Sequential Forward Selection Algorithm Research Articles

Estimation of the Bio-Parameters of Winter Wheat by Combining Feature Selection with Machine Learning Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Images

Accurate and timely monitoring of biochemical and biophysical traits associated with crop growth is essential for indicating crop growth status and yield prediction for precise field management. This study evaluated the application of three combinations of feature selection and machine learning regression techniques based on unmanned aerial vehicle (UAV) multispectral images for estimating the bio-parameters, including leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC), at key growth stages of winter wheat. The performance of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for the bio-parameters estimation was compared with that of Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) regression with internal feature selectors. A consumer-grade multispectral UAV was used to conduct four flight campaigns over a split-plot experimental field with various nitrogen fertilizer treatments during a growing season of winter wheat. Eighteen spectral variables were used as the input candidates for analyses against the three bio-parameters at four growth stages. Compared to LASSO and RF internal feature selectors, the SFS algorithm selects the least input variables for each crop bio-parameter model, which can reduce data redundancy while improving model efficiency. The results of the SFS-SVR method show better accuracy and robustness in predicting winter wheat bio-parameter traits during the four growth stages. The regression model developed based on SFS-SVR for LAI, LCC, and CCC, had the best predictive accuracy in terms of coefficients of determination (R2), root mean square error (RMSE) and relative predictive deviation (RPD) of 0.967, 0.225 and 4.905 at the early filling stage, 0.912, 2.711 μg/cm2 and 2.872 at the heading stage, and 0.968, 0.147 g/m2 and 5.279 at the booting stage, respectively. Furthermore, the spatial distributions in the retrieved winter wheat bio-parameter maps accurately depicted the application of the fertilization treatments across the experimental field, and further statistical analysis revealed the variations in the bio-parameters and yield under different nitrogen fertilization treatments. This study provides a reference for monitoring and estimating winter wheat bio-parameters based on UAV multispectral imagery during specific crop phenology periods.

Estimation of Biochemical Pigment Content in Poplar Leaves Using Proximal Multispectral Imaging and Regression Modeling Combined with Feature Selection.

Monitoring the biochemical pigment contents in individual plants is crucial for assessing their health statuses and physiological states. Fast, low-cost measurements of plants' biochemical traits have become feasible due to advances in multispectral imaging sensors in recent years. This study evaluated the field application of proximal multispectral imaging combined with feature selection and regressive analysis to estimate the biochemical pigment contents of poplar leaves. The combination of 6 spectral bands and 26 vegetation indices (VIs) derived from the multispectral bands was taken as the group of initial variables for regression modeling. Three variable selection algorithms, including the forward selection algorithm with correlation analysis (CORR), recursive feature elimination algorithm (RFE), and sequential forward selection algorithm (SFS), were explored as candidate methods for screening combinations of input variables from the 32 spectral-derived initial variables. Partial least square regression (PLSR) and nonlinear support vector machine regression (SVR) were both applied to estimate total chlorophyll content (Chla+b) and carotenoid content (Car) at the leaf scale. The results show that the nonlinear SVR prediction model based on optimal variable combinations, selected by SFS using multiple scatter correction (MSC) preprocessing data, achieved the best estimation accuracy and stable prediction performance for the leaf pigment content. The Chla+b and Car models developed using the optimal model had R2 and RMSE predictive statistics of 0.849 and 0.825 and 5.116 and 0.869, respectively. This study demonstrates the advantages of using a nonlinear SVR model combined with SFS variable selection to obtain a more reliable estimation model for leaf biochemical pigment content.

Enhancing multi-class SSVEP classification performance using regularized covariance estimators, spatiotemporal beamforming, and short-time EEG trials

