Discrete Wavelet Transform Features Research Articles

EMG Instrumentation Modeling and Feature Processing Based On Discrete Wavelet Transform

Electromyography (EMG) instrumentation is essential in generating electrical signals from skeletal muscles. EMG sensors are helpful in various cases requiring the detection of human muscle contractions, neuromuscular disorders, and rehabilitation. EMG instrumentation is divided into two parts, namely, the analogue part and the digital part. The EMG instrumentation design comprises a digital-to-analog converter (DAC), instrumentation amplifier, filter, and analog-to-digital converter (ADC). Meanwhile, in digital signal processing adopting the Discrete Wavelet Transform (DWT) method, frequency analysis using DWT has proven superior. It is used in various research and has exceptionally detailed coefficient features for classifying neuromuscular disease signals. Therefore, this research aims to design analogue and digital EMG instrumentation and identify features in the form of detailed coefficients. The data used are two Physionet signals from the anterior tibialis body with myopathy and neuropathy disorders. The results obtained for EMG analogue instrumentation provide the expected results until they reach the filter component stage. The resulting signal forms a block in the filter component, different from the initial EMG signal. Meanwhile, the DWT decomposition results are of the Daubechies4 wavelet type with the highest level 17, which has a high detail coefficient at low frequencies, high dilation and the result of a mixture of neuropathy and myopathy EMG signals, or in other words, at low energies, this result is by the DWT theorem. Determining the efficiency of the DWT detailed coefficient feature requires further study with the same signal subject. The DWT features obtained can then be developed for various needs in EMG signal recognition.

Multiclass Classification of Brain MRI through DWT and GLCM Feature Extraction with Various Machine Learning Algorithms

This study delves into the domain of medical diagnostics, focusing on the crucial task of accurately classifying brain tumors to facilitate informed clinical decisions and optimize patient outcomes. Employing a diverse ensemble of machine learning algorithms, the paper addresses the challenge of multiclass brain tumor classification. The investigation centers around the utilization of two distinct datasets: the Brats dataset, encompassing cases of High-Grade Glioma (HGG) and Low-Grade Glioma (LGG), and the Sartaj dataset, comprising instances of Glioma, Meningioma, and No Tumor. Through the strategic deployment of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) features, coupled with the implementation of Support Vector Machines (SVM), k-nearest Neighbors (KNN), Decision Trees (DT), Random Forest, and Gradient Boosting algorithms, the research endeavors to comprehensively explore avenues for achieving precise tumor classification. Preceding the classification process, the datasets undergo pre-processing and the extraction of salient features through DWT-derived frequency-domain characteristics and texture insights harnessed from GLCM. Subsequently, a detailed exposition of the selected algorithms is provided and elucidates the pertinent hyperparameters. The study's outcomes unveil noteworthy performance disparities across diverse algorithms and datasets. SVM and Random Forest algorithms exhibit commendable accuracy rates on the Brats dataset, while the Gradient Boosting algorithm demonstrates superior performance on the Sartaj dataset. The evaluation process encompasses precision, recall, and F1-score metrics, thereby providing a comprehensive assessment of the classification prowess of the employed algorithms.

Cross-Domain Conv-TasNet Speech Enhancement Model with Two-Level Bi-Projection Fusion of Discrete Wavelet Transform

Nowadays, time-domain features see wide use in speech enhancement (SE) networks such as frequency-domain features to achieve excellent performance in eliminating noise from input utterances. This study primarily investigates how to extract information from time-domain utterances to create more effective features in SE. We extend our recent work by employing sub-signals which dwell in multiple acoustic frequency bands in the time domain and integrating them into a unified time-domain feature set. The discrete wavelet transform (DWT) is applied to decompose each input frame signal to obtain sub-band signals, and a projection fusion process is performed on these signals to create the ultimate features. The corresponding fusion strategy is either bi-projection fusion (BPF) or multiple projection fusion (MPF). In short, MPF exploits the softmax function to replace the sigmoid function in order to create ratio masks for multiple feature sources. The concatenation of fused DWT features and time features serves as the encoder output of two celebrated SE frameworks, the fully convolutional time-domain audio separation network (Conv-TasNet) and the dual-path transformer network (DPTNet), to estimate the mask and then produce the enhanced time-domain utterances. The evaluation experiments are conducted on the VoiceBank-DEMAND and VoiceBank-QUT tasks, and the results reveal that the proposed method achieves higher speech quality and intelligibility than the original Conv-TasNet that uses time features only, indicating that the fusion of DWT features created from the input utterances can benefit time features to learn a superior Conv-TasNet/DPTNet network in SE.

