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Recursive queries have been traditionally studied in the framework of datalog, a language that restricts recursion to monotone queries over sets, which is guaranteed to converge in polynomial time in the size of the input. But modern big data systems require recursive computations beyond the Boolean space. In this article, we study the convergence of datalog when it is interpreted over an arbitrary semiring. We consider an ordered semiring, define the semantics of a datalog program as a least fixpoint in this semiring, and study the number of steps required to reach that fixpoint, if ever. We identify algebraic properties of the semiring that correspond to certain convergence properties of datalog programs. Finally, we describe a class of ordered semirings on which one can use the semi-naïve evaluation algorithm on any datalog program. [ABSTRACT FROM AUTHOR]

We have reanalyzed the rich plethora of ground state configurations of the asymmetric Wigner bilayer system that we had recently published in a related diagram of states [Antlanger et al., Phys. Rev. Lett. 117, 118002 (2016)], comprising roughly 60 000 state points in the phase space spanned by the distance between the plates and the charge asymmetry parameter of the system. In contrast to this preceding contribution where the classification of the emerging structures was carried out "by hand," we have used for the present contribution machine learning concepts, notably based on a principal component analysis and a k-means clustering approach: using a 30-dimensional feature vector for each emerging structure (containing relevant information, such as the composition of the configuration as well as the most relevant order parameters), we were able to reanalyze these ground state configurations in a considerably more systematic and comprehensive manner than we could possibly do in the previously published classification scheme. Indeed, we were now able to identify new structures in previously unclassified regions of the parameter space and could considerably refine the previous classification scheme, thereby identifying a rich wealth of new emerging ground state configurations. Thorough consistency checks confirm the validity of the newly defined diagram of states. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are a key class of endogenous non-coding RNAs that play a pivotal role in regulating diseases. Accurately predicting the intricate relationships between miRNAs and diseases carries profound implications for disease diagnosis, treatment, and prevention. However, these prediction tasks are highly challenging due to the complexity of the underlying relationships. While numerous effective prediction models exist for validating these associations, they often encounter information distortion due to limitations in efficiently retaining information during the encoding-decoding process. Inspired by Multi-layer Heterogeneous Graph Transformer and Machine Learning XGboost classifier algorithm, this study introduces a novel computational approach based on multi-layer heterogeneous encoder—machine learning decoder structure for miRNA-disease association prediction (MHXGMDA). First, we employ the multi-view similarity matrices as the input coding for MHXGMDA. Subsequently, we utilize the multi-layer heterogeneous encoder to capture the embeddings of miRNAs and diseases, aiming to capture the maximum amount of relevant features. Finally, the information from all layers is concatenated to serve as input to the machine learning classifier, ensuring maximal preservation of encoding details. We conducted a comprehensive comparison of seven different classifier models and ultimately selected the XGBoost algorithm as the decoder. This algorithm leverages miRNA embedding features and disease embedding features to decode and predict the association scores between miRNAs and diseases. We applied MHXGMDA to predict human miRNA-disease associations on two benchmark datasets. Experimental findings demonstrate that our approach surpasses several leading methods in terms of both the area under the receiver operating characteristic curve and the area under the precision-recall curve. [ABSTRACT FROM AUTHOR]

The rig state plays a crucial role in recognizing the operations carried out by the drilling crew and quantifying Invisible Lost Time (ILT). This lost time, often challenging to assess and report manually in daily reports, results in delays to the scheduled timeline. In this paper, the Naive Bayes algorithm was used to establish a novel rig state. Training data, consisting of a large set of rules, was generated based on drilling experts' recommendations. This dataset was then employed to build a Naive Bayes classifier capable of emulating the cognitive processes of skilled drilling engineers and accurately recognizing the actual drilling operation from surface data. The developed model was used to process high-frequency drilling data collected from three wells, aiming to derive the Key Performance Indicators (KPIs) related to each drilling crew's efficiency and quantify the ILT during the drilling connections. The obtained results revealed that the established rig state excelled in automatically recognizing drilling operations, achieving a high success rate of 99.747%. The findings of this study offer valuable insights for drillers and rig supervisors, enabling real-time visual assessment of efficiency and prompt intervention to reduce ILT. [ABSTRACT FROM AUTHOR]

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Data has become much widely available in recent years. Since the past years, Learning classifiers from unbalanced data is a crucial issue that comes up frequently in classification difficulties. In such cases, the majority of the instances belong to one class while many fewer belong to the other class, which is typically the more significant class. As important data is extracted from data during learning, if the ratio between classes are changed.Class Imbalances causes the classifier's performance to decrease. The imbalanced data issues is well-known in numerous application areas and has recently become a open research challenge in data mining and learning algorithms. In such situation, nearly all the instances belong to majority class, but very few belongs to the minority class, which is often the most crucial class for prediction or detection. Since typical classifications demand a high accuracy over a complete range of examples in this situation, machine learning techniques frequently overwhelm the majority class and neglect the minority class. This survey first evaluated academic initiatives specifically aimed at the issues of class imbalance. Then, we analyzed numerous solutions at four levels during the learning stages. The purpose of survey is to present an overview of class imbalance problem that includes their issues, solution and their disadvantages. The survey concluded with suggestions for future investigation, research problems, and developments in the field. The survey involved the adaptive processes as well. [ABSTRACT FROM AUTHOR]