One of the main challenges in BCI technologies is the problem of poor information transfer rate (ITR) of BCI systems and its effect on reducing the commands users can give. At the state of the art, the Linearly Constrained Minimum Variance (LCMV) beamformer is one of the most effective strategies that can provide satisfactory performance in the analysis of steady-state visual evoked potentials (SSVEP). When only a short-period SSVEP signal is available, a poor estimate of the covariance matrix used in the original LCMV method is obtained. Therefore, the main drawback of the conventional LCMV beamformer and its spatiotemporal form (LCMVst) is that they require a long-period SSVEP signal to achieve a reliable estimate for the beamformer weights. To cope with the mentioned problems, this paper presents two types of regularized estimators to achieve a robust estimate for the covariance matrix when only a short-period SSVEP signal is available. These two types, which are user-independent, include the convex combination (CC) and general linear combination (GLC) methods. In the next step, in order to provide suitable beamformer weights, the CC and GLC covariance estimators are combined with the original LCMVst beamformer. Furthermore, two new subject-based electrode selection algorithms, i.e., sequential forward electrode selection (SFES) algorithm and common spatial pattern (CSP) based technique, are applied to pick out the best collection of electrodes and to maximize the classification accuracy. The introduced methods were assessed using a four-class SSVEP dataset recorded on 20 subjects. Experimental results indicate that the proposed estimators optimally classify SSVEPs by estimating proper beamformer weights. The proposed LCMVst-CC and LCMVst-GLC beamformers improved the average classification accuracy by about 27.75% in comparison to the original LCMV-based beamformer at the 0.25-second segment length. Using SSVEP stimulations, we show that our proposed beamformers offer a better classification performance than the conventional LCMV-based spatiotemporal beamformer for all stimulation times.

Wind power plants hybridised with solar power: A generation forecast perspective

The association of different variable renewable technologies in hybrid power plants and the benefits of their aggregation for the operation of power systems is an area of recent research. Accurate forecasts are crucial for extracting those benefits and promote an optimal integration of such plants into power systems and electricity markets. This study focuses on the hybridisation of existing wind power plants with different shares of solar photovoltaic capacity and investigates how these power plants can reduce their combined forecast errors and thus, increasing profitability in electricity markets.The work uses a forecast methodology based on a sequential forward feature selection algorithm which employs two different objective functions and an artificial neural network approach previously presented but, in this case, it is applied to the specific case of hybrid power plants. The methodology uses as input data from a numerical weather prediction model and iteratively selects meteorological features to achieve the different objective functions implemented, namely i) minimisation of the root mean square error; or ii) maximisation of the market remuneration.The methodology developed was applied to three case studies in Portugal with different levels of wind and solar generation complementarity. The results show that the hybrid power plants can increase market value by up to 5% and total remuneration can increase by up to 30% when compared with the existing wind power plant, while it is possible to reduce the forecast errors by nearly 4%. The obtained results highlight the need to select the most relevant meteorological features to maximise the accuracy of the power forecast and the renewable power producers revenues in a market environment.

Optimized Extraction Method of Fruit Planting Distribution Based on Spectral and Radar Data Fusion of Key Time Phase

With China’s fruit tree industry becoming the largest in the world, accurately understanding the spatial distribution of fruit tree growing areas is crucial for promoting socio-economic development and rural revitalization. Remote sensing offers unprecedented opportunities for fruit tree monitoring. However, previous research has mainly focused on UAV and near-ground remote sensing, with limited accuracy in obtaining fruit tree distribution information through satellite remote sensing. In this study, we utilized the Google Earth Engine (GEE) remote sensing cloud platform and integrated data from Sentinel-1, Sentinel-2, and SRTM sources. We constructed a feature space by extracting original band features, vegetation index features, polarization features, terrain features, and texture features. The sequential forward selection (SFS) algorithm was employed for feature optimization, and a combined machine learning and object-oriented classification model was used to accurately extract fruit tree crop distributions by comparing key temporal phases of fruit trees. The results revealed that the backscatter coefficient features from Sentinel-1 had the highest contribution to the classification, followed by the original band features and vegetation index features from Sentinel-2, while the terrain features had a relatively smaller contribution. The highest classification accuracy for jujube plantation areas was observed in November (99.1% for user accuracy and 96.6% for producer accuracy), whereas the lowest accuracy was found for pear tree plantation areas in the same month (93.4% for user accuracy and 89.0% for producer accuracy). Among the four different classification methods, the combined random forest and object-oriented (RF + OO) model exhibited the highest accuracy (OA = 0.94, Kappa = 0.92), while the support vector machine (SVM) classification method had the lowest accuracy (OA = 0.52, Kappa = 0.31). The total fruit tree plantation area in Aksu City in 2022 was estimated to be 64,000 hectares, with walnut, jujube, pear, and apple trees accounting for 42.5%, 20.6%, 19.3%, and 17.5% of the total fruit tree area, respectively (27,200 hectares, 13,200 hectares, 12,400 hectares, and 11,200 hectares, respectively). The SFS feature optimization and RF + OO-combined classification model algorithm selected in this study effectively mapped the fruit tree planting areas, enabling the estimation of fruit tree planting areas based on remote sensing satellite image data. This approach facilitates accurate fruit tree industry and real-time crop monitoring and provides valuable support for fruit tree planting management by the relevant departments.

