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Cardiotocography (CTG) is a clinical procedure that is used to track and gauge the severity of fetal distress. Although CTG is the most often used equipment to monitor and assess the health of the fetus, the high rate of false positive results due to visual interpretation significantly contributes to needless surgical delivery or delayed intervention. In this study, a novel approach is introduced where both printing CTG paper is digitized and a machine learning approach is employed to detect the abnormality in the digitized CTG signal. Image processing-based preprocessing steps are employed to make the printing of CTG paper more convenient to extract the CTG signal. Various signal-processing techniques are used to calibrate the extracted CTG signal. Then, Empirical Mode Decomposition (EMD) is used to decompose the CTG signal into its frequency components and instantaneous frequency and spectral entropy features are extracted. After feature normalization and feature selection with ReliefF algorithm, support vector machines (SVM) is used for the classification of the normal and abnormal classes. A novel dataset is used in the experimental works and various performance evaluation metrics are used for the evaluation of the achievement of the proposed method. 10-fold cross-validation-based experiments show that the proposed method is quite efficient in abnormality detection in printing CTG papers where an average accuracy score of around 90.0% is produced.

Dimensionality Reduction (DR) is the process of reducing the numerous features or random variables under consideration to a limited number of features by obtaining a set of principal variables. These techniques cater great values in machine learning, which come in handy to simplify a classification or a regression dataset, thereby yielding a better-performing predictive model. Techniques used for DR include Feature Selection methods, Matrix Factorization, AutoEncoder methods, and Manifold Learning. Merits of DR include data compression, reduced space of storage, and removal of redundant features. This paper attempts to review various techniques used to carry out dimensionality reduction while providing an exhaustive comparative study over the merits and demerits of each of the techniques used under the empirical experiments performed by the authors whose work is being reviewed. [ABSTRACT FROM AUTHOR]

Cross project defect prediction (CPDP) is a key method for estimating defect-prone modules of software products. CPDP is a tempting approach since it provides information about predicted defects for those projects in which data are insufficient. Recent studies specifically include instructions on how to pick training data from large datasets using feature selection (FS) process which contributes the most in the end results. The classifier helps classify the picked-up dataset in specified classes in order to predict the defective and non-defective classes. The aim of our research is to select the optimal set of features from multi-class data through a search-based optimizer for CPDP. We used the explanatory research type and quantitative approach for our experimentation. We have F1 measure as our dependent variable while as independent variables we have KNN filter, ANN filter, random forest ensemble (RFE) model, genetic algorithm (GA), and classifiers as manipulative independent variables. Our experiment follows 1 factor 1 treatment (1F1T) for RQ1 whereas for RQ2, RQ3, and RQ4, there are 1 factor 2 treatments (1F2T) design. We first carried out the explanatory data analysis (EDA) to know the nature of our dataset. Then we pre-processed our data by removing and solving the issues identified. During data preprocessing, we analyze that we have multi-class data; therefore, we first rank features and select multiple feature sets using the info gain algorithm to get maximum variation in features for multi-class dataset. To remove noise, we use ANN-filter and get significant results more than 40% to 60% compared to NN filter with base paper (all, ckloc, IG). Then we applied search-based optimizer i.e., random forest ensemble (RFE) to get the best features set for a software prediction model and we get 30% to 50% significant results compared with genetic instance selection (GIS). Then we used a classifier to predict defects for CPDP. We compare the results of the classifier with base paper classifier using F1-measure and we get almost 35% more than base paper. We validate the experiment using Wilcoxon and Cohen's d test. [ABSTRACT FROM AUTHOR]

Process-monitoring laboratories in the pulp and paper industry generally use a combination of wet chemical analyses and physical measurements to certify the fitness of a production pulp for a specific end-use. These laboratory tests require time and the effort of trained personnel, limiting their utility for real-time process control. Here we show that Raman probes of unrefined cellulosic pulps, well-suited to the online measurement of in-process materials, can predict the quality attributes of manufactured papers. The accuracy of prediction improves when the covariance is modelled in a reduced measurement space selected by a data-driven, feature-selection technique referred to as a Template Oriented Genetic Algorithm (TOGA). TOGA, combined with discrete wavelet transform (DWT), isolates functional-group features that correlate best with mechanical properties paper derived from refined pulp. Paper makers refine market pulps to build sheet strength using a beating process that decreases freeness as it increases fibre-fibre bonding. Methods demonstrated here predict manufactured sheet properties obtainable after any specified degree of refining from the Raman spectrum of an unrefined pulp. This analysis capacity will enable both vendors of market pulp and makers of sheet paper to specify in advance the amount of beating required to produce a desired product, thereby saving cost and conserving resources. [ABSTRACT FROM AUTHOR]

Bearing fault diagnosis for equipment-safe operation has a crucial role. In recent years, more achievements have been made in bearing fault diagnosis. However, for the fault diagnosis model, the representation and sensitivity of bearing fault features have a great influence on the diagnosis output results; thus, the attention mechanism is particularly important for the selection of features. However, global attention focuses on all sequences, which is computationally expensive and not ideal for fault diagnosis tasks. The local attention mechanism ignores the relationship between non-adjacent sequences. To address the respective shortcomings of global attention and local attention, an adaptive sparse attention network is proposed in this paper to filter fault-sensitive information by soft threshold filtering. In addition, the effects of different signal representation domains on fault diagnosis results are investigated to filter out signal representation forms with better performance. Finally, the proposed adaptive sparse attention network is applied to cross-working conditions diagnosis of bearings. The adaptive sparse attention mechanism focuses on the signal characteristics of different frequency bands for different fault types. The proposed network model achieves better overall performance when comparing the cross-conditions diagnosis accuracy and model convergence speed. [ABSTRACT FROM AUTHOR]

