Selection Algorithm Research Articles

Sound insulation prediction and optimization of wooden support structure for high-speed train floor based on machine learning

In order to improve the sound insulation performance of high-speed train floors, this study first obtained the necessary data for model training based on the reverberation test method, and then conducted data sorting and feature selection. Next, the maximum mutual information minimum redundancy (mRMR) feature selection algorithm was used to calculate the selected features and screen out a subset of significant features. Subsequently, the decision tree, BP neural network, and support vector machine regression (SVR) methods were applied in sequence, and the standardized feature data were used for the high-speed train floor under the same evaluation criteria of the mean square error (MSE) and coefficient of determination (R2). We conducted training and validation of the sound insulation prediction models for timber-framed support structures. The prediction accuracy of the trained model was compared and evaluated with the finite element statistical energy analysis (FE-SEA) prediction model. Finally, the SVR model was used to optimize the design under constraint conditions. The research results show that based on the research object, sample library, and model training in this article, compared with the FE-SEA model, the prediction error of the SVR model is only 0.3 dB, showing better performance. In engineering practice, the SVR model can effectively optimize the wooden support structure in the floor under certain constraints, and it predicts that the weighted sound insulation of the entire floor is 50.45 dB, which has important engineering application value.

An ambiguity subset selection algorithm based on the variation of check factors for BDS-3/BDS-2/GPS precise point positioning

An ambiguity subset selection algorithm based on the variation of check factors for BDS-3/BDS-2/GPS precise point positioning

Artificial intelligence applications in ophthalmic optical coherence tomography: a 12-year bibliometric analysis.

To explore the current application and research frontiers of global ophthalmic optical coherence tomography (OCT) imaging artificial intelligence (AI) research. The citation data were downloaded from the Web of Science Core Collection database (WoSCC) to evaluate the articles in application of AI in ophthalmic OCT published from January 1, 2012 to December 31, 2023. This information was analyzed using CiteSpace 6.2.R2 Advanced software, and high-impact articles were analyzed. In general, 877 articles from 65 countries were studied and analyzed, of which 261 were published by the United States and 252 by China. The centrality of the United States is 0.33, the H index is 38, and the H index of two institutions in England reaches 20. Ophthalmology, computer science, and AI are the main disciplines involved. Hot keywords after 2018 include deep learning (DL), AI, macular degeneration, and automatic segmentation. The annual number of articles on AI applications in ophthalmic OCT has grown rapidly. The United States holds a prominent position. Institutions like the University of California System and the University of London are spearheading advancements. Initial researches centered on the automatic recognition and diagnosis of ocular diseases leveraging traditional machine learning (ML) technology and OCT images. Nowadays, the imaging process algorithm selection has shifted its focus towards DL. Concurrently, optical coherence tomography angiography (OCTA) and computer-aided diagnosis (CAD) have emerged as key areas of contemporary research.

On the calibration of multiscale geographically and temporally weighted regression models

Spatiotemporal analysis and modeling have long been key foci of geographical information science. In a recent paper, Wu et al. expanded the local spatial modeling technique of multiscale geographically weighted regression (MGWR) to incorporate a temporal component. This new model is named multiscale geographically and temporally weighted regression (MGTWR). Despite the utility of expanding MGWR to incorporate a temporal weighting function in addition to the existing spatial one, the approach developed by Wu et al. has two limitations: the bandwidth selection algorithm cannot guarantee an optimal result and no formulation for the effective number of parameters (ENPs) in the model is given. To address these issues, this paper describes a procedure to derive an optimal temporal and an optimal spatial bandwidth for MGTWR and also develops a formula for the ENPs in the model. The former ensures the reliability of the model outputs while the latter is essential for making reliable inferences from the local parameter estimates generated in the calibration of models by MGTWR. These advances in the MGTWR framework are demonstrated through applications to simulated and real-world data.

Crossover Operator Inspired by the Selection Operator for an Evolutionary Task Sequencing Algorithm

This paper proposes a novel crossover operator for evolutionary algorithms in task sequencing and verifies its efficacy. Unlike the conventional blind and entirely stochastic selection of sequence fragments exchanged with the second individual, the proposed operator employs a method where the probability of fragment selection is influenced by the total cost of internal connections within the exchanged fragments. At the same time, the new operator retains its stochastic nature and is not a deterministic operator, which reduces the risk of the evolutionary algorithm getting stuck in a local minimum. The idea of the proposed crossover operator was based on the main mechanism of the evolutionary algorithm that determines the success of this type of algorithm selection. To assess its effectiveness, the new operator was compared against previously employed crossover operators using a traveling salesman problem (TSP) instance in a multidimensional space in order to map the problem of symmetric sequencing tasks described with multiparameters (e.g., a symmetric variant of production tasks sequencing).

FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery

Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implemented and tested on popular ground-taken photos. This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery. The goal is to enable researchers to choose efficiently from algorithms that are trainable and usable in real time on a deep learning infrastructure with moderate requirements. Using the large HRPlanesV2 dataset, together with rigorous validation with the GDIT dataset, this research encompasses an array of methodologies including YOLO versions 5, 8, and 10, Faster RCNN, CenterNet, RetinaNet, RTMDet, DETR, and grounding DINO, all trained from scratch. This exhaustive training and validation study reveals YOLOv5 as the pre-eminent model for the specific case of identifying airplanes from remote sensing data, showcasing high precision and adaptability across diverse imaging conditions. This research highlights the nuanced performance landscapes of these algorithms, with YOLOv5 emerging as a robust solution for aerial object detection, underlining its importance through superior mean average precision, recall, and intersection over union scores. The findings described here underscore the fundamental role of algorithm selection aligned with the specific demands of satellite imagery analysis and extend a comprehensive framework to evaluate model efficacy. This aims to foster exploration and innovation in the realm of remote sensing object detection, paving the way for improved satellite imagery applications.

Grad-CAM-Based Feature Selection and Dementia Classification Algorithm Using Voice Data

Grad-CAM-Based Feature Selection and Dementia Classification Algorithm Using Voice Data

A multi-stage feature selection method to improve classification of potential super-agers and cognitive decliners using structural brain MRI data-a UK biobank study.

Cognitive aging is described as the age-related decline in areas such as memory, executive function, reasoning, and processing speed. Super-Agers, adults over 80 years old, have cognitive function performance comparable to middle-aged adults. To improve cognitive reserve and potentially decrease Alzheimer's disease (AD) risk, it is essential to contrast changes in regional brain volumes between "Positive-Agers" who have superior cognitive performance compared to their age peers but are not 80 years old yet and aging adults who show cognitive decline (i.e., "Cognitive Decliners"). Using longitudinal cognitive tests over 7-9 years in UK Biobank, principal component analysis (PCA) was first applied to four cognitive domains to create a general cognition (GC) composite score. The GC score was then used to identify latent cognitive groups. Given cognitive groups as the target variable and structural magnetic resonance imaging (sMRI) data and demographics as predictors, we developed a multi-stage feature selection algorithm to identify the most important features. We then trained a Random Forest (RF) classifier on the final set of 54 selected sMRI and covariate predictors to distinguish between Positive-Agers and Cognitive Decliners. The RF model achieved an AUC of 73%. The top 6 features were age, education, brain total surface area, the area of pars orbitalis, mean intensity of the thalamus, and superior frontal gyrus surface area. Prediction of cognitive trajectory types using sMRI may improve our understanding of successful cognitive aging.

Evaluation of Automatic Analysis Software for 1H NMR Spectra Quantification. Impact of Signal Broadening and EDTA Addition.

NMR is a well-established analytical technique that enables the identification and quantification of several compounds in complex mixtures. The implementation of automated analysis software enhances this process by comparing sample spectra with a library of standard reference spectra. One of the key parameters in libraries is the signal line width; however, this may result in a concentration bias for compounds with broad signals, particularly those that are complexing. This study aims to evaluate the quantification of seven compounds commonly present in wine with complexing activity. Four automatic programs and manual deconvolution were used to quantify mixtures of these compounds at known concentrations, both in the presence of cations and with the addition of ethylenediamine-tetraacetate (EDTA) as a chelating agent. Additionally, malate was quantified in a wine sample using the standard addition method, both with and without EDTA. The findings illustrate that three of the programs, which employ the signal width value, underestimate compound concentrations in the presence of cations due to signal broadening. This error was mitigated by the EDTA addition. In contrast, the remaining software, which did not utilize signal line width, obtained similar concentrations in both cases. Future investigations involving the automatic analysis of compounds with complexing activity should take care of sample preparation and/or algorithm selection. Furthermore, future software development should consider allowing flexible adjustments of signal line width in their workflow.