Gray-Level Co-occurrence Matrix Analysis of Nuclear Textural Patterns in Laryngeal Squamous Cell Carcinoma: Focus on Artificial Intelligence Methods.

Gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) analyses are two contemporary computational methods that can identify discrete changes in cell and tissue textural features. Previous research has indicated that these methods may be applicable in the pathology for identification and classification of various types of cancers. In this study, we present findings that squamous epithelial cells in laryngeal carcinoma, which appear morphologically intact during conventional pathohistological evaluation, have distinct nuclear GLCM and DWT features. The average values of nuclear GLCM indicators of these cells, such as angular second moment, inverse difference moment, and textural contrast, substantially differ when compared to those in noncancerous tissue. In this work, we also propose machine learning models based on random forests and support vector machine that can be successfully trained to separate the cells using GLCM and DWT quantifiers as input data. We show that, based on a limited cell sample, these models have relatively good classification accuracy and discriminatory power, which makes them suitable candidates for future development of AI-based sensors potentially applicable in laryngeal carcinoma diagnostic protocols.

Optimization of Discrete Wavelet Transform Feature Representation and Hierarchical Classification of G-Protein Coupled Receptor Using Firefly Algorithm and Particle Swarm Optimization

Ineffective protein feature representation poses problems in protein classification in hierarchical structures. Discrete wavelet transform (DWT) is a feature representation method which generates global and local features based on different wavelet families and decomposition levels. To represent protein sequences, the proper wavelet family and decomposition level must be selected. This paper proposed a hybrid optimization method using particle swarm optimization and the firefly algorithm (FAPSO) to choose the suitable wavelet family and decomposition level of wavelet transformation for protein feature representation. The suggested approach improved on the work of earlier researchers who, in most cases, manually selected the wavelet family and level of decomposition based solely on experience and not on data. The paper also applied the virtual class methods to overcome the error propagation problems in hierarchical classification. The effectiveness of the proposed method was tested on a G-Protein Coupled Receptor (GPCR) protein data set consisting of 5 classes at the family level, 38 classes at the subfamily level, and 87 classes at the sub-subfamily level. Based on the result obtained, the most selected wavelet family and decomposition level chosen to represent GPCR classes by FAPSO are Biorthogonal wavelets and decomposition level 1, respectively. The experimental results show that the representation of GPCR protein using the FAPSO algorithm with virtual classes can yield 97.9%, 86.9%, and 81.3% classification accuracy at the family, subfamily, and sub-subfamily levels, respectively. In conclusion, the result shows that the selection of optimized wavelet family and decomposition level by the FAPSO algorithm, and the virtual class method can be potentially used as the feature representation method and a hierarchical classification method for GPCR protein.

Multiclass tumor identification using combined texture and statistical features.

Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature.

Epileptic Seizures Detection from EEG Recordings Based on a Hybrid system of Gaussian Mixture Model and Random Forest Classifier

Epilepsy is the most common neurological disease defined as a central nervous system disorder that is characterized by recurrent seizures. While electroencephalography (EEG) is an essential tool for monitoring epilepsy patients' brain activity and diagnosing epilepsy, Visual detection of the EEG signal to identify epileptic seizures is a time-consuming approach that might result in human error. Therefore, an early and precise epilepsy diagnosis is critical to reducing the risk of future seizures. This paper aims to increase epileptic seizure detection accuracy in a balanced dataset while reducing the execution time. To address this, we proposed a hybrid system of supervised and unsupervised machine learning algorithms to construct a computationally efficient and scalable model for the early detection of epileptic seizures from two-class EEG datasets. First, Discrete Wavelet Transform (DWT) was applied to the EEG signal to decompose it into frequency sub-bands. Then these EEG extracted features were fed into the Gaussian Mixture Model (GMM) for partitioning these features into two clusters: epilepsy or not. Lastly, the clusters' output was evaluated with the random forest classifier. In addition, Principal Component Analysis (PCA) was used to reduce the EEG features and to reduce further the features obtained after conducting DWT on the EEG signal to determine the impacts of dimension reduction on this system performance. The experimental results show that the highest accuracy was achieved by the hybrid system of GMM with random forest with DWT features with an accuracy of 93.62 %.