In the realm of network intrusion detection using machine learning, feature selection aims for computational efficiency, enhanced performance, and model interpretability, preventing overfitting and optimizing data visualization. This paper proposes a filtering method for feature selection, which optimizes information quantity and linear correlation between resultant features. The method identifies Pareto dominant pairs of informative and correlated features, constructs a graph, and selects key features based on betweenness centrality in its connected components. The proposal yields a more concise and informative dataset representation. Experimental results, using three diverse datasets, demonstrate that the proposal achieves more than 95% accuracy in classifying network attacks with just 14% of the total number features in original datasets. [ABSTRACT FROM AUTHOR]

Crowdsourcing is a task-solving model in which human crowd is hired to solve a particular task. During the crowdsourcing process, the crowd selection is performed in order to select appropriate crowd workers for a specific task; without appropriate selection of crowd workers, the process of crowdsourcing is aimless. The main goal of this paper was to identify the features of crowd in crowdsourcing activity, reasons behind crowd participation in the activity of crowdsourcing, and the existing techniques that were utilized for crowd selection in crowdsourcing. Search strings with corresponding keywords were used to capture relevant studies related to crowdsourcing, and crowd selection was classified under conference papers, journal articles, proceedings, and book chapters. 81 relevant studies are selected from 7 digital data repositories using a search strategy. In crowdsourcing practices, crowd selection was considerably addressed. Nonetheless, it has been noticed that the selection is based only on a single crowd worker attribute such as confidence, past success, efficiency, and experience. For the efficiency and effectiveness of the crowdsourcing operation, crowd selection on multicriteria features is essential. [ABSTRACT FROM AUTHOR]

Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art. [ABSTRACT FROM AUTHOR]

While the musical instrument classification task is well-studied, there remains a gap in identifying non-pitched percussion instruments which have greater overlaps in frequency bands and variation in sound quality and play style than pitched instruments. In this paper, we present a musical instrument classifier for detecting tambourines, maracas and castanets, instruments that are often used in early childhood music education. We generated a dataset with diverse instruments (e.g., brand, materials, construction) played in different locations with varying background noise and play styles. We conducted sensitivity analyses to optimize feature selection, windowing time, and model selection. We deployed and evaluated our best model in a mixed reality music application with 12 families in a home setting. Our dataset was comprised of over 369,000 samples recorded in-lab and 35,361 samples recorded with families in a home setting. We observed the Light Gradient Boosting Machine (LGBM) model to perform best using an approximate 93 ms window with only 12 mel-frequency cepstral coefficients (MFCCs) and signal entropy. Our best LGBM model was observed to perform with over 84% accuracy across all three instrument families in-lab and over 73% accuracy when deployed to the home. To our knowledge, the dataset compiled of 369,000 samples of non-pitched instruments is first of its kind. This work also suggests that a low feature space is sufficient for the recognition of non-pitched instruments. Lastly, real-world deployment and testing of the algorithms created with participants of diverse physical and cognitive abilities was also an important contribution towards more inclusive design practices. This paper lays the technological groundwork for a mixed reality music application that can detect children's use of non-pitched, percussion instruments to support early childhood music education and play. [ABSTRACT FROM AUTHOR]

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes. Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction. Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data. Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data. [ABSTRACT FROM AUTHOR]

Automatic Speech Emotion Recognition (ASER) has recently garnered attention across various fields including artificial intelligence, pattern recognition, and human–computer interaction. However, ASER encounters numerous challenges such as a shortage of diverse datasets, appropriate feature selection, and suitable intelligent recognition techniques. To address these challenges, a systematic literature review (SLR) was conducted following established guidelines. A total of 60 primary research papers spanning from 2011 to 2023 were reviewed to investigate, interpret, and analyze the related literature by addressing five key research questions. Despite being an emerging area with applications in real-life scenarios, ASER still grapples with limitations in existing techniques. This SLR provides a comprehensive overview of existing techniques, datasets, and feature extraction tools in the ASER domain, shedding light on the weaknesses of current research studies. Additionally, it outlines a list of limitations for consideration in future work. [ABSTRACT FROM AUTHOR]

Aiming at the imbalance of industrial control system data and the poor detection effect of industrial control intrusion detection systems on network attack traffic problems, we propose an ETM-TBD model based on hybrid machine learning and neural network models. Aiming at the problem of high dimensionality and imbalance in the amount of sample data in the massive data of industrial control systems, this paper proposes an IG-based feature selection method and an oversampling method for SMOTE. In the ETM-TBD model, we propose a hyperparameter optimization method based on Bayesian optimization used to optimize the parameters of the four basic machine learners in the model. By introducing a multi-head-attention mechanism, the Transformer module increases the attention between local features and global features, enabling the discovery of the internal relationship between features. Additionally, the BiGRU is used to preserve the temporal features of the dataset, while the DNN is used to extract deeper features. Finally, the SoftMax classifier is used to classify the output. By analyzing the results of the comparison and ablation experiments, it can be concluded that the F1-score of the ETM-TBD model on a robotic arm dataset is 0.9665 and the model has very low FNR and FPR scores of 0.0263 and 0.0081, respectively. It can be seen that the model in this paper is better than the traditional single machine learning algorithm as well as the algorithm lacking any of the modules. [ABSTRACT FROM AUTHOR]

