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Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a given device but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioral biometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied to address this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nine participants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLog was downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while the participant interacted with the iPad. Five machine learning models, logistic regression (LR), support vector machine, neural net (NN), k-nearest neighbors (k-NN), and random forest (RF), were trained using 57 features generated from the sensor output to perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance was evaluated using F 1 score, accuracy, precision, and recall. Model performance was high, with F 1 scores ranging from 0.75 to 0.94. RF and k-NN had the highest performance across metrics, with F 1 scores of 0.94 for both models. This study highlights the potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen time measurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach, Deep Multiscale Soft-Threshold Support Vector Data Description (DMS-SVDD), designed for the detection of anomalies and prediction of faults in heavy-duty gas turbine generator sets (GENSETs). The model combines a support vector data description (SVDD) with a deep autoencoder backbone network framework, integrating a multiscale convolutional neural network (M) and soft-threshold activation network (S) into the Deep-SVDD framework. In comparison with conventional methods, such as One-Class Support Vector Machine (OCSVM) and autoencoder (AE), DMS-SVDD demonstrates improvements in accuracy (by 22.94%), recall (by 32%), F1 score (by 12.02%), and smoothness (by 39.15%). The model excels particularly in feature extraction, denoising, and early fault detection, offering a proactive strategy for maintenance. Furthermore, the DMS-SVDD demonstrated enhanced training efficiency with a reduction in the convergence rounds by 66% and overall training times by 34.13%. The study concludes that DMS-SVDD presents a robust and efficient solution for gas turbine anomaly detection, with practical advantages for decision support in turbine maintenance. Future research could explore additional refinements and applications of the DMS-SVDD model across diverse industrial contexts. [ABSTRACT FROM AUTHOR]

The widespread integration of renewable energy sources (RESs) into power systems using power electronics-based interface devices has led to a substantial rise in power quality (PQ) issues. There is an immediate requirement for effective monitoring, detection, and classification of power quality disturbances (PQDs) that is needed to take remedial measures and design planning of the system architecture. This study presents a hybrid approach with an objective for the feature extraction and classification of PQDs. The proposed hybrid approach is comprised of an optimized tunable-Q wavelet transform (OTQWT) for the feature extraction and incremental support vector machine (ISVM). A four-stage approach is suggested for the PQ detection and classification in this study. In the first stage, the various data are retrieved both in the form of synthetic data by mathematical formulations and real-time data with prototype design setup. In the second stage, regardless of the specified wavelet function, the PQD signals are decomposed into low-pass and high-pass sub-bands using the tunable-Q wavelet transform (TQWT). However, the utilization of default decomposition parameters to address nonstationary PQ signals may lead to information loss and reduced performance of the system. To avoid this limitation, an OTQWT as an enhanced technique to TQWT based on an Adaptive Particle Swarm Optimization (APSO) is suggested. A modified objective function based on the mean square error (MSE) is used to improve the decomposition process. In the third stage, an efficient classifier is suggested based on the ISVM. Lastly, to test and evaluate the performance of the proposed approach, twelve types of PQDs including noise and multiple occurrences are considered. The comparative analysis with other popular methods reflects the better performance of the proposed approach and justifies its use for PQ detection and classification purposes in real-time​ conditions. [ABSTRACT FROM AUTHOR]

Recent experience with the strongest earthquakes greater than magnitude 5.4 in Korea leads to public interest in the safety and resilience of critical infrastructures. There are many nuclear power plants near the epicenters of the earthquakes. Nuclear power plants are essential infrastructures that provide stable and enough energy for human life, and simultaneously, control systems for the safety and security of nuclear power plants are critical due to the risk of nuclear accidents on public health and the environment. The nuclear area uses probabilistic risk assessment to estimate the risk of structures, systems, and components in nuclear power plants, and the evaluation of the fragility curve is a key process for probabilistic risk assessment. The challenges of a seismic fragility analysis lie in estimating the influence of various uncertainties in material, geometry, and earthquake and improving the existing fragility analysis methods, which require time-consuming nonlinear time history analysis. Thus, this paper conducts a multivariate seismic fragility analysis using surrogate models for reinforced concrete squat shear walls and proposes a simplified closed form equation for a chosen surrogate seismic demand model. The surrogate models are trained and validated by several approaches: response surface method, support vector machine, Gaussian process regression, and neural network. In addition, a correlation analysis is used to evaluate the relative importance of the variables to the seismic demand to simplify the surrogate model further. Finally, simplified surrogate models based on the importance of the variables are proposed as closed form of polynomials, and the performance of these models on the fragility analysis is evaluated. [ABSTRACT FROM AUTHOR]

Gas reservoir development and the estimation of rock properties heavily rely on lithology classification, which can be difficult, time-consuming, and prone to errors. In this study, a novel deep learning-based approach has been developed for the rapid, accurate, and efficient prediction of lithology in a gas field from conventional well-log data. The well-logs, referred to as numerical well-logs (NWLs), are transformed into two-dimensional images through two proposed approaches: shallow images (SIs) and deep images (DIs). In these images, the pixels effectively represent the relationships between different logs. For this purpose, we developed residual convolutional neural networks (ResCNN) named SIs-ResCNN 2D and DIs-ResCNN 2D. The feed data for DIs-ResCNN 2D are images created and referred to as DIs, which are initially formed from a vector in which the order of logs is somehow repeated, ensuring that each pairwise combination occurs only once. This resulted in the incorporation of the connection between the logs within the pixels of the generated images, alongside the integration of unique binary combinations of the logs. We compared the proposed models, including DIs-ResCNN 2D, DIs-ResCNN 2D, and NWLs-ResCNN 1D with baseline methods such as random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM). Based on the evaluation metrics, DIs-ResCNN 2D outperformed the other proposed and baseline methods on the test dataset. A balanced DIs-ResCNN 2D model achieved 93% accuracy and F1-score of 80% on a test well, highlighting the importance of data balancing during CNN model training. [ABSTRACT FROM AUTHOR]

The respiratory disease of coronavirus disease 2019 (COVID-19) has wreaked havoc on the economy of every nation by infecting and killing millions of people. This deadly disease has taken a toll on the life of the entire human race, and an exact cure for it is still not developed. Thus, the control and cure of this disease mainly depend on restricting its transmission rate through early detection. The detection of coronavirus infection facilitates the isolation and exclusive care of infected patients. This research paper proposes a novel data mining system that combines the ensemble feature selection method and machine learning classifier for the effective identification of COVID-19 infection. Different feature selection approaches including chi-square test, recursive feature elimination (RFE), genetic algorithm (GA), particle swarm optimization (PSO), and random forest are evaluated for their effectiveness in enhancing the classification accuracy of the machine learning classifiers. The classifiers that are considered in this research work are decision tree, naïve Bayes, K -nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM). Two COVID-19 datasets were used for testing from which the best features supporting the dataset were extracted by the proposed system. The performance of the machine learning classifiers based on the ensemble feature selection methods is analyzed. [ABSTRACT FROM AUTHOR]

Bridges situated on small-radius reverse curves play a pivotal role within some railway networks, exerting influence over project-wide design progress. Typically, assessing the safety of bridge design parameters necessitates laborious vehicle-bridge dynamic coupling vibration numerical analysis or model experiments. To streamline the design process and enhance efficiency during the preliminary design phase, we propose an efficient method to assess the dynamic performance of bridges on small-radius reverse curves. This approach enables direct prediction of bridge dynamic performance based on design parameters, eliminating the need for numerical simulations and model experiments. We first develop a vehicle-bridge coupling vibration program grounded in train-curve bridge coupling vibration theory, validated using on-site measured data. Subsequently, through numerical simulation experiments, we evaluate 80 simply supported beam bridges on small-radius reverse curves under various operating conditions, generating ample dynamic response data for bridge pier tops and girders. These data are then compared with regulatory thresholds to assign dynamic performance labels. After identifying essential design parameters as data features using Fisher scores, we proceed to input these features into a support vector machine (SVM). Through supervised training with dynamic performance labels, this process empowers the SVM model to predict the dynamic performance of the bridge. Our results demonstrate that this method circumvents the need for detailed vehicle-bridge interaction analysis, yielding an impressive 86.9% accuracy in predicting dynamic performance and significantly boosting computational efficiency. Besides, the top five design parameters that significantly influence the prediction of bridge dynamic performance are obtained. This novel approach has the potential to expedite design assessments and enhance safety in railway bridge construction. [ABSTRACT FROM AUTHOR]

