Predictive Capability Of Model Research Articles

Prediction enhancement for surface water sodium adsorption ratio using limited inputs: Implementation of hybridized stacked ensemble model with feature selection algorithm

The Sodium Adsorption Ratio (SAR) is a widely used variable in water quality research, particularly in agriculture and environmental studies. In many cases, the key variables required for SAR calculation, namely Na+, Mg+2, and Ca+2, are not available. Consequently, the potential to calculate SAR using a limited number of water quality variables becomes critically important. The study implemented the Multilayer Perceptron Neural Network (MLPNN), Support Vector Regression (SVR), and K-Nearest Neighbors (KNN) models at level-0 for prediction purposes, along with the Boruta model for variable selection. A stacked ensemble learning model at level-1 enhanced the prediction accuracy. The discharge and water quality dataset from the Zarrin-Gol River in northern Iran was utilized to implement the modeling procedure. Results obtained from the variable selection process using the Boruta model revealed that using a limited number of water quality variables can effectively predict SAR even without the principal variables. Further investigation of the input combinations for the level-0 models demonstrated that, for the MLPNN, KNN, and SVR models, 4, 3, and 1 input variables, respectively, yielded optimal predictions. Among the level-0 models, the MLPNN model exhibited the highest accuracy, with RMSE = 0.54, MBE = 0.26, MAE = 0.44, R = 0.84, IA = 0.67, and KGE = 0.79. Implementing the stacked ensemble learning model at level-1 significantly improved the SAR prediction compared to the level-0 models. The ensemble-NN model yielded the best performance in estimating SAR within the range of recorded data, with RMSE = 0.53, MBE = 0.29, MAE = 0.41, R = 0.87, IA = 0.70, and KGE = 0.82. Residual analysis further confirmed the superior predictive capability of the level-1 models compared to the level-0 models. The generalized-logistic probability distribution function is used to estimate the extreme values data. The Ensemble-KNN model best predicted extreme values data, with RMSE = 0.69, MBE = −0.61, MAE = 0.61, R = 0.61, IA = 0.26, and KGE = 0.37. The findings underscore the substantial advancements achieved through stacked ensemble methods in enhancing the modeling of SAR across various aspects, including total data, extreme values, and models' residuals.

Deep convolutional neural networks with Bee Collecting Pollen Algorithm (BCPA)-based landslide data balancing and spatial prediction

Predicting the landslide-prone area is critical for various applications, including emergency response, land planning, and disaster mitigation. There needs to be a thorough landslide inventory in current studies and appropriate sampling uncertainty issues. Landslide risk mapping has expanded significantly as machine learning techniques have developed. However, one of the primary issues in Landslide Prediction is data imbalance (DI). This is problematic since it is challenging or expensive to generate an accurate inventory map of landslides based on previous data. This study proposes a novel landslide prediction method using Generative Adversarial Networks (GAN) for generating the synthetic data, Synthetic Minority Oversampling Technique (SMOTE) for overcoming the data imbalance problem, and Bee Collecting Pollen Algorithm (BCPA) for feature extraction. Combining 184 landslides and ten criteria, including topographic wetness index (TWI), aspect, distance from the road, total curvature, sediment transport index (STI), height, slope, stream, lithology, and slope length, a geographical database was produced. The data was generated using GAN, a Deep Convolutional Neural Network (DCNN) technique to populate the dataset. The proposed DCNN-BCPA approach findings were merged with current machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), k-Nearest Neighbours (k-NN), Decision Trees (DT), Support Vector Machine (SVM), logistic regression (LR). The model’s accuracy, precision, recall, f-score, and RMSE were measured using the following metrics: 92.675%, 96.298%, 90.536%, 96.637%, and 45.623%. This study suggests that harmonizing landslide data may have a substantial impact on the predictive capabilities of machine learning models.

