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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

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Towards in-Depth Trustworthy Communications in NextG Networks 15

Towards in-Depth Trustworthy Communications in NextG Network...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Ibrahim, Yomna Abdel-Malek, Mai A. Azab, Mohamed Rizk, Mohamed R.M. Alexandria University Faculty of Engineering Department of Electrical Engineering Alexandria Egypt The University of Arizona Department of Electrical and Computer Engineering AZ United States Virginia Military Institute Department of Computer and Information Sciences VA United States

ISBN: (纸本)9798331507695

The current 5G Authentication and Key Agreement (5G-AKA) protocol remains susceptible to several vulnerabilities that can lead to massive privacy leakage. The absence of mutual authentication in the connection between User Equipment (UE) and Serving Network (SN) elements, represented by the Base Station (BS), opens doors for false-base station deployment and user tracking. Those vulnerabilities are exacerbated by unsecured signaling in the 5G-AKA signaling process. In this paper, we identify and discuss the issues above in the 5G-AKA protocol that affect user privacy and network reliability, especially those evident during the initial bootstrapping phase and the plaintext message exchange in the AKA process. The paper presents initial designs for potential solutions that can help counter the lack of authentication between the UE and the SN and secure the unshielded messages in the 5G-AKA signaling. Initial evaluation is demonstrated to assess the effectiveness and the potential of the presented approach. © 2025 IEEE.

关键词： Authentication Protocol

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A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3765-3779页

作者： Renkhoff, Justus Feng, Ke Meier-Doernberg, Marc Velasquez, Alvaro Song, Houbing Herbert Department of Information Systems BaltimoreMD21250 United States Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Daytona BeachFL32114 United States University of Colorado Department of Computer Science BoulderCO80309 United States

Neurosymbolic artificial intelligence (AI) is an emerging branch of AI that combines the strengths of symbolic AI and subsymbolic AI. Symbolic AI is based on the idea that intelligence can be represented using semantically meaningful symbolic rules and representations, while deep learning (DL), or sometimes called subsymbolic AI, is based on the idea that intelligence emerges from the collective behavior of artificial neurons that are connected to each other. A major drawback of DL is that it acts as a 'black box,' meaning that predictions are difficult to explain, making the testing & evaluation (T&E) and validation & verification (V&V) processes of a system that uses subsymbolic AI a challenge. Since neurosymbolic AI combines the advantages of both symbolic and subsymbolic AI, this survey explores how neurosymbolic applications can ease the V&V process. This survey considers two taxonomies of neurosymbolic AI, evaluates them, and analyzes which algorithms are commonly used as the symbolic and subsymbolic components in current applications. Additionally, an overview of current techniques for the T&E and V&V processes of these components is provided. Furthermore, it is investigated how the symbolic part is used for T&E and V&V purposes in current neurosymbolic applications. Our research shows that neurosymbolic AI has great potential to ease the T&E and V&V processes of subsymbolic AI by leveraging the possibilities of symbolic AI. Additionally, the applicability of current T&E and V&V methods to neurosymbolic AI is assessed, and how different neurosymbolic architectures can impact these methods is explored. It is found that current T&E and V&V techniques are partly sufficient to test, evaluate, verify, or validate the symbolic and subsymbolic part of neurosymbolic applications independently, while some of them use approaches where current T&E and V&V methods are not applicable by default, and adjustments or even new approaches are needed. Our research shows that th

关键词： Neural networks

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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A Comparative Study on Deep Learning based Segmentation Techniques for Tamil Inscriptions 8

A Comparative Study on Deep Learning based Segmentation Tech...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Poornimathi, K. Muralibhaskaran, V. Priya, L. Rajalakshmi Engineering College Department of Computer Science and Business Systems India Rajalakshmi Engineering College Department of Computer Science and Engineering India Rajalakshmi Engineering College Department of Information Technology India

ISBN: (纸本)9798350376425

The digitization and preservation of Tamil inscriptions are crucial for safeguarding the rich cultural heritage they represent. This study presents an in-depth evaluation of deep learning-based segmentation methods specifically designed for the complex and ancient Tamil script. Evaluate several cutting-edge deep learning models, including Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, and attention-based mechanisms, using a dataset of Tamil inscriptions. The evaluation criteria encompass segmentation accuracy, computational efficiency, and the ability to handle the noise and degradation common in historical artifacts. Our findings indicate that traditional methods often falter when faced with the complexities of Tamil script, whereas advanced deep learning techniques markedly enhance segmentation quality. This improvement facilitates more precise transcription and analysis. The comparison emphasizes the benefits and challenges of each method, delivering important insights into their effectiveness for cultural heritage preservation and paving the way for future research in the digitization of ancient scripts. © 2024 IEEE.

关键词： Convolutional neural networks

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Evolutionary-based Coordination of Multi-Robot systems with Dynamic Constraints

Evolutionary-based Coordination of Multi-Robot Systems with ...

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2024 systems and information engineering Design Symposium, SIEDS 2024

作者： Shah, Vihar Heeter, Matthew Vallarino, Jose Sherman, Patrick Bramblett, Lauren Bezzo, Nicola University of Virginia Department of Systems and Information Engineering CharlottesvilleVA22903 United States University of Virginia Department of Electrical and Computer Engineering CharlottesvilleVA22903 United States

ISBN: (纸本)9798350385144

Large-scale manufacturing facilities and power plants comprise numerous subsystems that require consistent monitoring and inspection to ensure reliable and secure operations. The use of multiple robotic systems is an expanding, robust, and cost-effective solution for such operations. To enable automated infrastructure monitoring, there are many challenges, such as path planning and task allocation for multiple robots and dynamic constraints. We formulate such challenges as a multiple Traveling Salesman Problem (mTSP) and propose an efficient task assignment solution that uses an evolutionary algorithm considering priority, energy, and time constraints. Our solution incorporates: A digital twin of a monitored environment, A? and artificial potential field methods for path planning and navigation, and human-in-The-loop prioritization. We validate the proposed method with state-of-The-Art simulations in Gazebo/ROS for unmanned ground vehicles (UGVs) in cluttered environments. © 2024 IEEE.

