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Heart Disease Prediction Using Novel Ensemble and Blending Based Cardiovascular Disease Detection Networks: EnsCVDD-Net and BlCVDD-Net

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IEEE Access 2024年 12卷 109230-109254页

作者： Khan, Hira Javaid, Nadeem Bashir, Tariq Akbar, Mariam Alrajeh, Nabil Aslam, Sheraz Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Cyprus University of Technology Limassol3036 Cyprus

Cardiovascular Diseases (CVDs) have emerged as a significant physiological condition, being a primary contributor to mortality. Timely and precise diagnosis of heart disease is crucial to safeguard patients from additional harm. Recent studies show that the usage of data driven approaches, such as Deep Learning (DL) and Machine Learning (ML) techniques, in the field of medical science is highly useful in accurately diagnosing heart disease in less time. However, statistical learning and traditional ML approaches require feature engineering to generate robust and effective features from data, which are then used in the prediction models. In the case of large complex data, both processes pose many challenges. Whereas, DL techniques are capable of learning features automatically from the data and are effective at handling large and intricate datasets while outperforming the ML models. This study focuses on the accurate prediction of CVDs, considering the patient’s health and socio-economic conditions while mitigating the challenges presented by imbalanced data. The Adaptive Synthetic Sampling Technique is used for data balancing, while the Point Biserial Correlation Coefficient is used as a feature selection technique. In this study, two DL models, Ensemble based Cardiovascular Disease Detection Network (EnsCVDD-Net) and Blending based Cardiovascular Disease Detection Network (BlCVDD-Net), are proposed for accurate prediction and classification of CVDs. EnsCVDD-Net is made by applying an ensemble technique to LeNet and Gated Recurrent Unit (GRU), and BlCVDD-Net is made by blending LeNet, GRU and Multilayer Perceptron. SHapley Additive exPlanations is used to provide a clear understanding of the influence different factors have on CVD diagnosis. The network’s performance is evaluated on the basis of various performance metrics. The results indicate that the EnsCVDD-Net outperforms all base models with 88% accuracy, 88% F1-score, 91% precision, 85% recall, and 777s execu

关键词： Heart

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Integration of data science with the intelligent IoT(IIoT):Current challenges and future perspectives

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Digital Communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon UniversitySeongnamSujeong-gu 13120Republic of Korea University of Electronic Science and Technology of China Chengdu 611731China College of Information Science and Engineering Hohai University213000China Department of Computer Science University of SalernoItaly Graduate School of Informatics and Engineering The University of Electro-Communications 1-5-1ChofugaokaChofu-shiTokyo182-8585Japan

The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate *** science is the science of dealing with data and its relationships through intelligent *** state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their ***,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various *** paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based *** insights into IoT data security,privacy,and challenges are visualized in the context of data science for *** addition,this study reveals the current opportunities to enhance data science and IoT market *** current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.

关键词： Data science Internet of things(IoT) Big data Communication systems Networks Security Data science analytics

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Indirect Rate Distortion Functions with Side Information: Structural Properties and Multivariate Gaussian Sources 63

Indirect Rate Distortion Functions with Side Information: St...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Stylianou, Evagoras Gkagkos, Michail Charalambous, Charalambos D. Technical University of Munich Theoretical Information Technology Munich Germany Texas A&m University Department of Electrical and Computer Engineering College StationTX United States University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (纸本)9798350316339

In this paper, we analyze the indirect source coding problem with side information at both the encoder and decoder, as well as only at the decoder. We first derive structural properties of the two rate distortion functions (RDFs) for general abstract spaces and identify conditions under which the RDFs coincide. For multivariate jointly Gaussian random variables with square-error fidelity, we establish structural properties of the optimal test channels, show that side information at both the encoder and decoder does not reduce compression, and provide water-filling solutions using parallel Gaussian channel realizations. This paper uses a novel realization theory approach to establish achievability of the converse coding theorem lower bounds of the two RDFs. © 2024 IEEE.

