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There is an "Infodemic" of COVID-19 in which there are a lot of rumours and information disorders spreading rapidly, the purpose of the study is to build a predictive model for identifying whether the COVID-19 information in the Malay language in Malaysia is real or fake. Under the study of COVID-19 fake news detection, the synthetic minority oversampling technique (SMOTE) is used to generate synthetic instances of real news in the training set after natural language processing (NLP) and before data modelling because the number of fake news is approximately three times greater than that of real news. Logistic regression, Naïve Bayes, decision trees, support vector machines, random forests, and gradient boosting are employed and compared to determine the most suitable predictive model. In short, the gradient-boosting classifier model has the highest value of accuracy and F1-score. [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]

Pathogens, including viruses, bacteria, and fungus, are the biotic agents that cause illnesses in crops and are the major cause of yield losses of up to 16 percent in certain parts of the globe. Pathogens are the primary cause of yield losses in some parts of the world. Deep learning algorithms, which are at the cutting edge of technology, are now being used to identify crop disease at an earlier stage. Supervised learning (support vector machine and K-nearest neighbor), ensemble learning (random forest and AdaBoost), and deep learning approaches were used in this study to suggest a classification of paddy leaf diseases, including bacteria leaf blight, blast, hispa, leaf spot, and leaf folder (neural networks). In order to evaluate the performance of the learning approaches, accuracy, recall, precision, F 1 score, and area under the receiver operating characteristic curve were used to evaluate the performance of the interpretation (ROC and AUC). According to the results of the investigation, when the fold value grows, the value of the evaluation metrics (AUC, CA, F 1 , precision, and recall) increases in a progressive manner, i.e., the 0.001 value increases as compared to the values obtained with the previous folds. When comparing the neural network to the baseline classifiers, the assessment metrics demonstrate that the neural network performs much better. [ABSTRACT FROM AUTHOR]

Gene splicing site recognition is a very important research topic in smart healthcare. Gene splicing site recognition is of great significance, not only for the large-scale and high-quality computational annotation of genomes but also for the analysis and recognition of the gene sequences evolutionary process. It is urgent to study a reliable and effective algorithm for gene splice site recognition. Traditional Twin Support Vector Machine (TWSVM) algorithm has advantages in solving small-sample, nonlinear, and high-dimensional problems, but it cannot deal with parameter selection well. To avoid the blindness of parameter selection, particle swarm optimization algorithm was used to find the optimal parameters of twin support vector machine. Therefore, a Particle Swarm Optimization Twin Support Vector Machine (PSO-TWSVM) algorithm for gene splicing site recognition was proposed in this paper. The proposed algorithm was compared with traditional Support Vector Machine algorithm, TWSVM algorithm, and Least Squares Support Vector Machine algorithm. The comparison results show that the positive sample recognition rate, negative sample recognition rate, and correlation coefficient (CC) of the proposed algorithm are the best among the four different support vector machine algorithms. The proposed algorithm effectively improves the recognition rate and the accuracy of splice sites. The comparison experiments verify the feasibility of the proposed algorithm. [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]

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]

The high dimensionality and massive amount of data in open environments make the existing low-dimensional outlier detection methods time-consuming. The support vector machine (SVM) is a commonly used outlier detection method. However, the SVM still faces the problem of difficulty in obtaining optimal parameters quickly and effectively, resulting in low detection efficiency, poor stability, and difficulty in applying to open environment datasets. In order to improve the efficiency and stability of outlier detection, this paper proposes an improved sparrow search algorithm and uses it to optimize SVM parameters. First, the traditional sparrow search algorithm is improved by using improved backtracking learning and variable logarithmic spirals. Then, the improved sparrow search method is used to optimize SVM parameters, and the optimized support vector machine is applied to the field of outlier detection. Simulation experimental results show that the proposed method is significantly better than the compared classification algorithms in multiple evaluation indicators, with better detection efficiency, stability, and generalization ability. [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]

