Algorithms最新文献_第10页

Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces 基于ssvep的脑机接口刺激频率自动选择

Algorithms Pub Date : 2023-10-29 DOI: 10.3390/a16110502

Alexey Kozin, Anton Gerasimov, Maxim Bakaev, Anton Pashkov, Olga Razumnikova

引用次数: 0

Machine Learning-Based Approach for Predicting Diabetes Employing Socio-Demographic Characteristics 利用社会人口统计学特征预测糖尿病的机器学习方法

Algorithms Pub Date : 2023-10-29 DOI: 10.3390/a16110503

Md. Ashikur Rahman, Lway Faisal Abdulrazak, Md. Mamun Ali, Imran Mahmud, Kawsar Ahmed, Francis M. Bui

{"title":"Machine Learning-Based Approach for Predicting Diabetes Employing Socio-Demographic Characteristics","authors":"Md. Ashikur Rahman, Lway Faisal Abdulrazak, Md. Mamun Ali, Imran Mahmud, Kawsar Ahmed, Francis M. Bui","doi":"10.3390/a16110503","DOIUrl":"https://doi.org/10.3390/a16110503","url":null,"abstract":"Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. From a clinical perspective, the most significant approach to mitigating the effects of diabetes is early-stage control and management, with the aim of a potential cure. However, lack of awareness and expensive clinical tests are the primary reasons why clinical diagnosis and preventive measures are neglected in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, this study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes, due to the fact that clinical features are not always accessible to all people from lower-income countries. To find the best fit of the supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. This study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for developing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"59 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136136169","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

Denoising Diffusion Models on Model-Based Latent Space 基于模型潜在空间的扩散模型去噪

Algorithms Pub Date : 2023-10-28 DOI: 10.3390/a16110501

Carmelo Scribano, Danilo Pezzi, Giorgia Franchini, Marco Prato

引用次数: 0

Two-Way Linear Probing Revisited 双向线性探测

Algorithms Pub Date : 2023-10-28 DOI: 10.3390/a16110500

Ketan Dalal, Luc Devroye, Ebrahim Malalla

引用次数: 0

Discovering Non-Linear Boolean Functions by Evolving Walsh Transforms with Genetic Programming 用遗传规划进化Walsh变换发现非线性布尔函数

Algorithms Pub Date : 2023-10-27 DOI: 10.3390/a16110499

Luigi Rovito, Andrea De Lorenzo, Luca Manzoni

{"title":"Discovering Non-Linear Boolean Functions by Evolving Walsh Transforms with Genetic Programming","authors":"Luigi Rovito, Andrea De Lorenzo, Luca Manzoni","doi":"10.3390/a16110499","DOIUrl":"https://doi.org/10.3390/a16110499","url":null,"abstract":"Stream ciphers usually rely on highly secure Boolean functions to ensure safe communication within unsafe channels. However, discovering secure Boolean functions is a non-trivial optimization problem that has been addressed by many optimization techniques: in particular by evolutionary algorithms. We investigate in this article the employment of Genetic Programming (GP) for evolving Boolean functions with large non-linearity by examining the search space consisting of Walsh transforms. Especially, we build generic Walsh spectra starting from the evolution of Walsh transform coefficients. Then, by leveraging spectral inversion, we build pseudo-Boolean functions from which we are able to determine the corresponding nearest Boolean functions, whose computation involves filling via a random criterion a certain amount of “uncertain” positions in the final truth table. We show that by using a balancedness-preserving strategy, it is possible to exploit those positions to obtain a function that is as balanced as possible. We perform experiments by comparing different types of symbolic representations for the Walsh transform, and we analyze the percentage of uncertain positions. We systematically review the outcomes of these comparisons to highlight the best type of setting for this problem. We evolve Boolean functions from 6 to 16 bits and compare the GP-based evolution with random search to show that evolving Walsh transforms leads to highly non-linear functions that are balanced as well.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235709","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

On the Intersection of Computational Geometry Algorithms with Mobile Robot Path Planning 计算几何算法与移动机器人路径规划的交叉研究

Algorithms Pub Date : 2023-10-27 DOI: 10.3390/a16110498

Ehsan Latif, Ramviyas Parasuraman

引用次数: 0

Overview of the Special Issue on “Deep Neural Networks and Optimization Algorithms” 《深度神经网络与优化算法》特刊综述

Algorithms Pub Date : 2023-10-26 DOI: 10.3390/a16110497

Jia-Bao Liu, Muhammad Faisal Nadeem, Yilun Shang

引用次数: 0

Agreement, Accuracy, and Reliability of a New Algorithm for the Detection of Change of Direction Angle Based on Integrating Inertial Data from Inertial Sensors 基于惯性传感器惯性数据集成的方向角变化检测新算法的一致性、准确性和可靠性