The Comparison of SVM and ANN Classifier for COVID-19 Prediction

Coronavirus 2 (SARS-CoV-2) is the cause of an acute respiratory infectious disease that can cause death, popularly known as Covid-19. Several methods have been used to detect COVID-19-positive patients, such as rapid antigen and PCR. Another method as an alternative to confirming a positive patient for COVID-19 is through a lung examination using a chest X-ray image. Our previous research used the ANN method to distinguish COVID-19 suspect, pneumonia, or expected by using a Haar filter on Discrete Wavelet Transform (DWT) combined with seven Hu Moment Invariants. This work adopted the ANN method's feature sets for the Support Vector Machine (SVM), which aim to find the best SVM model appropriate for DWT and Hu moment-based features. Both approaches demonstrate promising results, but the SVM approach has slightly better results. The SVM's performances improve accuracy to 87.84% compared to the ANN approach with 86% accuracy.
 

SCDC-Net: Skin Cancer Detection and Classification using Deep Q Neural Network with Hybrid Features

Skin cancer is one of the most severe malignancies that may affect individuals of all ages. It can affect people of all races and ethnicities. Thus, early detection and treatment of skin cancer have the potential to save millions of lives. Because of the high degree of similarity between skin lesions, conventional machine learning approaches failed to provide the highest possible detection and classification accuracy in the skin cancer detection and classification (SCDC) system. To achieve the robust performance, this work is presented a SCDC-Net by employing deep learning, transfer learning models with hybrid features. Initially, UG-Net based transfer learning model is developed by combining generative adversarial networks (GAN) and U-Net. Here, UG-Net is used to remove hair from skin lesions and also preprocess enhances lesion. In addition, Hybrid U-Net (HU-Net) is used to localize the disease effected area of skin cancer. Further, various hybrid features are extracted from the segmented output by employing the gray-level cooccurrence matrix (GLCM) based texture characteristics, the discrete wavelet transform (DWT)-based low-level features, and the computation of image statistics for the texture, low-level, and colour features, respectively. In order to ensure the highest possible performance of the system, a deep q neural network (DQNN) was constructed for the classification of skin cancer using GLCM, DWT, and statistical colour features. The simulation results shows that the proposed method resulted in superior subjective and objective performance as compared to state of art approaches on ISIC-2019 public challenge dataset.

Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion

Tuberculosis (TB) is a fatal disease in developing countries, with the infection spreading through direct contact or the air. Despite its seriousness, the early detection of tuberculosis by means of reliable techniques can save the patients’ lives. A chest X-ray is a recommended screening technique for locating pulmonary abnormalities. However, analyzing the X-ray images to detect abnormalities requires highly experienced radiologists. Therefore, artificial intelligence techniques come into play to help radiologists to perform an accurate diagnosis at the early stages of TB disease. Hence, this study focuses on applying two AI techniques, CNN and ANN. Furthermore, this study proposes two different approaches with two systems each to diagnose tuberculosis from two datasets. The first approach hybridizes two CNN models, which are Res-Net-50 and GoogLeNet techniques. Prior to the classification stage, the approach applies the principal component analysis (PCA) algorithm to reduce the features’ dimensionality, aiming to extract the deep features. Then, the SVM algorithm is used for classifying features with high accuracy. This hybrid approach achieved superior results in diagnosing tuberculosis based on X-ray images from both datasets. In contrast, the second approach applies artificial neural networks (ANN) based on the fused features extracted by ResNet-50 and GoogleNet models and combines them with the features extracted by the gray level co-occurrence matrix (GLCM), discrete wavelet transform (DWT) and local binary pattern (LBP) algorithms. ANN achieved superior results for the two tuberculosis datasets. When using the first dataset, the ANN, with ResNet-50, GLCM, DWT and LBP features, achieved an accuracy of 99.2%, a sensitivity of 99.23%, a specificity of 99.41%, and an AUC of 99.78%. Meanwhile, with the second dataset, ANN, with the features of ResNet-50, GLCM, DWT and LBP, reached an accuracy of 99.8%, a sensitivity of 99.54%, a specificity of 99.68%, and an AUC of 99.82%. Thus, the proposed methods help doctors and radiologists to diagnose tuberculosis early and increase chances of survival.