关键词： Decoding

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Green hydrogen storage capacity estimations in depleted gas reservoirs 3

Green hydrogen storage capacity estimations in depleted gas ...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Konstantinou, Charalampos Giakoumi, Matheos Papanastasiou, Panos Olympios, Andreas V. Kourougianni, Fanourios Arsalis, Alexandros Georghiou, George E. University of Cyprus Department of Civil and Environmental Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Fondazione Eni Enrico Mattei Milan Italy University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (纸本)9798350375923

Green hydrogen is poised to play a crucial role in the transition to renewable energy sources. It acts as a versatile energy storage carrier, helping to balance supply and demand. However, storing hydrogen presents significant challenges, as current technologies can only handle limited volumes. One promising solution involves injecting hydrogen into depleted oil & gas reservoirs, where it can be stored and later extracted when needed. This study explores this approach using the material balance method, which estimates the volumes within a reservoir. It is applied to a depleted natural gas system supported by an aquifer, taking into account pressure and flow rate histories. As the natural gas production rate increases, the hydrogen storage capacity also rises preventing the water to invade in the reservoir. For example, at a reference pressure of 2000 psi, there is no hydrogen storage capacity at a flow rate of 10 MMMCF/day, whereas a storage capacity of 314 BSCF is observed at a flow rate of 1000 MMMCF/day. These insights highlight the potential of utilising depleted reservoirs for hydrogen storage, offering a feasible path to overcoming the current limitations in hydrogen storage technology. © 2024 IEEE.

关键词： Hydrogen storage

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AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

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Digital Communications and Networks 2024年第2期10卷 292-303页

作者： Yuteng Xiao Kaijian Xia Hongsheng Yin Yu-Dong Zhang Zhenjiang Qian Zhaoyang Liu Yuehan Liang Xiaodan Li School of Information and Control Engineering China University of Mining and TechnologyXuzhou221116China Department of Informatics University of LeicesterLeicesterLE17RHUK The Affiliated Changshu Hospital of Soochow University Changshu215500China School of Computer Science and Engineering Changshu Institute of TechnologySuzhou215500China

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other ***,it is important to construct a digital twin ***,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted *** this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)***,we fuse the spatial-temporal graph based on the interrelationship of spatial ***,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding ***,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)*** module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like *** dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted *** on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

关键词： Adaptive adjacency matrix Digital twin Graph convolutional network Multivariate time series prediction Spatial-temporal graph

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Comparison of Classical and Deep Learning-Based Feature Representations for Age-Related Macular Degeneration 5th

Comparison of Classical and Deep Learning-Based Feature Re...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Sinichi, Parsa Bernabeu, Miguel O. Javidi, Malihe Department of Computer Engineering Faculty of Electronic and Computer Engineering Quchan University of Technology Quchan Iran Centre for Medical Informatics University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9789819638628

Age-related Macular Degeneration (AMD) is a leading cause of visual impairment among the elderly worldwide. This study compares deep learning-based and classical feature extraction methods for AMD classification using colour fundus images, a cost-effective imaging modality. This comparative approach aims to evaluate the performance of advanced deep learning methods against traditional computer vision approaches in generating effective feature representation. To achieve this, we compared various classical feature extraction methods alongside deep learning-based approaches, specifically VGG16 pre-trained on ImageNet, RETFound and RETFound-Green, which are foundation models pre-trained on a significant number of retinal images. The results demonstrate that combinations of classical features still obtained notable performance and even outperformed the VGG16 model pre-trained on ImageNet. These results also highlight the need for deep learning models to be trained on domain-specific data to surpass classical approaches. In this regard, the pre-trained RETFound and RETFound-Green models yielded promising outcomes for AMD classification, and the highest performance was achieved using a fine-tuned RETFound model. Furthermore, the best proposed method was compared with the state-of-the-art methods. This approach achieved competitive results on the ADAM dataset, demonstrating its robustness and effectiveness in AMD classification while utilising a significantly simpler architecture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Optimized Price Prediction of Cryptocurrencies using Deep Learning on High-Volume Time Series Data 15

Optimized Price Prediction of Cryptocurrencies using Deep Le...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Vonitsanos, Gerasimos Kanavos, Andreas Grivokostopoulou, Foteini Sioutas, Spyros University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece Computer Technology Institute and Press 'Diophantus' Patras Greece

ISBN: (纸本)9798350368833

Statistical models, enhanced by deep learning techniques, have become pivotal in various predictive tasks, including financial forecasting. This paper addresses the challenge of predicting cryptocurrency prices, utilizing a dataset comprising various cryptocurrencies treated as time series. We employ several deep neural network architectures-including Multilayer Perceptrons (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), and Bidirectional LSTM networks-to forecast future prices. Our methodology involves a detailed analysis of cryptocurrency time series to inform the design of these networks. The performance of each model is rigorously compared, highlighting their predictive capabilities in the context of cryptocurrency markets. This study not only contributes to the empirical literature by applying advanced neural networks to high-volume financial data but also provides a comparative analysis that may guide future applications of deep learning in economic forecasting. © 2024 IEEE.

