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The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, the identification involved AE signals registered in laboratory conditions for eight basic classes of PDs that occur in paper-oil insulation systems of high-voltage power equipment. On the basis of acoustic signals emitted by PDs and by application of the frequency descriptor that took the form of a signal power density spectrum (PSD), the assessment involved the possibility of identifying individual types of PD by the analyzed classification algorithms. As part of the research, the results obtained with the use of five independent classification mechanisms were analyzed, namely: k-Nearest Neighbors method (kNN), Naive Bayes Classification, Support Vector Machine (SVM), Random Forests and Probabilistic Neural Network (PNN). The best results were achieved using the SVM classification tuned with polynomial core, which obtained 100% accuracy. Similar results were achieved with the kNN classifier. Random Forests and Naïve Bayes obtained high accuracy over 97%. Throughout the study, identification algorithms with the highest effectiveness in identifying specific forms of PD were established. [ABSTRACT FROM AUTHOR]

The identification of wetland vegetation is essential for environmental protection and management as well as for monitoring wetlands' health and assessing ecosystem services. However, some limitations on vegetation classification may be related to remote sensing technology, confusion between plant species, and challenges related to inadequate data accuracy. In this paper, vegetation classification in the Yancheng Coastal Wetlands is studied and evaluated from Sentinel-2 images based on a random forest algorithm. Based on consistent time series from remote sensing observations, the characteristic patterns of the Yancheng Coastal Wetlands were better captured. Firstly, the spectral features, vegetation indices, and phenological characteristics were extracted from remote sensing images, and classification products were obtained by constructing a dense time series using a dataset based on Sentinel-2 images in Google Earth Engine (GEE). Then, remote sensing classification products based on the random forest machine learning algorithm were obtained, with an overall accuracy of 95.64% and kappa coefficient of 0.94. Four indicators (POP, SOS, NDVIre, and B12) were the main contributors to the importance of the weight analysis for all features. Comparative experiments were conducted with different classification features. The results show that the method proposed in this paper has better classification. [ABSTRACT FROM AUTHOR]

Machine learning methods, such as support vector regression (SVR) and gradient boosting, have been introduced into the modeling of power amplifiers and achieved good results. Among various machine learning algorithms, XGBoost has been proven to obtain high-precision models faster with specific parameters. Hyperparameters have a significant impact on the model performance. A traditional grid search for hyperparameters is time-consuming and labor-intensive and may not find the optimal parameters. To solve the problem of parameter searching, improve modeling accuracy, and accelerate modeling speed, this paper proposes a PA modeling method based on CS-GA-XGBoost. The cuckoo search (CS)-genetic algorithm (GA) integrates GA's crossover operator into CS, making full use of the strong global search ability of CS and the fast rate of convergence of GA so that the improved CS-GA can expand the size of the bird nest population and reduce the scope of the search, with a better optimization ability and faster rate of convergence. This paper validates the effectiveness of the proposed modeling method by using measured input and output data of 2.5-GHz-GaN class-E PA under different temperatures (−40 °C, 25 °C, and 125 °C) as examples. The experimental results show that compared to XGBoost, GA-XGBoost, and CS-XGBoost, the proposed CS-GA-XGBoost can improve the modeling accuracy by one order of magnitude or more and shorten the modeling time by one order of magnitude or more. In addition, compared with classic machine learning algorithms, including gradient boosting, random forest, and SVR, the proposed CS-GA-XGBoost can improve modeling accuracy by three orders of magnitude or more and shorten modeling time by two orders of magnitude, demonstrating the superiority of the algorithm in terms of modeling accuracy and speed. The CS-GA-XGBoost modeling method is expected to be introduced into the modeling of other devices/circuits in the radio-frequency/microwave field and achieve good results. [ABSTRACT FROM AUTHOR]

The trend for listening to music online has greatly increased over the past decade due to the number of online musical tracks. The large music databases of music libraries that are provided by online music content distribution vendors make music streaming and downloading services more accessible to the end-user. It is essential to classify similar types of songs with an appropriate tag or index (genre) to present similar songs in a convenient way to the end-user. As the trend of online music listening continues to increase, developing multiple machine learning models to classify music genres has become a main area of research. In this research paper, a popular music dataset GTZAN which contains ten music genres is analysed to study various types of music features and audio signals. Multiple scalable machine learning algorithms supported by Apache Spark, including naïve Bayes, decision tree, logistic regression, and random forest, are investigated for the classification of music genres. The performance of these classifiers is compared, and the random forest performs as the best classifier for the classification of music genres. Apache Spark is used in this paper to reduce the computation time for machine learning predictions with no computational cost, as it focuses on parallel computation. The present work also demonstrates that the perfect combination of Apache Spark and machine learning algorithms reduces the scalability problem of the computation of machine learning predictions. Moreover, different hyperparameters of the random forest classifier are optimized to increase the performance efficiency of the classifier in the domain of music genre classification. The experimental outcome shows that the developed random forest classifier can establish a high level of performance accuracy, especially for the mislabelled, distorted GTZAN dataset. This classifier has outperformed other machine learning classifiers supported by Apache Spark in the present work. The random forest classifier manages to achieve 90% accuracy for music genre classification compared to other work in the same domain. [ABSTRACT FROM AUTHOR]

The rise of malware attacks presents a significant cyber-security challenge, with advanced techniques and offline command-and-control (C2) servers causing disruptions and financial losses. This paper proposes a methodology for dynamic malware analysis and classification using a malware Portable Executable (PE) file from the MalwareBazaar repository. It suggests effective strategies to mitigate the impact of evolving malware threats. For this purpose, a five-level approach for data management and experiments was utilised: (1) generation of a customised dataset by analysing a total of 582 malware and 438 goodware samples from Windows PE files; (2) feature extraction and feature scoring based on Chi2 and Gini importance; (3) empirical evaluation of six state-of-the-art baseline machine learning (ML) models, including Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), XGBoost (XGB), and K-Nearest Neighbour (KNN), with the curated dataset; (4) malware family classification using VirusTotal APIs; and, finally, (5) categorisation of 23 distinct APIs from 266 malware APIs. According to the results, Gini's method takes a holistic view of feature scoring, considering a wider range of API activities. The RF achieved the highest precision of 0.99, accuracy of 0.96, area under the curve (AUC) of 0.98, and F1-score of 0.96, with a 0.93 true-positive rate (TPR) and 0.0098 false-positive rate (FPR), among all applied ML models. The results show that Trojans (27%) and ransomware (22%) are the most risky among 11 malware families. Windows-based APIs (22%), the file system (12%), and registry manipulation (8.2%) showcased their importance in detecting malicious activity in API categorisation. This paper considers a dual approach for feature reduction and scoring, resulting in an improved F1-score (2%), and the inclusion of AUC and specificity metrics distinguishes it from existing research (Section Comparative Analysis with Existing Approaches). The newly generated dataset is publicly available in the GitHub repository (Data Availability Statement) to facilitate aspirant researchers' dynamic malware analysis. [ABSTRACT FROM AUTHOR]

