Search

Economic growth, air pollution, and forest fires in some states in the United States have increased the concentration of particulate matter with a diameter less than or equal to 2.5 μm (PM2.5). Although previous studies have tried to observe PM2.5 both spatially and temporally using aerosol remote sensing and geostatistical estimation, they were limited in accuracy by coarse resolution. In this paper, the performance of machine learning models on predicting PM2.5 is assessed with linear regression (LR), decision tree (DT), gradient boosting regression (GBR), AdaBoost regression (ABR), XGBoost (XGB), k‐nearest neighbors (K‐NN), long short‐term memory (LSTM), random forest (RF), and support vector machine (SVM) using PM2.5 station data from 2017 to 2021. To compare the accuracy of all the nine machine learning models, the coefficient of determination (R2), root mean square error (RMSE), Nash‐Sutcliffe efficiency (NSE), root mean square error ratio (RSR), and percent bias (PBIAS) were evaluated. Among all nine models, the RF (100 decision trees with a max depth of 20) and support vector regression (SVR; nonlinear kernel, degree 3 polynomial) models were the best for predicting PM2.5 concentrations. Additionally, comparison of the PM2.5 performance metrics displayed that the models had better predictive behavior in the western United States than that in the eastern United States. Key Points: We present the prediction of PM2.5 concentrations over the United States using various machine learning (ML) algorithmsWe show ML as a new approach for analyzing large data sets due to the computational speed and easy implementation for massive amounts of dataThe study is important for improving our understanding of the differences among ML algorithms for Earth Science research [ABSTRACT FROM AUTHOR]

High impedance fault (HIF) is difficult to detect by conventional overcurrent protection relays due to the lower fault current values, which are normally lower than the normal current. A fast and reliable algorithm is required to detect this type of fault. This paper proposes a novel method for detecting the location of HIF fault zone in a distribution system by using a novel graph theory‐based zone detection technique along with a Random Search Multilevel Support Vector Machine (RSMSVM) algorithm to classify the faulted zone. Due to shift in‐variance property of "Dual Tree Complex Wavelet Transform (DTCWT)," which has been used, in this paper, to decompose the voltage/current waveform to collect the signature of the signals and feed to the optimized RSMSVM model for classifying fault zone. The proposed method is evaluated on the IEEE 33‐bus system and also IEEE 39 bus test system under normal and noisy conditions. The proposed method is also evaluated for distribution network with the integration of distributed generation. [ABSTRACT FROM AUTHOR]

Background and Aim: Schizophrenia and bipolar disorder (BD) are critical and high‐risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease. Methods: A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers. Results: In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full‐text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity. Conclusion: ML can precisely predict results and assist in making clinical decisions‐concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research. [ABSTRACT FROM AUTHOR]

Because of the open property of information sharing in integrated sensing and communication, it is inevitable to face security problems such as user information being tampered, eavesdropped, and copied. Radio frequency (RF) individual identification technology is an important means to solve its security problems at present. Whether using machine learning methods or current deep learning-based target fingerprint identification, its performance is based on how well the radio frequency features (RFF) are extracted. Since the received signal is affected by various factors, we believe that we should first find the intrinsic features that can describe the properties of the target, which is the key to enhance the RF fingerprint recognition. In this paper, we try to analyze the intrinsic characteristics of the components that influenced the signal by the transmitting source and derive a mathematical formula to describe the RF characteristics. We propose a method using dynamic wavelet transform and wavelet spectrum (DWTWS) to enhance RFF features. The performance of the proposed method was evaluated by experimental data. Using a support vector machine classifier, the recognition accuracy is 99.6% for 10 individuals at a signal-to-noise ratio (SNR) of 10 dB. In comparison with the dual-tree complex wavelet transform (DT-CWT) feature extraction method and the wavelet scattering transform method, the DWTWS method has increased the interclass distance of different individuals and enhanced the recognition accuracy. The DWTWS method is superior at low SNR, with performance improvements of 53.1% and 10.7% at 0 dB. [ABSTRACT FROM AUTHOR]

A phoneme is perceptually the smallest distinct sound unit distinguished among words in a particular language. Every language has its own set of phonemes, and all the words are ordered sequences of phonemes. Therefore, phoneme recognition is essential to automatic speech recognition (ASR) systems. Phonemes of a language can be classified together using a single machine learning (ML) model through the direct classification (also known as the baseline or flat classification) approach. However, it is observed that the performance of such phoneme recognition degrades with the increase in the number of phoneme classes. The challenge is pronounced in languages with a larger number of phoneme classes, like Hindi, which has 48 phonemes. In this paper, we propose a speaker‐independent hierarchical classification approach for 33 Hindi‐Devanagari consonants/phonemes using cepstral features with ML techniques like support vector machine (SVM), random forest (RF) and fully connected deep neural network (DNN). In this hierarchical approach, a given phoneme is classified into successive subgroups until the particular phoneme class is identified. To perform the classification task, a binary or multi‐class classifier is invoked for each internal (non‐leaf) node in the hierarchy tree. Our model identified pairs of Optimal Feature Sets (based on mutual information) and the best suitable ML classifier for each internal decision node in the hierarchy using 10‐fold cross‐validation to help in efficient classification. Our proposed hierarchical model leads to better accuracy and 57% improved performance for phoneme recognition compared with the non‐hierarchical, that is, the direct classification approach. [ABSTRACT FROM AUTHOR]

Species distribution modeling (SDM) is widely used in ecology and conservation. Currently, the most available data for SDM are species presence‐only records (available through digital databases). There have been many studies comparing the performance of alternative algorithms for modeling presence‐only data. Among these, a 2006 paper from Elith and colleagues has been particularly influential in the field, partly because they used several novel methods (at the time) on a global data set that included independent presence–absence records for model evaluation. Since its publication, some of the algorithms have been further developed and new ones have emerged. In this paper, we explore patterns in predictive performance across methods, by reanalyzing the same data set (225 species from six different regions) using updated modeling knowledge and practices. We apply well‐established methods such as generalized additive models and MaxEnt, alongside others that have received attention more recently, including regularized regressions, point‐process weighted regressions, random forests, XGBoost, support vector machines, and the ensemble modeling framework biomod. All the methods we use include background samples (a sample of environments in the landscape) for model fitting. We explore impacts of using weights on the presence and background points in model fitting. We introduce new ways of evaluating models fitted to these data, using the area under the precision‐recall gain curve, and focusing on the rank of results. We find that the way models are fitted matters. The top method was an ensemble of tuned individual models. In contrast, ensembles built using the biomod framework with default parameters performed no better than single moderate performing models. Similarly, the second top performing method was a random forest parameterized to deal with many background samples (contrasted to relatively few presence records), which substantially outperformed other random forest implementations. We find that, in general, nonparametric techniques with the capability of controlling for model complexity outperformed traditional regression methods, with MaxEnt and boosted regression trees still among the top performing models. All the data and code with working examples are provided to make this study fully reproducible. [ABSTRACT FROM AUTHOR]

