Outdoor Plant Segmentation With Deep Learning for High-Throughput Field Phenotyping on a Diverse Wheat Dataset

Robust and automated segmentation of leaves and other backgrounds is a core prerequisite of most approaches in high-throughput field phenotyping. So far, the possibilities of deep learning approaches for this purpose have not been explored adequately, partly due to a lack of publicly available, appropriate datasets. This study presents a workflow based on DeepLab v3+ and on a diverse annotated dataset of 190 RGB (350 x 350 pixels) images. Images of winter wheat plants of 76 different genotypes and developmental stages have been acquired throughout multiple years at high resolution in outdoor conditions using nadir view, encompassing a wide range of imaging conditions. Inconsistencies of human annotators in complex images have been quantified, and metadata information of camera settings has been included. The proposed approach achieves an intersection over union (IoU) of 0.77 and 0.90 for plants and soil, respectively. This outperforms the benchmarked machine learning methods which use Support Vector Classifier and/or Random Forrest. The results show that a small but carefully chosen and annotated set of images can provide a good basis for a powerful segmentation pipeline. Compared to earlier methods based on machine learning, the proposed method achieves better performance on the selected dataset in spite of using a deep learning approach with limited data. Increasing the amount of publicly available data with high human agreement on annotations and further development of deep neural network architectures will provide high potential for robust field-based plant segmentation in the near future. This, in turn, will be a cornerstone of data-driven improvement in crop breeding and agricultural practices of global benefit.

Support Vector Machines and Support Vector Regression

In this chapter, the support vector machines (svm) methods are studied. We first point out the origin and popularity of these methods and then we define the hyperplane concept which is the key for building these methods. We derive methods related to svm: the maximum margin classifier and the support vector classifier. We describe the derivation of the svm along with some kernel functions that are fundamental for building the different kernels methods that are allowed in svm. We explain how the svm for binary response variables can be expanded for categorical response variables and give examples of svm for binary and categorical response variables with plant breeding data for genomic selection. Finally, general issues for adopting the svm methodology for continuous response variables are provided, and some examples of svm for continuous response variables for genomic prediction are described.

Transcriptomic Fingerprint of Bacterial Infection in Lower Extremity Ulcers

Clinicians and researchers utilize subjective classification systems based on clinical parameters to stratify lower extremity ulcer infections for treatment and research. This study compared clinical infection classifications (mild to severe) of lower extremity ulcers (n = 44) with transcriptomic profiles and direct measurement of bacterial RNA signatures by RNA-sequencing. Samples demonstrating similar transcriptomes were clustered and characterized by transcriptomic fingerprint. Clinical infection severity did not explain the major sources of variability among the samples and samples with the same clinical classification demonstrated high inter-sample variability. High proportions of bacterial RNA, however, resulted in a strong effect on transcription and increased expression of genes associated with immune response and inflammation. K-means clustering identified two clusters of samples, one of which contained all of the samples with high levels of bacterial RNA. A support vector classifier identified a fingerprint of 20 genes, including immune-associated genes such as CXCL8, GADD45B, and HILPDA, which accurately identified samples with signs of infection via cross-validation. This suggests that stratification of infection states based on a transcriptomic fingerprint may be a useful tool for studying host-bacterial interactions in these ulcers, as well as an objective classification method to identify the severity of infection.

Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier

Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.

Waiting for Baseline Stability in Single-Case Designs: Is It Worth the Time and Effort?

Researchers and practitioners often use single-case designs (SCDs), or n-of-1 trials, to develop and validate novel treatments. Standards and guidelines have been published to provide guidance as to how to implement SCDs, but many of their recommendations are not derived from the research literature. For example, one of these recommendations suggests that researchers and practitioners should wait for baseline stability prior to introducing an independent variable. However, this recommendation is not strongly supported by empirical evidence. To address this issue, we used a Monte Carlo simulation to generate a total of 480,000 AB graphs with fixed, response-guided, and random baseline lengths. Then, our analyses compared the Type I error rate and power produced by two methods of analysis: the conservative dual-criteria method (a structured visual aid) and a support vector classifier (a model derived from machine learning). The conservative dual-criteria method produced more power when using response-guided decision-making (i.e., waiting for stability) with negligeable effects on Type I error rate. In contrast, waiting for stability did not reduce decision-making errors with the support vector classifier. Our findings question the necessity of waiting for baseline stability when using SCDs with machine learning, but the study must be replicated with other designs to support our results.

Cyberbullying Detection in Social Networks: Artificial Intelligence Approach

Over the past decade, digital communication has reached a massive scale globally. Unfortunately, cyberbullying has become prevalent, with perpetrators hiding behind the mask of relative internet anonymity. In this work, efforts were made to review prominent classification algorithms and also to propose an ensemble model for identifying cases of cyberbullying, using Twitter datasets. The algorithms used for evaluation are Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, Linear Support Vector Classifier, Adaptive Boosting, Stochastic Gradient Descent and Bagging classifiers. Through experimentations, comparisons were made with the classifiers against four metrics: accuracy, precision, recall and F1 score. The results reveal the performances of all the algorithms used with their corresponding metrics. The ensemble model generated better results while Linear Support Vector Classifier (SVC) was the least effective of all. Random Forest classifier has shown to be the best performing classifier with medians of 0.77, 0.73 and 0.94 across the datasets. The ensemble model has shown to improve the results of its constituent classifiers with medians of 0.77, 0.66 and 0.94, as against the 0.59, 0.42 and 0.86 of Linear Support Vector Classifier.

RECOGNIZE HAPPY AND SADNESS EMOTIONS USING MUTUAL INFORMATION AND SUPPORT VECTOR CLASSIFICATION METHODS

In the past decade, the field of facial expression recognition has attracted the attention of scientists who play an important role in enhancing interaction between human and computers. The issue of facial expression recognition is not a simple matter of machine learning, because expression of the individual differs from one person to another based on the various contexts, backgrounds and lighting. The goal of the current system was to achieve the highest rate for two facial expressions ("happy" and "sad") The objective of the current work was to attain the highest rate in classification with computer vision algorithms for two facial expressions ("happy" and "sad"). This was accomplished through several phases started from image pre-processing to the Gabor filter extraction, which was then used for the extraction of important characteristics with mutual information. The expression was finally recognized by a support vector classifier. Cohn-Kanade database and JAFFE data base have been trained and checked. The rates achieved by the qualified data package were 81.09% and 92.85% respectively.