Algorithms最新文献_第2页

Computing RF Tree Distance over Succinct Representations 在简洁表征上计算射频树距离

IF 2.3

Algorithms Pub Date : 2023-12-28 DOI: 10.3390/a17010015

Ant'onio Pedro Branco, Cátia Vaz, Alexandre P. Francisco

{"title":"Computing RF Tree Distance over Succinct Representations","authors":"Ant'onio Pedro Branco, Cátia Vaz, Alexandre P. Francisco","doi":"10.3390/a17010015","DOIUrl":"https://doi.org/10.3390/a17010015","url":null,"abstract":"There are several tools available to infer phylogenetic trees, which depict the evolutionary relationships among biological entities such as viral and bacterial strains in infectious outbreaks or cancerous cells in tumor progression trees. These tools rely on several inference methods available to produce phylogenetic trees, with resulting trees not being unique. Thus, methods for comparing phylogenies that are capable of revealing where two phylogenetic trees agree or differ are required. An approach is then proposed to compute a similarity or dissimilarity measure between trees, with the Robinson–Foulds distance being one of the most used, and which can be computed in linear time and space. Nevertheless, given the large and increasing volume of phylogenetic data, phylogenetic trees are becoming very large with hundreds of thousands of leaves. In this context, space requirements become an issue both while computing tree distances and while storing trees. We propose then an efficient implementation of the Robinson–Foulds distance over tree succinct representations. Our implementation also generalizes the Robinson–Foulds distances to labelled phylogenetic trees, i.e., trees containing labels on all nodes, instead of only on leaves. Experimental results show that we are able to still achieve linear time while requiring less space. Our implementation in C++ is available as an open-source tool.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"20 s9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139150196","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 Intelligent Control Method for Servo Motor Based on Reinforcement Learning 基于强化学习的伺服电机智能控制方法

IF 2.3

Algorithms Pub Date : 2023-12-28 DOI: 10.3390/a17010014

Depeng Gao, Shuai Wang, Yuwei Yang, Haifei Zhang, Hao Chen, Xiangxiang Mei, Shuxi Chen, Jianlin Qiu

引用次数: 0

Machine Learning Model for Multiomics Biomarkers Identification for Menopause Status in Breast Cancer 用于识别乳腺癌绝经状态的多组学生物标记物的机器学习模型

IF 2.3

Algorithms Pub Date : 2023-12-28 DOI: 10.3390/a17010013

Firas Alghanim, Ibrahim Al-Hurani, H. Qattous, Abdullah Al-Refai, Osamah Batiha, A. Alkhateeb, Salama Ikki

{"title":"Machine Learning Model for Multiomics Biomarkers Identification for Menopause Status in Breast Cancer","authors":"Firas Alghanim, Ibrahim Al-Hurani, H. Qattous, Abdullah Al-Refai, Osamah Batiha, A. Alkhateeb, Salama Ikki","doi":"10.3390/a17010013","DOIUrl":"https://doi.org/10.3390/a17010013","url":null,"abstract":"Identifying menopause-related breast cancer biomarkers is crucial for enhancing diagnosis, prognosis, and personalized treatment at that stage of the patient’s life. In this paper, we present a comprehensive framework for extracting multiomics biomarkers specifically related to breast cancer incidence before and after menopause. Our approach integrates DNA methylation, gene expression, and copy number alteration data using a systematic pipeline encompassing data preprocessing and handling class imbalance, dimensionality reduction, and classification. The framework starts with MutSigCV for data preprocessing and ensuring data quality. The Synthetic Minority Over-sampling Technique (SMOTE) up-sampling technique is applied to address the class imbalance representation. Then, Principal Component Analysis (PCA) transforms the DNA methylation, gene expression, and copy number alteration data into a latent space. The purpose is to discard irrelevant variations and extract relevant information. Finally, a classification model is built based on the transformed multiomics data into a unified representation. The framework contributes to understanding the complex interplay between menopause and breast cancer, thereby revealing more precise diagnostic and therapeutic strategies in the future. The explainable artificial intelligence model Shapley based on the XGBoost regressor showed the power of the selected gene expressions for predicting the menopause status, and the potential biomarkers included RUNX1, PTEN, MAP3K1, and CDH1. The literature confirmed the findings.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"221 8","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139152897","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

Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI 数据增强对基于深度学习的长轴 Cine-MRI 分段的影响

IF 2.3

Algorithms Pub Date : 2023-12-25 DOI: 10.3390/a17010010

François Legrand, Richard Macwan, Alain Lalande, Lisa Métairie, Thomas Decourselle