Around the world, 5% of adults suffer from depression, which is often inadequately treated. Depression is caused by a complex relationship of cultural, psychological, and physical factors. This growing issue has become a significant public health problem globally. Medical datasets often contain redundant characteristics, missing information, and high dimensionality. By using an iterative floating elimination feature selection algorithm and considering various factors, we can reduce the feature set and achieve optimized outcomes. The research utilizes the 36-Item Short Form Survey (SF-36) from the NHANES 2015–16 dataset, which categorizes data into seven groups relevant to quality of life and depression. This dataset presents a challenge due to its imbalance, with only 8.08% of individuals diagnosed with depression. The Depression Ensemble Stacking Generalization Model (DESGM) employs stratified k-fold cross-validation and oversampling for training data. DESGM enhances the classification performance of both base learners (linear support vector machine, perceptron, artificial neural network, linear discriminant analysis, and K-nearest neighbor) and meta-learners (logistic regression). The model achieved an F1 score of 0.9904 and an accuracy of 98.17%, with no instances of depression misdiagnosed. [ABSTRACT FROM AUTHOR]

This paper explores the combination of a data mining-based state evaluation method for electrical equipment in substations, analyzing the effectiveness and accuracy. First, a Gaussian mixture model is applied to fit all raw data of electrical equipment. The Expectation Maximization algorithm summarizes the data distribution characteristics and identifies outliers. The a priori algorithm is then employed for data mining to derive frequent itemsets and association rules between equipment quality and measurement data. For new equipment samples, conditional probabilities of each feature are independently calculated and combined to classify and evaluate equipment quality. The results suggest that equipment reliability in smart substations can be inferred from historical and real-time operational data using improved association rule algorithms and Naive Bayes classifiers. Finally, the proposed method was applied to analyze statistical data from a 110 kV substation of a power supply company. The states prediction accuracy exceeded 95% when compared with actual equipment quality. The effectiveness evaluation metrics demonstrated that this method outperforms single-category algorithms in terms of accuracy and discrimination ability. [ABSTRACT FROM AUTHOR]

Prediction processes in areas ranging from climate and disease spread to disasters and air pollution rely heavily on spatial–temporal data. Understanding and forecasting the distribution patterns of disease cases and climate change phenomena has become a focal point of researchers around the world. Machine learning models for prediction can generally be classified into 2: based on previous patterns such as LSTM and based on causal factors such as Naive Bayes and other classifiers. The main drawback of models such as Naive Bayes is that it does not have the ability to predict future trends because it only make predictionsin the present time. In this study, we propose a novel approach that makes the Naive Bayes classifier capable of predicting future classification. The process of expanding the dimension of the feature matrix based on historical data from several previous time periods is performed to obtain a long-term classification prediction model using Naive Bayes. The case studies used are the prediction of the distribution of the annual number of dengue fever cases in Bandung City and the distribution of monthly rainfall in Java Island, Indonesia. Through rigorous testing, we demonstrate the effectiveness of this Time-Based Feature Expansion approach in Naive Bayes in accurately predicting the distribution of annual dengue fever cases in 30 sub-districts in Bandung City and monthly rainfall in Java Island, Indonesia with with both accuracy and F1-score reaching more than 97%. [ABSTRACT FROM AUTHOR]

A common situation in classification tasks is to deal with unbalanced datasets, an issue that appears when the majority class(es) has a large number of samples compared to the minority class(es). This problem is even more significant when the datasets have a large number of features but only a few samples, as is the case with microarray datasets. Traditionally, an approach to alleviate this problem has been the application of sampling methods to obtain more balanced classes, increasing the number of samples in the minority class (replicating samples or generating new synthetic samples), or decreasing the number of samples in the majority class. In this study, we have compared different balancing methods, including a novel method that applies sampling in both the minority and majority classes. The interest in applying feature selection in combination with balancing methods has also been explored. In view of the results, a recommendation of sampling method, feature selection, and classifier is proposed to improve the classification results according to the type of dataset. [ABSTRACT FROM AUTHOR]

An efficient Four-Grade Astrocytoma brain tumor classification is proposed in this research work. The work highlights the optimal feature selection using a hybrid combination of Ant Colony Optimization (ACO) algorithm and machine learning techniques, leading to better classification accuracy. A diverse set of features including spatial and frequency domain features will provide better discrimination of patterns. ACO provides a robust and efficient mechanism for exploring the vast space of possible feature subsets. A total of 135 features are extracted including Tamura features, Gabor Wavelet Features, Coiflet Wavelet Coefficients, Speeded Up Robust Features, and GLCM features. Ant Colony Optimization is used to find out the optimum set of features. The selected feature set is used for training a Decision Tree classifier, where the Astrocytoma MRI images are categorized into four grades of astrocyoma namely, pilocytic astrocytoma, diffuse astrocytoma, anaplastic astrocytoma and glioblastoma multiforne. Classification is also performed using KNN classifier, SVM classifier and Ensemble Bagged Tree Classifier. Performance metrics like accuracy, sensitivity, specificity, precision and FScore are utilized to evaluate the proposed system's performance. It is found out that the decision tree classifier outperforms the other classifiers. Thus this integrated approach of ACO with machine learning classifiers results in improved Astrocytoma classification performance aiding clinicians in making accurate treatment decisions. [ABSTRACT FROM AUTHOR]

