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In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches. • We evaluated process-based (PB) and machine-learning (ML) models for SOC prediction. • Random forest & support vector machine predicted SOC superior to PB models. • Reduced data availability and quality significantly weakened ML model performance. • ML-assisted PB model ensembles strongly improved model performance. [ABSTRACT FROM AUTHOR]

• FFS maps were produced using CTREE, KNN, SVM, RF, GBM and XGBoost algorithms. • The performance of six machine learning techniques was evaluated. • This study will be the first to use the CTREE algorithm for FFS mapping. • XGBoost gave the best results compared to other models. • The most important factor for producing FFS maps in XGBoost models was annual temperature. Antalya is one of the provinces with the highest number of forest fires in Türkiye. In 2021, 278 forest fires occurred within the administrative boundaries of Antalya Regional Directorate of Forestry. The main objective of this study is to produce forest fire susceptibility (FFS) maps of Antalya province using machine learning (ML) models. In addition to forest fire inventory data, 16 factors, including topographic, environmental, meteorological, and human-driven, were used in the study. Inventory data included 2166 fire ignition points from the General Directorate of Forestry. 70 % of the inventory dataset was used to train the ML models and 30 % to validate the models. Overall accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) approaches were considered as validation metrics. FFS maps of Antalya were produced using stand-alone ML algorithms, K-Nearest Neighbors, and Support Vector Machines, as well as tree-based Conditional Inference Trees (CTREE), Random Forest (RF), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGBoost) algorithms. To the best of our knowledge, this is the first study using the CTREE algorithm for forest fire susceptibility mapping. Therefore, this study is important for the related literature. The validation results revealed that the XGBoost model outperformed other models. It is thought that the FFS map produced using the XGBoost model will guide forest engineers, wildland firefighting teams, and firefighters to minimize damage and control forest fires. [ABSTRACT FROM AUTHOR]

The estimation of material performance plays a crucial role in practical life, enabling the rational allocation of time and resources while enhancing the practical application of materials. Therefore, this study investigates the analytical and predictive capabilities of five machine learning (ML) algorithms (the Random Forest algorithm – RF, the Adaboost algorithm – AB, the Gradient Boosting algorithm – GB, the Extra Trees algorithm – ET and linear models – LM) for comprehensive performance parameters of wood ceramics (ammonia nitrogen adsorption quantity - Q , open porosity - P , burn-off , and hardness). In the analysis of model prediction capabilities, five key statistical parameters (Root Mean Square Error - RMSE , Mean Square Error - MSE , Coefficient of Correlation - R , Determination Coefficient - R 2 , and Mean Absolute Relative Error - MARE) were calculated. The results indicate the following: (1) Among various ML models, the GB model exhibits the most superior performance with an R 2 ≥ 0.9594. (2) However, in predicting the four performance parameters of wood ceramics, the AB model with an R 2 ranging from 0.0078 to 0.45, indicating notably poor predictive capabilities. Despite integrating the Support Vector Regression (SVR) module, no enhancement in predictive accuracy was observed. (3) While forecasting the four performance parameters of wood ceramics, the LM model demonstrates predictive accuracy relatively akin to that of the RF and ET models among the ML models. (4) Though the feature importance scores vary across distinct input variable models, there is a consistent trend of change. (5) Variables B and D exhibit some level of correlation with other variables. In summary, the results of this study suggest that, for the analysis and prediction of wood ceramic performance, the GB model among the five regression models demonstrates outstanding simulation performance. [ABSTRACT FROM AUTHOR]

• This study examined the genes related to PD pathogenesis by tissues and blood samples datasets. • WGCNA and protein–protein interaction (PPI) network were used to pinpoint hub genes. • Elevation of FOXO3 , HIST2H2BE , and HDAC1 expression associated with PD pathogenesis. Parkinson's disease (PD) represents a multifaceted neurological disorder whose genetic underpinnings warrant comprehensive investigation. This study focuses on identifying genes integral to PD pathogenesis and evaluating their diagnostic potential. Initially, we screened for differentially expressed genes (DEGs) between PD and control brain tissues within a dataset comprising larger number of specimens. Subsequently, these DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to discern relevant gene modules. Notably, the yellow module exhibited a significant correlation with PD pathogenesis. Hence, we conducted a detailed examination of the yellow module genes using a cytoscope-based approach to construct a protein–protein interaction (PPI) network, which facilitated the identification of central hub genes implicated in PD pathogenesis. Employing two machine learning techniques, including XGBoost and LASSO algorithms, along with logistic regression analysis, we refined our search to three pertinent hub genes: FOXO3 , HIST2H2BE , and HDAC1 , all of which demonstrated a substantial association with PD pathogenesis. To corroborate our findings, we analyzed two PD blood datasets and clinical plasma samples, confirming the elevated expression levels of these genes in PD patients. The association of the genes with PD, as reflected by the area under the curve (AUC) values for FOXO3 , HIST2H2BE , and HDAC1 , were moderate for each gene. Collectively, this research substantiates the heightened expression of FOXO3 , HIST2H2BE , and HDAC1 in both PD brain and blood samples, underscoring their pivotal contribution to the pathogenesis of PD. [ABSTRACT FROM AUTHOR]

Machine learning applications in the medical field has proliferated in the past decade. One of the main usages of machine learning in medical field is the detection of diseases from text and image data. In this work, a new hybrid multi-layer algorithm (MLHA) based on machine learning algorithms is proposed to predict diabetes. The algorithm consists of two layers. The first layer consists of three different machine learning algorithms; SVM, random forest and K-mean, that work in parallel on the dataset. The output of this layer is fed into the second layer that consists of only one algorithm; XGBoost, that is trained from the output data of the first layer. We trained the proposed model with two different datasets and its accuracy has been compared with three different common well-known algorithms; feed forward back propagation neural network, logistic regression and random forest. Our results show that the accuracy of MLHA reached 86.5% for the first dataset and approximately 84.6% for the second dataset. This reported accuracy outperformed all the classical models. [ABSTRACT FROM AUTHOR]

VEGF is prototypic marker of neovascularization, repeatedly proposed as intrinsic characteristic of peri-implantitis. This study aimed to assess pattern of VEGF in peri-implantitis, its correlation with titanium particles (TPs) and capacity as respective biomarker. Pathological specificity of VEGF was assessed in peri-implant granulations using immunohistochemistry, periodontal granulations represented Ti-free positive controls. VEGF was correlated to TPs, identified using scanning electron microscopy coupled with dispersive x-ray spectrometry. Diagnostic accuracy, sensitivity and specificity of VEGF were estimated in PICF specimens from peri-implantitis, peri-implant mucositis (PIM) and healthy peri-implant tissues (HI) using machine learning algorithms. Peri-implantitis exhibited rich neovascular network with expressed density in contact zones toward neutrophil infiltrates without specific pattern variations around TPs, identified in all peri-implantitis specimens (mean particle size 8.9 ± 24.8 µm2; Ti-mass (%) 0.380 ± 0.163). VEGF was significantly more expressed in peri-implantitis (47,065 ± 24.2) compared to periodontitis (31,14 ± 9.15), and positively correlated with its soluble concentrations in PICF (p = 0.01). VEGF was positively correlated to all clinical endpoints and significantly increased in peri-implantitis compared to both PIM and HI, but despite high specificity (96%), its overall diagnostic capacity was average. Two patient clusters were identified in peri-implantitis, one with 8-fold higher VEGF values compared to HI, and second with lower values comparable to PIM. VEGF accurately reflects neovascularization in peri-implantitis that was expressed in contact zones toward implant surface without specific histopathological patter variation around TPs. VEGF answered requests for biomarker of peri-implantitis but further research is necessary to decrypt its exact underlying cause. [Display omitted] • Neovascularization was expressed in contact zones toward implant surface. • VEGF is highly specific to peri-implantitis and accurately reflects neovascularization. • VEGF could not be directly associated to titanium particles. • In-depth research is required to decipher the specific VEGF pattern in peri-implantitis. [ABSTRACT FROM AUTHOR]

Unlike many previous studies that often focus on optimizing energy efficiency for buildings when detailed design drawings are available, this paper introduces a newly integrated model for energy-efficient building envelope design in the early stages (when detailed design drawings are not yet available). The newly developed model includes three main components: a simulation model, a predictive model, and an optimization model. The simulation model simulates the building's energy performance, considering different values for various envelope parameters. The predictive model employs machine learning algorithms, including RF, ANN, DNN, SVM, GENLIN, and GB (in which GB has been identified as the most suitable algorithm), boasting a very high R 2 (0.994) to assess energy consumption. The optimization model which uses AI optimization algorithms (such as NSGA II, DSE, and MOPSO) integrates the machine learning predictive model into the evaluation function during the evolutionary process, efficiently searching for Pareto-optimal building envelope solutions. Results show simultaneous savings in cost and energy, with savings of 7.52 % in cost and 8.48 % in energy, or 21.17 % in cost and 0.4 % in energy, for a case study in Vietnam. This model establishes a foundation by providing design solutions for stakeholders to assess, and can incorporate additional objectives at later stages. [ABSTRACT FROM AUTHOR]