As a kind of thermal control device, fluid loop systems must operate with the demands of high safety, high reliability, and long life. In order to accurately assess the health status of fluid loop systems, a hierarchical multi-information fusion (HMIF) method is proposed in this paper. Considering that fluid loop systems generally have distinct structural hierarchies, the health evaluation process in this method is divided into three levels, which are the indicator level, the component level, and the system level. In the evaluation process, the health indices are, respectively, constructed to quantify the health status at the three levels. At the indicator level, One-Class support vector machine algorithm is used to obtain the distribution space of each state monitoring indicator under a normal state. The indicator-level health indices are evaluated by calculating the ratio of the data located in the distribution space to the overall data. At the component level, a fuzzy theory is used to calculate the health indices of the component level. Health indices at the indicator level are first converted to membership degree by membership degree function. Then, the evaluation fusion strategy is used to deduce the membership degree of the component level. The health indices at the component level are obtained from the mapping relationship between the membership degree and the health index. At the system level, an adaptive weight adjustment strategy is proposed to fuze all component-level health indices. Taking a practical fluid loop system as an example, health indices at the three levels evaluated by the HMIF method are compared with the actual status. The results indicate that the proposed method can correctly judge the health state of the system and provide a reference for the maintenance and fault diagnosis of fluid loop systems. [ABSTRACT FROM AUTHOR]

This paper proposes a diagnosis method based on time series and support vector machine (SVM) to improve the timeliness, accuracy, and feasibility of fault diagnosis for photovoltaic (PV) arrays. It obtains the nominal output power of the PV array based on real-time collected data such as voltage, current, radiation, and temperature and normalizes the power values at different time points throughout the day to form a time series. Using the time series values as input data for a "one-to-one" multiclass classifier, we can identify and classify typical operational faults such as random shading, fixed shading, and aging degradation of PV arrays. The developed algorithmic model is trained and tested for different fault conditions using the data sets generated by the PV array simulation device. The experimental results show that our model has fairly good reliability and accuracy, and to some extent, it solves the problem of classifying shading and aging faults, two of which exhibit rather similar degradation characteristics. [ABSTRACT FROM AUTHOR]

The objective of this study was to determine whether multi-microRNA analysis using a combination of four microRNA biomarkers (miR-1246, 202, 21, and 219B) could improve the diagnostic performance of mammography in determining breast cancer risk by age group (under 50 vs. over 50) and distinguish breast cancer from benign breast diseases and other cancers (thyroid, colon, stomach, lung, liver, and cervix cancers). To verify breast cancer classification performance of the four miRNA biomarkers and whether the model providing breast cancer risk score could distinguish between benign breast disease and other cancers, the model was verified using nonlinear support vector machine (SVM) and generalized linear model (GLM) and age and four miRNA qRT-PCR analysis values (dCt) were input to these models. Breast cancer risk scores for each Breast Imaging-Reporting and Data System (BI-RADS) category in multi-microRNA analysis were analyzed to examine the correlation between breast cancer risk scores and mammography categories. We generated two models using two classification algorithms, SVM and GLM, with a combination of four miRNA biomarkers showing high performance and sensitivities of 84.5% and 82.1%, a specificity of 85%, and areas under the curve (AUCs) of 0.967 and 0.965, respectively, which showed consistent performance across all stages of breast cancer and patient ages. The results of this study showed that this multi-microRNA analysis using the four miRNA biomarkers was effective in classifying breast cancer in patients under the age of 50, which is challenging to accurately diagnose. In addition, breast cancer and benign breast diseases can be classified, showing the possibility of helping with diagnosis by mammography. Verification of the performance of the four miRNA biomarkers confirmed that multi-microRNA analysis could be used as a new breast cancer screening aid to improve the accuracy of mammography. However, many factors must be considered for clinical use. Further validation with an appropriate screening population in large clinical trials is required. This trial is registered with (KNUCH 2022-04-036). [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) examination provides information on the brain's electricity, especially in cases of epilepsy. Since the characteristics of EEG signals are nonlinear and nonstationary, visual inspection becomes very difficult. To overcome this problem, digital EEG signal processing was developed. Automatic epileptic EEG recognition is an area of interest on which much research focuses. The complexity approach to EEG signal analysis is interesting to be used as feature extraction, referring to the nonlinear characteristics of the signal. This study proposed an automatic epileptic EEG classification method based on the multiscale Hjorth descriptor measurement. EEG signals consisting of normal, interictal, and seizure (ictal) were simulated. The signal is scaled into new signals using the coarse-grained procedure on a scale of 1–20. Then, the Hjorth parameter which consists of activity, mobility, and complexity is calculated on the new signal. This process produces a feature vector that is used in the classification stage. Support vector machine (SVM) is used to evaluate the proposed feature extraction method. Simulation results showed that the Hjorth parameter on a scale of 1–15 yields 99.5% accuracy. The proposed method is expected to be applied to digital EEG for seizure detection and prediction. [ABSTRACT FROM AUTHOR]

The cyber-physical power system (CPPS) is a modern infrastructure utilising information and communication technology that has become more vulnerable to cyberattacks in recent years. The attack magnitude injected by the adversary is stealthier and it cannot be detected using conventional bad data detection techniques. Protecting sensitive data from data integrity attacks (DIA) is essential for ensuring system security and reliability. A tragic event will occur if the attack goes unreported. Therefore, DIA detection is highly vital for the operator in the control centre to make important decisions. This paper addresses the attack impact on WAC applications and attack detection using the model-based method and data-driven-based methods. On the basis of the validation of performance indicators, various detection approaches are simulated and compared to determine the best detection strategy. Simulation results show that in the model-based anomaly detection method, the recursive polynomial model estimator (RPME) has better detection performance than the recursive least square estimator (RLSE). The convolutional neural network- (CNN-) based data-driven anomaly detection technique outperforms other machine learning (ML) techniques such as support vector machine (SVM), K-nearest neighbour (KNN), and random forest (RF). On the WSCC 3 machine 9-bus system, the efficacy of the suggested methods is evaluated. [ABSTRACT FROM AUTHOR]

Bearings are critical components in modern manufacturing, yet they are prone to failures in induction machines. Detecting these faults early can reduce repair costs. To achieve efficient and accurate fault detection, we explore vibration-based analysis. Traditional methods rely on manual feature extraction, which is time-consuming. In contrast, our work leverages deep learning, particularly convolutional neural networks, to automatically extract fault features from raw data. We investigate various image sizes (16 × 16, 32 × 32, 64 × 64, 128 × 128, 256 × 256) and their performance in bearing fault diagnosis. Our convolutional neural networks-based approach is compared to traditional methods such as support vector machine, nearest neighbors, and artificial neural networks. Results demonstrate the superior performance of our data-driven fault diagnosis using convolutional neural networks. [ABSTRACT FROM AUTHOR]

