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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abdelrahman Osheba Ahmed Abou-El-Ela Osama Adel Youssef Maaod Tamer Abuhmed Shaker El-Sappagh Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications.

关键词： Drugs Electric potential Hospitals Large language models Mortality Medical services Chatbots Real-time systems Safety Electronic medical records

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Zirkus Empathico 2.0: a multiplayer serious mobile game for children with autism spectrum disorder (ASD), with a focus on enhancing social and emotional development

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Multimedia Tools and Applications 2025年 1-24页

作者： Hassan, Ahmed Pinkwart, Niels Shafi, Muhammad Department of Computer Science and Informatics Berlin School of Business and Innovation Karl Marx Strasse 97 - 99 Berlin12043 Germany Institute for Informatic Humboldt University Berlin Rudower Chaussee 25 Berlin Berlin12489 Germany School of Computing Ulster University Belfast Campus United Kingdom Faculty of Computing & Information Technology Sohar University Sohar Oman

This study explores the effectiveness of the multiplayer serious mobile game "Zirkus Empathico (ZE) 2.0" in enhancing social-emotional behaviors in children diagnosed with autism spectrum disorder (ASD). It aims to assess how such games can foster emotional recognition, empathy, and prosocial behavior, thereby supporting the holistic development of children with ASD. An eight-week randomized controlled trial (RCT) was conducted between January and April 2021 at autism centers in Islamabad, Pakistan, and Berlin, Germany. The study involved 107 children aged 5–11 years, with 60 diagnosed with ASD and 47 typically developing children serving as an Active Control Group (ACG). Participants were engaged in sessions using the bilingual"ZE 2.0"game, incorporating multiplayer and emotional learning modules. The intervention required 120 min of weekly training supplemented by daily sessions. Post-intervention assessments demonstrated significant improvements in the ASD group compared to the control group in areas of empathy and emotional awareness, as evidenced by higher scores on the Griffith Empathy Measure (GEM) and multiplayer module evaluations. Notable was the ability of participants to apply learned social skills in dynamic, real-world contexts, which suggests the game’s effectiveness transcends the controlled settings typically associated with ASD interventions. ZE 2.0 adds to the social and teamwork competencies of children with ASD. Besides encouraging peers to be proactive in communications and cooperation, the multiplayer mode provides an immersion learning atmosphere for facilitating social and emotional development. Such an outcome underlines the perspectives of serious games for use as a framework for the treatment and education of children who have ASD. In this manner, serious games can be utilized as a supplementary or alternative means of treating ASD. Future research may focus on distinguishing the effects of intervention from those of other therapy modalit

关键词： Serious games

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Reported Challenges in Deep Learning-Based Human Pose Estimation: A Systematic Review

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IEEE Access 2025年 13卷 80520-80539页

作者： Fisal, Nagwa R. Fathalla, Ahmed Elmanakhly, Dina Salah, Ahmad Suez Canal University Faculty of Science Department of Mathematics Ismailia Egypt University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Faculty of Computers and Informatics Zagazig University Department of Computer Science Egypt

Human Pose Estimation (HPE) is the technique of precisely estimating the locations of human joints in images and videos. Deep learning has been frequently applied recently to improve HPE performance. However, a number of challenges, including occlusions, crowded environments, and hidden joints, make it difficult to create an effective HPE model. This systematic review provides a comprehensive analysis of HPE reported challenges, focusing on hidden joint issues due to their significant impact on performance. This study systematically examines a number of HPE models, choosing 74 papers on deep learning-based HPE that have been published since 2018 based on specific criteria. The reported challenges are listed along with their frequency and related references, the review also covers datasets, loss functions, evaluation metrics, and pre-trained models used in HPE. Our analysis demonstrates that occlusion and crowded scenes are still the main issues that affect the model performance, highlighting the importance of addressing hidden joint problem. The review presents a number of findings, such as the most widely utilized architecture in HPE literature is the residual network, the mean square error is the most frequently used loss function. Furthermore, MPII and Human3.6M are the most used datasets for 2D and 3D pose estimation, respectively. Finally, this study highlights the gaps in the field, which could lead to new research opportunities. © 2013 IEEE.

