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Fuzzy Quantum Group Decision Making and Its Application in Meteorological Disaster Emergency

IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1441-1454页

作者： Yan, Shuli Xu, Yizhao Gong, Zaiwu Herrera-Viedma, Enrique Nanjing University of Information Science & Technology School of Management Science and Engineering Research Institute for Risk Governance and Emergency Decision-Making Nanjing210044 China University of Granada Department of Computer Science and Artificial Intelligence Granada18071 Spain

This paper proposes a quantum group decision model that integrates intuitionistic fuzzy sets to represent the uncertainty of meteorological disaster information, addressing both vagueness and probabilistic uncertainty. This makes it particularly suitable for modeling the complex and dynamic decision-making processes during emergency responses. The model employs regret theory for attribute weight determination and constructs a quantum-like Bayesian network (QLBN), where Deng entropy is applied to measure the mutual interference effects among decision-makers. Decision-makers' weights are determined using grey relational analysis and incorporated as the initial layer in the Bayesian network. The conditional probabilities within the QLBN are derived by integrating attribute weights and regret utility functions, and the alternatives are ranked based on their final quantum probabilities. The effectiveness and stability of the model are demonstrated through its application in emergency alternative selection for meteorological disasters, confirmed by sensitivity and comparison analyses. © 1993-2012 IEEE.

关键词： Markov processes

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Clustering-based Image-Text Graph Matching for Domain Generalization 27th

Clustering-based Image-Text Graph Matching for Domain Genera...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Park, Nokyung Chae, Daewon Shim, Jeongyong Kim, Sangpil Kim, Eun-Sol Kim, Jinkyu Department of Computer Science and Engineering Korea University Seoul Korea Republic of Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Artificial Intelligence Application Hanyang University Seoul Korea Republic of Department of Computer Science Hanyang University Seoul Korea Republic of

ISBN: (纸本)9783031781919

Learning domain-invariant visual representations is important to train a model that can generalize well to unseen target task domains. Recent works demonstrate that text descriptions contain high-level class-discriminative information and such auxiliary semantic cues can be used as effective pivot embedding for domain generalization problems. However, they use pivot embedding in a global manner (i.e., aligning an image embedding with sentence-level text embedding), which does not fully utilize the semantic cues of given text description. In this work, we advocate for the use of local alignment between image regions and corresponding textual descriptions to get domain-invariant features. To this end, we first represent image and text inputs as graphs. We then cluster nodes within these graphs and match the graph-based image node features to the nodes of textual graphs. This matching process is conducted both globally and locally, tightly aligning visual and textual semantic sub-structures. We experiment with large-scale public datasets, such as CUB-DG and DomainBed, and our model achieves matched or better state-of-the-art performance on these datasets. The code is available at: https://***/noparkee/Graph-Clustering-based-DG. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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Identification of Infectious Disease Like COVID-19 Gene Biomarkers Using a Clear artificial intelligence Approach

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SN computer science 2025年第4期6卷 1-13页

作者： Dubey, Shivendra Verma, Dinesh Kumar Kumar, Mahesh Department of Artificial Intelligence and Machine Learning Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Jaypee University of Engineering and Technology MP Guna India

This study offers an approach that uses SARS-COV-2 mNGS (meta-genomic next-generation sequencing) samples to apply XAI (explainable artificial intelligence) methodologies derived from the use of machine learning methods. The research's data set contains 15,979 expressions of genes from 234 infected persons, of whom 39.68% (93) had SARS-COV-2 that was positive and 60.29% (141) had SARS-COV-2 that was negatives. The SARS-COV-2 related gene was chosen using the LASSO (least absolute shrinkage and selection operator) technique. The class imbalanced issue has been resolved using the SVM-SMOTE (Support Vector Machine - Synthetic Minority Oversampling Technique) approach. To identify SARS-COV-2 related biomarkers possibilities and increase the comprehensibility of the final approach, an explainable strategy that utilized SHAP (SHAPley Additive exPlanations) as well as LIME (local interpretable model-agnostic explanations) approaches were used. The XGBoost (92.98% accuracy) approach performs better for the identification of infectious diseases like COVID-19 than the LR (91.17% accuracy), SVM (87.68% accuracy), and RF (91.18% accuracy) models. The three most significant genes connected to COVID-19 by the SHAP method were FAM83A, LGR6, and IFI27. The LIME findings demonstrated that the likelihood of a positive class was particularly influenced by the elevated degree of IFI27 expression of genes. Infectious diseases like COVID-19 could be accurately predicted using the suggested framework (XGBoost). The findings demonstrate that SHAP and LIME, along with the exploit of ML methods, it could be clarify the biological marker prognosis for infectious disease and give health center a clear knowledge about how potential risk factors affect the forecasts made by the model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomarker Gene LIME SARS SHAP

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X-OODM:Leveraging Explainable Object-Oriented Design Methodology for Multi-Domain Sentiment Analysis