Neural network application for assessing thyroid-associated orbitopathy activity using orbital computed tomography

This study aimed to propose a neural network (NN)-based method to evaluate thyroid-associated orbitopathy (TAO) patient activity using orbital computed tomography (CT). Orbital CT scans were obtained from 144 active and 288 inactive TAO patients. These CT scans were preprocessed by selecting eleven slices from axial, coronal, and sagittal planes and segmenting the region of interest. We devised an NN employing information extracted from 13 pipelines to assess these slices and clinical patient age and sex data for TAO activity evaluation. The proposed NN’s performance in evaluating active and inactive TAO patients achieved a 0.871 area under the receiver operating curve (AUROC), 0.786 sensitivity, and 0.779 specificity values. In contrast, the comparison models CSPDenseNet and ConvNeXt were significantly inferior to the proposed model, with 0.819 (p = 0.029) and 0.774 (p = 0.04) AUROC values, respectively. Ablation studies based on the Sequential Forward Selection algorithm identified vital information for optimal performance and evidenced that NNs performed best with three to five active pipelines. This study establishes a promising TAO activity diagnosing tool with further validation.

Intentional-Deception Detection Based on Facial Muscle Movements in an Interactive Social Context

Micro-expression, which is generated by facial muscle movements, could be a crucial cue for deception detection. In the existed research investigating the relationship between facial muscles and deception detection, researchers have focused almost exclusively on two muscles, i.e., zygomaticus and corrugator supercilii, based on the theoretical basis that they are highly associated with positive and negative expressions. However, the aim of this study is to demonstrate the direct relationship between facial muscle movements and deception detection. Addressing this issue, this paper proposes an experimental paradigm with high ecological validity that uses electromyography (EMG) signals to precisely examine the role of facial muscle movements in deception detection. Moreover, we propose a vector-based sequential forward selection (VSFS) algorithm to identify the muscle (or muscle combination) most closely associated with lying. Based on our proposed approach, the importance of seven selected facial muscles was explored by comparing the corresponding facial EMG (fEMG) between truth and lying conditions. First, the present study found that the zygomaticus and corrugator supercilii could play important roles in deception detection, and our findings are consistent with existed research. Second, the experiment result verified that the muscles related to deception detection were consistent with those with higher frequency occurring in micro-expression. Moreover, the present study provides a theoretical basis that intelligent micro-expressions analysis could improve the lie detection performance by focusing on the area of the forehead, eyebrows, and cheeks.

CHILDHOOD MEDULLOBLASTOMA DIAGNOSIS USING MULTISCALE FRAMEWORK

This paper proposes an efficient Shearlet Based Childhood MedulloBlastoma (SBCMB) detection system. It is a classification system that extracts prominent characteristics for childhood MedulloBlastoma diagnosis from a given collection of histopathological images. Then, those extracted features are used with specific decision-making algorithms to categorize the histopathological images. After representing the histopathological images by Shearlet, a multiscale framework, Improved Sequential Forward Selection (ISFS) algorithm, is employed to select the dominant feature subset. Finally, Multi-Layer Perceptron (MLP) with ten hidden layers is utilized for melanoma classification. Based on the results of the evaluation of the SBCM system, it seems that the classification may be accomplished exclusively by the ISFS-based features and that the accuracy of the classifier depends on these selected features. The SBCM system provides 97.62% accuracy on 10x magnified images and 98.77% on 100x magnified images for childhood MedulloBlastoma diagnosis.    

Enhancing wind power forecast accuracy using the weather research and forecasting numerical model-based features and artificial neuronal networks

Forecasting with accuracy the quantity of energy produced by wind power plants is crucial to enabling its optimal integration into power systems and electricity markets. Despite the remarkable improvements in the wind forecasting systems in recent years, large errors can still be observed, especially for longer time horizons. This work focuses on identifying new numerical weather prediction (NWP)-based features aiming to improve the overall quality of wind power forecasts.The methodology also incorporates a sequential forward feature selection algorithm. This algorithm was designed to select iteratively the meteorological features which minimize the wind forecast errors.The methodology was applied separately to seven wind parks in Portugal with different climate characteristics. The proposed approach allowed a reduction between 13% and 37% in the root mean square errors of wind power forecasts, compared with a baseline scenario. While the meteorological features identified for each wind park showed similarities within regions with analogous wind power generation profiles, each wind park required specific meteorological parameters as input data to obtain the best performance. Thus, the results show to be crucial to select the most relevant features of a specific site to maximize the accuracy of a wind power forecast.

