Automated accurate fire detection system using ensemble pretrained residual network

COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.

To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Two experienced radiologists marked sets of 600 rectangular 20 × 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessed using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For integrated ROI data obtained from both scanners, the classification accuracies with the SVM and Bayesian classifiers were 92% and 77%, respectively. The selected features resulting from the classification process differed by scanner, with more features included for the classification of the integrated HRCT data than for the classification of the HRCT data from each scanner. For the integrated data, consisting of HRCT images of both scanners, the classification accuracy based on the SVM was statistically similar to the accuracy of the data obtained from each scanner. However, the classification accuracy of the integrated data using the Bayesian classifier was significantly lower than the classification accuracy of the ROI data of each scanner. The use of an integrated dataset along with a SVM classifier rather than a Bayesian classifier has benefits in terms of the classification accuracy of HRCT images acquired with more than one scanner. This finding is of relevance in studies involving large number of images, as is the case in a multicenter trial with different scanners.

PrimePatNet87: Prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition

In this chapter, deep learning-based approaches, namely deep feature extraction, fine-tuning of pre-trained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, are used to classify the malignant and normal breast X-ray images. For deep feature extraction, pre-trained deep CNN models such as ResNet18, ResNet50, ResNet101, VGG16, and VGG19 are used. For classification of the deep features, the support vector machines (SVM) classifier is used with various kernel functions namely linear, quadratic, cubic, and Gaussian, respectively. The aforementioned pre-trained deep CNN models are also used in fine-tuning procedure. A new CNN model is also proposed in end-to-end training fashion. The classification accuracy is used as performance measurements. The experimental works show that the deep learning has potential in detection of the breast cancer from the X-ray images. The deep features that are extracted from the ResNet50 model and SVM classifier with linear kernel function produced 94.7% accuracy score which the highest among all obtained.

기업의 부도를 예측하는 것은 회계나 재무 분야에서 중요한 연구주제이다. 지금까지 기업 부도예측을 위해 여러 가지 데이터마이닝 기법들이 적용되었으나 주로 단일 모형을 사용함으로서 복잡한 분류 문제에의 적용에 한계를 갖고 있었다. 본 논문에서는 최근에 각광받고 있는 SVM (support vector machine) 모형들을 결합한 앙상블 SVM 모형 (ensemble SVM model)을 부도예측에 사용하고자 한다. 제안된 앙상블 모형은 v-조각 교차 타당성 (v-fold cross-validation)에 의해 얻어진 여러 가지 모형 중에서 성능이 좋은 상위 k개의 단일 모형으로 구성하고 과반수 투표 방식 (majority voting)을 사용하여 미지의 클래스를 분류한다. 본 논문에서 제안된 앙상블 SVM 모형의 성능을 평가하기 위해 실제 기업의 재무비율 자료와 모의실험자료를 가지고 실험하였고, 실험결과 제안된 앙상블 모형이 여러 가지 평가척도 하에서 단일 SVM 모형들보다 좋은 성능을 보임을 알 수 있었다. Corporate bankruptcy prediction has been an important topic in the accounting and finance field for a long time. Several data mining techniques have been used for bankruptcy prediction. However, there are many limits for application to real classification problem with a single model. This study proposes ensemble SVM (support vector machine) model which assembles different SVM models with each different kernel functions. Our ensemble model is made and evaluated by v-fold cross-validation approach. The k top performing models are recruited into the ensemble. The classification is then carried out using the majority voting opinion of the ensemble. In this paper, we investigate the performance of ensemble SVM classifier in terms of accuracy, error rate, sensitivity, specificity, ROC curve, and AUC to compare with single SVM classifiers based on financial ratios dataset and simulation dataset. The results confirmed the advantages of our method: It is robust while providing good performance.

In this chapter, deep learning-based approaches, namely deep feature extraction, fine-tuning of pre-trained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, are used to classify the malignant and normal breast X-ray images. For deep feature extraction, pre-trained deep CNN models such as ResNet18, ResNet50, ResNet101, VGG16, and VGG19 are used. For classification of the deep features, the support vector machines (SVM) classifier is used with various kernel functions namely linear, quadratic, cubic, and Gaussian, respectively. The aforementioned pre-trained deep CNN models are also used in fine-tuning procedure. A new CNN model is also proposed in end-to-end training fashion. The classification accuracy is used as performance measurements. The experimental works show that the deep learning has potential in detection of the breast cancer from the X-ray images. The deep features that are extracted from the ResNet50 model and SVM classifier with linear kernel function produced 94.7% accuracy score which the highest among all obtained.