In the field of computer network communication, there are various types of data, and these data have strong commercial value. Therefore, it is particularly important to apply them to relevant security monitoring. This article analyzes and studies the problems in remote detection of computer networks using machine learning algorithms. Firstly, three key technologies based on classifiers, feature extraction, and association rule recognition were introduced. Secondly, how to use machine learning methods to evaluate the performance indicators and operational effectiveness data statistics of computer network security monitoring systems was explained. Finally, this article tests the monitoring technology of the system, and the test results show that the security of the technology for data monitoring can reach over 94%. [ABSTRACT FROM AUTHOR]

Issues of providing mental health support to people with emerging or current mental health disorders are becoming a significant concern throughout the world. One of the biggest effects of digital psychiatry during COVID-19 is its capacity for early identification and forecasting of a person's mental health decline resulting in chronic mental health issues. Therefore, through this study aims at addressing the hological problems by identifying people who are more likely to acquire mental health issues induced by COVID-19 epidemic. To achieve this goal, this study includes 1) Rajyoga practitioners' perceptions of psychological effects, levels of anxiety, stress, and depression are compared to those of the non practitioners 2) Predictions of mental health disorders such as stress, anxiety and depression using machine learning algorithms using the online survey data collected from Rajyoga meditators and general the population. Decision tree, random forest, naive bayeBayespport vector machine and K nearest neighbor algorithms were used for the prediction as they have been shown to be more accurate for predicting psychological disorders. The support vector machine showed the highest accuracy among all other algorithms. The f1 score was also the highest for support vector machine. [ABSTRACT FROM AUTHOR]

The progression of the fuzzy domain is propelled synergistically by the evolution of science via academic papers and the advancement of technology through technical patents. However, the intrinsic interrelationships within the science and technology (S&T) interactive landscape of this domain remain largely unknown, as few studies attempt to explore these close interrelationships. To bridge this research gap, based on 161,060 academic publications and 36,693 granted patents, we measured their mutual attribution between S&T from the perspective of textual semantics and structure information on top of a quantitative network monitoring process. The results showed that the fuzzy domain lacked technology-oriented topics in terms of semantic relations and presented an interactive pattern where science was ahead of technology; that is, science worked as an engineer to expand the knowledge boundaries of the research field, and technology existed more as a beneficiary. This article presents cutting-edge insights into the intrinsic S&T interactions of the fuzzy domain in a visual and quantitative approach for the first time and aims to aid researchers in theoretically grasping the S&T innovation laws. [ABSTRACT FROM AUTHOR]

• Experimentation of AWJ milling of alumina ceramic using Box-Behnken RSM. • Data augmentation using a bootstrapping technique. • Hyperparameter tuning using a grid search with 5-fold cross validation. • Development of machine learning algorithms using the augmented data samples. • The methodology was tested on dataset of WEDM of metal matrix composites. In abrasive waterjet (AWJ) milling process, the ability to accurately predict outcomes such as surface roughness (R a) and depth of cut (DoC) holds immense significance for cost reduction, resource optimization, and minimizing material wastage. However, a major challenge lies in the limited availability of AWJ milling datasets, hindering the development of robust machine learning models. To address the predicament of outcome, the present study employs a bootstrapping data augmentation technique to expand small AWJ milling datasets. Augmented data is used to train machine learning models, and hyperparameter fine-tuning is conducted using grid search and 5-fold cross-validation. In the prediction of surface roughness (R a) for AWJ milling of alumina ceramic, the study finds that a Multi-Layer Perceptron (MLP) Regression model with a 4–100-100–1 architecture, 'relu' activation function, 'adam' solver, alpha value of 0.0001, and 'adaptive' learning rate outperforms the decision tree and random forest regression models. In the case of prediction of AWJ milling depth of cut (DoC) in alumina ceramic, the random forest regression model surpasses MLP and decision tree regression models. It employs hyperparameters including 100 estimators, 'gini' criterion, 'log2′ for maximum features, a maximum depth of 50, a minimum sample split of 10, a minimum sample leaf of 2, bootstrap as 'True', and a minimum impurity decrease of 0. The methodology followed to develop the machine learning models in this study is also tested in wire electrical discharge machining (WEDM) process in the machining aspects of metal matrix composites (MMC) for the prediction of surface roughness (R a). The experimental dataset was adopted from (Satishkumar et al., 2011). In case of prediction of surface roughness (R a) in WEDM, the random forest regression model outperforms MLP and decision tree regression models. These developed machine learning models hold the potential to greatly assist manufacturing engineers in selecting optimal input parameters for AWJ milling and WEDM, thereby reducing the wastage of machine resources. [ABSTRACT FROM AUTHOR]

• Multi-sensor input machine learning vastly outperforms single-senor input models. • Recursive feature elimination reduces machine learning model complexity. • Gaussian process regression model best estimates wheat nitrogen nutrition index. • Transfer component analysis was used to improve model transferability. The nitrogen nutrition index (NNI) has been extensively applied for the diagnosis of crop nitrogen status, providing insights into efficient nitrogen utilization and plant growth. In this study, we utilized a low-altitude unmanned aerial vehicle (UAV) platform, equipped with multispectral (MS), red–green–blue (RGB), and thermal infrared (TIR) cameras, to comprehensively capture wheat spectral information. The analysis of the relationship between NNI and relative yield revealed an initially linear relationship, which saturated for high NNI values. To enhance accuracy and minimize complexity, we employed a random forest (RF) – recursive feature elimination (RFE) method to select features as inputs for four machine learning (ML) models: back propagation neural network (BPNN), extreme learning machine (ELM), support vector regression (SVR), and Gaussian process regression (GPR). After feature selection, the prediction accuracies of single-sensor models were ranked as: MS > RGB > TIR. The R2 values for the four ML models were in the range of 0.54–0.75. Among multi-sensor combinations, the GPR with MS + RGB + TIR input features achieved the best results with R2 = 0.89 and RPD = 2.52. Further, the dataset was partitioned into six subsets based on location and cultivar variety to evaluate model transferability. The results showed that the transferability largely suffered during the bivariate conditions of different varieties at different locations; the transferability of the model was average improved by 11 % when GPR was combined with transfer component analysis (TCA). The accuracy and transferability of the NNI estimation models significantly improved, offering valuable guidance and methodological support for diagnosing the nitrogen nutrient status of wheat. [ABSTRACT FROM AUTHOR]

• DSPA and CNN are the best ML algorithms for sepsis prediction. • Imbalance-XGBoost outperforms other traditional ML algorithms. • DSPA can improve sepsis diagnosis and management. • Further research needed on poor-performing ML algorithms. With the integration of artificial intelligence and clinical medicine, machine learning (ML) algorithms have been applied to develop sepsis predictive models for sepsis management. The purpose is to systematically summarize existing evidence to determine the effectiveness of ML algorithms in sepsis. We conducted a systematic electronic search of databases including PubMed, Cochrane Library, Embase, and the Web of Science, and included all case-control and cohort studies using terms reflecting sepsis and ML up to September 2023. statistical software STATA was used for network meta -analysis, and QUADAS-2 tool was used to assess the certainty of evidence. The SUCRA results for sensitivity, specificity, and predictive accuracy of various models are as follows: DSPA (77.0 %) > Imbalance-XGBoost (72.9 %) > CNN + Bi-LSTM (69.7 %) > CNN (67.3 %) > LR (62.4 %) > Ensemble model (55.9 %) > RF (53.2 %) > ET (51.3 %) > XGBoost (49.1 %) > DNN (48.1 %) > MLP (47.5 %) > RBF (47.1 %) > KNN (45.8 %) > NB (33.3 %) > SVM (13.7 %) > Bi-LSTM (5.7 %); CNN (78.3 %) > CNN + Bi-LSTM (77.6 %) > DSPA (75.1 %) > ET (69 %) > Bi-LSTM (68.5 %) > MLP (51 %) > RBF (50.2 %) > KNN (47.3 %) > RF (47 %) > Ensemble Model (43.4 %) > XGBoost (38.1 %) > SVM (37.3 %) > NB (34.2 %) > DNN (31.1 %) > LR (30.4 %) > Imbalance-XGBoost (21.5 %); DSPA (85.9 %) > CNN + Bi-LSTM (82.6 %) > CNN (81.9 %) > Imbalance-XGBoost (76.8 %) > ET (67.8 %) > RF (51.1 %) > Ensemble model (47.7 %) > XGBoost (44.4 %) > LR (42.7 %) > MLP (38.1 %) > RBF (37.8 %) > KNN (37.3 %) > DNN(35.8 %) > Bi-LSTM(33.3 %) > NB(21.5 %) > SVM(15.3 %). DSPA and CNN may be the best ML algorithms for predicting sepsis. Imbalance-XGBoost algorithm outperformed other traditional ML algorithms in terms of sensitivity and predictive accuracy. This study has several implications for clinical practice and research, highlighting the potential benefits of using ML algorithms in sepsis management, particularly in improving sepsis detection and reducing mortality rates. Through our systematic review and network meta -analysis, we have provided a comprehensive and accurate assessment of the effectiveness of ML algorithms in sepsis prediction, emphasizing the need for further exploration and evaluation of these algorithms to advance sepsis management. [ABSTRACT FROM AUTHOR]