The widespread availability of cutting-edge computer technologies has shed light on the relevance of artificial intelligence (AI) applications in almost all sectors of the economy. As a result of the incorporation of voice control processing into many Internet of Things (IoT) devices, many of these IoT devices may be operated using spoken commands. The environment that is controlled by speech may include several devices, each of which may be used for a separate activity; yet, all of the devices may collect and process the same command at the same time. This may be the case if the devices can communicate with one another. Because other devices may choose to ignore orders that are intended for particular devices if those devices are not equipped to deal with those orders, only the device that is designed to carry out the activity and process the command will be able to carry out the activity. This is because only the device that is designed to carry out the activity and process the command will be able to carry out the activity. On the other hand, when all of the voice-controlled devices capture the command through the microphone, there is a greater chance that it will mix with other sounds coming from a variety of sources. This is because the microphone is being used to capture the command from all of the voice-controlled devices. These noises may include those that are emanating from the television, music systems, and other sounds that are created by activities taking on inside the family, among other things. During the identification of instructions via processing, any blending of other sounds that are not the primary command is regarded as noise and has to be deleted. This is because any such blending is deemed to be noise. The direction of arrival (also known as DOA) of the sound waves is given primary consideration by this approach. This is done at the same time as the performance of the system, and the proposal for it are being evaluated. Based on the angle of arrival estimate, a specific room impulse response (RIR) from a collection of defined RIR is identified as a room acoustic characteristic, and source separation is carried out using the technique of independent component analysis (ICA). Following the completion of the analysis of the signals produced by the split command speech, the characteristics of the speech are retrieved from the signals. The Mel-frequency cepstral coefficients (MFCC) approach is used so that the operation of feature extraction may be carried out. This is the goal of the technique. Following that, a support vector machine classifier is used to the data in order to further split these characteristics into a large range of distinct groups. Comparisons are made between the performance of the SVM classifier and the performance of a large number of different classifiers, including decision trees, which are often used in applications that incorporate machine learning (DT). After analyzing its performance, the multiclass SVM classifier is found to have an accuracy of 91%, according to the conclusions of the study. Utilizing a classifier that is based on a probabilistic neural network, which is sometimes referred to as a PNN, is one way in which the accuracy of future classifications may be enhanced. This particular classifier is made up of three layers: one layer of gated recurrent units (GRU), one layer of long short-term memory (LSTM), and one layer that integrates the two of those different kinds of memory. This classification seems to have obtained an accuracy of 94.5 percent, which is higher than the classification accuracy attained by the multiclass SVM classifier. [ABSTRACT FROM AUTHOR]

In this study, a channel and feature selection methodology is devised for brain-computer interface (BCI) applications using functional near-infrared spectroscopy (fNIRS). A graph convolutional network (GCN) is employed to select the appropriate and correlated fNIRS channels. Furthermore, in the feature extraction phase, the performance of two filter-based feature selection algorithms, (i) the minimum redundancy maximum relevance (mRMR) and (ii) ReliefF, is investigated. The five most commonly used temporal statistical features (i.e., mean, slope, maximum, skewness, and kurtosis) are used, whereas the conventional support vector machine (SVM) is utilized as a classifier for training and testing. The proposed methodology is validated using an available online dataset of motor imagery (left- and right-hand), mental arithmetic, and baseline tasks. First, the efficacy of the proposed methodology is shown for two-class BCI applications (i.e., left- vs. right-hand motor imagery and mental arithmetic vs. baseline). Second, the proposed framework is applied to four-class BCI applications (i.e., left- vs. right-hand motor imagery vs. mental arithmetic vs. baseline). The results show that the number of appropriate channels and features was significantly reduced, resulting in a significant increase in classification accuracy for both two-class and four-class BCI applications, respectively. Furthermore, both mRMR (i.e., 87.8% for motor imagery, 87.1% for mental arithmetic, and 78.7% for four-class) and ReliefF (i.e., 90.7% for motor imagery, 93.7% for mental arithmetic, and 81.6% for four-class) yielded high average classification accuracy p < 0.05 . However, the results of the ReliefF algorithm are more stable and significant. [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]

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]

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]

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]

