Combined Feature Extraction Research Articles

Improving identification performance in iris recognition systems through combined feature extraction based on binary genetics

The process of iris recognition is composed of several stages. The most important stage is feature extraction. The majority of systems use a single method of feature extraction. We used a binary genetic algorithm with a new fitness criterion to find an efficient combination of feature extraction methods with the aim of improving the performance of iris recognition systems. The proposed method makes use of a large number of filters and transforms which are widely used to extract iris features. The best combination of these techniques is found over iterations of the proposed algorithm. The final combination, which is argued to be the most efficient method of feature extraction, consists of a number of wavelet transforms, Gabor filter, and Fourier transform. ROC graphs are used to show the experimental superiority of the performance of this method over methods that use a single filter. According to our findings, the proposed method shows better performance in most situations. An FAR value of 0 and an FRR value of 0.092 was obtained by this method.

A Computational Framework For Infrastructure Systems Under Multiple Factors In Deep Learning

The proposed framework leverages deep learning algorithms to incorporate multiple factors and their interconnections, enabling a comprehensive understanding of infrastructure performance. This paper outlines the framework's methodology, describes the deep learning techniques employed, presents a case study to demonstrate its effectiveness, and discusses potential applications in infrastructure planning and decision-making. Infrastructure systems, such as transportation networks, power grids, and water supply systems, are essential for modern societies. The presents a computational framework that leverages deep learning techniques to model and analyse infrastructure systems under the influence of multiple factors, enabling enhanced decision-making and system performance. The proposed framework integrates deep learning algorithms with comprehensive data sets collected from various sources, including sensors, social media, and historical records, to capture the intricate relationships and dependencies among system components and influencing factors. Through a combination of feature extraction, pattern recognition, and predictive modelling, the framework learns the underlying dynamics of the infrastructure system, enabling accurate predictions and decision support. The trained deep learning models are capable of simulating and predicting the behaviour and performance of the infrastructure system under different scenarios and conditions. This enables the identification of potential vulnerabilities, the optimization of resource allocation, and the development of proactive strategies to enhance system resilience, reliability, and efficiency. Moreover, the framework can provide real-time monitoring and decision support by analysing streaming data and detecting anomalies or critical events.

Real-Time Angular Velocity-Based Misfire Detection Using Artificial Neural Networks

In this paper, an artificial neural network (ANN) is introduced in order to detect the occurrence of misfire in an internal combustion (IC) engine by analyzing the crankshaft angular velocity. This study presents three reliable misfire detection procedures. In the first two methods, the fault features are extracted using both time domain and frequency domain techniques, and a multilayer perceptron (MLP) serves as the pattern recognition tool for detecting the misfiring cylinder. In the third method, a one-dimensional (1D) convolutional neural network (CNN) that combines feature extraction capability and pattern recognition is adopted for misfire detection. The experimental data are obtained by setting a six in-line diesel engine with different cylinder misfiring to work under representative operating conditions. Finally, all three diagnostic methods achieved satisfactory results, and the 1D CNN achieved the best performance. The current study provides a novel way to detect misfiring in IC engines.

Learning visual representations with optimum-path forest and its applications to Barrett’s esophagus and adenocarcinoma diagnosis

In this work, we introduce the unsupervised Optimum-Path Forest (OPF) classifier for learning visual dictionaries in the context of Barrett's esophagus (BE) and automatic adenocarcinoma diagnosis. The proposed approach was validated in two datasets (MICCAI 2015 and Augsburg) using three different feature extractors (SIFT, SURF, and the not yet applied to the BE context A-KAZE), as well as five supervised classifiers, including two variants of the OPF, Support Vector Machines with Radial Basis Function and Linear kernels, and a Bayesian classifier. Concerning MICCAI 2015 dataset, the best results were obtained using unsupervised OPF for dictionary generation using supervised OPF for classification purposes and using SURF feature extractor with accuracy nearly to 78% for distinguishing BE patients from adenocarcinoma ones. Regarding the Augsburg dataset, the most accurate results were also obtained using both OPF classifiers but with A-KAZE as the feature extractor with accuracy close to 73%. The combination of feature extraction and bag-of-visual-words techniques showed results that outperformed others obtained recently in the literature, as well as we highlight new advances in the related research area. Reinforcing the significance of this work, to the best of our knowledge, this is the first one that aimed at addressing computer-aided BE identification using bag-of-visual-words and OPF classifiers, being this application of unsupervised technique in the BE feature calculation the major contribution of this work. It is also proposed a new BE and adenocarcinoma description using the A-KAZE features, not yet applied in the literature.