Deep learning approaches for COVID-19 detection based on chest X-ray images

Novel three kernelled binary pattern feature extractor based automated PCG sound classification method

Major depressive disorder (MDD) is one of the most common modern ailments affected huge population throughout the world. The electroencephalogram (EEG) signal is widely used to screen the MDD. The manual diagnosis of MDD using EEG is time consuming, subjective and may cause human errors. Therefore, nowadays various automated systems have been developed to diagnose MDD accurately and rapidly. In this work, we have proposed a novel automated MDD detection system using EEG signals. Our proposed model has three steps: (i) Melamine pattern and discrete wavelet transform (DWT)- based multileveled feature generation, (ii) selection of most relevant features using neighborhood component analysis (NCA) and (iii) classification using support vector machine (SVM) and k nearest neighbor (kNN) classifiers. The novelty of this work is the application of melamine pattern. The molecular structure of melamine (also named chemistry spider- ChemSpider) is used to generate 1536 features. Also, various statistical features are extracted from DWT coefficients. The NCA is used to select the most relevant features and these selected features are classified using SVM and kNN classifiers. The presented model attained greater than 95% accuracies using all channels with quadratic SVM classifier. Our results obtained highest classification accuracy of 99.11% and 99.05% using Weighted kNN and Quadratic SVM respectively using A2A1 EEG channel. We have developed the automated depression model using a big dataset and yielded high classification accuracies. These results indicate that our presented model can be used in mental health clinics to confirm the manual diagnosis of psychiatrists.

In this study, EMG signals taken from the skin surface as a result of muscles' contraction are classified. Studied EMG signals include 400 different patterns relating to four different movements. Each pattern is obtained by adding EMG signals one after another, which are recorded synchronously from two different muscles relating to one movement. Support Vector Machine (SVM) classifier, a supervised method, is used to classify these pattterns. But signals need to be preprocessed before being used in SVM classifier. To this end, spectral methods are consulted. In this way, feature vectors which are more significant than raw data and are composed of coefficients are achieved. Four different methods are used for preprocessing and feature vectors obtained are classified by SVM. Success of SVM classifier is tested and performances of preprocessing methods are compared. Best achievement is 94.25%. Keywords: EMG; Spectral Methods; Autoregressive (AR); SVM Classifier.

Signal of humming sound is the input which is important for the Query-by-Humming system. This input signal which has variable dimension depend on humming time interval will always affect the feature vector. It cannot be used with some classifiers, which require non-variable dimension of feature vector, such as Artificial Neural Network (ANN) or Support Vector Machine (SVM). Especially, SVM is good classifier and it might be appropriate for our work. Because of each signal of humming sound has variable dimension and length, this is the main problem which we would like to come up with the idea to solve it. We have an idea to create a new feature space that has the same dimension in order to use with SVM classifier. In this paper, we propose indirect feature, it is used distance between template and observation sequence for creating new feature vector. This technique can be briefly described: Firstly, templates are distributed in original feature space. When the observation sequence gets into this space, Dynamic Time Warping (DTW) will measure the distance between observation sequence and existing templates. These distance are used to get the new feature vector in new space, called distance space. In this way, all feature vectors are non-variable dimension therefore we used SVM and ANN classifier. The experimental results show that the new feature vector which is used by SVM classifier gives better results than ANN.

Aim: The study aims to extract features from EEG signals and classify emotion using Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifier. Materials and methods: The study was conducted using the Support Vector Machine (SVM) and Hidden Markov Model (HMM) programs to analyze and compare the recognition of emotions classified under EEG signals. The results were computed using the MATLAB algorithm. For each group, ten samples were used to compare the efficiency of SVM and HMM classifiers. Result: The study’s performance exhibits the HMM classifier’s accuracy over the SVM classifier and the emotion detection from EEG signals computed. The mean value of the HMM classifier is 52.2, and the SVM classifier is 22.4. The accuracy rate of 35% with the data features is found in HMM classifier. Conclusion: This study shows a higher accuracy level of 35% for the HMM classifier when compared with the SVM classifier. In the detection of emotions using the EEG signal. This result shows that the HMM classifier has a higher significant value of P=.001 < P=.005 than the SVM classifier.

