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Automated heart disease prediction using improved explainable learning-based technique

Neural Computing and Applications 2024年第26期36卷 16289-16318页

作者： Bizimana, Pierre Claver Zhang, Zuping Hounye, Alphonse Houssou Asim, Muhammad Hammad, Mohamed El-Latif, Ahmed A. Abd School of Computer Science and Engineering Central South University Changsha410083 China School of Mathematics and Statistics Central South University Changsha410008 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom Egypt

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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ShennongMGS: An LLM-based Chinese Medication Guidance System

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ACM Transactions on Management Information Systems 2025年第2期16卷 1-14页

作者： Dou, Yutao Huang, Yuwei Zhao, Xiongjun Zou, Haitao Shang, Jiandong Lu, Ying Yang, Xiaolin Xiao, Jian Peng, Shaoliang College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Computer Science and Engineering Guilin University of Technology Guilin China National Supercomputing Center in Zhengzhou Zhengzhou China College of Military and Political Basic Education National University of Defense Technology Changsha China College of Finance and Statistics Hunan University Changsha China Department of Pharmacy Xiangya Hospital Central South University Changsha China

The rapidly evolving field of Large Language Models (LLMs) holds immense promise for healthcare, particularly in medication guidance and adverse drug reaction prediction. Despite their potential, existing LLMs face challenges in dealing with complex polypharmacy scenarios and often grapple with data lag issues. To address these limitations, we introduce an LLM-based Chinese medication guidance system, called ShennongMGS, specifically tailored for robust medication guidance and adverse drug reaction predictions. Our system transforms multi-source heterogeneous medication information into a knowledge graph and employs a two-stage training strategy to construct a specialized LLM (ShennongGPT). This method enables the simulation of professional pharmacists' decision-making processes and incorporates the capability for knowledge self-updating, thereby significantly enhancing drug safety and the overall quality of medical services. Rigorously evaluated by medical professionals and artificial intelligence experts, our method demonstrates superiority, outperforming existing general and specialized LLMs in performance. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Windows operating system

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A Survey on Image Semantic Segmentation Using Deep Learning Techniques

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computers, Materials & Continua 2023年第1期74卷 1941-1957页

作者： Jieren Cheng Hua Li Dengbo Li Shuai Hua Victor S.Sheng School of Computer Science and Technology Hainan UniversityHaikou570228China School of Cyberspace Security(School of Cryptology) Hainan UniversityHaikou570228China Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570228China Department of Computer Science Texas Tech University TX 79409USA

Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis,autonomous driving,virtual or augmented reality,*** recent years,due to the remarkable performance of transformer and multilayer perceptron(MLP)in computer vision,which is equivalent to convolutional neural network(CNN),there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning *** survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic ***,the commonly used image segmentation datasets are ***,extensive pioneering works are deeply studied from multiple perspectives(e.g.,network structures,feature fusion methods,attention mechanisms),and are divided into four categories according to different network architectures:CNN-based architectures,transformer-based architectures,MLP-based architectures,and ***,this paper presents some common evaluation metrics and compares the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value on the most widely used ***,possible future research directions and challenges are discussed for the reference of other researchers.

关键词： Deep learning semantic segmentation CNN MLP transformer

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Sapphire Fiber Scattering Array Interferometer for Multiplexable High-Temperature Sensing

Sapphire Fiber Scattering Array Interferometer for Multiplex...

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CLEO: Applications and technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Shi, Guannan Thomas, Joseph G. Pickrell, Gary Wang, Anbo Zhu, Yizheng Center for Photonics Technology The Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States Center for Photonics Technology Department of Material Science and Engineering Virginia Tech BlacksburgVA24061 United States

An intrinsic sapphire fiber scattering array interferometer (SAI) is presented. SAI generates robust interferometric signal with a ~20 dB signal-to-noise ratio and is immune to blackbody radiation-induced noise. Multiplexable high-temperature sensing to ~1500°C is demonstrated. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Sapphire

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Improving the Transmission Security of Vein Images Using a Bezier Curve and Long Short-Term Memory