关键词： Challenges deep learning hidden joints human pose estimation

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Optimality Versus Generality: Performance Assessment of Meta-Heuristics in Educational Timetabling

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IEEE Access 2025年 13卷 75679-75696页

作者： Sina Abdipoor Razali Yaakob Say Leng Goh Salwani Abdullah Hazlina Hamdan Khairul Azhar Kasmiran Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang Selangor Malaysia Optimization and Visual Analytics Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Labuan International Campus Labuan Malaysia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

Educational timetabling, a principal branch of operations research, presents challenging combinatorial optimization problems widely encountered in educational institutions. Meta-heuristics have commonly been applied to these problems and managed to attain promising performance in terms of optimality. However, their general applicability has been overlooked, hindering their effectiveness as versatile solvers. The limited generalizability of current approaches is the primary hurdle between the literature and real-world applications. This paper addresses this gap by introducing a generality taxonomy and conducting comprehensive theoretical and empirical analyses. This study highlights the adverse impact of extreme parameter tuning on generality, emphasizing the need for more generalized approaches. Furthermore, it introduces a performance assessment framework, penalizing problem-tailored solutions. It also examines the optimality vs. generality performance of the state-of-the-art approaches of the latest university course timetabling benchmark to further reinforce our claim and validate the efficacy of our framework. Our findings indicate that the current literature prioritizes optimality over generality. We believe adopting the proposed assessment framework is crucial for bridging the gap between research and practical applications, enabling fairer comparisons, and encouraging more adaptable approaches.

关键词： Metaheuristics Benchmark testing Operations research Tuning Terminology Taxonomy Performance evaluation Vehicle routing Schedules Job shop scheduling

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Deep learning for cerebral vascular occlusion segmentation: A novel ConvNeXtV2 and GRN-integrated U-Net framework for diffusion-weighted imaging

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Neuroscience 2025年 574卷 42-53页

作者： Ince, Suat Kunduracioglu, Ismail Algarni, Ali Bayram, Bilal Pacal, Ishak Department of Radiology University of Health Sciences Van Education and Research Hospital Van 65000 Turkey Department of Computer Engineering Faculty of Engineering Igdir University Igdir 76000 Turkey Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha 61421 Saudi Arabia Department of Neurology University of Health Sciences Van Education and Research Hospital Van 65000 Turkey Department of Electronics and Information Technologies Faculty of Architecture and Engineering Nakhchivan State University Nakhchivan AZ 7012 Azerbaijan

Cerebral vascular occlusion is a serious condition that can lead to stroke and permanent neurological damage due to insufficient oxygen and nutrients reaching brain tissue. Early diagnosis and accurate segmentation are critical for effective treatment planning. Due to its high soft tissue contrast, Magnetic Resonance Imaging (MRI) is commonly used for detecting these occlusions such as ischemic stroke. However, challenges such as low contrast, noise, and heterogeneous lesion structures in MRI images complicate manual segmentation and often lead to misinterpretations. As a result, deep learning-based computer-Aided Diagnosis (CAD) systems are essential for faster and more accurate diagnosis and treatment methods, although they can sometimes face challenges such as high computational costs and difficulties in segmenting small or irregular lesions. This study proposes a novel U-Net architecture enhanced with ConvNeXtV2 blocks and GRN-based Multi-Layer Perceptrons (MLP) to address these challenges in cerebral vascular occlusion segmentation. This is the first application of ConvNeXtV2 in this domain. The proposed model significantly improves segmentation accuracy, even in low-contrast regions, while maintaining high computational efficiency, which is crucial for real-world clinical applications. To reduce false positives and improve overall accuracy, small lesions (≤5 pixels) were removed in the preprocessing step with the support of expert clinicians. Experimental results on the ISLES 2022 dataset showed superior performance with an Intersection over Union (IoU) of 0.8015 and a Dice coefficient of 0.8894. Comparative analyses indicate that the proposed model achieves higher segmentation accuracy than existing U-Net variants and other methods, offering a promising solution for clinical use. © 2025 International Brain Research Organization (IBRO)

关键词： Brain stroke diagnosis Deep learning Health Medical image processing U-Net

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The Topology Diameters of the Asymmetric Cluster Affinity Cost

SSRN

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SSRN 2025年

作者： Górecki, Pawel Wagle, Sanket Eulenstein, Oliver Faculty of Mathematics Informatics and Mechanics University of Warsaw Poland Department of Computer Science Iowa State University United States

The asymmetric cluster affinity cost is based on the classic Robinson-Foulds metric and offers a more nuanced comparison of ordered tree pairs. When comparing the costs of these pairs, it is important to consider topology diameters to ensure a meaningful evaluation. The topology diameters of a cost represent the maximum values across all ordered tree pairs when the topology of either the first tree, the second tree, or both is fixed. This theoretical work outlines all topology diameters for the asymmetric cluster affinity cost and describes a linear-time algorithm that generates trees achieving the topology diameter for a given ordered pair of topologies. Our detailed experimental studies suggest that normalization strategies based on topology diameters can significantly improve comparative studies using the asymmetric cluster affinity cost. © 2025, The Authors. All rights reserved.