In this paper, we present a hybrid approach for credit scoring, and the classification performance of this approach is compared with 4 base learners in machine learning. A large credit default swap dataset covering the period from 2006 to 2016 is used to build classifiers and test their performances. The results from this empirical study indicate that the bagging ensemble method can substantially improve individual base learners such as decision tree, multilayer perceptron, and k‐nearest neighbours. The performance of support vector machine does not change after applying bagging ensemble. The overall results demonstrate that k‐nearest neighbour is more suitable than any other method when dealing with large unbalanced datasets in credit scoring. [ABSTRACT FROM AUTHOR]

Pore structure impacts the capability of seepage pattern of subsurface fluid mineral and mineral exploitation efficiency. Because of the strong heterogeneity in carbonate reservoirs, the pore structure is nonlinearly varying and complex in reservoirs. It is necessary to establish a method for pore structure type (PST) prediction. Machine learning provides an efficient prediction method by finding the relationship between core test data and well‐logging data. In this paper, a reservoir identification method based on Gradient Boost Decision Trees (GBDT) is proposed for the pore structure characteristics of carbonate reservoirs integrating core test and logging data. Core testing data are utilized to form the training set for PST prediction. Then, we adopt the mutual information method to optimize the logging data as the input of the machine learning model. GBDT hyperparameters are optimized using the learning curve to make it have optimal performance. Finally, we compare the predicted result of GBDT with K‐Nearest Neighbor and support vector machines, demonstrating the superior performance of GBDT. The results indicate that the four PS are different in some properties, such as permeability, pore radius and shape of pore and throat. 20%, 10 and 0.15 are recommended as the optimal parameter values for Maximum feature, iteration number and Sub‐sample number to improve GBDT accuracy in carbonate reservoir PST prediction. Evaluation indicators of machine learning, including the Kappa coefficient, Heming distance, Jaccard similarity coefficient and Confusion matrices, indicate that GBDT has better performance. This research proves the high accuracy and applicability of machine learning technology in the carbonate reservoir and also provides a meaningful reference for the identification of the carbonate reservoirs in the central area of Iraq. [ABSTRACT FROM AUTHOR]

It is a long‐term goal in external insulation studies to determine the discharge voltages of complicated engineering gaps by simulation methods. Based on the one‐to‐one correspondence between air gap structure and the static electric field (EF) distribution, this paper characterizes the transmission tower gap configuration by spatial EF features, which were used for machine learning to achieve switching impulse discharge voltage prediction. An interelectrode path and a conical zone between the energized sub‐conductor and the crossarm or the tower window were considered as EF regions strongly associated with gap breakdown, where 73 parameters were extracted to construct a feature set. Taking 15 extra‐high voltage (EHV) transmission tower gaps as training samples, with different gap distances and tower configurations, their EF features were input to a support vector machine (SVM) for model training to establish the relationships with discharge voltages. The trained SVM model was used to predict the impulse discharge voltages of 20 EHV and ultra‐high voltage (UHV) transmission tower gaps. The prediction results with different feature dimensions and various sizes of conical zones were compared to the experimental values, which demonstrate similar variation trends and acceptable errors. This study contributes to realize insulation strength calculation of engineering gaps. [ABSTRACT FROM AUTHOR]

The prevalence of musculoskeletal and neurological diseases such as knee osteoarthritis (KOA) and Parkinson's disease (PD) has grown speedily in recent years. The direct impact of these disorders on a person's gait has evoked many researchers to perform their analysis using gait because of its greater feasibility. In this paper, we proposed a framework to accurately classify abnormal considering KOA and PD and normal (NM) gait using a vision‐based (VB) approach. The aim of this study is four‐fold: firstly, a novel VB gait dataset is created and presented; secondly, the segmentation of the region of interest (ROIs) is performed using an improved technique; thirdly gait parameters, that is, spatiotemporal (SPT), linear kinematic (KNM) and additional body motion features are evaluated using statistical methods; fourthly, the classification of KOA, PD and NM subjects has been done based on artificial intelligence or a machine learning technique (MLT), that is, k‐nearest neighbour (KNN) at three severity levels and a comparison is performed with other methods namely support vector machine (SVM), random forest (RF) and linear regression (LR). The results reveal the relevance of considered gait features in this investigation. For classification purposes, the highest results are obtained by our proposed method in classifying the subjects with severity also, achieving an overall accuracy = 0.9006, sensitivity = 0.8554, specificity = 0.9044, and precision = 0.8966 respectively. This study thus successfully presents KOA, PD, and NM gait classification method based on the MLT which may be beneficial for clinicians to perform early diagnosis of such disorders objectively. [ABSTRACT FROM AUTHOR]

Today, educational institutions produce large amounts of data with the deployment of learning management systems. These large datasets provide an untapped potential to support and enhance decision‐making and operations. In recent times, machine learning (ML) has been applied to develop models utilizing this "big" data to assist in decision‐making. This study presents a systematic literature review into the application of ML to predict student performance. A total of 162 research articles from January 2010 to October 2022 were critically reviewed and analyzed by applying Kitchenham's systematic literature review approach. Our analysis categorized the literature predicting students' academic performance into two categories: (i) predicting student performance in assessments, courses or programs, and identifying students at‐risk of failing their course/program (129 studies); and (ii) predicting student dropout or retention in a course or program (33 studies). Classification is the most commonly used approach for predicting student performance (138 studies), followed by regression (25 studies) and clustering (9 studies). Supervised learning methods are used more often than semi‐supervised learning. Five most popular ML algorithms include the Decision Tree, Random Forest, Naïve Bayes, Artificial Neural Network, and Support Vector Machine. Historical records of students' grades and class performance, academic related data from learning management systems, and students' demographics are the most common features used for predicting students' performance. The most common methods used for feature selection are Information Gain‐based selection algorithms, Correlation‐based feature selection, and Gain Ratio. The general platforms/tools/libraries used in the studies include WEKA, Python, R, Rapid Miner, and MATLAB. We also investigated possible actions considered in the literature to help at‐risk students. We only found very few studies that deployed remedial actions and evaluated their impact on students' performance. In conclusion, ML has shown great potential in the prediction of student performance, but also has many areas of further research. [ABSTRACT FROM AUTHOR]

There have been numerous studies on predicting the production performance of the steam assisted gravity drainage (SAGD) process by data‐driven models with different machine learning algorithms since their introduction into industry. Similar efforts on SAGD infill wells, nevertheless, remain rare for this advanced alteration in improving the classical SAGD performance. On the other hand, predictive tools to optimize an infill well start time is useful in maximizing bitumen production and minimizing its costs. In this paper, a series of SAGD infill well models are constructed with selected ranges of operational conditions. Three SAGD infill well production performance indicators, namely, an increased ratio (Rincrease), a total steam–oil ratio (SORtotal), and a stolen ratio (RStolen) for each SAGD infill well, are calculated based on simulated infill well cases and control models. Five different machine learning algorithms (an artificial neural network [ANN] algorithm, three gradient boosting decision tree [GBDT] algorithms, and a support vector machine [SVM] algorithm) are trained, tested, and evaluated for their effectiveness in predicting those three indicators as output parameters, given seven SAGD relevant parameters as input parameters. Comparisons of different data sets show that the ANN is the best in predicting all three performance indicators under different infill well start times among all the above machine learning algorithms, while the GBDT algorithms have a better ability to learn a variation trend in the SAGD infill well performance. [ABSTRACT FROM AUTHOR]

