SBP-SITA: A sequence-based prediction tools for S-itaconation

As a recently-reported post-translational modification, S-itaconation plays an important role in inflammation suppression. In order to understand its regulatory mechanism in many life activities, the essential step is the recognition of S-itaconation. However, it is difficult to identify S-itaconation in the proteome for the high cost, which limits further investigation. In this study, we constructed an ensemble algorithm based on Soft Voting Classifier. The area under the ROC curve (AUC) value 0.73 for ensemble model. Accordingly, we constructed the on-line prediction tool dubbed SBP-SITA for easily identifying Cystine sites. SBP-SITA is available at http://www.bioinfogo.org/sbp-sita.

Recognition of Eye-Written Characters with Limited Number of Training Data Based on a Siamese Network

Developing a hum–computer interface (HCI) is essential, especially for those that have spinal cord injuries or paralysis, because of the difficulties associated with the application of conventional devices and systems. Eye-writing is an HCI that uses eye movements for writing characters such that the gaze movements form letters. In addition, it is a promising HCI because it can be utilized even when voices and hands are inaccessible. However, eye-writing HCI has low accuracy and encounters difficulties in obtaining data. This study proposes a method for recognizing eye-written characters accurately and with limited data. The proposed method is constructed using a Siamese network, an attention mechanism, and an ensemble algorithm. In the experiment, the proposed method successfully classified the eye-written characters (Arabic numbers) with high accuracy (92.78%) when the ratio of training to test data was 2:1. In addition, the method was tested as the ratio changed, and 80.80% accuracy was achieved when the number of training data was solely one-tenth of the test data.

Hybrid Classification for Face Spoof Detection

ML (machine learning) is consisted of a method of recognizing face. This technique is useful for the attendance system. Two sets are generated for testing and training phases in order to segment the image, to extract the features and develop a dataset. An image is considered as a testing set; the training set is contrasted when it is essential to identify an image. An ensemble classifier is implemented to classify the test images as recognized or non-recognized. The ensemble algorithm fails to acquire higher accuracy as it classifies the data in two classes. Thus, GLCM (Grey Level Co-occurrence Matrix) is projected for analyzing the texture features in order to detect the face. The attendance of the query image is marked after detecting the face. The simulation outcomes revealed the superiority of the projected technique over the traditional methods concerning accuracy. Keywords: DWT, GLCM, KNN, Decision Tree

A Combined Strategy of Improved Variable Selection and Ensemble Algorithm to Map the Growing Stem Volume of Planted Coniferous Forest

Remote sensing technology is becoming mainstream for mapping the growing stem volume (GSV) and overcoming the shortage of traditional labor-consumed approaches. Naturally, the GSV estimation accuracy utilizing remote sensing imagery is highly related to the variable selection methods and algorithms. Thus, to reduce the uncertainty caused by variables and models, this paper proposes a combined strategy involving improved variable selection with the collinearity test and the secondary ensemble algorithm to obtain the optimally combined variables and extract a reliable GSV from several base models. Our study extracted four types of alternative variables from the Sentinel-1A and Sentinel-2A image datasets, including vegetation indices, spectral reflectance variables, backscattering coefficients, and texture features. Then, an improved variable selection criterion with the collinearity test was developed and evaluated based on machine learning algorithms (classification and regression trees (CART), k-nearest neighbors (KNN), support vector regression (SVR), and artificial neural network (ANN)) considering the correlation between variables and GSV (with random forest (RF), distance correlation coefficient (DC), maximal information coefficient (MIC), and Pearson correlation coefficient (PCC) as evaluation metrics), and the collinearity among the variables. Additionally, we proposed a secondary ensemble with an improved weighted average approach (IWA) to estimate the reliable forest GSV using the first ensemble models constructed by Bagging and AdaBoost. The experimental results demonstrated that the proposed variable selection criterion efficiently obtained the optimal combined variable set without affecting the forest GSV mapping accuracy. Specifically, considering the first ensemble, the relative root mean square error (rRMSE) values ranged from 21.91% to 30.28% for Bagging and 23.33% to 31.49% for AdaBoost, respectively. After the secondary ensemble involving the IWA, the rRMSE values ranged from 18.89% to 21.34%. Furthermore, the variance of the GSV mapped by the secondary ensemble with various ranking methods was significantly reduced. The results prove that the proposed combined strategy has great potential to reduce the GSV mapping uncertainty imposed by current variable selection approaches and algorithms.