Algorithms Pub Date : 2023-10-25 DOI: 10.3390/a16110496

Roberto Avilés, Diego Brito de Souza, José Pino-Ortega, Julen Castellano

{"title":"Agreement, Accuracy, and Reliability of a New Algorithm for the Detection of Change of Direction Angle Based on Integrating Inertial Data from Inertial Sensors","authors":"Roberto Avilés, Diego Brito de Souza, José Pino-Ortega, Julen Castellano","doi":"10.3390/a16110496","DOIUrl":"https://doi.org/10.3390/a16110496","url":null,"abstract":"The development of algorithms applied to new technologies allows a better understanding of many of the movements in team sports. The purpose of this work was to analyze the validity, precision, and reproducibility of an algorithm to detect angulation of changes of direction (CoDs) while running, of between 45° and 180°, both to the left and the right at different speeds, in a standardized context. For this, five participants performed a total of 200 CoDs at 13 km/h and 128 CoDs at 18 km/h while wearing three inertial sensors. The information obtained from the sensors was contrasted with observation and coding using high-resolution video. Agreement between systems was assessed using Bland–Altman 95% limits of agreement as well as effect size (ES) and % difference between means. Reproducibility was evaluated using the standard error (CV%). The algorithm overestimated the angulation of 90° and 135° to the right (Cohen’s d > 0.91). The algorithm showed high precision when the angulations recorded at 13 km/h and 18 km/h were compared, except at 45° to the left (mean bias = −2.6°; Cohen’s d = −0.57). All angulations showed excellent reproducibility (CV < 5%) except at 45° (CV = 11%), which worsened when the pre-CoD speed was 18 km/h (CV < 16%). The algorithm showed a high degree of validity and reproducibility to detect angles during CoDs.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"21 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112944","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

Multi-Objective Order Scheduling via Reinforcement Learning 基于强化学习的多目标订单调度

Algorithms Pub Date : 2023-10-24 DOI: 10.3390/a16110495

Sirui Chen, Yuming Tian, Lingling An

{"title":"Multi-Objective Order Scheduling via Reinforcement Learning","authors":"Sirui Chen, Yuming Tian, Lingling An","doi":"10.3390/a16110495","DOIUrl":"https://doi.org/10.3390/a16110495","url":null,"abstract":"Order scheduling is of a great significance in the internet and communication industries. With the rapid development of the communication industry and the increasing variety of user demands, the number of work orders for communication operators has grown exponentially. Most of the research that tries to solve the order scheduling problem has focused on improving assignment rules based on real-time performance. However, these traditional methods face challenges such as poor real-time performance, high human resource consumption, and low efficiency. Therefore, it is crucial to solve multi-objective problems in order to obtain a robust order scheduling policy to meet the multiple requirements of order scheduling in real problems. The priority dispatching rule (PDR) is a heuristic method that is widely used in real-world scheduling systems In this paper, we propose an approach to automatically optimize the Priority Dispatching Rule (PDR) using a deep multiple-objective reinforcement learning agent and to optimize the weighted vector with a convex hull to obtain the most objective and efficient weights. The convex hull method is employed to calculate the maximal linearly scalarized value, enabling us to determine the optimal weight vector objectively and achieve a balanced optimization of each objective rather than relying on subjective weight settings based on personal experience. Experimental results on multiple datasets demonstrate that our proposed algorithm achieves competitive performance compared to existing state-of-the-art order scheduling algorithms.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"49 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266855","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

COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques 基于深度学习技术的胸部x线图像COVID-19检测

Algorithms Pub Date : 2023-10-23 DOI: 10.3390/a16100494

Shubham Mathesul, Debabrata Swain, Santosh Kumar Satapathy, Ayush Rambhad, Biswaranjan Acharya, Vassilis C. Gerogiannis, Andreas Kanavos

{"title":"COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques","authors":"Shubham Mathesul, Debabrata Swain, Santosh Kumar Satapathy, Ayush Rambhad, Biswaranjan Acharya, Vassilis C. Gerogiannis, Andreas Kanavos","doi":"10.3390/a16100494","DOIUrl":"https://doi.org/10.3390/a16100494","url":null,"abstract":"The COVID-19 pandemic has posed significant challenges in accurately diagnosing the disease, as severe cases may present symptoms similar to pneumonia. Real-Time Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) is the conventional diagnostic technique; however, it has limitations in terms of time-consuming laboratory procedures and kit availability. Radiological chest images, such as X-rays and Computed Tomography (CT) scans, have been essential in aiding the diagnosis process. In this research paper, we propose a deep learning (DL) approach based on Convolutional Neural Networks (CNNs) to enhance the detection of COVID-19 and its variants from chest X-ray images. Building upon the existing research in SARS and COVID-19 identification using AI and machine learning techniques, our DL model aims to extract the most significant features from the X-ray scans of affected individuals. By employing an explanatory CNN-based technique, we achieved a promising accuracy of up to 97% in detecting COVID-19 cases, which can assist physicians in effectively screening and identifying probable COVID-19 patients. This study highlights the potential of DL in medical imaging, specifically in detecting COVID-19 from radiological images. The improved accuracy of our model demonstrates its efficacy in aiding healthcare professionals and mitigating the spread of the disease.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135366312","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: 1