• Simulate and analyze the characteristics of LULC changes from 2000 to 2030. • UTFVI is used to describe the thermal environment of the PRD urban agglomeration. • Machine learning algorithms are applied to predict the seasonal LST, UTFVI, and UHI. • Explore the response between urban land expansion and the thermal environment. Land use practices in urban areas exert a profound influence on the urban thermal environment and the pursuit of sustainable development. This paper aims to investigate and forecast future changes in land use/land cover (LULC) and their response to seasonal variations in land surface temperatures (LST), the urban thermal field variance index (UTFVI), and the urban heat island effect (UHI). The artificial neural network based on cellular automata (ANN-CA) and the improved whale optimization based on long short-term memory (WOA-LSTM) algorithms are used to predict the LULC, UTFVI, and UHI characteristics in the Pearl River Delta (PRD) urban agglomeration. The results show that urban land will likely expand from 4335 km2 to 8292 km2 from 2000 to 2030. The LST continues to increase, and the maximum temperature in summer will likely increase to 44.6 °C in 2030. Without the intervention of effective cooling measures, the area with LST≥35 °C will likely increase to 4873 km2, and the proportion of areas with LST≥20 °C will likely reach 63.72 % in the winter of 2030. The strongest level of UTFVI expansion is significant in summer, and the area is likely to increase by 83.64 % in 2030. Urban land has the highest percentage in the high temperature region relative to other land use categories. Similar to the trend of LST changes, UHI is expected to notably increase by 2030, with minimum and maximum UHI values projected to rise. This study may provide new perspectives on thermal environment management and sustainable urban development. [ABSTRACT FROM AUTHOR]

[Display omitted] • The impact of the DTPA chelating agent on the rock/oil contact angle and rock surface charge was examined under varying concentrations, salinity, and potential-determining ions. • An experimental database comprising 240 sets of wettability data was acquired. • Two advanced machine-learning models, namely Random Forest and Boosted Regression Tree, were developed to accurately estimate the alteration in rock wettability. • Statistical analysis was conducted, demonstrating outstanding predictions for a wide range of input variables. Chelating agents have garnered increasing attention due to their unique capabilities in addressing challenges associated with chemical enhanced oil recovery (CEOR) materials. This research investigates the transformative potential of diethylenetriaminepentaacetic acid (DTPA) chelating agent in altering sandstone rock wettability. Wettability and zeta potential measurements were used to assess the impact of crucial factors including DTPA concentration, salinity, and potential determining ions (PDIs) on contact angle and rock surface charges. The findings revealed that there is an optimal level for DTPA concentration and salinity that can shift rock wettability from an oil-wet to strongly water-wet state. Moreover, while PDIs in their natural concentrations had no significant impact on DTPA performance, a threefold increase in their concentration was found to adversely affect DTPA efficacy. After conducting rock wettability tests, two advanced machine learning (ML) techniques, namely Random Forest (RF) and Boosted Regression Tree (BRT), were employed to assess rock/oil contact angle based on the aforementioned factors. Data were collected from 240 experimental datasets of contact angles. The findings indicated that both models performed well, but BRT exhibited exceptional performance. Sensitivity analysis, conducted using the Jackknife method, revealed the order of importance for parameters affecting wettability as follows: PDIs > salinity > time > DTPA concentration. [ABSTRACT FROM AUTHOR]

[Display omitted] • An automatic sorting machine is designed for grading pomegranates. • Machine vision is utilized in order to detect sunburn on pomegranates. • Three different image processing algorithms are proposed for feature extraction. • ANN and SVM classification models are used to sort the fruits automatically. • The experimental results of algorithms and overall sorting machine are provided. Sunburn is a widespread problem in pomegranate marketing. About 30 % of the harvested pomegranates suffer from sunburn. In this research, an automatic sorting machine was designed, developed, and tested to separate the sunburn pomegranates. To detect sunburn on pomegranates, three novel image processing algorithms were proposed: skin spot detection, brightness intensity distribution, and statistical surface parameter. Two supervised machine learning algorithms comprising artificial neural networks and support vector machines were utilized to classify the fruits. The proposed algorithms were used to control the developed sorting machine to separate the sunburn pomegranates from the healthy ones. Finally, the performance of the proposed algorithms was evaluated by the accuracy and response time of the sunburn sorting function of the developed machine. The brightness intensity distribution algorithm with the accuracy and response time of 98 % and 0.88 s, respectively, is suggested as the fastest and the most accurate algorithm. [ABSTRACT FROM AUTHOR]

• This study uses descriptive statistical features and five classifiers—DT, LDA, NB, SVM, and KNN—to recognise hand gestures. • Five classifiers were tested in two situations by dividing the dataset into 80%-20% and 50%-50% training and testing ratios. • The SVM classifies 8 hand gestures with the maximum accuracy at %97.1 for 80%-20% and %96.1 for 50%-50%. • Using seven performance metrics, the five classifiers showed strength and precision with good numerical values. • Our research outperformed nine previous studies using the same MMG signal, demonstrating superior classification accuracy. One of the most significant tools for Human–Computer Interaction (HCI) is body language, namely gestures. Therefore, Gesture recognition using Mechanomyography (MMG) for controlling prostheses and rehabilitation devices has been researched for years. The MMG is gauged as the mechanical vibrations engendered by the muscle in its activation. Hence, MMG data provides information regarding the mechanical activity of muscles. It is obtained from the limb and facilitates the comprehension of hand gestures. A MMG dataset named "MMG DATA" that has already been produced is utilized in this paper. To improve the accuracy of classification and the speed of processing, we have extracted the twelve descriptive statistical features of the eight game motions and their iterations contained in the "MMG DATA" for each subject. Five distinct machine learning algorithms—including Decision Tree (DT), Linear Discriminant Analysis (LDA), Naive Bayes (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN)—were used to categorize the hand gestures, with the results compared using several performance metrics. Several experiments have been carried out in order to validate the presented models. Additionally, two scenarios have been achieved by splitting the dataset into two different and far apart ratios: the first by randomly partitioning the dataset into 80% for training the models and 20% for testing them, while the second by evenly dividing the dataset into 50% for both training and testing phases. Drawing on the results of the performance metrics, it can be argued that this study effectively distinguishes and classifies the eight hand motions. Furthermore, it is worth noting that the results exhibit significant strength and stability, particularly when transitioning from the initial scenario to the subsequent scenario across all proposed models. Out of the classifiers that were chosen, the SVM emerged as the most successful, achieving a classification accuracy of 97.1%. [ABSTRACT FROM AUTHOR]

It is nowadays a hot topic to apply machine learning (ML) algorithms to illustrate water temperature dynamics in lentic waters. Due to the limited amount of in-situ temperature measurements from traditional sampling programmes, most of the related studies, however, restricted their analysis within the surface and rarely checked the results for whole depth profiles. Moreover, capability of such methods in projecting thermal dynamics under future climate conditions is even less illustrated. To fill the gap, we collected an unparalleled huge database including 9 million water temperature measurements, from 2013 to 2022, in Rappbode Reservoir, Germany as well as the corresponding climatic and hydrological observations, to train three commonly used ML models (Random Forest, XGBoost, and Long Short-Term Memory) and comprehensively evaluate their performance in reproducing thremal structure within the whole water column. We also systemically compared such simulation results with a well-established process-driven model (CE-QUAL-W2). Going beyond a pure reproduction of observatiosn, we further evaluated the ability of CE-QUAL-W2 and ML models in projecting thermal dynamics under RCP8.5 climate scenario up to 2100. Our results suggested three ML methods yielded high accuracy in capturing water temperature dynamics from the surface (epilimnion) to bottom (hypolimnion), and also satisfactorily reproduced temporal development of the stratification pattern, with the results corresponding well with those from CE-QUAL-W2. By contrast, the projections by ML models were rather insensitive to future climate warming under RCP8.5 comparing with those by CE-QUAL-W2, pointing to an important risk whenever ML-based models are extrapolated beyond their training data range. Supported by millions of in situ measurements, our research clearly illustrated the opportunities (limits) of ML models in projecting thermal dynamics in deep waters, and the conclusion also provides key guidance for scientists to select the appropriate tools in projecting water temperature, and other environmental factors, under changing climate. [Display omitted] • An unparalleled temperature database was collected to test performance of ML models. • ML models captured the seasonal temperature pattern from top to bottom water layers. • ML models accurately reproduced the temporal development of stratification structure. • Extrapolation in ML models should be cautious under climate warming conditions. [ABSTRACT FROM AUTHOR]