The process of identifying a person using their facial traits is referred to as face recognition, and it is a form of biometric identification. The use of facial recognition might range from that of an entertainment tool to one of a security tool. Even while other forms of biometric identification, such as fingerprints and iris scans, are reliable, they require the active participation of an individual. As a result, criminals cannot rely on them as the most reliable means of verification. When a criminal database, which stores the individual details of a criminal, and facial recognition technology are brought together, it can identify a criminal who is depicted in an image or seen in a video feed. Not only does a criminal recognition system needs to have a high level of accuracy, but it also needs to be able to adapt to significant changes in lighting, occlusion, aging, expressions, and other factors. In this study, they were analyzed and compared with the many methods of face detection and face recognition, such as HAAR cascades, local binary patterns histogram, support vector machines, convolutional neural networks, and ResNet-34. These methods include a variety of different approaches to recognizing faces. An analysis of these strategies is also conducted and then put into practice to those that seem to be the most effective for the designed criminal recognition system. In addition to that, a variety of uses of this criminal recognition in the real world are also discussed. [ABSTRACT FROM AUTHOR]

Around the world, road traffic accidents are the leading cause of serious injuries and deaths. Ethiopia is one of the countries that suffer the most from traffic accidents. Every government in every country wants to keep its citizens safe from accidents. To keep people safe from accidents, it is necessary to conduct a detailed analysis of the factors that contribute to high-severity accidents and deaths. As a result, we developed a data mining algorithm-based road traffic accident severity analysis for the Addis Ababa subcity in this study. The longest frequent factors in the dataset were generated using the Apriori algorithm. The Apriori algorithm generates the most frequent factors as sex: male, driver–vehicle relationship: employee, weather condition: normal, pedestrian movement: not a pedestrian, road surface type: asphalt, and accident severity: high severity, with 42.21% and 84.35% support and confidence, respectively. In addition, we created an accident severity level predictive model using a support vector machine. The predictive model has an accuracy of 85%. The proposed predictive model outperforms other well-known predictive models, such as K-nearest neighbors, decision trees, and random forests. As a result, when making decisions or policies in Ethiopia, the government or private organizations should consider the association of factors that lead to serious severe accidents. [ABSTRACT FROM AUTHOR]

The development of medical diagnostic models to support healthcare professionals has witnessed remarkable growth in recent years. Among the prevalent health conditions affecting the global population, diabetes stands out as a significant concern. In the domain of diabetes diagnosis, machine learning algorithms have been widely explored for generating disease detection models, leveraging diverse datasets primarily derived from clinical studies. The performance of these models heavily relies on the selection of the classifier algorithm and the quality of the dataset. Therefore, optimizing the input data by selecting relevant features becomes essential for accurate classification. This research presents a comprehensive investigation into diabetes detection models by integrating two feature selection techniques: the Akaike information criterion and genetic algorithms. These techniques are combined with six prominent classifier algorithms, including support vector machine, random forest, k-nearest neighbor, gradient boosting, extra trees, and naive Bayes. By leveraging clinical and paraclinical features, the generated models are evaluated and compared to existing approaches. The results demonstrate superior performance, surpassing accuracies of 94%. Furthermore, the use of feature selection techniques allows for working with a reduced dataset. The significance of feature selection is underscored in this study, showcasing its pivotal role in enhancing the performance of diabetes detection models. By judiciously selecting relevant features, this approach contributes to the advancement of medical diagnostic capabilities and empowers healthcare professionals in making informed decisions regarding diabetes diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is one of the significant common neurological disorders of the current age that causes uncontrollable movements like shaking, stiffness, and difficulty. The early clinical diagnosis of this disease is essential for preventing the progression of PD. Hence, an innovative method is proposed here based on combining the crow search algorithm and decision tree (CSADT) for the early PD diagnosis. This approach is used on four crucial Parkinson's datasets, including meander, spiral, voice, and speech-Sakar. Using the presented method, PD is effectively diagnosed by evaluating each dataset's critical features and extracting the primary practical outcomes. The used algorithm was compared with other machine learning algorithms of k-nearest neighbor (KNN), support vector machine (SVM), naive Baye (NB), multilayer perceptron (MLP), decision tree (DT), random tree, logistic regression, support vector machine of radial base functions (SVM of RBFs), and combined classifier in terms of accuracy, recall, and combination measure F1. The analytical results emphasize the used algorithm's superiority over the other selected ones. The proposed model yields nearly 100% accuracy through various trials on the datasets. Notably, a high detection speed achieved the lowest detection time of 2.6 seconds. The main novelty of this paper is attributed to the accuracy of the presented PD diagnosis method, which is much higher than its counterparts. [ABSTRACT FROM AUTHOR]

Credit card fraud is a common occurrence in today's society because the majority of us use credit cards as a form of payment more frequently. This is the outcome of developments in technology and an increase in online transactions, which have given rise to frauds that have caused significant financial losses. In order to detect fraud in credit card transactions, efficient and effective approaches are needed. In this study, we developed a hybrid CNN-SVM model for detecting fraud in credit card transactions. The effectiveness of our suggested hybrid CNN-SVM model for detecting fraud in credit card transactions was tested using real-world public credit card transaction data. The architecture of our hybrid CNN-SVM model was developed by replacing the final output layer of the CNN model with an SVM classifier. The first classifier is a fully connected layer with softmax that is trained using an end-to-end approach, whereas the second classifier is a support vector machine that is piled on top by deleting the final fully connected and softmax layer. According to experimental results, our hybrid CNN-SVM model produced classification performances with accuracy, precision, recall, F1-score, and AUC of 91.08%, 90.50%, 90.34%, 90.41, and 91.05%, respectively. [ABSTRACT FROM AUTHOR]

This work pursues two primary aims: identifying the precursory point by the CSD analysis of AE series and using acoustic emission parameters to predict the compressive strength of concrete utilizing the artificial neural network (ANN), extreme learning machine (ELM), and support vector machine (SVM). The concrete specimens with a water-cement ratio of 0.45 and 0.55 were tested for compressive strength, and the failure process of concrete was monitored by acoustic emission. The results demonstrate that the advantage of variance was being intuitive, robust, and less affected by the window. The fluctuation and abrupt increase of the variance can be regarded as the critical point and the formation of the main failure surface, which provided warning information for preventing catastrophic failures. The precursory point determined by the variance was 80%–90% of the ultimate strength, consistent with the improved b value (Ib). Then, an approach to predict the compressive strength of concrete was proposed to predict the compressive strength of concrete utilizing the ANN, ELM, and SVM. The results of the ELM were more outstanding than those of the ANN and SVM. The results suggest that it was possible to predict the compressive strength of concrete with a small number of samples using the AE parameters. [ABSTRACT FROM AUTHOR]

Facial emotion expressions are among the most potent, natural, and powerful means of human communication. Due to the COVID-19 pandemic, educational institutions worldwide are forced to switch rapidly to remote and online learning. Students are currently in an emergency state and must adapt to various and readily accessible learning methods, such as mobile learning applications or an e-learning system. A systematic literature review (SLR) is conducted to extract and synthesize information such as the emotion classifier used in the facial expression recognition (FER) system, the dataset used, the preprocessing technique applied, the feature extraction approach used, and the strength and limitation of the previous studies. Based on the search criteria, 701 publications were initially retrieved from five different digital databases, of which 48 studies have been chosen as primary studies for further analysis. Based on the findings of this study, the deep learning approach is the most frequently adopted approach in classifying student emotions during online learning. FER-2013 is the most commonly used FER dataset in FER studies, while DAiSEE is the most used academic emotion dataset. Moreover, support vector machine (SVM) is the conventional learning emotion classifier that is widely used in the FER systems, while convolutional neural network (CNN) is the most frequently used deep learning classifier. Next, it was found that the number of real-time FER systems is less than that of non-real-time FER systems. Finally, the top-1 accuracy of 94.6% was achieved by the long-term recurrent convolutional network on the academic emotion dataset, and the limitation is that it has low illumination and a lack of frontal pose. [ABSTRACT FROM AUTHOR]