The western Pacific seamount area is abundant in both biological and mineral resources, making it a crucial location for international investigation of regional seabed resources. An essential stage in comprehending and advancing seamounts is gaining knowledge about the distribution characteristics and laws governing the seabed substrate. Deep-sea geological sampling is challenging because of the intricate nature of the deep-sea environment, resulting in increased difficulty in identifying and evaluating substrates. This study addresses the aforementioned issues by utilizing in-situ video footage obtained from the "Jiaolong" manned deep submersible and shipborne deep-water multibeam data. This data is used as a foundation for constructing a Western Pacific seamount areas substrate classification point set. Additionally, the paper introduces the mRMR-XGBoost substrate classification model. Substrate categorization in deep sea and mountainous regions has been successfully accomplished, yielding a classification accuracy of 92.5%. The classification experiments and box sampling results demonstrate that the mRMR-XGBoost substrate classification model proposed in this paper can efficiently use acoustic and optical data to accurately divide the substrate types in seamount areas, with better classification accuracy, when compared with commonly used machine learning models. It has a significant application value and the best classification effect on the two types of substrates: nodules and gravel substrates. [ABSTRACT FROM AUTHOR]

In the field of risk assessment, the traditional econometric models are generally used to assess credit risk. And with the introduction of the "dual-carbon" goals to promote the development of a low-carbon economy, the scale of green credit in China has rapidly expanded. But with the advent of the big data era, due to the poor interpretability of a traditional single machine learning model, it is difficult to capture nonlinear relationships, and there are shortcomings in prediction accuracy and robustness. This paper selects the adjusted ensemble learning model based on the homogeneous and heterogeneous factors for user default prediction, which can efficiently process large quantities of high-dimensional data. This article adjusts each model to adapt to the task and innovatively compares various models. In this paper, the missing value filling method, feature selection, and ensemble model are studied and discussed, and the optimal ensemble model is obtained. When comparing the predictions of single models and ensemble models, the accuracy, sensitivity, specificity, F1-Score, Kappa, and MCC of Categorical Features Gradient Boosting (CatBoost) and Random undersampling Boosting (RUSBoost) all reach 100%. The experimental results prove that the algorithm based on adjusted homogeneous and heterogeneous ensemble learning can predict the user default efficiently and accurately. This paper also provides some references for establishing a risk assessment index system. [ABSTRACT FROM AUTHOR]

As 5G technology becomes more widespread, the significant improvement in network speed and connection density has introduced more challenges to network security. In particular, distributed denial of service (DDoS) attacks have become more frequent and complex in software-defined network (SDN) environments. The complexity and diversity of 5G networks result in a great deal of unnecessary features, which may introduce noise into the detection process of an intrusion detection system (IDS) and reduce the generalization ability of the model. This paper aims to improve the performance of the IDS in 5G networks, especially in terms of detection speed and accuracy. It proposes an innovative feature selection (FS) method to filter out the most representative and distinguishing features from network traffic data to improve the robustness and detection efficiency of the IDS. To confirm the suggested method's efficacy, this paper uses four common machine learning (ML) models to evaluate the InSDN, CICIDS2017, and CICIDS2018 datasets and conducts real-time DDoS attack detection on the simulation platform. According to experimental results, the suggested FS technique may match 5G network requirements for high speed and high reliability of the IDS while also drastically cutting down on detection time and preserving or improving DDoS detection accuracy. [ABSTRACT FROM AUTHOR]

The real world data are complex in nature and addition to that a large number of features add more value to the complexity. However, the features associated with the data may be redundant and erroneous in nature. To deal with such type of features, feature selection plays a vital role in computational learning. The reduction in the dimensionality of the dataset not only reduces the computational time required for classification but also enhances the classification accuracy by removing the misleading features. This paper presents a Filter Approach using Elitism based Multi-objective Differential Evolution algorithm for feature selection (FAEMODE) and the novelty lies in the objective formulation, where both linear and nonlinear dependency among features have been considered to handle the redundant and unwanted features of a dataset. Finally, the selected feature subsets of 23 benchmark datasets are tested using 10-fold cross validation with four well-known classifiers to endorse the result. A comparative analysis of the proposed approach with seven filter approaches and two conventional as well as three metaheuristic based wrapper approaches have been carried out for validation. The result reveals that the proposed approach can be considered as a powerful filter method for feature selection in various fields. [ABSTRACT FROM AUTHOR]

This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication. [ABSTRACT FROM AUTHOR]