• Hybrid models successfully predicted key performance metrics for antibody production in mammalian cells. • Such models allow predictions under varying process conditions, without the need to re-estimate parameters. • Performance could be predicted for a different product under similar process conditions with reasonable accuracy. • Algorithm choice did not significantly impact model performance, but computational requirements differed. Among the many approaches to build predictive models for bioprocesses, hybrid models have gained increased attention due to their ability to combine data driven approaches with process knowledge. This study develops and compares hybrid models that predict the performance of a mammalian CHO cell culture producing a therapeutic product. Three machine learning algorithms, MLP, Random Forest and XGBoost regressors are compared to understand the effect of algorithm choice on the rates predicted and overall performance of the hybrid model. When combined in series with mechanistic equations, all three algorithms could predict next day Viable Cell Density, titer and the cumulative amount of glucose consumed with low error, although decision tree-based algorithm required less computational time for training. The models performed well using a dataset for a new product, without further model training. Retraining the model on a subset of data from the new product improved the prediction accuracy of VCD and titer. The potential for application and further model developments are also outlined. [Display omitted] [ABSTRACT FROM AUTHOR]

Landslides pose a significant threat to lives and socio-economic stability globally. In this study, we conducted a comprehensive landslide susceptibility mapping (LSM) in the western region of Cameroon, focusing on Bafoussam and its surroundings. The integration of multi-criteria decision analysis models (AHP), statistical methods (Information Value IV, Shannon Entropy SE, Frequency Ratio FR), and machine learning algorithms (Naïve Bayes and Logistic Regression) provided a robust assessment of landslide risk. Our analysis, based on 54 recorded landslides, carefully selected Landslide Conditioning Factors (LCF), and influential parameters such as lithology, slope, altitude, and precipitation, resulted in susceptibility maps categorizing the area into five risk zones. The Spatial distribution shows the centre and northwestern regions as high-risk areas. Model sensitivity differences underscore the need for tailored LSM selection. Validation using the Area Under Curve/Receiver Operating Characteristics (AUC/ROC) method indicates the LR and NB methods have the highest accuracy (82.7% and 84.1%, respectively). Comparative analysis of landslide events in Gouaché, Sichuan, Souk Ahras, and Kekem reveals correlations between heavy rainfall and geological conditions. The study supplies valuable insights for decision-makers in landslide-prone areas, emphasizing the importance of integrating multiple methodologies for comprehensive risk assessment and management. [Display omitted] • A comprehensive landslide susceptibility mapping (LSM) was conducted in the west region of Cameroon, focusing on Bafoussam and its surroundings. • The study utilized a combination of Multi-Criteria Decision Analysis models, statistical methods, and machine learning algorithms for a robust assessment of landslide risk. • Susceptibility maps classified the area into five risk zones, with the centre and northwestern regions identified as high-risk areas. • Validation using the AUC/ROC method showed the Logistic Regression and Naïve Bayes methods had the highest accuracy (82.7% and 84.1%, respectively). • The study underscores the need for a tailored approach to LSM selection and the importance of integrating multiple methodologies for comprehensive risk assessment and management. [ABSTRACT FROM AUTHOR]

Continuous monitoring of wastewater treatment processes is key to mitigate the risk of inadequately treated wastewater on the environment and public health. However, effective control of wastewater treatment processes is challenging because of the numerous relevant variables and their complex physio-chemical-biological interdependence. Most published related studies focused on correlating the effluent concentration of chemical oxygen demand and/or suspended solids using only a limited number of wastewater influent variables. In addition, recent machine learning- (ML) based studies in wastewater treatment systems considered some individual classification algorithms rather than providing a comparison between different algorithm performances. In the current study, different algorithms were developed to categorize a range of wastewater treatment effluent characteristics based on multiple influent variables. To demonstrate their application, 23 ML classification algorithms were deployed on a wastewater treatment reactor-generated dataset and their performances were evaluated considering two different group of metrics related to the removal efficiency and effluent quality. The analysis results showed that, among all considered algorithms, the ensemble bagged trees algorithm had the most superior performance in terms of its overall classification accuracy. An interpretability analysis was further performed on the treatment process variables to detect the correlation between the input and output variables and to assess variable importance. In practice, the developed algorithms can facilitate optimal operation and effective management of wastewater treatment plants. ML algorithms also present efficient tools for rapidly classifying the effluent characteristics in lieu of typical sampling and laboratory analysis processes. [ABSTRACT FROM AUTHOR]

The study examines the role of data frequency and estimation methods in electricity price estimation by applying selected machine learning algorithms and time series econometric models. In this context, Turkey is selected as an emerging country example, seven explanatory variables including COVID-19 pandemic is considered, and daily and weekly data between February 20, 2019 and March 26, 2021 that includes pre-pandemic and pandemic periods are used. The empirical results show that (i) machine learning algorithms perform better than time series econometric models for both pre-pandemic and pandemic periods; (ii) high-frequency data increases the performance of estimation models; (iii) machine learning algorithms perform better with high-frequency (daily) data with regard to low-frequency (weekly) data; (iv) the pandemic causes an adverse effect on the performance of estimation models; (v) energy-related variables are more important than other variables although all are significant; (vi) the share of renewable sources in electricity production is the most important variable on the electricity prices in both periods and data types. Hence, the findings highlight the role of data frequency and method selection in electricity prices estimation. Moreover, policy implications are discussed. • The study examines role of data frequency and prediction methods in electricity prices. • This study applies machine learning algorithms and time series econometric models. • Machine learning algorithms perform better than time series econometric models. • High-frequency data increases performance of prediction models. • COVID-19 pandemic causes an adverse effect on performance of prediction models. [ABSTRACT FROM AUTHOR]

The present study provided the first-time comprehensive evaluation of 12 advanced statistical and machine learning (ML) algorithms for the Soil Moisture (SM) estimation from dual polarimetric Sentinel-1 radar backscatter. The ML algorithms namely support vector machine (SVM) with linear, polynomial, radial and sigmoid kernel, random forest (RF), multi-layer perceptron (MLP), radial basis function (RBF), Wang and Mendel's (WM), subtractive clustering (SBC), adaptive neuro fuzzy inference system (ANFIS), hybrid fuzzy interference system (HyFIS), and dynamic evolving neural fuzzy inference system (DENFIS) were used. Extensive field samplings were performed for collection of in-situ SM data and other parameters from the selected sites for seven different dates and at two different locations (Varanasi and Guntur District, India), concurrent to Sentinel-1 overpasses. The backscattering coefficients were considered as input variables and SM as output variable for the training, validation and testing of the ML algorithms. The site at Varanasi was used for the training, validation and testing of the models. On the other hand, the Guntur site was used as an independent site for checking the model performance, before finalizing the algorithms. The performances of different trained algorithms were evaluated in terms of correlation coefficient (r), root mean square error (RMSE) (in m3/m3) and bias (in m3/m3). The study identified the RF, SBC and ANFIS as the top three best performing models with comparable and promising SM estimation. In order to test the robustness of these best models (RF, SBC and ANFIS), further performance analysis was performed to the independent datasets of the Varanasi and Guntur test sites, which indicates that the performance of these three models were consistent and SBC can be recommended as the best among all for SM estimation. [ABSTRACT FROM AUTHOR]

The substantial amount of energy consumption in buildings and the associated adverse effects prompts the importance of understanding building energy efficiency. Developing an energy prediction model with high accuracy is considered one of the most effective approach to understanding building energy efficiency. Therefore, various studies have developed diverse models for predicting building energy consumption focused on the current building stock. However, to ensure future buildings are constructed to be more energy efficient, it is essential to consider energy efficiency at the design stage. Machine Learning (ML) algorithms are considered the most contemporary and best method for prediction, and these algorithms (such as Support Vector Machine (SVM) and Decision Tree (DT), among others) have gained much attention in the field of energy prediction. However, no study has explored the application of hyper parameter tuning and feature selection methods in developing a design stage Machine Learning (ML) energy predictive model. In this research, nine machine learning classification-based algorithms were compared for energy performance assessment at the design stage of residential buildings. Additionally, feature selection and hyper parameter tunning were implemented. The result shows that it is possible to develop a high performing ML model for building energy use prediction at the design stage. Furthermore, Gradient Boosting (GB) outperformed the other models with an accuracy of 0.67 for predicting building energy performance. • We explored the development of an efficient energy performance assessment model for building designers. • We developed nine models for assessing energy performance at the building design stage. • We investigated the effect of feature selection on model performance • We conducted parameter optimization to achieve the best performance [ABSTRACT FROM AUTHOR]