Microseismic monitoring is essential to image and map hydraulic fractures during and after the hydraulic fracturing stimulations for unconventional oil and gas reservoirs. Insights into the underlying reservoir geology and structure can be obtained and effectiveness of hydraulic fracturing engineering parameters can be evaluated through located microseismic events and interpreted hydraulic fractures. There are many important steps in a typical microseismic data processing workflow, including preprocessing, microseismic event detection, microseismic event location, etc. These steps can be implemented either automatically or manually. Automatic microseismic event detection is of particular interest in this thesis. Automatic microseismic event detection involves algorithms and/or workflows to discriminate genuine microseismic events, either P-wave events or S-wave events or both of them, from noise. From algorithm or workflow perspective, microseismic event detection methods can be classified into three major categories, including arrival-time picking, migration-based and waveform-based detection. Most of state-of-the-art arrival-time picking and migration-based methods are characteristic function and threshold based. Limitations in these traditional methods are that user-defined threshold imposes too much impact on the detection accuracy and inappropriate pre-set threshold is prone to bring about low detection accuracy, especially if the signal-to-noise ratio (SNR) of a given microseismic dataset is relatively low.Recently, machine learning and deep learning based methods have been investigated to overcome the drawbacks of these traditional physical model based methods. This thesis aims to develop a workflow that leverages the support vector machine (SVM) classifier to realize automatic microseismic event detection and investigate how to train a robust SVM classifier in order to improve the microseismic event detection accuracy. Here, a classifier is considered to be robust if its performance has the following property: it achieves “similar” performance on a testing sample and a training sample that are “close”. In this thesis, we proposed a “Classification Is Detection” strategy, where a machine learning based approach, specifically SVM classifier referred to as microseismic event detector (MED), was used to distinguish genuine microseismic events from noise. Thus, microseismic detection was cast as a supervised classification. Experiments in this work indicated a well-trained MED is able to achieve comparable, if not better, event detection accuracy with traditional methods.To improve the detection accuracy of a MED, enhanced feature engineering was investigated. We added more 1D features, including time, frequency and multi-channel domain features, into existing feature set published by other researchers. These features were referred to as “ZZ features” in this work. The multi-channel domain features, for example cross-correlation, proved to be effective in improving event detection accuracy. We introduced matched filter analysis (MFA) to enhance the 2D features through firstly applying matched filter to the low to ultra-low SNR dataset and then extracting 2D features from the MFA data. The results indicated that a MED trained with 2D features extracted from MFA data obtained higher detection accuracy than one trained with 2D features extracted from raw data, especially when low to ultra-low SNR dataset was presented. We also studied the impact of SNR on feature selection by carrying out many experiments with variable-SNR training datasets.These experiments indicated that 2D features were important for all training sets, regardless of their SNR, however, 1D features gained more importance weights when training a SVM using features extracted from higher-SNR training sets. As 2D features were more important to train a robust MED, we investigated if adding more 2D features, for example 2D features extracted from raw data, will improve the MED performance. The result suggested that 2D features in ZZ features were sufficient to obtain a robust MED. Lastly, the impact of SNR discrepancy between training and test sets on MED performance was investigated. It was found that a MED can only perform well when it was trained and tested with similar noise level datasets.In practice, both P-wave events and S-wave events are present in a individual seismic trace and an event detection algorithm needs to differentiate these two phases in order to feed them to following location processes with different velocity models and wave travel times. To further differentiate P-wave and S-wave, we leveraged the existing multiclass SVM classifier to cast the two-phase microseismic event detection problem into a multiclass SVM classification problem. In multiclass classification, we introduced the multivariate time series (MTS) concept to take the 3C microseismic data as MTS data and ZZ features were expanded into 3C-ZZR features by extracting ZZ features from X, Y and Z component of raw training dataset. We next used both One-vs-Rest (OVR) and One-vs-One (OVO) strategies to train and test multiclass SVM classifiers. Both synthetic and field examples indicated that multiclass SVM classifiers were still able to achieve acceptable detection accuracy, while the overall event detection performance cannot compete with the aforementioned binary SVM classifiers. During this course, we also found that 1D features gained more feature importance in feature selection process and a MED trained with 1D features only was able to achieve comparable performance with a MED trained with both 1D and 2D features in 3C-ZZR features. As mentioned, both the training and test data are either synthetic or field data in all of previous examples. However, it is a MED trained with synthetic data and tested on field data that is of particular interest to the industry. To find out if a MED trained by synthetic data can perform well on field data, we carried out feasibility study of applying a MED trained by synthetic data to field data. We compared the experiment in which a MED was trained with white Gaussian noise (WGN) polluted synthetic data and tested on field data with the experiment in which a MED was trained with field noise polluted synthetic data and tested on field data. It is found that the MED trained with ZZ features extracted from WGN polluted synthetic data achieved comparable high event detection accuracy with the MED trained with ZZ features extracted from field noise polluted synthetic data, though both of these MEDs cannot compete with the MED trained with field data in terms of event detection accuracy. Furthermore, the results of experiments in which less features were used in the training phase indicated that a MED trained with field noise polluted synthetic data was superior to a MED trained with WGN in terms of event detection accuracy.The machine learning based microseismic event detection methods and the robust MEDs developed and presented in this thesis can be utilised as standalone or concurrent microseismic event detection processes within a standard microseismic data processing workflow. These MEDs provides remedies to the aforementioned limitations of the conventional characteristic function and threshold based methods. Contributions made in this thesis offer an improvement on existing automatic microseismic event detection techniques and offer a new avenue for future research. Some of the key improvements provided by this research are that we developed a new feature set that was able to develop a robust MED and obtain improved event detection accuracy and we found a SVM classifier trained with features extracted from field noise polluted synthetic data can achieve comparable event detection accuracy with a classifier trained by field data.