Load2Load: Day-ahead load forecasting at aggregated level

A reliable and accurate short-term load forecasting (STLF) helps utilities and energy providers deal with the challenges posed by supply and demand balance, higher penetration of renewable energies and the development of electricity markets with increasingly complex pricing strategies in future smart grids. Recent advances in deep learning have been successively utilized to STLF. However, there is no certain study that evaluates the performances of different STLF methods at an aggregated level on different datasets with different numbers of daily measurements.In this study, a deep learning STLF architecture called Load2Load is proposed for day-ahead forecasting. Different forecasting methods have been evaluated and compared on two datasets with different temporal resolutions and features. An additive ensemble method as well as a selective ensemble method that selects the outputs of different forecasters in an hourly manner are proposed. Moreover; a modified sequential forward feature selection algorithm is proposed, resulting in better performance with a much smaller number of features.Numerical results show that the proposed Load2Load architecture has a competing performance compared to other advanced forecasters. When used together with the proposed ensemble methods, Load2Load can significantly improve the forecasting performance. The proposed feature selection algorithm results in better performance for the majority of the cases while reducing the dimensionality. According to the results with two different datasets; the proposed methods are shown to be robust to temporal resolutions, feature types and sequence lengths.

An individualized medication model of sodium valproate for patients with bipolar disorder based on machine learning and deep learning techniques.

Valproic acid/sodium valproate (VPA) is a widely used anticonvulsant drug for maintenance treatment of bipolar disorders. In order to balance the efficacy and adverse events of VPA treatment, an individualized dose regimen is necessary. This study aimed to establish an individualized medication model of VPA for patients with bipolar disorder based on machine learning and deep learning techniques. The sequential forward selection (SFS) algorithm was applied for selecting a feature subset, and random forest was used for interpolating missing values. Then, we compared nine models using XGBoost, LightGBM, CatBoost, random forest, GBDT, SVM, logistic regression, ANN, and TabNet, and CatBoost was chosen to establish the individualized medication model with the best performance (accuracy = 0.85, AUC = 0.91, sensitivity = 0.85, and specificity = 0.83). Three important variables that correlated with VPA daily dose included VPA TDM value, antipsychotics, and indirect bilirubin. SHapley Additive exPlanations was applied to visually interpret their impacts on VPA daily dose. Last, the confusion matrix presented that predicting a daily dose of 0.5g VPA had a precision of 55.56% and recall rate of 83.33%, and predicting a daily dose of 1g VPA had a precision of 95.83% and a recall rate of 85.19%. In conclusion, the individualized medication model of VPA for patients with bipolar disorder based on CatBoost had a good prediction ability, which provides guidance for clinicians to propose the optimal medication regimen.

A New Feature Analysis Approach to Selecting Channels of EEG for Fatigue Driving.

Fatigued driving is a significant contributor to traffic accidents. There are some issues with common EEG data of 32 channels, 64 channels, and 128 channels, such as difficult acquisition, high data redundancy, and difficult practical application. A new channel selection method called ReliefF_SFS is proposed to address the problem of how to reduce the number of channels while maintaining classification accuracy. It combines the ReliefF algorithm and the sequential forward selection (SFS) algorithm. When only T6, O1, Oz, T4, P3, and FC3 are used, the classification accuracy under Theta_Std+FE combined with ReliefF_SFS achieves 99.45%. The strategy suggested in this paper not only ensures the recognition accuracy but also reduces the number of channels when compared to other models based on the same data set.

Computational prediction of Calu-3-based in vitro pulmonary permeability of chemicals

Pulmonary is a potential route for drug delivery and exposure to toxic chemicals. The human bronchial epithelial cell line Calu-3 is generally considered to be a useful in vitro model of pulmonary permeability by calculating the apparent permeability coefficient (Papp) values. Since in vitro experiments are time-consuming and labor-intensive, computational models for pulmonary permeability are desirable for accelerating drug design and toxic chemical assessment. This study presents the first attempt for developing quantitative structure-activity relationship (QSAR) models for addressing this goal. A total of 57 chemicals with Papp values based on Calu-3 experiments was first curated from literature for model development and testing. Subsequently, eleven descriptors were identified by a sequential forward feature selection algorithm to maximize the cross-validation performance of a voting regression model integrating linear regression and nonlinear random forest algorithms. With applicability domain adjustment, the developed model achieved high performance with correlation coefficient values of 0.935 and 0.824 for cross-validation and independent test, respectively. The preliminary results showed that computational models could be helpful for predicting Calu-3-based in vitro pulmonary permeability of chemicals. Future works include the collection of more data for further validating and improving the model.

