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2023 International Conference on Sustainable Technology and engineering, i-COSTE 2023

作者： Ramful, Raviduth Shoaib Casseem, Mohammad University of Mauritius Mechanical Production Engineering Department Reduit Mauritius University of Mauritius Department of Software and Information Systems Reduit Mauritius

ISBN: (纸本)9798350329711

The interest to look into alternative engineered materials, which would maximize the efficiency and performance attributes in numerous modern engineering application as well as to push the boundary imposed by present-day material science in the field engineering, is ever so high. One commonly searched feature in engineered materials is their high strength-to-weight ratio. The new AI (Artificial Intelligence) -driven technique of generative design has revolutionized the method of design refinement in Computer-Aided Design (CAD) by enabling advanced optimization techniques which was formerly-unavailable. This study seeks to apply the technique of generative design to improve the flexural behaviour of small clear Polylactic acid (PLA) specimens produced through the additive manufacturing process. The results obtained have shown that despite the optimized material geometry and weight, the refined model was still able to bear significantly high bending loads. Further analysis via the Finite Element Method (FEM) was conducted to substantiate the experimental investigation. Findings of this study illustrates the benefits of generative design which can seamlessly provide advanced design refinements all while preserving or enhancing mechanical performance of the material. © 2023 IEEE.

关键词： Finite element method

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Usability assessment of scholarly publishers’ online journal interfaces

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Journal of Data, information and Management 2023年第4期5卷 363-374页

作者： Peker, Serhat Dalveren, Gonca Gokce Menekse Department of Management Information Systems Izmir Bakirçay University İzmir 35665 Turkey Department of Software Engineering Atilim University Ankara 06830 Turkey

A well-designed and user-friendly online journal interface not only enhances researchers’ satisfaction but also bolsters the reputation of academic publishers within the scholarly community. Consequently, the present study undertakes an examination of the usability aspects pertaining to online journal interfaces offered by five prominent academic publishers: Wiley, Sage, Elsevier, Springer, and Taylor & Francis. This investigation encompasses a combination of user testing and post-questionnaire methodologies, aiming to accrue both quantitative and qualitative data concerning user task execution and satisfaction. The research findings spotlight the varying degrees of usability across major academic publishers and emphasize the critical role of design in enhancing researchers’ experiences. By identifying both usability strengths and weaknesses, along with offering actionable areas for improvement, this study adds a valuable perspective to the enhancement of academic journal interfaces. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Human computer interaction Usability evaluation User interfaces Web information systems Web usability

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MedConceptsQA: Open source medical concepts QA benchmark

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Computers in Biology and Medicine 2024年 182卷 109089-109089页

作者： Shoham, Ofir Ben Rappoport, Nadav Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel

Background: Clinical data often includes both standardized medical codes and natural language texts. This highlights the need for Clinical Large Language Models to understand these codes and their differences. We introduce a benchmark for evaluating the understanding of medical codes by various Large Language Models. Methods: We present MedConceptsQA, a dedicated open source benchmark for medical concepts question answering. The benchmark comprises of questions of various medical concepts across different vocabularies: diagnoses, procedures, and drugs. The questions are categorized into three levels of difficulty: easy, medium, and hard. We conduct evaluations of the benchmark using various Large Language Models. Results: Our findings show that most of the pre-trained clinical Large Language Models achieved accuracy levels close to random guessing on this benchmark, despite being pre-trained on medical data. However, GPT-4 achieves an absolute average improvement of 9-11% (9% for few-shot learning and 11% for zero-shot learning) compared to Llama3-OpenBioLLM-70B, the clinical Large Language Model that achieved the best results. Conclusion: Our benchmark serves as a valuable resource for evaluating the abilities of Large Language Models to interpret medical codes and distinguish between medical concepts. We demonstrate that most of the current state-of-the-art clinical Large Language Models achieve random guess performance, whereas GPT-3.5, GPT-4, and Llama3-70B outperform these clinical models, despite their primary focus during pre-training not being on the medical domain. Our benchmark is available at https://***/datasets/ofir408/MedConceptsQA. © 2024 The Author(s)