Quality prediction in additive manufacturing (AM) processes is crucial, particularly in high-risk manufacturing sectors like aerospace, biomedicals, and automotive. Acoustic sensors have emerged as valuable tools for detecting variations in print patterns by analyzing signatures and extracting distinctive features. This study focuses on the collection, preprocessing, and analysis of acoustic data streams from a Fused Deposition Modeling (FDM) 3D-printed sample cube (10 mm × 10 mm × 5 mm). Time and frequency-domain features were extracted at 10-s intervals at varying layer thicknesses. The audio samples were preprocessed using the Harmonic–Percussive Source Separation (HPSS) method, and the analysis of time and frequency features was performed using the Librosa module. Feature importance analysis was conducted, and machine learning (ML) prediction was implemented using eight different classifier algorithms (K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Trees (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM)) for the classification of print quality based on the labeled datasets. Three-dimensional-printed samples with varying layer thicknesses, representing two print quality levels, were used to generate audio samples. The extracted spectral features from these audio samples served as input variables for the supervised ML algorithms to predict print quality. The investigation revealed that the mean of the spectral flatness, spectral centroid, power spectral density, and RMS energy were the most critical acoustic features. Prediction metrics, including accuracy scores, F-1 scores, recall, precision, and ROC/AUC, were utilized to evaluate the models. The extreme gradient boosting algorithm stood out as the top model, attaining a prediction accuracy of 91.3%, precision of 88.8%, recall of 92.9%, F-1 score of 90.8%, and AUC of 96.3%. This research lays the foundation for acoustic based quality prediction and control of 3D printed parts using Fused Deposition Modeling and can be extended to other additive manufacturing techniques. [ABSTRACT FROM AUTHOR]

Diabetes is one of the most deadly chronic diseases because most sufferers do not realize they have it. A more accurate prediction of diabetes disease must be made to reduce the risk of bad things happening to sufferers. This research will optimize the decision tree (DT) classification method for diabetes prediction. Optimization is done by splitting criteria, splitting data, particle swarm optimization (PSO), and parameter optimization to find the highest and most accurate forecast of diabetes. Splitting criteria is done by comparing the results of three criteria, namely gain ratio (GR), information gain (IG), and gini index (GI). Splitting data is done by dividing training data and testing data into three comparison groups, namely 70:30, 80:20, and 90:10. The application of PSO and parameter optimization is carried out to increase the accuracy value. The processed data is taken from the UCI machine learning repository with 520 records and 17 attributes (1 class/label attribute). From the experiments, the GI criterion with splitting data 90:10 obtained the greatest accuracy of 98.08%, and the combination with PSO resulted in an accuracy of 97.66%. Meanwhile, parameter optimization with splitting data 90:10 combined with GR criteria resulted in the highest accuracy of 97.90%. [ABSTRACT FROM AUTHOR]

Random forest is a powerful ensemble learning technique celebrated for its heightened predictive performance and robustness in handling complex datasets; nevertheless, it is criticized for its computational expense, particularly with a large number of trees in the ensemble. Moreover, the model's interpretability diminishes as the ensemble's complexity increases, presenting challenges in understanding the decision-making process. Although various pruning techniques have been proposed by researchers to tackle these issues, achieving a consensus on the optimal strategy across diverse datasets remains elusive. In response to these challenges, this paper introduces an innovative machine learning algorithm that integrates random forest with Naïve Bayes to predict student performance. The proposed method employs the Naïve Bayes formula to evaluate random forest branches, classifying data by prioritizing branches based on importance and assigning each example to a single branch for classification. The algorithm is utilized on two sets of student data and is evaluated against seven alternative machine-learning algorithms. The results confirm its strong performance, characterized by a minimal number of branches. [ABSTRACT FROM AUTHOR]

Text classification tasks, particularly those involving a large number of features, pose significant challenges in effective feature selection. This research introduces a novel methodology, MBO-NB, which integrates Migrating Birds Optimization (MBO) approach with naïve Bayes as an internal classifier to address these challenges. The motivation behind this study stems from the recognized limitations of existing techniques in efficiently handling extensive feature sets. Traditional approaches often fail to adequately streamline the feature selection process, resulting in suboptimal classification accuracy and increased computational overhead. In response to this need, our primary objective is to propose a scalable and effective solution that enhances both computational efficiency and classification accuracy in text classification systems. To achieve this objective, we preprocess raw data using the Information Gain algorithm, strategically reducing the feature count from an average of 62,221 to 2,089. Through extensive experiments, we demonstrate the superior effectiveness of MBO-NB in feature reduction compared to other existing techniques, resulting in significantly improved classification accuracy. Furthermore, the successful integration of naïve Bayes within MBO offers a comprehensive and well-rounded solution to the feature selection problem. In individual comparisons with Particle Swarm Optimization (PSO), MBO-NB consistently outperforms by an average of 6.9% across four setups. This research provides valuable insights into enhancing feature selection methods, thereby contributing to the advancement of text classification techniques. By offering a scalable and effective solution, MBO-NB addresses the pressing need for improved feature selection methods in text classification, thereby facilitating the development of more robust and efficient classification systems. [ABSTRACT FROM AUTHOR]

