This paper proposes algorithms to model fractional (dynamical) behaviors using non-singular rational kernels whose interest is first demonstrated on a pure power law function. Two algorithms are then proposed to find a non-singular rational kernel that allows the input-output data to be fitted. The first one derives the impulse response of the modeled system from the data. The second one finds the interlaced poles and zeros of the rational function that fits the impulse response found using the first algorithm. Several applications show the efficiency of the proposed work.
{"title":"Algorithms for Fractional Dynamical Behaviors Modelling Using Non-Singular Rational Kernels","authors":"Jocelyn Sabatier, C. Farges","doi":"10.3390/a17010020","DOIUrl":"https://doi.org/10.3390/a17010020","url":null,"abstract":"This paper proposes algorithms to model fractional (dynamical) behaviors using non-singular rational kernels whose interest is first demonstrated on a pure power law function. Two algorithms are then proposed to find a non-singular rational kernel that allows the input-output data to be fitted. The first one derives the impulse response of the modeled system from the data. The second one finds the interlaced poles and zeros of the rational function that fits the impulse response found using the first algorithm. Several applications show the efficiency of the proposed work.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":" 915","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139136476","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}
Artificial immune systems (AIS), as nature-inspired algorithms, have been developed to solve various types of problems, ranging from machine learning to optimization. This paper proposes a novel hybrid model of AIS that incorporates cellular automata (CA), known as the cellular automata-based artificial immune system (CaAIS), specifically designed for dynamic optimization problems where the environment changes over time. In the proposed model, antibodies, representing nominal solutions, are distributed across a cellular grid that corresponds to the search space. These antibodies generate hyper-mutation clones at different times by interacting with neighboring cells in parallel, thereby producing different solutions. Through local interactions between neighboring cells, near-best parameters and near-optimal solutions are propagated throughout the search space. Iteratively, in each cell and in parallel, the most effective antibodies are retained as memory. In contrast, weak antibodies are removed and replaced with new antibodies until stopping criteria are met. The CaAIS combines cellular automata computational power with AIS optimization capability. To evaluate the CaAIS performance, several experiments have been conducted on the Moving Peaks Benchmark. These experiments consider different configurations such as neighborhood size and re-randomization of antibodies. The simulation results statistically demonstrate the superiority of the CaAIS over other artificial immune system algorithms in most cases, particularly in dynamic environments.
{"title":"CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments","authors":"Alireza Rezvanian, S. M. Vahidipour, A. Saghiri","doi":"10.3390/a17010018","DOIUrl":"https://doi.org/10.3390/a17010018","url":null,"abstract":"Artificial immune systems (AIS), as nature-inspired algorithms, have been developed to solve various types of problems, ranging from machine learning to optimization. This paper proposes a novel hybrid model of AIS that incorporates cellular automata (CA), known as the cellular automata-based artificial immune system (CaAIS), specifically designed for dynamic optimization problems where the environment changes over time. In the proposed model, antibodies, representing nominal solutions, are distributed across a cellular grid that corresponds to the search space. These antibodies generate hyper-mutation clones at different times by interacting with neighboring cells in parallel, thereby producing different solutions. Through local interactions between neighboring cells, near-best parameters and near-optimal solutions are propagated throughout the search space. Iteratively, in each cell and in parallel, the most effective antibodies are retained as memory. In contrast, weak antibodies are removed and replaced with new antibodies until stopping criteria are met. The CaAIS combines cellular automata computational power with AIS optimization capability. To evaluate the CaAIS performance, several experiments have been conducted on the Moving Peaks Benchmark. These experiments consider different configurations such as neighborhood size and re-randomization of antibodies. The simulation results statistically demonstrate the superiority of the CaAIS over other artificial immune system algorithms in most cases, particularly in dynamic environments.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":" 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139140737","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}
This paper shows the development of a numerical analysis model, which enables the calculation of the cargo transport capacity of a vehicle that circulates through a vacuum tube at high speed, whose effectiveness in transport is analyzed. The simulated transportation system is based on vehicles moving in vacuum tubes at high speed, a concept commonly known as Hyperloop, but assuming the vehicles for cargo containers. For the specific vehicle proposed, which does not include a compressor and levitates on magnets, the system formed by the vehicle and the vacuum tube has been conceptually developed, establishing the corresponding mathematical relationships that define its behavior. To properly model the performance of this transport system, it has been necessary to establish the relationships between the design variables and the associated constraints, such as the Kantrowitz limit, aerodynamics, transport, energy consumption, etc. Once the model was built and validated, it was used to analyze the effects of the variation of the number of containers, the operating speed and the tube length, considering the total and specific consumption of energy. After finding the most efficient configuration regarding energy consumption and transport effectiveness, the complete system was calculated. The results obtained constitute a first approximation for the predesign of this transport system and the built model allows different alternatives to be compared according to the design variables.
