Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

<span lang="EN-US">Among the several ways followed for detecting Parkinson's disease, there is the one based on the speech signal, which is a symptom of this disease. In this paper focusing on the signal analysis, a data of voice records has been used. In these records, the patients were asked to utter vowels “a”, “o”, and “u”. Discrete wavelet transforms (DWT) applied to the speech signal to fetch the variable resolution that could hide the most important information about the patients. From the approximation a3 obtained by Daubechies wavelet at the scale 2 level 3, 21 features have been extracted: a <a name="_Hlk88480766"></a>linear predictive coding (LPC), energy, zero-crossing rate (ZCR), mel frequency cepstral coefficient (MFCC), and wavelet Shannon entropy. Then for the classification, the K-nearest neighbour (KNN) has been used. The KNN is a type of instance-based learning that can make a decision based on approximated local functions, besides the ensemble learning. However, through the learning process, the choice of the training features can have a significant impact on overall the process. So, here it stands out the role of the genetic algorithm (GA) to select the best training features that give the best accurate classification.</span>

Evaluation of the carotid artery using wavelet-based analysis of the pulse wave signal

<p>The use of pulse wave analysis may assist cardiologists in diagnosing patients with vascular diseases. However, it is not common in clinical practice to interpret and analyze pulse wave data and utilize them to detect the abnormalities of the signal. This paper presents a novel approach to the clinical application of pulse waveform analysis using the wavelet technique by decomposing the normal and pathology signal into many levels. The discrete wavelet transform (DWT) decomposes the carotid arterial pulse wave (CAPW) signal, and the continuous wavelet transform (CWT) creates images of the decomposed signal. The wavelet analysis technique in this work aims to strengthen the medical benefits of the pulse wave. The obtained results show a clear difference between the signal and the images of the arterial pathologies in comparison with normal ones. The certain distinct that were achieved are promising but further improvement may be required in the future.</p>

Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

IBRDM: An Intelligent Framework for Brain Tumor Classification Using Radiomics- and DWT-based Fusion of MRI Sequences

Brain tumors are one of the critical malignant neurological cancers with the highest number of deaths and injuries worldwide. They are categorized into two major classes, high-grade glioma (HGG) and low-grade glioma (LGG), with HGG being more aggressive and malignant, whereas LGG tumors are less aggressive, but if left untreated, they get converted to HGG. Thus, the classification of brain tumors into the corresponding grade is a crucial task, especially for making decisions related to treatment. Motivated by the importance of such critical threats to humans, we propose a novel framework for brain tumor classification using discrete wavelet transform-based fusion of MRI sequences and Radiomics feature extraction. We utilized the Brain Tumor Segmentation 2018 challenge training dataset for the performance evaluation of our approach, and we extract features from three regions of interest derived using a combination of several tumor regions. We used wrapper method-based feature selection techniques for selecting a significant set of features and utilize various machine learning classifiers, Random Forest, Decision Tree, and Extra Randomized Tree for training the model. For proper validation of our approach, we adopt the five-fold cross-validation technique. We achieved state-of-the-art performance considering several performance metrics, 〈 Acc , Sens , Spec , F1-score , MCC , AUC 〉 ≡ 〈 98.60%, 99.05%, 97.33%, 99.05%, 96.42%, 98.19% 〉, where Acc , Sens , Spec , F1-score , MCC , and AUC represents the accuracy, sensitivity, specificity, F1-score, Matthews correlation coefficient, and area-under-the-curve, respectively. We believe our proposed approach will play a crucial role in the planning of clinical treatment and guidelines before surgery.

Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

Classification of three pathological voices based on specific features groups using support vector machine

<span>Determining and classifying pathological human sounds are still an interesting area of research in the field of speech processing. This paper explores different methods of voice features extraction, namely: Mel frequency cepstral coefficients (MFCCs), zero-crossing rate (ZCR) and discrete wavelet transform (DWT). A comparison is made between these methods in order to identify their ability in classifying any input sound as a normal or pathological voices using support vector machine (SVM). Firstly, the voice signal is processed and filtered, then vocal features are extracted using the proposed methods and finally six groups of features are used to classify the voice data as healthy, hyperkinetic dysphonia, hypokinetic dysphonia, or reflux laryngitis using separate classification processes. The classification results reach 100% accuracy using the MFCC and kurtosis feature group. While the other classification accuracies range between~60% to~97%. The Wavelet features provide very good classification results in comparison with other common voice features like MFCC and ZCR features. This paper aims to improve the diagnosis of voice disorders without the need for surgical interventions and endoscopic procedures which consumes time and burden the patients. Also, the comparison between the proposed feature extraction methods offers a good reference for further researches in the voice classification area.</span>

Research on Virtual Color Restoration of Complex Building System Based on Discrete Wavelet Transform

At present, image restoration has become a research hotspot in computer vision. The purpose of digital image restoration is to restore the lost information of the image or remove redundant objects without destroying the integrity and visual effects of the image. The operation of user interactive color migration is troublesome, resulting in low efficiency. And, when there are many kinds of colors, it is prone to errors. In response to these problems, this paper proposes automatic selection of sample color migration. Considering that the respective gray-scale histograms of the visual source image and the target image are approximately normal distributions, this paper takes the peak point as the mean value of the normal distribution to construct the objective function. We find all the required partitions according to the user’s needs and use the center points in these partitions as the initial clustering centers of the fuzzy C-means (FCM) algorithm to complete the automatic clustering of the two images. This paper selects representative pixels as sample blocks to realize automatic matching of sample blocks in the two images and complete the color migration of the entire image. We introduced the curvature into the energy functional of the p-harmonic model. According to whether there is noise in the image, a new wavelet domain image restoration model is proposed. According to the established model, the Euler–Lagrange equation is derived by the variational method, the corresponding diffusion equation is established, and the model is analyzed and numerically solved in detail to obtain the restored image. The results show that the combination of image sample texture synthesis and segmentation matching method used in this paper can effectively solve the problem of color unevenness. This not only saves the time for mural restoration but also improves the quality of murals, thereby achieving more realistic visual effects and connectivity.