Regional estimates of gross primary production applying the Process-Based Model 3D-CMCC-FEM vs. Remote-Sensing multiple datasets

ABSTRACT Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of carbon fluxes and stocks in forests. Yet, their predictive capacity should be demonstrated not only at the stand-level but also in the context of broad spatial and temporal heterogeneity. We apply a stand scale PBFM (3D-CMCC-FEM) in a spatially explicit manner at 1 km resolution in southern Italy. We developed a methodology to initialize the model that includes information derived from the integration of Remote Sensing (RS) and the National Forest Inventory (NFI) data and regional forest maps to characterize structural features of the main forest species. Gross primary production (GPP) is simulated over 2005–2019 period and the model predictive capability of the model in simulating GPP is evaluated both aggregated as at species-level through multiple independent data sources based on different nature RS-based products. We show that the model is able to reproduce most of the spatial (~2800 km2) and temporal (32 years in total) patterns of the observed GPP at both seasonal, annual and interannual time scales, even at the species-level. These promising results open the possibility of confindently applying the 3D-CMCC-FEM to investigate the forests’ behaviour under climate and environmental variability over large areas across highly variable ecological and bio-geographical heterogeneity of the Mediterranean region.

Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling.

The manufacturing processes and design of metal and alloy products can be performed over a wide range of strain rates and temperatures. To design and optimize these processes using computational mechanics tools, the selection and calibration of the constitutive models is critical. In the case of hazardous and explosive impact loads, it is not always possible to test material properties. For this purpose, this paper assesses the efficiency and the accuracy of different architectures of ANNs for the identification of the Johnson-Cook material model parameters. The implemented computational tool of an ANN-based parameter identification strategy provides adequate results in a range of strain rates required for general manufacturing and product design applications. Four ANN architectures are studied to find the most suitable configuration for a reduced amount of experimental data, particularly for cases where high-impact testing is constrained. The different ANN structures are evaluated based on the model's predictive capability, revealing that the perceptron-based network of 66 inputs and one hidden layer of 30 neurons provides the highest prediction accuracy of the effective flow stress-strain behavior of Ti64 alloy and three virtual materials.

Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network and 18F-FDG PET/CT Radiomics.

The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein-protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p < 2.75 × 10-12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95% CI = 0.8401-0.8689, p-value < 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value < 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation.

Essential Structural Profile of Novel Adenosine Derivatives as Antiplatelet Aggregation Inhibitors based on 3D-QSAR Analysis

Aims: In this research, 3D-QSAR evaluation on a set of fresh purinoid compounds that we produced was conducted. This analysis aims to illustrate the correlation between the structure of purine and its ability to prevent platelet aggregation. Our findings could pave the way to discovering novel antithrombotic medications. background: Cardiovascular disease caused by platelet aggregation is a serious threat to human health. Purine derivatives are important molecules with antiplatelet aggregation activity. Background: The incidence of cardiovascular disease triggered by the clumping of platelets poses a significant danger to human health. Purine derivatives are important molecules with antiplatelet aggregation activity. Objective: The objectives of this research are to establish the correlation between the structure of purine and its ability to prevent platelet aggregation. Such a correlation could aid in the development of innovative antithrombotic medications. method: In this study, 3D-QSAR analysis was performed on a series of novel purine derivatives synthesized by us based on comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). Methods: In this study, 3D-QSAR investigation on a collection of 75 new purine derivatives, which we synthesized, was conducted, utilizing Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). result: Significant correlation coefficients (CoMFA, q2=0.843, r2=0.930, F value=266.755, SEE=0.165; CoMSIA, q2=0.869, r2=0.918, F value=222.571, SEE=0.179) were obtained, and the model prediction ability was validated using the test set. Results: Significant correlation coefficients (CoMFA, q2= 0.843, r2= 0.930, F value= 266.755, SEE= 0.165; CoMSIA, q2= 0.869, r2= 0.918, F value= 222.571, SEE= 0.179) were obtained, and assessed the model's predictive capabilities by validating it with the test set. conclusion: The results suggest that it is beneficial to introduce a group of appropriate size at position C-2 of the purine ring, and that an excessively large group is disadvantageous; a bulky substituent group cannot be directly attached at the C-6 position of the purine ring, and the attachment of a group with low electron cloud density increases the activity, and the attachment of a bulky group at the C-5' of the sugar ring is beneficial, and the presence of hydrogen bond receptors in this region also increases the activity. Conclusion: Our findings indicate that the introduction of an appropriately sized structure at position 2 of the compound yields significant benefits. Conversely, the attachment of an excessively large group is detrimental. Direct attachment of a bulky substituent at C-6 of the compound is not feasible, and its activity increases when the structure with low electron cloud density is added. Moreover, the presence of a voluminous functional group at the 5' position of the compound is advantageous, and its activity will be further increased by the presence of hydrogen bond receptors in this region. These discoveries furnish significant comprehension for the formation of innovative structures with heightened efficacy.