Smart at‐ or online process sensors, which employ machine learning (ML) to process data, have been the subject of extensive research in recent years, due to their potential for real‐time process control. In this paper, a passive acoustic emission process sensor has been used to detect gas–liquid regimes within a stirred, aerated vessel using novel ML approaches. Pressure fluctuations (acoustic emissions) in an air‐water system were recorded using a piezoelectric sensor installed on the external wall of three identical cylindrical tanks of diameter, T = 160 mm, filled to a volume of 5 L (height, H = 1.5 T). The tanks were made of either glass, steel, or aluminium, and each tank was equipped with a Rushton turbine of diameter, D = 0.35 T. The investigated flow regimes, flooding, loading, complete dispersion, and un‐gassed, were obtained by changing the air feed flow rates and by varying the impeller speed. The acoustic spectra obtained were processed to select an optimal number of features characterizing each of the regimes, and these were used to train three different ML algorithms. The pre‐processing includes a principal component analysis (PCA) step, which reduces the volume of data fed to the ML algorithms, saving computational time up to a factor of 5. The algorithms (decision tree, k‐nearest neighbour, and support vector machines) were challenged to use these features to identify the correct flow regime. Accurate predictions of the three gas–liquid regimes of interest have been achieved. The accuracy of the prediction ranges from 90% to 99%, and this difference is related to the material used for the vessel. [ABSTRACT FROM AUTHOR]

In most data assimilation and numerical weather prediction systems, the Gaussian assumption is prevalent for the behavior of the random variables/errors that are involved. At the Cooperative Institute for Research in the Atmosphere theory has been developed for different forms of variational data assimilation schemes that enables the Gaussian assumption to be relaxed. For certain variable types, a lognormally distributed random variable can be combined in a mixed Gaussian‐lognormal distribution to better capture the interactions of the errors of different distributions. However, assuming that a distribution can change in time, then developing techniques to know when to switch between different versions of the data assimilation schemes becomes very important. By dynamically changing the formulation of the data assimilation system we are able to assimilate observations in a way that reflects the flow‐dependent variability of their distribution. In this paper, we present results with a machine learning technique (the support vector machine) to switch between data assimilation methods based on the detection of a change in the Lorenz 1963 model's z component's probability distribution. Given the machine learning technique's detection/prediction of a change in the distribution, then either a Gaussian or a mixed Gaussian‐lognormal 3DVar based cost function is used to minimize the errors in this period of time. It is shown that switching from a Gaussian 3DVar to a lognormal 3DVar at lognormally distributed parts of the attractor improves the data assimilation analysis error compared to using one distribution type for the entire attractor. Key Points: Machine learning can be used to detect non‐Gaussian distributionsGiven the machine learning detection, we can switch between optimal data assimilation schemesData assimilation can be improved using machine learning techniques [ABSTRACT FROM AUTHOR]

Pandemic novel Coronavirus (Covid‐19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid‐19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid‐19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid‐19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)‐based meta‐analysis to predict the trend of epidemic Covid‐19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time‐series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid‐19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid‐19 observed symptoms, a list of Top‐20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid‐19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction. [ABSTRACT FROM AUTHOR]

Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population; thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm‐based feature selection design for thyroid disease classifier with rough type‐2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top‐n features. The proposed hybrid firefly butterfly optimization‐rough type‐2 fuzzy support vector machine (HFBO‐RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well‐known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixed‐kernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO‐RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively. [ABSTRACT FROM AUTHOR]

In the present era, enormous factors contribute to causing cancer. So cancer classification cannot rely only on doctor's thoughts. As a result, intelligent algorithms concerning doctor's help are inevitable. Therefore, the authors are motivated to suggest a novel algorithm to classify three cancer datasets; colon, ALL-AML, and leukaemia cancers. Their proposed algorithm is based on the deep neural network and emotional learning process. First of all, by applying the principal component analysis, they had a feature reduction. Then, they used deep neural as a feature extraction. Then, they implemented different classifiers; multi-layer perceptron, support vector machine (SVM), decision tree, and Gaussian mixture model. In the end, because in the real world, especially when working on systems biology, unpredictable events, and uncertainties are undeniable, the robustness of their model against uncertainties is important. So they added Gaussian noise to the input features of the first encoder in each dataset, then, they applied the stacked denoising method. Experimental results disclosed that, generally, using emotional learning increased the accuracy. In addition, the highest accuracy was gained by SVM, 91.66, 92.27, and 96.56% for colon, ALL-AML, and leukaemia, respectively. However, GMM led to the lowest accuracy. The best accuracy gained by GMM was 60%. [ABSTRACT FROM AUTHOR]

Abstract: We propose an approach to image segmentation that views it as one of pixel classification using simple features defined over the local neighborhood. We use a support vector machine for pixel classification, making the approach automatically adaptable to a large number of image segmentation applications. Since our approach utilizes only local information for classification, both training and application of the image segmentor can be done on a distributed computing platform. This makes our approach scalable to larger images than the ones tested. This article describes the methodology in detail and tests it efficacy against 5 other comparable segmentation methods on 2 well‐known image segmentation databases. Hence, we present the results together with the analysis that support the following conclusions: (i) the approach is as effective, and often better than its studied competitors; (ii) the approach suffers from very little overfitting and hence generalizes well to unseen images; (iii) the trained image segmentation program can be run on a distributed computing environment, resulting in linear scalability characteristics. The overall message of this paper is that using a strong classifier with simple pixel‐centered features gives as good or better segmentation results than some sophisticated competitors and does so in a computationally scalable fashion. [ABSTRACT FROM AUTHOR]

Marine renewable energy has made significant progress in the last few decades. Even after making substantial progress, the cost of electricity produced by tidal turbines is high. Therefore, the current paper concentrated on reducing the cost of transportation and installation of the turbine by performing a model. Extreme Learning Machine and Support Vector Machines as well as Genetic Programming were applied to predict the performance of the turbine model by creating short‐term, multistep‐ahead prediction models to compute the performance of the H‐rotor vertical axis Folding Tidal turbine. The performance of the turbine was verified by a numerical study using the three‐dimensional approach for the viscous model with the unsteady flow. Statistical evaluation of the outcomes pointed out that advanced Extreme Learning Machine simulation made the assurance in formulating an innovative forecasting strategy for investigating the performances of the tidal turbine. This study shows that the application of the new procedure resulted in confident generality performance and learns faster than orthodox learning algorithms. In conclusion, the assessment indicated that the advanced Extreme Learning Machine simulation was capable as a promising alternative to existing numerical methods for computing the coefficient of performance for turbines. [ABSTRACT FROM AUTHOR]