The k-nearest neighbor algorithm is a powerful classification method. However, its classification performance will be affected in small-size samples with existing outliers. To address this issue, a pre-averaged pseudo nearest neighbor classifier (PAPNN) is proposed to improve classification performance. In the PAPNN rule, the pre-averaged categorical vectors are calculated by taking the average of any two points of the training sets in each class. Then, k-pseudo nearest neighbors are chosen from the preprocessed vectors of every class to determine the category of a query point. The pre-averaged vectors can reduce the negative impact of outliers to some degree. Extensive experiments are conducted on nineteen numerical real data sets and three high dimensional real data sets by comparing PAPNN to other twelve classification methods. The experimental results demonstrate that the proposed PAPNN rule is effective for classification tasks in the case of small-size samples with existing outliers. [ABSTRACT FROM AUTHOR]

Transit deserts refer to regions with a gap in transit services, with the demand for transit exceeding the supply. This study goes beyond merely identifying transit deserts to suggest actionable solutions. Using a multi-class supervised machine learning framework, we analyzed factors leading to transit deserts, distinguishing demand by gender. Our focus was on peak-time periods. After assessing the Support Vector Machine, Decision Tree, Random Forest, and K-nearest Neighbor, we settled on the Random Forest method, supported by Diverse Counterfactual Explanation and SHapley Additive Explanation in our analysis. The ranking of feature importance in the trained Random Forest model revealed that factors such as density, design, distance to transit, diversity in the built environment, and sociodemographic characteristics significantly contribute to the classification of transit deserts. Diverse Counterfactual Explanation suggested that a reduction in population density and an increase in the proportion of green open spaces would likely facilitate the transformation of transit deserts into transit oases. SHapley Additive Explanation highlighted the differential impact of various features on each identified transit desert. Our analysis results indicate that identifying transit deserts can vary depending on whether the data is aggregated or separated by demographics. We found areas that have unique transit needs based on gender. The disparity in transit services was particularly pronounced for women. Our model pinpointed the core elements that define a transit desert. Broadly, to address transit deserts, strategies should prioritize the needs of disadvantaged groups and enhance the design and accessibility of transit in the built environment. Our research extends existing analyses of transit deserts by leveraging machine learning to develop a predictive model. We developed a machine learning-powered interactive dashboard. Integrating participatory planning approaches with the development of an interactive interface could enhance ongoing community engagement. Planning practices can evolve with AI in the loop. [ABSTRACT FROM AUTHOR]

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability. [ABSTRACT FROM AUTHOR]

Building shape cognition is essential for tasks, such as map generalization, urban modeling, and building semantics and distribution pattern recognition. Traditional geometric and statistical methods rely on human‐defined shape indicators, and spectral‐based graph neural networks (GNNs) require Laplacian eigendecomposition, resulting in high algorithmic complexity. Therefore, we proposed a low‐complexity and simple‐to‐use spatial‐domain GNN for differentiating building shapes. To examine the influence of the building vertices on their shape, we treated each building as a graph and proposed a graph isomorphic network with weighted multi‐aggregators (GIN‐WMA) by analyzing the node connectivity of a building graph. The GIN‐WMA utilizes a novel aggregator that combines the sum and max aggregators, enhancing its recognition and differentiation capabilities. This approach can effectively differentiate nodes that have identical features after aggregation by the sum aggregator. We extracted features considering both local node and global shape features, drawing inspiration from Gestalt cognitive psychology and GNN's “node–graph” differentiation strategy. In addition, we compared the performance of GIN‐WMA with existing methods, studying the effect of various node features and their combinations on classification accuracy. The results demonstrated that GIN‐WMA outperforms other methods in discriminating building shapes, demonstrating superior capabilities in shape classification and enabling end‐to‐end extraction and classification of building shapes. [ABSTRACT FROM AUTHOR]