Feature Selection Methods Comparison for EEG-based Classifier Constructed Using Discrete Wavelet Transform Features

The paper presents the results of a study in the application of electroencephalography (EEG) for user authentication using discrete wavelet transform. The Leipzig Study for Mind-Body-Emotion Interactions dataset (LEMON) was used. Mean value, standard deviation, and root mean square value are used as features. Feature selection methods based on correlation, on mutual information, and on the χ2 criterion are used for reduce feature space. The SVM model is used for classification. The efficiency of constructed classifier has been tested using cross-validation procedure. Classifier built on feature reduced data via mutual information criteria have improved accuracy (97.4%) with feature space nearly halved (183 features) compared to baseline classifier.

Comparative Analysis for Feature Extraction and Prediction of CKD Using Machine Learning

Purpose: Chronic Kidney Disease (CKD) is one of the world’s top 20 causes of death. This novel study focuses on creating a prediction system for chronic kidney disease. It leverages T2 weighted MRI images and machine learning for efficient CKD classification, replacing labour-intensive manual processes. The adaptability of machine learning models accommodates changing disease patterns and diverse data sources. The purpose of this study is to investigate CKD, characterized by a sustained reduction in renal function lasting at least three months. CKD severity is gauged by kidney damage extent and glomerular filtration rate decline. The ultimate stage of CKD is end-stage renal disease. Methods: The study focuses on various feature extraction from MRI data using (kNN), Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Gray level co-variance matrix (GLCM) along with a few morphological operations. Three Different sets of features are extracted, and Machine Learning Classification Models (Logistic Regression (LR), Support Vector Classifier (SVM), Decision Tree (DT), Random Forest (RF), k-Nearest-Neighbors (kNN), Naïve Bayes (NB)) are trained and tested on these set of features. Results: Experiment results show that LR Classifier gives the highest Accuracy of 92% for GLCM features. SVM and RF Classifier provide the highest Accuracy of 91.5% for DCT features, and RF Classifier gives the highest Accuracy of 86.6%. Based on predictions made by each model, a soft voting classifier is trained and tested to achieve the best Classification for each set of features. This study helps analyse the influence of the voting classifiers obtaining an accuracy of 90% for GLCM features, followed by 89% for DCT features and 84% for DWT features. Conclusion: The suggested system offers a comparative analysis of classification models and techniques for feature extraction. The results of performance evaluation illustrate the efficacy of different feature extraction and classification approaches. This will contribute to the timely detection, routine examination, and efficient control of CKD.

Machine Learning Based Flow Regime Identification using Ultrasonic Doppler Data and Feature Relevance Determination

Machine learning approaches are now being used in the process industries to objectively classify two-phase flow regimes in pipes such as into bubbly, slugging, churning, or annular flow. However, they lack explainability, which makes the results difficult to be trusted by plant operators in the field. This paper presents a complete machine learning workflow for flow regime identification with improved explainability through the use of virtual flow regime maps and feature relevance determination. The workflow was applied on an S-shape riser fitted with a non-intrusive ultrasonic Doppler sensor. Statistical features were first extracted via discrete wavelet transform (DWT). An improved virtual flow regime map was then constructed by projecting the DWT features onto 2-D space using kernel principal components analysis (KPCA). Within KPCA, anisotropic kernels are proposed in this study (referred to as Aniso-KPCA) to reveal which input DWT features have the best flow-distinguishing power. Kernel parameters in Aniso-KPCA were searched by maximizing the goodness of 2-D clustering of data points using the genetic algorithm. Finally, the support vector machine (SVM) emerged as the recommended classifier of flow regimes on the basis of repeatability and accuracy, as compared to other models such as neural networks, naive Bayes, k-nearest neighbors, and ensembled trees. Overall, the proposed workflow is a step towards improving the explainability of future machine learning based flow regime classifiers.

DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection.

