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Enhancing the Heart Disease Classification Using Multi-level Perceptron and Principal Component Analysis 1

International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Jaiswal, Vanshika Gupta, Sanyam Gupta, Shilpa Kumar, Deepika Hameed, Alaa Ali Department of Computer Science and Engineering JIMS Engineering Management Technical Campus U.P. Greater Noida India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul34396 Turkey

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

The ability to detect cardiac disease early is vital to saving lives. Heart attacks are one of the leading reasons for high death rates worldwide due to the high cost of identifying cardiac disorders, which is crucial to the healthcare industry, as well as the shortage of human and logistical resources. In order to find the best machine learning algorithm for early-stage heart disease prediction, a model is presented in this study. In order to train and evaluate the model based on the data for efficient decision-making, machine learning techniques are utilized. The goal of this research is to combine Principal Component Analysis and Multilevel Perceptron model to propose an updated diagnosis and classification model based on machine learning (ML) for predicting heart failure and heart disease detection. Using cutting-edge techniques and foundational classifiers like Support Vector machines, Random forests, and K-Nearest Neighbors, an empirical evaluation of the suggested methodology was carried out with accuracy, precision, recall, and F1-score serving as the evaluation criteria. The proposed method has shown the maximum accuracy of 95.1%. This innovative approach not only streamlines the diagnostic process but also offers personalized medical assistance, facilitating early detection and intervention in heart disease, thereby optimizing healthcare delivery and improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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An Attention-Based Model for Clinical Time Series Prediction: Enhancing ICU Readmission Prediction 14

An Attention-Based Model for Clinical Time Series Prediction...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Madinei, Hananeh Sadat Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis. ©2024 IEEE.

关键词： Electronic health record

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Multi-Site Aggregate Production Planning Using Particle Swarm Optimization

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Journal of engineering, Project, and Production Management 2022年第1期12卷 62-69页

作者： Wibawa, Aji Prasetya Mahmudy, Wayan F. Rizki, Agung Mustika Yuliastuti, Gusti Eka Tama, Ishardita Pambudi Electrical Engineering Department Universitas Negeri Malang Malang Indonesia Computer Science Faculty of Computer Science Brawijaya University Malang Indonesia Industrial Engineering Faculty of Engineering Brawijaya University Malang Indonesia

Aggregate planning is a crucial stage in the production process because it supports other processes. Careless production planning may cause production costs to spike sharply that hurts the company financially. This study explores the novel usage of particle swarm optimization (PSO) to discover a set of solutions among the objective of a multioptimization problem in aggregate production planning. The study uses a small home textile industry with complex production processes of school uniforms as a case study. The results show that the production cost difference between actual data and the proposed method is IDR330,670,000. Thus, PSO can solve the multi-site aggregate planning by reducing the company production cost. Copyright © Journal of engineering, Project, and Production Management (EPPM-Journal).

关键词： Aggregates

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A MODIFIED WEAK GALERKIN FINITE ELEMENTMETHOD FOR SINGULARLY PERTURBED PARABOLIC CONVECTION-DIFFUSION-REACTION PROBLEMS

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Journal of Computational Mathematics 2023年第6期41卷 1246-1280页

作者： Suayip Toprakseven Fuzheng Gao Faculty of Engineering Department of Computer ScienceArtvin Coruh UniversityArtvin08100Turkey School of Mathematics Shandong UniversityJinan 250100China

In this work,a modified weak Galerkin finite element method is proposed for solving second order linear parabolic singularly perturbed convection-diffusion *** key feature of the proposed method is to replace the classical gradient and divergence operators by the modified weak gradient and modified divergence operators,*** apply the backward finite difference method in time and the modified weak Galerkin finite element method in space on uniform *** stability analyses are presented for both semi-discrete and fully-discrete modified weak Galerkin finite element *** order of convergences are obtained in suitable *** have achieved the same accuracy with the weak Galerkin method while the degrees of freedom are reduced in our *** numerical examples are presented to support the theoretical *** is theoretically and numerically shown that the method is quite stable.