Non-invasive characterization of color variation in plant-based meat burgers using portable hyperspectral imaging device and multivariate image analysis

The excessive color variation in plant-based meat burgers (PB) during storage, arising from the interplay of multiple ingredients, suggests the acute need for real-time color monitoring techniques. This study reports the use of a smart-portable hyperspectral imaging device in the visible-near-infrared coupled with multivariate data and image analysis for the prediction of color in PB samples of different formulations during storage. Principal Components Analysis (PCA) and Partial Least Squares Regression (PLSR) were applied to explore the spectral features and predict the CIE - L*, a*, b*. PCA on spectral data showed successful spatial separation of PB samples based on color stability. The PLSR models in the full spectral range possessed good prediction accuracies for L* (R2P = 0.88, RMSEP = 0.94), a* (R2P = 0.98, RMSEP = 0.15), b* (R2P = 0.92, RMSEP = 0.31). Prediction capabilities of simplified PLSR models were improved for a* (R2P = 0.99, RMSEP = 0.09) with B-iPLS, b* (R2P = 0.95, RMSEP = 0.23) with B-iPLS. Pixel-by-pixel prediction maps unveiled the pattern of color deterioration concerning individual base ingredients in the PB samples. The results unravel the feasibility of developing a real-time multi-spectral technique for quantification and visualization of color evolution in plant-based meat analogs.

NO measurements in high temperature hydrogen flames: The crucial role of the hydrogen oxidation chemistry for accurate NO predictions

The current work investigates the formation of Nitric Oxide (NO) in hydrogen–air flames, over a wide range of flame temperatures. The use of hydrogen allows improved focus on the thermal-NO pathway by removing the complexity introduced by the prompt-NO pathway, which has been shown to be an important contributor to inaccurate predictions of absolute post-flame NO concentrations in hydrocarbon flames. This experimental study is conducted at atmospheric pressure using stoichiometric, premixed, laminar stagnation flames. Adiabatic flame temperatures ranging from 1600K to 2300K are achieved by varying the argon concentration in air. One-dimensional velocity, temperature, and NO concentration profiles are measured using non-intrusive laser diagnostics: Particle Tracking Velocimetry (PTV), NO multiline thermometry, and NO Laser Induced Fluorescence (NO-LIF), respectively. Results show that the experimental velocity profiles are incorrectly captured by the studied mechanisms, especially at low and high temperatures. This suggests that major inaccuracies are present in the hydrogen oxidation chemistry of the thermochemical models, regardless of their optimisation methodology. Furthermore, NO-LIF profiles show major discrepancies between all the studied mechanisms and the experiments, especially at elevated temperatures. The disagreement stems from an inaccurate description of the base chemistry of the models. These inaccuracies arise specifically from the description of the radical pool driving the flame behaviour and NO formation. This study demonstrates the need for model optimisation on experimental measurements using pure hydrogen 1D flames to obtain an accurate description of the hydrogen oxidation chemistry at play. This would lead to an improved description of the NOx sub-chemistry of any hydrogen, or hydrocarbon, combustion system.