Reducing downtime through predictive or condition‐based maintenance is a promising strategy to help reduce costs associated with wind farm operation and maintenance. To help effectively monitor wind turbine condition, operators now rely on multiply sources of data to make informed operational decisions which can minimise downtime, increasing availability and profitability of any given site. Two of such approaches are SCADA temperature and vibration monitoring, which are typically performed in isolation and compared over time for both fault diagnostics and reliability analysis. Presenting two separate case studies, this paper describes a methodology to bring multiple data sources together to diagnose faults by using a single‐class support vector machine classifier to assess normal behaviour model error, with results showing that anomalies can be detected more consistently when compared to more standard approaches of analysing each data source in isolation. [ABSTRACT FROM AUTHOR]

Copyright of Developmental Medicine & Child Neurology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we propose a machine‐learning‐based approach to automatically detect a satellite oscillator anomaly. A major challenge is to differentiate an oscillator anomaly from ionospheric scintillation. Although both scintillation and oscillator anomalies cause phase disturbances, their underlying physics are different and, therefore, show different carrier‐frequency dependency. By using triple‐frequency signals, distinct features are extracted from the disturbed signals and applied to the radial basis function (RBF) support vector machine (SVM) classifier to identify an oscillator anomaly. The results show that the proposed RBF SVM displays superior performance and outperforms several other classification methods. The proposed approach is applied to an extensive GNSS database to conduct automatic satellite oscillator anomaly detection. Preliminary detection results validate the effectiveness of the proposed method. On average, one‐to‐three satellite oscillator anomaly events are detected daily at each receiver location. [ABSTRACT FROM AUTHOR]

Background and Aims: Keratoconus is a progressive eye condition in which the normally round cornea thins and bulges outwards into a cone shape. This irregular shape causes light to scatter in multiple directions as it enters the eye, leading to distorted vision, increased sensitivity to light and frequent changes in the prescription of glasses or contact lenses. Detecting keratoconus at an early stage is not only difficult but also challenging. Methods: The study has proposed an ensemble‐based machine learning (ML) technique named KeratoEL to detect keratoconus at an early stage. The proposed KeratoEL model combines the basic machine learning algorithms, namely support vector machine (SVM), decision tree (DT), random forest (RF) and artificial neural network (ANN). Before employing the ML model for keratoconus detection, the data set is first preprocessed manually by eliminating some features that don't contribute any significant value to predict the exact class. Moreover, the output features are labelled into three different classes and Extra Trees Classifier is used to find out the important features. Then, the features are sorted in descending order and top 45, 30, and 15 features are taken as input datasets against the output. Finally, different machine learning models are tested using the input datasets and performance metrics are measured Results: The proposed model obtains 98.0%, 98.9% and 99.8% accuracy for top 45, 30, and 15 number of features respectively. Overall experimental results show that the proposed ensemble model outperforms the existing machine learning models. Conclusion: The proposed KeratoEL model effectively detects keratoconus at an early stage by combining SVM, DT, RF, and ANN algorithms, demonstrating superior performance over existing models. These results underscore the potential of the KeratoEL ensemble approach in enhancing early detection and treatment of keratoconus. [ABSTRACT FROM AUTHOR]

Oil‐immersed transformers play an important role in the stable operation of power systems. Aiming at the low accuracy of traditional transformer fault diagnosis methods, a transformer fault diagnosis method using an improved sparrow search algorithm (ISSA) optimised support vector machine (SVM) is proposed. First, use Sin chaotic to initialise the population, then introduce the fusion of Cauchy mutation and opposition‐based learning to optimise the selection of the population to improve the SSA global optimisation capability. Secondly, use the improved Sparrow algorithm to optimise the SVM kernel function parameters and penalty coefficients and establish a fault diagnosis model‐‐ISSA‐SVM for dissolved gas analysis. Enter the data into ISSA‐SVM for fault diagnosis and combine it with K nearest neighbour (KNN) algorithm, gradient boosting decision tree (GBDT), sparrow search optimised deep extreme learning machine (SSA‐DELM), SVM, sparrow search algorithm optimised Support vector machine (SSA‐SVM), and other diagnostic results are compared. The results show that the fault diagnosis rate of ISSA‐SVM is 91.43%, which is 12.86%, 7.14%, 5.71%, 2.86%, and 1.43% higher than that of KNN, GBDT, SSA‐DELM, SVM, and SSA‐SVM, respectively. It can more accurately judge the current operating state of the transformer. [ABSTRACT FROM AUTHOR]

Full-scale discharge tests of transmission line–tower air gaps are costly and time-consuming, and they cannot exhaustively simulate all the gap configurations in practical engineering. In this paper, a machine learning model established by support vector machine is introduced to predict the switching impulse discharge voltages of the ultra-high-voltage (UHV) AC transmission line–tower air gaps. The three-dimensional finite element models of a UHV cup-type tower and a UHV compact transmission line were established for electric field calculation, and some features were extracted from the hypothetic discharge channel and the shortest path between the bundled conductor and the tower. These features under a given voltage were normalised and input to the SVM model, while the output is two binary values, respectively, representing gap withstanding or breakdown. Trained by experimental data of one type of the UHV transmission line–tower gaps, the SVM model is able to predict the discharge voltages of another gap type. The mean absolute percentage errors of the two engineering gap types, under different gap distances, are 8.31 and 4.86%, respectively, which are acceptable for engineering applications. The results provide a possible way to obtain the discharge voltages of complicated engineering gaps by mathematical calculations. [ABSTRACT FROM AUTHOR]

This paper presents a rule-based adaptive protection scheme using machine-learning methodology for microgrids in extensive distribution automation (DA). The uncertain elements in a microgrid are first analysed quantitatively by Pearson correlation coefficients from data mining. Then, a so-called hybrid artificial neural network and support vector machine (ANN-SVM) model is proposed for state recognition in microgrids, which utilises the growing massive data streams in smart grids. Based on the state recognition in the algorithm, adaptive reconfigurations can be implemented with enhanced decision-making to modify the protective settings and the network topology to ensure the reliability of the intelligent operation. The effectiveness of the proposed methods is demonstrated on a microgrid model in Aalborg, Denmark and an IEEE 9 bus model, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a rule‐based adaptive protection scheme using machine‐learning methodology for microgrids in extensive distribution automation (DA). The uncertain elements in a microgrid are first analysed quantitatively by Pearson correlation coefficients from data mining. Then, a so‐called hybrid artificial neural network and support vector machine (ANN‐SVM) model is proposed for state recognition in microgrids, which utilises the growing massive data streams in smart grids. Based on the state recognition in the algorithm, adaptive reconfigurations can be implemented with enhanced decision‐making to modify the protective settings and the network topology to ensure the reliability of the intelligent operation. The effectiveness of the proposed methods is demonstrated on a microgrid model in Aalborg, Denmark and an IEEE 9 bus model, respectively. [ABSTRACT FROM AUTHOR]