Embedded system technologies are increasingly being incorporated into manufacturing, smart grid, industrial control systems, and transportation systems. However, the vast majority of today's embedded platforms lack the support of built-in security features which makes such systems highly vulnerable to a wide range of cyber-attacks. Specifically, they are vulnerable to malware injection code that targets the power distribution system of an ARM Cortex-M-based microcontroller chipset (ARM, Cambridge, UK). Through hardware exploitation of the clock-gating distribution system, an attacker is capable of disabling/activating various subsystems on the chip, compromising the reliability of the system during normal operation. This paper proposes the development of an Intrusion Detection System (IDS) capable of detecting clock-gating malware deployed on ARM Cortex-M-based embedded systems. To enhance the robustness and effectiveness of our approach, we fully implemented, tested, and compared six IDSs, each employing different methodologies. These include IDSs based on K-Nearest Classifier, Random Forest, Logistic Regression, Decision Tree, Naive Bayes, and Stochastic Gradient Descent. Each of these IDSs was designed to identify and categorize various variants of clock-gating malware deployed on the system. We have analyzed the performance of these IDSs in terms of detection accuracy against various types of clock-gating malware injection code. Power consumption data collected from the chipset during normal operation and malware code injection attacks were used for models' training and validation. Our simulation results showed that the proposed IDSs, particularly those based on K-Nearest Classifier and Logistic Regression, were capable of achieving high detection rates, with some reaching a detection rate of 0.99. These results underscore the effectiveness of our IDSs in protecting ARM Cortex-M-based embedded systems against clock-gating malware. [ABSTRACT FROM AUTHOR]

To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs the Bald Eagle Search (BES) optimization technique to fine-tune the Kernel Principal Component Analysis (KPCA) algorithm, enabling optimized dimensionality reduction. The processed data are then fed into a cost-sensitive Random Forest classifier for training, with subsequent model validation conducted on the reduced-dimension data. Experimental results demonstrate that compared to traditional Random Forest algorithms, the proposed method reduces the training time by 11.32 s and achieves a 5.59% increase in classification accuracy, an 11.7% improvement in specificity, and a 0.0558 increase in the G-mean value. These findings underscore the promising application potential and performance of this approach in the field of network intrusion detection. [ABSTRACT FROM AUTHOR]

This paper addresses the global surge in heart disease prevalence and its impact on public health, stressing the need for accurate predictive models. The timely identification of individuals at risk of developing cardiovascular ailments is paramount for implementing preventive measures and timely interventions. The World Health Organization (WHO) reports that cardiovascular diseases, responsible for an alarming 17.9 million annual fatalities, constitute a significant 31% of the global mortality rate. The intricate clinical landscape, characterized by inherent variability and a complex interplay of factors, poses challenges for accurately diagnosing the severity of cardiac conditions and predicting their progression. Consequently, early identification emerges as a pivotal factor in the successful treatment of heart-related ailments. This research presents a comprehensive framework for the prediction of cardiovascular diseases, leveraging advanced boosting techniques and machine learning methodologies, including Cat boost, Random Forest, Gradient boosting, Light GBM, and Ada boost. Focusing on "Early Heart Disease Prediction using Boosting Techniques", this paper aims to contribute to the development of robust models capable of reliably forecasting cardiovascular health risks. Model performance is rigorously assessed using a substantial dataset on heart illnesses from the UCI machine learning library. With 26 feature-based numerical and categorical variables, this dataset encompasses 8763 samples collected globally. The empirical findings highlight AdaBoost as the preeminent performer, achieving a notable accuracy of 95% and excelling in metrics such as negative predicted value (0.83), false positive rate (0.04), false negative rate (0.04), and false development rate (0.01). These results underscore AdaBoost's superiority in predictive accuracy and overall performance compared to alternative algorithms, contributing valuable insights to the field of cardiovascular health prediction. [ABSTRACT FROM AUTHOR]

Healthcare cost is an issue of concern right now. While many complex machine learning algorithms have been proposed to analyze healthcare cost and address the shortcomings of linear regression and reliance on expert analyses, these algorithms do not take into account whether each characteristic variable contained in the healthcare data has a positive effect on predicting healthcare cost. This paper uses hybrid machine learning algorithms to predict healthcare cost. First, network structure learning algorithms (a score-based algorithm, constraint-based algorithm, and hybrid algorithm) for a Conditional Gaussian Bayesian Network (CGBN) are used to learn the isolated characteristic variables in healthcare data without changing the data properties (i.e., discrete or continuous). Then, the isolated characteristic variables are removed from the original data and the remaining data used to train regression algorithms. Two public healthcare datasets are used to test the performance of the proposed hybrid machine learning algorithm model. Experiments show that when compared to popular single machine learning algorithms (Long Short Term Memory, Random Forest, etc.) the proposed scheme can obtain similar or higher prediction accuracy with a reduced amount of data. [ABSTRACT FROM AUTHOR]

Studying the impact of green and high-quality development is of great significance to the healthy growth and sustainable development of enterprises. This paper discusses the influencing factors of the green and high-quality development of enterprises from the perspective of ownership structure and innovation ability, aiming to clarify the impact mechanism of these influencing factors on the green development of enterprises, and combined with emerging machine learning technologies, to propose a novel and effective corporate green high-quality development using a regression prediction model for quality development. Linear regression and one-way ANOVA were used to analyze the influence of each variable on the green and high-quality development of the enterprise, and the weight proportions of each influencing factor under the linear model were obtained. Two machine learning models based on the random forest (RF) algorithm and support vector machine algorithm were established, and the random parameters in the two machine learning algorithms were optimized by a genetic algorithm (GA). The reliability and accuracy of machine learning models and multivariate linear models were compared. The results show that the GA–RF model has superior regression performance compared with other prediction models. This paper provides a convenient machine learning model, which can quickly and effectively predict the green and high-quality development of enterprises, and provide help for enterprise decision-making and government policy formulation. [ABSTRACT FROM AUTHOR]