Humans are familiar with "diabetes," a chronic metabolic disease that causes resistance to insulin in the human body, and about 425 million cases worldwide. Diabetes is a hazard to human health since it can gradually cause significant damage to the heart, blood vessels, eyes, kidneys, and nerves. As a result, it is critical to recognize diabetes early on to minimize its negative consequences. Over the years, artificial intelligence (AI) technology and data mining methods are playing a crucial role in detecting diabetic patients. Considering this opportunity, we present a fine-tuned random forest algorithm with the best parameters (RFWBP) that is used with the RF algorithm and feature engineering to detect diabetes patients at an early stage. We have employed several data processing techniques (e.g., normalization, conversion into numerical data) to raw data during the prepossessing phase. After that, we further applied some data mining techniques, adding related characteristics to the primary dataset. Finally, we train the proposed RFWBP and conventional methods like the AdaBoost algorithm, support vector machine, logistic regression, naive Bayes, multilayer perceptron, and a regular random forest with the dataset. Furthermore, we also utilized 5-fold cross-validation to enhance the performance of the RFWBP classifier. The proposed RFWBP achieved an accuracy of 95.83% and 90.68% with and without 5-fold cross-validation, respectively. Moreover, the proposed RFWBP is compared with conventional machine learning methods to evaluate the performance. The experimental results confirm that the proposed RFWBP outperformed conventional machine learning methods. [ABSTRACT FROM AUTHOR]

Almost 17.9 million people are losing their lives due to cardiovascular disease, which is 32% of total death throughout the world. It is a global concern nowadays. However, it is a matter of joy that the mortality rate due to heart disease can be reduced by early treatment, for which early-stage detection is a crucial issue. This study is aimed at building a potential machine learning model to predict heart disease in early stage employing several feature selection techniques to identify significant features. Three different approaches were applied for feature selection such as chi-square, ANOVA, and mutual information, and the selected feature subsets were denoted as SF1, SF2, and SF3, respectively. Then, six different machine learning models such as logistic regression (C1), support vector machine (C2), K-nearest neighbor (C3), random forest (C4), Naive Bayes (C5), and decision tree (C6) were applied to find the most optimistic model along with the best-fit feature subset. Finally, we found that random forest provided the most optimistic performance for SF3 feature subsets with 94.51% accuracy, 94.87% sensitivity, 94.23% specificity, 94.95 area under ROC curve (AURC), and 0.31 log loss. The performance of the applied model along with selected features indicates that the proposed model is highly potential for clinical use to predict heart disease in the early stages with low cost and less time. [ABSTRACT FROM AUTHOR]

Based on the brain signals, decoding and analyzing the gait features to make a reliable prediction of action intention are the core issues in the brain computer interface (BCI)-based hybrid rehabilitation and intelligent walking aid robot system. In order to realize the classification and recognition of the most basic gait processes such as standing, sitting, and quiet, this paper proposes a feature representation method based on the signal complexity and entropy of each brain region. Through the statistical analysis of these parameters between different conditions, these characteristics which sensitive to different actions are determined as a feature vector, and the classification and recognition of these actions are completed by combing support vector machine, linear discriminant analysis, and logistic regression. Experimental results show the proposed method can better realize the recognition of the aforementioned action intention. The recognition accuracy of standing, sitting, and quiet of 13 subjects is higher than 80.9%, and the highest one can reach 86.8%. Directed dynamic brain network analysis of the 8 brain regions shows that the occurrence of lower limb movement will weaken the dependence between brain regions, resulting in the weakening of network topological connection. The result has significant value for understanding human's brain cognitive characteristics in the process of lower limb movement and carrying out the study of BCI based strategy and system for lower limb rehabilitation. [ABSTRACT FROM AUTHOR]

Effective accident detection and traffic flow forecasting are of great importance for quick respond, impact elimination and intelligent control of the traffic flow consisting of autonomous vehicles. This paper proposes a traffic accident detection method for connected and automated transport systems by conducting a grid-based parameter extracting and SVC-based traffic state classification. Allowing for the dynamic spread of traffic flow over time, from upstream to downstream and from accident lanes to other lanes, a spatiotemporal Markov model is established to predict the evolution of traffic flow after accident by introducing the grid as state detection unit and fitting the spatiotemporal evolution with the parameter space mean speed to match the need of both detection accuracy and monitoring scope. Compared with actual accident data, the validation results indicate that the proposed methods present a good performance in accident detection with the accident detection rate as 87.72% and a higher precision rate than both SVM (support vector machine) and ANN (artificial neural network) models in traffic flow prediction. With the active traffic accident identification and dynamic traffic flow prediction, it is beneficial to shorten detection time, reduce possible impacts of traffic accidents and carbon emissions from congestion. The methods can be implied to traffic state recognition and traffic flow prediction, which is one of the significant sections of connected and automated transport systems, and serve as references for accident handling and urban traffic management. [ABSTRACT FROM AUTHOR]

In the process of multiscale network traffic prediction using a single model, the results are often single, resulting in a decline in the accuracy of multiscale network traffic prediction. In order to solve this problem effectively, a mathematical modeling method of multiscale network traffic combination prediction based on fuzzy SVM is proposed. Firstly, according to the multiscale network approximation signal, the multiscale network traffic feature function is constructed to complete the multiscale network traffic feature extraction. Secondly, according to the feature extraction results, the fuzzy membership function is introduced into the SVM, and the fuzzy SVM is used to classify the multiscale network traffic. Finally, based on the traffic classification results, the combination prediction of multiscale network traffic is completed by combining the grey Verhulst prediction model with the GNN model. The experimental results show that the prediction accuracy of this method for multiscale network traffic is higher, and the prediction accuracy can always maintain above 95%, and the MSE and MAE values are relatively low. [ABSTRACT FROM AUTHOR]

This article investigates an issue of road safety and a method for detecting drowsiness in images. More fatal accidents may be averted if fatigued drivers are using this technology accurately and the proposed models provide quick response by recognising the driver's state of falling asleep. There are the following drowsiness models for depicting the possible eye state classifications as VGG16, VGG19, RESNET50, RESNET101 and MobileNetV2. The absence of a readily available and trustworthy eye dataset is perceived acutely in the realm of eye closure detection. On extracting the deep features of faces with VGG16, 98.68% accuracy has been achieved, VGG19 provides an accuracy of 98.74%, ResNet50 works with 65.69% accuracy, ResNet101 has achieved 95.77%, and MobileNetV2 is achieving 96.00% accuracy with the proposed dataset. The put forth model using the support vector machine (SVM) has been used to evaluate several models, and the present results in terms of loss function and accuracy have been obtained. In the proposed dataset, 99.85% accuracy in detecting facial expressions has been achieved. These experimental results show that the eye closure estimation has a higher accuracy and cheap processing cost, as well as the ability of the proposed framework for drowsiness. [ABSTRACT FROM AUTHOR]

Background. Nasopharyngeal carcinoma (NPC) is one of the most prevalent cancers with a poor prognosis. Immunotherapy, especially immune checkpoint blockade (ICB), is becoming a potential therapeutic choice for NPC patients. Thus, the identification of patients who could benefit from immunotherapy is clinically significant. Methods. The NPC expression profiles from GSE102349 were used to calculate the cell scores of the tumor microenvironment (TME). The consensus clustering method was utilized to identify the potential molecular subtypes among NPC samples. The hub genes were selected from subtype-specific genes by bioinformatics analysis. Machine learning models, including random forest (RF) and support vector machine (SVM) algorithms, were constructed to predict the immune subtype. Results. In the present study, we identified two TME subtypes among NPC patients. Patients with the S1 subtype have higher levels of immune cells, immune checkpoint genes, and prognosis. Using expression data profiles of NPC patients, we constructed machine learning models for predicting TME subtypes of NPC patients. This model consists of 8 genes (LCK, CD247, FYN, ZAP70, SH2D1A, CD3D, CD3E, and CD3G). Among them, LCK, FYN, SH2D1A, and CD3D were associated with better prognoses. Among the two constructed models, SVM exhibited a higher area under curve (AUC) of 0.977, when compared with RF (AUC = 0.966). The web server based on the constructed machine learning models will contribute to the identification of NPC patients likely to benefit from ICB therapies. Conclusions. This study identified NPC subtypes and provided an accurate model to select individuals who are most likely to respond to ICB. [ABSTRACT FROM AUTHOR]

