time. We analyze interrupt-driven programs from the perspective of timeliness and present the Interrupt Control Flow Graph (ICFG), which is used to describe the control flow of interrupt-driven programs. The execution of interrupts may largely increase the number of program states, potentially leading to state explosion, resulting in a sizable ICFG. To solve this problem, we propose an algorithm for reducing the size of ICFG. On the basis of the reduced ICFG, the worst-case execution path can be determined and thus be used to verify whether the program execution time exceeds the expected time and identify the execution path that may cause a timeout problem.

关键词： Industries Uncertainty software algorithms software systems Timing Flow graphs Standards Automotive engineering software development management software engineering

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QSroute : A QoS Aware Routing Scheme for software Defined Networking

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

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International Conference on software engineering and Computer systems (ICSECS)

作者： Imran Edzereiq Kamarudin Mohamed Ariff Ameedeen Muhamad Idaham Umar Ong Azlee Zabidi Department of Computer Systems and Networking Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. software Defined Networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field.

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Multimodal Deep Neural Networks for Digitized Document Classification

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Computer systems Science & engineering 2024年第3期48卷 793-811页

作者： Aigerim Baimakhanova Ainur Zhumadillayeva Bigul Mukhametzhanova Natalya Glazyrina Rozamgul Niyazova Nurseit Zhunissov Aizhan Sambetbayeva Department of Computer Engineering Khoja Akhmet Yassawi International Kazakh-Turkish UniversityTurkistanKazakhstan Department of Computer and Software Engineering L.N.Gumilyov Eurasian National UniversityAstanaKazakhstan Department of Information and Computing Systems Non-Profit Joint Stock Company Abylkas Saginov Karaganda Technical UniversityKaragandaKazakhstan Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan

As digital technologies have advanced more rapidly,the number of paper documents recently converted into a digital format has exponentially *** respond to the urgent need to categorize the growing number of digitized documents,the classification of digitized documents in real time has been identified as the primary goal of our study.A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human *** intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits,which were not conceivable ten years *** learning aids in comprehending input patterns so that object classes may be *** segmentation process divides the input image into separate segments for a more thorough image *** study proposes a deep learning-enabled framework for automated document classification,which can be implemented in higher *** further this goal,a dataset was developed that includes seven categories:Diplomas,Personal documents,Journal of Accounting of higher education diplomas,Service letters,Orders,Production orders,and Student ***,a deep learning model based on Conv2D layers is proposed for the document classification *** the final part of this research,the proposed model is evaluated and compared with other machine-learning *** results demonstrate that the proposed deep learning model shows high results in document categorization overtaking the other machine learning models by reaching 94.84%,94.79%,94.62%,94.43%,94.07%in accuracy,precision,recall,F-score,and AUC-ROC,*** achieved results prove that the proposed deep model is acceptable to use in practice as an assistant to an office worker.

关键词： Document categorization deep learning machine learning classification digitization

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Medical Diagnosis Using Machine Learning:A Statistical Review

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Computers, Materials & Continua 2021年第4期67卷 107-125页

作者： Kaustubh Arun Bhavsar Jimmy Singla Yasser D.Al-Otaibi Oh-Young Song Yousaf Bin Zikria Ali Kashif Bashir Lovely Professional University Jalandhar144411India Department of Information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Department Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterM156BHUK

Decision making in case of medical diagnosis is a complicated process.A large number of overlapping structures and cases,and distractions,tiredness,and limitations with the human visual system can lead to inappropriate *** learning(ML)methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease *** academic papers involving the use of machine learning for disease diagnosis have been increasingly getting ***,to determine the use of ML to improve the diagnosis in varied medical disciplines,a systematic review is conducted in this *** carry out the review,six different databases are *** and exclusion criteria are employed to limit the ***,the eligible articles are classied depending on publication year,authors,type of articles,research objective,inputs and outputs,problem and research gaps,and ndings and *** the selected articles are analyzed to show the impact of ML methods in improving the disease *** ndings of this study show the most used ML methods and the most common diseases that are focused on by *** also shows the increase in use of machine learning for disease diagnosis over the *** results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.

关键词： Decision making disease diagnosis machine learning medical disciplines

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ChatGPT, Let Us Chat Sign Language: Experiments, Architectural Elements, Challenges and Research Directions

ChatGPT, Let Us Chat Sign Language: Experiments, Architectur...

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International Symposium on Networks, Computers and Communications (ISNCC)

作者： Nada Shahin Leila Ismail Department of CS and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of IT National Water and Energy Center UAE University Al-Ain UAE CLOUDS lab School of Computing and Information Systems The University of Melbourne Melbourne Australia

ChatGPT is a language model based on Generative AI. Existing research work on ChatGPT focused on its use in various domains. However, its potential for Sign Language Translation (SLT) is yet to be explored. This paper addresses this void. Therefore, we present GPT's evolution aiming a retrospective analysis of the improvements to its architecture for SLT. We explore ChatGPT's capabilities in translating different sign languages in paving the way to better accessibility for deaf and hard-of-hearing community. Our experimental results indicate that ChatGPT can accurately translate from English to American (ASL), Australian (AUSLAN), and British (BSL) sign languages and from Arabic Sign Language (ArSL) to English with only one prompt iteration. However, the model failed to translate from Arabic to ArSL and ASL, AUSLAN, and BSL to Arabic. Consequently, we present challenges and derive insights for future research directions.

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud computing System

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Procedia Computer Science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

关键词： Artificial Intelligence (AI) Cloud computing Diagnosis Digital Health Edge computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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