Model Selection Research Articles

The emergence and circulation of human immunodeficiency virus (HIV)-1 subtype C.

Introduction. Human immunodeficiency virus (HIV)-1 subtype C is the most prevalent globally and is thought to have originated in non-human primates in the Democratic Republic of Congo.Hypothesis/Gap Statement. Although the global dominance of HIV-1 subtype C is well established, a thorough understanding of its evolutionary history and transmission dynamics across various risk populations remains elusive. The current knowledge is insufficient to fully capture the global diversification and dissemination of this subtype.Aim. We for the first time sought to investigate the global evolutionary history and spatiotemporal dynamics of HIV-1 subtype C using a selection of maximum-likelihood-based phylodynamic approaches on a total of 1210 near full-length genomic sequences sampled from 32 countries, collected in 4 continents, with sampling dates between 1986-2019 among various risk groups were analysed.Methodology. We subsampled the HIV-1 subtype C genomic datasets based on continent and risk group traits, and performed nucleotide substitution model selection analysis, maximum likelihood (ML) phylogenetic reconstruction, phylogenetic tree topology similarity analysis, temporal signal analysis and traced the timings of viral spread both geographically and by risk group.Results. Based on the phylodynamic analyses of four datasets (full1210, locrisk626, loc562 and risk393), we inferred the time to the most recent common ancestor (TMRCA) in the 1930s and an evolutionary rate of 0.0023 substitutions per site per year. The total number of introduction events of HIV-1 subtype C between continents and between risk groups is estimated to be 71 and 115, respectively. The largest number of introductions occurred from Africa to Europe (n=32), from not-recorded to heterosexual (n=40) and from heterosexual to not-recorded (n=51) risk groups.Conclusion. Our results emphasize that HIV subtype C has mainly spread from Africa to Europe, likely through heterosexual transmission.

Sexual dimorphism in the environmental sensitivity of weaning weight in beef cattle

This study investigates the environmental sensitivity and sexual dimorphism in the genetic parameters of weaning weight (WW) in Nelore beef cattle. Five reaction norms models were employed to analyze the 148,131 WW records, considering different definitions of the environmental descriptor (ED). (Co)variance components and genetic parameters were separately estimated for males and females. Results reveal substantial sexual dimorphism in various parameters. Significant divergence across sexes was observed for environmental sensitivity, with males showing an increased response to favorable environments while females genetically responded better to more challenging environments. Including sex in defining the ED can inflate the estimated breeding values of sires with a higher number of male progeny records, highlighting the importance of choosing a reliable ED in analyses of traits influenced by sexual dimorphism and genotype-by-environment interaction (G × E). The observed sexual dimorphism and environmental sensitivity underscore the complexity of genetic evaluation of WW in beef cattle populations, with implications for selection strategies. The findings suggest that a sex-separated trait analysis considering G × E effects is most appropriate for genetic evaluations of WW. However, a simpler single-trait model excluding sex from the ED definition yielded a higher coincidence of selected sires with the sex-separated analysis. In conclusion, understanding sexual dimorphism and environmental sensitivity is crucial for genetic evaluations and selection decisions in beef cattle breeding programs, emphasizing the need for careful model selection and ED definition.

Inverse unit exponential probability distribution: Classical and Bayesian inference with applications

This article examines the new inverse unit exponential distribution, utilizing both classical and Bayesian methodologies; it begins by presenting the general properties of the proposed model, highlighting characteristic features, such as the presence of a reverse-J or increasing and inverted bathtub-shaped hazard rate function. Furthermore, it explores various statistical properties of the suggested distribution. It employs 12 methods to estimate the associated parameters. A Monte Carlo simulation is conducted to evaluate the accuracy of the estimation procedure. Even for small samples, the results indicate that biases and mean square errors decrease as the sample size increases, demonstrating the robustness of the estimation method. The application of the suggested distribution to real datasets is then discussed. Empirical evidence, using model selection criteria and goodness-of-fit test statistics, supports the assertion that the suggested model outperforms several existing models considered in the study. This article extends its analysis to the Bayesian framework. Using the Hamiltonian Monte Carlo with the no U-turn sampling algorithm, 8000 real samples are generated. The convergence assessment reveals that the chains are convergent and the samples are independent. Subsequently, using the posterior samples, the parameters of the proposed model are estimated, and credible intervals and highest posterior density intervals are computed to quantify uncertainty. The applicability of the suggested model to real data under both classical and Bayesian methodologies provides insights into its statistical properties and performance compared to existing models.