A Data-Driven Short-Term PV Generation and Load Forecasting Approach for Microgrid Applications

The data-driven (DD) is a systematic approach to improve the data and model by deriving/adding features to address the problem identified during the iterative loop of forecasting model development. This article proposes a DD framework for forecasting short-term PV generation and load demand. A framework of three stages with a unique contribution in each stage, such as generalizing data preprocessing steps (stage-1), multivariate feature generation and selection (stage-2), and model hyperparameter tuning (stage-3) for further improvement in forecasting is proposed. It focuses on data as well as forecasting models. The whole process is analyzed using the time-series measured data collected from a real-life demonstration project in Ireland. Data preprocessing is generalized for both generation and demand forecasting under the same framework. The relevant features are selected with the help of the proposed random forest sequential forward feature selection algorithm. Hyperparameters are tuned through tree-structured Parzen estimator algorithm for further improvement. In addition, the performance of the classical autoregressive integrated moving average model is compared with the machine learning-based gate recurrent unit, long short term memory, recurrent neural network, and convolutional neural network models. Results show that the data-driven forecasting model framework systematically improves the model performance. The seasonal variation has also a high impact on the model performances.

IoT botnet detection with feature reconstruction and interval optimization

The existing botnet detection methods have the problems of uneven sampling, poor feature selection, and weak generalization ability, resulting in low detection and classification results and poor adaptability to the internet of things (IoT) environment with limited computing and storage resources. This paper proposes an IoT botnet detection method using feature reconstruction and interval optimization to solve the above problems. Through the designed address triple and time window-based IP aggregation and feature reconstruction method (ATTW-IP-FR), the network traffic samples obtained from the IoT gateway are integrated, and the flow features are reconstructed to attain the reconstructed sample set. The proposed self-corrected hybrid weighted sampling algorithm balances the normal and botnet flow samples in the reconstructed sample set to get the resampling sample set. The introduced multiattribute decision-making and adjacency relation chain-based sequential forward selection algorithm is applied to eliminate the redundant features in the resampling sample set, and the optimal feature subset is obtained. The resampling sample set filtered by the optimal feature subset is detected and classified through the designed two-stage hybrid heterogeneous model optimized by the intermittent chaos and bald eagle search algorithm-based interval optimization algorithm. The experimental results show that the proposed method effectively detects the botnet in two real IoT scenarios. The detection accuracy is 99.17 % $ \% $ , the Matthews correlation coefficient is 98.35 % $ \% $ , the false positive rate is 0.25 % $ \% $ , and the false negative rate is 1.27 % $ \% $ , which are better than the existing methods. This method can effectively reduce sampling and feature selection time and space overhead and better adapt to the resource-constrained IoT environment.

Predicting Lapatinib Dose Regimen Using Machine Learning and Deep Learning Techniques Based on a Real-World Study.

Lapatinib is used for the treatment of metastatic HER2(+) breast cancer. We aim to establish a prediction model for lapatinib dose using machine learning and deep learning techniques based on a real-world study. There were 149 breast cancer patients enrolled from July 2016 to June 2017 at Fudan University Shanghai Cancer Center. The sequential forward selection algorithm based on random forest was applied for variable selection. Twelve machine learning and deep learning algorithms were compared in terms of their predictive abilities (logistic regression, SVM, random forest, Adaboost, XGBoost, GBDT, LightGBM, CatBoost, TabNet, ANN, Super TML, and Wide&Deep). As a result, TabNet was chosen to construct the prediction model with the best performance (accuracy = 0.82 and AUC = 0.83). Afterward, four variables that strongly correlated with lapatinib dose were ranked via importance score as follows: treatment protocols, weight, number of chemotherapy treatments, and number of metastases. Finally, the confusion matrix was used to validate the model for a dose regimen of 1,250 mg lapatinib (precision = 81% and recall = 95%), and for a dose regimen of 1,000 mg lapatinib (precision = 87% and recall = 64%). To conclude, we established a deep learning model to predict lapatinib dose based on important influencing variables selected from real-world evidence, to achieve an optimal individualized dose regimen with good predictive performance.

Real-time detection method of gear contact fatigue pitting based on machine vision.