{"title":"Analysis of the Effectiveness of a Freight Transport Vehicle at High Speed in a Vacuum Tube (Hyperloop Transport System)","authors":"David S. Pellicer, Emilio Larrodé","doi":"10.3390/a17010017","DOIUrl":"https://doi.org/10.3390/a17010017","url":null,"abstract":"This paper shows the development of a numerical analysis model, which enables the calculation of the cargo transport capacity of a vehicle that circulates through a vacuum tube at high speed, whose effectiveness in transport is analyzed. The simulated transportation system is based on vehicles moving in vacuum tubes at high speed, a concept commonly known as Hyperloop, but assuming the vehicles for cargo containers. For the specific vehicle proposed, which does not include a compressor and levitates on magnets, the system formed by the vehicle and the vacuum tube has been conceptually developed, establishing the corresponding mathematical relationships that define its behavior. To properly model the performance of this transport system, it has been necessary to establish the relationships between the design variables and the associated constraints, such as the Kantrowitz limit, aerodynamics, transport, energy consumption, etc. Once the model was built and validated, it was used to analyze the effects of the variation of the number of containers, the operating speed and the tube length, considering the total and specific consumption of energy. After finding the most efficient configuration regarding energy consumption and transport effectiveness, the complete system was calculated. The results obtained constitute a first approximation for the predesign of this transport system and the built model allows different alternatives to be compared according to the design variables.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"28 S102","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139145911","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}
Ant'onio Pedro Branco, Cátia Vaz, Alexandre P. Francisco
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.
有几种工具可用于推断系统发生树，系统发生树描述了生物实体之间的进化关系，如传染病爆发中的病毒和细菌菌株或肿瘤进展树中的癌细胞。这些工具依赖多种推断方法来生成系统发生树，但生成的系统发生树并不是唯一的。因此，需要能够揭示两棵系统发生树相同或不同之处的系统发生比较方法。罗宾逊-富尔德距离（Robinson-Foulds distance）是最常用的方法之一，可以在线性时间和空间内计算。然而，由于系统发育数据量巨大且不断增加，系统发育树变得非常庞大，树叶多达数十万片。在这种情况下，无论是计算系统树距离还是存储系统树，空间需求都成为一个问题。因此，我们提出了一种在树简洁表示上高效实现 Robinson-Foulds 距离的方法。我们的实现方法还将罗宾逊-福尔斯距离推广到带标签的系统发育树，即在所有节点上都包含标签的树，而不是只在叶子上包含标签的树。实验结果表明，我们仍能实现线性时间，同时所需的空间更少。我们的 C++ 实现是一个开源工具。
{"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}
Servo motors play an important role in automation equipment and have been used in several manufacturing fields. However, the commonly used control methods need their parameters to be set manually, which is rather difficult, and this means that these methods generally cannot adapt to changes in operation conditions. Therefore, in this study, we propose an intelligent control method for a servo motor based on reinforcement learning and that can train an agent to produce a duty cycle according to the servo error between the current state and the target speed or torque. The proposed method can adjust its control strategy online to reduce the servo error caused by a change in operation conditions. We verify its performance on three different servo motors and control tasks. The experimental results show that the proposed method can achieve smaller servo errors than others in most cases.
{"title":"An Intelligent Control Method for Servo Motor Based on Reinforcement Learning","authors":"Depeng Gao, Shuai Wang, Yuwei Yang, Haifei Zhang, Hao Chen, Xiangxiang Mei, Shuxi Chen, Jianlin Qiu","doi":"10.3390/a17010014","DOIUrl":"https://doi.org/10.3390/a17010014","url":null,"abstract":"Servo motors play an important role in automation equipment and have been used in several manufacturing fields. However, the commonly used control methods need their parameters to be set manually, which is rather difficult, and this means that these methods generally cannot adapt to changes in operation conditions. Therefore, in this study, we propose an intelligent control method for a servo motor based on reinforcement learning and that can train an agent to produce a duty cycle according to the servo error between the current state and the target speed or torque. The proposed method can adjust its control strategy online to reduce the servo error caused by a change in operation conditions. We verify its performance on three different servo motors and control tasks. The experimental results show that the proposed method can achieve smaller servo errors than others in most cases.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"55 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139150562","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}
Firas Alghanim, Ibrahim Al-Hurani, H. Qattous, Abdullah Al-Refai, Osamah Batiha, A. Alkhateeb, Salama Ikki
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.
确定与更年期相关的乳腺癌生物标志物对于加强该阶段的诊断、预后和个性化治疗至关重要。在本文中，我们提出了一个提取与绝经前后乳腺癌发病率特别相关的多组学生物标志物的综合框架。我们的方法使用一个系统管道整合了 DNA 甲基化、基因表达和拷贝数改变数据，该管道包括数据预处理、类不平衡处理、降维和分类。该框架从 MutSigCV 开始，进行数据预处理并确保数据质量。应用合成少数群体过度采样技术（SMOTE）向上采样技术来处理类不平衡表示。然后，主成分分析法（PCA）将 DNA 甲基化、基因表达和拷贝数改变数据转化为潜在空间。这样做的目的是摒弃无关变异，提取相关信息。最后，根据转换后的多组学数据建立一个统一表示的分类模型。该框架有助于理解更年期与乳腺癌之间复杂的相互作用，从而揭示未来更精确的诊断和治疗策略。基于 XGBoost 回归器的可解释人工智能模型 Shapley 显示了所选基因表达预测绝经状态的能力，潜在的生物标志物包括 RUNX1、PTEN、MAP3K1 和 CDH1。文献证实了这些发现。
{"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}
François Legrand, Richard Macwan, Alain Lalande, Lisa Métairie, Thomas Decourselle
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.
{"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}
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.
{"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}
Konstantin Gaipov, Daniil Tausnev, Sergey Khodenkov, N. Shepeta, Dmitry Malyshev, Aleksey Popov, L. Kazakovtsev
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.
{"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}
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.
{"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}
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