关键词： Motion planning

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A Self-Labeling Method for Adaptive Machine Learning by Interactive Causality

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2093-2102页

作者： Ren, Yutian Yen, Aaron Haohua Li, Guann-Pyng University of California Irvine Department of Electrical Engineering and Computer Science The California Institute for Telecommunications and Information Technology IrvineCA92697 United States

Learning from unlabeled data or self-learning, can substantially reduce the complexity of machine learning (ML) utilization in real-time deployment. While the development of un/semisupervised algorithms shows promising results in learning with reduced labels, the fundamental assumption of data smoothness restricts its scope of application, especially with nonstationary data distributions in different domains. Leveraging transdomain invariant causal relationships, causality has recently been employed to foster robust ML. In this article, we have developed a generic method for self-labeling data that relies on known causality among interactive objects and learned temporal relations among causal events for identifying and associating labels and input data. The causal time-interval between asynchronous cause and effect events is studied to achieve self-labeling. We utilize dynamical system theory in a 1-d setting to demonstrate that our proposed method outperforms traditional feature similarity-based semisupervised learning. A computer simulation experiment was conducted to generate high-dimensional data, and the comprehensive results reveal the potential of learning adaptation in dynamic environments to improve ML robustness against shifts in data distribution. © 2023 IEEE.

关键词： Timing circuits

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Design and Implementation of Safety Functions for the CNC Milling Machine 15

Design and Implementation of Safety Functions for the CNC Mi...

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15th International Conference Elektro, ELEKTRO 2024

作者： Brtis, Jozef Zdansky, Juraj Pirnik, Rastislav University of Žilina Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Žilina Slovakia

ISBN: (纸本)9798350372359

The paper deals with the design and implementation of safety functions for a CNC milling machine. It is a relatively small CNC milling machine that does not pose a great danger to trained persons. Because it is used in areas where even untrained persons can come into contact with it, it was necessary to implement a safety system that implements safety functions preventing accidents when entering the dangerous zone of the CNC milling machine. The safety system is implemented based on a safety PLC and the sensors are designed using a two-channel architecture to meet the required SIL (safety integrity level). © 2024 IEEE.

关键词： Security systems

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Empowering Internet of Underwater Things: An Integrated Simulator for Underwater Magneto-Coupled Wireless Sensor Networks

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IEEE Internet of Things Magazine 2025年第1期8卷 109-114页

作者： Songzuo, Liu Ullah, Irfan Muzzammil, Muhammad Ahmed, Niaz Wahid, Abdul Zhen, Shao Ministry of Industry and Information Technology Key Laboratory of Marine Information China College of Underwater Acoustic Engineering Harbin Engineering University China Sanya Nanhai Innovation and Development Base of Harbin Engineering University China FAST National University of Computer and Emerging Sciences Department of Electrical Engineering Islamabad Campus Pakistan

This research paper presents an integration of finite element method (FEM) based models into MATLAB for developing an end-to-end magneto-coupled (MC) communication system tailored for Internet of Underwater Things (IoUT)-based applications. Magneto-coupled communication has emerged as a promising solution for extreme environments like underwater where conventional wireless acoustic communication poses significant challenges. The article exploits the strengths of two software tools: MATLAB and COMSOL Multiphysics, to seamlessly integrate communication protocols with theoretical insights into magnetic channels. The article also demonstrates the development and evaluation of an end-to-end communication system with both simulation and experimental setup. We believe that this integrated approach is highly effective in achieving reliable communication performance in underwater scenarios, thereby advancing the capabilities of IoUT for various real-world applications. © 2025 IEEE.

关键词： MATLAB

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Fault-tolerant visual servo control for a robotic arm with actuator faults

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Neural Computing and Applications 2024年第25期36卷 15815-15828页

作者： Li, Jiashuai Peng, Xiuyan Li, Bing Sreeram, Victor Wu, Jiawei College of Intelligent Systems Science and Engineering Harbin Engineering University Heilongjiang Nantong Harbin150001 China Department of Electrical Electronic and Computer Engineering The University of Western Australia Stirling Highway PerthWA6009 Australia

The study targets uncertain coupling faults in robotic arm actuators and proposes a new fault-tolerant visual servo control strategy. Specifically, it considers both multiplicative and additive actuator faults within the dynamic of the robotic arm, treating the coupling faults and time-varying disturbances as an aggregate of concentrated uncertainties. A radial basis function neural network-based state observer is introduced to online approximate these concentrated uncertainties, which include fault information, eliminating the need for prior knowledge of faults. Furthermore, a fault-tolerant controller based on a non-singular fast terminal sliding mode is proposed, which separately decouples the nominal quantities and concentrated uncertainties and develops individual adaptive control laws for each. This effectively reduces the detrimental impact of coupled faults and disturbances on the system’s performance, facilitating image feature trajectory tracking control with minimal jitter, high precision, and strong transient response capabilities. The stability of the state observer and the fault-tolerant controller has been substantiated through Lyapunov’s theory. Lastly, numerical simulations validate the efficacy and robustness of the proposed fault-tolerant visual servo control approach. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Robotic arms

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