Diabetic vascular complications (DVC) are the main cause of death in diabetic patients. However, there is a lack of effective biomarkers or convenient methods for early diagnosis of DVC. In this study, the salivary glycopatterns from 130 of healthy volunteers (HV), 139 patients with type 2 diabetes mellitus (T2DM) and 167 patients with DVC were case-by-case analyzed by using lectin microarrays. Subsequently, diagnostic models were developed using logistic regression and machine learning algorithms based on the data of lectin microarrays in training set. The performance of diagnostic models was evaluated in an independent blind cohort. The results of lectin microarrays indicated that the glycopatterns identified by 16 lectins (e.g. BS-I, PWM and EEL) were significantly altered in DVC patients compared with patients with T2DM, which suggested the alterations in salivary glycopatterns could reflect onset of DVC. Notably, K-Nearest Neighbor (KNN) model exhibited better performance for distinguishing DVC (accuracy: 0.939) than other models in blind cohort. The integrated classifier, which combined three machine learning models, exhibited a higher overall accuracy (≥ 0.933) than other models in blind cohort. Our study provided a cost-effective and non-invasive method for auxiliary diagnosis DVC based on the combination of salivary glycopatterns and machine learning algorithms. • There were no convenient methods for early diagnosis of diabetic vascular complications (DVC) • Salivary glycopatterns showed significantly altered in DVC patients compared to type 2 diabetes mellitus patients • The K-Nearest Neighbor (KNN) model exhibited better performance for distinguishing DVC (accuracy: 0.939) than other models in blind cohort • The integrated classifier exhibited a higher overall accuracy (≥ 0.933) than other models in blind cohort [ABSTRACT FROM AUTHOR]

Blockchain technology, with its peer-to-peer trading feature, influences the management of energy consumption by offering the potential to transform transparency, efficiency, and sustainability within the energy sector. Nonetheless, there is a need to develop analytical decision-making models tailored for managing peer-to-peer energy transactions to improve energy resilience. Therefore, the purpose of this paper is to address the research question: How can energy distribution systems be protected via blockchain technology to enhance energy resilience and mitigate vulnerabilities to disruptions ? This paper employs a conceptual research model design and a mathematical decision-making model to address the research question by capturing the peer-to-peer trading capability of blockchain technology. The theory of planned behavior provides theoretical explanations for the proposed model. The sample includes longitudinal energy consumption data from 2015 to 2023 in Texas. The findings indicate a significant improvement in energy efficiency along with a considerable decrease in total electricity consumption. Post hoc analysis results reveal that the seasonal autoregressive integrated moving average algorithm is effective as a reliable input for the proposed mathematical model. The significant implications are to implement blockchain-based smart grids in which energy systems become more resilient to disruptions, as the peer-to-peer capability enables users to trade energy. The proposed model suggests that energy will be used more efficiently and effectively. This paper contributes to prior works by introducing a mathematical model that captures the trading behavior of energy consumers. Moreover, this paper proposes the SARIMA algorithm to predict energy demand. • A mathematical peer-to-peer trading model is presented to improve resilience. • A blockchain-based smart grid is presented for consumer energy consumption. • Five machine learning algorithms are presented in energy demand management. • Longitudinal energy consumption data of Texas is used in the model. • Theoretical support is provided using the theory of planned behavior. [ABSTRACT FROM AUTHOR]

Even though the World Health Organization declared the end of the pandemic, COVID-19 is still considered an endemic disease, affecting many people worldwide. As a result, hospitalization rates can continue to strain healthcare systems, particularly in low and middle-income countries. Thus, this article presents two predictive Machine Learning (ML) models to identify COVID-19 patients more likely to be hospitalized or face a higher mortality risk, aiming to assist physicians and healthcare administrators in making informed decisions. Previous research primarily focused on using expensive input data, such as clinical examinations and images, to implement ML models. In contrast, we implemented the models using information on patients' demographics, vaccination history, symptoms, and underlying health conditions. We trained the ML algorithms using data from the OpenDataSus database, focusing on patients undergoing hospital treatment in São Paulo, Brazil. Our study tested 14 different ML algorithms to determine their effectiveness: gradient boosting, light gradient boosting machine, logistic regression, linear discriminant analysis, ridge, AdaBoost, random forest, support vector machine, k-nearest neighbor, extra tree, decision tree, naive Bayes, quadratic discriminant analysis, and dummy. The results showed that the gradient-boosting model outperformed the others, achieving an accuracy rate of 83% and an Area Under the Curve (AUC) of 0.89 for predicting mortality risk. Moreover, the model achieved an accuracy rate of 71% and an AUC of 0.75 for predicting hospitalization risk. Our analysis evidence that including information on the number of vaccine doses can enhance the prediction results for hospitalization and mortality of patients. Based on these significant findings, we have developed a web application to assist healthcare professionals in identifying patients at a higher risk of severe outcomes from COVID-19. • Models for identifying COVID-19 patients at risk of mortality and hospitalization. • Gathered São Paulo hospital data for COVID-19 model training. • Evaluated 14 machine Learning algorithms; gradient boosting proved most effective. • Vaccine doses can improve the risk of mortality and hospitalization. • Web app aids healthcare professionals in identifying high-risk COVID-19 cases. [ABSTRACT FROM AUTHOR]

Soft 2D tactile sensors are becoming increasingly important in robotics and human-machine interaction. In this paper, we propose a new approach to develop a soft tactile sensor using fiber Bragg gratings (FBGs) and machine learning algorithms. The sensor consists of a layer of silicone elastomer with embedded 192 FBGs that can detect deformations caused by point impact. The FBG responses are then processed by machine learning algorithms to measure the position and the force of impacts with the mean absolute errors of 2.1 mm and 0.34 N, respectively. [Display omitted] • 2D flexible tactile sensor with array of 192 FBG sensors was shown. • Experimental calibration of 2D tactile sensors with assistance of machine learning algorithm was presented. • Prediction of pressing force with mean absolute error of 0.34 N was demonstrated. • Prediction of position of the impact with mean absolute error of 2.1 mm was achieved. [ABSTRACT FROM AUTHOR]

[Display omitted] • A dual-scale non-invasive technique for crack detection is demonstrated. • Resonance-based device is evaluated for its attenuation and frequency shift. • A comparison between sensors operating in microwave and radiowave regime is evaluated. • The microwave regime device show superior quality factor with ∼250 % improved sensitivity. • Out of multiple machine learning algorithms, k-NN algorithm proved to be most accurate with accuracy of around ∼99 %. Condition monitoring of structures built using metallic supports is a significant issue in structural engineering. Through this work, design, fabrication, and optimization of a dual-scale resonance-based microwave sensor is discussed for monitoring cracks developed over metallic surfaces. These sensors were initially simulated and later fabricated in micro-wave (4.59 GHz) and radio-wave region (418 MHz) for which the power attenuation and frequency shifts were observed. With the structure having crack depth of 2 mm, microwave sensor showed the power shift ∼24 %, while the radio-wave range sensors showed ∼9 %. The corresponding frequency shifts were 2.4 % and 0.85 %, respectively. The microwave sensor (power shift data) showed ∼280 % more sensitivity than the radio-wave sensor. By using dual scale, comprising of both frequency and power shifts, the microwave sensor classification was further improved to 99.72 %, as compared to their individual counterparts. A non-destructive crack detection system in microwave regime is hence demonstrated, which can be further explored for range of crack studies in metallic structures. [ABSTRACT FROM AUTHOR]

In recent years, there has been a notable surge in the prevalence of cardiovascular diseases (CVD), presenting a significant global public health challenge and a leading cause of mortality worldwide. Among the myriad complications stemming from CVD, heart failure stands out as a critical concern. Addressing heart failure through surgical means poses considerable challenges. The primary objective of this research is to identify pivotal attributes linked to heart failure and employ diverse machine learning methodologies to predict its occurrence, thereby enabling early estimation of mortality rates associated with heart failure. Leveraging a heart failure dataset, we conducted comprehensive model construction using pre-processing techniques such as feature scaling and correlation analysis. The Extreme Gradient Boosting (XGBoost) method was instrumental in evaluating feature relevance, leading to the selection of two distinct datasets: the whole dataset and the XGBoost dataset. In conclusion, we employed thirteen machine learning methods to predict the occurrence of death events within these datasets. Fine-tuning hyperparameters significantly enhanced model performance. Notably, our model demonstrated exceptional performance on this dataset, achieving the highest accuracy of 85.23% with Random Forest on the whole dataset and 86.36% with Flexible Discriminant Analysis on the XGBoost dataset. [Display omitted] • This research addresses the rising global prevalence of cardiovascular diseases, focussing heart failure. • The study aims to predict heart failure occurrence, enabling early estimation of mortalityrates. • Employing model, feature scaling and correlation analysis method to identify pivotal attributes linked to heart failure. [ABSTRACT FROM AUTHOR]