Tiegun yam is a typical food and medicine agricultural product, which has the effects of nourishing the kidney and benefitting the lungs. The quality and price of Tiegun yam are affected by its origin, and counterfeiting and adulteration are common. Therefore, it is necessary to establish a method to identify the origin and index component contents of Tiegun yam. Hyperspectral imaging combined with chemometrics was used, for the first time, to explore and implement the identification of origin and index component contents of Tiegun yam. The origin identification models were established by partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF) using full wavelength and feature wavelength. Compared with other models, MSC-PLS-DA is the best model, and the accuracy of the training set and prediction set is 100% and 98.40%. Partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR) models were used to predict the contents of starch, polysaccharide, and protein in Tiegun yam powder. The optimal residual predictive deviation (RPD) values of starch, polysaccharide, and protein prediction models selected in this study were 5.21, 3.21, and 2.94, respectively. The characteristic wavelength extracted by the successive projections algorithm (SPA) method can achieve similar results as the full-wavelength model. These results confirmed the application of hyperspectral imaging (HSI) in the identification of the origin and the rapid nondestructive prediction of starch, polysaccharide, and protein contents of Tiegun yam powder. Therefore, the HSI combined with the chemometric method was available for conveniently and accurately determining the origin and index component contents of Tiegun yam, which can expect to be an attractive alternative method for identifying the origin of other food. [ABSTRACT FROM AUTHOR]

The treadmill is widely used in running fatigue experiments, and the variation of plantar mechanical parameters caused by fatigue and gender, as well as the prediction of fatigue curves by a machine learning algorithm, play an important role in providing different training programs. This experiment aimed to compare changes in peak pressure (PP), peak force (PF), plantar impulse (PI), and gender differences of novice runners after they were fatigued by running. A support vector machine (SVM) was used to predict the fatigue curve according to the changes in PP, PF, and PI before and after fatigue. 15 healthy males and 15 healthy females completed two runs at a speed of 3.3 m/s ± 5% on a footscan pressure plate before and after fatigue. After fatigue, PP, PF, and PI decreased at hallux (T1) and second-fifth toes (T2–5), while heel medial (HM) and heel lateral (HL) increased. In addition, PP and PI also increased at the first metatarsal (M1). PP, PF, and PI at T1 and T2–5 were significantly higher in females than in males, and metatarsal 3–5 (M3–5) were significantly lower in females than in males. The SVM classification algorithm results showed the accuracy was above average level using the T1 PP/HL PF (train accuracy: 65%; test accuracy: 75%), T1 PF/HL PF (train accuracy: 67.5%; test accuracy: 65%), and HL PF/T1 PI (train accuracy: 67.5%; test accuracy: 70%). These values could provide information about running and gender-related injuries, such as metatarsal stress fractures and hallux valgus. Application of the SVM to the identification of plantar mechanical features before and after fatigue. The features of the plantar zones after fatigue can be identified and the learned algorithm of plantar zone combinations with above-average accuracy (T1 PP/HL PF, T1 PF/HL PF, and HL PF/T1 PI) can be used to predict running fatigue and supervise training. It provided an important idea for the detection of fatigue after running. [ABSTRACT FROM AUTHOR]

In the present scenario like COVID-19 pandemic, to maintain physical distance, the gait-based biometric is a must. Human gait identification is a very difficult process, but it is a suitable distance biometric that also gives good results at low resolution conditions even with face features that are not clear. This study describes the construction of a smart carpet that measures ground response force (GRF) and spatio-temporal gait parameters (STGP) using a polymer optical fiber sensor (POFS). The suggested carpet contains two light detection units for acquiring signals. Each unit obtains response from 10 nearby sensors. There are 20 intensity deviation sensors on a fiber. Light-emitting diodes (LED) are triggered successively, using the multiplexing approach that is being employed. Multiplexing is dependent on coupling among the LED and POFS sections. Results of walking experiments performed on the smart carpet suggested that certain parameters, including step length, stride length, cadence, and stance time, might be used to estimate the GRF and STGP. The results enable the detection of gait, including the swing phase, stance, stance length, and double supporting periods. The suggested carpet is dependable, reasonably priced equipment for gait acquisition in a variety of applications. Using the sensor data, gait recognition is performed using genetic algorithm (GA) and particle swarm optimization (PSO) technique. GA- and PSO-based gait template analyses are performed to extract the features with respect to the gait signals obtained from polymer optical gait sensors (POGS). The techniques used for classification of the obtained signals are random forest (RF) and support vector machine (SVM). The accuracy, sensitivity, and specificity results are obtained using SVM classifier and RF classifier. The results obtained using both classifiers are compared. [ABSTRACT FROM AUTHOR]

