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International Journal of Interactive Mobile Technologies 2025年第1期19卷 107-125页

作者： Al Tawil, Arar Fathi, Hanaa Alzoubi, Sharaf Shaban, Amneh Almazaydeh, Laiali H. Faculty of Information Technology Applied Science Private University Amman Jordan College of Computer Science and Informatics Amman Arab University Amman Jordan College of Engineering AbuDhabi University Abu Dhabi United Arab Emirates

The gaming industry produces vast amounts of user-generated feedback, making it challenging for developers to efficiently analyze and respond to real-time reviews. This study addresses the problem of classifying large-scale unstructured user feedback from Steam reviews. In this paper an approach that integrates traditional machine learning models and deep learning models is proposed. XGBoost is used to manage feature-rich datasets, reducing overfitting. Long-short-term memory (LSTM) and Bi-directional LSTM are used to enhance the accuracy and robustness of classification. Feature extraction techniques, such as sentiment analysis and topic modeling, are employed to enrich the dataset and improve model performance. The experimental results show that the XGBoost model achieved the highest performance with an accuracy of 0.9499 and a ROC-AUC score of 0.6113, demonstrating superior performance in distinguishing between positive and negative feedback. In comparison, deep learning models such as LSTM and Bi-directional LSTM showed lower ROC-AUC scores, indicating less effectiveness in handling the classification task. This approach offers game developers a reliable and scalable solution for classifying user sentiment, leading to better game improvements based on user reviews. © 2025 by the authors of this article.

关键词： Contrastive Learning

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AI-Progressions in Automated Facial Paralysis Evaluation: A Comprehensive Survey of Traditional to Deep Learning Approaches 4

AI-Progressions in Automated Facial Paralysis Evaluation: A ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kiruthiga, S. Anbuananth, C. Dhilipkumar, V. Annamalai University Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331523923

Deep learning mechanisms allow computers to solve complex real-time problems with complex neural networks, it employs a vital role in health sector, for assisting the medical practitioners with quick and accurate decision making. Various studies on this research area proves that deep learning approaches are more effective in detecting facial paralysis, a common visible symptom of acute stroke. The sealing sign of stroke is facial weakness which destroys the newline muscle movements in the face on one or both sides of the nerves in the brain stream. Accurate and timely detection is most important and need of the golden hour, to patients effectively. Human assessment and traditional diagnostic methods have demonstrated poor inter rated reliability, demanding improved diagnostic tools. By analysing facial asymmetry and other relevant features like Face feature analysis, extracting characteristics such as eye shape, movement, and wrinkles from static images to detect facial weakness is done using various deep learning techniques. This work encompass the real-time challenges such as curating datasets, ethical concerns and data privacy of patients. The objective of this paper is to review the potentiality of various traditional to deep learning algorithms in automising the facial paralysis detection for accurate stroke diagnosis in patients. AI models can provide objective assessments that enhance the reliability of stroke detection. © 2025 IEEE.

关键词： Convolutional neural networks

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Minimizing energy consumption in fixed node networks using a novel neutrosophic model: comparative analysis with standard and existing algorithms

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International Journal of Information Technology (Singapore) 2025年 1-15页

作者： Ghimire, Sangam Bhurtel, Nirjal Jha, Sudan Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Department of Computer Science and Engineering Kathmandu University 28 kilo Bagmati Dhulikhel 45210 Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj 11942 Saudi Arabia School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Lebanon

The growing development and utilization of networked systems has led to more concern regarding the energy efficiency of these systems. In this paper, we present a novel approach to minimizing energy consumption in fixed node network for which we propose a new Neutrosophic model. This model leverages the unique strengths of Neutrosophic logic to enhance the efficiency by minimizing the energy consumption. The simulation of results is performed using Matplotlib (Python) with implementation parameters such as number of nodes (n) and additive white Gaussian noise (AWGN) (η). Additionally, we show a comparative analysis between the proposed model and other prevalent approaches used in minimizing energy consumption in networked systems. The comparative results shows improvement in energy efficiency, showcasing the potential of the proposed model as an alternative for optimizing energy consumption in networked systems. This work highlights the effectiveness of the proposed model and opens new avenues for future advancements in energy-efficient networked system using Neutrosophic approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Comparative analysis Energy-consumption optimization Networked systems Neutrosophic model Sensitivity analysis

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A Case Study of Intelligent Street Lighting in Rural Areas for Energy Savings 33rd

A Case Study of Intelligent Street Lighting in Rural Areas f...