Classification is one of the most essential tasks in data mining. Unlike other methods, associative classification tries to find all the frequent patterns existing in the input categorical data satisfying a user-specified minimum support and/or other discrimination measures like minimum confidence or information-gain. Those patterns are used later either as rules for rule-based classifier or training features for support vector machine (SVM) classifier, after a feature selection procedure which usually tries to cover as many as the input instances with the most discriminative patterns in various manners. Several algorithms have also been proposed to mine the most discriminative patterns directly without costly feature selection. Previous empirical results show that associative classification could provide better classification accuracy over many datasets. Recently, many studies have been conducted on uncertain data, where fields of uncertain attributes no longer have certain values. Instead probability distribution functions are adopted to represent the possible values and their corresponding probabilities. The uncertainty is usually caused by noise, measurement limits, or other possible factors. Several algorithms have been proposed to solve the classification problem on uncertain data recently, for example by extending traditional rule-based classifier and decision tree to work on uncertain data. In this paper, we propose a novel algorithm uHARMONY which mines discriminative patterns directly and effectively from uncertain data as classification features/rules, to help train either SVM or rule-based classifier. Since patterns are discovered directly from the input database, feature selection usually taking a great amount of time could be avoided completely. Effective method for computation of expected confidence of the mined patterns used as the measurement of discrimination is also proposed. Empirical results show that using SVM classifier our algorithm uHARMONY outperforms the state-of-the-art uncertain data classification algorithms significantly with 4% to 10% improvements on average in accuracy on 30 categorical datasets under varying uncertain degree and uncertain attribute number.

Grasping of the objects is the most frequent activity performed by the human upper limb. The amputations of the upper limb results in the need for prosthetic devices. The myoelectric prosthetic devices use muscle signals and apply control techniques for identification of different levels of hand gesture and force levels. In this study; a different level force contraction experiment was performed in which Electromyography (EMGs) signals and fingertip force signals were acquired. Using this experimental data; a two-step feature selection process is applied for the designing of a pattern recognition algorithm for the classification of different force levels. The two step feature selection process consist of generalized feature ranking using ReliefF, followed by personalized feature selection using Neighborhood Component Analysis (NCA) from the shortlisted features by earlier technique. The classification algorithms applied in this study were Support Vector Machines (SVM) and Random Forest (RF). Besides feature selection; optimization of the number of muscles during classification of force levels was also performed using designed algorithm. Based on this algorithm; the maximum classification accuracy using SVM classifier and two muscle set was achieved as high as 99%. The optimal feature set consisted features such as Auto Regressive coefficients, Willison Amplitude and Slope Sign Change. The mean classification accuracy for different subjects, achieved using SVM and RF was 94.5% and 91.7% respectively.