Algorithms最新文献_第7页

A Learnheuristic Algorithm for the Capacitated Dispersion Problem under Dynamic Conditions 动态条件下容量分散问题的学习启发式算法

IF 2.3

Algorithms Pub Date : 2023-11-22 DOI: 10.3390/a16120532

Juan F. Gomez, Antonio R. Uguina, Javier Panadero, Angel A. Juan

引用次数: 0

Fast Local Laplacian Filter Based on Modified Laplacian through Bilateral Filter for Coronary Angiography Medical Imaging Enhancement 基于通过双侧滤波器修正的拉普拉斯的快速局部滤波器，用于冠状动脉造影医学成像增强

IF 2.3

Algorithms Pub Date : 2023-11-21 DOI: 10.3390/a16120531

S. Khan, Muzammil Khan, Yasser Alharbi

{"title":"Fast Local Laplacian Filter Based on Modified Laplacian through Bilateral Filter for Coronary Angiography Medical Imaging Enhancement","authors":"S. Khan, Muzammil Khan, Yasser Alharbi","doi":"10.3390/a16120531","DOIUrl":"https://doi.org/10.3390/a16120531","url":null,"abstract":"Contrast enhancement techniques serve the purpose of diminishing image noise and increasing the contrast of relevant structures. In the context of medical images, where the differentiation between normal and abnormal tissues can be quite subtle, precise interpretation might become challenging when noise levels are relatively elevated. The Fast Local Laplacian Filter (FLLF) is proposed to deliver a more precise interpretation and present a clearer image to the observer; this is achieved through the reduction of noise levels. In this study, the FLLF strengthened images through its unique contrast enhancement capabilities while preserving important image details. It achieved this by adapting to the image’s characteristics and selectively enhancing areas with low contrast, thereby improving the overall visual quality. Additionally, the FLLF excels in edge preservation, ensuring that fine details are retained and that edges remain sharp. Several performance metrics were employed to assess the effectiveness of the proposed technique. These metrics included Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Normalization Coefficient (NC), and Correlation Coefficient. The results indicated that the proposed technique achieved a PSNR of 40.12, an MSE of 8.6982, an RMSE of 2.9492, an NC of 1.0893, and a Correlation Coefficient of 0.9999. The analysis highlights the superior performance of the proposed method when contrast enhancement is applied, especially when compared to existing techniques. This approach results in high-quality images with minimal information loss, ultimately aiding medical experts in making more accurate diagnoses.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"35 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139252625","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

Period Cycle Optimization of Integrated Energy Systems with Long-Term Scheduling Consideration 考虑长期调度的综合能源系统周期优化

IF 2.3

Algorithms Pub Date : 2023-11-18 DOI: 10.3390/a16110530

Daoyu Ye, Shengxiang Deng

{"title":"Period Cycle Optimization of Integrated Energy Systems with Long-Term Scheduling Consideration","authors":"Daoyu Ye, Shengxiang Deng","doi":"10.3390/a16110530","DOIUrl":"https://doi.org/10.3390/a16110530","url":null,"abstract":"The economy and energy saving effects of integrated energy system dispatch plans are influenced by the coupling of different energy devices. In order to consider the impact of changes in equipment load rates on the optimization and scheduling of the system under long-term operation, a method for energy and component cycle optimization considering energy device capacity and load has been proposed. By improving the initial parameters of the components, energy economic parameters, and operational optimization parameters, the system is subjected to long-term scheduling and multi-cycle operational optimization analysis to evaluate the energy saving and emission reduction potential as well as the economic feasibility of the system. Finally, through numerical analysis, the effectiveness of this optimization approach in achieving energy savings, emission reductions, and cost benefits for the system is validated. Furthermore, compared to existing optimization methods, this approach also assesses the economic feasibility of the system. The case study resulted in a pre-tax IRR of 23.14% and a pre-tax NPV of 66.38 million. It is inferred that the system could generate profits over a 10-year operation period, thereby offering a more rational and cost-effective scheduling scheme for the integrated energy system.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"20 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139260944","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 Narrow-Down Approach Based on Machine Learning for Indoor Localization 基于机器学习的狭义室内定位方法

IF 2.3

Algorithms Pub Date : 2023-11-17 DOI: 10.3390/a16110529

Sahibzada Muhammad Ahmad Umair, T. Arslan

{"title":"A Narrow-Down Approach Based on Machine Learning for Indoor Localization","authors":"Sahibzada Muhammad Ahmad Umair, T. Arslan","doi":"10.3390/a16110529","DOIUrl":"https://doi.org/10.3390/a16110529","url":null,"abstract":"Over the past decade, the demand and research for indoor localization have burgeoned and Wi-Fi fingerprinting approach has been widely considered because it is cheap and accessible. However, most existing methods lack in terms of positioning accuracy and high computational complexity. To cope with these issues, we formulate a two-stage, coarse and accurate positioning narrow-down approach (NDA). Furthermore, a three-step source domain refinement (SDR) scheme that involves outlier removal, stable AP’s weight enhancement, and a data averaging technique by applying the K-means clustering algorithm is also proposed. The collaboration of SDR scheme with the training data selection, area division, and overlapping schemes reduces the computational complexity and improves coarse positioning accuracy. The effect of the proposed SDR scheme on the performance of the support vector machine (SVM) and random forest algorithms is also presented. In the final/accurate positioning phase, a set of lightweight neural networks (DNNs), trained on different sub-areas, predict the user’s location. This approach significantly increases positioning accuracy while reducing the online computational complexity at the same time. The experimental results show that the proposed approach outperforms the best solutions presented in the literature.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"41 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139264378","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

