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>

Power quality event classification using complex wavelets phasor models and customized convolution neural network

Origin and triggers of power quality (PQ) events must be identified in prior, in order to take preventive steps to enhance power quality. However it is important to identify, localize and classify the PQ events to determine the causes and origins of PQ disturbances. In this paper a novel algorithm is presented to classify voltage variations into six different PQ events considering the space phasor model (SPM) diagrams, dual tree complex wavelet transforms (DTCWT) sub bands and the convolution neural network (CNN) model. The input voltage data is converted into SPM data, the SPM data is transformed using 2D DTCWT into low pass and high pass sub bands which are simultaneously processed by the 2D CNN model to perform classification of PQ events. In the proposed method CNN model based on Google Net is trained to perform classification of PQ events with default configuration as in deep neural network designer in MATLAB environment. The proposed algorithm achieve higher accuracy with reduced training time in classification of events than compared with reported PQ event classification methods.

Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model

The sedimentation problem is one of the critical issues affecting the long-term use of rivers, and the study of sediment variation in rivers is closely related to water resource, river ecosystem and estuarine delta siltation. Traditional research on sediment variation in rivers is mostly based on field measurements and experimental simulations, which requires a large amount of human and material resources, many influencing factors and other restrictions. With the development of computer technology, intelligent approaches have been applied to hydrological models to establish small information in river areas. In this paper, considering the influence of multiple factors on sediment transport, the validity of predicting sediment transport combined with wavelet transforms and neural network was analyzed. The rainfall and runoff cycles are extracted and decomposed into time series sub-signals by wavelet transforms; then, the data post-processing is used as the neural network training set to predict the sediment model. The results show that wavelet coupled neural network model effectively improves the accuracy of the predicted sediment model, which can provide a reference basis for river sediment prediction.

Towards Electron-Beam-Driven Soft / Polymer Fiber Microrobotics for Vacuum Conditions

The possibility of creating vacuum robotics based on the polymer structures irradiated by an electron beam, in particular, polymer fibers, which provide high functional flexibility and a variety of states, is discussed. The possibility of using polymer fibers as different types of MEMS-like electromechanical elements is demonstrated - from elastic cantilevers to springs that change their state under the electron beam. Experimentally proved the presence of different functional types of fibers, correlating with their thickness, as well as the phenomenon of the fiber break. A number of exotic forms of dynamics have been demonstrated and a method for their detection has been developed using 2D Fourier spectra, integral spatial characteristics, time resolved correlograms and wavelet transforms (visualized as the scaleograms / scalograms). Access barcodes for the full video records of the corresponding experiments are provided.

Cortical and STN spectral changes during limb movements in PD patients with and without dystonia

Background: Dystonia is a prevalent yet under-studied motor feature of Parkinson disease (PD). Although considerable efforts have focused on brain oscillations related to the cardinal symptoms of PD, whether dystonia is associated with specific electrophysiological features is unclear. Objectives: To investigate subcortical and cortical field potentials at rest and during contralateral hand and foot movements in PD patients with versus without dystonia. Methods: We examined the prevalence and somatotopy of dystonia in PD patients undergoing deep brain stimulation (DBS) surgery. We recorded intracranial electrophysiology from sensorimotor cortex and directional DBS electrodes in subthalamic nucleus (STN), during both rest and voluntary contralateral limb movements. We used wavelet transforms and linear mixed models to characterize spectral content in patients with and without dystonia (n=25). Results: Dystonia was highly prevalent at enrollment (61%) and most common in the foot (78%). PD patients with dystonia display greater subthalamic theta and alpha power during movement (p < 0.05) but not at rest. Regardless of dystonia status, cortical recordings display prominent beta desynchronization (13-30 Hz) during movement, whereas STN signals show increases in spectral power at lower frequencies (4-20 Hz), with peaks at 6.0 +/- 3.3 and 4.2 +/- 2.9 Hz during hand and foot movements, respectively (p < 0.03). Conclusions: Whereas cortex was characterized by beta desynchronization during hand and foot movements similarly, STN showed limb-specific low frequency activity which was increased in PD patients with dystonia. These findings may help elucidate why PD-related dystonia is most common in the foot and help guide future closed-loop DBS devices.

Enhanced Epileptic Seizure diagnosis using EEG Signals with Support vector machine and Bagging Classifiers

Many approaches have been proposed using Electroencephalogram (EEG) to detect epilepsy seizures in their early stages. Epilepsy seizure is a severe neurological disease. Practitioners continue to rely on manual testing of EEG signals. Artificial intelligence (AI) and Machine Learning (ML) can effectively deal with this problem. ML can be used to classify EEG signals employing feature extraction techniques. This work focuses on automated detection for epilepsy seizures using ML techniques. Various algorithms are investigated, such as Bagging, Decision Tree (DT), Adaboost, Support vector machine (SVM), K-nearest neighbors(KNN), Artificial neural network(ANN), Naïve Bayes, and Random Forest (RF) to distinguish injected signals from normal ones with high accuracy. In this work, 54 Discrete wavelet transforms (DWTs) are used for feature extraction, and the similarity distance is applied to identify the most powerful features. The features are then selected to form the features matrix. The matrix is subsequently used to train ML. The proposed approach is evaluated through different metrics such as F-measure, precision, accuracy, and Recall. The experimental results show that the SVM and Bagging classifiers in some data set combinations, outperforming all other classifiers

An Automatic Detection by Classification of Cracked Pixels or Noncracked Pixels in Road Surface

Automatic detection and monitoring of the condition of cracks in the road surface are essential elements to ensure road safety and quality of service. A crack detection method based on wavelet transforms (2D-DWT) and Jerman enhancement filter is used. This paper presents different contributions corresponding to the three phases of the proposed system. The first phase presents the contrast enhancement technique to improve the quality of roads surface image. The second phase proposes an effective detection algorithm using discrete wavelet (2D-DWT) with “db8” and two-level sub-band decomposition. Finally, in the third phase, the Jerman enhancement filter is usually used with different parameters of the control response uniformity “ τ ” to enhance for cracks detection. The experimental results in this article provide very powerful results and the comparisons with five existing methods show the effectiveness of the proposed technique to validate the recognition of surface cracks.

Analysis of image compression methods based on wavelet transforms for maritime applications

The purpose of the article is to develop recommendations for choosing image compression method based on wavelet transformation, depending on image type, quality and compression requirements. Among the wavelet image compression methods, Embedded Zerotree Wavelet coder (EZW) and Set Partition In Hierarchical Trees (SPIHT) are considered, and the Haar wavelet and wavelet transformation in the oriented basis with the first, third, fifth and seventh decomposition levels is used as the base wavelet transform. These compression methods were compared with each other and with the standard JPEG method on the following parameters: mean square error, maximum error, peak to noise ratio, number of bits per pixel, compression ratio, and image size. The proposed methods can be successfully applied in the transmission of seabed relief images obtained from satellites or sea buoys.