scholarly journals Classification of Plant Electrophysiology Signals for Detection of Spider Mites Infestation in Tomatoes

2021 ◽  
Vol 11 (4) ◽  
pp. 1414
Author(s):  
Elena Najdenovska ◽  
Fabien Dutoit ◽  
Daniel Tran ◽  
Carrol Plummer ◽  
Nigel Wallbridge ◽  
...  
Keyword(s):  
Stressed State ◽  
Electrical Response ◽  
Electrical Potential ◽  
External Perturbation ◽  
Relevant Information ◽  
Spider Mites ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Feature Subset ◽  
Plant Electrophysiology

Herbivorous arthropods, such as spider mites, are one of the major causes of annual crop losses. They are usually hard to spot before a severe infestation takes place. When feeding, these insects cause external perturbation that triggers changes in the underlying physiological process of a plant, which are expressed by a generation of distinct variations of electrical potential. Therefore, plant electrophysiology data portray information of the plant state. Analyses involving machine learning techniques applied to plant electrical response triggered by spider mite infestation have not been previously reported. This study investigates plant electrophysiological signals recorded from 12 commercial tomatoes plants contaminated with spider mites and proposes a workflow based on Gradient Boosted Tree algorithm for an automated differentiation of the plant’s normal state from the stressed state caused by infestation. The classification model built using the signal samples recorded during daylight and employing a reduced feature subset performs with an accuracy of 80% in identifying the plant’s stressed state. Furthermore, the Hjorth complexity encloses the most relevant information for discrimination of the plant status. The obtained findings open novel access towards automated detection of insect infestation in greenhouse crops and, consequently, more optimal prevention and treatment approaches.

Predicting Hub Genes of Glioblastomas Based on Support Vector Machine Combined with CFS algorithms

2020 ◽  
Vol 15 ◽  
Author(s):  
Chun Qiu ◽  
Sai Li ◽  
Shenghui Yang ◽  
Lin Wang ◽  
Aihui Zeng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Expression Profiles ◽  
Independent Set ◽  
Classification Model ◽  
Support Vector ◽  
Feature Subset ◽  
Hub Genes ◽  
Effective Prevention ◽  
Key Genes ◽  
Control Samples

Aim: To search the genes related to the mechanisms of the occurrence of glioma and to try to build a prediction model for glioblastomas. Background: The morbidity and mortality of glioblastomas are very high, which seriously endangers human health. At present, the goals of many investigations on gliomas are mainly to understand the cause and mechanism of these tumors at the molecular level and to explore clinical diagnosis and treatment methods. However, there is no effective early diagnosis method for this disease, and there are no effective prevention, diagnosis or treatment measures. Methods: First, the gene expression profiles derived from GEO were downloaded. Then, differentially expressed genes (DEGs) in the disease samples and the control samples were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, the correlation-based feature subset (CFS) method was applied to the selection of key DEGs. In addition, the classification model between the glioblastoma samples and the controls was built by an Support Vector Machine (SVM) based on selected key genes. Results and Discussion: Thirty-six DEGs, including 17 upregulated and 19 downregulated genes, were selected as the feature genes to build the classification model between the glioma samples and the control samples by the CFS method. The accuracy of the classification model by using a 10-fold cross-validation test and independent set test was 76.25% and 70.3%, respectively. In addition, PPP2R2B and CYBB can also be found in the top 5 hub genes screened by the protein– protein interaction (PPI) network. Conclusions: This study indicated that the CFS method is a useful tool to identify key genes in glioblastomas. In addition, we also predicted that genes such as PPP2R2B and CYBB might be potential biomarkers for the diagnosis of glioblastomas.

scholarly journals Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 327 ◽  
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Relevant Information ◽  
Forest Monitoring ◽  
Machine Learning Techniques ◽  
Planted Forests ◽  
Systematic Analysis ◽  
Aerial Photogrammetry ◽  
Wide Range ◽  
Aerial Vehicle

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

scholarly journals Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1581
Author(s):  
Jimy Oblitas ◽  
Jezreel Mejia ◽  
Miguel De-la-Torre ◽  
Himer Avila-George ◽  
Lucía Seguí Gil ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cucurbita Pepo ◽  
Performance Metrics ◽  
Microstructural Analysis ◽  
Relevant Information ◽  
Machine Learning Techniques ◽  
Radial Basis Neural Networks ◽  
Vegetable Foods ◽  
Vegetal Tissue

Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)—when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1–score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods.

Improved Intrusion Detection Algorithm based on TLBO and GA Algorithms

2021 ◽  
Vol 18 (2) ◽  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Optimization Algorithm ◽  
Feature Subset Selection ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Feature Subset ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Optimization algorithms are widely used for the identification of intrusion. This is attributable to the increasing number of audit data features and the decreasing performance of human-based smart Intrusion Detection Systems (IDS) regarding classification accuracy and training time. In this paper, an improved method for intrusion detection for binary classification was presented and discussed in detail. The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). The process of selecting the least number of features without any effect on the result accuracy in FSS was considered a multi-objective optimization problem. The NTLBO was proposed in this paper as an FSS mechanism; its algorithm-specific, parameter-less concept (which requires no parameter tuning during an optimization) was explored. The experiments were performed on the prominent intrusion machine-learning datasets (KDDCUP’99 and CICIDS 2017), where significant enhancements were observed with the suggested NTLBO algorithm as compared to the classical Teaching-Learning-Based Optimization algorithm (TLBO), NTLBO presented better results than TLBO and many existing works. The results showed that NTLBO reached 100% accuracy for KDDCUP’99 dataset and 97% for CICIDS dataset

