Input Features For Classification Research Articles

Identification of male schizophrenia patients using brain morphology based on machine learning algorithms.

Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task. To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls. The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC). A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886. Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.

A hybrid-extreme learning machine based ensemble method for online dynamic security assessment of power systems

• A hybrid model is developed, blending ELM and Levenberg-Marquardt. • Particle swarm optimization-based weighted averaging ensemble is developed. • Energy margin and time margin are calculated for better application of techniques. • Hybrid ELM is found most suited technique for online DSA application. This manuscript develops a new hybrid-extreme learning machine (ELM) based ensemble model for real-time dynamic security assessment (DSA) of power systems. In order to boost the forecasting accuracy of ELM algorithm, a Levenberg-Marquardt (LM) backpropagation algorithm is used. The Ensemble strategy takes advantage of complementary information to improve the generalization capability of model. To realize an accurate ensemble model, particle swarm optimization (PSO) based weighted averaging is used to combine the individual forecasts. Transient energy function (TEF) terms, generator real and reactive power outputs and fault location are taken as input features for classification as well as prediction of transient energy margin (TEM) and time margin (TM) for a given operating condition and fault location. The potential of the proposed technique is validated on New England 39-bus and 68-bus test power systems. The results demonstrate that the developed model outperforms other state-of-the-art methods.

PERFORMANCE ENHANCEMENT OF DEEP NEURAL NETWORK BASED AUTOMATIC VOICE DISORDER DETECTION SYSTEM WITH DATA AUGMENTATION — DETECTION OF LEUKOPLAKIA: A CASE STUDY

Laryngeal pathologies resulting in voice disorders are normally diagnosed using invasive methods such as rigid laryngoscopy, flexible nasopharyngo-laryngoscopy and stroboscopy, which are expensive, time-consuming and often inconvenient to patients. Automatic Voice Disorder Detection (AVDD) systems are used for non-invasive screening to give an indicative direction to the physician as a preliminary diagnosis. Deep neural networks, known for their superior discrimination capabilities, can be used for AVDD Systems, provided there are sufficient samples for training. The most popular datasets used for developing AVDD systems lack sufficient samples in several pathological categories. Leukoplakia — a premalignant lesion, which may progress to carcinoma unless detected early — is one such pathology. Data augmentation is a technique used in deep learning environments to increase the size of the training datasets which lack sufficient samples for effective data analysis and classification. This study aims at investigating the performance enhancement of a deep learning-based AVDD system through a novel time domain data augmentation technique named ‘TempAug’. This method segments each data sample into short voice segments, so as to get multiple data from each sample, thereby generating a larger database (augmented database) for training a deep learning model. A deep neural network model, Long Short-Term Memory (LSTM) with Short Term Fourier Transform (STFT) coefficients as input features for classification, was used in this study for the detection of the voice disorder Leukoplakia. A series of experiments were done to investigate the effect of data augmentation and to find the optimum duration for segmentation. Based on experimental results, a detection strategy was developed and evaluated using an AVDD system, which gave an accuracy of 81.25%. The percentage increase in accuracy was found to be 46.9% with respect to the accuracy obtained for unaugmented data.

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability and environmental carrying capacity. This research proposed a method to integrate time series Sentinel-1 (S1) and Sentinel-2 (S2) data for crop type mapping over oasis agricultural areas through a case study in Northwest China. Previous studies using synthetic aperture radar (SAR) data alone often yield quite limited accuracy in crop type identification due to speckles. To improve the quality of SAR features, we adopted a statistically homogeneous pixel (SHP) distributed scatterer interferometry (DSI) algorithm, originally proposed in the interferometric SAR (InSAR) community for distributed scatters (DSs) extraction, to identify statistically homogeneous pixel subsets (SHPs). On the basis of this algorithm, the SAR backscatter intensity was de-speckled, and the bias of coherence was mitigated. In addition to backscatter intensity, several InSAR products were extracted for crop type classification, including the interferometric coherence, master versus slave intensity ratio, and amplitude dispersion derived from SAR data. To explore the role of red-edge wavelengths in oasis crop type discrimination, we derived 11 red-edge indices and three red-edge bands from Sentinel-2 images, together with the conventional optical features, to serve as input features for classification. To deal with the high dimension of combined SAR and optical features, an automated feature selection method, i.e., recursive feature increment, was developed to obtain the optimal combination of S1 and S2 features to achieve the highest mapping accuracy. Using a random forest classifier, a distribution map of five major crop types was produced with an overall accuracy of 83.22% and kappa coefficient of 0.77. The contribution of SAR and optical features were investigated. SAR intensity in VH polarization was proved to be most important for crop type identification among all the microwave and optical features employed in this study. Some of the InSAR products, i.e., the amplitude dispersion, master versus slave intensity ratio, and coherence, were found to be beneficial for oasis crop type mapping. It was proved the inclusion of red-edge wavelengths improved the overall accuracy (OA) of crop type mapping by 1.84% compared with only using conventional optical features. In comparison, it was demonstrated that the synergistic use of time series Sentinel-1 and Sentinel-2 data achieved the best performance in the oasis crop type discrimination.