An assessment of model capability on rapid intensification prediction of tropical cyclones in the South China Sea

The absence of robust quantitative evaluation methods has led to insufficient knowledge of models capability on the rapid intensification (RI) prediction of tropical cyclones (TCs). In this study, we propose a method and define some indicators aiming to evaluate model capability on predicting RI in a more accurate manner. An assessment of model predictive capability on RI of TCs based on 10 years of operational forecasts has been conducted using different RI criteria. The Tropical Regional Atmosphere Model for the South China Sea of China Meteorological Administration (CMA-TRAMS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) high resolution (HRES) operational forecasts were used. Analysis results revealed that the criterion of 6-hour sea level pressure (SLP) change is more appropriate to be used in RI operational forecast. The maximum lead time (MLT) of CMA-TRAMS and HRES was 72 and 78 h, and the maximum deviation of RI occurrence time of CMA-TRAMS and HRES was 48 h delay and 24 h ahead, respectively. Overall results suggest that the model predictive capability of RI is currently limited, and both models have inadequate capability in providing sufficient heat and energy to support RI in the long run. A tendency of CMA-TRAMS to have a lag in RI occurrence time was also demonstrated due to an air-sea interaction lag resulting from the fixed skin sea surface temperature used. Results of the present study provide insights and could be the basis for future efforts on improving parametrization schemes for properly describing RI process of TCs.

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.

Aging prediction in single based propellants using hybrid strategy of machine learning and genetic algorithm

This study proposes novel approach that combines optimization and machine learning to predict the aging of single-base propellants in alignment with the high-performance liquid chromatography (HPLC). To obtain aging and reduce HPLC experimentation, the proposed algorithm provides an efficient procedure. A hybrid strategy of genetic algorithm and ML models including support vector machine, ensemble trees, Gaussian process, and regression tree is combined to optimize SBPs aging. The correlation map provides an interdependence table of aging with initial composition, caliber, and environmental factors. The coefficient of determination and RMSE will determine predictive capabilities of ML models. ET-GA is an optimum-performing ML model with a 0.89 coefficient of determination. Partial dependence plots give an overview of the impact on aging by several variables with maximum impact by temperature, humidity, and diphenylamine. ET-GA shows 95% agreement with experimental data. This results in an economically viable interface that reduces experimentation and provides real-time aging prediction.

ABCNet: A comprehensive highway visibility prediction model based on attention, Bi-LSTM and CNN.

Meteorological disasters along highways significantly reduce road traffic efficiency. Low visibility caused by heavy fog is a severe meteorological disaster that greatly increases highway traffic accidents. Accurately predicting highway visibility and taking timely response measures can reduce the impact of meteorological disasters and improve traffic safety. We proposed an Attention-based BiLSTM-CNN (ABCNet) model, which synergized attention mechanisms with BiLSTM and CNN technologies to forecast atmospheric visibility more accurately. First, the Bi-LSTM module processed information both forward and backward, capturing intricate temporal dependencies in the model. Second, the multi-head attention mechanism following the Bi-LSTM distilled and prioritized salient features from multiple aspects of the sequence data. Third, the CNN module recognized local spatial features, and a singular attention mechanism refined the feature map after the CNN module, further enhancing the model's accuracy and predictive capability. Experiments showed that the model was accurate, effective, and significantly advanced compared to conventional models. It could fully extract the spatiotemporal characteristics of meteorological elements. The model was integrated into practical systems with positive results. Additionally, this study provides a self-collected meteorological dataset for highways in high-altitude mountainous areas.

Design, 3D-QSAR, molecular docking, ADMET, molecular dynamics and MM-PBSA simulations for new anti-breast cancer agents

Breast cancer is the most frequent form of malignant tumor in women, and represents a major public health problem due to its high mortality rate. Although a multitude of therapeutic options exist for control of this disease, the emergence of resistance to current pharmaceutical treatments underscores the urgency of developing new anti- breast cancer drugs, with a focus on reducing the adverse effects associated with current therapeutic agents. The present study concerns a new series of (23) compounds based on 1,4-quinone and quinoline derivatives to design candidate drugs against breast cancer. For this purpose, integrated computational techniques were applied, including 3D-QSAR, molecular docking and molecular dynamics simulations (MD). CoMFA and CoMSIA were used to build a robust and highly reliable 3D-QSAR models. To validate the model's predictive capabilities, an external validation was carried out. The results of the best model (CoMSIA/SEA) revealed that electrostatic, steric and hydrogen bond acceptor fields had a significant effect on the anti-breast cancer activity of molecules studied. In addition, evaluation of ADMET properties determined whether these newly designed ligands were likely to be selected as drug-candidates. To confirm the binding stability of the selected ligands to aromatase (3S7S) and validate the molecular docking results, molecular dynamics simulations lasting 100 nanoseconds were performed by calculating RMSD, RMSF, RoG, H-bond, SASA and MM-PBSA parameters. As a result, only one designed compound (ligand 5) emerged as the most promising drug candidate for experimental in vitro and in vivo testing, due to its potential inhibition of breast cancer.

