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8th International Conference on Electrical, Mechanical and computer engineering, ICEMCE 2024

作者： Xue, Tongxia Shi, Xuhua Tongxia Xue Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (纸本)9798331506230

Dynamic multi-objective optimization problems (DMOPs) are common in real-world applications. To effectively address these problems, algorithms are required to maintain solution diversity and quickly adapt to environmental changes. Transfer learning is a promising approach for tackling dynamic multi-objective optimization. In this paper, a transfer learning evolutionary optimization method based on population distribution characteristics is proposed for solving DMOPs. First, mutual information is used to calculate the correlation between the optimal solutions from two consecutive environments before the environmental change. Then, the distribution characteristics of the highly correlated population individuals are extracted as valuable knowledge for the transfer process. This information is employed to guide the generation of new population individuals, which are then randomly introduced into the initial population generated by the DNSGA-II-A algorithm in the new environment, forming an improved initial population. Experimental results demonstrate that, the proposed DMOEA-TLF exhibits significant advantages in convergence and diversity in most cases. © 2024 IEEE.

关键词： Optimization algorithms

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Explainable AI for Discovering Disease Biomarkers: A Survey 7th

Explainable AI for Discovering Disease Biomarkers: A Survey

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7th EAI International Conference on Robotics and Networks, ROSENET 2023

作者： Temkov, Slave Faculty of Computer Science and Engineering University ss. Cyril and Methodius Skopje Macedonia

ISBN: (纸本)9783031644948

Artificial intelligence (AI) and machine learning (ML) have found its applications in biomedical research for a long time. As these emerging technologies proceed to evolve, their utilization in different fields also adapts, including the field of biomedicine. Explainable AI (XAI) is a hot topic nowadays, and its different aspects and use cases are being explored more and more. Biomedicine is one of those disciplines where these tools and methods find their employment. More specifically, XAI has been found very significant in uncovering genetic and molecular biomarkers across various diseases. Thus, it is facilitating enhanced diagnostic and therapeutic strategies for a broad spectrum of diseases—from cancers to neurological and infectious illnesses. In this paper, we are going to give an overview of some state-of-the-art use cases of diverse XAI techniques in the field of biomarker discovery. Ultimately, it is a survey that should serve as a beacon for researchers and practitioners to further explore this interdisciplinary domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Gene expression

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Evaluating Azure Container, Backend, and Function as a Service 32

Evaluating Azure Container, Backend, and Function as a Servi...

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32nd Telecommunications Forum, TELFOR 2024

作者： Ristovski, Daniel Gusev, Marjan Ss Cyril and Methodius University in Skopje Faculty of Computer Science and Engineering Skopje Macedonia

ISBN: (纸本)9798350391053

In this research paper, we investigate the performance of Container, Backend, and Function as a Service as various Microsoft cloud service models for specific computational and memory-bound use cases. The research method involves practical experiments to present insights into functionalities, advantages, and limitations. Our research shows that AKS performs the best compared to Web Apps and Azure Functions, reaching the highest throughput for a quick sort application. © 2024 IEEE.

关键词： Cloud platforms

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A Study of Deep Learning Techniques for Predicting Breast Cancer Types

A Study of Deep Learning Techniques for Predicting Breast Ca...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Ashwini, P. Suguna, N. Vadivelan, N. Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Associate Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Professor Department of Computer Science and Engineering Teegala Krishna Reddy Engineering College Hyderabad India

ISBN: (纸本)9789819947164

Breast cancer is today’s deadly health issue which causes high mortality in woman worldwide. The preliminary detection and classification may help for proper treatment of the same. Understanding the causes of this cancer using traditional machine learning techniques misread feature extraction are leading to a complication. Traditional machine learning models are best suited for feature-based raw data. The novel deep learning techniques have been introduced for diagnosis of breast abnormalities using various imaging modalities like mammogram, magnetic resonance imaging (MRI) and ultrasound with high accuracy. This survey focuses on challenges of classical machine learning models and supervene efficient prediction models using novel deep learning techniques. This review presents comparison analysis of traditional machine learning and deep learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Visual Analytics of Streaming Data in Concept Drift Detection 11

Visual Analytics of Streaming Data in Concept Drift Detectio...