Research on Low-Carbon Site Selection Decision Model and Algorithm for Green Logistics

This work proposes a low-carbon site selection decision model and algorithm for green logistics. The focus is to minimize carbon emissions while optimizing the distribution network. With the development of smart shared logistics and increasing environmental awareness, there is an urgent need to tackle issues in urban logistics, including pollution, resource inefficiency, high carbon energy usage, and greenhouse gas emissions. By integrating geographic, environmental, and operational data, the proposed site selection model incorporates multi-objective optimization to balance carbon efficiency with cost-effectiveness in logistics site selection. We employ a hybrid algorithm combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to solve the complex decision-making process. The model is tested using real-world logistics data, and the results demonstrate significant reductions in carbon emissions and transportation costs when compared to conventional logistics site selection methods. Further sensitivity analysis reveals that the model is robust under varying transportation demands and geographic constraints. Also, we highlight the role of green logistics in achieving corporate sustainability goals aligning with global carbon neutrality initiatives. We believe this model offers a practical approach to fostering environmentally responsible supply chains and will contribute to the advancement of sustainable logistics operations, helping organizations make informed, eco-friendly decisions in site selection.

Development of an Intrusion Detection System using ANOVA Feature Selection and Support Vector Machine Algorithms

Development of an Intrusion Detection System using ANOVA Feature Selection and Support Vector Machine Algorithms

Multi-class Financial Distress Prediction Based on Feature Selection and Deep Forest Algorithm

Multi-class Financial Distress Prediction Based on Feature Selection and Deep Forest Algorithm

Development of an Algorithm and Software for Optimal Route Selection

The paper deals with developing an optimal route selection algorithm and software, which, taking into account various criteria, ensures the adoption of efficient and economical route selection decisions. The algorithm is based on permutations for defining routes. It utilizes multi-criteria analysis methods such as TOPSIS and CRITIC, which enable the selection of optimal routes based on various factors, including cost optimization, time savings, safety measures, etc. The paper also presents the proposed algorithm's software, which helps to solve real-world problems related to route selection.

Optimized Configuration and Operation Of Isolated Microgrid Systems For Rural Electrification: Baron Technopark

Microgrid systems represent a significant advancement in energy supply technologies, particularly for rural communities lacking access to electricity; however, these systems are predominantly reliant on diesel generators (DG). The formulation and selection of suitable configurations, alongside operational patterns, must be meticulously evaluated in the pursuit of economically viable and dependable microgrid systems. Consequently, this research sought to devise an optimal configuration and operational for microgrid systems situated in isolation, utilizing the Baron Techno Park (BTP) in Indonesia as a case study. The optimization process was executed utilizing HOMER software, integrated with an operating cost comparison, with particular emphasis placed on daily load fluctuations, the selection of control algorithms, the reconfiguration of the power supply system, and the regulation of diesel generator operational hours. The proposed microgrid system yielded a surplus energy production of 16.7%, a renewable fraction (RF) of 100%, a levelized cost of electricity (LCOE) of $5.6 per kWh, a net present cost (NPC) of $3,97M. In summary, the study shows that by slightly increasing the capital cost of PV system procurement, it can reduce the operating cost of $629 from the base system in the long term.

A Comparative Analysis of Advanced Routing and Cluster Head Selection Algorithm Using Lagrange Interpolation

This paper presents a unified performance metric for evaluating the chronological wild geese optimization (CWGO) algorithm in wireless sensor networks (WSNs). The metric combines key performance factors—energy consumption, delay, distance, and trust—into a single measure using Lagrange interpolation, providing a more comprehensive assessment of WSN algorithms. We evaluate CWGO against E-CERP, EECHIGWO, DUCISCA, and DE-SEP across static and dynamic sensor node configurations in various wireless technologies, including LoRa, Wi-Fi, Zigbee, and Bluetooth low energy (BLE). The results show that CWGO consistently outperforms the other algorithms, especially in larger node configurations, demonstrating its scalability and robustness in static and dynamic environments. Moreover, the unified metric reveals significant performance gaps with EECHIGWO, which underperforms all wireless technologies. DUCISCA and DE-SEP show moderate and fluctuating results, underscoring their limitations in larger networks. While E-CERP performs competitively, it generally lags behind CWGO. The unified metric offers a holistic view of algorithm performance, conveying clearer comparisons across multiple factors. This study emphasized the importance of a unified evaluation approach for WSN algorithms and positions CWGO as a superior solution for efficient cluster head selection and routing optimization in diverse WSN scenarios. While CWGO demonstrates superior performance in simulation, future research should validate these findings in real-world deployments, accounting for hardware limitations and in a highly dynamic environment. Further optimization of the unified metrics’ computational efficiency could enhance its real-time applicability in larger, energy-resource-constrained WSNs.