Software defect prediction (SDP) is designed to assist software testing, which can reasonably allocate test resources to reduce costs and improve development efficiency. In order to improve the prediction performance, researchers have designed many defect-related features for SDP. However, feature redundancy (FR) and irrelevance caused by the increasing dimensions of data will greatly degrade the performance of defect prediction. In order to solve the problems, researchers have proposed various data dimensionality reduction methods. These methods can be simply divided into two categories of methods: feature selection and feature extraction. However, the two categories of methods have their own advantages and limitation. In this paper, we propose a Hybrid Feature Dimensionality Reduction Approach (HFDRA) for SDP, which combines the two different kinds of methods, to improve the performance of SDP. HFDRA approach can be divided into two stages: feature selection and feature extraction. First, HFDRA divides the original features into several feature subsets through a clustering algorithm in the feature selection stage. Then, in the feature extraction stage, kernel principal component analysis (KPCA) is used to reduce the dimensionality of each feature subset. Finally, the reduced-dimensional data is used to build the prediction model. In the empirical study, we use 22 projects from AEEEM, SOFTLAB, MORP, and ReLink as experiment object. In this paper, we first compare our approach with seven baseline methods and three state-of-the-art methods. Then, we analyze the relationship between FR and prediction performance. Experiment results show that our approach outperforms the state-of-the-art data dimensionality reduction methods for defect prediction. [ABSTRACT FROM AUTHOR]

Short-term power load forecasting is of great significance for the reliable and safe operation of power systems. In order to improve the accuracy of short-term load forecasting, for the problems of random fluctuation in load and the complexity of load-influencing factors, this paper proposes a two-stage short-term load forecasting method, SSA–VMD-LSTM-MLR-FE (SVLM–FE) based on sparrow search algorithm (SSA), to optimize variational mode decomposition (VMD) and feature engineering (FE). Firstly, an evaluation criterion on the loss of VMD decomposition is proposed, and SSA is used to find the optimal combination of parameters for VMD under this criterion. Secondly, the first stage of forecasting is carried out, and the different components obtained from SSA–VMD are predicted separately, with the high-frequency components input to a long short-term memory network (LSTM) for forecasting and the low-frequency components input to a multiple linear regression model (MLR) for forecasting. Finally, the forecasting values of the components obtained in the first stage are input to the second stage for error correction; factors with a high degree of influence on the load are selected using the Pearson correlation coefficient (PCC) and maximal information coefficient (MIC), and the load value at the moment that has a great influence on the load value at the time to be predicted is selected using autocorrelation function (ACF). The forecasting values of the components are fused with the selected feature values to construct a vector, which is fed into the fully connected layer for forecasting. In this paper, the performance of SVLM–FE is evaluated experimentally on two datasets from two places in China. In Place 1, the RMSE, MAE, and MAPE are 128.169 MW, 102.525 MW, and 1.562%, respectively; in Place 2, the RMSE, MAE, and MAPE are 111.636 MW, 92.291 MW, and 1.426%, respectively. The experimental results show that SVLM–FE has high accuracy and stability. [ABSTRACT FROM AUTHOR]

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively. [ABSTRACT FROM AUTHOR]

The temperature profile is an important parameter of the atmospheric thermal state in atmospheric monitoring and weather forecasting. The hyperspectral infrared sounder of a geostationary satellite provides abundant spectral information and can retrieve the temperature profile. Based on the mediumwave channel data (independent variable and model input data) of FY-4A/GIIRS (geosynchronous interferometric infrared sounder) and ERA5 reanalysis data (dependent variable and model output data), the atmospheric temperature profile is retrieved by generalized ensemble learning. Firstly, the feature variables of the model are constructed. Because there are many GIIRS channels, a two-step feature selection method is adopted: step 1--establish a blacklist of GIIRS channels; step 2--select feature variables by using the method of importance permutation. Secondly, they are integrated based on optimizing and adjusting the hyperparameters of three basic machine learning models (Random Forest, XGBoost and LightGBM). Generalized ensemble learning nonlinear convex optimization is used to optimize the weight of each basic model. Finally, based on high-frequency GIIRS observations of Typhoon Lekima and Typhoon Higos, testing and method evaluation of the temperature profile retrievals are carried out. The results show that LightGBM achieves the best retrieval result among the three basic models, followed by Random Forest and finally XGBoost. The root-mean-square error of the whole temperature profile in the training dataset of generalized ensemble learning is less than 0.3 K, while that of the testing dataset is less than 1.4 K, and that between 150 hPa and 925 hPa is less than 1 K. The retrieval results correlate well with the radiosonde temperature profile. The performance of generalized ensemble learning is better than the performances of the three basic models, but it depends on the retrieval results of LightGBM. In the Lekima experimental case, compared to other channels selected for temperature retrieval models, the importance of mediumwave channels 9 and 307 of GIIRS ranks first and second, respectively. The method in this paper provides a new solution and technical support for retrieving atmospheric parameters from hyperspectral and other satellite data. [ABSTRACT FROM AUTHOR]

Due to the difficulties of system modeling, nonlinearity effects, uncertainties, and the availability of Wind Turbines (WTs) SCADA system data, data‐driven Fault Detection and Isolation (FDI) methods for WTs have received increasing attention. In this paper, using the wind turbine SCADA data, an effective FDI scheme is proposed using the K‐Nearest Neighbors (KNN) classifier. The operational data set is labeled by the status and warning data sets, and the labeled operational data set, after eliminating invalid data, feature selection, and standardization, is used for training and validation of the FDI model. Data imbalance, which is common in real data sets, does not affect the performance of the proposed method, hence there is no need for data balancing methods in this algorithm and the performance is not deteriorated by occurring false alarms. Therefore, the proposed method has provided impressive performance in FDI compared with previous research on this data set. Also, many of the fault classes addressed in this paper were not considered in previous works on this data set. [ABSTRACT FROM AUTHOR]