Intelligent system (IS) using synchronous phasor measurements for transient stability assessment (TSA) has received continuous interests recently. For post-disturbance TSA, one pivotal concern is the response time, which was reported in the literature as a fixed value ranging from 4 cycles to 3 s after fault clearance. Since transient instability can develop very fast, there is a pressing need for faster response speed. This paper develops a novel IS to balance the response speed and accuracy requirements. A set of classifiers are sequentially organised, each is an ensemble of extreme learning machines (ELMs), whose inputs are post-disturbance generator voltage trajectories and outputs are the classification on the stable/unstable status of the post-disturbance system and an evaluation of the credibility of the classification. A self-adaptive TSA decision-making mechanism is designed to progressively adjust the response time, such that the IS can do the classification faster, thereby allowing more time for emergency controls. The ELM ensemble classifiers can also be updated by on-line pre-disturbance TSA results due to its very fast learning speed. Case studies on the New England system and IEEE 50-machine system have validated the high efficiency and accuracy of the IS. [ABSTRACT FROM AUTHOR]

Abstract: Data stream is a sequence of data that has unique features. In many data streams, data from one concept is available, and detection of other types of data in data stream is an essential task. One‐class classification is a famous approach to data classification when data from one class is accessible. The principal task of one‐class classification is separating input data in two different parts: target and outlier data. One of the main challenges concerning data classification on the stationary data streams is the response time, which causes negative effects on effective runtime. Most classifiers, that have been proposed on Rd feature space, solve a quadratic problem for classification that leads to extreme runtime increase. In this paper, an incremental one‐class classification on stationary data streams is proposed using two‐quarter sphere (IOCTQ) in order to achieve lower computation cost of classification time, and a linear optimization problem is solved. IOCTQ divides one data classification problem into two data classifiers, and each data point will be individually classified in one‐quarter sphere. The two‐quarter spheres can be run parallel. The results of the experiments have been compared with state‐of‐the‐art methods and show superiority of the IOCTQ method in classification accuracy and time complexity. [ABSTRACT FROM AUTHOR]

Machine learning algorithms for sound classification can be supported by multiple temporal, spectral, and perceptual features extracted from the sound signal. The number of features affects the classification accuracy but also the computational resources requested, so the number of features has to be carefully selected. In this work, we propose a methodology for feature selection based on the principal component analysis. The case study has been the classification of classroom sounds during face‐to‐face module delivery and six sound types have been defined. The proposed method is applied upon a set of 143 sound features to produce feature ranking. The ranking results are compared with those provided by the Relief‐F. Then the selected features are used by five classification algorithms, Linear Discriminant Analysis (LDA), Quadratic Support Vector Machine (QSVM), k Nearest Neighbors, Boosted Trees, and Random Forest. The algorithms are executed with increasing number of features, from 1 to 143, considering both feature rankings, creating 1430 models. The performance of the classification algorithms increases rapidly with the number of features with LDA, QSVM, and Boosted Trees outperforming other methods and surpassing the accuracy ratio of 90% with 25 features. [ABSTRACT FROM AUTHOR]

For the past few years, the addition of renewable energy sources has been on the rise, but the unregulated addition of these sources can cause severe harm to the grid. Therefore, it is necessary to have a predefined limit for a grid, beyond which no further addition of renewables should be allowed without reinforcement. That limit is called the hosting capacity (HC), which is addressed in the literature by search‐based methods with heavy computational burdens. This manuscript first presents a framework to find the HC for a grid. The same framework is then applied to 503 different simulated but realistic LV distribution feeders in Finland to find their HCs and the most effective network parameters in defining a network specific HC. Next, different machine learning models, that is, Decision Tree (DT), Random Forest (RF), Linear Regression (LR), K nearest neighbours (KNN), Logistic Regression, and Support Vector Machine (SVM) are implemented on the generated data. For the classification case, the accuracy values for logistic regression, KNN, and SVM were 0.89, 0.84, and 0.81, respectively. The findings demonstrate that the developed machine learning based technique will enable distribution network operators in finding the HC without applying any deterministic or probabilistic approaches. [ABSTRACT FROM AUTHOR]

Here we present a method for using the spatial x–y coordinate of an image cropped from the cylindrical surface of digital 3D drill core images and demonstrate how this spatial metadata can be used to improve unsupervised machine learning performance. This approach is applicable to any data set with known spatial context, however, here it is used to classify 400 m of drillcore imagery into 12 distinct classes reflecting the dominant rock types and alteration features in the core. We modified two unsupervised learning models to incorporate spatial metadata and an average improvement of 25% was achieved over equivalent models that did not utilize metadata. Our semi‐supervised workflow involves unsupervised network training followed by semi‐supervised clustering where a support vector machine uses a subset of M expert labeled images to assign a pseudolabel to the entire data set. Fine‐tuning of the best performing model showed an f1 (macro average) of 90%, and its classifications were used to estimate bulk fresh and altered rock abundance downhole. Validation against the same information gathered manually by experts when the core was recovered during the Oman Drilling Project revealed that our automatically generated data sets have a significant positive correlation (Pearson's r of 0.65–0.72) to the expert generated equivalent, demonstrating that valuable geological information can be generated automatically for 400 m of core with only ∼24 hr of domain expert effort. Plain Language Summary: This work presents a novel method for using the spatial context of digital core images to improve the descriptive accuracy of unsupervised machine learning algorithms. The addition of spatial metadata improves model performance by an average of 25%, with the best performing model in this study achieving an accuracy score of 90%. The output of this model was then used to estimate the amount of fresh and altered rock within a 400 m long drill core, which was shown to be of comparable quality to the same estimations made by geologists on the cores themselves. Key Points: Use of spatial metadata improves state‐of‐the‐art unsupervised feature extractionSemi‐supervised methods using spatial metadata outperform supervised methods for the same expert labeling effortNew methods described enable the standardization of core‐logging processes and improve cross‐dataset comparisons [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) presents a significant risk to human well‐being and has emerged as a worldwide public health concern. Twenty‐eight children with ASD and 33 healthy children (HC) were selected for the quantitative determination of their plasma metabolites using an ultraperformance liquid chromatography‐tandem mass spectrometry (UPLC‐MS/MS) platform. A total of 1997 metabolites were detected in the study cohort, from which 116 metabolites were found to be differentially expressed between the ASD and HC groups. Through analytical algorithms such as least absolute shrinkage selection operator (LASSO), support vector machine (SVM), and random forest (RF), three potential metabolic markers were identified as FAHFA (18:1(9Z)/9‐O‐18:0), DL‐2‐hydroxystearic acid, and 7(S),17(S)‐dihydroxy‐8(E),10(Z),13(Z),15(E),19(Z)‐docosapentaenoic acid. These metabolites demonstrated superior performance in distinguishing the ASD group from the HC group, as indicated by the area under curves (AUCs) of 0.935, 0.897, and 0.963 for the three candidate biomarkers, respectively. The samples were divided into training and validation sets according to 7:3. Diagnostic models were constructed using logistic regression (LR), SVM, and RF. The constructed three‐biomarker diagnostic model also exhibited strong discriminatory efficacy. These findings contribute to advancing our understanding of the underlying mechanisms involved in the occurrence of ASD and provide a valuable reference for clinical diagnosis. [ABSTRACT FROM AUTHOR]