Whole-life carbon emissions (WLCE) studies are critical in assessing the environmental impact of buildings and promoting sustainable design practices. However, existing methods for estimating WLCE are time-consuming and data-intensive, limiting their usefulness in the early building design stages. In response to this, this research introduces a novel approach by harnessing various machine learning algorithms to predict WLCE and WLCE intensity (normalised by floor area) for buildings. To evaluate the suitability of machine learning algorithms, we conducted an experiment involving ten algorithms to build the prediction models. These models were trained using data from 150 typical residential properties in Cornwall, UK, along with 28 features obtained from a comprehensive survey, including floor area, heating type, and occupant characteristics. The ten algorithms include Multiple Linear Regression, and non-linear algorithms such as Decision Tree, Random Forest. Performance evaluation metrics, such as coefficient of determination (R2), mean absolute error (MAE), means squared error (MSE), root-mean-square error (RMSE), and elapsed time, were employed. Our research contributes to the field by showcasing the effectiveness of machine learning models in predicting building WLCE. We reveal that all the tested machine learning algorithms have the capability to predict WLCE and WLCE intensity, non-linear models outperform linear ones, and the Random Forest (RF) model demonstrates superior performance in terms of accuracy, stability, and efficiency. This research encourages the integration of life cycle studies into the early design stage, even within tight building design schedules, offering practical guidance to architects and designers. Furthermore, these results also benefit a wide range of stakeholders, not only the architects but also the engineers, policymakers, and life cycle assessment (LCA) researchers, contributing to the advancement of data-driven sustainability approaches within the building sector. • Novel method using machine learning algorithms for building WLCE prediction. • Promotes WLCE integration into the early stages of building design. • Leverage a comprehensive UK residential dataset for robust model development. [ABSTRACT FROM AUTHOR]

The transformative impact of Artificial Intelligence (AI) technologies, particularly Machine Learning (ML), on the analysis of spectroscopic data in water quality assessment cannot be overstated. We remark the ways in which AI and ML have revolutionized the analysis and prediction of water quality parameters. These technologies efficiently process spectral data from various sources, identify contaminants, and support early detection systems. However, AI tools have limitations, including the need for a large and diverse dataset for optimal performance, and some studies used small datasets, limiting the predictive power of the models. Open databases can aid in expanding AI applications in water quality control and treatment. The potential of AI and spectroscopic techniques reduce costs, promote environmentally sustainable water treatment, and enhance water and environmental quality. Finally, we emphasize the need for legislative changes and collaboration between organizations to harness the synergy between these technologies, and its vital water resources. • Evaluation of machine learning techniques as tools for water quality control. • Establishment of ANN as the most used ML algorithm in water quality control. • Highlighting the need to implement regulations that include AI-based solutions for water quality control and treatment. [ABSTRACT FROM AUTHOR]

• Vibration-based method: changes in structural frequencies utilized to estimate the residual fatigue life. • Comparison of theoretical and machine learning algorithms such as Support Vector Machines (SVM) and ANN. • Check prediction accuracy with different input schemes, including single-mode and multi-mode frequencies. Fiber reinforced polymer (FRP) structures may experience cumulative fatigue damage during their service life which can lead to structural failure. This paper focuses on predicting the residual fatigue life of FRP structures using vibration parameters. The relationship between residual fatigue life and natural frequencies is examined through modal testing and fatigue measurements on FRP beam specimens. Two prediction methods; semi-empirical models and machine learning (ML) algorithms, are utilized. The semi-empirical models are derived from existing "residual stiffness" models based on the relationship between bending stiffness and flexural frequencies. ML algorithms Support Vector Machine (SVM) and Artificial Neural Network (ANN), are developed for fatigue life prediction. Experimental validation is performed using measured frequencies during fatigue testing of FRP beams. The ML algorithms can use multimode frequencies unlike single mode of semi-empirical models. The verification results show that the ML algorithms can be used to predict the residual fatigue life with the selection of the appropriate mode of frequency. The results show that ML algorithms outperform single-mode frequency inputs, and the use of higher modes of measured frequencies improves the precision of fatigue life prediction. An inverse algorithm based on SVM exhibits higher prediction accuracy and stability, even with limited training samples. [ABSTRACT FROM AUTHOR]

This paper introduces a novel model-free approach to synthesize virtual sensors for the estimation of dynamical quantities that are unmeasurable at runtime but are available for design purposes on test benches. After collecting a dataset of measurements of such quantities, together with other variables that are also available during on-line operations, the virtual sensor is obtained using machine learning techniques by training a predictor whose inputs are the measured variables and the features extracted by a bank of linear observers fed with the same measures. The approach is applicable to infer the value of quantities such as physical states and other time-varying parameters that affect the dynamics of the system. The proposed virtual sensor architecture — whose structure can be related to the Multiple Model Adaptive Estimation framework — is conceived to keep computational and memory requirements as low as possible, so that it can be efficiently implemented in embedded hardware platforms. The effectiveness of the approach is shown in different numerical examples, involving the estimation of the scheduling parameter of a nonlinear parameter-varying system, the reconstruction of the mode of a switching linear system, and the estimation of the state of charge (SoC) of a lithium-ion battery. [ABSTRACT FROM AUTHOR]

The article presents the results of applying machine learning techniques to detect fraudulent banking transactions. The market of antifraud systems was studied. Ensemble methods for solving classification problem as well as dimensionality reduction techniques were examined. The proposed analysis procedure is based on the selection of the best machine learning model and the identification of the most significant features for detecting fraud. Results-based recommendations can be used in financial institutions as well as in other organizations, where it is required to identify and prevent entities' fraudulent actions that pose a threat to the functioning of business processes and electronic systems. The proposed fraud detection methodology was implemented on the cloud-based analytical platform Statistical Analysis System (SAS) Viya. [ABSTRACT FROM AUTHOR]

This research studies the implementation of artificial neural networks (ANN) in predicting the concentration of total suspended solids (TSS) for the Fei Tsui reservoir in Taiwan. The prediction model developed in this study is designed to be used for monitoring the water quality in the Fei Tsui reservoir. High concentrations of total suspended solids (TSS) have been a crucial problem in the Fei Tsui reservoir for decades. As the Fei Tsui reservoir is a primary water source for Taipei City, this issue impacts the drinking water supply for the city due to etherification problems in the reservoir. 10-year average monthly records and 13-year average annual records have been collected for 26 parameters and correlated with the TSS concentrations to determine the parameters that have a strong relationship with the TSS concentrations. The parameters that were shown to have a strong correlation with the TSS concentration are the trophic state index (TSI), nitrate (NO 3) concentration, total phosphorous (TP) concentration, iron concentration (IRON), and turbidity. Linear regression was used to develop the model that estimates the TSS concentration in the Fei Tsui Reservoir. The results show that model 3, a three-layer ANN model that uses three-input parameters namely NO 3 concentration , TP concentration, and turbidity, with five neurons, to predict the output parameter which is TSS concentration, produces the highest coefficient of determination (R2) and Willmott Index (WI), which are 0.9589 and 0.9933 respectively, and the lowest root mean square error, which is 0.4753. Based on these performance criteria, model 3 is concluded as the best model to predict TSS concentrations in this study. [ABSTRACT FROM AUTHOR]

Rainfall plays a main role in managing the water level in the reservoir. The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir. In this study, several models and methods were applied to predict the rainfall data in Tasik Kenyir, Terengganu. The comparative study was conducted focusing on developing and comparing several Machine Learning (ML) models, evaluating different scenarios and time horizon, and forecasting rainfall using two types of methods. Data involved for this research consist of taking the average rainfall from 10 stations around the study area using Thiessen polygon to weight the station area and projected rainfall. The forecasting model uses four different ML algorithms, which are Bayesian Linear Regression (BLR), Boosted Decision Tree Regression (BDTR), Decision Forest Regression (DFR) and Neural Network Regression (NNR). On the other hand, the rainfall was predicted on different time horizon by using different ML's algorithms which is method 1 (M1): Forecasting Rainfall Using Autocorrelation Function (ACF) and method 2 (M2): Forecasting Rainfall Using Projected Error. In M1, the best regression developed for ACF is BDTR since it has the highest coefficient of determination, R2, after tuning the hyperparameter. The results show coefficient between 0.5 and 0.9 with the highest of each scenarios for daily (0.9739693), weekly (0.989461), 10-days (0.9894429) and monthly (0.9998085). In M2, overall model performances show that normalization using LogNormal is preferably giving a good result of each categories except for 10-days with BDTR and DFR are the most acceptable result than NNR and BLR. It is concluded that, two different methods have been applied with different scenarios and different time horizons, and M1 shows a rather high accuracy than M2 using BDTR modeling. [ABSTRACT FROM AUTHOR]