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33rd International Conference on Organization and Technology of Maintenance, OTO 2024

作者： Križanović, Višnja Pracny, Patrik Pejković, Ana Grgić, Krešimir Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9783031805967

The aim of this paper is to analyze the implementation of intelligent lighting within the concept of smart energy based on the possibility of saving and efficient use of energy, which is largely based on non-renewable sources. In selected case studies of rural environments, the possibility of implementing smart lighting was analyzed through simulation procedures, from the point of view of energy saving, showing the importance of this aspect in the context of building smart energy lighting systems. The conclusions in the paper show that the analyzed solution provides useful guidelines for the development of the concept of smart lighting, bringing benefits and reducing costs. Considerations given in the paper show a quantitative assessment of energy savings through the replacement of traditional lighting systems in rural areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy saving

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Leveraging Large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

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

ISBN: (纸本)9798331507817

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. © 2025 IEEE.

关键词： Electronic health record

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Superconductivity in Bi1.6Pb0.4Sr2Ca2Cu3O10+δ ceramics with cobalt ferrite (CoFe2O4) nanoparticle addition

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Ceramics International 2025年第12期51卷 16134-16142页

作者： Tran, Duc H. Pham, An T. Binh, Nguyen Thanh Anh, Do Thi Kim Nam, Nguyen Hoang Hong, Nguyen T.M. Cuong, Le Viet Pham, Phong V. Huy, Nguyen Quang Huong, Tran Thi Man, Nguyen K. Lim, Kean Pah Kechik, Mohd Mustafa Awang Chen, Soo Kien Le, Tien Faculty of Physics University of Science Vietnam National University Hanoi100000 Viet Nam Nano and Energy Center University of Science Vietnam National University Hanoi100000 Viet Nam Faculty of Engineering Physics and Nanotechnology VNU University of Engineering and Technology Hanoi100000 Viet Nam University of Science Vietnam National University Hanoi100000 Viet Nam Faculty of Electronics and Computer Engineering University of Economics - Technology for Industries Hanoi100000 Viet Nam Faculty of Electronic Materials and Devices School of Materials Science and Engineering Hanoi University of Science and Technology Hanoi100000 Viet Nam Department of Physics Faculty of Science Universiti Putra Malaysia Selangor Serdang43400 Malaysia University of Science and Technology of Hanoi Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi100000 Viet Nam

This study explores the use of cobalt ferrite (CoFe2O4) nanoparticles as artificial pinning centers to investigate flux pinning characteristics in the Bi1.6Pb0.4Sr2Ca2Cu3O10+δ ceramics. Polycrystalline samples with the composition (Bi1.6Pb0.4Sr2Ca2Cu3O10+δ)1−x(CoFe2O4)x, where x ranges from 0.000 to 0.010, are synthesized based on the solid-state formation. Structural and surface analyses reveal that the samples predominantly comprise superconducting phases, with volume fractions varying with x. An increase in x leads to a drop in the critical temperature (Tc). However, an improvement in the critical current density (Jc) is observed at x = 0.002, owing to the addition of CoFe2O4. The collective pinning theory indicates a shift from a single-vortex to small bundle vortex pinning at the crossover field (Bsb) in all samples. The dependence of Bsb on temperatures is consistent with δl pinning, attributed to changes in the mean free path of the charge carrier. Geometries of artificial pinning centers are identified as 2D using the Dew–Hughes model. CoFe2O4 nanoparticles are primarily located at the grain boundaries, filling voids and supporting pinning through grain boundaries. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Flux pinning

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Empowering SMEs in the Fourth Industrial Revolution: A Framework for Maintenance 4.0 Adoption 6

Empowering SMEs in the Fourth Industrial Revolution: A Frame...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Nasirinejad, Majid Afshari, Hamid Sampalli, Srinivas Faculty of Computer Science Dalhousie University Halifax Canada Department of Industrial Engineering Dalhousie University Halifax Canada

