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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Volodymyr Khilenko Andrii Zinchenko Oleksii Stepanov Ivan Kotuliak Michal Ries Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Bratislava Slovakia Institute of Transport Systems and Technologies National Academy of Sciences of Ukraine Dnipro Ukraine Department of Information Technologies National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

The theses is in line with works aimed at studying the possibility of using mathematical algorithms for parallel processing of information in reverse blockchain technology. The paper examines the use of parallel signal processing algorithms to improve the speed and quality of control systems for autonomous dynamic objects using reverse blockchain technology. The use of reverse blockchain technology is due to the possibility of loss of control signals and, at the same time, the inadmissibility of loss of control in the event of changes in the conditions and parameters of the operation of the obj ect. The purpose of the work is to analyze the implementation of solutions for parallel information processing for various models of industrially produced equipment and to develop solutions to increase the efficiency of reverse blockchain technology when using parallel data processing.

关键词： Analytical models Heuristic algorithms Instruction sets Signal processing algorithms Process control Parallel processing Control systems Smart systems Data models Blockchains

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Prediction of Heart Disease Based on Robust Artificial Intelligence Techniques 6

Prediction of Heart Disease Based on Robust Artificial Intel...

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6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

作者： Ghorbani, Hamzeh Krasnikova, Alla Ghorbani, Parvin Ghorbani, Simin Hovhannisyan, Harutyun S. Minasyan, Arsen Minasian, Natali Alvar, Mehdi Ahmadi Stepanyan, Harutyun Azodinia, Mohammazreza Faculty of General Medicine 38a Marshal Babajanyan St. Yerevan0040 Armenia Yerevan State Medical University Faculty of Medicine Department of Cardiology Yerevan0031 Armenia Ahvaz Jundishapur University of Medical Sciences Faculty of Medicine Department of Cardiology Ahvaz Iran Ahvaz Jundishapur University of Medical Sciences Faculty of Nursing Department of Nursing and Midwifery Ahvaz Iran Yerevan State Medical University Department of Internal Disease Propaedeutics Yerevan Armenia Yaroslavl State Medical University Faculty of General Medicine Yaroslavl Russia Shahid Chamran University Faculty of Engineering Department of Computer Engineering Ahwaz Iran Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary

ISBN: (纸本)9798350328752

Heart disease (HD) is a concerning health condition that demands immediate attention. This article employs three artificial intelligence classification algorithms: DT, NB, and SGD to address this issue. The primary goal is to determine the most effective algorithm for early-stage prediction of heart disease, using easily accessible patient information. A dataset of 290 simple input variables, including sex, age, blood sugar, fasting blood sugar levels, and maximal heart rate, was collected from a hospital in Armenia. The results reveal that the DT algorithm outperforms NB and SGD in accuracy, with CAtrain of 0.96 and CAtest of 0.93. Accurate early prediction enables timely interventions and support for patients, improving healthcare management. Leveraging artificial intelligence, particularly DT, enables swift identification of potential heart disease cases, aiding healthcare professionals in providing essential care and preventive measures. Furthermore, the pragmatic approach of employing easily accessible patient data for prediction underscores the feasibility and applicability of the proposed methodology. By relying on readily available and cost-effective information, medical practitioners can efficiently screen and diagnose individuals at risk of heart disease, leading to improved healthcare management and better patient outcomes. © 2023 IEEE.

关键词： Decision trees

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AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach

AI Based Prediction for Heart Disease: A Comparative Analysi...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Jay Raval Jai Prakash Verma Sardar N. M. Islam Rachna Jain Narina Thakur Computer Science Engineering Department Institute of Technology Nirma University Ahmedabad India Applied Informatics Research Victoria University Melbourne Australia Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology Delhi India

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm.

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Improving the Estimation of Coronary Artery Disease by Classification Machine Learning Algorithm 6

Improving the Estimation of Coronary Artery Disease by Class...

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6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

ISBN: (纸本)9798350328752

This research paper aims to predict coronary artery disease (CAD) using data from 350 patients collected at one of the hospitals in Armenia. CAD is a critical parameter which can have a significant impact on patients' life and survival. The study considers several input variables, including level of cholesterol (LOC), patient's age (PA), type of chest pain (TCP), number of arteries blocked (NAB), sex (S), and family history (FH), to make accurate predictions. To achieve this crucial task of CAD prediction, the researchers employed three powerful classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). Among these, the Random Forest algorithm stands out for its robustness and numerous advantages, including high accuracy, ability to handle outliers effectively, provision of feature importance insights, and reduced risk of overfitting. The research findings presented in this article demonstrate the impressive performance of the Random Forest algorithm, showcasing an accuracy value of 0.95 and a precision value of 0.94. These results indicate the model's ability to make precise and reliable predictions, essential when dealing with a life-or-death parameter like CAD. By conducting a comparative analysis based on statistical parameters, the researchers establish that Random Forest outperforms both SVM and LR. Thus, the conclusion drawn from the study suggests that the ranking of the algorithms based on their performance is as follows: RF > SVM > LR. © 2023 IEEE.