This study trains three machine learning models with varying complexity—Random Forest, Support Vector Machine, and Neural Network—to predict cyclogenesis at a forecast lead time of 24 hr for given tropical disturbances identified by an optimized Kalman Filter algorithm. The overall performance is competent in terms of f1‐scores (∼0.8) compared to previous research of the same kind. An assessment by SHapley Additive exPlanations (SHAP) values reveals that mid‐level (500 hPa) vorticity is the most influential factor in deciding if a tropical disturbance is developing or non‐developing for all three models. Wind shear and tilting are found to hold a certain level of importance as well. These results encourage further experiments that use physical models to explore the dynamical, mid‐level pathway to tropical cyclogenesis. Another usage of SHAP values in this work is to explain how a machine learning model decides if an individual tropical disturbance case will develop, by listing the contribution of each feature to the output genesis probability, illustrated by a case study of Typhoon Halong. This increases the reliability of the machine learning models, and forecasters can take advantage of such information to issue tropical cyclone formation warnings more accurately. Several caveats of the current machine learning application in the studies of tropical cyclogenesis are discussed and can be considered for future research. These can benefit the interpretation and emphasis of certain output fields in the operational dynamical prediction system, which can contribute to more timely cyclogenesis forecasts. Plain Language Summary: Machine learning methods are utilized to improve the prediction of typhoon formation. In addition to high accuracy, the models also provide additional information on decisive formation factors. The most evident general relationship found in typhoon formation is that the stronger the mid‐tropospheric circulation, the higher the probability of typhoon formation. The models are capable of showing why individual disturbances may or may not grow into typhoons. The results are found to be physically consistent and helpful in enhancing the trustworthiness of the machine learning product. Hence, the methods and models have promising potentials for being applied to real‐life typhoon formation forecasting as convenient and reliable tools. Key Points: Machine learning methods are trained to forecast tropical cyclone genesis events with a decent accuracy of ∼80%Mid‐level vorticity and wind shear are important fields in the cyclogenesis predictions informed by the machine learning modelsSHAP values are used to interpret what information the models used to estimate the probability of cyclogenesis [ABSTRACT FROM AUTHOR]

Falls have a global impact, affecting people worldwide, with a notably high occurrence among the elderly. This study employs machine learning techniques to analyze falls and simulate Activities of Daily Living (ADL). The objective is to predict human falls by leveraging signals from accelerometers and gyroscopes as wearable sensors. By deriving statistical features such as mean, standard deviation, and range the authors successfully trained and assessed six machine learning models allowing them to compare solutions based on both wrist and waist data. The combination of these characteristics and sensors resulted in the Random Forest waist model achieving the most favorable metrics, with an accuracy rate of 97.22% in a 5‐s window. [ABSTRACT FROM AUTHOR]

The eddy current method of control analyzed and the possibility of its use for the identification of metals and alloys substantiated. It is shown that this can be done by modernizing the eddy current radio engineering system with dynamic scanning of metal samples, which consists in the use of modern microcontrollers with high‐precision ADC operation. The structural of the modernized system is presented, some aspects of processing the received response signal are considered, which made it possible to highlight the information coefficients that would represent the metal or alloy as a generalized image. It is proposed to use machine learning methods to compare an unknown sample of a metal or alloy under study with those available in the collected database, which is shown on the example of the support vector machine method, which allows for real‐time identification of metals and alloys, to carry out express control, without human intervention in the identification process. [ABSTRACT FROM AUTHOR]

Vision-based human activity recognition (HAR) finds its application in many fields such as video surveillance, robot navigation, telecare and ambient intelligence. Most of the latest researches in the field of automated HAR based on skeleton data use depth devices such as Kinect to obtain three-dimensional (3D) skeleton information directly from the camera. Although these researches achieve high accuracy but are strictly device dependent and cannot be used for videos other than from specific cameras. Current work focuses on the use of only 2D skeletal data, extracted from videos obtained through any standard camera, for activity recognition. Appearance and motion features were extracted using 2D positions of human skeletal joints through OpenPose library. The approach was trained and tested on publically available datasets. Supervised machine learning was implemented for recognising four activity classes including sit, stand, walk and fall. Performance of five techniques including K-nearest neighbours (KNNs), support vector machine, Naive Bayes, linear discriminant and feed-forward backpropagation neural network was compared to find the best classifier for the proposed method. All techniques performed well with best results obtained through the KNN classifier. [ABSTRACT FROM AUTHOR]

Electricity theft is a primary concern for utility providers, as it leads to substantial financial losses. To address the issue, a novel extreme gradient boosting (XGBoost)‐based model utilizing the consumers' electricity consumption patterns for analysis is proposed for electricity theft detection (ETD). To remove the imbalance in the real‐world electricity consumption dataset and ensure an even distribution of theft and non‐theft data instances, six different artificially created theft attacks were used. Moreover, the utilization of the XGBoost algorithm for classification, especially to identify malicious instances of electricity theft, yielded commendable accuracy rates and a minimal occurrence of false positives. The proposed model identifies electricity theft specific to the regions, utilizing electricity consumption parameters, and other variables as input features. The authors' model outperformed existing benchmarks like k‐neural networks, light gradient boost, random forest, support vector machine, decision tree, and AdaBoost. The simulation results using the false attacks for balancing the dataset have improved the proposed model's performance, achieving a precision, recall, and F1‐score of 96%, 95%, and 95%, respectively. The results of the detection rate and the false positive rate (FPR) of the proposed XGBoost‐based detection model have achieved 96% and 3%, respectively. [ABSTRACT FROM AUTHOR]

Snowfall forecasting has historically been an area of difficulty for operational meteorologists, particularly in regions of complex terrain, such as the western United States. Attempts at improving forecasts have been made, but skill is still poor, with snowfall routinely overpredicted. A major reason for this overprediction has been the failure to accurately predict snow–liquid ratios (SLR) ahead of major events. This research proposes, develops, and tests multiple machine learning methods for dynamic SLR prediction for the Sky Islands of southeast Arizona by objectively comparing a multiple linear regression (MLR) against several more complex and flexible machine learning methods. Input parameters for each method were chosen based on variables found by previous studies to have a regression‐based relationship with SLR, with a focus on the lower mid‐levels of the troposphere. These parameters were also used to construct the MLR model, and its performance was compared objectively with the machine learning methods. When tested on historical events, a very high percentage of the network‐predicted SLR values fall within the margin of error of observed SLRs, which were calculated using gridded snow depth and snow water equivalent (SWE) data from the University of Arizona daily 4‐km SWE, SD, and SCE dataset (UASnow). A support vector machine (SVM), a k‐nearest neighbor (KNN) algorithm, and a random forest also showed high accuracies when tested on the dataset, and each showed a significant gain in skill compared with the MLR model, with skill being evaluated by multiple metrics. [ABSTRACT FROM AUTHOR]