Exploring the role of alternative lengthening of telomere-related genes in diagnostic modeling for non-alcoholic fatty liver disease

Previous studies have reported an association between telomere length and non-alcoholic fatty liver disease (NAFLD). This study aimed to explore the involvement of alternative lengthening of telomere-related genes (ALTRGs) in the pathology of NAFLD, construct a risk signature, and evaluate both treatment and prognosis. Three NAFLD datasets (GSE48452, GSE89632, and GSE63067) were collected from the GEO database and merged into combined GEO datasets. ALTRGs were collected from GeneCards and PubMed databases. Differentially expressed genes (DEGs) were identified, and functional enrichment analysis was performed. This study employed a support vector machine algorithm and least absolute shrinkage and selection operator regression analysis to identify key genes for constructing a diagnostic model. High- and low-risk groups were identified from the combined GEO datasets using the diagnostic model. Gene set enrichment analysis, regulatory network analysis, and intergroup immune infiltration analysis were performed. This study identified the key genes using receiver operating characteristic and Friends analysis. Expression of these genes was validated in a mouse model of NAFLD. Twenty-five genes were differentially expressed, with a positive correlation between FOS and EGR1 and a negative correlation between MYC and CEBPA. A diagnostic model was constructed using 12 genes, and high- and low-risk groups were identified. CAMK2G, ERBB2, FOSB, WT1, and CEBPA showed certain accuracy, and their expression levels were significantly different in the model. Immune infiltration analysis between the risk groups revealed that six immune cells were statistically significant. This includes a strong negative interaction between type 2 T helper cells and SPHK2 in the high-risk group. These findings suggest that ALTRDEGs are potential therapeutic targets and prognostic indicators for NAFLD. However, further investigations are required to elucidate the specific underlying mechanisms.

Advancements in evaporation prediction: introducing the Gated Recurrent Unit–Multi-Kernel Extreme Learning Machine (MKELM)–Gaussian Process Regression (GPR) model

Predicting evaporation is an essential topic in water resources management. It is critical to plan irrigation schedules, optimize hydropower production, and accurately calculate the overall water balance. Thus, researchers have developed many prediction models for predicting evaporation. Despite the development of these models, there are still unresolved challenges. These challenges include selecting the most important input parameters, handling nonstationary data, extracting critical information from data, and quantifying the uncertainty of predicted values. Thus, the main aim of this study is to address these challenges by developing a new prediction model. The new prediction model, named Gated Recurrent Unit–Multi-Kernel Extreme Learning Machine (MKELM)–Gaussian Process Regression (GPR), was used to predict one-month ahead evaporation in the Kashafrood basin, Iran. This model was executed in multiple stages. First, a feature selection algorithm was used to determine the most critical input parameters. A data processing technique was then employed to decompose nonstationary data into stationary intrinsic mode functions (IMFs). The GRU model then processed these components to extract their essential information. In the following step, the extracted information was inserted into the MKELM model to predict evaporation. Finally, the GPR model quantified the uncertainty of predicted values. Our research also introduces a new optimizer called the Salp Swarm Optimization Algorithm–Sine Cosine Optimization Algorithm. This algorithm was used to tune the model parameters. This algorithm's performance and the prediction models’ accuracy were evaluated using several error indices. According to the study results, the GRU–MKELM–GPR model performed better than other models in predicting monthly evaporation. It improved the training and testing mean absolute error values of the other models by 21%-43% and 8.2–33%, respectively. Moreover, the new model improved the R2 (R-squared or coefficient of determination) values of other models by 5–12%. Generally, the main findings of this paper included the superior performance of the new model in predicting evaporation data and the superior performance of a new optimizer in adjusting model parameters. These findings highlighted the effectiveness of the suggested model in addressing the challenges associated with evaporation prediction.

