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Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems

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Scientific Reports 2025年第1期15卷 1-17页

作者： Medani, Mohamed Elhessewi, Ghada Moh. Samir Alqahtani, Mohammed Asklany, Somia A. Alamro, Sulaiman Albalawneh, Da’ad Alshammeri, Menwa Assiri, Mohammed Department of Information Systems Applied College at Mahayil King Khalid University Abha Saudi Arabia Department of Health Sciences College of Health and Rehabilitation Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha 61922 Saudi Arabia Department of Computer Science and Information Technology Faculty of Sciences and Arts Northern Border University Turaif Arar 91431 Saudi Arabia Department of Computer Science College of Computer Qassim University Buraydah 51452 Saudi Arabia Department of Computer Science University College in Umluj University of Tabuk Tabuk Saudi Arabia Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University P.O. BOX 16273 Al-Kharj 3963 Saudi Arabia

The prevalence of dementia is growing worldwide due to the fast ageing of the population. Dementia is an intricate illness that is frequently produced by a mixture of genetic and environmental risk factors. There is no treatment for dementia yet;therefore, the early detection and identification of persons at greater risk of emerging dementia becomes crucial, as this might deliver an opportunity to adopt lifestyle variations to decrease the risk of dementia. Many dementia risk prediction techniques to recognize individuals at high risk have progressed in the past few years. Accepting a structure uniting explainability in artificial intelligence (XAI) with intricate systems will enable us to classify analysts of dementia incidence and then verify their occurrence in the survey as recognized or suspected risk factors. Deep learning (DL) and machine learning (ML) are current techniques for detecting and classifying dementia and making decisions without human participation. This study introduces a Leveraging Explainability Artificial Intelligence and Optimization Algorithm for Accurate Dementia Prediction and Classification Model (LXAIOA-ADPCM) technique in medical diagnosis. The main intention of the LXAIOA-ADPCM technique is to progress a novel algorithm for dementia prediction using advanced techniques. Initially, data normalization is performed by utilizing min–max normalization to convert input data into a beneficial format. Furthermore, the feature selection process is performed by implementing the naked mole‐rat algorithm (NMRA) model. For the classification process, the proposed LXAIOA-ADPCM model implements ensemble classifiers such as the bidirectional long short-term memory (BiLSTM), sparse autoencoder (SAE), and temporal convolutional network (TCN) techniques. Finally, the hyperparameter selection of ensemble models is accomplished by utilizing the gazelle optimization algorithm (GOA) technique. Finally, the Grad‐CAM is employed as an XAI technique to enhance

关键词： Data normalization Dementia prediction Explainability artificial intelligence Feature selection Gazelle optimization algorithm

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Immersive Embedded Consumer Model Leveraging AI with Zero-Trust Architecture for Cyber-Physical System

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IEEE Transactions on Consumer Electronics 2025年

作者： Rehman, Amjad Haseeb, Khalid Kolivand, Hoshang Saba, Tanzila Al-Khasawneh, Mahmoud A. Ahmad, Shabir Ullah, Inam Lab Riyadh12435 Saudi Arabia Liverpool John Moores University School of Computer Science and Mathematics United Kingdom School of Computing and Digital Technologies Staffordshire University Liverpool Staffordshire United Kingdom University City Sharjah 1797 United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Jordan School of Computing Skyline University College Sharjah United Arab Emirates Incheon Startup Park Center of Artificial Intelligence for Medical Instruments Convensia-daero Incheon204 Korea Republic of Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of

