Development of empirical mode decomposition based neural network for power quality disturbances classification

The complexity of the electric power network causes a lot of distortion, such as a decrease in power quality (PQ) in the form of voltage variations, harmonics, and frequency fluctuations. Monitoring the distortion source is important to ensure the availability of clean and quality electric power. Therefore, this study aims to classify power quality using a neural network with empirical mode decomposition-based feature extraction. The proposed method consists of 2 main steps, namely feature extraction, and classification. Empirical Mode Decomposition (EMD) was also applied to categorize the PQ disturbances into several intrinsic mode functions (IMF) components, which were extracted using statistical parameters and the Hilbert transformation. The statistical parameters consist of mean, root mean squared, range, standard deviation, kurtosis, crest factor, energy, and skewness, while the Hilbert transformation consists of instantaneous frequency and amplitude. The feature extraction results from both parameters were combined into a set of PQ disturbances and classified using Multi-Layer Feedforward Neural Networks (MLFNN). Training and testing were carried out on 3 feature datasets, namely statistical parameters, Hilbert transforms, and a combination of both as inputs from 3 different MLFNN architectures. The best results were obtained from the combined feature input on the network architecture with 2 layers of ten neurons, by 98.4 %, 97.75, and 97.4 % for precision, recall, and overall accuracy, respectively. The implemented method is used to classify PQ signals reliably for pure sinusoids, harmonics with sag and swell, as well as flicker with 100 % precisio

Toward Suicidal Ideation Detection with Lexical Network Features and Machine Learning

In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts with epilepsy patients, with a few of them admitting to experiencing suicidal ideation in the past (32 suicidal and 77 control). The selected methods detect suicidal ideation with an average area under the curve (AUC) score of 95% on the merged collection with high suicidal ideation, and the trained models generalize over the third collection with an average AUC score of 69%. Results reveal that lexical networks are promising for classification and feature extraction as successful as the deep learning model. We also observe that a logistic classifier’s performance was comparable with the deep learning method while promising explainability

Utilising Convolutional Neural Networks for Pavement Distress Classification and Detection

This paper examines deep learning models for accurate and efficient identification and classification of pavement distresses. In it, a variety of related studies conducted on the topic as well as the various identification and classification methods proposed, such as edge detection, machine learning classification informed by statistical feature extraction, artificial neural networks, and real-time object detection systems, are discussed. The study investigates the effect of image processing techniques such as grayscaling, background subtraction, and image resizing on the performance and generalizability of the models. Using convolutional neural networks (CNN) architectures, this paper proposes a model that correctly classifies images into five pavement distress categories, namely fatigue (or alligator), longitudinal, transverse, patches, and craters, with an accuracy rate of 90.4% and a recall rate of 90.1%. The model is contrasted to a current state-of-the-art model based on the You Only Look Once framework as well as a baseline CNN model to demonstrate the impact of the image processing and architecture building techniques discussed on performance. The findings of this paper contribute to the fields of computer vision and infrastructure monitoring by demonstrating the efficacy of convolutional neural networks (CNNs) in image classification and the viability of using CNNbased models to automate pavement condition monitoring