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computers, Materials & Continua 2025年第3期82卷 4977-4994页

作者： Abqa Javed Muhammad Shoaib Abdul Jaleel Mohamed Deriche Sharjeel Nawaz Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science(RCET Campus GRW)University of Engineering and TechnologyLahore52250Pakistan Artificial Intelligence Centre(AIRC) Ajman UniversityAjman346United Arab Emirates Smith School of Business University of MarylandCollege ParkMD 20742-5151USA

Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the ***-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user *** design methodologies such as object-oriented design methodology(OODM)have been proposed for web-based application development,which facilitates code reuse,quantification,and security at the design ***,OODM did not provide the feature of explainability in web-based decision-making systems.X-OODM modifies the OODM with added explainable models to introduce the explainability feature for such *** research introduces an explainable model leveraging X-OODM for designing transparent applications for multidomain sentiment *** proposed design is evaluated using the design quality metrics defined for the evaluation of the X-OODM explainable model under user *** design quality metrics,transferability,simulatability,informativeness,and decomposability were introduced one after another over time to the evaluation of the X-OODM user *** metrics of accessibility and algorithmic transparency were added to increase the degree of explainability for the *** study results reveal that introducing such explainability parameters with X-OODM appropriately increases system transparency,trustworthiness,and user *** experimental results validate the enhancement of decision-making for multi-domain sentiment analysis with integration at the design level of *** work can be built in this direction by extending this work to apply the proposed X-OODM framework over different datasets and sentiment analysis applications to further scrutinize its effectiveness in real-world scenarios.

关键词： Measurable explainable web-based application object-oriented design sentiment analysis multi-domain

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From 5G to 6G: The Role of AI, Machine Learning, and Deep Learning in Wireless Systems 4

From 5G to 6G: The Role of AI, Machine Learning, and Deep Le...

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

作者： Gote, Pradnyawant M. Kumar, Praveen Verma, Prateek Yesankar, Prajyot Pawar, Adesh Saratkar, Saniya Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible. © 2025 IEEE.

关键词： Adversarial machine learning

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Legal Query RAG

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IEEE Access 2025年 13卷 36978-36994页

作者： Wahidur, Rahman S. M. Kim, Sumin Choi, Haeung Bhatti, David S. Lee, Heung-No Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju61005 Korea Republic of Gwangju Institute of Science and Technology Artificial Intelligence Graduate School Gwangju61005 Korea Republic of

Recently, legal practice has seen a significant rise in the adoption of artificial intelligence (AI) for various core tasks. However, these technologies remain in their early stages and face challenges such as understanding complex legal reasoning, managing biased data, ensuring transparency, and avoiding misleading responses, commonly referred to as hallucinations. To address these limitations, this paper introduces Legal Query RAG (LQ-RAG), a novel Retrieval-Augmented Generation framework with a recursive feedback mechanism specifically designed to overcome the critical shortcomings of standard RAG implementations in legal applications. The proposed framework incorporates four key components: a custom evaluation agent, a specialized response generation model, a prompt engineering agent, and a fine-tuned legal embedding LLM. Together, these components effectively minimize hallucinations, improve domain-specific accuracy, and deliver precise, high-quality responses for complex queries. Experimental results demonstrate that the fine-tuned embedding LLM achieves a 13% improvement in Hit Rate and a 15% improvement in Mean Reciprocal Rank (MRR). Comparisons with general LLMs reveal a 24% performance gain when using the Hybrid Fine-Tuned Generative LLM (HFM), the specialized response generation model integrated into the LQ-RAG framework. Furthermore, LQ-RAG achieves a 23% improvement in relevance score over naive configurations and a 14% improvement over RAG with Fine-Tuned LLMs (FTM). These findings underscore the potential of domain-specific fine-tuned LLMs, combined with advanced RAG modules and feedback mechanisms, to significantly enhance the reliability and performance of AI in legal practice. The reliance of this study on a proprietary model as the evaluation agent, combined with the lack of feedback from human experts, highlights the need for improvement. Future efforts should focus on developing a specialized legal evaluation agent and enhancing its performance

关键词： Information management

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ML computational inference techniques and indicator metrics for analyzing uncertainties in stock market data

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Multimedia Tools and Applications 2025年 1-21页

作者： Henge, Santosh Kumar Mandal, Sanjeev Kumar Sharma, Amit Upadhyay, Aditya Kundu, Shakti Iyer, Srinivasan Saini, Ashok Kumar Yadav, Manoj Kumar Singh, Bhupinder Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Bangalore India Department of Computer Applications Lovely Professional University Punjab Jalandhar India Center for Distance and Online Education Manipal University Jaipur Rajasthan Jaipur India Haryana Gurugram122413 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science & Engineering Lovely Professional University Punjab Jalandhar India