The rapid expansion of the wireless network and its combination with the Consumer Internet of Things (CIoT) has introduced significant research challenges for Cyber-Physical Systems (CPS). The distributed nature of these embedded networks provides big data analytics with the integration of sensors, electronic devices, hardware, and network components. They provide diverse functionalities while sensing the observing environment based on the demand of network users with timely feedback, which supports efficient decision-making. However, CIoT devices in CPS increasingly interact with both physical and digital environments, such interaction raises the research challenges of performance optimization, data breaches, and long-run seamless connectivity. In addition, the constraint devices demanded lightweight communication methods to remain robust and enhance network stability. This paper presents an adaptive model that leverages AI-based optimization techniques with efficient data management and utilization of resources in consumer applications. Moreover, the integrated edges provide consistency and resilience of communication services in unpredictable systems, while adopting a Zero-Trust architecture with more trustworthy and secure transmissions. It increases the system's scalability and improves the response time with the limited computing power of devices for real-time processing. Simulation results demonstrated the significant outcomes of the proposed model over the dynamic scenarios for packet delivery ratio by 35.5%, network throughput by 38.7%, energy consumption by 41.6%, and latency by 45% against existing solutions. © 1975-2011 IEEE.

关键词： consumer technologies edge intelligence embedded systems information security technological development

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An Integrated Preprocessing and Drift Detection Approach With Adaptive Windowing For Fraud Detection In Payment Systems (February 2025)

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IEEE Access 2025年 13卷 92036-92056页

作者： Lawati, Hadi M. R. Al Zainal, Anazida Al-Rimy, Bander Ali Saleh Al-Azawi, Mohammad Kassim, Mohamad Nizam Almalki, Sultan Ahmed Alghamdi, Tami Abdulrahman Universiti Teknologi Malaysia (UTM) Faculty of Computing Johor Bahru Malaysia Universiti Teknologi Malaysia (UTM) Anti-Financial Crime Lab Faculty of Computing Johor Bahru Malaysia University of Portsmouth PAIDS Research Center School of Computing Buckingham Building Lion Terrace Portsmouth PO1 3HE United Kingdom Gulf College Muscat Oman National Anti-Financial Crime Centre (NFCC) Putrajaya Malaysia Najran University Applied College Computer Department Najran 66462 Saudi Arabia Al-Baha University Faculty of Computing and Information Computer Science Department Al-Baha 65779 Saudi Arabia

As fraudulent transaction methods evolve rapidly;it becomes progressively more challenging to detect them in payment systems. Static machine learning and rule-based traditional detection methods cannot capture all the dynamic and evolving nature of fraudulent behaviors, resulting in lower detection accuracy and a higher false positive rate. This study proposes a complete framework that brings together advanced data preprocessing, effective drift detection, and a reliable detection model to address these issues. The method uses Mutual Information and SelectKBest for selecting important features, applies ADASYN to handle class imbalance, and adopts Convolutional Neural Networks (CNN) to capture complex transaction patterns. By implementing Early Drift Detection Method (EDDM) and ADaptive WINdowing (ADWIN), the drift can be detected in advance and the system can respond to changes, both gradual and *** framework was evaluated on three datasets, including real-world transactions and mixed-data environments, achieving superior accuracy, precision, and drift detection rates, with values up to 99.99% accuracy and 1.0 respectively. The findings show that the framework can adjust to changing patterns of fraud, reduce false positives, and enhance detection performance. These insights demonstrate the significance of dynamic preprocessing and drift-aware approaches in the context of real-time fraud detection. This also serves as a basis for future work in adaptive fraud detection model research areas such as of integrating online learning for improved speed and efficiency in high-frequency transactional environments. © 2013 IEEE.

关键词： Banking fraud class imbalance concept drift credit cards data pre-processing debit cards deep learning drift detection fraud detection payment systems prepaid cards real-time fraud detection supervised feature selection

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Enhanced Arithmetic Optimization Algorithm for Intrusion Detection in Wireless Sensor Networks

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International Journal of Communication Systems 2025年第10期38卷