A Novel Approach to a Multi-Model Ensemble for Climate Change Models: Perspectives on the Representation of Natural Variability and Historical and Future Climate

This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rainfall in different scenarios. Our focus was to combine 10 regional climate model (RCM) simulations, forced by two global climate models (GCM), especially for estimation of design rainfall in a changing climate. We hypothesized that the natural variability and statistically higher moment attributes in the extreme rainfall simulations from RCMs were not fully preserved. Therefore, the MME approach could be more effective in climate change studies, largely due to the use of multiple climate models. First, an experimental study was proposed to validate the efficacy of the proposed modeling framework approach adopting L-moments to quantify relative importance among climate models and their use for representing natural variability in the MME construction. The proposed approach was then applied to climate change scenarios collected from multiple RCMs for the Han-River watershed during both historical (1981 to 2005) and future (2006 to 2100) periods. The results showed that the climate model selection informed by natural variability demonstrated better performance, representing nearly identical distribution to the observed annual maximum rainfall (AMR) in the Han River watershed. The range of the selected scenario was relatively narrower than that of all the scenarios, and the change rate was more consistent with the limited zero crossing, reflecting improvement in both model performance and consistency over historical and future periods, respectively. The change rate of the MME under RCP8.5 appeared to be an approximately 20% increase for the near (2011∼2040) and far (2071∼2100) future, and the degree of increase in the rate for the mid-future (2041∼2070) was slightly lower than that for the other periods, with increase of approximately 10%.

TriOS Schwarzschild Orbit Modeling: Robustness of Parameter Inference for Masses and Shapes of Triaxial Galaxies with Supermassive Black Holes

Evidence for the majority of the supermassive black holes in the local Universe has been obtained dynamically from stellar motions with the Schwarzschild orbit superposition method. However, there have been only a handful of studies using simulated data to examine the ability of this method to reliably recover known input black hole masses M BH and other galaxy parameters. Here, we conduct a comprehensive assessment of the reliability of the triaxial Schwarzschild method at simultaneously determining M BH, stellar mass-to-light ratio M*/L, dark matter mass, and three intrinsic triaxial shape parameters of simulated galaxies. For each of 25 rounds of mock observations using simulated stellar kinematics and the TriOS code, we derive best-fitting parameters and confidence intervals after a full search in the 6D parameter space with our likelihood-based model inference scheme. The two key mass parameters, M BH and M*/L, are recovered within the 68% confidence interval, and other parameters are recovered between the 68% and 95% confidence intervals. The spatially varying velocity anisotropy of the stellar orbits is also well recovered. We explore whether the goodness-of-fit measure used for galaxy model selection in our pipeline is biased by variable complexity across the 6D parameter space. In our tests, adding a penalty term to the likelihood measure either makes little difference, or worsens the recovery in some cases.

Ensembles of Multiple Models for Soil Moisture Retrieval from Remote Sensing Data over Agricultural Areas: A Deep Learning-based Framework

