Algorithms最新文献_第3页

Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques 利用卷积神经网络，通过特征还原技术对脑电图信号中的睡眠阶段进行分类

Algorithms Pub Date : 2024-05-24 DOI: 10.3390/a17060229

Maadh Rajaa Mohammed, A. Sagheer

{"title":"Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques","authors":"Maadh Rajaa Mohammed, A. Sagheer","doi":"10.3390/a17060229","DOIUrl":"https://doi.org/10.3390/a17060229","url":null,"abstract":"One of the most essential components of human life is sleep. One of the first steps in spotting abnormalities connected to sleep is classifying sleep stages. Based on the kind and frequency of signals obtained during a polysomnography test, sleep phases can be separated into groups. Accurate classification of sleep stages from electroencephalogram (EEG) signals plays a crucial role in sleep disorder diagnosis and treatment. This study proposes a novel approach that combines feature selection techniques with convolutional neural networks (CNNs) to enhance the classification performance of sleep stages using EEG signals. Firstly, a comprehensive feature selection process was employed to extract discriminative features from raw EEG data, aiming to reduce dimensionality and enhance the efficiency of subsequent classification using mutual information (MI) and analysis of variance (ANOVA) after splitting the dataset into two sets—the training set (70%) and testing set (30%)—then processing it using the standard scalar method. Subsequently, a 1D-CNN architecture was designed to automatically learn hierarchical representations of the selected features, capturing complex patterns indicative of different sleep stages. The proposed method was evaluated on a publicly available EDF-Sleep dataset, demonstrating superior performance compared to traditional approaches. The results highlight the effectiveness of integrating feature selection with CNNs in improving the accuracy and reliability of sleep stage classification from EEG signals, which reached 99.84% with MI-50. This approach not only contributes to advancing the field of sleep disorder diagnosis, but also holds promise for developing more efficient and robust clinical decision support systems.","PeriodicalId":502609,"journal":{"name":"Algorithms","volume":"21 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Context Privacy Preservation for User Validation by Wireless Sensors in the Industrial Metaverse Access System 工业元接入系统中通过无线传感器进行用户验证的上下文隐私保护

Algorithms Pub Date : 2024-05-23 DOI: 10.3390/a17060225

John Owoicho Odeh, Xiaolong Yang, C. I. Nwakanma, Sahraoui Dhelim

引用次数: 0

Gain and Pain in Graph Partitioning: Finding Accurate Communities in Complex Networks 图谱划分中的得与失：在复杂网络中寻找准确的群落

Algorithms Pub Date : 2024-05-23 DOI: 10.3390/a17060226

Arman Ferdowsi, Maryam Dehghan Dehghan Chenary

引用次数: 0

Bayesian Estimation of Simultaneous Regression Quantiles Using Hamiltonian Monte Carlo 利用哈密尔顿蒙特卡洛对同步回归量进行贝叶斯估计

Algorithms Pub Date : 2024-05-23 DOI: 10.3390/a17060224

Hassan Hachem, Candy Abboud

引用次数: 0

Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review 基于惯性传感器的人类呼吸模式评估：系统性文献综述

Algorithms Pub Date : 2024-05-23 DOI: 10.3390/a17060223

R. Martins, Fátima Rodrigues, Susana Costa, Nélson Costa

{"title":"Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review","authors":"R. Martins, Fátima Rodrigues, Susana Costa, Nélson Costa","doi":"10.3390/a17060223","DOIUrl":"https://doi.org/10.3390/a17060223","url":null,"abstract":"Breathing pattern assessment holds critical importance in clinical practice for detecting respiratory dysfunctions and their impact on health and wellbeing. This systematic literature review investigates the efficacy of inertial sensors in assessing adult human breathing patterns, exploring various methodologies, challenges, and limitations. Utilizing the PSALSAR framework, incorporating the PICOC method and PRISMA statement for comprehensive research, 22 publications were scrutinized from the Scopus, Web of Science, and PubMed databases. A diverse range of sensor fusion methods, data signal analysis techniques, and classifier performances were investigated. Notably, Madgwick’s algorithm and the Principal Component Analysis showed superior performance in tracking respiratory movements. Classifiers like Long Short-Term Memory Recurrent Neural Networks exhibited high accuracy in detecting breathing events. Motion artifacts, limited sample sizes, and physiological variability posed challenges, highlighting the need for further research. Optimal sensor configurations were explored, suggesting improvements with multiple sensors, especially in different body postures. In conclusion, this systematic literature review elucidates methods, challenges, and potential future developments in using inertial sensors for assessing adult human breathing patterns. Overcoming the challenges related to sensor placement, motion artifacts, and algorithm development is essential for progress. Future research should focus on extending sensor applications to clinical settings and diverse populations, enhancing respiratory health management.","PeriodicalId":502609,"journal":{"name":"Algorithms","volume":"17 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141104677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiobjective Path Problems and Algorithms in Telecommunication Network Design—Overview and Trends 电信网络设计中的多目标路径问题与算法--概述与趋势