关键词： Computational cost

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Performance Comparison of Interpolation Methods for Enriching WiFi Fingerprint Database 24

Performance Comparison of Interpolation Methods for Enrichin...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Vu-Thanh, Trung He, Jing Duong-Bao, Ninh Thi, Luong Nguyen Lee, Seon-Woo Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Mathematics and Informatics Dalat University Dalat Viet Nam Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of Department of Electronics and Telecommunications Dalat University Dalat Viet Nam

ISBN: (纸本)9798400717161

Recently, WiFi Fingerprinting-based indoor positioning systems have gained significant attention due to the low cost of infrastructure deployment. In the data collection process, a higher density of reference points may lead to better positioning results. However, it makes this process time-consuming and laborious. To solve this problem, this study applies and compares different interpolation methods to enrich the fingerprint database without increasing the cost of the collection process. To evaluate the performance of the chosen methods, five interpolation methods are implemented in a multi-condition WiFi Fingerprinting dataset. Moreover, to check the robustness of the methods, different numbers of reference points are considered when creating the new interpolated fingerprint databases. According to the results, inverse distance weighting (IDW) is the most suitable interpolation method with low complexity and stable performance. The IDW-based database achieves an average interpolation error of less than 6 dBm compared to the original database. When applying this interpolated database to perform positioning tasks, it helps reduce the collection cost by 30% with slightly lower positioning results compared to the original database. This work demonstrates the feasibility of using interpolation to ease the data collection process of WiFi Fingerprinting, especially in complex and big areas such as airports or hospitals. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Wi-Fi

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Onboard Perception-Assisted High Fidelity Simulation Framework for Autonomous Planetary Soft-Landing

Onboard Perception-Assisted High Fidelity Simulation Framewo...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Alibekov, Ulugbek Banerjee, Avijit Satpute, Sumeet Gajanan Nikolakopoulos, George ERASMUS MUNDUS JOINT MASTER in Intelligent Field Robotic Systems Faculty of Informatics Eötvös Loránd University Budapest Hungary Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents an onboard perception-assisted high-fidelity simulation framework for autonomous planetary soft-landing, enabling visual information processing tightly integrated with advanced onboard guidance system in a realistic simulation environments. Utilizing the open source Unity game engine, a physics-based simulation toolbox integrated with the Robotic Operating System (ROS2), we effectively emulate the motion of spacecraft approaching planetary terrain. The simulation environment features a realistic 3D terrain model and a lander spacecraft equipped with onboard camera to effectively capture detailed local terrain images. The simulation engine accurately captures the spacecraft’s 6-degrees of freedom motion, seamlessly integrating space craft orientation with the associated camera feed while incorporating various illumination conditions using a light source representing the Sun. To demonstrate the efficacy of the framework, we implement a recently developed attitude-constrained minimum jerk guidance algorithm to emulate spacecraft motion. Concurrently, the visual feed from the onboard RGB camera is processed through a gradient extraction-based perception system, providing the visual odometer. This system identifies provably safe landing sites, effectively avoiding potential risks of landing on uneven terrain. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Planetary landers

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A Smart Farming System for Rubber Nursery Management in Monitoring Plant Growth Performance Using IoT Technology 1

A Smart Farming System for Rubber Nursery Management in Moni...

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1st Engineering and science International Conference 2024, ESIC 2024

作者： Sadikan, S.F.N. Aziz, M.L.H.A. Umor, M.A.S. Mahzan, S. Marjudi, S. Salamat, M.A. Dunne, R.S. Faculty of Plantation and Agrotechnology Universiti Teknologi MARA Merlimau Melaka77300 Malaysia College of Computing Informatics and Media Universiti Teknologi MARA Merlimau Melaka77300 Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat86400 Malaysia Faculty of Communication Visual art and Computing Universiti Selangor Kuala Selangor45600 Malaysia

Rubber plantations are an integral part of economy in Malaysia. However, inadequate soil moisture poses a significant problem for rubber trees as it can lead to water stress, ultimately hindering their growth, leaf development, and latex production. Water stress occurs when the trees are unable to obtain sufficient water from the soil, which resulting in stunted growth, leaf development, leading to fewer and smaller leaves. There is needed an effective water management strategy to ensure optimal growth of rubber trees for latex production. Thus, this project is aim to design and build an active soil monitoring system based on the Internet of Things (IoT). This system is made up of two major components: soil moisture and light intensity. The first component is a soil moisture sensor, which determines the soil's moisture content;light intensity sensor, which detects the intensity of the light and ANSELF Brushless DC Pump connected to an Arduino. This system provides sustainable solutions to increase crop output, reduce waste, and develop a greener plant cultivation. This smart monitoring plant growth system prototype is important in agriculture and plant production. It has potential to promote healthy plant growth and increase agricultural production. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Soil moisture

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