关键词： Zero-shot learning

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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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nPGSAO: A Hybrid Particle Swarm Optimization and Genetic Algorithm With Niching Technology for Edge Server Placement

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IEEE Internet of Things Journal 2025年第12期12卷 19370-19383页

作者： Wang, Bo Zhang, Zhifeng Song, Ying Chen, Ming Liu, Dongqing Zhengzhou University of Light Industry Software Engineering College Zhengzhou450002 China Beijing Information Science and Technology University Computer School Beijing100101 China Tongling University School of Mathematics and Computer Science Tongling China The First Affiliated Hospital of Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450052 China

In this article, we focus on the edge server placement problem (ESPP). ESPP is to decide positions (edge stations) where purchased edge servers (ESs) are placed when a service provider builds or upgrades its edge computing solution. The decision of ESPP largely determines the overall service quality and resource efficiency. We aim at providing ESPP solutions for optimizing the service quality in terms of the system response time, given a set of purchased ESs. To achieve this aim, we first deduce the optimal request dispatch for each edge station based on queue theory and Binomial theorem. Then, based on the deduced result, we formulate ESPP into a discrete nonlinear constrained optimization model. Because ESPP is NP-hard and generally multimodality, we designed a hybrid meta-heuristic algorithm to combine advantages of particle swarm optimization (PSO) and genetic algorithm (GA) as well as the niching technology, which is named nPGSAO. The basic idea of nPGSAO is grouping similar individuals into a niche and finding the niche best. Then, nPGSAO evolves each individual by integrating the swarm intelligence of PSO into the evolutionary process of GA based on its personal and niching best. To evaluate the performance of nPGSAO, we conducted extensive simulated experiments. The results show that nPGSAO achieves 5.78%–40.4% better response time with varied ESPP scale, compared to 11 up-to-date algorithms, and has a linear time complexity with the ESPP scale. These confirm the superior performance and scalability of nPGSAO. © 2014 IEEE.

关键词： Genetic algorithms

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Research and development of an information system for speech recognition based on neural network algorithms metrics 4

Research and development of an information system for speech...

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4th IEEE International Conference on Smart information systems and Technologies, SIST 2024

作者： Kanashev, Maksat Mukasheva, Assel Yedilkhan, Didar Rakhmetulaeva, Sabina Gnatyuk, Sergiy Nazym, Mirambayeva Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan The School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan The Department of Computer Engineering Astana IT University Astana Kazakhstan The Department of Information Systems International University of Information Technologies Almaty Kazakhstan The Faculty of Cybersecurity Computer and Software Engineering National Aviation University National Aviation University Kyiv Ukraine

ISBN: (纸本)9798350374865

This article examines the parameters of speech recognition in audio recordings containing a variety of phrases uttered by different people with different accents and intonations. The aim of the study is to evaluate the recognition speed, the accuracy of audio-to-text conversion and resource consumption when using SpeechRecognition and Levenshtein libraries. During the analysis, the above libraries were used to record and recognize audio files, as well as to calculate the distance between two lines and determine the recognition accuracy. The findings of this study can be useful for improving speech recognition systems and creating more adaptive and efficient solutions for audio data processing. The results of comparing the accuracy of speech recognition models are recorded in JSON format, after which, using the matplotlib library, they are visualized as graphs, comparing the speed and accuracy of recognition. Visualization of data in this way contributes to their improvement and more effective integration into educational systems. In conclusion, the authors emphasize the prospects for further development of speech recognition technologies in different fields. © 2024 IEEE.