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11th International Conference on Electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Ilic, Aleksandra Stojnev Stojanovic, Dragan University of Nis Faculty of Electronic engineering Dept. Computer Science Nis Serbia

ISBN: (纸本)9798350386998

Recent developments in sensor technology have enabled real-time data acquisition, high-frequency and multimodal data capturing thus underlying the need for monitoring physical or operational conditions in various aspects of the data. High dimensionality and volume of the data poses significant statistical challenges creating a need for accurate concept drift detection and prompt responses, a task that can be performed either by an algorithm or by human expert. Visual analytics plays a crucial role in concept drift detection by enabling analysts to visually explore, analyze, and interpret complex data streams, facilitating real-time monitoring, decision-making, and communication with stakeholders. This paper examines the usability of radar graphs for concept drift exploration, as well as their usage in other steps of analysis. For the purpose of demonstration, a pipeline for real-time health data analysis is presented and steps are visualized using radar graphs. © 2024 IEEE.

关键词： Visual analytics

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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Improvement of myopia using brain vision training with variable focal length VR

Improvement of myopia using brain vision training with varia...

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2024 International Conference on Advanced Mechatronic Systems, ICAMechS 2024

作者： Shindou, Kazuma Taguchi, Akira Bao, Yue Tokyo City University Faculty of Information Engineering Department of Computer Science Tokyo Japan

ISBN: (纸本)9798350366204

There is growing interest in vision improvement methods because poor vision impairs quality of life and causes a variety of problems in daily living and cognitive function. However, many existing vision improvement methods are invasive, expensive, and require large training spaces and long training *** this study, we focused on the positive effects of HMD use on visual acuity and devised a noninvasive visual acuity training method using an HMD with variable focal distance and Gabor stimuli to enhance its effectiveness. In this training method, the image formation distance during training is adjusted according to the subject's visual acuity, providing training at the optimal difficulty level for each subject. It also aims to efficiently enhance visual function through perceptual learning using Gabor *** results of this study showed that this training method improved visual acuity by an average of 35% in 1/8th the training time of the conventional method. This indicates that the proposed method is an effective way to improve visual acuity in a short period of time without the need for expensive equipment or a large training space. © 2024 IEEE.

关键词： Vision aids

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Analysis and Classification of Fake News Using Sequential Pattern Mining

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Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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A Transfer-Learning-based Strategy for Autonomous Driving: Leveraging Driver Experience for Vision-Based Control 20

A Transfer-Learning-based Strategy for Autonomous Driving: L...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Babaahmadi, Amirreza Masoumi, Fatemeh Sadat Ayati, Moosa Amirpour, Mehrimah University of Tehran School of Mechanical Engineering Tehran Iran Allameh Tabataba'i University Faculty of Mathematics Statistics and Computer Science Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

This paper explores the utilization of a novel transformer-based architecture for end-to-end learning in predicting steering angles in self-driving scenarios while leveraging a novel robust image processing pipeline to handle diverse environmental situations. Our approach relies solely on visual perception as the input to generate control commands. We trained and evaluated our methodology using a proprietary dataset from a self-driving car simulator consisting of image frames paired with their corresponding steering angles. The presented methodology is robust against overfitting, and it shows superior performance in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE) compared to previous methods. © 2024 IEEE.

关键词： Autonomous vehicles

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Improved Alzheimer's Disease Early Prediction using Machine Learning Method : A Comprehensive Diagnostic Approach with Random Forest Classifier 4

Improved Alzheimer's Disease Early Prediction using Machine ...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Munawar, Madiha Monika Singh, T. Kishor Kumar Reddy, C. Hanafiah, Marlia Mohd Stanley College of Engineering and Technology for Women Department of Computer Science and Engineering Hyderabad India Universiti Kebangsaan Malaysia Faculty of Science and Technology Malaysia

ISBN: (纸本)9798331515720

Alzheimer's Disease (AD) often affects the elder persons and is the prevalent kind of Dementia. AD has a huge expense, especially when it comes to the treatment. AD is a main reason the older generations die whereas when Alzheimer Disease is detected early there will be minimal deaths and accurate diagnosing likely occurs in most cases. Using anonymized Oasis longitudinal MRI data, the study trains several machine learning algorithms to identify AD patients. Analyzing the test result includes utilizing different learning strategies or algorithms e.g., Ensemble Random Forest (RF), Hierarchical Decision Tree (DT), Logistic Regression (LR), Convolutional Neural Network (CNN), Deep Neural Network binary classifier (DNN), soft voting classifier (SVC), transfer learning approaches (TLA), Support Vector Machine (SVM) and boosting AdaBoost in reference of their Accuracy, Error Rate, AUC (area under the curve), Time Taken and Recall for each method. The obtained results concluded that the proposed Random Forest classifier achieves higher accuracy (92.84%), Error Rate (7.16%), AUC (95%) (Area Under Curve) and Recall (89%). The Random Forest classifier also achieves higher Time Taken that is 483.654 sec. © 2024 IEEE.

关键词： Alzheimer's Disease Decision Trees Deep Learning Dementia Learning Approaches Machine Learning Random Forest

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