Recently, there are lots of literature on improving the robustness of SVM by constructing nonconvex functions, but they seldom theoretically study the robust property of the constructed functions. In this paper, based on our recent work, we present a novel capped asymmetric elastic net (CaEN) loss and equip it with the SVM as CaENSVM. We derive the influence function of the estimators of the CaENSVM to theoretically explain the robustness of the proposed method. Our results can be easily extended to other similar nonconvex loss functions. We further show that the influence function of the CaENSVM is bounded, so that the robustness of the CaENSVM can be theoretically explained. Other theoretical analysis demonstrates that the CaENSVM satisfies the Bayes rule and the corresponding generalization error bound based on Rademacher complexity guarantees its good generalization capability. Since CaEN loss is concave, we implement an efficient DC procedure based on the stochastic gradient descent algorithm (Pegasos) to solve the optimization problem. A host of experiments are conducted to verify the effectiveness of our proposed CaENSVM model. [ABSTRACT FROM AUTHOR]

The portable executable header (PEH) information is commonly used as a feature for malware detection systems to train and validate machine learning (ML) or deep learning (DL) classifiers. We propose to extract the deep features from the PEH information through hidden layers of a feed-forward deep neural network (FFDNN). The extraction of deep features of hidden layers represents the dataset with a better generalization for malware detection. While feeding the deep feature of one hidden layer to the succeeding layer, the Gaussian error linear unit (GeLU) activation function is applied. The FFDNN is trained with the GeLU activation function using the deep features of individual layers as well as concatenated deep features of all hidden layers. Similarly, the ML classifiers are also trained and validated in with individual layer deep features and concatenated features. Three highly effective ML classifiers, random forest (RF), support vector machine (SVM), and k-nearest neighbour (k-NN) have been investigated. The performance of the proposed model is demonstrated using a statically significant large dataset. The obtained results are interesting and encouraging in terms of classification accuracy. The classification accuracy reaches 99.15% with the internal discriminative deep feature for the proposed FFDNN-ML classifier with the GeLU activation function. [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]

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]

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]

The mechanization and intelligentization of the production process are the main trends in research and development of agricultural products. The realization of an unmanned and automated picking process is also one of the main research hotspots in China's agricultural product engineering technology field in recent years. The development of automated apple-picking robot is directly related to imaging research, and its key technology is to use algorithms to realize apple identification and positioning. Aiming at the problem of false detection and missed detection of densely occluded targets and small targets by apple picking robots under different lighting conditions, two different apple recognition algorithms are selected based on the apple shape features to study the traditional machine learning algorithm: histogram of oriented gradients + support vector machine (HOG + SVM) and a fast recognition method for multiple apple targets in a complex occlusion environment based on improved You-Only-Look-Once-v5 (YOLOv5). The first is the improvement of the CSP structure in the network. Using parameter reconstruction, the convolutional layer (Conv) and the batch normalization (BN) layer in the CBL (Conv + BN + Leaky_relu activation function) module are fused into a batch-normalized convolutional layer Conv_B. Subsequently, the CA (coordinate attention) mechanism module is embedded into different network layers in the improved designed backbone network to enhance the expressive ability of the features in the backbone network to better extract the features of different apple targets. Finally, for some targets with overlapping occlusions, the loss function is fine-tuned to improve the model's ability to recognize occluded targets. By comparing the recognition effects of HOG + SVM, Faster RCNN, YOLOv6, and baseline YOLOv5 on the test set under complex occlusion scenarios, the F 1 value of this method was increased by 13.47%, 6.01%, 1.26%, and 3.63%, respectively, and the F 1 value of this method was increased by 19.36%, 13.07%, 1.61%, and 4.27%, respectively, under different illumination angles. The average image recognition time was 0.27 s faster than that of HOG + SVM, 0.229 s faster than that of Faster RCNN, and 0.006 s faster than that of YOLOv6. The method is expected to provide a theoretical basis for apple-picking robots to choose a pertinent image recognition algorithm during operation. [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]