Research on personal adornments depends on the reliable characterisation of materials to trace provenance and model complex social networks. However, many analytical techniques require the transfer of materials from the museum to the laboratory, involving high insurance costs and limiting the number of items that can be analysed, making the process of empirical data collection a complicated, expensive and time-consuming routine. In this study, we compiled the largest geochemical dataset of Iberian personal adornments (n = 1243 samples) by coupling X-ray fluorescence compositional data with their respective X-ray diffraction mineral labels. This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. As a proof of concept, we developed a multiclass model and evaluated its performance on two assemblages from different Portuguese sites with current mineralogical characterisation: Cova das Lapas (n = 15 samples) and Gruta da Marmota (n = 10 samples). Our results showed that decisión-tres based classifiers outperformed other classification logics given the discriminative importance of some chemical elements in determining the mineral phase, which fits particularly well with the decision-making process of this type of model. The comparison of results between the different validation sets and the proof-of-concept has highlighted the risk of using synthetic data to handle imbalance and the main limitation of the framework: its restrictive class system. We conclude that the presented approach can successfully assist in the mineral classification workflow when specific analyses are not available, saving time and allowing a transparent and straightforward assessment of model predictions. Furthermore, we propose a workflow for the interpretation of predictions using the model outputs as compound responses enabling an uncertainty reduction approach currently used by our team. The Python-based framework is packaged in a public repository and includes all the necessary resources for its reusability without the need for any installation. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to enhance the accuracy of patch-level Gleason grading in prostate histopathology images, a critical task in the diagnosis and prognosis of prostate cancer. This study shows that the Gleason grading accuracy can be improved by addressing the prevalent issue of label inconsistencies in the SICAPv2 prostate dataset, which employs a majority voting scheme for patch-level labels. We propose a multi-label ensemble deep-learning classifier that effectively mitigates these inconsistencies and yields more accurate results than the state-of-the-art works. Specifically, our approach leverages the strengths of three different one-vs-all deep learning models in an ensemble to learn diverse features from the histopathology images to individually indicate the presence of one or more Gleason grades (G3, G4, and G5) in each patch. These deep learning models have been trained using transfer learning to fine-tune a variant of the ResNet18 CNN classifier chosen after an extensive ablation study. Experimental results demonstrate that our multi-label ensemble classifier significantly outperforms traditional single-label classifiers reported in the literature by at least 14% and 4% on accuracy and f1-score metrics respectively. These results underscore the potential of our proposed machine learning approach to improve the accuracy and consistency of prostate cancer grading. [ABSTRACT FROM AUTHOR]

This article presents the discovery of an explicit 16-stage Runge–Kutta method that numerically satisfies the Runge–Kutta order conditions (in Butcher form) to 10th order, conjecturally improving the best-known number of stages in an explicit 10th-order Runge–Kutta method from 17 to 16. Unlike the vast majority of published Runge–Kutta methods, the method presented in this paper was not constructed via symbolic analysis or the use of simplifying assumptions, but instead by directly applying numerical optimization and root-finding algorithms to the order conditions. A naïve implementation of these algorithms would be made computationally infeasible by the considerable size and complex structure of the Butcher equations. However, we present a collection of software optimizations that greatly accelerate the evaluation of the Butcher equations and their derivatives while mitigating the effects of destructive cancellation, pushing these techniques within the realm of feasibility. While we do not have a formal proof of order, we present the results of numerical experiments to demonstrate that our method satisfies the Butcher equations to an accuracy of over 3000 decimal digits. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1 , EPHA2 , CLSPN , OLFML3 , and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ. [ABSTRACT FROM AUTHOR]

The worldwide dispersal of the ectoparasitic mite Varroa destructor from its Asian origins has fundamentally transformed the relationship of the honey bee (Apis mellifera) with several of its viruses, via changes in transmission and/or host immunosuppression. The extent to which honey bee-virus relationships change after Varroa invasion is poorly understood for most viruses, in part because there are few places in the world with several geographically close but completely isolated honey bee populations that either have, or have not, been exposed long-term to Varroa, allowing for separate ecological, epidemiological, and adaptive relationships to develop between honey bees and their viruses, in relation to the mite's presence or absence. The Azores is one such place, as it contains islands with and without the mite. Here, we combined qPCR with meta-amplicon deep sequencing to uncover the relationship between Varroa presence, and the prevalence, load, diversity, and phylogeographic structure of eight honey bee viruses screened across the archipelago. Four viruses were not detected on any island (ABPV-Acute bee paralysis virus, KBV-Kashmir bee virus, IAPV-Israeli acute bee paralysis virus, BeeMLV-Bee macula-like virus); one (SBV-Sacbrood virus) was detected only on mite-infested islands; one (CBPV-Chronic bee paralysis virus) occurred on some islands, and two (BQCV-Black queen cell virus, LSV-Lake Sinai virus,) were present on every single island. This multi-virus screening builds upon a parallel survey of Deformed wing virus (DWV) strains that uncovered a remarkably heterogeneous viral landscape featuring Varroa-infested islands dominated by DWV-A and -B, Varroa-free islands naïve to DWV, and a refuge of the rare DWV-C dominating the easternmost Varroa-free islands. While all four detected viruses investigated here were affected by Varroa for one or two parameters (usually prevalence and/or the Richness component of ASV diversity), the strongest effect was observed for the multi-strain LSV. Varroa unambiguously led to elevated prevalence, load, and diversity (Richness and Shannon Index) of LSV, with these results largely shaped by LSV-2, a major LSV strain. Unprecedented insights into the mite-virus relationship were further gained from implementing a phylogeographic approach. In addition to enabling the identification of a novel LSV strain that dominated the unique viral landscape of the easternmost islands, this approach, in combination with the recovered diversity patterns, strongly suggests that Varroa is driving the evolutionary change of LSV in the Azores. This study greatly advances the current understanding of the effect of Varroa on the epidemiology and adaptive evolution of these less-studied viruses, whose relationship with Varroa has thus far been poorly defined. Author summary: Honey bees are plagued by many enemies, and the Varroa mite is one of the most important of these. Varroa hurts bees by feeding on their haemolymph, but more importantly, by facilitating the transmission and development of many viruses. The impact of Varroa on most honey bee viruses remains poorly understood. Here, we capitalized on the exceptional Azores setting, which contains islands with and without Varroa, to gain unprecedented insights into the complex mite-virus interactions. We uncovered a very heterogenous viral landscape, with one virus (SBV) occurring only on mite-infested islands, two (CBPV and DWV) on some islands, and two (BQCV and LSV) on every single island. While Varroa influenced the prevalence and/or diversity of all four viruses, its strongest effect was observed for LSV, with the mite leading to elevated LSV prevalence, loads, and diversity (number of variants and their relative abundance). Furthermore, we discovered a novel LSV strain and showed for the first time that the epidemiology of LSV-2, a major strain of LSV, is unambiguously linked to the presence of Varroa. Our findings not only deepen current scientific understanding of the mite-virus relationships but are also of value for assisting veterinary authorities in decision-making regarding the movement of bees across territories. [ABSTRACT FROM AUTHOR]