关键词： Forecasting

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An Enhanced Residual Networks Based Framework for Early Alzheimer’s Disease Classification and Diagnosis 6th

An Enhanced Residual Networks Based Framework for Early Alzh...

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6th International Conference on Communication, Devices and Networking, ICCDN 2022

作者： Awotunde, Joseph Bamidele Sur, Nath Samarendra Imoize, Agbotiname Lucky Misra, Sanjay Gaber, Tarek Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University East Sikkim Rangpo Majitar737136 India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Applied Data Science Institute of Energy Technology Halden Norway Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom

ISBN: (纸本)9789819919826

Alzheimer's disease (AD) is a long-lasting, degenerative brain illness for which there is now no successful treatment. However, there are medications that can delay its growth and stop its tracks. Therefore, stopping and regulating the progression of AD depends greatly on its early diagnosis. computer-aided diagnosis (CAD) in health care has become more and more dependent on deep learning technologies. This has help in early diagnosis and detection of various disease in health care. New techniques for the early diagnosis of neurocognitive abnormalities are now possible thanks to advancements in medical imaging and computational capacity in an effort to stop or slow down cognitive aging. Therefore, this study proposes a residual network (ResNet) type 50 for early diagnosis of AD. Convolutional neural networks (CNNs) are first used in the study for features extraction and then make the ultimate diagnosis using the ResNet-50. To create a better architecture, the problem of AD diagnosis will be addressed by utilizing and incorporating ResNet-50, which improves the current categorization problem's accuracy to its maximum. With a 98.8% accuracy rate, the proposed model using ResNet-50 produces encouraging findings for AD prediction. The study reveals that the model can effectively circumvent CNNs for the diagnostic issue with AD. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer aided diagnosis

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Classification of Wind Turbine Defects based on the SqueezeNet Neural Network

Classification of Wind Turbine Defects based on the SqueezeN...

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International computer Sciences and Information Technologies (CSIT)

作者： Lesia Dubchak Anatoliy Sachenko Carsten Wolff Nadiia Vasylkiv Zenovii Bernas Department of Computer Engineering West Ukrainian National University Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Tele-Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Dortmund University of Applied Science and Arts Dortmund Germany Department of Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The article presents a study on classifying wind turbine defects using the SqueezeNet neural network. Wind turbines are critical for renewable energy, but defects such as corrosion, erosion, and cracks can significantly reduce their efficiency. The study proposes using image classification techniques with neural networks, particularly SqueezeNet, to automate defect detection. The compact nature of SqueezeNet makes it suitable for real-time applications with limited computing resources. Through training and testing, the network achieved an accuracy of 89%, demonstrating its potential to improve wind turbine maintenance and reduce operational costs.

关键词： Training Accuracy Costs Neural networks Real-time systems Wind turbines Maintenance Defect detection Testing Image classification

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applied Federated Model Personalisation in the Industrial Domain: A Comparative Study

arXiv

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arXiv 2024年

作者： Siniosoglou, Ilias Argyriou, Vasileios Fragulis, George Fouliras, Panagiotis Papadopoulos, Georgios Th. Lytos, Anastasios Sarigiannidis, Panagiotis The Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece The Department of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom The Department of Applied Informatics University of Macedonia Thessaloniki Greece The Department of Informatics and Telematics Harokopio University of Athens Greece Sidroco Holdings Ltd. Nicosia Cyprus The R&D Department MetaMind Innovations P.C. Kozani Greece