Accurate and reliable traffic state identification is the prerequisite for developing intelligent traffic programs. With the improvement of intelligent traffic control measures, the traffic state of some highways has gradually stabilized. The current research on traffic state identification needs to fully meet the highly informative intelligent traffic system and traffic state subcategory analysis. To fill the gap above, we propose an improved support tensor machine (STM) method based on self-training and multiclassification for traffic state subcategory identification (ISTM) with ETC gantry data. This paper takes the excellent application of the support vector machine (SVM) in traffic state identification as the starting point of method design and extends to the STM. The ETC gantry data are represented as a third-order tensor model. This paper utilizes the similarity among tensor samples to construct the kernel function and recognize the traffic states. We simplify STM calculation with a one-against-one model and a self-training idea. An optimal fit of the characteristics is supplied by maximizing inter-subcategory tensor block distances and minimizing intra-subcategory tensor block distances throughout a joint utilization of the STM and multiscale training theories. The experiment in this paper uses ETC gantry data from the Jingtai highway in Shandong Province, and the findings reveal that the ISTM has optimum values of 0.2578 and 0.3254 for the SumD and 0.1718 and 0.1901 for the DBI as compared to K-mean clustering and the SVM. The ISTM trains the traffic state subcategory classifiers with high accuracy and strong generalization ability. [ABSTRACT FROM AUTHOR]

In order to estimate the power system accurately and identify anomaly detection in real time, an identification method of anomaly detection in power system state estimation based on Fuzzy C-means algorithm is proposed. Considering the problems of scale and redundancy of power system measurement data, effective measurement data of the power system is extracted by the principal component analysis method. On this basis, the power system state estimation model established by particle swarm optimization support vector machines is used to judge the operational state of the power system. An anomaly detection identification method based on fuzzy C-means algorithm is proposed to cluster the measured data and identify the anomaly detection of power system. The experimental results show that this method can accurately estimate the state of the power system and has the highest identification accuracy for anomaly detection compared with similar methods. When the equivalent measurement data is affected by noise, the identification delay of this method for anomaly detection in a power system is 1 s, and the real-time performance is high. [ABSTRACT FROM AUTHOR]

ROP is an important index to evaluate the efficiency of oil and gas drilling. In order to accurately predict the ROP of an oilfield in Xinjiang working area, a ROP prediction model based on the historical drilling data of this working area was established based on stacking ensemble learning. This model integrates the K -nearest neighbor algorithm and support vector machine algorithm by stacking ensemble strategy and uses genetic algorithm to optimize model parameters, forming a new method of ROP prediction suitable for this oilfield. The prediction results show that the accuracy of ROP prediction by this method is up to 92.5%, and the performance is stable, which can provide reference for the optimization of drilling parameters in this oilfield and has specific guiding significance for improving the efficiency of drilling operations. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the feasibility of applying a clinical multimodal radiomics nomogram based on ultrasonography (US) and multiparametric magnetic resonance imaging (MRI) for the prediction of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) preoperatively. We performed retrospective evaluations of 133 patients with pathologically confirmed PTC, who were assigned to the training cohort and validation cohort (7 : 3), and extracted radiomics features from the preoperative US, T2-weighted (T2WI),diffusion-weighted (DWI), and contrast-enhanced T1-weighted (CE-T1WI) images. Optimal subsets were selected using minimum redundancy, maximum relevance, and recursive feature elimination in the support vector machine (SVM). For LNM prediction, the radiomics model was constructed by SVM, and Multi-Omics Graph cOnvolutional NETworks (MOGONET) was used for the effective classification of multiradiomics data. Multivariable logistic regression incorporating multiradiomics signatures and clinical risk factors was used to generate a nomogram, whose performance and clinical utility were assessed. Results showed that the nine most predictive features were separately selected from US, T2WI, DWI, and CE-T1WI images, and 18 features were selected in the combined model. The combined radiomics model showed better performance than models based on US, T2WI, DWI, and CE-T1WI. In a comparison of the combined radiomics and MOGONET model, receiver operating curve analysis showed that the area under the curve (AUC) value (95% CI) was 0.84 (0.76–0.93) and 0.84 (0.71–0.96) for the MOGONET model in the training and validation cohorts, respectively. The corresponding values (95% CI) for the combined radiomics model were 0.82 (0.74–0.90) and 0.77 (0.61–0.94), respectively. The MOGONET model had better performance and better prediction specificity compared with the combined radiomics model. The nomogram including the MOGONET signature showed a better predictive value (AUC: 0.81 vs. 0.88) in the training and validation (AUC: 0.74vs. 0.87) cohorts, as compared with the clinical model. Calibration curves showed good agreement in both cohorts. The applicability of the clinical multimodal radiomics (CMR) nomogram in clinical settings was validated by decision curve analysis. In patients with PTC, the CMR nomogram could improve the prediction of cervical LNM preoperatively and may be helpful in clinical decision-making. [ABSTRACT FROM AUTHOR]

Atrial flutter (AFL) is a common arrhythmia with two significant mechanisms, namely, focal (FAFL) and macroreentry (MAFL). Discrimination of the AFL mechanism through noninvasive techniques can improve radiofrequency ablation efficacy. This study aims to differentiate the AFL mechanism using a 12-lead surface electrocardiogram. P-P interval series variability is hypothesized to be different in FAFL and MAFL and may be useful for discrimination. 12-lead ECG signals were collected from 46 patients with known AFL mechanisms. Features for a proposed classifier are extracted through descriptive statistics of the interval series. On the other hand, the class ratio of MAFL and FAFL was 41 : 5, respectively, which was highly imbalanced. To resolve this, different data augmentation techniques (SMOTE, modified-SMOTE, and smoothed-bootstrap) have been applied on the interval series to generate synthetic interval series and minimize imbalance. Modification is introduced in the classic SMOTE technique (modified-SMOTE) to properly produce data samples from the original distribution. The characteristics of modified-SMOTE are found closer to the original dataset than the other two techniques based on the four validation criteria. The performance of the proposed model has been evaluated by three linear classifiers, namely, linear discriminant analysis (LDA), logistic regression (LOG), and support vector machine (SVM). Filter and wrapper methods have been used for selecting relevant features. The best average performance was achieved at 400% augmentation of the FAFL interval series (90.24% sensitivity, 49.50% specificity, and 76.88% accuracy) in the LOG classifier. The variation of consecutive P-wave intervals has been shown as an effective concept that differentiates FAFL from MAFL through the 12-lead surface ECG. [ABSTRACT FROM AUTHOR]

Social media platforms play a key role in fostering the outreach of extremism by influencing the views, opinions, and perceptions of people. These platforms are increasingly exploited by extremist elements for spreading propaganda, radicalizing, and recruiting youth. Hence, research on extremism detection on social media platforms is essential to curb its influence and ill effects. A study of existing literature on extremism detection reveals that it is restricted to a specific ideology, binary classification with limited insights on extremism text, and manual data validation methods to check data quality. In existing research studies, researchers have used datasets limited to a single ideology. As a result, they face serious issues such as class imbalance, limited insights with class labels, and a lack of automated data validation methods. A major contribution of this work is a balanced extremism text dataset, versatile with multiple ideologies verified by robust data validation methods for classifying extremism text into popular extremism types such as propaganda, radicalization, and recruitment. The presented extremism text dataset is a generalization of multiple ideologies such as the standard ISIS dataset, GAB White Supremacist dataset, and recent Twitter tweets on ISIS and white supremacist ideology. The dataset is analyzed to extract features for the three focused classes in extremism with TF-IDF unigram, bigrams, and trigrams features. Additionally, pretrained word2vec features are used for semantic analysis. The extracted features in the proposed dataset are evaluated using machine learning classification algorithms such as multinomial Naïve Bayes, support vector machine, random forest, and XGBoost algorithms. The best results were achieved by support vector machine using the TF-IDF unigram model confirming 0.67 F1 score. The proposed multi-ideology and multiclass dataset shows comparable performance to the existing datasets limited to single ideology and binary labels. [ABSTRACT FROM AUTHOR]