SMEs play a vital role in driving economic growth, creating jobs, and fostering innovation. However, unlike larger businesses, SMEs often struggle to adopt Industry 4.0 technologies due to limited resources. Addressing these challenges is essential to help SMEs leverage advanced technologies, enhance competitiveness, and support economic development. This paper presents a framework for SMEs to adopt Industry 4.0 technologies in maintenance operations. The framework leverages Reliability, Availability, Maintainability, Safety, and Sustainability (RAMS2) benefits and offers optimization opportunities to enhance production efficiency, reduce costs, and improve product quality. Based on the comprehensive literature review the gaps are identified and technical components of the proposed framework are matched with RAMS2 objectives. A case study illustrates its practical application, including a mathematical model to balance cost and reliability in maintenance. The proposed framework, compared to traditional systems, provides SMEs with a competitive edge by achieving operational and financial objectives. © 2025 Elsevier B.V.. All rights reserved.

关键词： Scheduled maintenance

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A Hybrid Embedding Approach to Classify Crisis-Related Short Text 12th

A Hybrid Embedding Approach to Classify Crisis-Related Short...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Nath, Basudev Sahoo, Deepak Paul, Nayan Ranjan Faculty of Engineering Technologies Sri Sri University Cuttack India Department of Computer Science and Engineering Silicon Institute Technology Bhubaneswar India

ISBN: (纸本)9789819788354

High mobile phone usage and internet access on phones make it simple to connect to the globe and share your thoughts, feelings, and views on local or global issues on social media. This social media content helps government and corporate institutions grasp public opinion. People utilise social media to report disasters and request medical, food, and water aid. It is difficult to recognise disaster-related tweets and determine people's needs. This paper proposed a hybrid model to recognise disaster-related tweets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tweets

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End-to-end optimized ensemble model for Alzheimer’s disease detection 19

End-to-end optimized ensemble model for Alzheimer’s disease...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Hassan, Abdelrahman Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities—demographics, cognitive scores, neuropsychological assessments, and MRI data—via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management. © 2025 IEEE.

关键词： Adversarial machine learning

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Optimizing Heart-Kidney Interaction for Cancer Detection through Physiological Process Simulation as a Decision Support System

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IEEE Access 2025年 13卷 79169-79179页

作者： Jaddi, Najmeh Sadat Abdullah, Salwanj Othnman, Zalinda Yahya, Yazrina Nazri, Mohd Zakree Ahmad Goh, Say Leng Alvankarian, Fatemeh Alvankarian, Jafar Iranian eUniversity Faculty of Computer Engineering Tehran Iran Faculty of Information Science and Technology Center for Artificial Intelligence Technology Selangor Bangi43600 Malaysia Center for Software Technology and Management Faculty of Information Science and Technology UniversitiKebangsaan Malaysia Selangor Bangi43600 Malaysia Universiti Malaysia Sabah KampusAntarabangsa Labuan Optimisation and Analytics Research Group Faculty of Computing and Informatics Labuan87000 Malaysia University of Maryland Department of Electrical & Computer Engineering College ParkMD20742 United States Tehran University Science and Technology Park RayanMiniFab Tehran Iran

Decision support system (DSS) is a computer-based tool used to improve decision-making capabilities for any organization, using analysis of the available *** heart-kidney (HK) model proposed in this paper as a DSS simulates the physiological processes of the heart and kidneys in the human body to support cancer detection. The first part of the HK algorithm is inspired by the functions of the heart, where blood molecules (solutions) are sent to the heart, simulating the processes of oxygenation and the pumping of oxygenated blood to the kidneys as a local search. The solutions generated by the heart are then fed into the kidney-inspired algorithm (KA), which performs filtration, reabsorption, secretion, and excretion. The heart component enhances the KA's local search capability, guiding the algorithm toward optimal solutions. The HK model is evaluated on eight test functions, with results compared to those of the heart algorithm and KA alone. Additionally, the HK algorithm's effectiveness as a decision support system is tested on benchmark classification and time series prediction problems, with statistical analysis against existing methods in the literature. Finally, the HK algorithm is applied to cancer detection from microRNA data, demonstrating its potential in optimizing decision-making processes in healthcare. © 2013 IEEE.

关键词： Diseases

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