A New Evolutionary Fuzzy Instance-Based Learning Approach: Application for Detection of Parkinson’s Disease

The present paper introduces the Bacterial Foraging Optimization (BFO) technique to develop an efficient forecasting model for prediction of various stock indices. The connecting weights of the adaptive linear combiner based model are optimized by the BFO so that its mean square error(MSE) is minimized. The short and long term prediction performance of the model is evaluated with test data and the results obtained are compared with those obtained from the multilayer perceptron (MLP) based model. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and takes less training time compared to the standard AMP based model.

In this paper, we proposed a home energy management (HEM) scheme for minimization in electricity bills and reduction in peak load. This can be achieved by scheduling the usage timings of appliances (APP) for shifting load from peak hours (PHs) to OFF-peak hours (OPHs). In this study we proposed a technique which is hybrid of two bio-inspired optimization techniques chicken swarm optimization (CSO) and bacterial foraging optimization (BFA). Simulation results shows that proposed hybrid technique reduces the cost and load peaks by shifting load from PHs to OPHs and reduction in load peaks.

High speed data transmission over communication channels distort the transmitted signals in both amplitude and phase due to presence of inter symbol interference (ISI). Other impairments like thermal noise, impulse noise and cross talk also cause further distortions to the received symbols. Adaptive equalization of the digital channels at the receiver removes/reduces the effects of such ISIs and attempts to recover the transmitted symbols. The multilayer perceptron (MLP), fuzzy logic (FL) and radial basis function (RBF) based equalizers are relatively new soft computing based equalizers which aim to minimize the ISI present in the channels particularly for nonlinear channels. However they suffer from long training time and undesirable local minima. In the present paper we propose a new adaptive channel equalizer using a novel bacterial foraging optimization (BFO) technique which is essentially a derivative free optimization tool. This algorithm has been suitably used to update the weights of the equalizer. The performance of the proposed equalizer has been evaluated and has been compared with its LMS based counter part. It is observed that the new equalizer offers improved performance both in terms of rate of convergence as well as bit-error-rate

Mechanomyographic (MMG) signal used for prosthetic hands has aroused the interest of a growing number of scholars in recent years and some considerable results have been achieved, however most MMG based approaches are limited to hand movements identification and control. In order to achieve a higher degree of freedom in hand movements, this paper proposed a novel method aiming at identifying the finger-motion patterns. Four-channel MMG signal was adopted to identify six single and combined finger-motion patterns. A total of 50 time-domain and frequency-domain features were extracted and diffusion maps were utilized to reduce the dimension of feature space. The fuzzy K-Nearest Neighbor (f-KNN) classifier was used to identify the six finger-motion patterns. The results showed that the average identification rate reaches to a high accuracy of 95.48 2.47%, which indicates that this algorithm is feasible and effective to identify the six finger-motion patterns and the MMG signal is a prospective alternative in the control of high freedom prosthetic hand.

This paper aims to improve recognition accuracies of single sources of partial discharge (PD) by employing image-oriented feature extraction and selection algorithms. 419 diversified samples of PD data acquired from typically artificial defect models, where the defect size, applied voltage and insulation aging are taken into account, are employed for algorithms validation. In the proposed method, the PD gray images are decomposed into various vectors by two-dimensional principal component analysis (2DPCA) on horizontal and vertical directions, respectively, in which 9 representative parameters are extracted from each image vector. Based on the 419 PD samples, the proposed image features are confirmed to achieve better PD recognition performances than typical image compression methods relevant to 2DPCA. To further improve the recognition performance and reduce the feature dimension, the feature selection technique based on non-dominated sorting genetic algorithm II (NSGA-II) is adopted to search the optimal 2DPCA features. By using the proposed image-oriented feature extraction and selection algorithm, fuzzy k-nearest neighbor classifier (FkNNC), back-propagation neural network (BPNN) and support vector machine (SVM) with 16-dimensional 2DPCA features can now achieve 96.4%, 94.4% and 97.5% accuracies, respectively. The increment of average recognition accuracies of 5%-7% are obtained by different classifiers compared with phase-resolved partial discharge (PRPD) statistical features in previous studies. The investigations on the recognition results relevant to image distortion, cavity discharge change with time and PD contaminated by random noises with different level confirm the robustness of the 16-dimensional 2DPCA features, which signify a great potential in applying the proposed method to field PD pattern recognition in the future.