Arithmetic Optimization Algorithm with Adaptive Neuro-Fuzzy Interference System for Predicting Financial Crisis

Financial technology (Fintech) is paramount in driving advanced technologies, economies, society, and several other sectors. Smart Fintech is the new-generation Fintech, primarily stimulated and endowed by compuational technology. Smart Fintech syndicates DSAI and renovates economies and finance for dynamic, smart, customized, automated services and systems, economies and financial companies, and the industry. The strength and development of the country’s economies are assessed by the correct forecasting. Financial crisis prediction (FCP) has the substantial consequence on the economies. Previous studies mainly emphasise statistical, DL, and ML methodologies for predicting the financial well-being of the business. Therefore, this article develops a new Arithmetic Optimization Algorithm with Adaptive Neuro-Fuzzy Interference System (AOA-ANFIS) technique for Predicting Financial Crisis. The presented AOA-ANFIS technique aims to predict the presence of financial crises or not. The model incorporates three major elements: Arithmetic Optimization Algorithm (AOA) for feature selection, Adaptive Neuro-Fuzzy Inference System (ANFIS) as the classification algorithm, and Bat Optimization Algorithm (BOA) for parameter tuning. The AOA feature selection model effectively detects the important attributes from a large proportion of financial indicators, augmenting the model's prediction capability while decreasing computational difficulty. Subsequently, the ANFIS classifier exploits the features selected for capturing the intricate non-linear relations intrinsic in financial data, permitting accurate crisis calculation. Additionally, the BOA parameter tuning model augments the ANFIS model's parameters, ensuring robustness and optimum performance. Experimental outcomes on varied financial databases validate the higher efficiency of the AOA-ANFIS technique over underlying processes, demonstrating its effectiveness in forecasting financial crises with great reliability and precision.

The Traffic Congestion Prediction Using Machine Learning

Around the world, traffic congestion is a major issue that affects everyday commutes, economic activity, and the sustainability of the environment. The goal of this research project is to use machine learning approaches to address the problem of traffic congestion. Through the utilization of an extensive dataset that encompasses several spatiotemporal parameters, including date, time, weather, and holiday indicators, the research creates predictive models that enable precise forecasting of traffic congestion levels. In order to determine how well machine learning predicts congestion dynamics, the research uses multiple regression methods, such as MLP Regressor, Stacking Regressor, and SVR. Historical traffic volume data is used to train and assess the models, making it possible to identify the main variables impacting patterns of congestion. The findings demonstrate how weather, time of day, and holidays all have a major impact on traffic congestion. Transportation authorities and urban planners may enhance overall urban mobility, optimise infrastructure utilisation, and regulate traffic flows proactively by leveraging the predictive capabilities of machine learning models. This work advances intelligent transport systems by offering a strong framework for anticipating and controlling traffic congestion. The results highlight the significance of utilising data-driven methodologies to tackle intricate urban issues, ultimately cultivating more effective and sustainable urban settings.

Composition design and optimization of Fe-C-Mn-Al steel based on machine learning.

The purpose of this study is to explore the composition space of Fe-C-Mn-Al steel using machine learning in order to identify materials with high-strength mechanical properties. A dataset of 580 steel samples was collected from the literature, each containing information on elemental composition, heat treatment processes, specimen dimensions, and mechanical properties (ultimate tensile strength and total elongation). Eight common machine learning models were constructed to predict the ultimate tensile strength (UTS) and total elongation (TE) of the steel. It was observed that the random forest regression (RFR) model, when trained, demonstrated superior overall performance in predicting UTS, with an average absolute error of approximately 90 MPa, and TE, with an average absolute error of about 7.9%. Validation of the model using eight sets of data that were not part of the dataset revealed that the predictions were in close agreement with experimental results, indicating the strong predictive capability of the RFR model. Subsequently, the trained RFR model was used to explore the composition space of Fe-C-Mn-Al steel, identifying the top fifty combinations of elemental compositions and heat treatment parameters, all of which manifest high ultimate tensile strength (UTS). This provides valuable research directions and methods to expedite the development of high-strength Fe-C-Mn-Al steel.

