Thermal Image Diseases Identification Using Hybrid Genetic Algorithm with Relevance Vector Machine Classification

This research has three parts addressing classification, especially the imbalanced data problem, which is one of the most popular and essential issues in the domain of classification. The first part is to study the Adaptive Boosting (AdaBoost) algorithm. AdaBoost is an effective solution for classification, but it still needs improvement in the imbalanced data problem. This part proposes a method to improve the AdaBoost algorithm using the new weighted vote parameters for the weak classifiers. Our proposed weighted vote parameters are determined not only by the global error rate but also by the classification accuracy rate of the positive class, which is our primary interest. The imbalanced index of the data is also a factor in constructing our algorithms. The numeric studies show that our proposed algorithms outperform the traditional ones, especially regarding the evaluation criterion of the F--1 Measure. Theoretic proofs of the advantages of our proposed algorithms are presented. The second part treats the Relevance Vector Machine (RVM), which is a supervised learning algorithm extended from the Support Vector Machine (SVM) based on the Bayesian sparsity model. Compared with the regression problem, RVM classification is challenging to conduct because there is no closed-form solution for the weight parameter posterior. The original RVM classification algorithm uses Newton's method in optimization to obtain the mode of weight parameter posterior, then approximates it by a Gaussian distribution in Laplace's method. This original model would work, but it just applies the frequency methods in a Bayesian framework. This part first proposes a Generic Bayesian RVM classification, which is a pure Bayesian model. We conjecture that our algorithm achieves convergent estimates of the quantities of interest compared with the nonconvergent estimates of the original RVM classification algorithm. Furthermore, a fully Bayesian approach with the hierarchical hyperprior structure for RVM classification is proposed, which improves the classification performance, especially in the imbalanced data problem. The third part is an extended work of the second one. The original RVM classification model uses the logistic link function to build the likelihood, which makes the model hard to conduct since the posterior of the weight parameter has no closed-form solution. This part proposes the probit link function approach instead of the logistic one for the likelihood function in RVM classification, namely PRVM (RVM with the Probit link function). We show that the posterior of the weight parameter in our model follows the multivariate normal distribution and achieves a closed-form solution. A latent variable is needed in our algorithm to simplify the Bayesian computation greatly, and its conditional posterior follows a truncated normal distribution. Compared with the original RVM classification model, our proposed one is another pure Bayesian approach and it has a more efficient computation process. For the prior structure, we first consider the Normal-Gamma independent prior to propose a Generic Bayesian PRVM algorithm. Furthermore, the Fully Bayesian PRVM algorithm with a hierarchical hyperprior structure is proposed, which improves the classification performance, especially in the imbalanced data problem.

Mental fatigue (MF) is a common phenomenon in our daily lives. In the workplace, mental fatigue can increase the risk of human errors. Moreover, if it is not detected and handled correctly, it can cause various problems. Electroencephalogram (EEG) is one of many modalities that is widely used by researchers for detecting mental fatigue. However, due to the difficulty in provoking fatigue condition during the EEG measurement, this study proposed two experimental designs for detecting mental fatigue, the first design is by using physical induction (PI) and the 2nd design is by using mental induction (MI). We obtained the EEG signals from 20 healthy participants, from 14 channels of wireless EEG headset. Each participant was given four types of cognitive tests in the form of a computer game including Trail, Span, Stroop, and Arithmetic. The subjective measurement of fatigue condition was measured by using the Swedish Occupational Fatigue Inventory (SOFI) questionnaires. The EEG signal was extracted by using power percentage (PP) features of alpha ( $\alpha$ ), beta ( $\beta$ ), and theta ( $\theta$ ) bands of frequency from all channels. As for classification, we introduced the use Relevance Vector Machine (RVM) approach which is claimed to have better performance than SVM as the precedence method. According to the results, all of the cognitive tests has increased average value of response time (RT) and decreased correction score (CS) on post-induction compared to pre-induction. Another result showed that the best mental fatigue detection is obtained from mental induction rather than physical induction. The classification results demonstrate an accuracy of 93.7% (MI) and 92.6% (PI) are achieved by RVM approach. Furthermore, RVM has also less execution time compared to SVM.

Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called <i>relevance vectors</i>. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.

ABSTRACTAddressing the challenges of low feature extraction dimensions and insufficient distinct information for gesture differentiation for smart home appliances, this article proposed an innovative gesture recognition algorithm, integrating fractional Fourier transform (FrFT) with relevance vector machine (RVM). The process involves using FrFT to transform raw gesture data into the fractional domain, thereby expanding the dimensions of information extraction. Subsequently, high‐dimensional feature vectors are created from fractional domain, and RVM classifiers are employed for joint optimization of feature selection and classification decision functions, achieving optimal classification performance. A dataset was constructed using five different types of gestures recorded on the TI millimeter‐wave radar platform to validate the effectiveness of this method. The experimental results demonstrate that the RVM selected the optimal FrFT order of 0.6, with the best feature set comprising fractional spectral entropy, peak factor, and second‐order central moment. Recognition rates for each gesture exceeded 96.2%, with an average rate of 98.5%. This performance surpasses three comparative methods in both recognition accuracy and real‐time processing, indicating high potential for future applications.