关键词： The modified weak Galerkin finite element method Backward Euler method Parabolic convection-diffusion problems Error estimates

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A Framework for Skin Cancer Prediction Using CNN Ensembles 2

A Framework for Skin Cancer Prediction Using CNN Ensembles

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Samushonga, Wieslaw Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Department of Computer Science and Engineering Punjab Gharuan India University of Kufa Faculty of Computer Science and Mathematics Najaf Iraq Technical Engineering College Al-Ayen University Department of Computer Technical Engineering Thi-Qar Iraq

ISBN: (纸本)9798350370188

Skin cancer is among the deadliest diseases engulfing the world. The early detection of this disease may provide a great relief to the patients and healthcare systems worldwide. Recently, Machine Learning (ML)-based detection techniques have become popular in early skin cancer prediction. This paper comprehensively presents an overview of the latest research in the field of skin cancer prediction using ML techniques, especially the use of Convolutional Neural Networks (CNN) for the said purpose. Various methodologies employed in the past have been discussed along with the outcomes achieved, limitations, and advantages of these predictive models. A detailed review of the latest key references in the area is also discussed. At the end, a way of achieving ensemble using CNN methods for skin care has also been discussed, which we will implement in the next phase of our research. © 2024 IEEE.

关键词： Diseases

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UPB at IberLEF-2023 AuTexTification: Detection of Machine-Generated Text using Transformer Ensembles

UPB at IberLEF-2023 AuTexTification: Detection of Machine-Ge...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Preda, Andrei-Alexandru Cercel, Dumitru-Clementin Rebedea, Traian Chiru, Costin-Gabriel Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest060042 Romania

This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, featured as part of IberLEF-2023. Our team participated in the first subtask, identifying text documents produced by large language models instead of humans. The organizers provided a bilingual dataset for this subtask, comprising English and Spanish texts covering multiple domains, such as legal texts, social media posts, and how-to articles. We experimented mostly with deep learning models based on Transformers, as well as training techniques such as multi-task learning and virtual adversarial training to obtain better results. We submitted three runs, two of which consisted of ensemble models. Our best-performing model achieved macro F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep learning

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Energy Preserving Techniques for CPS in Smart Healthcare System 13

Energy Preserving Techniques for CPS in Smart Healthcare Sys...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Srivastava, Jyoti Routray, Sidheswar School of Engineering Indrashil University Department of Computer Science and Engineering Gujarat Rajpur Mehsana India Ganpat University Faculty of Engineering and Technology Department of Computer Engineering Gujarat Kherwa Mehsana India School of Technology Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar382007 India

ISBN: (纸本)9798350380583

Cyber Physical System (CPS) enhances the functionality of various cyber and physical equipment of Smart Healthcare System (SHS) and provides automation in the healthcare sector using Artificial Intelligence (AI) techniques. SHS collects medical data with the help of sensors or actuators equipped with smart medical devices that act as the physical component of CPS, then processes and analyzes these medical data with the help of cyber components of CPS to provide automation in health monitoring, doctor consultancy, medical prescriptions, medicine delivery and various other perspectives of the healthcare sector. These advancements in the healthcare sector result in great assistance for healthcare individuals like doctors, patients, nursing staff, etc, but at the same time deal with very sensitive parameters like time, efficiency, reliability, security, etc, that need to be managed with utmost accuracy for preserving each life in this global smart healthcare environment. There can be various efficiency parameters in CPS enabled SHS namely the number of hops, average energy consumption, resource utilization, etc, This work elaborately defines various characteristics of CPS in SHS, efficiency parameters, and major challenges for the successful implementation of CPS enabled SHS. © 2024 IEEE.