In agricultural areas, most surface soil moisture (SM) retrieval models are unstable in terms of their accuracy and performance during crop growth. As a result, there is no consensus on which model performs optimally during the agricultural season. This is because of the uncertainties associated with model physics, initial conditions, model inversion processes, input data, vegetation attenuation and soil characteristics. To better deal with these practical concerns, we propose a simple, but robust SM retrieval method of using combination of multiple models based on deep learning and multi-model ensemble approach, called the DL-MME method, which makes use of the ‘collective intelligence’ and ‘wisdom of crowds’ concepts. The advantages of this method are: (1) robustness to model selection, and (2) robustness to model calibration during the growing season. In addition, this method is less dependent on one type of data across various agricultural areas compared to the single model approach. Firstly, the coupled water cloud model (WCM) and soil backscattering models (Oh model or advanced integral equation model (AIEM)) with different vegetation descriptors were calibrated and validated during the growing season in sugarcane and winter wheat fields for Sentinel-1 backscattering coefficients (VV and VH). SM was also retrieved by employing the trapezoid model (OPTRAM) with different parameters from Sentinel-2 images. To optimize SM retrieval computations, we used the outputs from optical and SAR models, auxiliary features, and reliable in situ SM measurements as inputs to a deep learning convolutional neural network (DL-CNN). For sugarcane and wheat fields in the early stages of crop growth, WCM models retrieved more accurate time-series SM than optical models. OPTRAM soil moisture retrievals showed greater accuracy in the late crop growing season. Time-series SM retrieval accuracy using DL-MME was higher than for the optical and semi-empirical SAR models. According to the results of the in situ validation for wheat (sugarcane) fields, the minimum MAE by an optimal combination of models was around 0.01 (0.02) m3m-3 (RMSE= 0.036 (0.074) m3m-3; R= 0.87 (0.71)). The findings demonstrate that our method is reliable and feasible for SM retrieval. Additionally, our method provides a way to select an optimal model for retrieving time-series SM during the crop growing season.

Model selection reveals selective regulation of blood amino acid and lipid metabolism by insulin in humans

Model selection reveals selective regulation of blood amino acid and lipid metabolism by insulin in humans

MLCompare: Platform to Compare Machine Learning Models

Abstract: The field of machine learning (ML) continues to advance, the demand for efficient and effective methods for model development and selection becomes increasingly pressing. Automated Machine Learning (AutoML) platforms have emerged as powerful tools to address this need by automating the process of model selection, hyperparameter tuning, and feature engineering. By integrating a wide array of ML techniques and leveraging cloud computing resources, the platform enables researchers and practitioners to efficiently explore and compare the performance of various algorithms across diverse datasets. Key features include a user-friendly interface, a robust evaluation framework encompassing traditional metrics and advanced techniques, and parallel processing capabilities. Through experiments on benchmark datasets from multiple domains, the platform demonstrates its ability to streamline algorithm selection and provide valuable insights into algorithm performance. This contribution advances the field of AutoML by facilitating informed decision-making and accelerating innovation in ML applications.

Real Time Hotel Booking Demand Optimization

The significance of having proper revenue management and convenient operations in hotels rely on accurate daily demand. The main challenges that are being tackled conventionally are the forecasting of the number of cancellations and the calculation of the Average Daily Rate (ADR). One of the distinct characteristics of the hotel industry being so unstable is to adopt proactive strategies by dealing with a lot of external changes such as pandemics, disasters caused by nature, and economic fluctuations around the world. Booking cancellations are instrumental in helping hotel managers to optimise resources and inventory while ADR forecasting offers them equally essential projections concerning anticipated profit or loss margins. This research relies on several data modelling steps, including time series aggregation and decision merging, which are later followed by decomposition and model selection. SARIMAX and LSTM models are adopted for future traffic flow modelling, which demonstrates better forecasting performances. Binary classification is employed for feature engineering techniques together with model selection methods. Binary Classification is performed with a number of experiments with machine learning algorithms, AdaBoost turned out to be the best model which surpassed CART , KNN , Random Forest, Gradient boosting algorithms , and Light Gradient Boosting algorithms. The results of this are of great help to the hotel management for taking better decisions which are connected to the changing situations of the market

Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data.