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computers, Materials & Continua 2024年第6期79卷 4825-4844页

作者： Ahmed H.Alhadethi Ikram Smaoui Ahmed Fakhfakh Saad M.Darwish Computer Science and School of Electronics and Telecommunications University of SfaxSfax3029Tunisia LETI Laboratory University of SfaxSfax3029Tunisia Digital Research Center of Sfax Technopark of SfaxSfax3029Tunisia Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandriaEgypt

The act of transmitting photos via the Internet has become a routine and significant *** the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further *** study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous *** main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image *** paper introduces a content-based image authentication mechanism that is suitable for usage across an untrusted network and resistant to data loss during *** employing scale attributes and a key-dependent parametric Long Short-Term Memory(LSTM),it is feasible to improve the resilience of digital signatures against image deterioration and strengthen their security against malicious ***,the successful implementation of transmitting biometric data in a compressed format over a wireless network has been *** applications involving the transmission and sharing of images across a *** suggested technique utilizes the scalability of a structural digital signature to attain a satisfactory equilibrium between security and picture *** effective adaptive compression strategy was created to lengthen the overall lifetime of the network by sharing the processing of *** scheme ensures a large reduction in computational and energy requirements while minimizing image quality *** approach employs multi-scale characteristics to improve the resistance of signatures against image *** proposed system attained a Gaussian noise value of 98%and a rotation accuracy surpassing 99%.

关键词： Image transmission image compression text hiding Bezier curve Histogram of Oriented Gradients(HOG) LSTM image enhancement Gaussian noise rotation

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Sapphire Fiber Scattering Array Interferometer for Multiplexable High-Temperature Sensing

Sapphire Fiber Scattering Array Interferometer for Multiplex...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Sapphire

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Active label-denoising algorithm based on broad learning for annotation of machine health status

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science China(Technological sciences) 2022年第9期65卷 2089-2104页

作者： LIU GuoKai SHEN WeiMing GAO Liang KUSIAK Andrew State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Industrial and System Engineering The University of IowaSeamans CenterIowa CityIA 52242USA

Deep learning has led to tremendous success in machine maintenance and fault ***,this success is predicated on the correctly annotated *** in large industrial datasets can be noisy and thus degrade the performance of fault diagnosis *** emerging concept of broad learning shows the potential to address the label noise *** with existing deep learning algorithms,broad learning has a simple architecture and high training *** active label denoising algorithm based on broad learning(ALDBL)is ***,ALDBL captures the embedded representation from the time-frequency features by a recurrent memory ***,it augments wide features with a sparse autoencoder and projects the sparse features into an orthogonal space.A proposed corrector then iteratively changes the weights of source examples during the training and corrects the labels by using a label adaptation ***,ALDBL finetunes the model parameters with actively sampled target data with reliable pseudo *** performance of ALDBL is validated with three benchmark datasets,including 30 label denoising *** results demonstrate the effectiveness and advantages of the proposed algorithm over the other label denoising algorithms.

关键词： data annotation broad learning deep learning domain adaptation fault diagnosis noisy label

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Recent Progress in the Auxiliary Phase Enhanced Flexible Piezocomposites

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Energy & Environmental Materials 2025年第1期8卷 292-312页

作者： Xin Gao Mupeng Zheng Boyue Hou Junshu Wu Mankang Zhu Yunfan Zhang Ke Wang Bing Han College of Materials Science and Engineering Beijing University of TechnologyBeijing 100124China School of Basic Medical Sciences Capital Medical UniversityBeijing 100069China Department of Orthodontics Peking University School and Hospital of Stomatology&National Center of Stomatology&National Engineering Laboratory for Digital and Material Technology of Stomatology&NMPA Key Laboratory for Dental MaterialsBeijing 100089China State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China

Piezocomposites with both flexibility and electromechanical conversion characteristics have been widely applied in various fields,including sensors,energy harvesting,catalysis,and biomedical *** the composition of piezocomposites or their preparation process,a category of materials is commonly employed that do not possess piezoelectric properties themselves but play a crucial role in performance *** this review,the concept of auxiliary phase is first proposed to define these materials,aiming to provide a new perspective for designing high-performance *** different categories of modulation forms of auxiliary phase in piezocomposites are systematically summarized,including the modification of piezo-matrix,the modification of piezo-fillers,and the construction of special *** category emphasizes the role of the auxiliary phase and systematically discusses the latest advancements and the physical mechanisms of the auxiliary phase enhanced flexible ***,a summary and future outlook of piezocomposites based on the auxiliary phase are provided.