作者： Otair, Mohammed Qawaqzeh, Faten Ali Alomari, Saleh Ali Zitar, Raed Abu Migdady, Hazem Saleem, Kashif Smerat, Aseel Alsoud, Anas Ratib Abualigah, Laith Department of Computer Science Faculty of Information Technology Middle East University Amman Jordan School of Information Engineering Sanming University Sanming China Faculty of Science and Information Technology Jadara University Irbid Jordan Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates CSMIS Department Oman College of Management and Technology Barka Oman Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman Jordan Centre for Research Impact & Outcome Institute of Engineering and Technology Chitkara University Punjab Rajpura India Computer Technologies Engineering Mazaya University College Nasiriyah Iraq TECH Harvard John A. Paulson School of Engineering & Applied Sciences Harvard University BostonMA United States Computer Science Department Al al-Bayt University Mafraq Jordan

Wireless sensor networks (WSNs) face significant security challenges because of their resource constraints and exposure to malicious attacks. Traditional intrusion detection systems (IDSs) often suffer from low detection accuracy, high false alarm rates, and long processing times. To address these issues, this paper proposes an enhanced IDS framework based on the arithmetic optimization algorithm (AOA) for feature selection, combined with support vector machine (SVM) for classification. A new cost function is introduced to guide the feature selection process and improve model performance. The approach, named AS_IDS, is evaluated on the NSL-KDD dataset, achieving an accuracy of 96.65%, a detection rate of 98.69%, and a false alarm rate of 0.04% using only 15 features, with a significant reduction in execution time. Comparative results with state-of-the-art methods demonstrate the effectiveness and efficiency of the proposed framework in enhancing intrusion detection in WSNs. © 2025 John Wiley & Sons Ltd.

关键词： Cost functions

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Subgrid informed neural networks for high-resolution flood mapping

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Journal of Hydrology 2025年 660卷

作者： Herath, Herath Mudiyanselage Viraj Vidura Marshall, Lucy Saha, Abhishek Rasnayaka, Sanka Seneviratne, Sachith Faculty of Science and Engineering Macquarie University SydneyNSW Australia Faculty of Engineering The University of Sydney SydneyNSW Australia Delft Institute of Applied Mathematics Delft University of Technology Delft Netherlands Hydroinformatics Institute Singapore Department of Computer Science School of Computing National University of Singapore Singapore Transport Health and Urban Systems Research Lab Melbourne School of Design The University of Melbourne MelbourneVIC Australia Faculty of Engineering and Information Technology The University of Melbourne MelbourneVIC Australia

Physics-based hydrodynamic models are essential for accurate flood prediction but are computationally expensive, limiting their applicability for real-time forecasting and probabilistic analyses. Conversely, pure machine learning (ML) models offer both computational efficiency and accuracy but often lack interpretability. To address this gap, we propose SGUnet, a physics-informed ML model and a hybrid theory-guided data science approach, for rapid, high-resolution flood mapping. It utilizes a neural network with U-Net architecture and integrates subgrid-based coarse-grid hydrodynamic model predictions as initial estimates, upskilling them to achieve fine-grid model accuracy. Unlike traditional hydrodynamic models, the subgrid method embeds fine-scale topographic details within coarse-grid cells, enhancing both computational efficiency and predictive accuracy. SGUnet processes flood depth raster patches (512 × 512 pixels) and corresponding digital elevation models as inputs. It functions as a deep learning-based corrector, refining flood predictions from numerical simulators. Trained through supervised learning, SGUnet learns to correct deviations in coarse-grid predictions using fine-grid model outputs as target values. The model is evaluated across three large Australian watersheds—Wollombi, Chowilla, and Burnett River—using HEC-RAS flood simulations with subgrid formulation. SGUnet reduces root mean squared error by a factor of 4.5–5.3 compared to coarse-grid models, achieves a critical success index exceeding 0.9 for flood extent mapping, and delivers a 50x speed-up over fine-grid hydrodynamic models. Furthermore, SGUnet outperforms a state-of-the-art ML-based upskilling model in depth and extent predictions. By effectively correcting flood artifacts from coarse-grid models, SGUnet achieves near fine-grid accuracy with significantly reduced computational cost, demonstrating its potential for real-time flood risk assessment. © 2025 The Authors

关键词： Deep learning

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A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI

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Scientific Reports 2025年第1期15卷 1-14页

作者： Hussain, Sayyed Shahid Shah, Pir Masoom Dawood, Hussain Degang, Xu Alshamayleh, Ahmad Khan, Muhammad Adnan Ghazal, Taher M. School of Automation Central South University Changsha 410083 China School of Computer Science and Engineering Central South University Changsha 410010 China Department of Computer Science Bacha Khan University Charsadda Pakistan School of Computing Skyline University College Sharjah United Arab Emirates Department of Data Science and Artificial Intelligence Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13120 South Korea Riphah School of Computing and Innovation Riphah International University Lahore Campus Lahore 54000 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Networks and Cybersecurity Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

Parkinson’s disease ranks as the second most prevalent neurological disorder after Alzheimer’s disease. Convolutional neural networks (CNNs) have been extensively employed in Parkinson’s disease (PD) detection using MR images. However, CNN models generally focus on local features while prone to capture global representations. On the other hand, the vision transformer (ViT) excels at capturing global features through its self-attention mechanism, but it compromises local feature representations. Additionally, the varying magnitude of MR data poses a challenge for ViT, potentially leading to the gradient vanishing problem. To address these limitations, this paper proposed a novel framework that combines the Swin-Transformer and CNN to capture both local and global features effectively. To mitigate the gradient vanishing issue in ViT, we used skipped connections and cosine attention mechanism in VIT that preserves the output distribution regardless of input magnitude variations. The proposed model comprises three primary blocks: Transformer-block, convolutional block, and dense-block. The input image is processed concurrently by the cosine transformer and convolutional block. Subsequently, the extracted features from both blocks are concatenated and fed to the dense block for decision-making. The proposed model achieved promesing results of 96%, 97%, 95%, and 95% in terms of accuracy, sensitivity, specificity, and area under the curve, respectively. © The Author(s) 2025.

关键词： CNN Cosine similarity MRI Parkinson’s disease Swin-transformer

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Blockchain-Assisted Trust Framework for Increased Survivability in Internet of Vehicles

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Internet Technology Letters 2025年第3期8卷

作者： Sharma, Kanta Prasad Al-Rashed, Atheer Johar, M.G.M. Alsoud, Anas Ratib Singh, Amrita Dey, Protyay Pant, Shivani Amity University Uttar Pradesh Greater Noida India College of Administrative Sciences Applied Science University Al Eker Bahrain Management and Science University Selangor Shah Alam Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Jaipur India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

VANETs enhance traffic efficiency and road safety, but they can be attacked by malicious vehicles. These malicious vehicles can cause accidents or endanger lives by broadcasting false event messages and disrupting Internet of Vehicles applications. Before responding to sender messages, receiver vehicles must estimate the legitimacy and trustworthiness of the source vehicles. Existing solutions struggle to balance security and efficiency effectively. This paper introduces a model that combines the advantages of blockchain technology and trust model models to improve the trustworthiness, efficacy, and security of vehicular networks, alongside secure trust-based optimized routing. Extensive experiments demonstrate the security and efficiency of the proposed model. The proposed model is adaptable to diverse VANET scenarios, addressing all security and privacy needs more comprehensively than current trust schemes. Efficiency analysis and simulation results show that our proposed framework outperforms baseline models, highlighting its security, effectiveness, and robustness in enhancing IoV communication security. © 2025 John Wiley & Sons Ltd.