The COVID-19 pandemic caused by the novel SARS-COV-2 virus poses a great risk to the world. During the COVID-19 pandemic, observing and forecasting several important indicators of the epidemic (likenew confirmed cases, new cases in intensive care unit, andnew deaths for each day) helped prepare the appropriate response (e.g., creating additional intensive care unit beds, andimplementing strict interventions). Various predictive models and predictorvariables have been used to forecast these indicators. However, the impact of prediction models and predictorvariables on forecasting performance has not been systematically well analyzed. Here,we compared the forecasting performance using a linear mixed model in terms of prediction models (mathematical, statistical, andAI/machine learning models) and predictor variables (vaccination rate, stringency index, andOmicron variantrate) for seven selected countries with the highest vaccination rates. We decided on our best models based on the Bayesian Information Criterion (BIC) and analyzed the significance of each predictor. Simple models were preferred. The selection of the bestprediction models and the use of Omicron variantrate were considered essential in improving prediction accuracies. For the test dataperiod before Omicron variantemergence, the selection of the bestmodels was the most significant factor in improving prediction accuracy. For the test period after Omicron emergence, Omicron variantrate use was considered essential in deciding forecasting accuracy. For prediction models, ARIMA, lightGBM, and TSGLM generally performed well in both test periods. Linear mixed models with country as a random effect has proven that the choice of prediction models and the use of Omicron data was significant in determining forecasting accuracies for the highly vaccinated countries. Relatively simple models, fit with either prediction model or Omicron data, produced best results in enhancing forecasting accuracies with test data.

Experimental analysis and evaluation of cohesive subgraph discovery

Retrieving cohesive subgraphs in networks is a fundamental problem in social network analysis and graph data management. These subgraphs can be used for marketing strategies or recommendation systems. Despite the introduction of numerous models over the years, a systematic comparison of their performance, especially across varied network configurations, remains unexplored. In this study, we evaluated various cohesive subgraph models using task-based evaluations and conducted extensive experimental studies on both synthetic and real-world networks. Thus, we unveil the characteristics of cohesive subgraph models, highlighting their efficiency and applicability. Our findings not only provide a detailed evaluation of current models but also lay the groundwork for future research by shedding light on the balance between the interpretability and cohesion of the subgraphs. This research guides the selection of suitable models for specific analytical needs and applications, providing valuable insights.

Comparative Study of G+6 Irregular Multistoryed Building of Different Shape of Diaphragm

Abstract: This study investigates the effectiveness of incorporating openings in the roof of re-entrant corner plan irregular buildings at different locations under earthquake loads. Five models with varying degrees of diaphragm discontinuity were analysed using STAAD Pro software. Node displacement, storey shear in X and Z directions, stress values, and maximum bending moment were evaluated to assess structural performance. Results indicate that models with higher diaphragm discontinuity exhibit increased node displacements, storey shear, and stress levels. Model M3, with 17% diaphragm discontinuity, consistently demonstrates superior performance in terms of lower displacements and stress values compared to other models. However, final model selection should consider specific project requirements and design considerations.

EVALUATING HYBRID NEURAL NETWORK ARCHITECTURES FOR PREDICTING SLEEP DISORDERS FROM STRUCTURED DATA

The accurate diagnosis of sleep disorders is crucial for effective treatment and management, yet current methods often rely on subjective assessments and are not always reliable. This research examines the efficacy of various neural network architectures, including dense networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and innovative hybrid models, in predicting sleep disorders from structured health data. Our study focuses on comparing the performance of these models using metrics such as accuracy, precision, recall, and F1 score across a dataset comprising 400 individuals with detailed sleep and lifestyle data. Our findings demonstrate that while traditional models like dense networks and CNNs for structured data yield robust results, hybrid models, particularly the CNN-Transformer, significantly outperform others. This model effectively integrates convolutional layers with Transformer’s attention mechanisms, excelling in handling complex data interactions and providing superior predictive accuracy with an F1 score and accuracy reaching as high as 0.91. Conversely, RNN models, designed to capture temporal data dependencies, showed less efficacy, underscoring the importance of model selection aligned with data characteristics. This suggests that for datasets not exhibiting strong temporal features, models leveraging spatial relationships or advanced attention mechanisms are more suitable. This study not only advances our understanding of neural network applications in medical diagnostics but also highlights the potential of hybrid models in enhancing diagnostic accuracy. These insights could lead to significant improvements in the early detection and treatment of sleep disorders, thereby enhancing patient outcomes and contributing to the broader field of medical informatics.