This paper proposes a real-time detection method for gear contact fatigue pitting based on machine vision in order to improve the detection accuracy and detection efficiency of specimen fatigue pitting in gear contact fatigue tests and to realize the visualization, quantification, and real-time detection of gear pitting. Under the principle of gear meshing and the shooting principle of a line-scan camera, a test detection system for gear contact fatigue is established, and the optimal centrifugal shooting distance for the gear tooth surface is obtained by analyzing the gear rotation process. In response to the phenomenon of image overlap caused by the inconsistency between the speed of each point on the gear tooth profile and the line frequency set by the camera, an image correction algorithm of the gear meshing surface has been proposed, which has been proven to have improved the accuracy of the detection results of gear contact fatigue pitting corrosion. The detection accuracy of fatigue pitting corrosion is improved by combining the preliminary detection and the accurate detection of the fatigue features. The depth information of the extracted contour pitting pits is extracted by the sequential forward selection (SFS) algorithm. The experimental results showed that 0.1216mm2 is the average absolute error of pitting corrosion detection, the average relative error is 2.2188%, and the detection accuracy is 97.7812%. The proposed pitting corrosion detection system advances in visualization, quantification, real-time monitoring, and failure judgment with a new, to the best of our knowledge, experimental approach for gear contact fatigue pitting corrosion detection.

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Perovskite materials are central to the fields of energy conversion and storage, especially for fuel cells. However, they are challenged by overcomplexity, coupled with a strong desire for new materials discovery at high speed and high precision. Herein, we propose a new approach involving a combination of extreme feature engineering and automated machine learning to adaptively learn the structure-composition-property relationships of perovskite oxide materials for energy conversion and storage. Structure-composition-property relationships between stability and other features of perovskites are investigated. Extreme feature engineering is used to construct a great quantity of fresh descriptors, and a crucial subset of 23 descriptors is acquired by sequential forward selection algorithm. The best descriptor for stability of perovskites is determined with linear regression. The results demonstrate a high-efficient and non-priori-knowledge investigation of structure-composition-property relationships for perovskite materials, providing a new road to discover advanced energy materials.

Decoding Multi-Class EEG Signals of Hand Movement Using Multivariate Empirical Mode Decomposition and Convolutional Neural Network.

Brain-computer interface (BCI) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients. One specific challenge of such BCI is that the classification accuracy is not high enough for multi-class movements. In this study, by using Multivariate Empirical Mode Decomposition (MEMD) and Convolutional Neural Network (CNN), a novel algorithm (MECN) was proposed to decode EEG signals for four kinds of hand movements. Firstly, the MEMD was used to decompose the movement-related electroencephalogram (EEG) signals to obtain the multivariate intrinsic empirical functions (MIMFs). Then, the optimal MIMFs fusion was performed based on sequential forward selection algorithm. Finally, the selected MIMFs were input to the CNN model for discriminating four kinds of hand movements. The average classification accuracy of thirteen subjects over the six-fold cross-validation reached 81.14% for 2s-data before the movement onset and 81.08% for 2s-data after the movement onset. The MECN method achieved statistically significant improvement on the state-of-the-art methods. The results showed that the algorithm proposed in this study can effectively decode four kinds of hand movements based on EEG signals.

PREDAIP: Computational Prediction and Analysis for Anti-inflammatory Peptide via a Hybrid Feature Selection Technique

Background: Anti-Inflammatory Peptides (AIPs) are potent therapeutic agents for inflammatory and autoimmune disorders due to their high specificity and minimal toxicity under normal conditions. Therefore, it is greatly significant and beneficial to identify AIPs for further discovering novel and efficient AIPs-based therapeutics. Recently, three computational approaches, which can effectively identify potential AIPs, have been developed based on machine learning algorithms. However, there are several challenges with the existing three predictors. Objective: A novel machine learning algorithm needs to be proposed to improve the AIPs prediction accuracy. Methods: This study attempts to improve the recognition of AIPs by employing multiple primary sequence-based feature descriptors and an efficient feature selection strategy. By sorting features through four enhanced minimal redundancy maximal relevance (emRMR) methods, and then attaching seven different classifiers wrapper methods based on the sequential forward selection algorithm (SFS), we proposed a hybrid feature selection technique emRMR-SFS to optimize feature vectors. Furthermore, by evaluating seven classifiers trained with the optimal feature subset, we developed the Extremely Randomized Tree (ERT) based predictor named PREDAIP for identifying AIPs. Results: We systematically compared the performance of PREDAIP with the existing tools on independent test dataset. It demonstrates the effectiveness and power of the PREDAIP. Conclusion: The correlation criteria used in emRMR would affect the selection results of the optimal feature subset at the SFS-wrapper stage, which justifies the necessity for considering different correlation criteria in emRMR.