Search on an NK Landscape with Swarm Intelligence: Limitations and Future Research Opportunities 利用蜂群智能搜索 NK 景观：局限性与未来研究机会

IF 2.3

Algorithms Pub Date : 2023-11-16 DOI: 10.3390/a16110527

Ren-Raw Chen, Cameron D. Miller, P. Toh

引用次数: 0

Optimal Integrated Single-Framework Algorithm for the Multi-Level School Bus Network Problem 多级校车网络问题的最优集成单框架算法

IF 2.3

Algorithms Pub Date : 2023-11-16 DOI: 10.3390/a16110528

A. Nickkar, Young-Jae Lee

引用次数: 0

An Intelligent Injury Rehabilitation Guidance System for Recreational Runners Using Data Mining Algorithms 使用数据挖掘算法的休闲跑步者智能损伤康复指导系统

IF 2.3

Algorithms Pub Date : 2023-11-15 DOI: 10.3390/a16110523

Theodoros Tzelepis, George Matlis, Nikos Dimokas, Petros Karvelis, P. Malliou, A. Beneka

{"title":"An Intelligent Injury Rehabilitation Guidance System for Recreational Runners Using Data Mining Algorithms","authors":"Theodoros Tzelepis, George Matlis, Nikos Dimokas, Petros Karvelis, P. Malliou, A. Beneka","doi":"10.3390/a16110523","DOIUrl":"https://doi.org/10.3390/a16110523","url":null,"abstract":"In recent years the number of people who exercise every day has increased dramatically. More precisely, due to COVID period many people have become recreational runners. Recreational running is a regular way to keep active and healthy at any age. Additionally, running is a popular physical exercise that offers numerous health advantages. However, recreational runners report a high incidence of musculoskeletal injuries due to running. The healthcare industry has been compelled to use information technology due to the quick rate of growth and developments in electronic systems, the internet, and telecommunications. Our proposed intelligent system uses data mining algorithms for the rehabilitation guidance of recreational runners with musculoskeletal discomfort. The system classifies recreational runners based on a questionnaire that has been built according to the severity, irritability, nature, stage, and stability model and advise them on the appropriate treatment plan/exercises to follow. Through rigorous testing across various case studies, our method has yielded highly promising results, underscoring its potential to significantly contribute to the well-being and rehabilitation of recreational runners facing musculoskeletal challenges.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"46 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139270918","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 General Model for Side Information in Neural Networks 神经网络侧边信息的通用模型

IF 2.3

Algorithms Pub Date : 2023-11-15 DOI: 10.3390/a16110526

Tameem Adel, Mark Levene

引用次数: 0

Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence 关系费舍尔分析：关系数据的降维与全局收敛

IF 2.3

Algorithms Pub Date : 2023-11-15 DOI: 10.3390/a16110522

Lina Wang, Guoqiang Zhong, Yaxin Shi, Mohamed Cheriet

引用次数: 0

Utilizing Mixture Regression Models for Clustering Time-Series Energy Consumption of a Plastic Injection Molding Process 利用混合回归模型对塑料注射成型工艺的时间序列能耗进行聚类

IF 2.3

Algorithms Pub Date : 2023-11-15 DOI: 10.3390/a16110524

Massimo Pacella, Matteo Mangini, G. Papadia

{"title":"Utilizing Mixture Regression Models for Clustering Time-Series Energy Consumption of a Plastic Injection Molding Process","authors":"Massimo Pacella, Matteo Mangini, G. Papadia","doi":"10.3390/a16110524","DOIUrl":"https://doi.org/10.3390/a16110524","url":null,"abstract":"Considering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption. The industrial case study considered in our work is one of the most energy-intensive processes in the plastics industry: the plastic injection molding process. Concerning the industrial setting, the energy consumption of the injection molding machine was monitored across multiple injection molding cycles. The collected data were then analyzed to establish patterns and trends in the energy consumption of the injection molding process. To this end, we considered mixtures of regression models given their flexibility in modeling heterogeneous time series and clustering time series in an unsupervised machine learning framework. Given the assumption of autocorrelated data and exogenous variables in the mixture model, we implemented an algorithm for model fitting that combined autocorrelated observations with spline and polynomial regressions. Our results demonstrate an accurate grouping of energy-consumption profiles, where each cluster is related to a specific production schedule. The clustering method also provides a unique profile of energy consumption for each cluster, depending on the production schedule and regression approach (i.e., spline and polynomial). According to these profiles, information related to the shape of energy consumption was identified, providing insights into reducing the electrical demand of the plant.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"70 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139271606","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