Accurate load forecasting (LF) plays an important role in the operation and decision-making process of the power grid. Although the stochastic and nonlinear behavior of loads is highly dependent on consumer energy requirements, that demands a high level of accuracy in LF. In spite of several research studies being performed in this field, accurate load forecasting remains an important consideration. In this article, the design of a hybrid short-term load forecasting model (STLF) is proposed. This work combines the features of an artificial neural network (ANN), ensemble forecasting, and a deep learning network. RBFNNs and CNNs are trained in two phases using the functional link artificial neural network (FLANN) optimization method with a deep learning structure. The predictions made from RBFNNs have been computed and produced as the forecast of each activated cluster. This framework is known as fuzzy-RBFNN. This proposed framework is outlined to anticipate one-week ahead load demand on an hourly basis, and its accuracy is determined using two case studies, i.e., Hellenic and Cretan power systems. Its results are validated while comparing with four benchmark models like multiple linear regression (MLR), support vector machine (SVM), ML-SVM, and fuzzy-RBFNN in terms of accuracy. To demonstrate the performance of RBF-CNN, SVMs replace the RBF-CNN regressor, and this model is identified as an ML-SVM having 3 layers. [ABSTRACT FROM AUTHOR]

As one of the foods commonly eaten all over the world, eggs have attracted more and more attention for their quality and price. A method based on elemental profiles and chemometrics to discriminate between free-range and caged eggs was established. Free-range (n1 = 127) and caged (n2 = 122) eggs were collected from different producing areas in China. The content of 16 elements (Zn, Pb, Cd, Co, Ni, Fe, Mn, Cr, Mg, Cu, Se, Ca, Al, Sr, Na, and K) in the eggshell was determined using a inductively coupled plasma atomic emission spectrometer (ICP-AES). Outlier diagnosis is performed by robust Stahel–Donoho estimation (SDE) and the Kennard and Stone (K-S) algorithm for training and test set partitioning. Partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) were used for classification of the two types of eggs. As a result, Cd, Mn, Mg, Se, and K make an important contribution to the classification of free-range and caged eggs. By combining column-wise and row-wise rescaling of the elemental data, the sensitivity, specificity, and accuracy were 91.9%, 91.1%, and 92.7% for PLS-DA, while the results of LS-SVM were 95.3%, 95.6%, and 95.1%, respectively. The result indicates that chemometrics analysis of the elemental profiles of eggshells could provide a useful and effective method to discriminate between free-range and caged eggs. [ABSTRACT FROM AUTHOR]

The half-bridge converter series Y-connection microgrid (HCSY-MG) is a new type of series microgrid. In order to reduce the harmonic content in HCSY-MG grid-connected current and at the same time simplify the parameter design process of the LCL filter, this study proposed an LCL filter parameter design method based on an improved particle swarm optimization-least squares support vector machine (PSO-LSSVM) by analyzing the harmonic characteristics of the HCSY-MG grid-connected current. In addition, to enhance the convergence speed of PSO-LSSVM, the inertia factor during its parameters' update is made to adjust adaptively according to the direction of two consecutive parameter changes to constitute an improved PSO-LSSVM. Through simulation and comparative analysis, it is demonstrated that the improved PSO-LSSVM can enhance the convergence speed; the proposed filter parameter design method can effectively reduce the harmonic content in the HCSY-MG grid-connected current and is simpler and more comprehensive than the existing design method. [ABSTRACT FROM AUTHOR]

Chemotherapy is not recommended for patients with deficient mismatch repair (dMMR) in colorectal cancer (CRC); therefore, assessing the status of MMR is crucial for the selection of subsequent treatment. This study is aimed at building predictive models to accurately and rapidly identify dMMR. A retrospective analysis was performed at Wuhan Union Hospital between May 2017 and December 2019 based on the clinicopathological data of patients with CRC. The variables were subjected to collinearity, least absolute shrinkage and selection operator (LASSO) regression, and random forest (RF) feature screening analyses. Four sets of machine learning models (extreme gradient boosting (XGBoost), support vector machine (SVM), naive Bayes (NB), and RF) and a conventional logistic regression (LR) model were built for model training and testing. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive performance of the developed models. In total, 2279 patients were included in the study and were randomly divided into either the training or test group. Twelve clinicopathological features were incorporated into the development of the predictive models. The area under curve (AUC) values of the five predictive models were 0.8055 for XGBoost, 0.8174 for SVM, 0.7424 for NB, 8584 for RF, and 0.7835 for LR (Delong test, P value < 0.05). The results showed that the RF model exhibited the best recognition ability and outperformed the conventional LR method in identifying dMMR and proficient MMR (pMMR). Our predictive models based on routine clinicopathological data can significantly improve the diagnostic performance of dMMR and pMMR. The four machine learning models outperformed the conventional LR model. [ABSTRACT FROM AUTHOR]

Antinuclear antibodies (ANAs) testing is the main serological diagnosis screening test for autoimmune diseases. ANAs testing is conducted principally by the indirect immunofluorescence (IIF) on human epithelial cell-substrate (HEp-2) protocol. However, due to its high variability and human subjectivity, there is an insistent need to develop an efficient method for automatic image segmentation and classification. This article develops an automatic segmentation and classification framework based on artificial intelligence (AI) on the ANA images. The Otsu thresholding method and watershed segmentation algorithm are adopted to segment IIF images of cells. Moreover, multiple texture features such as scale-invariant feature transform (SIFT), local binary pattern (LBP), cooccurrence among adjacent LBPs (CoALBP), and rotation invariant cooccurrence among adjacent LBPs (RIC-LBP) are utilized. Firstly, this article adopts traditional machine learning methods such as support vector machine (SVM), k-nearest neighbor algorithm (KNN), and random forest (RF) and then uses ensemble classifier (ECLF) combined with soft voting rules to merge these machine learning methods for classification. The deep learning method InceptionResNetV2 is also utilized to train on the classification of cell images. Eventually, the best accuracy of 0.9269 on the Changsha dataset and 0.9635 on the ICPR 2016 dataset for the traditional methods is obtained by a combination of SIFT and RIC-LBP with the ECLF classifier, and the best accuracy obtained by the InceptionResNetV2 is 0.9465 and 0.9836 separately, which outperforms other schemes. [ABSTRACT FROM AUTHOR]

Pipelines are crucial for transporting liquid and gaseous resources at various industrial sites. As unexpected pipeline failures and improper spill responses often cause detrimental effects on the environment and public safety, real-time leakage monitoring and detection are important. This study proposes a nonintrusive sensor system to measure the pressure and integrity of a pipeline in real time by easily installing nonintrusive sensors on the exterior of the pipelines. Initially, a prototype of the nonintrusive pressure-monitoring sensor was designed and fabricated. This sensor correlated the changes in pressure inside a pipe with those in the contact force exerted between the pipe and the surrounding high-stiffness band; a force-sensing resistor was used to measure the contact force. Furthermore, parametric studies were performed on a six-inch pipe, which indicated that the developed sensor can accurately monitor the inner pressure of the pipe. We also developed a methodology to determine the optimal sensor placement in a pipeline network for detecting damages using the Euclidean distance method. This was combined with the methodology of determining the decision boundary for classifying the damage location using a support vector machine to rapidly detect the damage using a minimum number of sensors. Finally, a nonintrusive sensor system was installed on the fire main pipeline system, and the damage detection performance was experimentally validated. The developed nonintrusive sensor system can be easily installed on operating pipes to monitor the inner pressure of a pipe in real-time and accurately detect damages in a pipeline network. [ABSTRACT FROM AUTHOR]