Algorithms Pub Date : 2024-05-22 DOI: 10.3390/a17060222

J. Craveirinha, J. Clímaco, Rita Girão-Silva, Marta Pascoal

{"title":"Multiobjective Path Problems and Algorithms in Telecommunication Network Design—Overview and Trends","authors":"J. Craveirinha, J. Clímaco, Rita Girão-Silva, Marta Pascoal","doi":"10.3390/a17060222","DOIUrl":"https://doi.org/10.3390/a17060222","url":null,"abstract":"A major area of application of multiobjective path problems and resolution algorithms is telecommunication network routing design, taking into account the extremely rapid technological and service evolutions. The need for explicit consideration of heterogeneous Quality of Service metrics makes it advantageous for the development of routing models where various technical–economic aspects, often conflicting, should be tackled. Our work is focused on multiobjective path problem formulations and resolution methods and their applications to routing methods. We review basic concepts and present main formulations of multiobjective path problems, considering different types of objective functions. We outline the different types of resolution methods for these problems, including a classification and overview of relevant algorithms concerning different types of problems. Afterwards, we outline background concepts on routing models and present an overview of selected papers considered as representative of different types of applications of multiobjective path problem formulations and algorithms. A broad characterization of major types of path problems relevant in this context is shown regarding the overview of contributions in different technological and architectural network environments. Finally, we outline research trends in this area, in relation to recent technological evolutions in communication networks.","PeriodicalId":502609,"journal":{"name":"Algorithms","volume":"67 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141109958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hybrid Learning-Architecture for Improved Brain Tumor Recognition 改进脑肿瘤识别的混合学习架构

Algorithms Pub Date : 2024-05-21 DOI: 10.3390/a17060221

Jose Dixon, Oluwatunmise Akinniyi, Abeer Abdelhamid, Gehad A. Saleh, M. Rahman, Fahmi Khalifa

{"title":"A Hybrid Learning-Architecture for Improved Brain Tumor Recognition","authors":"Jose Dixon, Oluwatunmise Akinniyi, Abeer Abdelhamid, Gehad A. Saleh, M. Rahman, Fahmi Khalifa","doi":"10.3390/a17060221","DOIUrl":"https://doi.org/10.3390/a17060221","url":null,"abstract":"The accurate classification of brain tumors is an important step for early intervention. Artificial intelligence (AI)-based diagnostic systems have been utilized in recent years to help automate the process and provide more objective and faster diagnosis. This work introduces an enhanced AI-based architecture for improved brain tumor classification. We introduce a hybrid architecture that integrates vision transformer (ViT) and deep neural networks to create an ensemble classifier, resulting in a more robust brain tumor classification framework. The analysis pipeline begins with preprocessing and data normalization, followed by extracting three types of MRI-derived information-rich features. The latter included higher-order texture and structural feature sets to harness the spatial interactions between image intensities, which were derived using Haralick features and local binary patterns. Additionally, local deeper features of the brain images are extracted using an optimized convolutional neural networks (CNN) architecture. Finally, ViT-derived features are also integrated due to their ability to handle dependencies across larger distances while being less sensitive to data augmentation. The extracted features are then weighted, fused, and fed to a machine learning classifier for the final classification of brain MRIs. The proposed weighted ensemble architecture has been evaluated on publicly available and locally collected brain MRIs of four classes using various metrics. The results showed that leveraging the benefits of individual components of the proposed architecture leads to improved performance using ablation studies.","PeriodicalId":502609,"journal":{"name":"Algorithms","volume":"63 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141114061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Integer-Fractional Gradient Algorithm for Back Propagation Neural Networks 反向传播神经网络的整数-分数梯度算法

Algorithms Pub Date : 2024-05-20 DOI: 10.3390/a17050220

Yiqun Zhang, Honglei Xu, Yang Li, G. Lin, Liyuan Zhang, Chaoyang Tao, Yonghong Wu

引用次数: 0

The Knapsack Problem with Conflict Pair Constraints on Bipartite Graphs and Extensions 双方形图上的冲突对约束条件下的 Knapsack 问题及其扩展

Algorithms Pub Date : 2024-05-18 DOI: 10.3390/a17050219

Abraham P. Punnen, Jasdeep Dhahan

引用次数: 0

An Oracle Bone Inscriptions Detection Algorithm Based on Improved YOLOv8 基于改进的 YOLOv8 的甲骨文检测算法

Algorithms Pub Date : 2024-04-24 DOI: 10.3390/a17050174

Qianqian Zhen, Liang Wu, Guoying Liu

引用次数: 0