{"title":"Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI","authors":"François Legrand, Richard Macwan, Alain Lalande, Lisa Métairie, Thomas Decourselle","doi":"10.3390/a17010010","DOIUrl":"https://doi.org/10.3390/a17010010","url":null,"abstract":"Automated Cardiac Magnetic Resonance segmentation serves as a crucial tool for the evaluation of cardiac function, facilitating faster clinical assessments that prove advantageous for both practitioners and patients alike. Recent studies have predominantly concentrated on delineating structures on short-axis orientation, placing less emphasis on long-axis representations due to the intricate nature of structures in the latter. Taking these consideration into account, we present a robust hierarchy-based augmentation strategy coupled with the compact and fast Efficient-Net (ENet) architecture for the automated segmentation of two-chamber and four-chamber Cine-MRI images. We observed an average Dice improvement of 0.99% on the two-chamber images and of 2.15% on the four-chamber images, and an average Hausdorff distance improvement of 21.3% on the two-chamber images and of 29.6% on the four-chamber images. The practical viability of our approach was validated by computing clinical metrics such as the Left Ventricular Ejection Fraction (LVEF) and left ventricular volume (LVC). We observed acceptable biases, with a +2.81% deviation on the LVEF for the two-chamber images and a +0.11% deviation for the four-chamber images.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"23 S2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139157677","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 Self-Adaptive Meta-Heuristic Algorithm Based on Success Rate and Differential Evolution for Improving the Performance of Ridesharing Systems with a Discount Guarantee 基于成功率和差分进化的自适应元算法，用于提高有折扣保证的共享乘车系统的性能

IF 2.3

Algorithms Pub Date : 2023-12-25 DOI: 10.3390/a17010009

Fu-Shiung Hsieh

{"title":"A Self-Adaptive Meta-Heuristic Algorithm Based on Success Rate and Differential Evolution for Improving the Performance of Ridesharing Systems with a Discount Guarantee","authors":"Fu-Shiung Hsieh","doi":"10.3390/a17010009","DOIUrl":"https://doi.org/10.3390/a17010009","url":null,"abstract":"One of the most significant financial benefits of a shared mobility mode such as ridesharing is cost savings. For this reason, a lot of studies focus on the maximization of cost savings in shared mobility systems. Cost savings provide an incentive for riders to adopt ridesharing. However, if cost savings are not properly allocated to riders or the financial benefit of cost savings is not sufficient to attract riders to use a ridesharing mode, riders will not accept a ridesharing mode even if the overall cost savings is significant. In a recent study, the concept of discount-guaranteed ridesharing has been proposed to provide an incentive for riders to accept ridesharing services through ensuring a minimal discount for drivers and passengers. In this study, an algorithm is proposed to improve the performance of the discount-guaranteed ridesharing systems. Our approach combines a success rate-based self-adaptation scheme with an evolutionary computation approach. We propose a new self-adaptive metaheuristic algorithm based on success rate and differential evolution for the Discount-Guaranteed Ridesharing Problem (DGRP). We illustrate effectiveness of the proposed algorithm by comparing the results obtained using our proposed algorithm with other competitive algorithms developed for this problem. Preliminary results indicate that the proposed algorithm outperforms other competitive algorithms in terms of performance and convergence rate. The results of this study are consistent with the empirical experience that two people working together are more likely to come to a correct decision than they would if working alone.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"19 10","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139158624","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

Heuristic Greedy-Gradient Route Search Method for Finding an Optimal Traffic Distribution in Telecommunication Networks 在电信网络中寻找最佳流量分布的启发式贪婪梯度路由搜索法

IF 2.3

Algorithms Pub Date : 2023-12-23 DOI: 10.3390/a17010007

Konstantin Gaipov, Daniil Tausnev, Sergey Khodenkov, N. Shepeta, Dmitry Malyshev, Aleksey Popov, L. Kazakovtsev

{"title":"Heuristic Greedy-Gradient Route Search Method for Finding an Optimal Traffic Distribution in Telecommunication Networks","authors":"Konstantin Gaipov, Daniil Tausnev, Sergey Khodenkov, N. Shepeta, Dmitry Malyshev, Aleksey Popov, L. Kazakovtsev","doi":"10.3390/a17010007","DOIUrl":"https://doi.org/10.3390/a17010007","url":null,"abstract":"Rapid growth in the volume of transmitted information has lead to the emergence of new wireless networking technologies with variable heterogeneous topologies. With limited radio frequency resources, optimal routing problems arise, both at the network design stage and during its operation. We propose an algorithm based on a minimum loss intensity (greedy-gradient algorithm) to search for optimal routes of information transmission in telecommunication networks. The relevance of the developed algorithm is determined by its practical use in data-transmitting modeling systems. The proposed algorithm satisfies several requirements, such as the speed of the calculations performed, the fulfillment of the conditions for its convergence, and its independence on the selected loss probability function, as well as on the network topology. The idea of the algorithm is a step-by-step recalculation of metrics based on derivatives of the loss intensity function with simultaneous redistribution of information flows along the routes determined by the Floyd algorithm. The comparative efficiency of the proposed algorithm is demonstrated by computational experiments on various network topologies (up to 100 nodes) with various traffic intensities.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"46 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139161415","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

Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning 利用机器学习预测重症监护室患者的脱功能风险

IF 2.3

Algorithms Pub Date : 2023-12-22 DOI: 10.3390/a17010006

Nosa Aikodon, S. Ortega-Martorell, I. Olier

{"title":"Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning","authors":"Nosa Aikodon, S. Ortega-Martorell, I. Olier","doi":"10.3390/a17010006","DOIUrl":"https://doi.org/10.3390/a17010006","url":null,"abstract":"Patients in Intensive Care Units (ICU) face the threat of decompensation, a rapid decline in health associated with a high risk of death. This study focuses on creating and evaluating machine learning (ML) models to predict decompensation risk in ICU patients. It proposes a novel approach using patient vitals and clinical data within a specified timeframe to forecast decompensation risk sequences. The study implemented and assessed long short-term memory (LSTM) and hybrid convolutional neural network (CNN)-LSTM architectures, along with traditional ML algorithms as baselines. Additionally, it introduced a novel decompensation score based on the predicted risk, validated through principal component analysis (PCA) and k-means analysis for risk stratification. The results showed that, with PPV = 0.80, NPV = 0.96 and AUC-ROC = 0.90, CNN-LSTM had the best performance when predicting decompensation risk sequences. The decompensation score’s effectiveness was also confirmed (PPV = 0.83 and NPV = 0.96). SHAP plots were generated for the overall model and two risk strata, illustrating variations in feature importance and their associations with the predicted risk. Notably, this study represents the first attempt to predict a sequence of decompensation risks rather than single events, a critical advancement given the challenge of early decompensation detection. Predicting a sequence facilitates early detection of increased decompensation risk and pace, potentially leading to saving more lives.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"13 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139163593","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

Special Issue “Algorithms in Data Classification” 数据分类算法 "特刊

IF 2.3

Algorithms Pub Date : 2023-12-22 DOI: 10.3390/a17010005

Ioannis G. Tsoulos

引用次数: 0

Comparing Direct Deliveries and Automated Parcel Locker Systems with Respect to Overall CO2 Emissions for the Last Mile 比较直接递送和自动包裹柜系统在最后一英里的总体二氧化碳排放量

IF 2.3

Algorithms Pub Date : 2023-12-21 DOI: 10.3390/a17010004

K. Gutenschwager, Markus Rabe, Jorge Chicaiza-Vaca

{"title":"Comparing Direct Deliveries and Automated Parcel Locker Systems with Respect to Overall CO2 Emissions for the Last Mile","authors":"K. Gutenschwager, Markus Rabe, Jorge Chicaiza-Vaca","doi":"10.3390/a17010004","DOIUrl":"https://doi.org/10.3390/a17010004","url":null,"abstract":"Fast growing e-commerce has a significant impact both on CEP providers and public entities. While service providers have the first priority on factors such as costs and reliable service, both are increasingly focused on environmental effects, in the interest of company image and the inhabitants’ health and comfort. Significant additional factors are traffic density, pollution, and noise. While in the past direct delivery with distribution trucks from regional depots to the customers might have been justified, this is no longer valid when taking the big and growing numbers into account. Several options are followed in the literature, especially variants that introduce an additional break in the distribution chain, like local mini-hubs, mobile distribution points, or Automated Parcel Lockers (APLs). The first two options imply a “very last mile” stage, e.g., by small electrical vehicles or cargo bikes, and APLs rely on the customers to operate the very last step. The usage of this schema will significantly depend on the density of the APLs and, thus, on the density of the population within quite small regions. The relationships between the different elements of these technologies and the potential customers are studied with respect to their impact on the above-mentioned factors. A variety of scenarios is investigated, covering different options for customer behaviors. As an additional important point, reported studies with APLs only consider the section up to the APLs and the implied CO2 emission. This, however, fully neglects the potentially very relevant pollution created by the customers when fetching their parcels from the APL. Therefore, in this paper this impact is systematically estimated via a simulation-based sensitivity analysis. It can be shown that taking this very last transport step into account in the calculation significantly changes the picture, especially within areas in outer city districts.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"8 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951031","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 Survey on Swarm Robotics for Area Coverage Problem 针对区域覆盖问题的蜂群机器人技术概览

IF 2.3

Algorithms Pub Date : 2023-12-20 DOI: 10.3390/a17010003

Dena Kadhim Muhsen, Ahmed T. Sadiq, Firas Abdulrazzaq Raheem

引用次数: 0