Brain Computer Interface technology enables a pathway for analyzing EEG signals for seizure detection. EEG signal decomposition, features extraction and machine learning techniques are more familiar in seizure detection. However, selecting decomposition technique and concatenation of their features for seizure detection is still in the state-of-the-art phase. This work proposes DWT-EMD Feature level Fusion-based seizure detection approach over multi and single channel EEG signals and studied the usability of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) feature fusion with respect to individual DWT and EMD features over classifiers SVM, SVM with RBF kernel, decision tree and bagging classifier for seizure detection. All classifiers achieved an improved performance over DWT-EMD feature level fusion for two benchmark seizure detection EEG datasets. Detailed quantification results have been mentioned in the Results section.

Valence State Analysis Using Discrete Wavelet Transform Features for Early Detection of Autism Spectrum Disorder in Young Kids

Autism spectrum disorder is a developmental disorder that has affected many children around the globe in recent years. It is possible to reduce the severity of the symptoms when the affected children are identified and treated early. Hence, early detection and treatment of this neurodevelopmental disorder significantly help the patient’s (young ASD kids) well-being. In this regard, the research has been initiated by developing an algorithm based on a neural network that can efficiently differentiate the brain activity of a normal young subject and an autistic young subject. In this research, Electroencephalography (EEG) data were collected from normal kids and kids with ASD from age 4 to 6. Discrete Wavelet Transform (DWT) is used for feature extraction of EEG data for valence state analysis on younger kids. It was inferred that there is a linear increase in Power Spectral Density (PSD) irrespective of age during valence state analysis of various EEG bands such as gamma, beta, alpha, and theta. When comparing the PSD of normal subjects with subjects of ASD, the PSD of ASD subjects is comparatively higher than the PSD of normal subjects. The trained network can classify the EEG data as normal subjects and subjects with ASD with good accuracy from the datasets.

Classification of flow regimes using a neural network and a non-invasive ultrasonic sensor in an S-shaped pipeline-riser system

A method for classifying flow regimes is proposed that employs a neural network with inputs of extracted features from Doppler ultrasonic signals of flows using either the Discrete Wavelet Transform (DWT) or the Power Spectral Density (PSD). The flow regimes are classified into four types: annular, churn, slug, and bubbly flow regimes. The neural network used in this work is a feedforward network with 20 hidden neurons. The network comprises four output neurons, each of which corresponds to the target vector's element number. 13 and 40 inputs are used for features extracted from PSD and DWT respectively. Experimental data were collected from an industrial-scale multiphase flow facility. Using the PSD features, the neural network classifier misclassified 3 out of 31 test datasets in the classification and gave 90.3% accuracy, while only one dataset was misclassified with the DWT features, yielding an accuracy of 95.8%, thus showing the superiority of the DWT in feature extraction of flow regime classification. The approach demonstrates the applicability of a neural network and DWT for flow regime classification in industrial applications using a clamp-on Doppler ultrasonic sensor. The scheme has significant advantages over other techniques as only a non-radioactive and non-intrusive sensor is used. To the best of our knowledge, this is the first known successful attempt for the classification of liquid-gas flow regimes in an S-shape riser system using an ultrasonic sensor, PSD-DWTs features, and a neural network.

Diagnosis of Pulpitis from Dental Panoramic Radiograph Using Histogram of Gradients with Discrete Wavelet Transform and Multilevel Neural Network Techniques

Radiograph plays the major role of diagnosis, treatment and surgery in the Dental field. There are many types of Intra and extra oral radiographs in which Dental Panoramic Radiograph helps in visualising the full view of the oral cavity. Pulpitis is the dental diseases caused due to the inflammation of the dental pulp from untreated caries, trauma or multiple restorations which leads to Apical Periodontitis. To predict the severity of pulp vitality pulp inflammation has to be evaluated. Radiographs helps the dentist in diagnosing the extent of tooth decay and inflammation. An automatic diagnostic model is proposed using robust algorithms to diagnose pulpits. Dental Panoramic Radiograph is used in the proposed research to diagnose the pulpitis and to classify the normal teeth from the pulpitis. The collected images are pre-processed using Histogram Equalization and filtered using Gaussian and Median filters. Modified K-Means algorithm is applied to segment the bony and teeth area from the oral cavity area. Integral Histogram of Gradients with Discrete Wavelet Transform feature extraction techniques and Multi-Layer Neural Network Classifier is proposed to achieve the accuracy of 91.09% which can be used as an assistive tool by the dentist to diagnose pulpitis.