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital‐acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language analysis. The former evidence was gathered through PRISMA guidelines. The logical analysis was applied via an R programming language. ML algorithms based on usage rate included logistic regression (LR), Random Forest (RF), Distributed tree (DT), Artificial neural networks (ANN), SVM (Support Vector Machine), Batch normalisation (BN), GB (Gradient Boosting), expectation–maximisation (EM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Six cases were related to risk predictions of HAPI in the ICU based on an ML algorithm from seven obtained studies, and one study was associated with the Detection of PI risk. Also, the most estimated risksSerum Albumin, Lack of Activity, mechanical ventilation (MV), partial pressure of oxygen (PaO2), Surgery, Cardiovascular adequacy, ICU stay, Vasopressor, Consciousness, Skin integrity, Recovery Unit, insulin and oral antidiabetic (INS&OAD), Complete blood count (CBC), acute physiology and chronic health evaluation (APACHE) II score, Spontaneous bacterial peritonitis (SBP), Steroid, Demineralized Bone Matrix (DBM), Braden score, Faecal incontinence, Serum Creatinine (SCr) and age. In sum, HAPI prediction and PI risk detection are two significant areas for using ML in PI analysis. Also, the current data showed that the ML algorithm, including LR and RF, could be regarded as the practical platform for developing AI tools for diagnosing, prognosis, and treating PI in hospital units, especially ICU. [ABSTRACT FROM AUTHOR]

The current study aims at assessing the capabilities of five machine learning models in terms of mapping tungsten polymetallic prospectivity in the Gannan region, China. The five models include logistic regression (LR), support vector machine (SVM), random forest, convolutional neural network (CNN), and light gradient boosting machine (LGBM) models. Three types of data sets including geochemical data, lithostratigraphic contacts, and faults were employed to generate 16 evidential maps that were used to build the machine learning models. Tungsten polymetallic deposits were randomly separated into two parts: 80% for training and 20% for validating. Performance of the models was evaluated through receiver operating characteristic and K‐fold cross validation, with an emphasis on the variable influence within different machine learning methods. The results show that the models are especially sensitive to the chemical elements: Be, Bi, Pb, and Cd, implying that they are closely related to tungsten polymetallic mineralization. Compared to other models, the LGBM and CNN models performed best, while the LR model was the most stable. The results also indicated that the CNN model can predict the maximum number of tungsten polymetallic deposits within the minimum area based on the prediction‐area plot analysis, while the SVM model can capture the least amount of tungsten polymetallic deposits within the largest area. Finally, 18 prospective areas were delineated according to the prediction results of the machine learning models, which provided important guidance for tungsten polymetallic exploration in the study area. Key Points: Five machine learning models were used and compared for tungsten polymetallic prospectivity analysis in the Gannan region, ChinaPerformances of the machine learning models were evaluated through receiver operating characteristic, P‐A plot, and K‐fold cross validationConvolutional neural network model is most efficient in mapping favorable areas for tungsten polymetallic exploration [ABSTRACT FROM AUTHOR]

Boosting has been shown to improve the predictive performance of unstable learners such as decision trees, but not of stable learners like Support Vector Machines (SVM), k-nearest neighbors and Naive Bayes classifiers. In addition to the model stability problem, the high time complexity of some stable learners such as SVM prohibits them from generating multiple models to form an ensemble for large data sets. This paper introduces a simple method that not only enables Boosting to improve the predictive performance of stable learners, but also significantly reduces the computational time to generate an ensemble of stable learners such as SVM for large data sets that would otherwise be infeasible. The method proposes to build local models, instead of global models; and it is the first method, to the best of our knowledge, to solve the two problems in Boosting stable learners at the same time. We implement the method by using a decision tree to define local regions and build a local model for each local region. We show that this implementation of the proposed method enables successful Boosting of three types of stable learners: SVM, k-nearest neighbors and Naive Bayes classifiers. [ABSTRACT FROM AUTHOR]

Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors. [ABSTRACT FROM AUTHOR]

Engines are prone to various types of faults, and it is crucial to detect and indeed classify them accurately. However, manual fault type detection is time‐consuming and error‐prone. Automated fault type detection promises to reduce inter‐ and intra‐observer variability while ensuring time invariant attention during the observation duration. We have proposed an automated fault‐type detection model based on sound signals to realize these advantageous properties. We have named the detection model prism pattern network (PrismPatNet) to reflect the fact that our design incorporates a novel feature extraction algorithm that was inspired by a 3D prism shape. Our prism pattern model achieves high accuracy with low‐computational complexity. It consists of three main phases: (i) prism pattern inspired multilevel feature generation and maximum pooling operator, (ii) feature ranking and feature selection using neighbourhood component analysis (NCA), and (iii) support vector machine (SVM) based classification. The maximum pooling operator decomposes the sound signal into six levels. The proposed prism pattern algorithm extracts parameter values from both the signal itself and its decompositions. The generated parameter values are merged and fed to the NCA algorithm, which extracts 512 features from that input. The resulting feature vectors are passed on to the SVM classifier, which labels the input as belonging to 1 of 27 classes. We have validated our model with a newly collected dataset containing the sound of (1) a normal engine and (2) 26 different types of engine faults. Our model reached an accuracy of 99.19% and 98.75% using 80:20 hold‐out validation and 10‐fold cross‐validation, respectively. Compared with previous studies, our model achieved the highest overall classification accuracy even though our model was tasked with identifying significantly more fault classes. This performance indicates that our PrismPatNet model is ready to be installed in real‐world applications. [ABSTRACT FROM AUTHOR]

Candesartan is an angiotensin receptor blocker (ARB) used to treat hypertension. However, its poor aqueous solubility and oral bioavailability have limited its therapeutic applications. In order to increase bioavailability and control the release of candesartan condensation, microspheres containing biodegradable polymers (polyvinylpyrrolidone [PVP K30] and polylactic acid [PLA]) in different ratios were prepared by the o/o solvent evaporation method using Span 80 as a surfactant. In addition, the impact of encapsulation parameters (i.e., PVP K30 and PLA concentrations) on the encapsulation ratio and release percentage was investigated by the mixed factorial design method. The release kinetics of the microspheres was simulated by combining two methods, the Dragonfly algorithm and a support vector machine (DA‐SVM). The experimental data were in good agreement with the predicted data, with a coefficient of determination close to unity and a mean square error close to zero. Fourier‐transfer infrared spectrometry (FTIR) analysis revealed the presence of condensation in all formulations without reporting distortion in the spectra. Scanning electron microscopy (SEM) confirmed the successful synthesis of microspheres, whose sizes were between 12 and 26 μm. Formulations with a PLA‐drug ratio of 6:1 (N15, N17, and N18) showed the highest encapsulation efficiency (68%, 71%, and 70%, respectively), while formulations that do contain PVP K30, such as N5, N4, and N3, showed a higher release (83%, 84%, and 89%, respectively), indicating that the agent (PVP K30) enhanced the bioavailability and release of candesartan. Overall, N3 showed a higher drug release rate at 12 h and important encapsulation efficiency, making it the optimal formulation obtained in this study. [ABSTRACT FROM AUTHOR]

