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Frontiers in Education (FIE) Conference

作者： V. Uskov Department of computer Science and Information Systems Bradley University Peoria IL USA

The state-of-the-art InterLabs Web-lecturing tool has been designed, developed, and tested at the Department of computer Science and information systems and the InterLabs Research Institute, Bradley University, Peoria, Illinois as a part of a National Science Foundation grant. It is based on active utilization of innovative Web-based software engineering, portal, multiagent, streaming multimedia, communication, teaching, learning and information technologies. The introductory "computer information systems" CIS102 course is a required course for all freshman students with major in either engineering, business, science or education areas. As a result, the innovative Web-based CIS102 course was aimed to (1) provide students of various major areas with highest-possible learning convenience, and (2) meet different requirements for CIS102 course of various Bradley departments. The new CIS102 online course was designed and developed by faculty of the computer Science and information systems department in cooperation with faculty from other Bradley colleges. Student formative and summative evaluations and academic performance clearly demonstrate the advantages of the innovative online teaching and learning technologies based on the InterLabs Web-lecturing tool.

关键词： information systems computer science Streaming media System testing Software engineering Portals Multimedia communication information technology computer science education systems engineering education

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Blockchain technology adoption in healthcare: a systematic review and conceptual framework

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International Journal of Industrial and systems Engineering 2025年第1期50卷 1-57页

作者： Abdou, Ashraf Sayed El-Demerdash, Basma E. Mazen, Sherif A. Darwish, Nagy Ramadan Department of Information Systems Faculty of Graduate Studies for Statistical Research Cairo University Egypt Department of Operations Research and Management Faculty of Graduate Studies for Statistical Research Cairo University Egypt Department of Information Systems Faculty of Computer and Artificial Intelligence Cairo University Egypt

Recently, blockchain technology has attracted a lot of interest from different researchers and academics due to its unique properties like immutability, interoperability, and confidentiality. However, to date, their adoption in the healthcare sector is still very limited. Few studies applied a systematic literature review (SLR) for blockchain adoption in healthcare. In this research study, the first contribution is to identify the factors that influence the adoption of blockchain by applying the SLR approach, understand how these factors are interrelated, and discuss the main challenges of blockchain adoption. The findings demonstrated that, the unified theory of acceptance and use of technology (UTAUT), the technology acceptance model (TAM) and its extension were the most popular models used for blockchain adoption. Then, we identified the key research gaps and proposed a conceptual framework to address the identified gaps to be a reference and guide for organisations adopting blockchain in healthcare. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： blockchain adoption blockchain technology healthcare SLR systematic literature review TAM technology acceptance model TOE UTAUT

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Novel Feature Extractor Framework in Conjunction with Supervised Three Class-XGBoost Algorithm for Osteosarcoma Detection from Whole Slide Medical Histopathology Images

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computers, Materials & Continua 2025年第2期82卷 3337-3353页

作者： Tanzila Saba Muhammad Mujahid Shaha Al-Otaibi Noor Ayesha Amjad Rehman Khan Artificial Intelligence&Data Analytics Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.

关键词： Medical image processing deep learning healthcare image classification histopathology

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Velocity control of a Stephenson III six-bar linkage-based gait rehabilitation robot using deep reinforcement learning

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Neural Computing and Applications 2025年第7期37卷 5671-5682页

作者： Kapsalyamov, Akim Brown, Nicholas A. T. Goecke, Roland Jamwal, Prashant K. Hussain, Shahid School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Clinical Sciences Queensland University of Technology BrisbaneQLD Australia School of Systems and Computing UNSW CanberraACT Australia Department of Electrical and Computer Engineering Nazarbayev University Nur-Sultan City Kazakhstan

Lower limb rehabilitation robots can help to improve the locomotor capabilities of patients experiencing gait impairments and help medical workers by reducing strain on them. However, since commercially available exoskeletons are expensive and there is a lack of number of physiotherapists many patients are still not able to get proper rehabilitation training. The closed-loop linkage mechanisms have recently drawn much attention in the realization of gait rehabilitation robots. Such mechanisms are affordable and capable of providing suitable trajectories for gait training therapy. In this work, we have proposed a fully operational one degree-of-freedom mechanism which can generate complex naturalistic lower limb trajectories. Although in theory, it is assumed that the constant speed applied at the input crank is sufficient to control the system, in reality, the external forces exerted by human legs and the inertia of the links can greatly alter the rotational velocity at the crank, which may negatively affect the training process. Therefore, we have explored the performance of a deep reinforcement learning-based control algorithm designed to regulate the speed of the input crank to reach satisfactory performance needed for gait rehabilitation training. Experimental evaluations with healthy human subjects were conducted to demonstrate that the mechanism is capable of directing lower limbs on naturalistic gait trajectories with a required walking speed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Exoskeleton (Robotics)

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LLM-Driven Cross-Platform Code Generation for Polyhedral Optimized NPDP Codes 15th

LLM-Driven Cross-Platform Code Generation for Polyhedral Op...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Palkowski, Marek Faculty of Computer Science and Information Systems West Pomeranian University of Technology in Szczecin Zolnierska 49 Szczecin71210 Poland

ISBN: (纸本)9783031856990

This paper explores using large language models (LLM) for automatic and source-to-source programming across various cross-platform languages and libraries, including generating CUDA code for high performance computing (HPC). Our proposed solution aims to demonstrate the capabilities of ChatGPT in generating valid CUDA code based on existing OpenMP code obtained from a polyhedral compilers Traco and Dapt. Specifically, we intend to showcase the model’s proficiency in producing equivalent and valid GPU code. Furthermore, we extend the application of our solution by prompting the AI model to generate similar code for another input benchmark, emphasizing its adaptability and versatility across different scenarios. This approach seeks to highlight the model’s capacity to provide automated and efficient solutions for diverse programming challenges. We chose non-trivial kernels from the non-serial polyadic dynamic programming (NPDP) benchmark with non-uniform loops. The focus is on ensuring code validity and understanding limitations in obtaining valid CUDA code. Additionally, we assess the efficiency and scalability of NVIDIA A100 GPU codes generated by artificial intelligence (AI) models, comparing them with optimized Intel Xeon Gold CPU codes from polyhedral optimizers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dynamic programming

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Utilizing TOGAF 10 to Design an Enterprise Architecture for BPRBCo SME Digital Transformation

Utilizing TOGAF 10 to Design an Enterprise Architecture for ...