Background: Health system performance indicators are widely used to assess primary healthcare (PHC) performance. Despite the numerous tools and some convergence on indicator criteria, there is not a clear understanding of the common features of indicator selection processes. We aimed to review the literature to identify papers that document indicator selection processes for health system performance indicators in PHC. Methods: We searched the online databases Scopus, Medline, and CINAHL, as well as the grey literature, without time restrictions, initially on July 31, 2019 followed by an update November 13, 2020. Empirical studies or reports were included if they described the selection of health system performance indicators or frameworks, that included PHC indicators. A combination of the process focused research question and qualitative analysis meant a quality appraisal tool or assessment of bias could not meaningfully be applied to assess individual studies. We undertook an inductive analysis based on potential indicator selection processes criteria, drawn from health system performance indicator appraisal tools reported in the literature. Results: We identified 16 503 records of which 28 were included in the review. Most studies used a descriptive case study design. We found no consistent variations between indicator selection processes of health systems of high income and low- or lower-middle income countries. Identified common features of selection processes for indicators in PHC include literature review or adaption of an existing framework as an initial step; a consensus building process with stakeholders; structuring indicators into categories; and indicator criteria focusing on validity and feasibility. The evidence around field testing with utility and consideration of reporting burden was less clear. Conclusion: Our findings highlight several characteristics of health system indicator selection processes. These features provide the groundwork to better understand how to value indicator selection processes in PHC. [ABSTRACT FROM AUTHOR]

Mass spectrometry technology can realize dynamic detection of many complex matrix samples in a simple, rapid, compassionate, precise, and high-throughput manner and has become an indispensable tool in accurate diagnosis. The mass spectrometry data analysis is mainly to analyze all metabolites in the organism quantitatively and to find the relative relationship between metabolites and physiological and pathological changes. A feature construction of mass spectrometry data (MSFS) method is proposed to construct the features of the original mass spectrometry data, so as to reduce the noise in the mass spectrometry data, reduce the redundancy of the original data and improve the information content of the data. Chi-square test is used to select the optimal non-redundant feature subset from high-dimensional features. And the optimal feature subset is visually analyzed and corresponds to the original mass spectrum interval. Training in 10 kinds of supervised learning models, and evaluating the classification effect of the models through various evaluation indexes. Taking two public mass spectrometry datasets as examples, the feasibility of the method proposed in this paper is verified. In the coronary heart disease dataset, during the identification process of mixed batch samples, the classification accuracy on the test set reached 1.000; During the recognition process, the classification accuracy on the test set advanced to 0.979. On the colorectal liver metastases data set, the classification accuracy on the test set reached 1.000. This paper attempts to use a new raw mass spectrometry data preprocessing method to realize the alignment operation of the raw mass spectrometry data, which significantly improves the classification accuracy and provides another new idea for mass spectrometry data analysis. Compared with MetaboAnalyst software and existing experimental results, the method proposed in this paper has obtained better classification results. [ABSTRACT FROM AUTHOR]

An alternative energy source such as solar is one of the most important renewable resources. A reliable solar radiation prediction is essential for various applications in agriculture, industry, transport, and the environment because they reduce greenhouse gases and are environmentally friendly. Solar radiation data series have embedded fluctuations and noise signals due to complexity, stochasticity, non-stationarity, and nonlinearity with uncertain and time-varying nature. Aside from being highly nonlinear, solar radiation is highly influenced by the environment and environmental parameters such as air temperature, cloud cover, surface reflectivity, and aerosols. In addition, the spatial measurements of these variables are not readily available. To tackle these challenges, it is necessary to consider data preprocessing techniques and to develop and test precise solar radiation predicting models at different forecast horizons. There is, however, controversy regarding the performance of such models in various studies. Comparisons are not conducted systematically among the different studies. Using a critical literature review, the authors hope to answer these questions and believe that further investigation of solar radiation can benefit researchers and practitioners alike. This study presents a comprehensive evaluation of solar radiation modeling using artificial intelligence in the last 15 years and provides a novel detailed analysis of the available models. The studies conducted in different climates of the world that were published in distinguished journals were considered (i.e., 90 papers in total) for this purpose. Newly discovered procedures for optimizing forecasts, data cleaning, feature selection, classification methods, and stand-alone or hybrid data-driven models for solar radiation prediction and modeling were evaluated. The results strikingly showed that the most used artificial intelligence methods were artificial neural network, adaptive neuro-fuzzy inference system, and decision tree family of models. In addition, the extreme learning machine, support vector machine, and particle swarm optimization were the most used optimization techniques in solar radiation modeling. In terms of forecast horizons, the most common forecast horizon found in papers was on the daily scale (51% of studies), followed by the hourly scale (26%), and the least common was the monthly scale (18%). Based on the regional studies, the highest number of solar radiation papers originated from Asia, with Europe in second place and African countries in third place. An increasing trend in the number of papers from 2011 to 2015 was noted, and the second peak started from 2018 till the present. Under each section, a summary of findings is provided. The paper concludes with future thoughts and directions on solar radiation modeling. [ABSTRACT FROM AUTHOR]