In numerous perilous cases, a quick medical decision is needed for the early detection of chronic diseases to avoid austere consequences that may be fatal. Chronic kidney disease (CKD) is a prevalent disease that presents a variety of challenges, including soaring costs for intervention, urgency, and, more importantly, difficulty in early detection of the disease. The current study carries out a prediction-based method that helps in detecting and diagnosing CKD patients which enables a fast and accurate decision-making process at the early stage. A combination of preprocessing and feature selection methods was developed; additionally, several prediction models, such as K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and bagging, were trained based on the processed dataset. The performance evaluation shows higher reliability of all models in terms of accuracy, precision, sensitivity, F-measure, specificity, and area under the curve (AUC) score. Specifically, KNN outperformed with an accuracy of 99.50%, sensitivity of 99.2%, precision of 100%, specificity of 98.7%, and F-measure and AUC score of 99.6%. The experimental results of KNN show the best fitted model compared to the existing state-of-the-art methods. Moreover, the reduced feature set proves that just a few clinical tests are enough to detect CKD, resulting in diagnosis cost reduction. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are the core of the Internet of Things and are used in healthcare, locations, the military, and security. Threats to the security of wireless sensor networks built on the Internet of Things (IoT-WSNs) can come from a variety of sources. This study proposes secure attack localization and detection in IoT-WSNs to improve security and service delivery. The technique used blockchain-based cascade encryption and trust evaluation in a hierarchical design to generate blockchain trust values before beacon nodes broadcast data to the base station. Simulation results reveal that cascading encryption and feature assessment measure the trust value of nodes by rewarding each other for service provisioning and trust by removing malicious nodes that reduce localization accuracy and quality of service in the network. Federated machine learning improves data security and transmission by merging raw device data and placing malicious threats in the blockchain. Malicious nodes are classified through federated learning. Federated learning combines hybrid random forest, gradient boost, ensemble learning, K -means clustering, and support vector machine approaches to classify harmful nodes via a feature assessment process. Comparing the proposed system to current ones shows an average detection and classification accuracy of 100% for binary and 99.95% for multiclass. This demonstrates that the suggested approach works well for large-scale IoT-WSNs, both in terms of performance and security, when utilizing heterogeneous wireless senor networks for the providing of secure services. [ABSTRACT FROM AUTHOR]

This study aims to develop a finite difference time-domain (FDTD) modeling method for complex terrain using a paper contour map. First, the image preprocessing technology is employed to segment, denoise, and number the drawing area to arrive at independent figures and curve matrices. Subsequently, the support vector machine (SVM) classifier is implemented to identify the number and obtain the elevation value pertinent to the curve in the figure. Finally, the scattered points in the curve matrix are meshed by the Delaunay triangulation algorithm, and a three-dimensional triangular mesh model is established according to the recognition results. The FDTD calculation model is established by the method of intersection of Yee cell grid lines and triangle projection. The simulation results reveal that the recognition rate of the SVM classification model in this study is close to 100% for printed numerals. This approach is capable of quickly and accurately establishing the FDTD-based calculation model for complex terrain on the basis of the paper contour map. [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 Diabetes-Mellitus (DM) disease is considered a persistent ailment that is triggered by excessive sugar levels in the blood of a person. It gives rise to severe health complications when left untreated and can also give rise to related diseases such as cardiac attack, nervous damage, foot problems, liver and kidney damage, and eye problems. These problems are caused by a series of factors interrelated to one another such as age, gender, family history, BMI, and Blood Glucose. Various Machine-Learning (ML) algorithms are being used in order to predict and detect the disease to avoid further complications of health. The Diabetes prediction process can be further improvised by identifying the type a person is being affected by and the probability of the occurrence of the related diseases. In order to perform the mentioned task, two types of the dataset are used in the study, namely, PIMA and a clinical survey dataset. Various ML algorithms such as Random Forest, Light Gradient Boosting Machine, Gradient Boosting Machine, Support Vector Machine, Decision Tree, and XGBoost are being used. The performance metrics used are accuracy, precision, recall, specificity, and sensitivity. Techniques such as Data Augmentation and Sampling are used. In comparison with the research conducted previously, the paper focuses on improvisation of the accuracy with a percentage of 95.20 using the LGBM Classifier, and Diabetes is also classified as Prediabetes or Diabetes using many Classification mechanisms. [ABSTRACT FROM AUTHOR]

