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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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Dynamic Quantum Federated Learning for UAV-Based Autonomous Surveillance

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8158-8170页

作者： Park, Soohyun Son, Seok Bin Jung, Soyi Kim, Joongheon Sookmyung Women's University Division of Computer Science Seoul04310 Korea Republic of Korea University School of Electrical Engineering Seoul02841 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

In recent years, unmanned aerial vehicles (UAVs) have proven their effectiveness in surveillance due to their superior mobility. By utilizing multiple UAVs with collaborated learning, surveillance of a huge area while consuming minimum resources is possible. However, the performance of UAV collaborative learning systems is still severely limited because the number of parameters in a classical NN is generally very large, which is unsuitable for unstable wireless communication. To address this issue, this paper uses a quantum neural network (QNN), which has large computational capabilities and uses fewer parameters to overcome the problems caused by many parameters. However, it is still difficult to send all the parameters of the QNN to the server under poor channel conditions. Therefore, this paper proposes dynamic quantum federated learning (DQFL), a novel framework designed for UAVs employing quantum computing (QC) and federated learning (FL). The proposed DQFL uses a dynamic quantum neural network (DQNN) with a multi-depth circuit and employs dynamic control of the circuit layer to improve the efficiency of local parameter transmission between UAVs in unstable wireless communication environments. Extensive simulations conducted under real-world autonomous surveillance conditions demonstrate the robustness of DQFL to non-iidness, varying signal-to-noise ratios (SNRs), and poor communication channel conditions in the UAV environment. © 1967-2012 IEEE.

关键词： Federated learning

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High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments

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Tsinghua science and Technology 2024年第4期29卷 1118-1137页

作者： Azam Fazel-Najafabadi Mahdi Abbasi Hani H.Attar Ayman Amer Amir Taherkordi Azad Shokrollahi Mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster

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Occupancy Map Guided Attributes Artifacts Removal for Video-Based Point Cloud Compression

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ACM Transactions on Multimedia Computing, Communications and Applications 2024年第12期20卷 1-20页

作者： Chen, Peilin Wang, Shiqi Li, Zhu Department of Computer Science City University of Hong Kong Kowloon Hong Kong Department of Computer Science and Electrical Engineering University of Missouri-Kansas City KansasMO United States

Point clouds offer realistic 3D representations of objects and scenes at the expense of large data volumes. To represent such data compactly in real-world applications, Video-Based Point Cloud Compression (V-PCC) converts their texture into 2D attributes and occupancy maps before applying lossy video compression. Unfortunately, the coding artifacts introduced in the decoded attribute maps eventually degrade the quality of the reconstructed point cloud, thereby influencing its immersive experience. This article proposes a deep learning-based attribute map enhancement method that fully leverages the occupancy map's guidance. The design philosophy is that the cross-modality guidance from occupancy can be leveraged as critical information to enhance the attribute. Therefore, instead of treating attribute and occupancy as two separate sources of signals, occupancy serves as an indispensable auxiliary, such that the proposed framework explicitly provides the model with abundant clues by conducting local feature modification and global dependencies aggregation. In particular, the proposed framework is compatible with existing V-PCC bitstreams and can be feasibly incorporated into the standardized decoder pipeline. Extensive evaluations show the effectiveness of the proposed framework in attribute enhancement, with equivalently 6.0% Bjontegaard Delta-rate (BD-rate) savings obtained. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Image compression

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Fast Adversarial Attacks on Language Models In One GPU Minute 41

Fast Adversarial Attacks on Language Models In One GPU Minut...

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41st International Conference on Machine Learning, ICML 2024

作者： Sadasivan, Vinu Sankar Saha, Shoumik Sriramanan, Gaurang Kattakinda, Priyatham Chegini, Atoosa Feizi, Soheil Department of Computer Science United States Department of Electrical & Computer Engineering United States

In this paper, we introduce a novel class of fast, beam search-based adversarial attack (BEAST) for Language Models (LMs). BEAST employs interpretable parameters, enabling attackers to balance between attack speed, success rate, and the readability of adversarial prompts. The computational efficiency of BEAST facilitates us to investigate its applications on LMs for jailbreaking, eliciting hallucinations, and privacy attacks. Our gradient-free targeted attack can jailbreak aligned LMs with high attack success rates within one minute. For instance, BEAST can jailbreak Vicuna-7B-v1.5 under one minute with a success rate of 89% when compared to a gradient-based baseline that takes over an hour to achieve 70% success rate using a single Nvidia RTX A6000 48GB GPU. BEAST can also generate adversarial suffixes for successful jailbreaks that can transfer to unseen prompts and unseen models such as GPT-4-Turbo. Additionally, we discover a unique outcome wherein our untargeted attack induces hallucinations in LM chatbots. Through human evaluations, we find that our untargeted attack causes Vicuna-7B-v1.5 to produce ∼15% more incorrect outputs when compared to LM outputs in the absence of our attack. We also learn that 22% of the time, BEAST causes Vicuna to generate outputs that are not relevant to the original prompt. Further, we use BEAST to generate adversarial prompts in a few seconds that can boost the performance of existing membership inference attacks for LMs. We believe that our fast attack, BEAST, has the potential to accelerate research in LM security and privacy. Copyright 2024 by the author(s)