RailNet: A Segmentation Network for Railroad Detection

Future trains will use more computer vision aids to help achieve fully autonomous driving. One of the most important parts of the train's visual function is the detection of railroad obstacles. This makes it important to identify and segment the railroad region within each video frame as it allows the train to identify the driving area so that it can do effective obstacle detection. Traditional railroad detection methods rely on hand-crafted features or highly specialized equipment such as lidar, which typically require expensive equipment to be maintained and are less reliable in scene changes. RailNet is a deep learning segmentation algorithm for railroad detection for videos captured by the front-view on-board cameras. RailNet provides an end-to-end solution that combines feature extraction and segmentation. We have modified the backbone network to extract multi-convolution features and use a pyramid structure to make the features have a top-to-bottom propagation. Our model can detect the railroad without generating large numbers of regions, which greatly increases the detection speed. Tested on a railroad segmentation dataset (RSDS) which we have built, RailNet exhibits very good performance while achieving 20 frames per second processing speed.

Iris recognition system based on a new combined feature extraction method

Iris recognition system based on a new combined feature extraction method

Iris recognition system based on a new combined feature extraction method

Recent science studies are interested in automatic systems without human intervention. The security field is in a great need of automatic identification system based on biometric properties. The uniqueness of the texture present in the human iris is a natural password. In this paper, we propose a novel iris recognition approach based on a combination of two systems. The first system extracts the local variation of the mean and the variance. The second system is based on the rotation invariant neighbourhoodbased binary pattern method (Hamouchene and Aouat, 2014a). Two sets of support vector machines (SVM) are used to train each system. Dempster-Shafer theory is used to distribute unitary mass over the two output sets of SVMs. Finally, the combined belief measures are transformed to a probability by applying the Dezert-Smarandache theory. In the experiments, the proposed system is compared to famous iris recognition systems and obtained better recognition rates.

Classification of needle-EMG resting potentials by machine learning.

The diagnostic importance of audio signal characteristics in needle electromyography (EMG) is well established. Given the recent advent of audio-sound identification by artificial intelligence, we hypothesized that the extraction of characteristic resting EMG signals and application of machine learning algorithms could help classify various EMG discharges. Data files of 6 classes of resting EMG signals were divided into 2-s segments. Extraction of characteristic features (384 and 4,367 features each) was used to classify the 6 types of discharges using machine learning algorithms. Across 841 audio files, the best overall accuracy of 90.4% was observed for the smaller feature set. Among the feature classes, mel-frequency cepstral coefficients (MFCC)-related features were useful in correct classification. We showed that needle EMG resting signals were satisfactorily classifiable by the combination of feature extraction and machine learning, and this can be applied to clinical settings. Muscle Nerve 59:224-228, 2019.

A Robust License Plate Detection and Character Recognition Algorithm Based on a Combined Feature Extraction Model and BPNN

The rapid development of the license plate recognition technology has made great progress for its widespread uses in intelligent transportation system (ITS). This paper has proposed a novel license plate detection and character recognition algorithm based on a combined feature extraction model and BPNN (Backpropagation Neural Network) which is adaptable in weak illumination and complicated backgrounds. Firstly, a preprocessing is first used to strengthen the contrast ratio of original car image. Secondly, the candidate regions of license plate are checked to verify the true plate, and the license plate image is located accurately by the integral projection method. Finally, a new feature extraction model is designed using three sets of features combination, training the feature vectors through BPNN to complete accurate recognition of the license plate characters. The experimental results with different license plate demonstrate effectiveness and efficiency of the proposed algorithm under various complex backgrounds. Compared with three traditional methods, the recognition accuracy of proposed algorithm has increased to 97.7% and the consuming time has decreased to 46.1ms.

Arrhythmia detection using deep convolutional neural network with long duration ECG signals

This article presents a new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis. Cardiovascular disease prevention is one of the most important tasks of any health care system as about 50 million people are at risk of heart disease in the world. Although automatic analysis of ECG signal is very popular, current methods are not satisfactory. The goal of our research was to design a new method based on deep learning to efficiently and quickly classify cardiac arrhythmias. Described research are based on 1000 ECG signal fragments from the MIT - BIH Arrhythmia database for one lead (MLII) from 45 persons. Approach based on the analysis of 10-s ECG signal fragments (not a single QRS complex) is applied (on average, 13 times less classifications/analysis). A complete end-to-end structure was designed instead of the hand-crafted feature extraction and selection used in traditional methods. Our main contribution is to design a new 1D-Convolutional Neural Network model (1D-CNN). The proposed method is 1) efficient, 2) fast (real-time classification) 3) non-complex and 4) simple to use (combined feature extraction and selection, and classification in one stage). Deep 1D-CNN achieved a recognition overall accuracy of 17 cardiac arrhythmia disorders (classes) at a level of 91.33% and classification time per single sample of 0.015 s. Compared to the current research, our results are one of the best results to date, and our solution can be implemented in mobile devices and cloud computing.