U.S. background ozone is defined as ground-level ozone in the absence of any domestic anthropogenic emissions. It plays an important role in the U.S. regulatory framework, as it corresponds to the theoretical minimum ozone that can be achieved at a location. Although some studies estimated background ozone from observations, it cannot be measured directly, and therefore air quality models have been used to estimate its levels across different parts of the country. These estimates are uncertain given the inherent uncertainty in air quality modeling. Recently data fusion techniques have been used to improve those estimates using ground-level ozone observations and satellite data. Here, we used two adjustment methods to improve on air quality model-derived estimates of background ozone: a Multi-Variate Linear Regression (MVLR) model and a machine learning (ML) algorithm, called Random Forest (RF), with a focus on thirteen urban areas in the U.S. These adjustments regress observed ozone on the simulated background and anthropogenic ozone (by a regional air quality model) and their combinations with spatial and temporal variables. The RF-ML algorithm showed the biggest improvement in model performance, compared to MVLR and the base air quality model predictions. Applying the MVLR adjustment, the background ozone estimates increased in most locations. Furthermore, applying the RF ML model adjustment increased background ozone estimates even more, highlighting the enhanced contribution of background ozone in recent years that constitutes a larger portion of total ozone and exceeds NAAQS in some regions. Our results are consistent with other observational and modeling studies in that the highest background ozone levels were estimated in the western U.S. and over higher elevations. • We first estimated background ozone in the U.S. using air quality models. • We used data fusion methods and machine learning to adjust the background ozone. • Adjustment methods improved the air quality model performance for total ozone. • Applying adjustment methods increased background ozone estimates in most locations. • The highest background ozone levels were estimated in the western U.S. [ABSTRACT FROM AUTHOR]

Text normalization (TN) for text-to-speech (TTS) synthesizer is the transformation of non-standard words like times, ordinal numbers, equations, ranges, dates, etc. into standard words that have similarities with their pronunciations. An essential part of all TTS synthesizers is text normalization. Without text normalization, generated voice from the TTS synthesizer will be unintelligible. For the unsatisfactory performance of previous research, a text normalization method for the Bangla language is proposed in this paper. At first, we have produced a tokenized dataset with a semiotic class using regular expressions from a Bangla corpus. Then, each token has been trained using the XGBClassifier algorithm. After that, it identifies the semiotic class for each token in a new Bangla text corpus using the trained XGBClassifier model. Finally, it produces a normalized text for each token by calling the class function according to the predicted class. This text normalization method will help the Bangla TTS synthesizer in producing more intelligible voices. The token classification accuracy of this method is 99.997%. [ABSTRACT FROM AUTHOR]

• An enhanced estimation of temperature-dependent radiative properties of heterogenous porous media. • Using Monte Carlo ray tracing algorithm for ground truth data generation. • Developing expertly-designed engineered features for geometry abstraction. • Implementing artificial neural networks for quick and efficient predictions. The accurate and efficient prediction of radiative properties of materials plays a crucial role in numerous industrial and engineering applications. However, experimental measurement can be cumbersome, particularly for complex materials. As a result, numerical techniques such as Monte Carlo ray tracing (MCRT) have been developed to assess radiative properties. MCRT is a well-known tool for solving radiation heat transport (RHT). Despite its advantages, MCRT requires tracing a large bundle of light beams for convergency and veracity of outcomes, which necessitates further advancements in its employment. To this end, supervised learning algorithms can be used in conjunction with MCRT to provide a quick and accurate substitution for typical MCRT simulations. In this study, machine learning algorithms employing expertly designed features are used to evaluate the radiative properties of heterogeneous porous media, while considering the effects of conductive heat transfer. Directional features have been introduced into the sets of the features implemented in our previous study for the enhancement of predictions. The results show that the applied method can improve prediction accuracy compared to prior studies. Moreover, this approach is generalizable to other radiative heat transfer problems by extracting relevant characteristics in accordance with the involved physics. [ABSTRACT FROM AUTHOR]

This review provides an overview of recent advances in hydrothermal liquefaction (HTL) biocrude production processes using plastics as feedstock, seawater as the processing medium, and microwave irradiation as a process intensification method. Additionally, the review examines the application of aid-in investigation tools such as kinetics, machine learning, and feasibility analysis to HTL research. All these aspects have been underexplored in review literature compared to process optimization, biocrude upgrading, continuous HTL, and aqueous phase reutilization. The potential of HTL as an effective method for the depolymerization of plastics is initially evaluated. The ease of plastic depolymerization follows the order of polycarbonate (300 °C) > polystyrene (350 °C) > polyethylene = polypropylene (420 °C) > polyethylene terephthalate (>450 °C). Both synergism and antagonism are observed for co-HTL of plastics with biomass, ranging from −48.3% to 79.2%. Using seawater as an alternative HTL processing medium shows promising potential, while the effect of sea salts on biocrude yield/quality is still controversial especially when carbohydrate-rich feedstocks are utilized, necessitating more comprehensive examination. Microwave irradiation has been shown to increase biocrude yield from lipid, produce comparable yields from protein and lignin, and decrease yield from carbohydrate compared to conventional heating. As for the aid-in investigation tools, limited efforts have been made to apply kinetic modeling to the HTL of plastics, which could be particularly useful when synergism or antagonism is observed during co-HTL of plastics and biomass. Machine learning-enabled predictions of product yield and quality have been found to be more accurate than traditional mathematical models. Future research could focus on using machine learning algorithms to elucidate product formation mechanisms. The techno-economic and life cycle assessment reveal that the commercialization of HTL technology remains a distant prospect, further improvements in product yield, quality, and process energy efficiency are essential. Overall, this review offers augmented insights into HTL technology and facilitates the identification of novel opportunities, which is of value to promote the biocrude production. [Display omitted] • Plastics as feedstock, seawater as the processing medium, and microwave irradiation as a heating method. • Aid-in investigation tools such as kinetics, machine learning, and feasibility analysis to HTL research. • Augmented insights into HTL technology and the identification of novel opportunities. [ABSTRACT FROM AUTHOR]

• QSPR models for predicting critical temperature of binary mixtures are built. • Linear and non-linear methods are adopted for QSPR modeling. • Internal and external validations and application domain are conducted. • The R ext 2 and AARD of the best QSPR model are 0.98 and 1.27%, respectively. The critical temperature is an important parameter in the design and selection of binary organic mixtures. Rapid and accurate prediction has been a focus of research. Strong nonlinear relationships exist between molecular characteristics and critical temperature. Developing nonlinear models is an important measure to improve the prediction accuracy. In this paper, 56 different QSPR models are developed to predict the critical temperature using 14 types of mixture descriptors and four modeling methods (MLR, XGBoost, RBFNN and SVM). A dataset containing 2540 data points is adopted. Results show x 1 d 1 + x 2 d 2 is the optimum mixture descriptor type, and the accuracy of the nonlinear models is better than that of the linear models. The model combining x 1 d 1 + x 2 d 2 and SVM exhibits the best predictive power; the R ext 2 , RMSE ext , and AARD are 0.988, 10.057 K, and 1.27%, respectively. For this model, 78.98% of the data have an absolute deviation of less than 5 K, and the accuracy is better than that of existing QSPR models for critical temperature of mixtures. The application domain analysis shows the model has good performance for novel binary organic mixtures. In addition, compared with the empirical calculation methods for predicting the critical temperature, the results show the developed model has higher reliability. [ABSTRACT FROM AUTHOR]

• Diurnal LST variations across different block types were analyzed. • Performances of BRT and RF in exploring the diurnal impacts were compared. • Relative importance of urban landscapes on diurnal LST were determined. • Nonlinear patterns between key urban landscapes and diurnal LST were revealed. • Landscapes optimization strategies were proposed to develop block thermal comfort. Urban landscapes significantly affect land surface temperature (LST) and are considered crucial factors affecting urban heat island (UHI). The impacts of urban landscapes on LST have been extensively explored, mainly focusing on grid scale and the daytime. However, how 2D/3D urban landscapes affect diurnal LST at the block scale is unclear. Therefore, taking 1, 536 blocks (including low-rise blocks (LRB), middle-rise blocks (MRB), and high-rise blocks (HRB)) in Beijing as samples, the performances of boosted regression tree (BRT) and random forest (RF) were first evaluated, and the impacts of 2D/3D urban landscapes on diurnal LST across different block types were explored. The results showed that the mean LST was the highest in MRB (daytime) and HRB (nighttime). BRT performed better than RF in investigating diurnal impacts at the block scale. Vegetation and buildings are the domain factors influencing daytime and nighttime LST in LRB and MRB, while buildings are the domain factor in HRB except at 03:09 (impervious surface). The relationships between the key 2D/3D urban landscape metrics and block diurnal LST are nonlinear. The findings can serve as f basis for UHI mitigation and urban renewal strategies by urban planners to develop thermal comfort. [ABSTRACT FROM AUTHOR]