关键词： Differential privacy

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Stable switching behavior of low-temperature ZrO_(2)RRAM devices realized by combustion synthesis-assisted photopatterning

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Journal of materials science & Technology 2024年第22期189卷 68-76页

作者： Bongho Jang Junil Kim Jieun Lee Jaewon Jang Hyuk-Jun Kwon Department of Electrical Engineering and Computer Science DGISTDaegu 42988Korea School of Electronic and Electrical Engineering Kyungpook National UniversityDaegu 41566Korea School of Electronics Engineering Kyungpook National UniversityDaegu 41566Korea Convergence Research Advanced Centre for Olfaction DGISTDaegu 42988Korea

We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures(250℃)using these *** synthesis reduces the energy required for oxide conversion,thus accelerating the decomposition of organic ligands in the UV-exposed area,and promoting the formation of metal-oxygen networks,contributing to *** analysis confirmed a reduction in the conversion temperature of combustion precursors,and the prepared combustion ZrO_(2)films exhibited a high proportion of metal-oxygen bonding that constitutes the oxide lattice,along with an amorphous ***,the synergistic effect of combustion synthesis and UV/O_(3)-assisted photochemical activation resulted in patterned ZrO_(2)films forming even more complete metal-oxygen *** devices fabricated with patterned ZrO_(2)films using combustion synthesis exhibited excellent switching characteristics,including a narrow resistance distribution,endurance of 103 cycles,and retention for 105 s at 85℃,despite low-temperature *** synthesis not only enables the formation of high-quality metal oxide films with low external energy but also facilitates improved photopatterning.

关键词： ZrO_(2) Combustion Sol-gel RRAM Patterning

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Regression analysis of squeezing-induced hybrid nanofluid flow in Darcy-Forchheimer porous medium

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Applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 193-208页

作者： K.MUHAMMAD M.SARFRAZ Department of Humanities and Sciences School of Electrical Engineering and Computer ScienceNational University of Sciences and TechnologyIslamabad 44000Pakistan Department of Mathematics Quaid-i-Azam UniversityIslamabad 44000Pakistan

This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel *** plate remains stationary,while the other moves downward at a squeezing *** space between these plates contains a Darcy-Forchheimer porous medium.A mixture of water-based fluid with gold(Au)and silicon dioxide(Si O2)nanoparticles is *** contrast to the conventional Fourier's heat flux equation,this study employs the Cattaneo-Christov heat flux equation.A uniform magnetic field is applied perpendicular to the flow direction,invoking magnetohydrodynamic(MHD)***,the model accounts for Joule heating,which is the heat generated when an electric current passes through the *** problem is solved via NDSolve in *** and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow,energy transport,and skin *** findings of this study have potential applications in enhancing cooling systems and optimizing thermal management *** is observed that the squeezing motion generates additional pressure gradients within the fluid,which enhances the flow rate but reduces the frictional ***,the fluid is pushed more vigorously between the plates,increasing the flow *** the fluid experiences higher flow rates due to the increased squeezing effect,it spends less time in the region between the *** thermal relaxation,however,abruptly changes the temperature,leading to a decrease in the temperature fluctuations.

关键词： convective boundary condition Darcy-Forchheimer medium hybrid nanofuid Joule heating magnetohydrodynamic(MHD) numerical solution squeezing flow regression analysis

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in engineering & sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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xCViT:Improved Vision Transformer Network with Fusion of CNN and Xception for Skin Disease Recognition with Explainable AI

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computers, materials & Continua 2025年第4期83卷 1367-1398页

作者： Armughan Ali Hooria Shahbaz Robertas Damaševicius Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Applied Informatics Vytautas Magnus UniversityKaunas44309Lithuania

Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)*** identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor *** study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception *** were evaluated using two benchmark datasets,HAM10000 and Skin Cancer *** the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC *** findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin *** addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.

关键词： Skin lesions vision transformer CNN Xception deep learning network fusion explainable AI Grad-CAM skin cancer detection

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Modified Frequency Scheduling Droop Control for Decentralized State of Charge Balancing of Battery in Islanded Microgrid 25

Modified Frequency Scheduling Droop Control for Decentralize...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Rahman, Irham Izzatur Miyauchi, Hajime Takano, Hirotaka Gifu University Department of Electrical Electronic and Computer Engineering Gifu Japan Kumamoto University Department of Computer Science and Electrical Engineering Kumamoto Japan

ISBN: (纸本)9798350378573

This study proposes a decentralized frequency scheduling method to achieve SoC balancing with no communication protocol. Since the frequency scheduling method is based on modifying droop control of inverter, to make the power output of inverter to be proportional to the SoC, the frequency output of inverter would be affected. This study considers to minimize the frequency deviation within European Norm EN50160 (±1% of the nominal value of the frequency), while also able to satisfy SoC balancing. To achieve it, we propose fuzzy logic controller to weight the value of droop coefficient ( ) instead of linear scaling in conventional frequency scheduling. The proposed method is studied in several test to show the performance and compare to conventional method. From the result, the proposed method performs better in SoC balancing, and results in better system reliability. We also study the proposed battery balancing system in various condition of PV and load profile, as well "plug and play"condition of microgrid. The proposed method can perform well in both conditions. © 2024 IEEE.

关键词： State of charge

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