Radiations are especially harmful to children and infants as their body cells divide rapidly, thus providing radiations more chances to interfere with the organs, leading to a number of diseases, especially those related to the skin. Therefore, the demand for a system that can detect harmful radiations timely and effectively becomes high. Many new and modern techniques comprising radiation protection and alerting systems are being introduced along with improvements and enhancements. This study demonstrates the practical implementation of an IoT-enabled intelligent system based on machine learning for radiation monitoring and warning by classifying radiations and their corresponding effects on infants. The proposed system alerts humans about the danger zones with audio/visual announcements or a buzz so that they can move to a safer place. Along with this automatic sensor system, a real-time dataset is also collected, in which sensor values are recorded along with their effects on infants for experiments. Additionally, the outcomes of the effect of radiations corresponding to the recorded sensor values are classified by using support vector machines, Gaussian naïve Bayes, decision trees, extra trees, bagging classifiers, random forests, logistic regression, and adaptive boosting classifiers. The experiment reveals that the adaptive boosting classifier gives the best accuracy of 81.77% compared to other classifiers. [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]

This paper presents the estimation of accuracy in male, female, and transgender identification using different classifiers with the help of voice signals. The recall value of each gender is also calculated. This paper reports the third gender (transgender) identification for the first time. Voice signals are the most appropriate and convenient way to transfer information between the subjects. Voice signal analysis is vital for accurate and fast identification of gender. The Mel Frequency Cepstral Coefficients (MFCCs) are used here as an extracted feature of the voice signals of the speakers. MFCCs are the most convenient and reliable feature that configures the gender identification system. Recurrent Neural Network–Bidirectional Long Short-Term Memory (RNN-BiLSTM), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) are utilized as classifiers in this work. In the proposed models, the experimental result does not depend on the text of the speech, the language of the speakers, and the time duration of the voice samples. The experimental results are obtained by analyzing the common voice samples. In this article, the RNN-BiLSTM classifier has single-layer architecture, while SVM and LDA have a k-fold value of 5. The recall value of genders and accuracy of the proposed models also varied according to the number of voice samples in training and testing datasets. The highest accuracy for gender identification is found as 94.44%. The simulation results show that the accuracy of the RNN is always found at a higher value than SVM and LDA. The gender-wise highest recall value of the proposed model is 95.63%, 96.71%, and 97.22% for males, females, and transgender, respectively, using voice signals. The recall value of the transgender is high in comparison to other genders. [ABSTRACT FROM AUTHOR]

Epilepsy is a common electrophysiological disorder of the brain, detected mainly by electroencephalogram (EEG) signals. Since correctly diagnosing epilepsy seizures by monitoring the EEG signal is very tedious and time-consuming for a neurologist, a growing number of studies have been conducted on developing automated epileptic seizure detection (AESD). In this work, a novel supervised model is proposed for AESD. Initially, the EEG signals are collected from Bonn University EEG (BU-EEG) database. Then, empirical mode decomposition and feature extraction (combination of entropy, frequency, and statistical features) are applied to extract the features. Furthermore, Siamese network is utilized to lessen the number of extracted features and obtain the most discriminative features. Then, these features are exploited to classify seizure and non-seizure EEG signals by using a support vector machine classifier. This paper examines the Siamese network's contribution as a learning-based feature transformation in improving seizure detection performance. The numerical results confirm that the proposed framework can achieve a perfect classification performance (100 % accuracy). This approach can constructively help doctors to detect epileptic seizure activity and reduce their workload. [ABSTRACT FROM AUTHOR]