关键词： Audio recordings

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In silico prediction methods of self-interacting proteins:an empirical and academic survey

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Frontiers of Computer Science 2023年第3期17卷 199-216页

作者： Zhanheng CHEN Zhuhong YOU Qinhu ZHANG Zhenhao GUO Siguo WANG Yanbin WANG College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China Institute of Machine Learning and Systems Biology School of Electronics and Information EngineeringTongji UniversityShanghai 201804China School of Cyber Science and Technology Zhejiang UniversityHangzhou 310058China

In silico prediction of self-interacting proteins(SIPs)has become an important part of *** is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab *** goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction,to provide important references for actual work in the *** this review,we first describe the data required for the task of DTIs ***,some interesting feature extraction methods and computational models are presented on this topic in a timely ***,an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding ***,we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.

关键词： proteomics self-interacting proteins feature extraction prediction model

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Optimizing Clustering Approaches in Cloud Environments

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 70-94页

作者： Al-Ghuwairi, Abdel-Rahman Al-Fraihat, Dimah Sharrab, Yousef Kreishan, Yazeed Alsarhan, Ayoub Idhaim, Hasan Qahmash, Ayman Department of Software Engineering Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Computer Science College King Khalid University Abha Saudi Arabia

This study focuses on the challenge of developing abstract models to differentiate various cloud resources. It explores the advancements in cloud products that offer specialized services to meet specific external needs. The study proposes a new approach to request processing in clusters, improving downtime, load distribution, and overall performance. A comparison of three clustering approaches is conducted: local single cluster, local multiple clusters, and multiple cloud clusters. Performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness are evaluated through experiments with 50 requests. All three approaches achieve a 100% success rate, but processing times vary. The local single cluster has the longest duration, while the local multiple clusters and multiple cloud clusters perform better and offer faster processing, scalability, fault tolerance, and availability. From a cost perspective, the local single cluster and local multiple clusters incur capital and operational expenses, while the multiple cloud clusters follow a pay-as-you-go model. Overall, the local multiple clusters and multiple cloud clusters outperform the local single cluster in terms of performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness. These findings provide valuable insights for selecting appropriate clustering strategies in cloud environments. © 2023 by the authors of this article. Published under CC-BY.

关键词： Fault tolerance

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Gloss: Guiding Large Language Models to Answer Questions from System Logs 31

Gloss: Guiding Large Language Models to Answer Questions fro...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Huang, Shaohan Liu, Yi Qi, Jiaxing Shang, Jing Xiao, Zhiwen Fung, Carol Wu, Zhihui Yang, Hailong Luan, Zhongzhi Qian, Depei Sino-German Joint Software Institute Beihang University Beijing China China Mobile Information Technology Center Beijing China Concordia Institute for Information Systems Engineering Concordia University Quebec Canada

ISBN: (纸本)9798350330663

System logs contain valuable information and they have emerged as one of the most crucial data sources for system monitoring aimed at enhancing service quality. IT support teams and system administrators are in dire need of an intelligent log-based QA system to help them quickly identify, diagnose, and resolve issues. In this paper, we propose a novel method for constructing log-based question-answering (QA) data using large language models, addressing challenges associated with limited dataset size and diversity in existing log-based QA systems. Our pipeline consists of three steps: generating questions, answering log questions, and refining question-answer pairs. The purpose of the generating questions is to create a diverse set of log-related queries that cover a wide range of potential issues. The second step, answering log questions, aims to extract relevant information from the logs to address the generated questions. This step ensures accurate and context-aware responses. Refining question-answer pairs is intended to improve the overall quality and consistency of the generated log-based QA data. We present a case study using ChatGPT to generate a new dataset, LogQuAD, containing over 28,000 question-answer pairs derived from more than 31,000 raw logs, representing a significant increase compared to existing datasets like LogQA. In our experimental setting, we sample half of the data as the training set and use memory-effect fine-tuning to fine-tune the model, named Gloss. Experimental results show that our method can generate high-quality log-based QA data, leading to improved performance of log-based QA models. Notably, our fine-tuned 7B model outperforms the LLaMA-65B model. This approach can potentially save valuable time for IT support teams and system administrators, enabling proactive problem resolution and optimal system performance. © 2024 IEEE.

关键词： Large datasets

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