Age Invariant Face Recognition using Convolutional Neural Network

<span>In the recent years, face recognition across aging has become very popular and challenging task in the area of face recognition. Many researchers have contributed in this area, but still there is a significant gap to fill in. Selection of feature extraction and classification algorithms plays an important role in this area. Deep Learning with Convolutional Neural Networks provides us a combination of feature extraction and classification in a single structure. In this paper, we have presented a novel idea of 7-Layer CNN architecture for solving the problem of aging for recognizing facial images across aging. We have done extensive experimentations to test the performance of the proposed system using two standard datasets FGNET and MORPH</span><span>(Album II). Rank-1 recognition accuracy of our proposed system is 76.6% on FGNET and 92.5% on MORPH</span><span>(Album II). Experimental results show the significant improvement over available state-of- the-arts with the proposed CNN architecture and the classifier.</span>

Development of an expert system for iron ore classification

The accurate classification of iron ores is one of the challenging tasks in the mining industry, and at the same time, it is essential for the future destination of the ores. The present study attempts to develop a machine-vision-based system, combining feature extraction, feature selection, and image classification, in order to predict the class of the iron ores. A total of 280 image features were extracted from each of the captured images of iron ores. The feature includes 10 statistical features for each component of the six colour spaces (RGB, HSI, XYZ, CMYK, Lab, and Grey) and four frequency-transformed components (DCT, DFT, DWT, and Gabor filter). In order to select the optimum feature subset for the model development, a sequential forward floating selection (SFFS) algorithm was used. The optimum feature contains only 3% of the total number of features. The support vector machine (SVM) algorithm was used for the development classification model. The four confusion matrix parameters (sensitivity, specificity, accuracy, and misclassification) along with the Q-statistics and the correlation were used as performance criteria for the model classification and were found to be 0.9792, 0.9949, 0.9918, 0.0082, 0.9999, and 0.9695, respectively. The high value of sensitivity, specificity, and accuracy and the low value of the misclassification indicate a good performance of the model. The performance of the model was also compared with other classification algorithms (k-nearest neighbours, classification tree, discriminant classifier, naive Bayes). It was observed that the proposed algorithm performs reasonably well in terms of categorising the classes of iron ore.

Personalised meal eating behaviour analysis via semi-supervised learning.

Automated monitoring and analysis of eating behaviour patterns, i.e., "how one eats", has recently received much attention by the research community, owing to the association of eating patterns with health-related problems and especially obesity and its comorbidities. In this work, we introduce an improved method for meal micro-structure analysis. Stepping on a previous methodology of ours that combines feature extraction, SVM micro-movement classification and LSTM sequence modelling, we propose a method to adapt a pretrained IMU-based food intake cycle detection model to a new subject, with the purpose of improving model performance for that subject. We split model training into two stages. First, the model is trained using standard supervised learning techniques. Then, an adaptation step is performed, where the model is fine-tuned on unlabeled samples of the target subject via semisupervised learning. Evaluation is performed on a publicly available dataset that was originally created and used in [1] and has been extended here to demonstrate the effect of the semisupervised approach, where the proposed method improves over the baseline method.

Switching impulse discharge voltage prediction of EHV and UHV transmission lines–tower air gaps by a support vector classifier

Discharge voltage prediction of practical air gaps in transmission projects is a long-sought goal and also a great challenge in high-voltage (HV) engineering. An approach combined electric field simulation, feature extraction and machine learning algorithm is presented in this study to predict the switching impulse discharge voltages of extra-HV (EHV) and ultra-HV (UHV) transmission lines-tower air gaps. Some features extracted from the electrostatic field distribution are used to characterise the air-gap configuration and taken as input parameters of a prediction model established by a support vector classifier (SVC). Three kinds of actual gap configurations in EHV and UHV transmission lines are taken as test samples to validate the validity of the SVC model. Trained by experimental data of rod-plane gaps and one of the engineering gap configurations, this model is able to predict the discharge voltages of the other two conductor-tower gaps with acceptable accuracy. The mean absolute percentage errors of the three prediction results are 6.84, 4.19 and 3.46%. This research demonstrates the feasibility of discharge voltage prediction for complicated engineering gaps, which is useful to reduce the costly full-scale tests and helpful to guide the external insulation design.