Rockburst is a mine dynamic disaster caused by the rapid release of elastic strain energy of surrounding rock. As the depth of engineering project operations increases, accurate classification of rockburst intensity cannot be achieved based on conventional criteria due to high uncertainty and unpredictability of rockburst. In this regard, an AOA-Voting-Soft ensemble machine learning was proposed in this study by combining seven individual classifiers, i.e., eXtreme gradient boosting, support vector machines, multilayer perceptron, k-nearest neighbor, random forest, naive Bayesian, and gradient boosting decision Tree. In addition, outliers were eliminated by means of density-based spatial clustering of applications with noise, and CURE-MeanradiusSMOTE was adopted to obtain a balanced data structure. Furthermore, the optimal combination of classifiers in Voting was determined by the game theory and the exhaustive search method. Weights of individual learners in Voting were determined through the arithmetic optimization algorithm and fivefold cross-validation. The results show that the prediction accuracy of the ensemble algorithm proposed in this study is 4.4% higher than that of the individual classifier with optimal performance. The importance analysis indicates that the elastic energy index is the most important variable that affects rockburst intensity grades. Moreover, this rockburst ensemble method can be applied further to solve other classification problems in underground engineering projects. Highlights: This study improves the data preprocessing method, outliers were eliminated by means of density-based spatial clustering of applications with noise, and CURE-MeanradiusSMOTE was proposed to obtain a balanced data structure. This study presents a hybrid ensemble model for Rockburst intensity grade prediction, combining a new metaheuristic method with the Voting-Soft model. This study combines game theory and method of exhaustion to determine the best classifier combination in voting. The weights of individual learners in Voting were determined through arithmetic optimization algorithm and fivefold cross-validation. Sensitivity study was conducted on input variables with RBD-FAST, and the results suggest that W et is the most important input variable. [ABSTRACT FROM AUTHOR]

Unsafe behavior is considered the primary cause of construction safety accidents. However, the main measures for unsafe behavior management are real-time monitoring and postevent correction, which cannot prevent unsafe behavior. Therefore, this study attempted to construct an artificial cognition approach to predict the potential unsafe behavior of workers and explain why workers engage in unsafe behaviors. First, based on the cognitive model of unsafe behavior, data on workers were collected with a questionnaire, and the cognitive model was validated. Second, the cognitive process of unsafe behaviors was analyzed using latent class analysis, and the cognitive characteristics of four types of unsafe behaviors were obtained. Subsequently, with the cognitive model of unsafe behavior as the input attribute, seven types of algorithms (gradient Boosting, random forest, naïve bayes, back propagation, K-nearest neighbor, logistic regression, and support vector machine) were used to construct artificial cognition to predict the potential unsafe behaviors of workers. The results showed that all seven algorithms performed well for prediction. Thus, artificial cognition that simulates the cognitive process of unsafe behavior is not limited to particular algorithms. Finally, artificial cognition was empirically validated in a construction project. The findings demonstrated that artificial cognition could effectively predict the potential unsafe behavior of workers and provide an explanation for why workers engage in unsafe behaviors. [ABSTRACT FROM AUTHOR]