Prediction of groundwater contamination in an open landfill area using a novel hybrid clustering-based ai model

Groundwater is a vital resource that provides drinking water to over half of the world’s population. However, groundwater contamination has become a serious issue due to human activities such as industrialization, agriculture, and improper waste disposal. The impacts of groundwater contamination can be severe, including health risks, environmental damage, and economic losses. A list of unknown groundwater contamination sources has been developed for the Wang-Tien landfill using a groundwater modeling system (GMS). Further, AI-based models have been developed which accurately predict the contamination from the sources at this site. A serious complication with most previous studies using artificial neural networks (ANN) for contamination source identification has been the large size of the neural networks. We have designed the ANN models which use three different ways of presenting inputs that are categorized by hierarchical K-means clustering. Such an implementation reduces the overall complexity of the model along with high accuracy. The predictive capability of developed models was assessed using performance indices and compared with the ANN models. The results show that the hybrid model of hierarchical K-means clustering and ANN model (HCA-ANN) is a highly accurate model for identifying pollution sources in contaminated water.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches.

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

Creation of innovative production systems in the machinery repair direction

This article examines the impact of digitization on machine repair production through the use of mathematical models. The main research tasks include analyzing recent studies and publications, identifying practical aspects of implementing innovative digital approaches in the machine repair production of Ukraine, developing mathematical models that describe the influence of «Industry 4.0» integration on machine repair processes and their research, and providing recommendations for machine repair enterprises to use innovative approaches to achieve stability in a changing market environment. The main focus is on analyzing the combination of «Industry 4.0» concepts and flexible production in machine repair processes. The research demonstrates significant potential for improving efficiency and productivity in this field with the implementation of innovative digital technologies. Based on the modeling, the possibility of assessing the impact of innovativeness on results in the context of machine repair has been considered. It is important to consider that this is only the most likely scenario of innovativeness, and actual results may depend on various factors, such as the amount of resources invested in research and implementation of innovations, as well as the level of competence of the personnel and other factors. Taking into account these additional factors allows understanding which combination of factors has the greatest impact on the results. Recommendations for further research include the use of machine learning algorithms and artificial intelligence to increase the accuracy and predictive capabilities of mathematical models. It is also recommended to explore the possibilities of integrating various digital technologies to achieve a synergy effect. For successful implementation of recommendations, it is important to systematically monitor their implementation and make necessary adjustments. Overall, this study opens up prospects for using digital technologies to improve the efficiency of machine repair production. Implementing the recommendations can lead to a significant increase in productivity and quality of processes, thereby enhancing the competitiveness of this market sector.

Machine Learning-Powered Prediction of molecule Solubility: Paving the Way for environmental, and energy applications

Predicting aqueous solubility is pivotal for selecting materials in pharmaceuticals, environmental, and renewable energy fields. For instance, it plays a vital role in drug development and the design of chemical and synthetic routes. In the realm of Cheminformatics, the accurate prediction of molecule solubility is indispensable for drug discovery and development. Traditional methods often rely on labor-intensive experimental assays, presenting challenges in terms of time and cost. To address these limitations, this study leverages advanced machine learning techniques to predict molecule solubility with exceptional accuracy. Using the PyCaret library, a versatile low-code machine learning tool, we develop and evaluate a diverse set of linear regression models. Key performance metrics, including R², RMSLE, MAE, MSE, MAPE, and RMSE, are employed to assess model performance comprehensively. Through rigorous model comparison and evaluation, we identify the optimal model for predicting molecule solubility. Our findings not only demonstrate the efficacy of machine learning in Cheminformatics but also offer insights into the complex relationship between molecular features and solubility. This study contributes to the advancement of computational chemistry by bridging the gap between theory and practice. By elucidating the predictive capabilities of machine learning models, we pave the way for more efficient and cost-effective drug discovery processes.