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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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MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time Systems

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ACM Transactions on Design Automation of Electronic Systems 2025年第3期30卷 1-29页

作者： Ahmadi-Pour, Sallar Saha, Sangeet Mcdonald-Maier, Klaus Drechsler, Rolf Institute of Computer Science University of Bremen Faculty 3 Mathematics and Computer Science Bremen Bremen Germany Robotics and Embedded Systems Research Group University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Cyber-Physical Systems DFKI Rheinland-Pfalz Kaiserslautern Germany

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today's Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this article, we consider the challenge of scheduling an RT application modeled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTG on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6% and area usage reductions of 46.3% are possible compared to full Software (SW) and HW execution, respectively. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Integer programming

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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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Current Trends and Challenges of Cloud Computing and Emerging Technological Utilization in Technical Education 5

Current Trends and Challenges of Cloud Computing and Emergin...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kaswan, Suresh Goyal, Parul Khirasaria, Vivek Yugal, Lisha Amita Chandigarh University Department of Computer Science & Engineering Punjab Mohali India M. M. Engineering College Maharishi Markandeshwar Deemed to Be University Computer Science & Engineering Department Haryana Mullana India Sharda University Faculty of Engineering and Technology Uzbekistan Sharda University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350379945

This research study explores the impact of cloud computing on engineering education in India, focusing onhow it enhances educational outcomes. This study examines the potentialof strategic cloud implementation in higher education to accelerate development and competitiveness. The study analyzes the current state of technical education in India and identifies areas where cloud integration can drive positive changes. Key research questions are formulated to guide the exploration of creating intelligent cloud applications for effective technical education. © 2024 IEEE.

关键词： Cloud platforms

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COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

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