关键词： auxiliary phase flexibility microstructure piezocomposites piezoelectricity

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Cyber Twin technology for AI-Driven Real-Time Software Security in IoT Ecosystems 4

Cyber Twin Technology for AI-Driven Real-Time Software Secur...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Sunkara, Krishna Chaitanya Dhasmana, Gulshan Ravi, K.S. Nayana Yadav, M. Desai, Ishan Shankar, Rajmani Oracle Cloud Engineering Data Center NC United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Vidyavardhaka College of Engineering Department of Mechanical Engineering Mysuru India Aj Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electrical Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

As Internet of Things (IoT) ecosystems grow more complex, ensuring real-time security has become a major challenge. Traditional security approaches are insufficient for handling dynamic and interconnected IoT networks, which are increasingly targeted by sophisticated cyber-attacks. To address these issues, new methodologies that combine real-time monitoring and adaptive security mechanisms are needed. Cyber Twin technology, an innovative extension of digital twin technology, presents a promising solution by creating AI-driven digital replicas of IoT devices and software systems for continuous security monitoring and management. This paper introduces a Cyber Twin technology Framework for AI-driven real-time software security in IoT ecosystems. The framework employs advanced AI models, including Convolutional Neural Networks (CNNs) for anomaly detection and Generative Adversarial Networks (GANs) for synthetic data generation to simulate potential attack scenarios. A dynamic reinforcement learning module is integrated to optimize threat response strategies based on evolving threat patterns. By creating real-time digital replicas of IoT components, the Cyber Twin framework continuously monitors device behaviors, identifies anomalies, and autonomously initiates mitigation actions. The system is evaluated in a simulated IoT environment with over 500 interconnected devices. Experimental results demonstrate that the Cyber Twin framework achieved a 99.2% detection accuracy in identifying cyber threats, with a false positive rate of 1.3%. The dynamic response module reduced incident response time by 35% compared to traditional methods, enhancing the system's ability to neutralize potential threats in real-time. The use of GAN-based synthetic data also enabled proactive defense strategies, reducing attack success rates by 40% during testing. The Cyber Twin technology Framework provides a robust solution for real-time software security in complex IoT ecosystems. By leveraging A

关键词： Generative adversarial networks

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Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks

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computers, Materials & Continua 2023年第5期75卷 2491-2508页

作者： Ashir Javeed Ana Luiza Dallora Johan Sanmartin Berglund Arif Ali Peter Anderberg Liaqat Ali Aging Research Center Karolinska InstitutetStockholm17165Sweden Department of Health Blekinge Institute of TechnologyKarlskrona37141Sweden School of Health Sciences University of SkövdeSkövdeSE 54128Sweden Department of Computer Science University of Science and Technology BannuBannu28100Pakistan Department of Electrical Engineering University of Science and Technology BannuBannu28100Pakistan

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity,mortality,and *** there is a consensus that dementia is a multifactorial disorder,which portrays changes in the brain of the affected individual as early as 15 years before its onset,prediction models that aim at its early detection and risk identification should consider these *** study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data,which comprised 75 *** are two automated diagnostic systems developed that use genetic algorithms for feature selection,while artificial neural network and deep neural network are used for dementia *** proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%,sensitivity of 93.15%,specificity of 91.59%,MCC of 0.4788,and performed superior to other 11 machine learning techniques which were presented in the past for dementia *** identified best predictors were:age,past smoking habit,history of infarct,depression,hip fracture,single leg standing test with right leg,score in the physical component summary and history of TIA/*** identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset.

关键词： Dementia prediction feature selection genetic algorithm neural networks

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