The time-consuming nature of training and deploying complicated Machine and Deep Learning (DL) models for a variety of applications continues to pose significant challenges in the field of Machine Learning (ML). These challenges are particularly pronounced in the federated domain, where optimizing models for individual nodes poses significant difficulty. Many methods have been developed to tackle this problem, aiming to reduce training expenses and time while maintaining efficient optimisation. Three suggested strategies to tackle this challenge include Active Learning, Knowledge Distillation, and Local Memorization. These methods enable the adoption of smaller models that require fewer computational resources and allow for model personalization with local insights, thereby improving the effectiveness of current models. The present study delves into the fundamental principles of these three approaches and proposes an advanced Federated Learning System that utilises different Personalisation methods towards improving the accuracy of AI models and enhancing user experience in real-time NG-IoT applications, investigating the efficacy of these techniques in the local and federated domain. The results of the original and optimised models are then compared in both local and federated contexts using a comparison analysis. The post-analysis shows encouraging outcomes when it comes to optimising and personalising the models with the suggested techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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CLASS ATTENDANCE SYSTEM IN EDUCATION WITH DEEP LEARNING METHOD

arXiv

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arXiv 2023年

作者： Demir, Hüdaverdi Savaş, Serkan Department of Electronics and Computer Engineering Graduate School of Natural and Applied Sciences Çankırı Karatekin University Çankırı Turkey Department of Informatics Technology Yenikent Ahmet Çiçek Vocational and Technical Anatolian High School Ankara Turkey Department of Computer Engineering Faculty of Engineering and Architecture Kırıkkale University Kırıkkale Turkey

With the advancing technology, the hardware gain of computers and the increase in the processing capacity of processors have facilitated the processing of instantaneous and real-time images. Face recognition processes are also studies in the field of image processing. Facial recognition processes are frequently used in security applications and commercial applications. Especially in the last 20 years, the high performances of artificial intelligence (AI) studies have contributed to the spread of these studies in many different fields. Education is one of them. The potential and advantages of using AI in education;can be grouped under three headings: student, teacher, and institution. One of the institutional studies may be the security of educational environments and the contribution of automation to education and training processes. From this point of view, deep learning methods, one of the sub-branches of AI, were used in this study. For object detection from images, a pioneering study has been designed and successfully implemented to keep records of students' entrance to the educational institution and to perform class attendance with images taken from the camera using image processing algorithms. The application of the study to real-life problems will be carried out in a school determined in the 2022-2023 academic year. © 2023, CC BY.

关键词： Face recognition

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Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning

arXiv

引用

arXiv 2023年

作者： De Lellis, Francesco Coraggio, Marco Russo, Giovanni Musolesi, Mirco Bernardo, Mario di The Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy The Scuola Superiore Meridionale School for Advanced Studies Naples Italy The Department of Computer and Electrical Engineering & Applied Mathematics University of Salerno DIEM Salerno Italy The Department of Computer Science University College London United Kingdom The Department of Informatics - Science and Engineering University of Bologna Bologna Italy

In addressing control problems such as regulation and tracking through reinforcement learning, it is often required to guarantee that the acquired policy meets essential performance and stability criteria such as a desired settling time and steady-state error prior to deployment. Motivated by this necessity, we present a set of results and a systematic reward shaping procedure that (i) ensures the optimal policy generates trajectories that align with specified control requirements and (ii) allows to assess whether any given policy satisfies them. We validate our approach through comprehensive numerical experiments conducted in two representative environments from OpenAI Gym: the Inverted Pendulum swing-up problem and the Lunar Lander. Utilizing both tabular and deep reinforcement learning methods, our experiments consistently affirm the efficacy of our proposed framework, highlighting its effectiveness in ensuring policy adherence to the prescribed control requirements. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

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Padding in the mathematics of arrays 7

Padding in the mathematics of arrays

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7th ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming, ARRAY 2021, held in association with PLDI 2021

作者： Chetioui, Benjamin Abusdal, Ole Haveraaen, Magne Järvi, Jaakko Mullin, Lenore University of Bergen Department of Informatics Bergen Hordaland Norway Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Hordaland Norway University of Turku Department of Computing Turku Finland College of Engineering and Applied Sciences University at Albany SUNY AlbanyNY United States

ISBN: (纸本)9781450384667

Multi-dimensional array manipulation constitutes a core component of numerous numerical methods, e.g. finite difference solvers of Partial Differential Equations (PDEs). The efficiency of such computations is tightly connected to traversing array data in a hardware-friendly way. The Mathematics of Arrays (MoA) allows reasoning about array computations at a high level and enables systematic transformations of array-based programs. We have previously shown that stencil computations reduce to MoA's Denotational Normal Form (DNF). Here we bring to light MoA's Operational Normal Forms (ONFs) that allow for adapting array computations to hardware characteristics. ONF transformations start from the DNF. Alongside the ONF transformations, we extend MoA with rewriting rules for padding. These new rules allow both a simplification of array indexing and a systematic approach to introducing halos to PDE solvers. Experiments on various architectures confirm the flexibility of the approach. © 2021 ACM.

关键词： Finite difference method

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