Forecasting US Inflation Trends: Insights from Time Series Analysis

Abstract: In our research, we dive into the world of forecasting US inflation using the ARIMA (Auto-Regressive Integrated Moving Average) model. We crafted a predictive framework by meticulously analyzing data, conducting tests like the Augmented Dickey-Fuller, and crunching metrics such as RMSE and MSE. Our meticulous model selection procedures involve carefully examining many ARIMA configurations to identify the ideal parameters, ensuring robustness and accuracy in forecasting inflation trends over the study period. Our findings show that the ARIMA (0,1,2) model outperforms others, offering reliable forecasts. These results not only showcase the model's effectiveness but also provide valuable insights for both policymakers and market players. By understanding potential inflationary trends, they can confidently navigate risks and make informed decisions, ultimately fostering stability and growth in the economy.

Comparative Study of G+6 Irregular Multistoryed Building of L Shape of Diaphragm Discontinuity

Abstract: This study investigates the effectiveness of incorporating roof openings in re-entrant corner plan irregular buildings at various locations under earthquake loads. Three models L-shaped diaphragm discontinuity were analyzed using STAAD Pro software. Node displacement and storey shear in both the X and Z directions were evaluated to assess structural performance. The results indicate that models with square diaphragm discontinuity exhibit increased node displacements and storey shear compared to those with L-shaped diaphragm discontinuity. The model with L-shaped diaphragm discontinuity consistently demonstrates superior performance in terms of lower displacements and shear stress values. However, the final model selection should take into account specific project requirements and design considerations.

Rapid outlier detection, model selection and variable selection using penalized likelihood estimation for general spatial models

The outliers in the data set have a potential influence on the statistical inference and can provide some useful information behind the data set, the methodology for outlier detection and accommodation is always an important topic in data analysis. For spatial data, its influence not only affects coefficient estimation but model selection. The traditional method usually carries out outlier detection, model selection and variable selection step by step, so the data processing efficiency is not high. In order to further improve the efficiency and accuracy of data processing, based on the general spatial model, we consider a technique to achieve outlier detection, along with model and variable estimation in one step. In the general spatial model, we add a mean shift parameter for each data point to identify outliers. Penalized likelihood estimation (PLE) is proposed to simultaneously detect outliers, and to select spatial models and explanatory variables for spatial data. This method correctly identifies multiple outliers, provides a proper spatial model, and corrects coefficient estimation without removing outliers in numerical simulation and case analysis. Compared to current methods, PLE detects outliers more quickly, and solves the optimization problem to select spatial models and explanatory variables. Calculation is easy using the optimized solnp function in R software.

OpenWeedGUI: An Open-Source Graphical Tool for Weed Imaging and YOLO-Based Weed Detection

Weed management impacts crop yield and quality. Machine vision technology is crucial to the realization of site-specific precision weeding for sustainable crop production. Progress has been made in developing computer vision algorithms, machine learning models, and datasets for weed recognition, but there has been a lack of open-source, publicly available software tools that link imaging hardware and offline trained models for system prototyping and evaluation, hindering community-wise development efforts. Graphical user interfaces (GUIs) are among such tools that can integrate hardware, data, and models to accelerate the deployment and adoption of machine vision-based weeding technology. This study introduces a novel GUI called OpenWeedGUI, designed for the ease of acquiring images and deploying YOLO (You Only Look Once) models for real-time weed detection, bridging the gap between machine vision and artificial intelligence (AI) technologies and users. The GUI was created in the framework of PyQt with the aid of open-source libraries for image collection, transformation, weed detection, and visualization. It consists of various functional modules for flexible user controls and a live display window for visualizing weed imagery and detection. Notably, it supports the deployment of a large suite of 31 different YOLO weed detection models, providing flexibility in model selection. Extensive indoor and field tests demonstrated the competencies of the developed software program. The OpenWeedGUI is expected to be a useful tool for promoting community efforts to advance precision weeding technology.