Modelling icing growth on overhead transmission lines: Current advances and future directions

AbstractThe increasing impact of climate change raises concerns regarding the vulnerability of overhead transmission lines to ice disasters. To address this issue, this study reviews icing growth modelling in two categories: physical‐driven models (PDMs) and data‐driven models (DDMs), covering current advances and future directions. First, PDMs are summarised, focusing on the thermodynamic and fluid mechanics mechanisms. Existing PDMs are compared based on principles, analysing their advantages, disadvantages, and challenges faced. Second, the summarisation of DDMs involves four aspects: data preparation, algorithm selection, model training, and model evaluation. In data preparation, techniques such as preprocessing methods are reviewed to handle multisource data. In algorithm selection, various modelling algorithms are compared and analysed, from basic to deep learning approaches. In model training, processes are summarised to enhance practical applicability, including data partitioning, hyperparameter adjustment, generalisation capability, and model interpretability. In model evaluation, the predictive capabilities are analysed, covering both regression and classification tasks. Subsequently, based on the analyses, a comparison of PDMs and DDMs across various aspects is presented. Finally, future directions in icing growth modelling are outlined. The aim is to enhance icing assessment by understanding the underlying mechanism in attempt to reduce vulnerability and ensure reliability against adverse weather conditions.

Effects of alcohol-related problems on the costs of frequent emergency department use: an economic analysis of a case-control study in Spain.

Alcohol-related problems increase the probability of frequent emergency department (ED) use. In this study, we compared the direct healthcare expenses incurred during a single visit among frequent and non-frequent ED users and analyzed the impact of alcohol-related issues in healthcare costs arising from ED usage. The study relied on secondary analyses of economic data from a 1:1 matched case-control study with the primary aim of identifying the clinical characteristics of hospital ED frequent users in a Mediterranean European environment with a public, universal, and tax-funded health system. The participants ranged in age from 18 to 65 years and underwent ED visits at a high-complexity Spanish hospital (cases ≥5 times, controls <5) from December 2018 to November 2019. Each case was matched to a control with the same age, gender, and date of attendance at the ED. Clinical data and direct healthcare costs for a single ED visit were obtained by a retrospective review of the first electronic medical register. Costs and duration of stay were compared between cases and controls using paired-samples t-tests, and ED users with and without alcohol-related problems were compared using bivariate (independent-samples t-tests, one-way analysis of variance, Chi square tests, and multiple linear regression) and multivariate analyses (multiple linear regression models with backward stepwise selection algorithm, and dependent variable: total mean direct costs). Among 609 case-control pairs (total n = 1218), mean total healthcare direct costs per ED visit were 22.2% higher among frequent compared with non-frequent users [mean difference 44.44 euros; 95% confidence interval (CI) 13.4-75.5; t(608) = 2.811; p = 0.005]. Multiple linear regression identified length of stay, triage level, ambulance arrival, and the specialty discharging the patient as associated with total healthcare costs for frequent users. In bivariate analyses, a history of alcohol-related problems was associated with a 32.5% higher mean total healthcare costs among frequent users [mean difference 72.61 euros; 95% confidence interval 25.24-119.97; t(320.016) = 3.015; p = 0.003]. The findings confirm the high cost of frequent ED use among people with alcohol-related problems, suggesting that costs could be reduced through implementation of intervention protocols.

A Simple Credit Rating Prediction Model for FinTech Companies Using SMOTE and MRMR Techniques

FinTech companies have made the financial industry more efficient and have increased financial inclusion. However, it has also brought new risks to the financial system. Regulators, investors, and researchers are concerned that their financial difficulties could affect the financial system. Our study aims to delve deeper into the effectiveness of machine learning techniques in identifying early warnings of FinTech companies’ credit risk impairment. Using commonly employed accounting and market measures in the literature, we created various classifiers to predict FinTech credit ratings. Classification algorithms face a challenge when the number of observations between classes is not equivalent, affecting their performance. Due to the limited size of publicly traded FinTech stocks with an issuer-level credit rating, our database has few observations and is highly imbalanced. The results of our study show that the SMOTE oversampling technique improves the predictive power of machine learning algorithms and that feature selection algorithms such as MRMR allow the generation of less complex and easierto-understand models. Our results suggest that the KNN classification algorithm has higher accuracy in predicting FinTech’s credit ratings.