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2025 International Conference on Advancement in Data Science, E-learning and information System, ICADEIS 2025

作者： Mumtaza, Farisa Fikri Mulyana, Rahmat Mukti, Iqbal Yulizar Telkom University Information Systems Department Bandung Indonesia Stockholm University Department of Computer and System Sciences Stockholm Sweden

ISBN: (纸本)9798331513320

Digital Transformation (DT) has become a strategic imperative for Small and Medium Enterprise (SME) banks, including for Bank Perekonomian Rakyat (BPRBCo) in Indonesia, which often face constraints in infrastructure, technology adoption, and regulatory compliance. While prior research has underscored the importance of DT strategy and architecture for success in large banking institutions, how these insights apply to smaller-scale banks remains underexplored. This paper develops an Enterprise Architecture (EA) blueprint for BPRBCo, an SME-scale bank, following a Design Science Research approach that encompasses problem explication, requirement specification, design and development, demonstration, and evaluation. Data was collected through semistructured interviews and triangulated with internal documents. By employing the latest best practice TOGAF Standard 10th Edition from preliminary planning to migration, the study proposes an integrated architecture featuring core banking enhancements and loan management automation. The resulting blueprint, incorporated into BPRBCo's DT Strategy, provides a replicable roadmap for aligning technology investments with business objectives. This research contributes both theoretically by extending EA discourse to smaller-scale banking contexts - and practically by offering actionable guidance for BPR-scale organizations seeking to achieve successful DT despite limited resources. © 2025 IEEE.

关键词： Investments

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th International Conference on Advances in information Communication Technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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History of Artificial Lighting, From Fire to LED [History]

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IEEE INDUSTRY APPLICATIONS MAGAZINE 2025年第2期31卷 10-25页

作者： Canale, Laurent Hamady, Mohamad S & C Electric Company Chicago Illinois USA Research Engineer CNRS LAPLACE Laboratory UMR 5213 Paul Sabatier University Toulouse Cedex 9 France Computer Systems Department Faculty of Computer and Information Sciences Ain Shams University Cairo Cairo Egypt

Having become so present and accessible, artificial light has been forgotten in the same way as the air we breathe. It is obvious and is only noticed by its often-exceptional absence. Its omnipresence is such that it goes to excess, resulting in light pollution;environmental impacts;impacts on fauna and flora;and, even more serious, impacts on human health. But it has also been an essential driving force in the evolution of humanity. From the first traces of the mastery of fire as a light source up to the "electronic" light of LEDs, we will travel through more than 500,000 years of history of lighting technologies and more particularly focus on the last 150 years, which have ended with the LED revolution.

关键词： Force Flora Fauna Light emitting diodes Artificial light History Light sources

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Autonomous vehicle surveillance through fuzzy C-means segmentation and DeepSORT on aerial images

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PeerJ computer Science 2025年 11卷 1-23页

作者： Qureshi, Asifa Mehmood Alotaibi, Moneerah Alotaibi, Sultan Refa AlHammadi, Dina Abdulaziz Jamal, Muhammad Asif Jalal, Ahmad Lee, Bumshik Department of Computer Science Air University Islamabad Pakistan Department of Computer Science College of Science and Humanities Shaqra University Dawadmi Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Information and Communication Engineering Chosun University Gwangju Korea Republic of Department of Computer Science and Engineering College of Informatics Korea University Seoul Korea Republic of

The high mobility of uncrewed aerial vehicles (UAVs) has led to their usage in various computer vision applications, notably in intelligent traffic surveillance, where it enhances productivity and simplifies the process. Yet, there are still several challenges that must be resolved to automate these systems. One significant challenge is the accurate extraction of vehicle foregrounds in complex traffic scenarios. As a result, this article proposes a novel vehicle detection and tracking system for autonomous vehicle surveillance, which employs Fuzzy C-mean clustering to segment the aerial images. After segmentation, we employed the YOLOv4 deep learning algorithm, which is efficient in detecting small-sized objects in vehicle detection. Furthermore, an ID assignment and recovery algorithm based on Speed-Up Robust Feature (SURF) is used for multi-vehicle tracking across image frames. Vehicles are determined by counting in each image to estimate the traffic density at different time intervals. Finally, these vehicles were tracked using DeepSORT, which combines the Kalman filter with deep learning to produce accurate results. Furthermore, to understand the traffic flow direction, the path trajectories of each tracked vehicle is projected. Our proposed model demonstrates a noteworthy vehicle detection and tracking rate during experimental validation, attaining precision scores of 0.82 and 0.80 over UAVDT and KIT-AIS datasets for vehicle detection. For vehicle tracking, the precision is 0.87 over the UAVDT dataset and 0.83 for the KIT-AIS dataset. © 2025 Qureshi et al.

关键词： Image segmentation

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