Machine Learning Techniques to Predict Software Defect

2014 ◽  
pp. 1422-1434 ◽  
Author(s):  
Ramakanta Mohanty ◽  
Vadlamani Ravi
Keyword(s):  
Machine Learning ◽  
Feature Subset Selection ◽  
Machine Learning Techniques ◽  
Group Method ◽  
Software Project ◽  
Feature Subset ◽  
Software Defects ◽  
Software Defect ◽  
Learning Techniques ◽  
Sensitivity Specificity

The past 10 years have seen the prediction of software defects proposed by many researchers using various metrics based on measurable aspects of source code entities (e.g. methods, classes, files or modules) and the social structure of software project in an effort to predict the software defects. However, these metrics could not predict very high accuracies in terms of sensitivity, specificity and accuracy. In this chapter, we propose the use of machine learning techniques to predict software defects. The effectiveness of all these techniques is demonstrated on ten datasets taken from literature. Based on an experiment, it is observed that PNN outperformed all other techniques in terms of accuracy and sensitivity in all the software defects datasets followed by CART and Group Method of data handling. We also performed feature selection by t-statistics based approach for selecting feature subsets across different folds for a given technique and followed by the feature subset selection. By taking the most important variables, we invoked the classifiers again and observed that PNN outperformed other classifiers in terms of sensitivity and accuracy. Moreover, the set of ‘if- then rules yielded by J48 and CART can be used as an expert system for prediction of software defects.

scholarly journals Liver Cancer Classification Model Using Hybrid Feature Selection Based on Class-Dependent Technique for the Central Region of Thailand

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 187
Author(s):  
Rattanawadee Panthong ◽  
Anongnart Srivihok
Keyword(s):  
Feature Selection ◽  
Liver Cancer ◽  
Predictive Model ◽  
Information Gain ◽  
Classification Performance ◽  
Cancer Classification ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Feature Subset ◽  
Cancer Data

Liver cancer data always consist of a large number of multidimensional datasets. A dataset that has huge features and multiple classes may be irrelevant to the pattern classification in machine learning. Hence, feature selection improves the performance of the classification model to achieve maximum classification accuracy. The aims of the present study were to find the best feature subset and to evaluate the classification performance of the predictive model. This paper proposed a hybrid feature selection approach by combining information gain and sequential forward selection based on the class-dependent technique (IGSFS-CD) for the liver cancer classification model. Two different classifiers (decision tree and naïve Bayes) were used to evaluate feature subsets. The liver cancer datasets were obtained from the Cancer Hospital Thailand database. Three ensemble methods (ensemble classifiers, bagging, and AdaBoost) were applied to improve the performance of classification. The IGSFS-CD method provided good accuracy of 78.36% (sensitivity 0.7841 and specificity 0.9159) on LC_dataset-1. In addition, LC_dataset II delivered the best performance with an accuracy of 84.82% (sensitivity 0.8481 and specificity 0.9437). The IGSFS-CD method achieved better classification performance compared to the class-independent method. Furthermore, the best feature subset selection could help reduce the complexity of the predictive model.

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

2019 ◽  
Vol 18 (03) ◽  
pp. 1950020 ◽  
Author(s):  
Alok Kumar Shukla ◽  
Pradeep Singh ◽  
Manu Vardhan
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Classification Accuracy ◽  
B Cell Lymphoma ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Significant Feature ◽  
Support Vector ◽  
Feature Subset ◽  
Binary Genetic Algorithm

The explosion of the high-dimensional dataset in the scientific repository has been encouraging interdisciplinary research on data mining, pattern recognition and bioinformatics. The fundamental problem of the individual Feature Selection (FS) method is extracting informative features for classification model and to seek for the malignant disease at low computational cost. In addition, existing FS approaches overlook the fact that for a given cardinality, there can be several subsets with similar information. This paper introduces a novel hybrid FS algorithm, called Filter-Wrapper Feature Selection (FWFS) for a classification problem and also addresses the limitations of existing methods. In the proposed model, the front-end filter ranking method as Conditional Mutual Information Maximization (CMIM) selects the high ranked feature subset while the succeeding method as Binary Genetic Algorithm (BGA) accelerates the search in identifying the significant feature subsets. One of the merits of the proposed method is that, unlike an exhaustive method, it speeds up the FS procedure without lancing of classification accuracy on reduced dataset when a learning model is applied to the selected subsets of features. The efficacy of the proposed (FWFS) method is examined by Naive Bayes (NB) classifier which works as a fitness function. The effectiveness of the selected feature subset is evaluated using numerous classifiers on five biological datasets and five UCI datasets of a varied dimensionality and number of instances. The experimental results emphasize that the proposed method provides additional support to the significant reduction of the features and outperforms the existing methods. For microarray data-sets, we found the lowest classification accuracy is 61.24% on SRBCT dataset and highest accuracy is 99.32% on Diffuse large B-cell lymphoma (DLBCL). In UCI datasets, the lowest classification accuracy is 40.04% on the Lymphography using k-nearest neighbor (k-NN) and highest classification accuracy is 99.05% on the ionosphere using support vector machine (SVM).

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