Abstract—Existing electroencephalography (EEG) based Alzheimer’s disease (AD) diagnostic systems typically rely on experts to visually inspect and segment the collected signals into artefact-free epochs and on support vector machine (SVM) based classifiers. The manual selection process, however, introduces biases and errors into the diagnostic procedure, renders it “semi-automated, ” and makes the procedure costly and labour-intensive. In this paper, we overcome these limitations by proposing the use of an automated artefact removal (AAR) algorithm to remove artefacts from the EEG signal without the need for human intervention. We investigate the effects of the so-called wavelet-enhanced independent component analysis (wICA) AAR on three classes of EEG features, namely spectral power, coherence, and amplitude modulation, and ultimately, on diagnostic accuracy, specificity and sensitivity. Furthermore, we propose to replace the binary SVM classifier with a soft-decision relevance vec-tor machine (RVM) classifier. Experimental results show the proposed RVM-based system outperforming the SVM trained on features extracted from both manually-selected and wICA-processed epochs. Moreover, the class membership information output by the RVM is shown to provide clinicians with a richer pool of information to assist with AD assessment. Keywords—Alzheimer’s disease, electroencephalography, support vector machine (SVM), relevance vector machine (RVM), wICA. I.

Currently, huge sizes of indeterminate data are effortlessly collected or created at a very high pace in numerous real-life applications. Classifying this indefinite big data, is computationally intensive as large amount of data is related with existential probability of undefined or undetermined values of raw data. In this study, we propose a data mining approach for the classification of big dataset based on death toll by epidemic outbreak of Ebola virus and comparing its relevance with other epidemic diseases and generalizing error and intra class separability using relevance vector machine classifier.

Although Relevance Vector Machine (RVM) is the most popular algorithms in machine learning and computer vision, outliers in the training data make the estimation unreliable. In the paper, a robust RVM model under non-parametric Bayesian framework is proposed. We decompose the noise term in the RVM model into two components, a Gaussian noise term and a spiky noise term. Therefore the observed data is assumed represented as: y= Dw+s+e where Dw is the relevance vector component, of which D is the kernel function matrix and w is the weight matrix, s is the spiky term and e is the Gaussian noise term. A spike-slab sparse prior is imposed on the weight vector, w which gives a more intuitive constraint on the sparsity than the Student’s t-distribution described in the traditional RVM. For the spiky component, s a spike-slab sparse prior is also introduced to recognize outliers in the training data effectively. Several experiments demonstrate the better performance over the RVM regression.

Hughes phenomenon is a serious problem in supervised classification of hyperspectral images in particular for small land-cover patches. A solution for this problem through integrating the capabilities of a nonlinear discriminating analysis with relevance vector machine (RVM) is proposed in this paper. It first transforms the hyperdimensional data to a new space with a better class separability. Then, a multiclass RVM classifier processes the transformed data for precise labeling of the classes. The results show that the proposed approach outperforms both RVM as well as support vector machine (SVM), when they are applied to the original hyperdimensional data space. Indeed, it is an advantage for key information detection in the classification context.

Deep learning (DL) has a tremendous deal of potential for accurate brain age prediction using neuroimaging data, but its performance is frequently limited by the size of the training dataset and the computer’s memory needs. Under this circumstance, a unique brain age prediction model with the following five phases of development. The input image is first pre-processed via median filtering, which keeps the edges of the raw image while removing the noise. The pre-processed images are segmented using the improved Balanced Iterative Reducing and Clustering employing Hierarchies (BIRCH) algorithm. Then, features including statistics (Mean, Median, Standard Deviation), Improved Median Robust Extended LBP (MRELBP), and sharpness score are extracted from these segmented images. The suggested coot interfaced Archimedes with Gaussian Map Estimation (CIAGME) optimization approach, which combines the Archimedes and coot optimization techniques, will select the optimal features from those extracted features. The deep ensemble approach, which includes deep classifiers like CNN, Bi-LSTM, and Deep maxout, will provide a prediction based on the optimal features that have been chosen. Lastly, the recommended CIAGME-based predictions model’s outperformance is evaluated, and the result is effectively evaluated.

Digital signals are often contaminated by noise during signal acquisition and transmission for sewage sensing signal treatment such as aeration volume, oxygen content and water transparency etc. Sometimes, noise is a mixed one of gaussian noise and impulse noise. Unfortunately, existing denoising algorithms are often designed for removing single gaussian noise or impulse noise. In this paper, an efficient algorithm for mixed noise removal in signal is proposed, including space impulse noise removal and wavelet Gaussian noise removal. An impulse noise detection algorithm based on median filter is given to filter impulse signal, also a lifting wavelet was constructed by lifting original wavelet. The threshold based on lifting wavelet transform for signal was applied to denoising gaussian noise. Simulations are conducted on the presented algorithm, and the simulation result shows that this algorithm can remove mixed Gaussian and impulse noise in signal efficiently.