Crown pears are an important economic crop, but their quality and economy are seriously affected by the different levels of damage. To improve the overall quality of crown pears, sorting of crown pears with different levels of damage is required. However, there are some shortcomings in the traditional detection methods, such as low efficiency and large error. Therefore, the hyperspectral technology was used to discriminate between sound and 3 different levels of damage (defined as level I, II, and III damage, respectively) of crown pears in this study. To improve the discriminatory accuracy of the model, absorbance (A) spectra and Kubelka–Munk (K-M) spectra were added to reflectance (R) spectra. The three spectra were pretreated; then, the partial least squares discriminant analysis (PLS-DA) model and the support vector machine (SVM) model were established to discriminate the crown pears with different levels of damage. The results of the discriminant model show that the discrimination accuracy of the SVM based on R, A, and K-M spectra is higher than that of PLS-DA of them; the A-RAW-SVM model has the best discrimination performance with an overall discrimination accuracy of 100% for the test and 98.98% for calibration sets, respectively. Finally, the spectra were selected by the competitive adaptive reweighted sampling (CARS) and the uninformative variables elimination (UVE) to obtain the characteristic wavelengths, and the SVM models were built based on the filtered R, A, and K-M. Their discrimination results show that the A-RAW-CARS-SVM model has the best discrimination ability, and the discrimination accuracies of the test and calibration sets of the model are 96.88% and 100%, respectively. The results show that the best discrimination of different levels of damage of crown pears is the SVM model based on a spectra. This study provides a theoretical basis and experimental basis for detecting the damage of crown pears using hyperspectral. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) are widely used for image recognition and text analysis and have been suggested for application on one-dimensional data as a way to reduce the need for preprocessing steps. In this study, the performance of one-dimensional convolutional neural network (1DCNN) machine learning algorithm was investigated for regression analysis of Antai pills spectral data. This algorithm was compared with other chemometric methods, including support vector machine regression (SVR) and partial least-square regression (PLSR) methods. The results showed that the 1DCNN model outperformed the PLSR and SVR models with similar data preprocessing for the three analytes (wogonoside, scutellarin, and ferulic acid) in Antai pills. Taking wogonoside as an example, the indices such as the correction coefficient of determination (R v 2), the root mean-squared error of cross validation (RMSECV) for calibration set, the prediction coefficient of determination (R p 2), and the root mean-squared error of prediction (RMSEP) obtained by PLSR modeling were 0.9340, 0.5568, 0.9491, and 0.5088; the indices obtained by SVR modeling were 0.9520, 0.4816, 0.9667, and 0.4117; and the indices obtained by 1DCNN modeling were 0.9683, 0.3397, 0.9845, and 0.2807, respectively. The evaluation metrics of 1DCNN are better than those of PLSR and SVR, and the prediction effect is the best, proving that 1DCNN has a good generalization ability. Especially with outliers of spectra, PLSR's R p 2 decreased by 0.0181, SVR's R v 2 decreased by 0.01, and 1DCNN's R v 2 increased by 0.0009 and R p 2 decreased by 0.0057. The evaluation indices of 1DCNN have no significant change in comparison with no outliers and can still show good performance, which reflects the inclusiveness of the 1DCNN model for outliers. Simultaneously, the feasibility and robustness of the 1DCNN model in the application of near-infrared spectroscopy was verified, which has a certain application value. [ABSTRACT FROM AUTHOR]