Numerical modeling is increasingly used to analyze practical rock engineering problems. The geological strength index (GSI) is a critical input for many rock engineering problems. However, no available method allows the quantification of GSI input parameters, and engineers must consider a range of values. As projects progress, these ranges can be narrowed down. Machine learning (ML) algorithms have been coupled with numerical modeling to create surrogate models. The concept of surrogate models aligns well with the deductive nature of data availability in rock engineering projects. In this paper, we demonstrated the use of surrogate models to analyze two common rock slope stability problems: (1) determining the maximum stable depth of a vertical excavation and (2) determining the allowable angle of a slope with a fixed height. Compared with support vector machines and K-nearest algorithms, the random forest model performs best on a data set of 800 numerical models for the problems discussed in the paper. For all these models, regression-type models outperform classification models. Once the surrogate model is confirmed to preform accurately, instantaneous predictions of maximum excavation depth and slope angle can be achieved according to any range of input parameters. This capability is used to investigate the impact of narrowing GSI range estimation. [ABSTRACT FROM AUTHOR]

Due to the transition toward the Internet of Everything (IOE), the prediction of energy consumed by household appliances has become a progressively more difficult topic to model. Even with advancements in data analytics and machine learning, several challenges remain to be addressed. Therefore, providing highly accurate and optimized models has become the primary research goal of many studies. This paper analyzes appliance energy consumption through a variety of machine learning-based strategies. Utilizing data recorded from a single-family home, input variables comprised internal temperatures and humidities, lighting consumption, and outdoor conditions including wind speed, visibility, and pressure. Various models were trained and evaluated: (a) multiple linear regression, (b) support vector regression, (c) random forest, (d) gradient boosting, (e) xgboost, and (f) the extra trees regressor. Both feature engineering and hyperparameter tuning methodologies were applied to not only extend existing features but also create new ones that provided improved model performance across all metrics: root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and mean absolute percentage error (MAPE). The best model (extra trees) was able to explain 99% of the variance in the training set and 66% in the testing set when using all the predictors. The results were compared with those obtained using a similar methodology. The objective of performing these actions was to show a unique perspective in simulating building performance through data-driven models, identifying how to maximize predictive performance through the use of machine learning-based strategies, as well as understanding the potential benefits of utilizing different models. [ABSTRACT FROM AUTHOR]

The public's support for emergency infrastructure projects, which will affect the government's credibility, social stability, and development, is very important. However, there are few systematic research findings on public support for emergency infrastructure projects. In order to explore the factors influencing the public's support and the degree of influence of each factor on the public's support, this paper employs K-Nearest Neighbors (KNN), a learning curve with m-fold cross-validation, grid search, and random forest to study the public's support for emergency infrastructure projects and its influencing factors. In this paper, a prediction model of the public's support for emergency infrastructure projects is developed based on KNN from data drawn from a questionnaire survey of 445 local residents concerning Wuhan Leishenshan Hospital, China. Two optimization algorithms, the learning curve with m-fold cross-validation and the grid search algorithm, are proposed to optimize the key parameters of the KNN predictive model. Additionally, quantitative analysis is conducted by using the random forest algorithm to assess the importance of various factors influencing public support. The results show that the prediction accuracy and model stability of the KNN prediction model based on the grid search algorithm are better than those using a learning curve with m-fold cross-validation. Furthermore, the random forest algorithm quantitative analysis shows that the most important factor influencing the public's support is government attention. The conclusions drawn from this paper provide a theoretical reference and practical guidance for decision making and the sustainable development of emergency infrastructure projects in China. [ABSTRACT FROM AUTHOR]

With technological transformations such as big data, blockchain, artificial intelligence, and cloud computing, digital techniques are infiltrating the field of finance. Digital finance (DF) is a resource-saving and environmentally friendly innovative financial service. It shows great green attributes and can drive the flow of financial resources towards environmentally-friendly enterprises, thereby promoting green low-carbon circular development (GLCD). However, few studies have explored the coupling mechanism between DF and GLCD. To fill this gap, this paper explores the effect of DF on GLCD, and established a mediating effect model to investigate the mechanism of DF in promoting GLCD. Additionally, this paper established a random forest model and a CatBoost model based on machine learning to examine the relative importance of DF and the factors affecting GLCD. The results show that DF has significant positive effects on GLCD, and technological innovation plays a key role in the effect of DF on GLCD; meanwhile, the effect of DF on GLCD shows nonlinear features with an increasing "marginal effect"; moreover, both DF and conventional factors have significant impacts on GLCD. Our study highlights the effect of DF on GLCD and underscores the importance of developing policies for DF and GLCD. This study provides an empirical basis and path reference for DF to achieve "carbon peak, carbon neutralization" in China. [ABSTRACT FROM AUTHOR]

The engineering challenge of rolling bearing condition monitoring has led to a large number of method developments over the past few years. Most commonly, vibration measurement data are used for fault diagnosis using machine learning algorithms. In current research, purely data-driven deep learning methods are becoming increasingly popular, aiming for accurate predictions of bearing faults without requiring bearing-specific domain knowledge. Opposing this trend in popularity, the present paper takes a more traditional approach, incorporating domain knowledge by evaluating a variety of feature engineering methods in combination with a random forest classifier. For a comprehensive feature engineering study, a total of 42 mathematical feature formulas are combined with the preprocessing methods of envelope analysis, empirical mode decomposition, wavelet transforms, and frequency band separations. While each single processing method and feature formula is known from the literature, the presented paper contributes to the body of knowledge by investigating novel series connections of processing methods and feature formulas. Using the CWRU bearing fault data for performance evaluation, feature calculation based on the processing method of frequency band separation leads to particularly high prediction accuracies, while at the same time being very efficient in terms of low computational effort. Additionally, in comparison with deep learning approaches, the proposed feature engineering method provides excellent accuracies and enables explainability. [ABSTRACT FROM AUTHOR]

Studying the influencing factors of venture capital fund investment performance is crucial for the decision making of venture capital institutions. This paper explored the influencing factors of venture capital institutions from the perspective of startups, aiming to elucidate the mechanisms of these factors on the performance of venture capital funds and to propose a novel and effective predictive model of investment performance. Linear regression and one-way ANOVA were used to analyze the influence of each variable on investment performance, and the weight proportion of each influencing factor was obtained under the linear model. Two machine learning models, including the random forest algorithm and extreme learning machine algorithm, are established, and the particle swarm algorithm and machine learning algorithm were combined to optimize the random parameters in the two models. Compare the reliability and accuracy of machine learning models and multivariate linear regression models. The analysis results indicate that the PSO-ELM hybrid model has a better predictive performance than other prediction models. A convenient machine learning algorithm provided in this paper can quickly and effectively predict the investment performance of various investment portfolios and provide investors with decision-making assistance. [ABSTRACT FROM AUTHOR]

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory. [ABSTRACT FROM AUTHOR]