SENTIMENT ANALYSIS APPROACH BASED N-GRAM AND KNN CLASSIFIER

The sentiment analysis is the approach which is design to analysis positive, negative and neural aspects towards any approach. In the past years, many techniques are designed for the sentiment analysis of twitter data. Based on the previous study about sentiment analysis, novel approach is presented in this research paper for the sentiment analysis of twitter data. The proposed approach is the combination of feature extraction and classification techniques. The N-gram algorithm is applied for the feature extraction and KNN classifier is applied to classify input data into positive, negative and neural classes. To validate the proposed system, performance is analyzed in terms of precision, recall and accuracy. The experiments results of proposed system show that it performs well as compared to existing system which is based on SVM classifier.

Power production prediction of wind turbines using a fusion of MLP and ANFIS networks

Access to accurate power production prediction of a wind turbine in future hours enables operators to detect possible underperformance and anomalies in advance. This may enable more proactive and strategic operations optimisation. This study examines common Supervisory Control And Data Acquisition (SCADA) data over a period of 20 months for 21 pitch regulated 2.3 MW turbines. In this study, an algorithm is proposed to impute values of data that are missing, out-of-range, or outliers. It is shown that an appropriate combination of a decision tree and mean value for imputation can improve the data analysis and prediction performance by the creation of a smoother dataset. In addition, principal component analysis is employed to extract parameters with power production influence based on all available signals in the SCADA data. Then, a new data fusion technique is applied, combining dynamic multilayer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS) networks to predict future performance of wind turbines. This prediction is made on a scale of one-hour intervals. This novel combination of feature extraction, imputation, and MLP/ANFIS fusion performs well with favourably low prediction error levels. Thus, such an approach may be a valuable tool for turbine power production prediction.

CAD system: a content based image retrieval approach for pulmonary nodule detection in CT images

This paper proposes Computerized Aided Detection System (CAD) which uses Content Based Image Retrieval (CBIR) to detect cancer nodules present in an image. The CAD system is concerned for the radiologists to identify lung cancer at premature stages, which are very tiny nodules that are not able to seen by naked eye. In modern years, Image processing techniques play a key role in predicting diseases at early stages in particular in various cancer types such as liver cancer, breast cancer etc. This paper comprises of four steps: i) preprocessing an image in order to lessen the noise level and the accuracy of the image is to be improved, so that the accuracy in detection will be higher. ii) The image is segmented based on Marker-Controlled Watershed Segmentation. iii) The features of the nodules present in the image are extracted using GLCM. iv) The nodules are classified based on the extracted features using KNN classifier. The Content Based Image Retrieval Technique is used which is used to redeem query based images in the database by combining feature extraction and similarity matching methods. For experimentation of proposed technique, CT images are used which are extracted from Lung Image Database Consortium database (LIDC).

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

Fuzzy ARTMAP Approach for Arabic Writer Identification using Novel Features Fusion

Arabic writer identification and associated tasks are still fresh due to huge variety of Arabic writer's styles. Current research presents a fusion of statistical features, extracted from fragments of Arabic handwriting samples to identify the writer using fuzzy ARTMAP classifier. Fuzzy ARTMP is supervised neural model, especially suited to classification problems. It is faster to train and need less number of training epochs to learn from input data for generalization. The extracted features are fed to Fuzzy ARTMP for training and testing. Fuzzy ARTMAP is employed for the first time along with a novel fusion of statistical features for Arabic writer identification. The entire IFN/ENIT database is used in experiments such that 75% handwritten Arabic words from 411 writers are employed in training and 25% for testing the system at random. Several combinations of extracted features are tested using fuzzy ARTMAP classifier and finally one combination exhibited promising accuracy of 94.724% for Arabic writer identification on IFN/ENIT benchmark database.

Instance selection and feature extraction using cuttlefish optimization algorithm and principal component analysis using decision tree

Instance selection and feature extraction is one of the most important task in data mining, due to the huge amount of data is constantly being produced in many fields. If the dataset is very large means most of the existing machine learning algorithms are inapplicable to handle such huge amount of data and computational cost is high. Two of the approaches have been used for solving this problem. One is scaling up algorithms and another one is data reduction. Scaling up data mining algorithm is not always feasible, but data reduction is possible. In this paper we take both, instance selection and feature extraction for data reduction. Instance selection is a technique that will reduce the size of the original training data. Feature extraction is input data having m dimension space that should be mapped into lower dimension space i.e., eliminate those components which are contributing less information. In this paper Cuttlefish optimization algorithm is used for instance selection, while principal component analysis is used for feature extraction. The combination of feature extraction and instance selection will reduce the large amount of computational time of training the classifiers. The optimal extracted subset of data points and reduced feature space are providing almost similar detection rate, accuracy rate, false positive rate and takes less amount of computational time for training the classifiers what we obtained from using original dataset.