With the progress of control and communication technology, coal mining is gradually transformed and upgraded from traditional mechanism to automation, the concept of intelligent mining in fully mechanized caving is also put forward in the mining technology research of thick and extra-thick coal seams. As a key technology and necessary condition of intelligent mining in fully mechanized caving, intelligent recognition of coal and gangue is a technical bottleneck to be broken through, which has seriously affected the coal mining efficiency and recovery rate. Based on this, this paper carries out a systematic study on coal gangue recognition in fully mechanized caving from the perspective of tail beam vibration. Firstly, the impact-contact dynamic theoretical model between coal gangue and the hydraulic support tail beam and its rigid-flexible coupling dynamic simulation model was constructed respectively. The theoretical and numerical simulation was combined to study the vibration characteristics induced by the contact between coal gangue and the tail beam, and the vibration characteristics caused by the difference of coal and gangue properties were compared and analyzed. Meanwhile, coal gangue recognition capability is summarized, which provides theoretical support for the vibration characteristics-based coal gangue recognition. Secondly, through analysis of fully mechanized caving environment, a single coal drawing experimental platform and vibration information acquisition system are designed and built in the laboratory environment. Then, a research on signal eigenvalue recognition sensitivity based on "coal gangue binary classification" recognition was carried out through the combination of signal acquisition and features extraction as well as machine learning, the optimal recognition eigenvector based on recognition effect analysis was obtained. On that premise, recognition research of "coal gangue multi-category recognition" based on the single point and multi-point vibration signals were proposed and conducted, which successfully realized coal gangue mixture ratio recognition under the condition of multi-category. The research provided the theoretical basis and technical support for accurate and real-time coal gangue mixture ratio recognition in top coal caving. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to determine propeller damage based on acoustic recordings taken from unmanned aerial vehicle (UAV) propellers operated at different thrust conditions on a test bench. Propeller damage is especially critical for fixed-wing UAVs to sustain a safe flight. The acoustic characteristics of the propeller vary with different propeller damages. Design/methodology/approach: For the research, feature extraction methods and machine learning techniques were used during damage detection from propeller acoustic data. First of all, sound recordings were obtained by operating five different damaged propellers and undamaged propellers under three different thrusts. Afterwards, the harmonic-to-noise ratio (HNR) feature extraction technique was applied to these audio recordings. Finally, model training and validation were performed by applying the Gaussian Naive Bayes machine learning technique to create a diagnostic approach. Findings: A high recall value of 96.19% was obtained in the performance results of the model trained according to damaged and undamaged propeller acoustic data. The precision value was 73.92% as moderate. The overall accuracy value of the model, which can be considered as general performance, was obtained as 81.24%. The F1 score has been found as 83.76% which provides a balanced measure of the model's precision and recall values. Practical implications: This study include provides solid method to diagnose UAV propeller damage using acoustic data obtain from the microphone and allows identification of differently damaged propellers. Using that, the risk of in-flight failures can be reduced and maintenance costs can be lowered with addressing the occurred problems with UAV propeller before they worsen. Originality/value: This study introduces a novel method to diagnose damaged UAV propellers using the HNR feature extraction technique and Gaussian Naive Bayes classification method. The study is a pioneer in the use of HNR and the Gaussian Naive Bayes and demonstrates its effectiveness in augmenting UAV safety by means of propeller damages. Furthermore, this approach contributes to UAV operational reliability by bridging the acoustic signal processing and machine learning. [ABSTRACT FROM AUTHOR]

The development of e-commerce in Indonesia is driven by the large population and extensive geography. One of the leading e-commerce applications in Indonesia is Shopee. User reviews of this application reflect public sentiment towards Shopee. The Naive Bayes method was used to classify reviews, with an accuracy rate of 90.76%. Using TF-IDF helps calculate the weight of words in reviews. Performance evaluation shows that the model has high accuracy in scenario 1 with a 60:40 split between training data and test data. The use of information technology has changed the business paradigm, including in e-commerce such as Shopee. The importance of understanding consumer sentiment can be seen from social media platforms, where sentiment analysis using Naive Bayes and Topic Modeling shows negative sentiment at the 11:11 Shopee Flashsale event. Suggestions for Shopee include improving event strategies such as Flashsale by inviting artists and improving customer service. Responsiveness to technical application problems is also emphasized to increase customer satisfaction. The increase in e-commerce transactions in Indonesia shows rapid growth, with many platforms emerging. Sentiment analysis such as that done with K-Nearest Neighbor (K-NN) on Google Play Store reviews provides insight into the experience of e-commerce users in Indonesia, with an accuracy rate of up to 82%. This research provides significant implications for e-commerce companies in better understanding and meeting customer expectations. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) data obtained at multiple frequencies and polarizations offers valuable complementary information for classifying mixed classes that exhibit similar backscattering response. Although deep learning-based convolutional neural networks (CNNs) effectively extract features from multi-frequency SAR data, the arbitrary ordering of SAR features may hinder optimal convolution of the best feature sub-space for a specific class and underutilize available multi-frequency data. To address this, a novel CNN transforming SAR feature-space from 1-D to 2-D and employing varied dilation-rate convolutions is introduced. This transformation maximizes unique and localized feature combinations, efficiently utilizing the available feature sub-spaces and extracting discriminative features for accurate classifications, addressing the challenge of arbitrary band neighborhoods. Utilizing dual-polarization SAR data from ALOS-2 PALSAR-2 and Sentinel-1 sensors, the proposed CNN achieves an average f-score of 0.97 and a kappa coefficient of 0.97, an improvement of 11 %, 7 % and 3 % in OA compared to the 1-D, 2-D and 3-D CNN classifiers, without feature transformation. The classifier's generalization ability is evaluated using ground truth knowledge of various heterogeneous classes, and the proposed CNN classifier outperforms others in terms of accuracy metrics and generalization ability. [ABSTRACT FROM AUTHOR]