RED WINE QUALITY PREDICTION USING MACHINE LEARNING

Wine quality assessment is a critical task in the beverage industry, as it directly impacts consumer satisfaction and market competitiveness. Among various types of wine, red wine stands out for its complexity in flavor, aroma, and texture, making its quality prediction a challenging yet crucial endeavor. This project aims to develop a machine learning model for predicting the quality of red wine based on its physicochemical properties. The dataset utilized for this project consists of various attributes such as acidity, pH, alcohol content, and volatile acidity, among others, collected from red wine samples. These attributes serve as input features for the machine learning model, while the quality of wine, typically rated on a scale, serves as the target variable. The proposed approach involves several stages: data preprocessing, feature selection, model selection, and evaluation. During data preprocessing, techniques like normalization and handling missing values are employed to ensure data quality. Feature selection techniques such as correlation analysis and feature importance are utilized to identify the most relevant attributes for predicting wine quality. Several machine learning algorithms, including but not limited to, linear regression, decision trees, random forests, and support vector machines, are trained and evaluated to determine the most suitable model for the task. Performance metrics such as mean squared error, accuracy, and F1 score are utilized to assess the model's predictive capability. Key Words: Wine quality, market, aroma, texture, physicochemical, alcohol, acidity.

Optimizing spatio-temporal correlation structures for modeling food security in Africa: a simulation-based investigation

This study investigates the impact of spatio- temporal correlation using four spatio-temporal models: Spatio-Temporal Poisson Linear Trend Model (SPLTM), Poisson Temporal Model (TMS), Spatio-Temporal Poisson Anova Model (SPAM), and Spatio-Temporal Poisson Separable Model (STSM) concerning food security and nutrition in Africa. Evaluating model goodness of fit using the Watanabe Akaike Information Criterion (WAIC) and assessing bias through root mean square error and mean absolute error values revealed a consistent monotonic pattern. SPLTM consistently demonstrates a propensity for overestimating food security, while TMS exhibits a diverse bias profile, shifting between overestimation and underestimation based on varying correlation settings. SPAM emerges as a beacon of reliability, showcasing minimal bias and WAIC across diverse scenarios, while STSM consistently underestimates food security, particularly in regions marked by low to moderate spatio-temporal correlation. SPAM consistently outperforms other models, making it a top choice for modeling food security and nutrition dynamics in Africa. This research highlights the impact of spatial and temporal correlations on food security and nutrition patterns and provides guidance for model selection and refinement. Researchers are encouraged to meticulously evaluate the biases and goodness of fit characteristics of models, ensuring their alignment with the specific attributes of their data and research goals. This knowledge empowers researchers to select models that offer reliability and consistency, enhancing the applicability of their findings.

Genetic Analyses of Primary Liver Cancer Cell Lines: Correspondence With Morphological Features of Original Tumors.

Advancements in genetic analysis technologies have led to establishment of molecular classifications systems for primary liver cancers. The correlation between pathological morphology and genetic mutations in hepatocellular carcinoma (HCC) is becoming increasingly evident. To construct appropriate experimental models, it is crucial to select cell lines based on their morphology and genetic mutations. In this study, we conducted comprehensive genetic analyses of primary liver cancer cell lines and examined their correlations with morphology. Thirteen primary liver cancer cell lines established in our Department were investigated. Eleven cell lines were HCC cell lines, whereas 2 were combined hepatocellular-cholangiocarcinoma (CHC) cell line characteristics. Whole exome sequencing and fusion gene analyses were conducted using a next generation sequencing platform. We also examined correlations between cell mutations and morphological findings and conducted experiments to clarify the association between morphological findings and genetic alterations. Mutations in TP53, HMCN1, PCLO, HYDIN, APOB, and EYS were found in 11, 5, 4, 4, 3, and 3 cell lines, respectively. CTNNB1 mutation was not identified in any cell line. The original tumor of four cell lines (KYN-1, KYN-2, KYN-3, and HAK-6) showed morphologically macrotrabecular massive patterns and these cell lines harbor TP53 mutations. Two cell lines (KYN-2 and KMCH-2) showed an extremely high tumor mutation burden. These two cell lines possess ultra-mutations associated with DNA repair and/or DNA polymerase. The study identified correlations between morphological findings and genetic mutations in several HCC cell lines. Cell lines with unique genetic mutations were found. This information will be a valuable tool for the selection of suitable experimental models in HCC research.