Probabilistic and Deep Learning Algorithms for the Analysis of Imagery Data

Accurate object classification is a challenging problem for various low to high resolution imagery data. This applies to both natural as well as synthetic image datasets. However, each object recognition dataset poses its own distinct set of domain-specific problems. In order to address these issues, we need to devise intelligent learning algorithms which require a deep understanding and careful analysis of the feature space. In this thesis, we introduce three new learning frameworks for the analysis of both airborne images (NAIP dataset) and handwritten digit datasets without and with noise (MNIST and n-MNIST respectively). First, we propose a probabilistic framework for the analysis of the NAIP dataset which includes (1) an unsupervised segmentation module based on the Statistical Region Merging algorithm, (2) a feature extraction module that extracts a set of standard hand-crafted texture features from the images, (3) a supervised classification algorithm based on Feedforward Backpropagation Neural Networks, and (4) a structured prediction framework using Conditional Random Fields that integrates the results of the segmentation and classification modules into a single composite model to generate the final class labels. Next, we introduce two new datasets SAT-4 and SAT-6 sampled from the NAIP imagery and use them to evaluate a multitude of Deep Learning algorithms including Deep Belief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Autoencoders (SAE) for generating class labels. Finally, we propose a learning framework by integrating hand-crafted texture features with a DBN. A DBN uses an unsupervised pre-training phase to perform initialization of the parameters of a Feedforward Backpropagation Neural Network to a global error basin which can then be improved using a round of supervised fine-tuning using Feedforward Backpropagation Neural Networks. These networks can subsequently be used for classification. In the following discussion, we show that the integration of hand-crafted features with DBN shows significant improvement in performance as compared to traditional DBN models which take raw image pixels as input. We also investigate why this integration proves to be particularly useful for aerial datasets using a statistical analysis based on Distribution Separability Criterion. Then we introduce a new dataset called noisy-MNIST (n-MNIST) by adding (1) additive white gaussian noise (AWGN), (2) motion blur and (3) Reduced contrast and AWGN to the MNIST dataset and present a learning algorithm by combining probabilistic quadtrees and Deep Belief Networks. This dynamic integration of the Deep Belief Network with the probabilistic quadtrees provide significant improvement over traditional DBN models on both the MNIST and the n-MNIST datasets. Finally, we extend our experiments on aerial imagery to the class of general texture images and present a theoretical analysis of Deep Neural Networks applied to texture classification. We derive the size of the feature space of textural features and also derive the Vapnik-Chervonenkis dimension of certain classes of Neural Networks. We also derive some useful results on intrinsic dimension and relative contrast of texture datasets and use these to highlight the differences between texture datasets and general object recognition datasets

Bottom-up design of artificial neural network for single-lead electrocardiogram beat and rhythm classification

Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined in terms of perfecting individual sub-systems to improve the over all system performance is used. Sub-systems include pre-processing, QRS detection and fiducial point estimations, feature calculations, and pattern classification. Inaccuracies in time-domain fiducial point estimations are overcome with the derivation of features in the frequency domain. Feature extraction in frequency domain is based on a spectral estimation technique (combination of simulation and subtraction of a normal beat). Auto-regressive spectral estimation methods yield a highly sensitive spectrum, providing several local features with information on beat classes like flutter, fibrillation, and noise. A total of 27 features, including 16 in time domain and 11 in frequency domain are calculated. The entire data and problem are divided into four major groups, each group with inter-related beat classes. Classification of each group into related sub-classes is performed using smaller feed-forward neural networks. Input feature sub-set and the structure of each network are optimized using an iterative process. Optimal implementations of feed-forward neural networks provide high accuracy in beat classification. Associated neural networks are used for the more deterministic rhythm-classification task. An accuracy of more than 85% is achieved for all 13 classes included in this study. The system shows a graceful degradation in performance with increasing noise, as a result of the noise consideration in the design of every sub-system. Results indicate a neural network-based bottom-up design of single-lead ECG classification is able to provide very high accuracy, even in the presence of noise, flutter, and fibrillation

Context sensitive optical character recognition using neural networks and hidden Markov models

This thesis investigates a method for using contextual information in text recognition. This is based on the premise that, while reading, humans recognize words with missing or garbled characters by examining the surrounding characters and then selecting the appropriate character. The correct character is chosen based on an inherent knowledge of the language and spelling techniques. We can then model this statistically. The approach taken by this Thesis is to combine feature extraction techniques, Neural Networks and Hidden Markov Modeling. This method of character recognition involves a three step process: pixel image preprocessing, neural network classification and context interpretation. Pixel image preprocessing applies a feature extraction algorithm to original bit mapped images, which produces a feature vector for the original images which are input into a neural network. The neural network performs the initial classification of the characters by producing ten weights, one for each character. The magnitude of the weight is translated into the confidence the network has in each of the choices. The greater the magnitude and separation, the more confident the neural network is of a given choice. The output of the neural network is the input for a context interpreter. The context interpreter uses Hidden Markov Modeling (HMM) techniques to determine the most probable classification for all characters based on the characters that precede that character and character pair statistics. The HMMs are built using an a priori knowledge of the language: a statistical description of the probabilities of digrams. Experimentation and verification of this method combines the development and use of a preprocessor program, a Cascade Correlation Neural Network and a HMM context interpreter program. Results from these experiments show the neural network successfully classified 88.2 percent of the characters. Expanding this to the word level, 63 percent of the words were correctly identified. Adding the Hidden Markov Modeling improved the word recognition to 82.9 percent