Background. Cancer-associated fibroblasts (CAFs) have reported widely involved in cancer progression. However, its underlying mechanism in gastric cancer is still not clarified. Methods. The data used in this study were all downloaded from the Cancer Genome Atlas database. R software and the R packages were used for all the analyses. Results. In our study, we first quantified the CAFs infiltration using the ssGSEA algorithm. The clinical correlation result showed that CAFs were associated with a worse prognosis and clinical features. Pathway enrichment also indicated several oncogenic pathways in GC patients with high CAFs infiltration, including epithelial-mesenchymal transition (EMT), myogenesis, allograft rejection, the inflammatory response, and IL2/STAT5 signaling. Furthermore, FNDC1 and RSPO3 were identified as the characteristic genes of CAFs through two machine learning algorithms, LASSO logistic regression and SVM-RFE. The following analysis showed that FNDC1 and RSPO3 were associated with more progressive clinical features and had a good prediction efficiency of the CAFs infiltration status in GC patients. Pathway enrichment and genomic instability were performed to explore the underlying mechanisms of FNDC1 and RSPO3. Immune infiltration analysis showed that CAFs were positively correlated with M2 macrophages. Moreover, we found that the GC patients with low CAFs infiltration were more sensitive to immunotherapy. Also, the CAFs, FNDC1, and RSPO3 could generate a certain effect on the sensitivity of doxorubicin, mitomycin, and paclitaxel. Conclusions. In summary, our study comprehensively investigated the role of CAFs in GC, which might be associated with immunotherapy sensitivity. Meanwhile, FNDC1 and RSPO3 were identified as the underlying targets of GC. [ABSTRACT FROM AUTHOR]

Motivation. Immunoglobulin proteins (IGP) (also called antibodies) are glycoproteins that act as B-cell receptors against external or internal antigens like viruses and bacteria. IGPs play a significant role in diverse cellular processes ranging from adhesion to cell recognition. IGP identifications via the in-silico approach are faster and more cost-effective than wet-lab technological methods. Methods. In this study, we developed an intelligent theoretical deep learning framework, "IGPred-HDnet" for the discrimination of IGPs and non-IGPs. Three types of promising descriptors are feature extraction based on graphical and statistical features (FEGS), amphiphilic pseudo-amino acid composition (Amp-PseAAC), and dipeptide composition (DPC) to extract the graphical, physicochemical, and sequential features. Next, the extracted attributes are evaluated through machine learning, i.e., decision tree (DT), support vector machine (SVM), k-nearest neighbour (KNN), and hierarchical deep network (HDnet) classifiers. The proposed predictor IGPred-HDnet was trained and tested using a 10-fold cross-validation and independent test. Results and Conclusion. The success rates in terms of accuracy (ACC) and Matthew's correlation coefficient (MCC) of IGPred-HDnet on training and independent dataset (Dtrain Dtest) are ACC = 98.00%, 99.10%, and MCC = 0.958, and 0.980 points, respectively. The empirical outcomes demonstrate that the IGPred-HDnet model efficacy on both datasets using the novel FEGS feature and HDnet algorithm achieved superior predictions to other existing computational models. We hope this research will provide great insights into the large-scale identification of IGPs and pharmaceutical companies in new drug design. [ABSTRACT FROM AUTHOR]

Classification of imbalanced data is a challenging task that has captured considerable interest in numerous scientific fields by virtue of the great practical value of minority accuracy. Some methods for improving generalization performance have been developed to address this classification situation. Here, we propose a cost-sensitive ensemble learning method using a support vector machine as a base learner of AdaBoost for classifying imbalanced data. Considering that the existing methods are not well studied in terms of how to precisely control the classification accuracy of the minority class, we developed a novel way to rebalance the weights of AdaBoost, and the weights influence the base learner training. This weighting strategy increases the sample weight of the misclassified minority while decreasing the sample weight of the misclassified majority until their distributions are even in each round. Furthermore, we included P-mean as one of the assessment markers and discussed why it is necessary. Experiments were conducted to compare the proposed and comparison 10 models on 18 datasets in terms of six different metrics. Through comprehensive experimental findings, the statistical study is performed to verify the efficacy and usability of the proposed model. [ABSTRACT FROM AUTHOR]

The bogie traction seat is the main part of urban rail vehicles and its fault status will affect the safe and smooth operation of the vehicles. For the low accuracy of the traditional detection methods, an intelligent fault diagnosis model of the traction seat based on principal component analysis with one versus one (PCA-OVO) and support vector machine (SVM) optimized by modified arithmetic optimization algorithm is proposed. Firstly, for the difficulty of high-frequency data collection under real working conditions, the simulation platform of the bogie of an urban rail vehicle is built, and the vibration signals of the traction seat are collected and processed in different domains, and then the feature extraction and fusion method based on PCA-OVO is used to obtain the sensitive feature set of the traction seat. Finally, the SVM intelligence recognition model is constructed based on the sensitive feature set data, and its parameters are optimally combined and selected by the modified arithmetic optimization algorithm after introducing the cosine factor. The experiments prove the effectiveness of the model. Experimental results show that the model is effective and provides a new model for fault diagnosis of traction seat of urban rail vehicles. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (LIHC) is the fifth common cancer worldwide, and it requires effective diagnosis and treatment to prevent aggressive metastasis. The purpose of this study was to construct a machine learning-based diagnostic model for the diagnosis of liver cancer. Using weighted correlation network analysis (WGCNA), univariate analysis, and Lasso-Cox regression analysis, protein-protein interactions network analysis is used to construct gene networks from transcriptome data of hepatocellular carcinoma patients and find hub genes for machine learning. The five models, including gradient boosting, random forest, support vector machine, logistic regression, and integrated learning, were to identify a multigene prediction model of patients. Immunological assessment, TP53 gene mutation and promoter methylation level analysis, and KEGG pathway analysis were performed on these groups. Potential drug molecular targets for the corresponding hepatocellular carcinomas were obtained by molecular docking for analysis, resulting in the screening of 2 modules that may be relevant to the survival of hepatocellular carcinoma patients, and the construction of 5 diagnostic models and multiple interaction networks. The modes of action of drug-molecule interactions that may be effective against hepatocellular carcinoma core genes CCNA2, CCNB1, and CDK1 were investigated. This study is expected to provide research ideas for early diagnosis of hepatocellular carcinoma. [ABSTRACT FROM AUTHOR]

The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations. Rapid and accurate monitoring of wheat growth in hilly areas is critical for determining plant protection operations and strategies. Currently, the operation time for FHB prevention and plant protection is primarily determined by manual tour inspection of plant growth, which has the disadvantages of low information gathering and subjectivity. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was used to collect wheat canopy multispectral images and heading rate information during the heading and flowering stages in order to develop a method for detecting the appropriate time for preventive control of FHB. A 1D convolutional neural network + decision tree model (1D CNN + DT) was designed. All the multispectral information was input into the model for feature extraction and result regression. The regression revealed that the coefficient of determination (R2) between multispectral information in the wheat canopy and the heading rate was 0.95, and the root mean square error of prediction (RMSE) was 0.24. This result was superior to that obtained by directly inputting multispectral data into neural networks (NN) or by inputting multispectral data into NN via traditional VI calculation, support vector machines regression (SVR), or decision tree (DT). On the basis of FHB prevention and control production guidelines and field research results, a discrimination model for FHB prevention and plant protection operation time was developed. After the output values of the regression model were input into the discrimination model, a 97.50% precision was obtained. The method proposed in this study can efficiently monitor the growth status of wheat during the heading and flowering stages and provide crop growth information for determining the timing and strategy of FHB prevention and plant protection operations. [ABSTRACT FROM AUTHOR]