关键词： Costs

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Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

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Tsinghua Science and technology 2024年第5期29卷 1615-1632页

作者： Abdul Basit Dogar Sami Ullah Yiran Zhang Hisham Alasmary Muhammad Waqas Sheng Chen Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China and also with Department of Informatics and SystemsUniversity of Management and TechnologyLahore 54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir 18050Pakistan School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha 61421Saudi Arabia Department of Computer Engineering Faculty of Information TechnologyUniversity of BahrainSakheer 32038Bahrainand also with School of EngineeringEdith Cowan UniversityPerth WA 6027Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow *** this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance *** look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a *** are used to assess the proposed *** findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

关键词： software defined data center networks network updates DCTCP out-of-order packets packet drop SDN

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Optimizing Resource Allocation in 5G Networks Through Downlink and Uplink Decoupling 15

Optimizing Resource Allocation in 5G Networks Through Downli...

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15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2023

作者： Bouras, Christos Chatzigeorgiou, Charalampos Kokkinos, Vasileios Gkamas, Apostolos Pouyioutas, Philippos University of Patras Computer Engineering and Informatics Dept Greece University Ecclisiastical Academy of Vella Computer Science Dept Greece University of Nicosia Computer Science Dept Cyprus

ISBN: (纸本)9798350393286

This research paper sets out to explore the multi-faceted and complex topic of decoupling downlink and uplink in 5G networks, with a view to optimizing resource allocation. Drawing on an extensive range of current literature and industry trends, the authors conduct a comprehensive and in-depth analysis of the potential benefits and drawbacks of decoupling, taking into account the impact on network capacity, flexibility, and user experience. Additionally, the article offers valuable insights into the practical implementation of decoupling in real-world 5G networks, including an exploration of the challenges and considerations that may arise, and potential future developments in this field. Through a rigorous and meticulous examination of the topic, the authors aim to provide an understanding of the subject, offering valuable guidance and insights to future researchers and developers working in this area. With a focus on depth and detail, this paper represents a vital contribution to the ongoing dialogue surrounding decoupling in 5G networks and has the potential to drive significant advancements in the field in the years to come. © 2023 IEEE.

关键词： 5G Decoupling Downlink Resource Allocation Uplink

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Predictive Analytics for Maximizing the Photovoltaic System Performance 52

Predictive Analytics for Maximizing the Photovoltaic System ...

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52nd IEEE Photovoltaic Specialist Conference, PVSC 2024

作者： Livera, Andreas Marangis, Demetris Tziolis, Georgios Paraskeva, Vasiliki Makrides, George Georghiou, George E. University of Cyprus Pv Technology Laboratory Department of Electrical and Computer Engineering Nicosia2109 Cyprus Phaethon Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia2109 Cyprus

ISBN: (纸本)9781665464260

To ensure the smooth Terawatt (TW) photovoltaic (PV) transition and secure the TWh power generation over the service lifetime, enhanced digitalization, and automation approaches for effective operation and maintenance (O&M) of PV systems are required. This highlights the urgent need for corrective and predictive analytics to detect and predict possible anomalies in PV systems and to effectively manage and optimally schedule the field O&M activities. To fulfill this need, a failure detection algorithm along with a predictive maintenance model, that leverages statistical and machine learning (ML) principles, are proposed to accurately detect failures and to predict potential fault issues before they occur. The developed algorithm and the model were benchmarked using historical field data from a PV power plant in Greece. The obtained results showed the effectiveness of the algorithm for accurate failure detection and the capability of the predictive model for generating predictive maintenance alarms. The integration of corrective and predictive analytics into a monitoring software solution can help PV plant operators to schedule the field O&M activities, by providing valuable fault insights and actionable maintenance recommendations. © 2024 IEEE.

关键词： Predictive analytics

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