Ensemble of Local Texture Descriptor for Accurate Breast Cancer Detection from Histopathologic Images

Histopathological analysis is important for detection of the breast cancer (BC). Computer-aided diagnosis and detection systems are developed to assist the radiologist in the diagnosis process and to relieve the patient from unnecessary pain. In this study, a computer-aided diagnosis system for the early detection of benign and malignant breast cancer is proposed. The proposed system consists of feature extraction, feature ensemble, and classification stages. Various preprocessing steps such as grayscale conversion, noise filtering, and image resizing are employed on the input histopathological images. The local texture descriptors namely Local Binary Pattern (LBP), Frequency Decoded LBP (FDLBP), Binary Gabor Pattern (BGP), Local Phase Quantization (LPQ), Binarized Statistical Image Features (BSIF), CENsus TRansform hISTogram (CENTRIST), and Pyramid Histogram of Oriented Gradients (PHOG) are employed for feature extraction from the histopathologic images. The obtained features are then concatenated for the construction of the ensemble of the features. Three classifiers namely Support Vector Machines (SVM), K-nearest neighbor (KNN), and Neural Networks (NN) are used in the detection of the BC and the classification accuracy score is used for performance evaluation. A dataset called BreaKHis is used in the studies. There are 9109 microscopic pictures in BreaKHis, with 2480 benign samples and 5429 malignant samples. During the collection of the data, 82 patients\u27 breast tumor tissues were envisioned using various magnification factors such as 40X, 100X, 200X, and 400X. The accuracy score is used to assess the acquired findings. The results show that the proposed method has the potential to use accurate BC detection

Intelligent Thermal Condition Monitoring Of Electrical Equipment Using Infrared Thermography

Infrared thermographic inspection system is widely being utilized for defect detection in electrical equipment. Conventional inspection based on the temperature data interpretation and evaluation the condition of the equipment is subjective and depends on the human experts. Implementation of an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In this thesis, an automatic features extraction system from thermal image of defects and the intelligent classification of thermal condition based on neural network are proposed. The proposed system extracts first order histogram based features and grey level co-occurrence matrix features from the segmented regions and evaluates the effectiveness of these features for defect characterization. Three feature selection techniques namely principal component analysis, the discriminant analysis and individual feature performance analysis are employed to find out the useful and important statistical features. In this study, multilayered perceptron network is proposed for classifying thermal condition into two classes namely normal and defective. The multilayered perceptron neural networks are trained using various training algorithms. Additionally, the present research introduces a computer aided defect diagnosis system where the defected region is found by manual thresholding and intensity features are extracted from each segmented region. The results prove that the statistical features are capable to classify thermal condition and the neural networks achieve the accuracy around 73~78

EEG-based driving fatigue detection using multilevel feature extraction and iterative hybrid feature selection

Brain activities can be evaluated by using Electroencephalogram (EEG) signals. One of the primary reasons for traffic accidents is driver fatigue, which can be identified by using EEG signals. This work aims to achieve a highly accurate and straightforward process to detect driving fatigue by using EEG signals. Two main problems, which are feature generation and feature selection, are defined to achieve this aim. This work solves these problems by using two different approaches. Deep networks are efficient feature generators and extract features in low, medium, and high levels. These features can be generated by using multileveled or multilayered feature extraction. Therefore, we proposed a multileveled feature generator that uses a one-dimensional binary pattern (BP) and statistical features together, and levels are created using a one-dimensional discrete wavelet transform (1D-DWT). A five-level fused feature extractor is presented by using BP, statistical features of 1D-DWT together. Moreover, a 2-layered feature selection method is proposed using ReliefF and iterative neighborhood component analysis (RFINCA) to solve the feature selection problem. The goals of the RFINCA are to choose the optimal number of features automatically and use the effectiveness of ReliefF and neighborhood component analysis (NCA) together. A driving fatigue EEG dataset was used as a testbed to denote the effectiveness of eighteen conventional classifiers. According to the experimental results, a highly accurate EEG classification approach is presented. The proposed method also reached 100.0% classification accuracy by using a k-nearest neighborhood classifier.</p