Vibration Analysis of Gearbox Fault Diagnosis using DWT and Statistical Features

The gearbox is one of the critical subsystems in any rotating machinery, which plays a significant role in machine-driven power transmission in terms of change in speed and torque. It plays a vital role in patching different industrial functionalities. The advent of developing different gear technologies and the requirement to fulfil the desired mechanical benefits lead to add more importance to the gearbox health condition monitoring from various types of fault occurrences at an earlier stage. This study presents the vibration analysis of gearbox fault diagnosis using discrete wavelet transform (DWT) and statistical features. It is observed that, using wavelet reconstruction in the fault diagnosis better fault classification is achieved. The fault diagnosis has presented with an emphasis in time-domain followed by two different approaches. The approach-1 is illustrated as windowing of raw signal, feature extraction and feature classification using support vector machine (SVM). In the approach-2, after windowing the raw signal - each window of original vibration signal has converted into wavelet coefficients reconstructed signals (without leaving the time domain) using discrete wavelet transform (DWT) at different levels of decomposition followed by approach-1. The fault diagnostic accuracy of SVM is presented with 100 Monte-Carlo -runs to validate the consistency in the accomplished result. By observing the success rates in two approaches, it is clear that approach-2 with wavelet coefficient’s reconstructed signal is providing better classification accuracy, which can be practically deployed to diagnose the gearbox fault.

COVID-19 detection with traditional and deep features on cough acoustic signals.

The COVID-19 epidemic, in which millions of people suffer, has affected the whole world in a short time. This virus, which has a high rate of transmission, directly affects the respiratory system of people. While symptoms such as difficulty in breathing, cough, and fever are common, hospitalization and fatal consequences can be seen in progressive situations. For this reason, the most important issue in combating the epidemic is to detect COVID-19(+) early and isolate those with COVID-19(+) from other people. In addition to the RT-PCR test, those with COVID-19(+) can be detected with imaging methods. In this study, it was aimed to detect COVID-19(+) patients with cough acoustic data, which is one of the important symptoms. Based on these data, features were obtained from traditional feature extraction methods using empirical mode decomposition (EMD) and discrete wavelet transform (DWT). Deep features were also obtained using pre-trained ResNet50 and pre-trained MobileNet models. Feature selection was applied to all obtained features with the ReliefF algorithm. In this case, the highest 98.4% accuracy and 98.6% F1-score values were obtained by selecting the EMD + DWT features using ReliefF. In another study in which deep features were used, features obtained from ResNet50 and MobileNet using scalogram images were used. For the features selected using the ReliefF algorithm, the highest performance was found with support vector machines-cubic as 97.8% accuracy and 98.0% F1-score. It has been determined that the features obtained by traditional feature approaches show higher performance than deep features. Among the chaotic measurements, the approximate entropy measurement was determined to be the highest distinguishing feature. According to the results, a highly successful study is presented with cough acoustic data that can easily be obtained from mobile and computer-based applications. We anticipate that this study will be useful as a decision support system in this epidemic period, when it is important to correctly identify even one person.

Image Authenticity Detection Using DWT and Circular Block-Based LTrP Features

Copy-move forgery is the most common type of digital image manipulation, in which the content from the same image is used to forge it. Such manipulations are performed to hide the desired information. Therefore, forgery detection methods are required to identify forged areas. We have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern (LTrP) features to detect the single and multiple copy-move attacks from the images. The proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing operations. It also uses discrete wavelet transform (DWT) for dimension reduction. The obtained approximate image is distributed into circular blocks on which the LTrP algorithm is employed to calculate the feature vector as the LTrP provides detailed information about the image content by utilizing the direction-based relation of central pixel to its neighborhoods. Finally, Jeffreys and Matusita distance is used for similarity measurement. For the evaluation of the results, three datasets are used, namely MICC-F220, MICC-F2000, and CoMoFoD. Both the qualitative and quantitative analysis shows that the proposed method exhibits state-of-the-art performance under the presence of post-processing operations and can accurately locate single and multiple copy-move forgery attacks on the images.