Education is essential and increasingly crucial for the development of almost all countries worldwide. As educational data has become increasingly available, scholars have shown a growing interest in exploring the correlation between students’ academic achievements and other factors that may impact their performance using machine learning algorithms. This research paper introduces a novel hybrid classifier that aims to predict the academic performance of students by using a combination of different single algorithms. The proposed hybrid classifier (PHC) is compared to six available classification algorithms (random forest (RF), C4.5, classification and regression trees (CART), support vector machines (SVM), naive Bayes (NB) and K-nearest neighbors (KNN)) using recall, precision, F1-score, and accuracy evaluation measures. Our experimental results reveal that the PHC classifier consistently outperforms the individual classifiers across multiple evaluation metrics. Specifically, the PHC classifier achieved an accuracy rate of 92.40%, surpassing the RF, C4.5, and CART classifiers, which were the next best performers. In terms of precision and F1 score, the PHC also demonstrated superior performance, indicating its robustness in correctly identifying positive instances and providing balanced accuracy. While the C4.5 classifier performed comparably to the PHC classifier concerning the recall metric, the hybrid model’s overall performance highlights its effectiveness in leveraging the complementary strengths of the included classifiers. The suggested hybrid model has the potential to enhance students’ academic performance and success more effectively and efficiently. It could benefit students, educators, and academic institutions. Additionally, it provides practical insights for educators and institutions striving to improve student achievement using predictive analysis. [ABSTRACT FROM AUTHOR]

As a popular nutraceutical, collagen powder is the target of adulteration like other high-value food products, therefore detection of adulteration in collagen powder is of great practical significance to ensure market order and the health of the people. To achieve accurate detection of adulterants in collagen powder, a modeling method of fluorescence hyperspectral technology combined with machine learning algorithm was proposed. This study employed various preprocessing methods for denoising and spectral correction to enhance the effective spectral data. Principal component analysis was used to visualize the spectral data and initially revealed the spatial distribution of adulterated collagen powder. Genetic algorithm-k nearest neighbor, particle swarm optimization-support vector machine, and gradient boosting decision tree were used to construct classification models to identify adulterated collagen powder, with the best 2-class discriminant model accuracy, 4-class discriminant model accuracy, and 5-class discriminant model accuracy reaching 99%, 94%, and 98%, respectively. In the quantitative detection models of adulteration level, the random forest model performed best with correlation coefficient (R2) of 0.95 to 0.99. These results suggested that fluorescence hyperspectral technology combined with machine learning algorithm can be effectively used to detect adulterated collagen powder. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance individually. Prior studies have mostly concentrated on the identification of high-power appliances like HVAC systems while overlooking the existence of low-power appliances. Low-power consumer appliances have comparable power consumption patterns, which can complicate the detection task and can be mistaken as noise. This research tackles the problem of classification of low-power appliances and uses turn-on current transients to extract novel features and develop unique appliance signatures. A hybrid feature extraction method based on mono-fractal and multi-fractal analysis is proposed for identifying low-power appliances. Fractal dimension, Hurst exponent, multifractal spectrum and the Hölder exponents of switching current transient signals are extracted to develop various 'turn-on' appliance signatures for classification. Four classifiers, i.e. deep neural network, support vector machine, decision trees, and K-nearest neighbours have been optimized using Bayesian optimization and trained using the extracted features. The simulated results showed that the proposed method consistently outperforms state-of-the-art feature extraction methods across all optimized classifiers, achieving an accuracy of up to 96 % in classifying low-power appliances. [ABSTRACT FROM AUTHOR]

In the process of crowdsourced testing, massive bug reports are submitted. Among them, the severity level of the bug report is an important indicator for traigers of crowdsourced platforms to arrange the order of reports efficiently so that developers can prioritize high‐severity defects. A lot of work has been devoted to the study of automatically assigning severity levels to a large number of bug reports in crowdsourcing test systems. The research objects of these works are standard bug reports, focusing on the text part of the report, using various feature engineering methods and classification techniques. However, while achieving good performance, these methods still need to overcome two challenges: no consideration of image information in mobile testing and discontinuous semantic information of words in bug reports. In this paper, we propose a new method of severity prediction by using heterogeneous graph convolutional networks with screenshots (SPHGCN‐S), which combines text features and screenshots information to understand the report more comprehensively. In addition, our approach applies the heterogeneous graph convolutional network (HGCN) architecture, which can capture the global word information to alleviate the semantic problem of word discontinuity and underlying relations between reports. We conduct a comprehensive study to compare seven commonly adopted bug report severity prediction methods with our approach. The experimental results show that our approach SPHGCN‐S can improve severity prediction performance and effectively predict reports with high severity. [ABSTRACT FROM AUTHOR]

Remote sensing image classification plays a crucial role in the field of remote sensing interpretation. With the exponential growth of multi-source remote sensing data, accurately extracting target features and comprehending target attributes from complex images significantly impacts classification accuracy. To address these challenges, we propose a Canny edge-enhanced multi-level attention feature fusion network (CAF) for remote sensing image classification. The original image is specifically inputted into a convolutional network for the extraction of global features, while increasing the depth of the convolutional layer facilitates feature extraction at various levels. Additionally, to emphasize detailed target features, we employ the Canny operator for edge information extraction and utilize a convolution layer to capture deep edge features. Finally, by leveraging the Attentional Feature Fusion (AFF) network, we fuse global and detailed features to obtain more discriminative representations for scene classification tasks. The performance of our proposed method (CAF) is evaluated through experiments conducted across three openly accessible datasets for classifying scenes in remote sensing images: NWPU-RESISC45, UCM, and MSTAR. The experimental findings indicate that our approach based on incorporating edge detail information outperforms methods relying solely on global feature-based classifications. [ABSTRACT FROM AUTHOR]