Deep learning is widely used for the classification of images that have various attributes. Image data are used to extract colour, texture, form, and local features. These features are combined in feature-level image fusion to create a merged remote sensing image. A trained depth belief network (DBN) processes and divides fusion images, while a Softmax classifier determines the land type. As tested, the proposed approach can categorise all types of land. Traditional methods of detecting distant sensing photographs have limitations that can be overcome by using convolutional neural networks (CNN). Traditional techniques are incapable of combining deep learning elements while doing badly in classification. After PCA decreases data dimensionality, deep learning is applied to generate effective features that employ deep learning after PCA has reduced the dimensionality of the data. Principal component analysis is commonly used because of its effectiveness in attaining linear dimension reduction. It may be used on its own or as a starting point for further study into various different dimensionality reduction approaches. Data can be altered by remapping onto a new set of orthogonal axes using a process known as projection-based principal component analysis. Following remote sensing of land resources, the pictures were classified using a support vector machine. Euroset satellite images are used to assess the suggested approach. Accuracy and kappa have both increased. It was accurate and within 95.83 % of the planned figures. The classification findings' kappa value and reasoning time were 95.87 % and 128 milliseconds, respectively. Both the model's performance and the classification effect are excellent. [ABSTRACT FROM AUTHOR]

The effect of aluminum oxide nanoparticles (Al2O3) on the 60/70 penetration of asphalt cement (AC) was investigated in terms of the physical and rheological characteristics by using the Superpave testing procedures. Al2O3 at 3, 5, and 7% concentrations were blended with 60/70 penetration of grade AC. Conventional testing procedures were adopted regarding the physical characteristics, while dynamic shear rheometer (DSR) testing procedures were conducted to evaluate the high and low temperature failure parameters. In addition, heuristic modelling techniques, artificial neural networks (ANN), and support vector machines (SVM) were employed to predict the performance characteristics of AC by using the mechanical testing conditions. The frequency sweep test and multiple stress creep recovery (MSCR) test results revealed that the optimum composition of Al2O3 was at 5% concentration considering the high temperature performance characteristics since further addition of the Al2O3 resulted in degradation in the enhanced properties due to agglomeration of the nanoparticles in the blend. On the contrary, Al2O3 5% demonstrated the lowest viscoelastic behavior at intermediate temperatures. The higher complex modulus (G ∗) and lower phase angle (δ) parameters indicated that the increase in stiffness due to the modification process was at the cost of losing elastic properties against fatigue cracking. Moreover, based on the statistical performance indicator, coefficient of determination (R2), it was observed that the ANN models for predicting G ∗ and δ achieved a prediction accuracy of 0.989 and 0.911 while SVM models were able to achieve 0.984 and 0.929, respectively, considering the training datasets. On the other hand, it was noted that SVM models outperformed the ANN models in terms of a smaller gap between the results obtained from the training and testing datasets. The difference between the training and testing datasets for G ∗ and δ parameters for the SVM models were 3.2% and 6.8% while for the ANN models, the differences were 11.6% and 9.5%, respectively, indicating that the ANN models were more prone to the overfitting phenomenon. [ABSTRACT FROM AUTHOR]

Cuprotosis is a novel cell death mechanism that can be explored to treat various tumors. A few studies on the role of cuprotosis-related long noncoding RNA (lncRNA) in the development and prognosis of kidney renal clear cell carcinoma (KIRC) have been reported. We aimed to study the relationship between the prognosis of patients suffering from KIRC and lncRNAs associated with cuprotosis. The Cancer Genome Atlas (TCGA) database was analyzed, and the transcriptome data and clinical information on the patients with KIRC were obtained. The cuprotosis-related lncRNAs were identified by using Pearson correlation analysis, and the significant changes in the lncRNAs associated with KIRC were studied by conducting the T -test. The cuprotosis-related lncRNAs with KIRC prognostic values were identified by using the univariate Cox analysis, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) methods. A prognostic marker composed of three cuprotosis-related lncRNAs was identified following the multivariate regression analysis method. Patients with KIRC were divided into two groups based on the expression characteristics of three cuprotosis-related lncRNAs by using the K nearest neighbor (KNN) cluster analysis method. Significant differences in survival were observed between the two groups. In addition, the results obtained following the independent prognostic analysis of the risk score (RS) and clinical correlation revealed that the three cuprotosis-related lncRNA prognostic markers could accurately predict the prognosis of patients with KIRC. The results reported herein provide new insights into the pathogenesis of KIRC and the contribution of lncRNAs associated with cuprotosis. The results also helped identify a prognostic indicator that could potentially provide information for KIRC treatment. [ABSTRACT FROM AUTHOR]

Background. To identify an optimal model for diabetic retinopathy (DR) prediction in Chinese rural population by establishing and comparing different algorithms based on the data from Handan Eye Study (HES). Methods. Five algorithms, including multivariable logistic regression (MLR), classification and regression trees (C&RT), support vector machine (SVM), random forests (RF), and gradient boosting machine (GBM), were used to establish DR prediction models with HES data. The performance of the models was assessed based on the adjusted area under the ROC curve (AUROC), sensitivity, specificity, and accuracy. Results. The data on 4752 subjects were used to build the DR prediction model, and among them, 198 patients were diagnosed with DR. The age of the included subjects ranged from 30 to 85 years old, with an average age of 50.9 years (SD = 3.04). The kappa coefficient of the diagnosis between the two ophthalmologists was 0.857. The MLR model revealed that blood glucose, systolic blood pressure, and body mass index were independently associated with the development of DR. The AUROC obtained by GBM (0.952), RF (0.949), and MLR (0.936) was similar and statistically larger than that of CART (0.682) and SVM (0.765). Conclusions. The MLR model exhibited excellent prediction performance and visible equation and thus was the optimal model for DR prediction. Therefore, the MLR model may have the potential to serve as a complementary screening tool for the early detection of DR, especially in remote and underserved areas. [ABSTRACT FROM AUTHOR]

With the increase in the number of cardiovascular patients in China, this paper must use better detection methods to examine these patients. Blood lipid testing is a quantitative method for the determination of lipids contained in blood (plasma). Blood lipids are the general term for neutral fats (triglycerides and cholesterol) and lipids (phospholipids, glycolipids, sterols, and steroids) in plasma, which are widely present in the human body. At the same time, this paper also applies this technology to different examination procedures at various current stages. Cardiovascular disease, also known as circulatory system disease, is a series of diseases involving the circulatory system. The circulatory system refers to the organs and tissues that transport blood in the human body, mainly including the heart and blood vessels (arteries, veins, and microvessels), which can be subdivided into acute and chronic and are generally related to arteriosclerosis. In recent years, the most widely used is blood lipid measurement. Therefore, this paper proposes a study on the safety evaluation and effect analysis of blood lipid testing in the diagnosis of cardiovascular disease patients. This article focuses on the causes of cardiovascular disease and introduces the purpose, methods, results, and conclusions of blood lipid detection. Combined with the deep learning method combined with wavelet analysis and support vector machine (SVM), the in-depth analysis is carried out, and suitable patients are selected for blood lipid detection. The final experimental results showed that apolipoprotein B (APOB), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), and other indicators were significantly different from the control group. The values of apolipoprotein A1 (APOA1) and high-density lipoprotein cholesterol (HDL-C) were significantly different from those in the control group, P < 0.05. This shows that blood lipid examination has a good effect on the diagnosis of cardiovascular patients; it can make accurate diagnosis and prediction of the patient's condition and has good clinical application value. [ABSTRACT FROM AUTHOR]