A Novel Fault Classification Scheme for Series Capacitor Compensated Transmission Line Based on Bagged Tree Ensemble Classifier

This paper presents a novel intelligent fault classification scheme for fixed series capacitor compensated transmission line. The singular value decomposition principle is applied along with the fast discrete orthonormal S-transform (FDOST) and bagged tree ensemble classifier for classification of faults under different scenarios. Classification input features are extracted from 1/2 cycle post-fault current data through the FDOST. The feasibility of the proposed relaying scheme is tested on a modified WSCC 3-machine 9-bus system under different fault conditions using PSCAD/EMTDC, and the results indicate the proposed scheme to be fast and accurate. The robustness of the proposed scheme to noise and current transformer saturation is also established. The obtained results under different fault scenarios confirm the efficacy of the proposed scheme.

Performance comparison of second order statistics texture features for variety identification of paddy seeds

The aim of this research was to evaluate the second order statistics texture features - grey level coocurrence matrix and grey level run length using the neural network architectures for cereal grain classification. For the purpose of classifying four paddy (rice) grains, viz. Karjat-6, Karjat-2, Ratnagiri-4 and Ratnagiri-24, an evaluation of the classification accuracy of texture features and neural network was done. To extract the features from the high-resolution images of kernels of four grain types, algorithms were written and these were used as input features for classification. Different feature models were observed for their capability to categorise these cereal grains. For the accurate classification, effect of using different features was studied and the most suitable feature from the feature set was also identified. The Texture-GLCM feature set outperformed the Texture-GLRL feature set in most of the instances of classification. Also the performance of three training functions viz. Levenberg-Marquardt (LM) backpropagation, resilient backpropogation (RP) and scaled conjugate gradient (SCG) training functions was compared and the most reliable training functions was identified from the three functions for accurate classification of four paddy varieties.

Evaluation of Shape and Color Features for Classification of Four Paddy Varieties

This research is aimed at evaluating the shape and color features using the most commonly used neural network architectures for cereal grain classification. An evaluation of the classification accuracy of shape and color features and neural network was done to classify four Paddy (Rice) grains, viz. Karjat-6, Ratnagiri-2, Ratnagiri-4 and Ratnagiri-24. Algorithms were written to extract the features from the high-resolution images of kernels of four grain types and use them as input features for classification. Different feature models were tested for their ability to classify these cereal grains. Effect of using different parameters on the accuracy of classification was studied. The most suitable feature set from the features was identified for accurate classification. The Shape-nColor feature set outperformed in almost all the instances of classification.

Evaluation of Texture and Shape Features for Classification of Four Paddy Varieties

This research is aimed at evaluating the texture and shape features using the most commonly used neural network architectures for cereal grain classification. An evaluation of the classification accuracy of texture and shape features and neural network was done to classify four Paddy (rice) grains, namely, Karjat-6(K6), Ratnagiri-2(R2), Ratnagiri-4(R4), and Ratnagiri-24(R24). Algorithms were written to extract the features from the high-resolution images of kernels of four grain types and used as input features for classification. Different feature models were tested for their ability to classify these cereal grains. Effect of using different parameters on the accuracy of classification was studied. The most suitable feature from the features for accurate classification was identified. The shape feature set outperformed the texture feature set in almost all the instances of classification.