关键词： Electronic health record

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Battery Monitoring System on IoT-Based Smart Micro Grid 4

Battery Monitoring System on IoT-Based Smart Micro Grid

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4th International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2024

作者： Iksan, Nur Widodo, Djoko Adi Djuniadi, Djuniadi Putri, Riana Defi Mahadji Suryanto, Agus Udayanti, Erika Devi Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Department of Information System Indonesia

ISBN: (纸本)9798350378368

The use of batteries as an energy storage medium has a very important role in the installation of renewable energy power plants, such as photovoltaics to overcome intermittency in photovoltaics and to maintain stable power quality. In this study, the installation of solar plants that was used as the research object was a smart micro grid that integrates photovoltaic electrical energy sources with the Utility electricity network for residential scale. The battery monitoring system is used to manage the performance of existing batteries on the electricity grid through analysis of the battery's electrical values such as current, voltage, power and energy, so that it can function optimally in storing electrical energy and supplying electricity to users. Internet of things (IoT) technology is used to provide easy transfer of data obtained through the process of data acquisition to data visualization provided to be accessed by users in real time. This research aims to develop an IoT-based battery monitoring system. The development carried out focuses on the data acquisition process, the data transfer process using IoT technology, the process of analyzing and visualizing data on the IoT cloud and remote access to users. © 2024 IEEE.

关键词： Battery IoT Monitoring Smart Micro Grid

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Negative Impact of Approximated Signed Addition on Power Reduction

Negative Impact of Approximated Signed Addition on Power Red...

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2023 International Symposium on Devices, Circuits and systems, ISDCS 2023

作者： Hama, Hiroyuki Ukezono, Tomoaki Sato, Toshinori Graduate School of Engineering Fukuoka University Department of Electronics Engineering and Computer Science Fukuoka Japan Faculty of Engineering Fukuoka University Department of Electronics Engineering and Computer Science Fukuoka Japan

ISBN: (纸本)9798350315042

This paper presents evaluation results of a power- efficient JPEG compression circuit utilizing approximate computing. To achieve power efficiency, we replace summations in Discrete Cosine Transform (DCT) with approximate additions that follow Carry-Maskable Adder (CMA) [1]. We evaluate both the quality of the compressed images and the power consumed by the circuit. However, we observed unexpected results where power consumption increased despite the use of approximation, contradicting our expectations. Our detailed investigations revealed that the addition of negative values in 2's complement representation led to an increased wasted power consumption. Although reports on investigations related to power reduction through approximate computing offer valuable insights for researchers in this field, they are often difficult to find. This paper shows the reasons why power reduction could not be achieved in this specific case study. © 2023 IEEE.

关键词： Discrete cosine transforms

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Performance Evaluation of Blob Detection Techniques Using Image Processing 4

Performance Evaluation of Blob Detection Techniques Using Im...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Sathva, S. Madhumathi, R. Manjula, D. MeenaKowshalya, A. Sri Ramakrishna Engineering College Department of Computer Science & Engineering Coimbatore India Department of Community Medicine Karpagam Faculty of Medical Sciences and Research Coimbatore India Government College of Technology Department of Computer Science & Engineering Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Blob detection is a primary requirement in computer vision and image processing tasks. Unique visual traits are obtained by identifying blobs in an image. Variations in colour, texture, intensity, or shape are just examples of these attributes, which make blob detection a flexible tool for a variety of image analysis applications. The technique of separating the objects in the binary image is called Binary Large Object (blob) extraction. Every representation technique provides distinct insights into the properties of the blob, which facilitates further processing and interpretation. Techniques for classifying blobs according to their size, shape, texture, and colour are presented, highlighting their importance involving object recognition and classification. The categorization of blob detection techniques presented in this work includes, encompassing Laplacian of Gaussian (LoG), Difference of Gaussian (DoG), Difference of Hessian (DOH), and Connected Components (Labelling). The results demonstrate that LoG and DoG detect a higher number of blobs, with LoG detecting the most (1,366 blobs), while DoH focuses on larger blobs but with a smaller count (777 blobs). Moreover, DoH had the largest mean blob size and higher variability in blob size, while DoG exhibited the fastest detection time (4.27 seconds). © 2024 IEEE.

关键词： Binary images

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