Social media platforms like Instagram have become a haven for online scams, employing various deceptive tactics to exploit unsuspecting users. This paper investigates advanced algorithmic approaches to combat this growing threat. We explore various machine learning models for scam profile detection on Instagram. Our methodology involves collecting a comprehensive dataset from a trusted source and meticulously preprocessing the data for analysis. We then evaluate the effectiveness of a suite of machine learning algorithms, including decision trees, logistic regression, SVMs, and other ensemble methods. Each model's performance is measured using established metrics like accuracy, precision, recall, and F1-scores. Our findings indicate that ensemble methods, particularly random forest, XGBoost, and gradient boosting, outperform other models, achieving accuracy of 90%. The insights garnered from this study contribute significantly to the body of knowledge in social media forensics, offering practical implications for the development of automated tools to combat online deception. [ABSTRACT FROM AUTHOR]

Motor power models are a key tool in robotics for modeling and simulations related to control and optimization. The authors collect the dataset of motor power using the ABB IRB 120 industrial robot. This paper applies a multilayer perceptron (MLP) model to the collected dataset. Before the training of MLP models, each of the variables in the dataset is evaluated using the random forest (RF) model, observing two metrics-mean decrease in impurity (MDI) and feature permutation score difference (FP). Pearson's correlation coefficient was also applied Based on the scores of these values, a total of 15 variables, mainly static variables connected with the position and orientation of the robot, are eliminated from the dataset. The scores demonstrate that while both MLPs achieve good scores, the model trained on the pruned dataset performs better. With the model trained on the pruned dataset achieving R ¯ 2 = 0.99924 , σ = 0.00007 and M A ¯ P E = 0.33589 , σ = 0.00955 , the model trained on the original, non-pruned, data achieves R ¯ 2 = 0.98796 , σ = 0.00081 and M A ¯ P E = 0.46895 , σ = 0.05636 . These scores show that by eliminating the variables with a low influence from the dataset, a higher scoring model is achieved, and the created model achieves a better generalization performance across five folds used for evaluation. [ABSTRACT FROM AUTHOR]

In this paper, we explore a novel model for pricing Chinese convertible bonds that seamlessly integrates machine learning techniques with traditional models. The least squares Monte Carlo (LSM) method is effective in handling multiple state variables and complex path dependencies through simple regression analysis. In our approach, we incorporate machine learning techniques, specifically support vector regression (SVR) and random forest (RF). By employing Bayesian optimization to fine-tune the random forest, we achieve improved predictive performance. This integration is designed to enhance the precision and predictive capabilities of convertible bond pricing. Through the use of simulated data and real data from the Chinese convertible bond market, the results demonstrate the superiority of our proposed model over the classic LSM, confirming its effectiveness. The development of a pricing model incorporating machine learning techniques proves particularly effective in addressing the complex pricing system of Chinese convertible bonds. Our study contributes to the body of knowledge on convertible bond pricing and further deepens the application of machine learning in the field in an integrated and supportive manner. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) equipped with RGB, multispectral, or thermal cameras have demonstrated their potential to provide high-resolution data before, during, and after wildfires and prescribed burns. Pre-burn point clouds generated through the photogrammetric processing of UAV images contain geometrical and spectral information of vegetation, while active fire imagery allows for deriving fire behavior metrics. This paper focuses on characterizing the relationship between the fire rate of spread (RoS) in prescribed burns and a set of independent geometrical, spectral, and neighborhood variables extracted from UAV-derived point clouds. For this purpose, different flights were performed before and during the prescribed burning in seven grasslands and open forest plots. Variables extracted from the point cloud were interpolated to a grid, which was sized according to the RoS semivariogram. Random Forest regressions were applied, obtaining up to 0.56 of R2 in the different plots studied. Geometric variables from the point clouds, such as planarity and the spectral normalized blue–red difference index (NBRDI), are related to fire RoS. In analyzing the results, the minimum value of the eigenentropy (Eigenentropy_MIN), the mean value of the planarity (Planarity_MEAN), and percentile 75 of the NBRDI (NBRDI_P75) obtained the highest feature importance. Plot-specific analyses unveiled distinct combinations of geometric and spectral features, although certain features, such as Planarity_MEAN and the mean value of the grid obtained from the standard deviation of the distance between points (Dist_std_MEAN), consistently held high importance across all plots. The relationships between pre-burning UAV data and fire RoS can complement meteorological and topographic variables, enhancing wildfire and prescribed burn models. [ABSTRACT FROM AUTHOR]

Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle's position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position. [ABSTRACT FROM AUTHOR]

This paper focuses on the emissions of the three most sold categories of light vehicles: sedans, SUVs, and pickups. The research is carried out through an innovative methodology based on GPS and machine learning in real driving conditions. For this purpose, driving data from the three best-selling vehicles in Ecuador are acquired using a data logger with GPS included, and emissions are measured using a PEMS in six RDE tests with two standardized routes for each vehicle. The data obtained on Route 1 are used to estimate the gears used during driving using the K-means algorithm and classification trees. Then, the relative importance of driving variables is estimated using random forest techniques, followed by the training of ANNs to estimate CO2, CO, NOX, and HC. The data generated on Route 2 are used to validate the obtained ANNs. These models are fed with a dataset generated from 324, 300, and 316 km of random driving for each type of vehicle. The results of the model were compared with the IVE model and an OBD-based model, showing similar results without the need to mount the PEMS on the vehicles for long test drives. The generated model is robust to different traffic conditions as a result of its training and validation using a large amount of data obtained under completely random driving conditions. [ABSTRACT FROM AUTHOR]

The prognosis of wind turbine failures in real operating conditions is a significant gap in the academic literature and is essential for achieving viable performance parameters for the operation and maintenance of these machines, especially those located offshore. This paper presents a framework for estimating the remaining useful life (RUL) of the main bearing using regression models fed operational data (temperature, wind speed, and the active power of the network) collected by a supervisory control and data acquisition (SCADA) system. The framework begins with a careful data filtering process, followed by creating a degradation profile based on identifying the behavior of temperature time series. It also uses a cross-validation strategy to mitigate data scarcity and increase model robustness by combining subsets of data from different available turbines. Support vector, gradient boosting, random forest, and extra trees models were created, which, in the tests, showed an average of 20 days in estimating the remaining useful life and presented mean absolute error (MAE) values of 0.047 and mean squared errors (MSE) of 0.012. As its main contributions, this work proposes (i) a robust and effective regression modeling method for estimating RUL based on temperature and (ii) an approach for dealing with a lack of data, a common problem in wind turbine operation. The results demonstrate the potential of using these forecasts to support the decision making of the teams responsible for operating and maintaining wind farms. [ABSTRACT FROM AUTHOR]