In a growing demand of accurately predicting the stock market and inefficient complex markets the rising accurate relationship prediction is not adequately addressed by the conventional methods. The dynamic and complex natures of data sources become the issue and we need to propose an effective method for adaptive algorithms to accurately forecasting and make decision efficiently. The system framed with two stages of execution: The first stage has covered time series-based predictions for different stock values involving the LSTM Model;the second stage integrated the experimental scenarios based on trend, value, indicators, and it's supporting impacted data metrics, which experimented on various large-cap stock companies. The dataset is sourced online by yahoo finance API with consecutive days data and dependable, non-dependable influenced indicators, which helped to intend trend-based evaluation performances. The proposed model experimented on distinct stock values and test cases executed based on indicator-based prediction with a 76.92% accurate performance rate using a Random Forest Classifier and 76.92% of Logistic Regression. The value-based prediction test case has achieved a performance rate of 97.5% using SVR Regressor and 97.2% using Linear Regressor. It computed value of root mean square error for the Ten large cap companies for performance evaluation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive algorithms

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GENOCARE PROGNOSTICATOR MODEL: HOST GENETICS PREDICT SEVERITY OF INFECTIOUS DISEASE

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Scalable Computing 2025年第2期26卷 924-939页

作者： DUBEY, SHIVENDRA SINGH, SHWETA VERMA, DINESH KUMAR LODHI, SUDHEER KUMAR DUBEY, SAKSHI Department of Artificial Intelligence and Machine Learning Manipal University Rajasthan Jaipur303007 India School of Engineering and Technology Jagran Lakecity University Madhya Pradesh Bhopal462042 India Department of Computer Science and Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Gujarat Vadodara391760 India

Scientific community understanding of the variance in severity of infectious disease like COVID-19 across patients is an important area of focus. The article presents an innovative voting ensemble GenoCare Prognosticator (GCP) model that incorporates XGBoost and Random Forest classifiers, two cutting-edge machine learning approaches. A large dataset that incorporates medical covariates like gender and age along with biological WES (Whole Exome Sequencing) data was used to train these models. Five-fold stratified cross-validation was used to process the dataset in order to improve model stability and avoid overfitting. Two medical covariates and sixteen recognized candidate gene variants were among the eighteen major features on which our GCP model had been verified using data from earlier studies. Specific post-hoc clarification of the model's predictions was provided by ExplainerDashboard, a Python open-source library, to improve interpretability. Furthermore, we utilized OpenTarget and Enrichr, two bioinformatic resources, to establish connections between the discovered variations in genetics and pertinent ontologies, biological pathways, and possible drug/disease relationships. Unsupervised clustering of SHAP key feature values was included in the analysis, which revealed intricate genetic interactions that affect the severity of the disease. Our results show that although gender and age are the main factors influencing the severity of COVID-19, complex genetic interactions cause severe symptoms in a specific subset of patients. This work contributes to our comprehension of the biological variables influencing the severity of COVID-19 and offers a reliable, comprehensible model that can help recognize patients at high risk and guide individualized treatment plans. © (2025), (West University of Timisoara). All rights reserved.

关键词： COVID-19

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Unlocking the potential of RNN and CNN models for accurate rehabilitation exercise classification on multi-datasets

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Multimedia Tools and Applications 2025年第3期84卷 1261-1301页

作者： Zaher, Moamen Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Software Engineering Program Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Computer Science Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Information System Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Cairo Ain Helwan11795 Egypt Giza October12451 Egypt

Physical rehabilitation is crucial in healthcare, facilitating recovery from injuries or illnesses and improving overall health. However, a notable global challenge stems from the shortage of professional physiotherapists, particularly acute in some developing countries, where the ratio can be as low as one physiotherapist per 100,000 individuals. To address these challenges and elevate patient care, the field of physical rehabilitation is progressively integrating computer Vision and Human Activity Recognition (HAR) techniques. Numerous research efforts aim to explore methodologies that assist in rehabilitation exercises and evaluate patient movements, which is crucial as incorrect exercises can potentially worsen conditions. This study investigates applying various deep-learning models for classifying exercises using the benchmark KIMORE and UI-PRMD datasets. Employing Bi-LSTM, LSTM, CNN, and CNN-LSTM, alongside a Random Search for architectural design and Hyper-parameter tuning, our investigation reveals the (CNN) model as the top performer. After applying cross-validation, the technique achieves remarkable mean testing accuracy rates of 93.08% on the KIMORE dataset and 99.7% on the UI-PRMD dataset. This marks a slight improvement of 0.75% and 0.1%, respectively, compared to previous techniques. In addition, expanding beyond exercise classification, this study explores the KIMORE dataset’s utility for disease identification, where the (CNN) model consistently demonstrates an outstanding accuracy of 89.87%, indicating its promising role in both exercises and disease identification within the context of physical rehabilitation. © The Author(s) 2024.

关键词： Classification (of information)

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