Many smart applications, including smart cities, healthcare, smart agriculture, manufacturing, etc., have widely adopted the Internet of Things (IoT). Smart Farming (SF) integrates a set of technologies, such as cloud and edge computing and greenhouse concepts, to improve agricultural processes. Due to its capacity to generate fresh agricultural products at an incredible pace of development and output, the greenhouse business has recently attracted the attention of the farming community. However, the cost of labor and energy consumption causes the greenhouse's production cost to rise by roughly 40%–50%. To avoid this, SF concepts have been introduced. Data management in SF has received a lot of attention in recent years due to its high potential for more accurate and cost-effective agriculture. Traditional client–server and cloud-based data management systems are plagued by single faults, data protection, centralized data management, and system vulnerabilities. In this paper, an IoT-based framework is proposed for SF to improve the productivity of crops. AI-based precision model is introduced in the framework, which can be integrated with remote sensor data for correct prediction of crop growth in smart agriculture. The result shows that the random forest model works better with 97% accuracy compared to Support Vector Classifier (SVC) have 93%, K-Nearest Neighbors (KNN) have 90%, and Logistic Regression 84%. This technology helps to reduce costs while also improving the overall quality of the agricultural process and product management with blockchain-based supply chain management to enhance farmers' economic backdrop. The blockchain performance is evaluated on gas consumption, execution time, throughput and latency. • IoT based Framework has been proposed for collection and processing of smart agricultural data. • Machine Learning models has been developed for effective monitoring system. • A blockchain network has been provided for end to end security in smart farming. [ABSTRACT FROM AUTHOR]

• The performance of five techniques for estimating lettuce LA was compared. • Artificial intelligence-based models could be a reliable alternative to conventional leaf area estimation methods. • The proposed image analysis algorithm performed best among other methods examined. • The ANFIS method was more accurate than the ANNs method. Artificial intelligence (AI) as a simulation approach is increasingly being used in precision agriculture. The current study evaluated the performance of five methods for estimating lettuce leaf area (LA), including simple linear regression (SLR), artificial neural networks (ANNs), adaptive neuro fuzzy inference system (ANFIS), support vector regression (SVR), and image analysis technique. The standardized Ranking Performance Index (sRPI) was used to determine the overall ranking of the simulation approaches. Image analysis and ANFIS methods were found to have superior performance to other methods evaluated, according to the evaluation criteria of R2 values of 0.98 and 0.92, and RRMSE values of 4.3 and 2.32, respectively. The SLR, on the other hand, was found to be the least ranked method. Overall, the results indicated that the image analysis method could be a cost-effective option, and the ANFIS method could serve as a non-destructive alternative to conventional LA determination methods. [ABSTRACT FROM AUTHOR]

• The developed numerical model can accurately simulate the gas-particle flow and decomposition process of magnesium nitrate in the pyrolysis furnace under multi-jet flow. • Coupling CFD and MLAs methods to predict the decomposition rate of magnesium nitrate in a pyrolysis furnace. • RFR algorithm has better performance in predicting decomposition rate compared to other machine learning algorithms. • The optimal combination is obtained by optimizing the selected parameters using a genetic algorithm. This paper investigates the effect of multi-jet arrays on the decomposition of magnesium nitrate in a pyrolysis furnace through computational fluid dynamics (CFD) and machine learning algorithms (MLAs). The effects of temperature, arrangement, cone angle of injections and distance between pyrolysis gas inlets on the decomposition rate were studied in the CFD method. Then, regression models for decomposition rates were built using four machine learning algorithms: Neural Networks (NN), Decision Tree Regressor (DTR), Random Forest Regression (RFR), and Polynomial Regression (PR). RFR was integrated with a genetic algorithm for parameter optimization since it was judged to have the best performance after comparison. Specifically, the arrangement of gas inlets mainly affects the residence time of the particles rather than the temperature uniformity. Increasing the temperature and cone angle both favor the decomposition of magnesium nitrate in the pyrolysis furnace. The interaction of gas-solid multiple jets prolongs the residence time of particles in the pyrolysis furnace thus increasing the decomposition rate when the value of d/D is taken around 0.625. The maximum decomposition rate of 99.67% is achieved when the parameters are set to arrangement 2, temperature = 897 °C, d/ D = 0.63, and cone angle = 73.8° [ABSTRACT FROM AUTHOR]

• Visibility forecast has been tackled by means of machine and deep learning methods. • Influence of reanalysis variables addiction as predictive variables has been assessed. • A feature selection algorithm has been used to select the optimal reanalysis variables. • Evolutionary optimization algorithms (DE and PSO) were employed. • Improvements of up to 17.3% with respect to the baseline prediction have been achieved. Low visibility events are a severe problem for road transport, causing accidents and major economic losses. Their accurate prediction may help prevent these problems. For that purpose, machine and deep learning techniques have been applied for fog prediction using in situ meteorological data and persistence variables as baseline predictors. These techniques have been evaluated for different prediction time-horizons: 1 h, 3 h and 6 h. The effect of including data extracted from ERA5 Reanalysis as predictive variables has been studied. A database, covering 23 months, has been used, which contains visibility and other meteorogical variables measured in Mondoñedo, Galicia, Spain. A 222000 km2 region around Mondoñedo has been delimited. Thus, a proposed iterative forward selection algorithm based on evolutionary algorithms has been applied to determine the optimal variables and nodes in the region for each regressor model. Both Differential Evolution and Particle Swarm Optimization have been used as optimization algorithms, and an improvement of up to 17.3% with respect to the baseline databases have been obtained. Finally, an analysis of the most frequently selected variables by the evolutionary algorithms has been conducted, leading us to conclude which variables and geographical nodes provide better information to the prediction models. [ABSTRACT FROM AUTHOR]

• PPBL's morphological hallmark is binucleated lymphocytes in peripheral blood. • High SDV and SDAL2 cell population data values imply morphological heterogeneity. • PPBL presented a "round bottom flask" shape scattergram in the DxH 800 analyzer. • Our ML model combines easily accessible laboratory data to detect PPBL and SMZL. The diagnosis of persistent polyclonal B-cell lymphocytosis (PPBL) is often challenging because of the lack of features and the overlap with the peripheral expression of splenic marginal zone lymphomas (SMZL). To obtain new clues for PPBL detection and diagnosis, all data provided by the DxH 800 analyzer (including scatter and cell population data (CPD)) was exploited and combined using a machine learning (ML) approach. A total 211 samples from 101 normal controls and 110 patients (PPBL and SMZL) were assessed. Age, gender, full blood count, CPD, scatter, flags and CellaVision differentials were also considered. A ML model was built for true classification purposes. PPBL and SMZL shared increased absolute lymphoid counts, atypical lymphoid flag presence and CPD values (8 out of 14). A typical "round-bottom-flask" shape scattergram was described for the first time for PPBL which was also present in 51.4% of SMZL cases. The developed ML model render a global classification accuracy of 93.4%, allowing the detection of all pathological cases, with mean misclassification rates of 12% among PPBL and SMZL. Our ML model represents a new unbiased tool than can be widely applied in the laboratory as an aid for detection of PPBL. [ABSTRACT FROM AUTHOR]

Stone dust induced risk and vulnerability in the developing nations is very thought-provoking and therefore is necessary to address scientifically to manage it. Identifying vulnerable areas using the robust method is preliminary and essential steps. The present study has intended to delineate Human health vulnerability models (HHVMs) using machine learning algorithms and justify whether ensemble prediction can provide improved results in stone quarry and crushing dominated Middle catchment of Dwarka river basin. Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Reduced Error Pruning (REP) Tree, Gaussian Process algorithms and ensemble prediction have been applied for HHV modelling. Total 6.9 to 7.37% in cluster 1, 9.11 to 9.6% in cluster 2, 19.63 to 26.79% and 5.34 to 12.85% areas in cluster 3 and 4 is found into very high human health vulnerable class as per the five applied models. REPTree model is found consistent for predicting vulnerability in case of all the clusters followed by GP model. As per the result of RMSE, the RF model is appeared as the most consistent model followed by REPTree. Some of the ensemble prediction models are potential for yielding improved result than individual algorithm. [ABSTRACT FROM AUTHOR]

Today, condition monitoring (CM) is unarguably the most important field in any industrial applications. CM of wind turbines (WT's) has in the past few years grown substantially. Although numerous initiatives to develop CM techniques and make operations more efficient were launched, most developed tools failed to respond on time to unpredictable events. One area that shows great potential in the battle against machine damages and their exploits is the diagnosis and prognosis of WT gears. In the world of big varying modulated data, analysis of health conditions of WT gears by traditional CM methods is no longer sufficient. Example for this is the high dimensionality and very extremely modulated vibration dataset, provided by Suzlon company. Suzlon unworkably attempted to online discriminate its machines using a set of well-known CM analysis methods. However, only visual inspection could identify the faulty WT gear. Hence, Suzlon flagged up a top priority need to identify more efficient online tools for improving CM processes. In the response to this essential need, the author employs Signal Intensity Estimator (SIE) method and some machine learning (ML) algorithms to analyse Suzlon dataset. A conclusion was reached that these techniques could successfully provide a reliable estimate of WT's conditions. • Undetectable faulty WT's gear due to very extremely modulation was reported. • Feasibility of SIE for tackling modulated data from WT gears was proven. • FFT, and MVF were successfully employed as a counterpart for SIE. • Proposed ML algorithms could accurately classify WT's and estimate RUL values. [ABSTRACT FROM AUTHOR]