Although breast cancer, with easy recurrence and high mortality, has become one of the leading causes of cancer death in women, early and accurate diagnosis of breast cancer can effectively increase the likelihood of a cure. Therefore, it is particularly important to improve the accuracy of early diagnosis of breast cancer. However, conventional early diagnosis relies on human experience and has a low accuracy rate. Therefore, many researchers have proposed various machine learning methods to improve the accuracy and efficiency of prediction. Most of the existing studies around breast cancer classification adopt a single algorithm to fit breast cancer data but ignore the applicability of different breast cancer data features to the model. In this paper, we adopt machine algorithms to strip the features of machine learning methods from the rest of the features and attempt to enhance the model effect by designing deep learning model structures to find the hidden patterns in the rest of the features. In addition, due to strict medical data privacy requirements and high collection difficulty and cost, the model designed in this paper will be trained on a small number of samples. As a result, we attempt to find a minimization model for a breast cancer classification algorithm that features both low cost and high efficiency. At the same time, the deep learning model is further designed to complement the original model when it is possible to introduce complex data indicators. Experimental values show that the design model in this paper performs best not only under limited data and limited indicators but also under limited data complex indicators, demonstrating the effectiveness of the approach of mixed comparison and feature selection of multiple classification algorithms. In summary, the fusion model designed and implemented in this paper performs well in the experiments, and the accuracy of the model test reaches 98.3%. [ABSTRACT FROM AUTHOR]

Anomaly detection for network traffic aims to analyze the characteristics of network traffic in order to discover unknown attacks. Currently, existing detection methods have achieved promising results against high-intensity attacks that aim to interrupt the operation of the target system. In reality, attack behaviors that are commonly exhibited are highly concealed and disruptive. In addition, the attack scales are flexible and variable. In this paper, we construct a multiscale network intrusion behavior dataset, which includes three attack scales and two multiscale attack patterns based on probability distribution. Specifically, we propose a stacked ensemble learning-based detection model for anomalous traffic (or SEDAT for short) to defend against highly concealed multiscale attacks. The model employs a random forest (RF)-based method to select features and introduces multiple base learning autoencoders (AEs) to enhance the representation of multiscale attack behaviors. In addressing the challenge of a single model's inability to capture the regularities of multiscale attack behaviors, SEDAT is capable of adapting to the complex multiscale characteristics in network traffic, enabling the prediction of network access behavior. Comparative experiments demonstrate that SEDAT exhibits superior detection capabilities in multiscale network attacks. In particular, SEDAT achieves an improvement of at least 5% accuracy over baseline methods for detecting multiscale attacks. [ABSTRACT FROM AUTHOR]

The hot spot temperature of transformer windings is an important indicator for measuring insulation performance, and its accurate inversion is crucial to ensure the timely and accurate fault prediction of transformers. However, existing studies mostly directly input obtained experimental or operational data into networks to construct data-driven models, without considering the lag between temperatures, which may lead to the insufficient accuracy of the inversion model. In this paper, a method for inverting the hot spot temperature of transformer windings based on the SA-GRU model is proposed. Firstly, temperature rise experiments are designed to collect the temperatures of the entire side and top of the transformer tank, top oil temperature, ambient temperature, the cooling inlet and outlet temperatures, and winding hot spot temperature. Secondly, experimental data are integrated, considering the lag of the data, to obtain candidate input feature parameters. Then, a feature selection algorithm based on mutual information (MI) is used to analyze the correlation of the data and construct the optimal feature subset to ensure the maximum information gain. Finally, Self-Attention (SA) is applied to optimize the Gate Recurrent Unit (GRU) network, establishing the GRU-SA model to perceive the potential patterns between output feature parameters and input feature parameters, achieving the precise inversion of the hot spot temperature of the transformer windings. The experimental results show that considering the lag of the data can more accurately invert the hot spot temperature of the windings. The inversion method proposed in this paper can reduce redundant input features, lower the complexity of the model, accurately invert the changing trend of the hot spot temperature, and achieve higher inversion accuracy than other classical models, thereby obtaining better inversion results. [ABSTRACT FROM AUTHOR]

The fusion tracking of RGB and thermal infrared image (RGBT) has attracted widespread interest within target tracking by leveraging the complementing benefits of information from both visible and thermal infrared modalities, but achieving robustness while operating in real time remains a challenge. Aimed at this problem, this paper proposes a real-time tracking network based on the attention mechanism, which can improve the tracking speed with a smaller model, and at the same time, introduce the attention mechanism in the module to strengthen the attention to the important features, which can guarantee a certain tracking accuracy. Specifically, the modal features of visible and thermal infrared are extracted separately by using the backbone of the dual-stream structure; then, the important features in the two modes are selected and enhanced by using the channel attention mechanism in the feature selection enhancement module (FSEM) and the Transformer, while noise is reduced by using gating circuits. Finally, the final enhancement fusion is performed by using the spatial channel adaptive adjustment fusion module (SCAAM) in both the spatial and channel dimensions. The PR/SR of the proposed algorithm tested on the GTOT, RGBT234 and LasHeR datasets are 90.0%/73.0%, 84.4%/60.2%, and 46.8%/34.3%, respectively, and generally good tracking accuracy has been achieved, with a speed of up to 32.3067 fps, meeting the model's real-time requirement. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) perform well in text analysis tasks. Their unique structure allows them to capture complex patterns and dependencies in text, making them ideal for processing natural language tasks. At the same time, XGBoost (version 1.6.2.) outperforms other machine learning methods on heterogeneous tabular data. However, traditional graph neural networks mainly study isomorphic and sparse data features. Therefore, when dealing with tabular data, traditional graph neural networks encounter challenges such as data structure mismatch, feature selection, and processing difficulties. To solve these problems, we propose a novel architecture, XGNN, which combines the advantages of XGBoost and GNNs to deal with heterogeneous features and graph structures. In this paper, we use GAT for our graph neural network model. We can train XGBoost and GNN end-to-end to fit and adjust the new tree in XGBoost based on the gradient information from the GNN. Extensive experiments on node prediction and node classification tasks demonstrate that the performance of our proposed new model is significantly improved for both prediction and classification tasks and performs particularly well on heterogeneous tabular data. [ABSTRACT FROM AUTHOR]