This paper proposes a new approach to enhance peak signal to noise ratio of highly corrupted image affected by impulse noise. The proposed technique is implemented using an adaptive median Alter and the bacterial foraging optimization (BFO) technique. The adaptive median Alter is used to identify pixels affected by noise and replace them with median value to keep the information uncorrupted. The BFO technique minimizes errors between adaptive median filter output image and noisy image to maintain an error percentage of 0.0001. It has been observed that results of the proposed method are superior to conventional methods in terms of perceptual image quality as well as clarity and smoothness in edge regions of the resultant image. This technique can remove the salt-and-pepper noise of highly corrupted images with a noise as high as 90%.

This paper presents a new parameter extraction method for photovoltaic (PV) modules exploiting Bacterial Foraging Optimization (BFO) technique. In a PV system, validation of the model of a PV module with correctly chosen parameters is essential. An efficient parameter extraction method is required to estimate the parameters of PV module. Although, a number of parameter extraction methods are available in literature but there is a need to explore parameter extraction methods that could extract globally optimized parameters in changing weather conditions. One of the recent evolutionary computing approaches called BFO exhibits global optimization performance. Therefore, we employ BFO for extraction of parameters of a PV module. The proposed BFO-based parameter extraction method has been tested for different types of PV modules at different test conditions. Analyzing both the simulation and experimental results obtained using BFO; it is found that the module parameters are more accurate compared to that of Newton-Raphson, particle swarm optimization, and enhanced simulated annealing methods.

Self-adaptive differential evolution for feature selection in hyperspectral image data

An ensemble classifier based prediction of G-protein-coupled receptor classes in low homology

Role of hippocampal subfields in neurodegenerative disease progression analyzed with a multi-scale attention-based network

Benefits of intensive speech treatment have been documented for a range of speech signs in English speakers with Parkinson's Disease (PD). However, the answer to a critical question that whether the same treatment benefits speech variables including intelligibility in Mandarin speakers is still unclear. In order to develop a targeted speech treatment for Mandarin speakers with PD, we reviewed the efficacy of intensive speech treatment to improve vocal loudness and functional communication and discuss possible explanations for efficacy on Mandarin speakers with PD. Literatures about intensive speech treatment for Mandarin speakers with PD were retrieved from PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), Wanfang and Weipu Database for Chinese Technical Periodicals (VIP) Database. Search strategy was (voice therapy OR speech therapy OR voice treatment OR speech treatment OR voice training OR speech training OR voice rehabilitation OR speech rehabilitation OR Lee Silverman voice treatment OR intensive speech treatment) and (Parkinson's disease) and (Mandarin speakers OR Chinese OR Chinese people). Five randomized controlled trials were selected and possible explanations for efficacy on individuals with PD are discussed. Further research directions are suggested. The existing evidence from treatment efficacy studies of intensive speech treatment provides support for improving vocal loudness, speech intelligibility, pitch and rate in Mandarin speakers with PD. Our future research will continue to work to conduct a large sample multicenter randomized controlled trial to provide high quality evidence and understand the basic mechanisms accompanying treatment-related change.

Prenatal phenytoin exposure and spatial navigation in offspring: effects on reference and working memory and on discrimination learning.

José Obeso: on a pilgrimage to unravel Parkinson's disease

A novel hybrid method for feature selection based on gender analysis for early Parkinson's disease diagnosis using speech analysis

Parkinson's disease (PD) is a neurodegenerative disease which is prevalent among the elder population and severely affects the life quality of patients and their families. Therefore, it is important to conduct an early diagnosis for potential patients with PD, so as to promote prompt treatment and avoid the aggravation of the disease. Recently, the in-vitro PD diagnosis based on facial expressions has received increasing attention because of its distinguishability (i.e., PD patients always possess the characteristics of “masked face”) and affordability. However, the performance of the existing facial expression-based PD diagnosis approaches is limited by: 1) the small-scale training data on PD patients' facial expressions, and 2) the weak prediction model. To address these two problems, we propose a new facial expression guided PD diagnosis method based on high-quality training data augmentation and deep neural network prediction. Specifically, the proposed method consists of three stages: Firstly, we synthesize virtual facial expression images with 6 basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise) based on multi-domain adversarial learning to approximate the premorbid expressions of PD patients. Secondly, we introduce three facial image quality assessment (FIQA) criteria to measure the quality of these synthesized facial expression images and design a fusion screening strategy that shortlists the high-quality ones to augment the training data. Finally, we train a deep neural network prediction model based on the original and synthesized high-quality facial expression images for PD diagnosis. To show real-world impacts and evaluate the proposed method under different facial expressions, we also create a (currently largest) multiple facial expressions-based PD face dataset in collaboration with a hospital. Extensive experiments are performed to demonstrate the effectiveness of the multi-domain adversarial learning-based facial expression synthesis and the fusion screening strategy, particularly the superior performance of the proposed method for PD diagnosis.