This study aims to forecast New York and Los Angeles gasoline spot prices on a daily frequency. The dataset includes gasoline prices and a big set of 128 other relevant variables spanning the period from 17 February 2004 to 26 March 2022. These variables were fed to three tree-based machine learning algorithms: decision trees, random forest, and XGBoost. Furthermore, a variable importance measure (VIM) technique was applied to identify and rank the most important explanatory variables. The optimal model, a trained random forest, achieves a mean absolute percent error (MAPE) in the out-of-sample of 3.23% for the New York and 3.78% for the Los Angeles gasoline spot prices. The first lag, AR (1), of gasoline is the most important variable in both markets; the top five variables are all energy-related. This paper can strengthen the understanding of price determinants and has the potential to inform strategic decisions and policy directions within the energy sector, making it a valuable asset for both industry practitioners and policymakers. [ABSTRACT FROM AUTHOR]

In order to solve the problem of the poor adaptability of the TBM digging process to changes in geological conditions, a new TBM digging model is proposed. An ensemble learning prediction model based on XGBoost, combined with Optuna for hyperparameter optimization, enables the real-time identification of surrounding rock grades. Firstly, an original dataset was established based on the TBM tunneling parameters under different surrounding rock grades based on the KS tunnel. Subsequently, the RF–RFECV was employed for feature selection and six features were selected as the optimal feature subset according to the importance measure of random forest features and used to construct the XGBoost identification model. Furthermore, the Optuna framework was utilized to optimize the hyperparameters of XGBoost and validated by applying the established TBM dataset of the KS Tunnel. In order to verify the applicability and efficiency of the proposed model in surrounding rock grade identification, the prediction results of five commonly used machine learning models, Optuna–XGBoost, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Decision Tree (DT), XGBoost, and PSO–XGBoost, were compared and analyzed. The main conclusions are as follows: the feature selection method based on RF–RFECV improved the accuracy by 8.26%. Among the optimal feature subset, T was the most essential feature for the model's input, while PR was the least important. The Optuna–XGBoost model proposed in this paper had higher accuracy (0.9833), precision (0.9803), recall (0.9813), and F1 score (0.9807) than other models and could be used as an effective means for the lithological identification of surrounding rock grade. [ABSTRACT FROM AUTHOR]

Mapping coastal regions is important for environmental assessment and for monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) methods present more advantageous solutions for pattern-finding tasks such as the automated detection of landscape patches in heterogeneous landscapes. This study aimed to discriminate landscape patterns along the eastern coasts of Mozambique using the ML modules of a Geographic Resources Analysis Support System (GRASS) GIS. The random forest (RF) algorithm of the module 'r.learn.train' was used to map the coastal landscapes of the eastern shoreline of the Bight of Sofala, using remote sensing (RS) data at multiple temporal scales. The dataset included Landsat 8-9 OLI/TIRS imagery collected in the dry period during 2015, 2018, and 2023, which enabled the evaluation of temporal dynamics. The supervised classification of RS rasters was supported by the Scikit-Learn ML package of Python embedded in the GRASS GIS. The Bight of Sofala is characterized by diverse marine ecosystems dominated by swamp wetlands and mangrove forests located in the mixed saline–fresh waters along the eastern coast of Mozambique. This paper demonstrates the advantages of using ML for RS data classification in the environmental monitoring of coastal areas. The integration of Earth Observation data, processed using a decision tree classifier by ML methods and land cover characteristics enabled the detection of recent changes in the coastal ecosystem of Mozambique, East Africa. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is crucial to preventing system failures and enhancing operational performance. Knowing the RUL of a battery enables one to perform preventative maintenance or replace the battery before its useful life expires, which is vital in safety-critical applications. The prediction of the RUL of Li-ion batteries plays a critical role in their optimal utilization throughout their lifetime and supporting sustainable practices. This paper conducts a comparative analysis to assess the effectiveness of multiple machine learning (ML) models in predicting the capacity fade and RUL of Li-ion batteries. Three case studies are analyzed to assess the performances of the state-of-the-art ML models, considering two distinct datasets. These case studies are conducted under various operating conditions such as temperature, C-rate, state of charge (SOC), and depth of discharge (DOD) of the batteries in Cases 1 and 2, and a different set of features and charging policies for the second dataset in Case 3. Meanwhile, diverse extracted features from the initial cycles of the second dataset are considered in Case 3 to predict the RUL of Li-ion batteries in all cycles. In addition, a multi-feature multi-target (MFMT) feature mapping is introduced to investigate the performance of the developed ML models in predicting the battery capacity fade and RUL in the entire life cycle. Multiple ML models that are developed for the comparison analysis in the proposed methodology include Random Forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), multi-layer perceptron (MLP), long short-term memory (LSTM), and attention-LSTM. Furthermore, hyperparameter tuning is applied to improve the performance of the XGBoost and LightGBM models. The results demonstrate that the extreme gradient boosting with hyperparameter tuning (XGBoost-HT) model outperforms the other ML models in terms of the root-mean-squared error (RMSE) and mean absolute percentage error (MAPE) of the battery capacity fade and RUL for all cycles. The obtained RMSE and MAPE values for XGBoost-HT in terms of cycle life are 69 cycles and 6.5%, respectively, for the third case. In addition, the XGBoost-HT model handles the MFMT feature mapping within an acceptable range of RMSE and MAPE, compared to the rest of the developed ML models and similar benchmarks. [ABSTRACT FROM AUTHOR]

The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing geopolymer concrete with a higher compressive strength challenging. In the performance prediction of geopolymer concrete compressive strength, machine learning models have the advantage of being more accurate and faster. However, only a single machine learning model is usually used at present, there are few applications of ensemble learning models, and model optimization processes is lacking. Therefore, this paper proposes to use the Firefly Algorithm (AF) as an optimization tool to perform hyperparameter tuning on Logistic Regression (LR), Multiple Logistic Regression (MLR), decision tree (DT), and Random Forest (RF) models. At the same time, the reliability and efficiency of four integrated learning models were analyzed. The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing ability. According to the experimental data, the RF-AF model had the lowest RMSE value. The RMSE value of the training set and test set were 4.0364 and 8.7202, respectively. The R value of the training set and test set were 0.9774 and 0.8915, respectively. Therefore, compared with the other three models, RF-AF has a stronger generalization ability and higher prediction accuracy. In addition, the molar concentration of NaOH was the most important influencing factors, and its influence was far greater than the other possible factors including NaOH content. Therefore, it is necessary to pay more attention to NaOH molarity when designing geopolymer concrete. [ABSTRACT FROM AUTHOR]

Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of operating cycles. Therefore, it is necessary to estimate the Remaining Useful Life (RUL) and the prediction of the Li-ion batteries' capacity to prevent the UAVs' loss of autonomy, which can cause accidents or material losses. In this paper, the authors propose a method of prediction of the RUL for Li-ion batteries using a data-driven approach. To maximize the performance of the process, the performance of three machine learning models, Support Vector Machine for Regression (SVMR), Multiple Linear Regression (MLR), and Random Forest (RF), were compared to estimate the RUL of Li-ion batteries. The method can be implemented within UAVs' Predictive Maintenance (PdM) systems. [ABSTRACT FROM AUTHOR]