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability. [ABSTRACT FROM AUTHOR]

This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)–electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who have lost the ability to communicate, such as people with paralysis. A set of experiments were conducted to assess the efficacy of the proposed method for feature selection. It was determined that the classification of mouth movements was meaningful. EEG-EMG signals were also collected during silent mouthing of phonemes. A few-shot neural network was trained to classify the phonemes from the EEG-EMG signals, yielding classification accuracy of 95%. This technique in data collection and processing bioelectrical signals for phoneme recognition proves a promising avenue for future communication aids. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The article discusses the key steps in digital visual design reengineering, with a special emphasis on the importance of information decoding and feature extraction for flat cultural heritage. These processes not only minimize damage to the aesthetic heritage itself but also feature high quality, efficiency, and recyclability. OBJECTIVES: The aim of the article is to explore the issues of gene extraction methods in digital visual design reengineering, proposing a visual gene extraction method through an improved K-means clustering algorithm. METHODS: A visual gene extraction method based on an improved K-means clustering algorithm is proposed. Initially analyzing the digital visual design reengineering process, combined with a color extraction method using the improved JSO algorithm-based K-means clustering algorithm, a gene extraction and clustering method for digital visual design reengineering is proposed and validated through experiments. .ASA-RESULT: The results show that the proposed method improves the accuracy, robustness, and real-time performance of clustering. Through comparative analysis with Dunhuang murals, the effectiveness of the color extraction method based on the K-means-JSO algorithm in the application of digital visual design reengineering is verified. The method based on the K-means-GWO algorithm performs best in terms of average clustering time and standard deviation. The optimization curve of color extraction based on the K-means-JSO algorithm converges faster and with better accuracy compared to the K-means-ABC, K-means-GWO, K-means-DE, K-means-CMAES, and K-means-WWCD algorithms. CONCLUSION: The color extraction method of the K-means clustering algorithm improved by the JSO algorithm proposed in this paper solves the problems of insufficient standardization in feature selection, lack of generalization ability, and inefficiency in visual gene extraction methods. [ABSTRACT FROM AUTHOR]

While the world struggles to recover from the devastation wrought by the widespread spread of COVID-19, monkeypox virus has emerged as a new global pandemic threat. In this paper, a high precision and lightweight classification network MpoxNet based on ConvNext is proposed to meet the need of fast and safe detection of monkeypox classification. In this method, a two-branch depthseparable convolution residual Squeeze and Excitation module is designed. This design aims to extract more feature information with two branches, and greatly reduces the number of parameters in the model by using depth-separable convolution. In addition, our method introduces a convolutional attention module to enhance the extraction of key features within the receptive field. The experimental results show that MpoxNet has achieved remarkable results in monkeypox disease classification, the accuracy rate is 95.28%, the precision rate is 96.40%, the recall rate is 93.00%, and the F1-Score is 95.80%. This is significantly better than the current mainstream classification model. It is worth noting that the FLOPS and the number of parameters of MpoxNet are only 30.68% and 31.87% of those of ConvNext-Tiny, indicating that the model has a small computational burden and model complexity while efficient performance. [ABSTRACT FROM AUTHOR]

Intrusion Detection Systems (IDSs) are an important tool to mitigate cybersecurity threats in the ICT infrastructures. Preferable properties of the IDSs are the optimization of the attack detection accuracy and the minimization of the computing resources and time. A signification portion of IDSs presented in the research literature is based on Machine Learning (ML) and Deep Learning (DL) elements, but they may be prone to adversarial attacks, which may undermine the overall performance of the IDS algorithm. This paper proposes a novel IDS focused on the detection of cybersecurity attacks in 5G networks, which addresses in a simple but effective way two specific adversarial attacks: (1) tampering of the labeled set used to train the ML algorithm, (2) modification of the features in the training data set. The approach is based on the combination of two algorithms, which have been introduced recently in the research literature. The first algorithm is the Extremely Randomized Tree (ERT) algorithm, which enhances the capability of Decision Tree (DT) and Random Forest (RF) algorithms to perform classification in data sets, which are unbalanced and of large size as IDS data sets usually are (legitimate traffic messages are more numerous than attack related messages). The second algorithm is the recently introduced Infinite Feature Selection algorithm, which is used to optimize the choice of the hyper-parameter defined in the approach and improve the overall computing efficiency. The result of the application of the proposed approach on a recently published 5G IDS data set proves its robustness against adversarial attacks with different degrees of severity calculated as the percentage of the tampered data set samples. [ABSTRACT FROM AUTHOR]