Combining Graph and Machine Learning Methods to Analyze Differences in Functional Connectivity Across Sex

In this work we combine machine learning methods and graph theoretical analysis to investigate gender associated differences in resting state brain network connectivity. The set of all correlations computed from the fMRI resting state data is used as input features for classification. Two ensemble learning methods are used to perform the detection of the set of discriminative edges between groups (males vs. females) of brain networks: 1) Random Forest and 2) an ensemble method based on least angle shrinkage and selection operator (lasso) regressors. Permutation testing is used not only to assess significance of classification accuracy but also to evaluate significance of feature selection. Finally, these methods are applied to data downloaded from the Connectome Project website. Our results suggest that gender differences in brain function may be related to sexually dimorphic regional connectivity between specific critical nodes via gender-discriminative edges.

Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems

Crop cover and crop rotation mapping is an important and still evolving field in remote sensing science for which robust and highly automated processing chains are required. This study presents an improved mapping procedure for crop rotations of irrigated areas in Central Asia by using classification and regression trees (CARTs) applied to transformations of 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series. The time series were divided into several temporal segments, from which metrics were derived as input features for classification. This temporal aggregation was applied to suppress within-class temporal variability. Various lengths of temporal segments were tested for their potential to increase classification accuracy. In addition, tests of enhancing the classification accuracy were done by combining different classification results using the majority rule for voting. These different processing strategies were applied to four annual time series (2004–2007) of the Khorezm region, where 270 000 ha of irrigated land is dominated by rotations of cotton, wheat and rice. Improved classification results were obtained for CARTs applied to metrics derived from a mixture of different segment lengths. The sole use of either long or short temporal segments was inferior. CART prioritized segments representing active phases of the phenological development. The best result, the optimized segment-based approach, achieved an overall accuracy between 83 and 85% for classifications between 2004 and 2007; in particular, the small range demonstrated the robustness regarding inter-annual variations. These accuracies exceeded those of the original time series without temporal segmentation by 6–7%. With some adjustments to other crops and field heterogeneity influencing the usefulness of a respective sensor, the approach can be applied to other irrigation systems in Central Asia.

Bayesian Network Modeling for Discovering “Dependent Synergies” Among Muscles in Reaching Movements

The coordinated activities of muscles during reaching movements can be characterized by appropriate analysis of simultaneously-recorded surface electromyograms (sEMGs). Many recent sEMG studies have analyzed muscle synergies using statistical methods such as Independent Component Analysis, which commonly assume a small set of influences upstream of the muscles (e.g., originating from the motor cortex) produce the sEMG signals. Traditionally only the amplitude of the sEMG signal was investigated. Here, we present a fundamentally different approach and model sEMG signals after the effects of amplitude have been minimized. We develop the framework of Bayesian networks (BNs) for modeling muscle activities and for analyzing the overall muscle network structure. Instead of assuming that synergies may be independently activated, we assume that neuronal activity driving a given muscle may be conditionally dependent upon neurons driving other muscles. We call the resulting interactions between muscle activity patterns "dependent synergies". The learned BN networks were explored for the purpose of classification across subjects based on hand dominance or affliction by stroke. Network structure features were investigated as classification input features and it was determined that specific edge connection patterns of 3-node subnetworks were selectively recruited during reaching movements and were differentially recruited after stroke compared to normal control subjects. The resulting classification was robust to inter-subject and within-group variability and yielded excellent classification performance. The proposed framework extends muscle synergy analysis and provides a framework for thinking about muscle activity interactions in motor control.

Comparison of a Neural Network and a Non-parametric Classifier for Grain Kernel Identification

The performances of a four-layer backpropagation neural network and a non-parametric statistical classifier were compared for classification of barley, Canada Western Amber Durum wheat, Canada Western Red Spring wheat, oats, and rye. A total of 230 features (51 morphological, 123 colour, and 56 textural) from the high-resolution images of kernels of the five grain types were extracted and used as input features for classification. Different feature models, viz . morphological, colour, texture, and a combination of the three, were tested for their ability to classify these cereal grains. To make the classification process fast, the number of input features were reduced to 60 and 30. A set of features consisting of an equal number of morphological, colour, and textural features gave the best classification accuracies. The neural network classifier outperformed the non-parametric classifier in almost all the instances of classification.