In the modern times of technological development, it is important to select adequate methods to support various food and industrial problems, including innovative techniques with the help of artificial intelligence (AI). Effective analysis and the speed of algorithm implementation are key points in assessing the quality of food products. Non-invasive solutions are being sought to achieve high accuracy in the classification and evaluation of various food products. This paper presents various machine learning algorithm architectures to evaluate the efficiency of identifying blackcurrant powders (i.e., blackcurrant concentrate with a density of 67 °Brix and a color coefficient of 2.352 (E520/E420) in combination with the selected carrier) based on information encoded in microscopic images acquired via scanning electron microscopy (SEM). Recognition of blackcurrant powders was performed using texture feature extraction from images aided by the gray-level co-occurrence matrix (GLCM). It was evaluated for quality using individual single classifiers and a metaclassifier based on metrics such as accuracy, precision, recall, and F1-score. The research showed that the metaclassifier, as well as a single random forest (RF) classifier most effectively identified blackcurrant powders based on image texture features. This indicates that ensembles of classifiers in machine learning is an alternative approach to demonstrate better performance than the existing traditional solutions with single neural models. In the future, such solutions could be an important tool to support the assessment of the quality of food products in real time. Moreover, ensembles of classifiers can be used for faster analysis to determine the selection of an adequate machine learning algorithm for a given problem. [ABSTRACT FROM AUTHOR]

The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) attacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantity and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimization algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimization, RF-GA Optimization, and SVM-GA Optimization methods, employing Evolutionary Algorithms (EAs) Optimization with Tree-based Pipelines Optimization Tool (TPOT)-Genetic Programming. Datasets pertaining to DDoS attacks were utilized to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimized using EAs, achieving remarkable accuracy scores: 99.99% with the XGB-GA method, 99.50% with RF-GA, and 99.99% with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats. [ABSTRACT FROM AUTHOR]

Complex oil and gas two-phase flow exists within an aero-engines bearing cavity scavenge pipe, prone to lubricated self-ignition and coking. Lubricant system designers must be able to accurately identify and understand the flow state of the scavenge pipe. The prediction accuracy of previous models is insufficient to meet the more demanding needs. This paper establishes a visualized flow pattern identification test system for the scavenge pipe, with a test temperature of up to 370 k, using a high-speed camera to photograph four flow patterns, decomposing the pressure signals obtained from high-frequency dynamic pressure sensors using the ensemble empirical mode decomposition (EEMD) method, and then performing Hilbert transform, using the Hilbert spectrum to quantify the changes of amplitude and frequency with time, and establishing the energy and flow pattern correspondence analysis. Then the energy percentage of IMFs is used as the input of feature values, and the random forest algorithm machine learning is used for predictive classification. The experimental results show that the flow pattern recognition rate established in this paper can reach 98%, which can identify the two-phase flow pattern in the scavenge pipe more objectively and accurately. [ABSTRACT FROM AUTHOR]

The translation of traffic flow data into images for the purposes of classification in machine learning tasks has been extensively explored in recent years. However, the method of translation has a significant impact on the success of such attempts. In 2019, a method called DeepInsight was developed to translate genetic information into images. It was then adopted in 2021 for the purpose of translating network traffic into images, allowing the retention of semantic data about the relationships between features, in a model called MAGNETO. In this paper, we explore and extend this research, using the MAGNETO algorithm on three new intrusion detection datasets—CICDDoS2019, 5G-NIDD, and BOT-IoT—and also extend this method into the realm of multiclass classification tasks using first a One versus Rest model, followed by a full multiclass classification task, using multiple new classifiers for comparison against the CNNs implemented by the original MAGNETO model. We have also undertaken comparative experiments on the original MAGNETO datasets, CICIDS17, KDD99, and UNSW-NB15, as well as a comparison for other state-of-the-art models using the NSL-KDD dataset. The results show that the MAGNETO algorithm and the DeepInsight translation method, without the use of data augmentation, offer a significant boost to accuracy when classifying network traffic data. Our research also shows the effectiveness of Decision Tree and Random Forest classifiers on this type of data. Further research into the potential for real-time execution is needed to explore the possibilities for extending this method of translation into real-world scenarios. [ABSTRACT FROM AUTHOR]

Rampant terrorism poses a serious threat to the national security of many countries worldwide, particularly due to separatism and extreme nationalism. This paper focuses on the development and application of a temporal self-exciting point process model to the terror data of three countries: the US, Turkey, and the Philippines. To account for occurrences with the same time-stamp, this paper introduces the order mark and reward term in parameter selection. The reward term considers the triggering effect between events in the same time-stamp but different order. Additionally, this paper provides comparisons between the self-exciting models generated by day-based and month-based arrival times. Another highlight of this paper is the development of a model to predict the number of terror events using a combination of simulation and machine learning, specifically the random forest method, to achieve better predictions. This research offers an insightful approach to discover terror event patterns and forecast future occurrences of terror events, which may have practical application towards national security strategies. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the use of a Romanian data source, different classifiers, and text data augmentation techniques to implement a fake news detection system. The paper focusses on text data augmentation techniques to improve the efficiency of fake news detection tasks. This study provides two approaches for fake news detection based on content and context features found in the Factual.ro data set. For this purpose, we implemented two data augmentation techniques, Back Translation (BT) and Easy Data Augmentation (EDA), to improve the performance of the models. The results indicate that the implementation of the BT and EDA techniques successfully improved the performance of the classifiers used in our study. The results of our content-based approach show that an Extra Trees Classifier model is the most effective, whether data augmentation is used or not, as it produced the highest accuracy, precision, F1 score, and Kappa. The Random Forest Classifier with BT yielded the best results of the context-based experiment overall, with the highest accuracy, recall, F1 score, and Kappa. Furthermore, we found that BT and EDA led to an increase in the AUC scores of all models in both content-based and context-based data sets. [ABSTRACT FROM AUTHOR]

Malignant melanoma (MM) is the "great mime" of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a constant challenge, and when it is not diagnosed in a timely manner, it can even lead to death. In recent years, artificial intelligence has revolutionised much of what has been achieved in the biomedical field, and what once seemed distant is now almost incorporated into the diagnostic therapeutic flow chart. In this paper, we present the results of a machine learning approach that applies a fast random forest (FRF) algorithm to a cohort of naevoid melanomas in an attempt to understand if and how this approach could be incorporated into the business process modelling and notation (BPMN) approach. The FRF algorithm provides an innovative approach to formulating a clinical protocol oriented toward reducing the risk of NM misdiagnosis. The work provides the methodology to integrate FRF into a mapped clinical process. [ABSTRACT FROM AUTHOR]