Based on the coordinate time series from Shenzhen CORS (Continuously Operating Reference Stations) during a period from 2010 to 2015, the algorithm of PCA (Principal Component Analysis) is employed in this paper to remove the CME (Common Mode Error) contained in the original series. After that, the optimal noise models of all stations’ components are determined by means of computing the MLE (Maximum Likelihood Estimation) values of selective noise combinations. Meanwhile, impacts of different surface mass loadings to station displacement are computed, including atmospheric pressure, non-tidal ocean, snow and soil moisture, which partly accounts for the existence of CME. The results of these experiments show that the SNR (Signal Noise Ratio) of the original time series can be improved by 2 or 3 times after the employment of PCA, and the filtered series shows a noise characteristic of flick noise and white noise. When the colored noise is to be considered, the velocity uncertainties is 3–9 times as large as that of white noise. Surface mass loadings as a whole, is one of the sources of CME. After the deducting the impact of CME, construction and depiction of horizontal velocity field of Shenzhen area is presented in the paper.

This study present a new islanding detection technique based on relevance vector machine (RVM) containing various types of distributed generations (DGs). The proposed scheme is based on utilising negative sequence component of current (I 2), acquired at the terminal of the target DG. Various islanding and non-islanding events with variable real and reactive power, change in network topology and diverse X/R ratio have been generated by modelling IEEE 34 bus system in real time digital simulator (RTDS®/RSCAD) environment. The samples of I 2 for one cycle duration from the inception of islanding/non-islanding events are given as input to the proposed RVM classifier. The proposed classifier is able to discriminate between islanding and non-islanding events with an accuracy of 98.62% by utilising only 40% of the total dataset in training. Moreover, it is capable to classify islanding condition rapidly and correctly even with perfect power balance situation. Furthermore, it remains immune to change in network configuration/entirely different network and X/R ratio of various types of DGs. Comparative analysis of the proposed scheme in terms of accuracy, testing time and number of relevance vectors (RVs) with the existing schemes shows its superiority in discriminating islanding situation with non-islanding events.

Personal emotions accompany us in our daily life, affecting our learning and work, therefore it is necessary to obtain better understanding of human behavior through emotional assessment. This paper proposes a method for recognizing emotions electroencephalography(EEG) based on relevance vector machine(RVM). Emotional states of two types as positive and negative were selected from a standard database of DEAP, with relevance vector machine and support vector machine(SVM) to apply classification and comparison. Experimental results show that RVM classification accuracy was 93.33% and the test run time was 0.0156s, while SVM classification accuracy was 78.67% and the test run time was 0.0211s. Compared with SVM, RVM's time complexity and test error rate are lower, and its classification performance is better.

The Wigner distribution (WD) produces highly concentrated time-frequency (TF) representation of nonstationary signals. It may be used as an efficient signal analysis tool, including the cases of frequency modulated signals corrupted with the Gaussian noise. In some applications, a significant amount of impulse noise is present. Then, the WD fails to produce satisfactory results. The robust periodogram has been introduced for spectral estimation of this kind of noisy signals. It can produce good concentration for pure harmonic signals. However, it is not so efficient in the cases of signals with rapidly varying frequency. This is the motivation for introducing the robust WD. It is a reliable TF representation tool for wide class of nonstationary signals corrupted with impulse noise. This distribution produces good accuracy of the instantaneous frequency (IF) estimation. Using the Huber (1981) loss function, a generalization of the WD is presented. It includes both the standard and the robust WD as special cases. This distribution can be used for TF analysis of signals corrupted with a mixture of impulse and Gaussian noise. The presented theory is illustrated on examples, including applications on the IF estimation and time-varying filtering of signals corrupted with a mixture of the Gaussian and impulse noise. The case study analysis of the IF estimators' accuracy, based on the standard and the robust WD forms, is performed. In order to improve the IF estimation, a median filter is applied on the obtained IF estimate.

We evaluate the performance of a feature-preserving filtering algorithm over a range of images corrupted by typical additive random noise against three common spatial filter algorithms: median, sigma and averaging. The concept of the new algorithm is based on a corrupted- pixel identification methodology over a variable subimage size. Rather than processing every pixel indiscriminately in a digital image, this corrupted-pixel identification algorithm interrogates the image in variable-sized subimage regions to determine which are the corrupted pixels and which are not. As a result, only the corrupted pixels are being filtered, whereas the uncorrupted pixels are untouched. Extensive evalu- ation of the algorithm over a large number of noisy images shows that the corrupted-pixel identification algorithm exhibits three major charac- teristics. First, its ability in removing additive random noise is better vi- sually (subjective) and has the smallest mean-square errors (objective) in all cases compared with the median filter, averaging filter and sigma filter. Second, the effect of smoothing introduced by the new filter is minimal. In other words, most edge and line sharpness is preserved. Third, the corrupted-pixel identification algorithm is consistently faster than the median and sigma filters in all our test cases. © 1996 Society of Photo-Optical Instrumentation Engineers. Subject terms: noise removal; feature preserving filtering; corrupted-pixel identi- fication; impulse noise; Gaussian white noise; mean-square error.