关键词： Highway accidents

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Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies

arXiv

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arXiv 2025年

作者： Bensberg, Moritz Eckhoff, Marco Thomasen, F. Emil Bro-Jørgensen, William Teynor, Matthew S. Sora, Valentina Weymuth, Thomas Husistein, Raphael T. Knudsen, Frederik E. Krogh, Anders Lindorff-Larsen, Kresten Reiher, Markus Solomon, Gemma C. ETH Zurich Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 2 Zurich8093 Switzerland University of Copenhagen Department of Biology Linderstrøm-Lang Centre for Protein Science Ole Maaløes Vej 5 Copenhagen NDK-2200 Denmark University of Copenhagen Department of Chemistry and Nano-Science Center Universitetsparken 5 Copenhagen ØDK-2100 Denmark University of Copenhagen Niels Bohr Institute NNF Quantum Computing Programme Blegdamsvej 21 Copenhagen ØDK-2100 Denmark University of Copenhagen Department of Computer Science Universitetsparken 1 Copenhagen ØDK-2100 Denmark Center for Health Data Science Department of Public Health Denmark

Binding free energies are a key element in understanding and predicting the strength of protein–drug interactions. While classical free energy simulations yield good results for many purely organic ligands, drugs including transition metal atoms often require quantum chemical methods for an accurate description. We propose a general and automated workflow that samples the potential energy surface with hybrid quantum mechanics/molecular mechanics (QM/MM) calculations and trains a machine learning (ML) potential on the QM energies and forces to enable efficient alchemical free energy simulations. To represent systems including many different chemical elements efficiently and to account for the different description of QM and MM atoms, we propose an extension of element-embracing atom-centered symmetry functions for QM/MM data as an ML descriptor. The ML potential approach takes electrostatic embedding and long-range electrostatics into account. We demonstrate the applicability of the workflow on the well-studied protein–ligand complex of myeloid cell leukemia 1 and the inhibitor 19G and on the anti-cancer drug NKP1339 acting on the glucose-regulated protein 78. Copyright © 2025, The Authors. All rights reserved.

关键词： Ligands

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Carbon-Aware Edge computing for Internet of Everything Networks: A Digital Twin Approach

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IEEE Internet of Things Journal 2025年

作者： Huynh, Dang Van Khosravirad, Saeed R. Sharma, Vishal Kim, Joongheon Canberk, Berk Duong, Trung Q. Memorial University Faculty of Engineering and Applied Science St. John’sNLA1B 3X5 Canada Nokia Bell Labs Murray HillNJ07964 United States Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of Engineering and The Built Environment at Edinburgh Napier University School of Computing EdinburghEH11 4BN United Kingdom

The rapid growth of edge computing has enabled low-latency and high-efficiency processing for a wide range of applications;however, it also leads to significant energy consumption and carbon emissions. In this context, this study investigates a CO2 emission minimisation problem in a digital twin-aided edge computing system, aiming to optimise task offloading decisions, transmit power, and processing rates of Internet of Things (IoT) devices. To address the formulated mixed-integer non-linear programming problem, we propose two solutions: an alternating optimisation method based on the successive convex approximation framework and a deep reinforcement learning (DRL) approach. Extensive simulations validate the effectiveness of the proposed solutions, demonstrating significant reductions in CO2 emissions, robust optimisation performance, and superior results compared to benchmark schemes. The findings highlight the feasibility of integrating advanced optimisation and artificial intelligence-driven techniques to achieve environmentally sustainable and high-performance edge computing systems, paving the way for greener technological innovation. © 2014 IEEE.

关键词： Deep reinforcement learning

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Bayesian regression filters and the issue of priors

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NEURAL computing & APPLICATIONS 1996年第3期4卷 130-142页

作者： Zhu, HY Rohwer, R Neural Computing Research Group Department of Computer Science and Applied Mathematics Aston University Birmingham UK

We propose a Bayesian framework for regression problems, which covers areas usually dealt with by function approximation. An online learning algorithm is derived which solves regression problems with a Kalman filter. its solution always improves with increasing model complexity, without the risk of over-fitting. In the infinite dimension limit it approaches the hue Bayesian posterior. The issues of prior selection and over-fitting are also discussed, showing that some of the commonly held beliefs are misleading. The practical implementation is summarised. Simulations using 13 popular publicly available data sets are used to demonstrate the method and highlight important issues concerning the choice of priors.

关键词： approximation Bayesian method Kalman filter online learning prior selection radial basis functions regression

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