Machine learning is the main technical means for lithofacies logging identification. As the main target of shale oil spatial distribution prediction, mud shale petrography is subjected to the constraints of stratigraphic inhomogeneity and logging information redundancy. Therefore, choosing the most applicable machine learning method for different geological characteristics and data situations is one of the key aspects of high-precision lithofacies identification. However, only a few studies have been conducted on the applicability of machine learning methods for mud shale petrography. This paper aims to identify lithofacies using commonly used machine learning methods. The study employs five supervised learning algorithms, namely Random Forest Algorithm (RF), BP Neural Network Algorithm (BPANN), Gradient Boosting Decision Tree Method (GBDT), Nearest Neighbor Method (KNN), and Vector Machine Method (SVM), as well as four unsupervised learning algorithms, namely K-means, DBSCAN, SOM, and MRGC. The results are evaluated using the confusion matrix, which provides the accuracy of each algorithm. The GBDT algorithm has better accuracy in supervised learning, while the K-means and DBSCAN algorithms have higher accuracy in unsupervised learning. Based on the comparison of different algorithms, it can be concluded that shale lithofacies identification poses challenges due to limited sample data and high overlapping degree of type distribution areas. Therefore, selecting the appropriate algorithm is crucial. Although supervised machine learning algorithms are generally accurate, they are limited by the data volume of lithofacies samples. Future research should focus on how to make the most of limited samples for supervised learning and combine unsupervised learning algorithms to explore lithofacies types of non-coring wells. [ABSTRACT FROM AUTHOR]

Imbalanced learning problems often occur in application scenarios and are additionally an important research direction in the field of machine learning. Traditional classifiers are substantially less effective for datasets with an imbalanced distribution, especially for high-dimensional longitudinal data structures. In the medical field, the imbalance of data problem is more common, and correctly identifying samples of the minority class can obtain important information. Moreover, class imbalance in imbalanced AD (Alzheimer's disease) data presents a significant challenge for machine learning algorithms that assume the data are evenly distributed within the classes. In this paper, we propose a random forest-based feature selection algorithm for imbalanced neuroimaging data classification. The algorithm employs random forest to evaluate the value of each feature and combines the correlation matrix to choose the optimal feature subset, which is applied to imbalanced MRI (magnetic resonance imaging) AD data to identify AD, MCI (mild cognitive impairment), and NC (normal individuals). In addition, we extract multiple features from AD images that can represent 2D and 3D brain information. The effectiveness of the proposed method is verified by the experimental evaluation using the public ADNI (Alzheimer's neuroimaging initiative) dataset, and results demonstrate that the proposed method has a higher prediction accuracy and AUC (area under the receiver operating characteristic curve) value in NC-AD, MCI-AD, and NC-MCI group data, with the highest accuracy and AUC value for the NC-AD group data. [ABSTRACT FROM AUTHOR]

A fuel cell is an energy conversion device that utilizes hydrogen energy through an electrochemical reaction. Despite their many advantages, such as high efficiency, zero emissions, and fast startup, fuel cells have not yet been fully commercialized due to deficiencies in service life, cost, and performance. Efficient evaluation methods for performance and service life are critical for the design and optimization of fuel cells. The purpose of this paper was to review the application of common machine learning algorithms in fuel cells. The significance and status of machine learning applications in fuel cells are briefly described. Common machine learning algorithms, such as artificial neural networks, support vector machines, and random forests are introduced, and their applications in fuel cell performance prediction and optimization are comprehensively elaborated. The review revealed that machine learning algorithms can be successfully used for performance prediction, service life prediction, and fault diagnosis in fuel cells, with good accuracy in solving nonlinear problems. Combined with optimization algorithms, machine learning models can further carry out the optimization of design and operating parameters to achieve multiple optimization goals with good accuracy and efficiency. It is expected that this review paper could help the reader comprehend the state of the art of machine learning applications in fuel fuels and shed light on further development directions in fuel cell research. [ABSTRACT FROM AUTHOR]

Intrusion detection systems are widely utilized in the cyber security field, to prevent and mitigate threats. Intrusion detection systems (IDS) help to keep threats and vulnerabilities out of computer networks. To develop effective intrusion detection systems, a range of machine learning methods are available. Machine learning ensemble methods have a well-proven track record when it comes to learning. Using ensemble methods of machine learning, this paper proposes an innovative intrusion detection system. To improve classification accuracy and eliminate false positives, features from the CICIDS-2017 dataset were chosen. This paper proposes an intrusion detection system using machine learning algorithms such as decision trees, random forests, and SVM (IDS). After training these models, an ensemble technique voting classifier was added and achieved an accuracy of 96.25%. Furthermore, the proposed model also incorporates the XAI algorithm LIME for better explainability and understanding of the black-box approach to reliable intrusion detection. Our experimental results confirmed that XAI LIME is more explanation-friendly and more responsive. [ABSTRACT FROM AUTHOR]

Leak detection techniques based on Machine Learning (ML) models can assist or even replace manual work in leak detection operations in water distribution systems (WDSs). However, studies on leakage detection based on on-site leak signals are limited compared to studies on lab-scale leak detection. The on-site leak signals have stronger interference and randomness, while leak signals in the laboratory are relatively simpler. To better assist on-site leak detection operations, the present paper develops and compares three ML-based models. For this purpose, many on-site tests were carried out, and tens of thousands of sets of on-site leak detection signals were collected. More than 6000 sets of these signals were marked and the signal features were extracted and analyzed from a statistical point of view. It was found that features such as the main frequency, the spectral roll-off rate, the spectral flatness, and one-dimensional (1-D) Mel Frequency Cepstrum Coefficient (MFCC) could well distinguish the leakage signals from non-leakage signals. After training the decision tree model, the performances of the random forest and Adaboost models were thoroughly compared. It was found that the false positive rates of the three models were 9.80%, 8.27% and 7.35%, all lower than 10%. In particular, the Adaboost model had the lowest false positive rate of 7.35%. The recall rate of the random forest and Adaboost models were 100% and 99.52%. [ABSTRACT FROM AUTHOR]

Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models—such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier—is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively. [ABSTRACT FROM AUTHOR]