With the onset of pandemics and accrescent stress among people, heart disease has become one of the leading contributors to premature deaths worldwide. Early prognosis of this disorder is the most viable strategy for increasing the prospects of individuals' survival. Numerous methods exploiting machine learning algorithms for heart disease prediction have been reported in the literature, but they all suffer from overfitting problems. Conspicuously, to improve the prediction accuracy, in this research work, an efficient meta‐heuristic‐based feature selection technique, namely NSGA‐II, is employed. The proposed solution aims to reduce the feature set and thus improve the prediction accuracy supported by intelligent machine learning models. The presented classifier is trained and validated using a comprehensive heart disease dataset by utilizing NSGA‐II selected features with 10‐fold cross‐validation. For performance validation, the results of the proposed model are compared with the state‐of‐the‐art machine learning algorithms, such as a k‐nearest neighbour, support vector machine, Bayesian belief networks, random forest, and naive bayes. The simulation results highlight the improved performance of the proposed model with NSGA‐II and achieve a high prediction accuracy of 97.32%. Furthermore, results of sensitivity (92.84%), specificity (92.60%), precision (91.25%), and F‐measure (92.17%) prove the utility of the proposed approach for heart disease prediction over all other variants. [ABSTRACT FROM AUTHOR]

Instance is important in data analysis and mining; it filters out unrepresentative, redundant, or noisy data from a given training set to obtain effective model learning. Various instance selection algorithms are proposed in the literature, and their potential and applicability in data cleaning and preprocessing steps are demonstrated. For multiclass classification datasets, the existing instance selection algorithms must deal with all the instances across the different classes simultaneously to produce a reduced training set. Generally, every multiclass classification dataset can be regarded as a complex domain problem, which can be effectively solved using the divide‐and‐conquer principle. In this study, the one‐versus‐all (OVA) and one‐versus‐one (OVO) decomposition approaches were used to decompose a multiclass dataset into multiple binary class datasets. These approaches have been widely employed when constructing the classifier but have never been considered in instance selection. The results of instance selection performance obtained with the OVA, OVO, and baseline approaches were assessed and compared for 20 different domain multiclass datasets as the first study and five medical domain datasets as the validation study. Furthermore, three instance selection algorithms were compared, including IB3, DROP3, and GA. The results demonstrate that using the OVO approach to perform instance selection can make the support vector machine (SVM) and k‐nearest neighbour (k‐NN) classifiers perform significantly better than the OVA and baseline approaches in terms of the area under the ROC curve (AUC) rate, regardless of the instance selection algorithm used. Moreover, the OVO approach can provide reasonably good data reduction rates and processing times, which are all better than those of the OVA approach. [ABSTRACT FROM AUTHOR]

Precise knowledge of faults is very exigent to reduce the outage duration as most of the customer minute losses in distribution grids occur due to longer period of interruptions caused by faults. This study proposes a fault location technique combining advanced signal processing and machine learning tools for distribution grids. The proposed technique decomposes three-phase currents measured from sending end employing wavelet transform (WT) and collects useful features to fetch them as inputs of extreme learning machine (ELM). Satisfactory values of the selected statistical performance measures validate the efficacy of proposed fault location technique. Besides, the efficacy of support vector regression (SVR) and artificial neural network (ANN) are also tested employing the WT extracted features. The presented results show the superiority of ELM-WT technique over SVR-WT and ANN-WT techniques in terms of the selected performance measures and training times. Additionally, the proposed technique is independent of fault resistance, inception angle, the presence of measurement noise, thermal expansion/contraction of the distribution line and pre-fault loading condition. Furthermore, the hybrid method detects and classifies different types of faults before locating them with different machine learning tools. [ABSTRACT FROM AUTHOR]

One of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial component of agricultural water management. A hybrid model (HM) of Bayesian random forest (BRF), Bayesian support vector machine (BSVM), and Bayesian artificial neural network (BANN) is built in this study. The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. These three HMs are employed to help automate logical inference and decision‐making in business intelligence for groundwater management. For this purpose, data on 8 separate climatic factors that impact GWL changes in the study area were acquired. Nine distinct farming communities' GWL change data were utilised as the dependent variables for each model fit (community data). The effectiveness of the HM techniques was assessed using the evaluation metrics of mean absolute error (MAE), coefficient of determination (R2), mean absolute percent error (MAPE), and root mean square error (RMSE). The model fit in Suhum had the greatest performance with the highest accuracy (R2 varied from 0.9051 to 0.9679) and the lowest error scores (RMSE ranged from 0.0653 to 0.0727, and MAE ranged from 0.0121 to 0.0541), according to the models' evaluation results. The BRF delivered the greatest results when compared to the two independent HMs, the BSVM and BANN. Future GWL and climatic variable data may be trained using the trained HM techniques to determine the effects of climate change. Farmers, businesses, and civil society organisations might benefit from continuous monitoring of GWL data and education on climate change to help control and prevent excessive deteriorations of global climate change on GWL. [ABSTRACT FROM AUTHOR]

Automatic techniques have not been applied in the geochemical discrimination of marine, terrestrial and hydrothermal cherts, which is critical to retrieving the paleoenvironmental information of ancient sedimentary basins. Here, we applied three supervised machine‐learning methods to analyse the labelled geochemical compositional data (32 major and trace elements) of cherts, including the support vector machine (SVM), sparse multinomial regression and linear discrimination analysis. Our results show that these machine‐learning methods can successfully discriminate different chert types, as the lowest classification score is 79%. Among the methods, the SVM is the most powerful approach (classification score 88%, on average), even though it is hard to be geochemically interpreted. Sparse multinomial regression and linear discrimination analysis are also efficient approaches (mean classification score 84%), which can be used to extract the important elements and the most representative sample for each chert type. Especially, the linear discrimination analysis can construct binary discrimination diagram superposed with biplot rays and posterior probability‐based decision boundaries, which could be quickly applied and explained by other researchers. Furthermore, the successful discrimination of different chert types indicates that cherts derived from different silica sources have different geochemical signatures, albeit mixing between different sources of silica during chert formation is common. Last, the general geochemical features of each chert type were summarised based on geochemical information extracted by machine‐learning methods and previous geochemical studies. [ABSTRACT FROM AUTHOR]