Background: Acute respiratory and circulatory collapse might occasionally happen after pulmonary endarterectomy (PEA). We aimed to investigate the utilization of extracorporeal membrane oxygenation (ECMO) after PEA and potential risk factors. Methods: Demographic characteristics, clinical and surgical data were collected for all patients who underwent PEA from December 2016 to June 2022. All factors were compared between patients in the ECMO group and those in the other group. The most characteristic risk factors were obtained by least absolute shrinkage and selection operator regression and support vector machine machine learning, and receiver operating characteristics (ROC) Curve analysis was performed to verify the diagnostic value of the obtained risk factors. Results: A total of 117 patients underwent PEA, and 8 (6.8%) of them received ECMO treatment intraoperatively or postoperatively. There were significant differences between the two groups in terms of cardiac function, pulmonary vascular resistance (PVR), preoperative inflammation and cardiopulmonary bypass time. The PVR and neutrophil‐to‐lymphocyte ratio (N/L ratio) were the most characteristic risk factors with an area under the ROC curve of 0.847 (95% confidence interval [CI] = 0.7517–0.9420, p =.005) and 0.896 (95% CI = 0.803–0.989, p =.001), respectively. The ECMO group had higher PVR (1549.4 ± 600.7 vs. 952.9 ± 466.9 dyn.s.cm−5, p =.004) and N/L ratio (6.3 ± 5.6 vs. 2.4 ± 1.7, p =.001). Conclusions: PVR and N/L ratio can correctly predict who is likely to receive ECMO treatment after PEA. Therefore, addressing the preoperative inflammatory status might be beneficial but further research is needed. [ABSTRACT FROM AUTHOR]

Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the patient's EEG signal into multiple intrinsic modal functions (IMFs) using empirical modal decomposition, then compute the mean, standard deviation, fluctuation index, and sample entropy of IMF1, and finally classify them using a fusion algorithm of support vector machine and K‐nearest neighbor optimized by particle swarm algorithm. The results of validation using the epileptic EEG data set from Bonn University show that the auto‐detection and fast recognition method proposed in this paper can achieve a high seizure accuracy recognition rate (≥95%) with only a small number of training samples, which has a good clinical application value. [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]

Objectives. The diagnosis of leukemia relies very much on the results of bone marrow examinations, which is never generally performed in routine physical examination. In many rural areas even community hospitals and primary care clinics, the lack of hematological specialist and facility does not allow a definite diagnosis of leukemia. Thus, there will be a significant benefit if machine learning (ML) models could help early predict leukemia using preliminary blood test data in a routine physical examination in community hospitals to save time before a definite diagnosis. Methods. We collected the routine physical examination data of 1230 newly diagnosed leukemia patients and 1300 healthy people. We trained and tested 3 machine learning (ML) models including linear support vector machine (LSVM), random forest (RF), and XGboost models. We not only examined the accordance between model results and statistical analysis of the input data but also examined the consistency of model accuracy scores and relative importance order of model factors with regard to different input data sets and different model arguments to check the applicability of both the models and the input data. Results. Generally, the RF and XGboost models give more identical, consistent, and robust relative importance order of factors that is also accordant with the statistical analysis, while the LSVM gives much different and nonsense orders for different inputs. Results of the RF and XGboost models show that (1) generally, the models achieve accuracy scores above 0.9, indicating effective identification of leukemia, and (2) the top three factors that contribute most to the identification of leukemia include red blood cell (RBC), hematocrit (HCT), and white blood cell (WBC), while the other factors contribute relatively less. Conclusions. This study shows a feasible case example for early identification of leukemia using routine physical examination data with the assistance of ML models, which can be conveniently, cheaply, and widely applied in community hospitals or primary care clinics to save time before definite diagnosis; however, more studies are still needed to validate the applicability of more ML models to a larger variety of input data sets. [ABSTRACT FROM AUTHOR]

In order to fully exploit the effect of intelligent evaluation of English teaching ability and explore the quality of English teaching, an intelligent evaluation method of English teaching ability based on improved machine learning algorithm is proposed, which can ensure the rational allocation of English teaching resources, analyze the big data of constraint parameters of English teaching ability evaluation, and obtain frequent item sets of English teaching quality based on big data mining technology. The particle swarm optimization (PSO) method was used to improve the parameters of the SVM, and the optimal parameters of the support vector machines (SVM) were obtained, which were input into the sample of English teaching effect evaluation, and a method was constructed to maximize the SVM. The optimal parameters are introduced into the decision function, so as to achieve the purpose of English teaching quality assessment. The experimental results show that the proposed method has a high convergence speed and can achieve rapid convergence with only 30 network trainings. At the same time, the evaluation accuracy of English teaching quality is as high as 95%, the correlation coefficient of the results is as high as 0.95, and the evaluation time is low. It is less than 150 ms, and the keyword frequency identification is better and better, which can realize the objective evaluation of online teaching quality. [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]

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]