The rapid proliferation of new technologies such as Internet of Things (IoT), cloud computing, virtualization, and smart devices has led to a massive annual production of over 400 zettabytes of network traffic data. As a result, it is crucial for companies to implement robust cybersecurity measures to safeguard sensitive data from intrusion, which can lead to significant financial losses. Existing intrusion detection systems (IDS) require further enhancements to reduce false positives as well as enhance overall accuracy. To minimize security risks, data analytics and machine learning can be utilized to create data-driven recommendations and decisions based on the input data. This study focuses on developing machine learning models that can identify cyber-attacks and enhance IDS system performance. This paper employed logistic regression, support vector machine, decision tree, and random forest algorithms on the UNSW-NB15 network traffic dataset, utilizing in-depth exploratory data analysis, and feature selection using correlation analysis and random sampling to compare model accuracy and effectiveness. The performance and confusion matrix results indicate that the Random Forest model is the best option for identifying cyber-attacks, with a remarkable F1 score of 97.80%, accuracy of 98.63%, and low false alarm rate of 1.36%, and thus should be considered to improve IDS system security. [ABSTRACT FROM AUTHOR]

Seismic vulnerability assessment in urban areas would, in principle, require the detailed modeling of every single building and the implementation of complex numerical calculations. This procedure is clearly difficult to apply at an urban scale where many buildings must be considered; therefore, it is essential to have simplified, but at the same time reliable, approaches to vulnerability assessment. Among the proposed strategies, one of the most interesting concerns is the application of machine learning algorithms, which are able to classify buildings according to their vulnerability on the basis of training procedures applied to existing datasets. In this paper, machine learning algorithms were applied to a dataset which collects and catalogs the structural characteristics of a large number of buildings and reports the damage observed in L'Aquila territory during the intense seismic activity that occurred in 2009. A combination of a trained neural network and a random forest algorithm allows us to identify an opportune "a-posteriori" vulnerability score, deduced from the observed damage, which is compared to an "a-priori" vulnerability one, evaluated taking into account characteristic indexes for building's typologies. By means of this comparison, an inverse approach to seismic vulnerability assessment, which can be extended to different urban centers, is proposed. [ABSTRACT FROM AUTHOR]

Determining fire source in underground commercial street fires is critical for fire analysis. This paper proposes a method based on temperature and machine learning to determine information about fire source in underground commercial street fires. Data was obtained through consolidated fire and smoke transport (CFAST) software, and a fire database was established based on the sampling to ascertain fire scenarios. Temperature time series were chosen for feature processing, and three machine learning models for fire source determination were established: decision tree, random forest, and LightGBM. The results indicated that the trained models can determine fire source information based on processed features, achieving a precision exceeding 95%. Among these, the LightGBM model exhibited superior performance, with macro averages of precision, recall, and F1 score being 99.01%, 98.45%, and 99.04%, respectively, and a kappa value of 98.81%. The proposed method for determining the fire source provides technical support for grasping the fire situation in underground commercial streets and has good application prospects. [ABSTRACT FROM AUTHOR]

This paper presents a novel geohash-based approach for predicting traffic incident locations using machine learning algorithms. The study utilized a three-stage model for predicting the locations of traffic incidents, which encompassed accidents, breakdowns, and other incidents. In the model, firstly, ArcGIS was used to convert the coordinates of traffic incidents into geohash areas, leading to the definition of incident locations. Secondly, variables affecting traffic incidents were extracted, and a dataset was created by utilizing the values of these variables in geohash fields. Finally, machine learning algorithms such as decision tree (DT), k-nearest neighbor (k-NN), random forest (RF), and support vector machine (SVM) algorithms were used to predict the geohash region of traffic incidents. After conducting hyperparameter optimization, we evaluated the efficacy of various machine learning algorithms in predicting the location of traffic incidents using different evaluation metrics. Our findings indicate that the RF, SVM, and DT models performed the best, with accuracy percentages of 91%, 88%, and 87%, respectively. The findings of the research revealed that traffic incident locations can be successfully predicted with the geohash-based forecasting model. The results offer traffic managers and emergency responders new perspectives on how to manage traffic incidents more effectively and improve drivers' safety. [ABSTRACT FROM AUTHOR]

As an indicator of the optical characteristics of perovskite materials, the band gap is a crucial parameter that impacts the functionality of a wide range of optoelectronic devices. Obtaining the band gap of a material via a labor-intensive, time-consuming, and inefficient high-throughput calculation based on first principles is possible. However, it does not yield the most accurate results. Machine learning techniques emerge as a viable and effective substitute for conventional approaches in band gap prediction. This paper collected 201 pieces of data through the literature and open-source databases. By separating the features related to bits A, B, and X, a dataset of 1208 pieces of data containing 30 feature descriptors was established. The dataset underwent preprocessing, and the Pearson correlation coefficient method was employed to eliminate non-essential features as a subset of features. The band gap was predicted using the GBR algorithm, the random forest algorithm, the LightGBM algorithm, and the XGBoost algorithm, in that order, to construct a prediction model for organic–inorganic hybrid perovskites. The outcomes demonstrate that the XGBoost algorithm yielded an MAE value of 0.0901, an MSE value of 0.0173, and an R2 value of 0.991310. These values suggest that, compared to the other two models, the XGBoost model exhibits the lowest prediction error, suggesting that the input features may better fit the prediction model. Finally, analysis of the XGBoost-based prediction model's prediction results using the SHAP model interpretation method reveals that the occupancy rate of the A-position ion has the greatest impact on the prediction of the band gap and has an A-negative correlation with the prediction results of the band gap. The findings provide valuable insights into the relationship between the prediction of band gaps and significant characteristics of organic–inorganic hybrid perovskites. [ABSTRACT FROM AUTHOR]

In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff-to-patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different beacons by relabeling to the locations with less samples, resolving data imbalance. Standard deviation and Kullback–Leibler divergence between minority and majority classes are used to measure signal pattern to find matching beacons to relabel. By matching beacons between classes, two variations of relabeling are implemented, specifically full and partial matching. The performance is evaluated using the real-world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. A Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the beacon data with our proposed relabeling method for data augmentation, we achieve a higher minority class F1-score compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected beacon data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score. [ABSTRACT FROM AUTHOR]

The presence of malicious software (malware), for example, in Android applications (apps), has harmful or irreparable consequences to the user and/or the device. Despite the protections app stores provide to avoid malware, it keeps growing in sophistication and diffusion. In this paper, we explore the use of machine learning (ML) techniques to detect malware in Android apps. The focus is on the study of different data pre-processing, dimensionality reduction, and classification techniques, assessing the generalization ability of the learned models using public domain datasets and specifically developed apps. We find that the classifiers that achieve better performance for this task are support vector machines (SVM) and random forests (RF). We emphasize the use of feature selection (FS) techniques to reduce the data dimensionality and to identify the most relevant features in Android malware classification, leading to explainability on this task. Our approach can identify the most relevant features to classify an app as malware. Namely, we conclude that permissions play a prominent role in Android malware detection. The proposed approach reduces the data dimensionality while achieving high accuracy in identifying malware in Android apps. [ABSTRACT FROM AUTHOR]