In the context of increasing data scale, contemporary optimization algorithms struggle with cost and complexity in addressing the feature selection (FS) problem. This paper introduces a Harris hawks optimization (HHO) variant, enhanced with a multi-strategy augmentation (CXSHHO), for FS. The CXSHHO incorporates a communication and collaboration strategy (CC) into the baseline HHO, facilitating better information exchange among individuals, thereby expediting algorithmic convergence. Additionally, a directional crossover (DX) component refines the algorithm's ability to thoroughly explore the feature space. Furthermore, the soft-rime strategy (SR) broadens population diversity, enabling stochastic exploration of an extensive decision space and reducing the risk of local optima entrapment. The CXSHHO's global optimization efficacy is demonstrated through experiments on 30 functions from CEC2017, where it outperforms 15 established algorithms. Moreover, the paper presents a novel FS method based on CXSHHO, validated across 18 varied datasets from UCI. The results confirm CXSHHO's effectiveness in identifying subsets of features conducive to classification tasks. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information." The original publication contained errors in Table 2, Table 3, Table 4, Figure 2, and Figure 7. Additionally, there were mistakes in some equations. The corrected versions of these tables, figures, and equations are provided in the document. The authors state that these corrections do not affect the scientific conclusions of the paper. The document also includes figures and tables related to the paper's content, providing information on computational complexity and derivatives of formulas. The authors of the document are Yugen Yi, Haoming Zhang, Ningyi Zhang, Wei Zhou, Xiaomei Huang, Gengsheng Xie, and Caixia Zheng. [Extracted from the article]

High-resolution remote sensing image-based vegetation monitoring is a hot topic in remote sensing technology and applications. However, when facing large-scale monitoring across different sensors in broad areas, the current methods suffer from fragmentation and weak generalization capabilities. To address this issue, this paper proposes a multisource high-resolution remote sensing image-based vegetation extraction method that considers the comprehensive perception of multiple features. First, this method utilizes a random forest model to perform feature selection for the vegetation index, selecting an index that enhances the otherness between vegetation and other land features. Based on this, a multifeature synthesis perception convolutional network (MSCIN) is constructed, which enhances the extraction of multiscale feature information, global information interaction, and feature cross-fusion. The MSCIN network simultaneously constructs dual-branch parallel networks for spectral features and vegetation index features, strengthening multiscale feature extraction while reducing the loss of detailed features by simplifying the dense connection module. Furthermore, to facilitate global information interaction between the original spectral information and vegetation index features, a dual-path multihead cross-attention fusion module is designed. This module enhances the differentiation of vegetation from other land features and improves the network's generalization performance, enabling vegetation extraction from multisource high-resolution remote sensing data. To validate the effectiveness of this method, we randomly selected six test areas within Anhui Province and compared the results with three different data sources and other typical methods (NDVI, RFC, OCBDL, and HRNet). The results demonstrate that the MSCIN method proposed in this paper, under the premise of using only GF2 satellite images as samples, exhibits robust accuracy in extraction results across different sensors. It overcomes the rapid degradation of accuracy observed in other methods with various sensors and addresses issues such as internal fragmentation, false positives, and false negatives caused by sample generalization and image diversity. [ABSTRACT FROM AUTHOR]

Cervical cancer has under the top cancer found in women of developing countries since last many years. Classification of cervical cancer through a traditional microscopic approach is a monotonous and prolonged task. Most of the time hospital doctors cannot identify the cancer cells as sometimes the nucleus of a cell, which contains the genetic material (DNA), is typically very small and often not visible to the naked eye. Due to the different perspectives of doctors, cancer stages are classified falsely which leads to low recovery and late medication. The use of Image Processing and Machine Learning technologies can take off misclassification and inaccurate prediction. Although many deep learning techniques are available for cervical cancer cell detection and classification, the performance of such techniques for prediction and classification with real and sample datasets is the main challenge. In this paper, we did a thorough state-of-the-art review of the available current literature. The objective of this paper is to bring forth in-depth knowledge to novice researchers with a thorough understanding of the architecture of the computer-assisted classification process. The current literature is studied, analyzed, and discussed with their approaches, results, and methodologies. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is the leading cause of global mortality and responsible for millions of deaths annually. The mortality rate and overall consequences of cardiac disease can be reduced with early disease detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment and misdiagnoses, which can impede the course of treatment and raise healthcare costs. The application of artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes the central role of machine learning in cardiac health and focuses on precise cardiovascular disease prediction. In particular, this paper is driven by the urgent need to fully utilize the potential of machine learning to enhance cardiovascular disease prediction. In light of the continued progress in machine learning and the growing public health implications of cardiovascular disease, this paper aims to offer a comprehensive analysis of the topic. This review paper encompasses a wide range of topics, including the types of cardiovascular disease, the significance of machine learning, feature selection, the evaluation of machine learning models, data collection & preprocessing, evaluation metrics for cardiovascular disease prediction, and the recent trends & suggestion for future works. In addition, this paper offers a holistic view of machine learning's role in cardiovascular disease prediction and public health. We believe that our comprehensive review will contribute significantly to the existing body of knowledge in this essential area. [ABSTRACT FROM AUTHOR]