Independent Component Analysis Technique Research Articles

An integrated approach combining randomized kernel PCA, Gaussian mixture modeling and ICA for fault detection in non-linear processes

Principal component analysis (PCA) and independent component analysis (ICA), as well as their kernel extensions, have been widely applied in the past for industrial fault detection with Gaussian or non-Gaussian process data with linear or non-linear characteristics. Kernel-based techniques lead to computational complexity due to the high dimensionality of the dataset in the feature space. In this work, a randomization approach is used to obtain a low-rank approximation of the high-dimensional kernel matrix. A hybrid machine learning technique is proposed that integrates randomized kernel PCA (RKPCA) with ICA and Gaussian mixture modeling (GMM). The proposed approach, ICA-RKPCA-GMM, addresses the Gaussian and non-Gaussian characteristics of non-linear process data. Another hybrid algorithm combining three basic techniques of ICA, PCA and GMM is also developed (ICA-PCA-GMM). The fault detection performances of the proposed techniques (ICA-RKPCA-GMM and ICA-PCA-GMM) are compared with PCA, ICA, KPCA and combined ICA-PCA techniques by applying the techniques to two benchmark systems. Monitoring performances were evaluated by determining the false alarm rate and fault detection rate for different types of process and sensor faults. The simulation results show that the proposed ICA-RKPCA-GMM approach yields better results than individual ICA, PCA and KPCA techniques, the combined ICA-PCA and the proposed ICA-PCA-GMM technique.

A Comparative Study of Different Dimensionality Reduction Techniques for Arabic Machine Translation

Word embeddings are widely deployed in a tremendous range of fundamental natural language processing applications and are also useful for generating representations of paragraphs, sentences, and documents. In some contexts involving constrained memory, it may be beneficial to reduce the size of word embeddings since they represent a core component of several natural language processing tasks. By reducing the dimensionality of word embeddings, their usefulness in memory-limited devices can be significantly improved, yielding gains in many real-world applications. This article aims to provide a comparative study of different dimensionality reduction techniques to generate efficient lower-dimensional word vectors. Based on empirical experiments carried out on the Arabic machine translation task, we found that the post-processing algorithm combined with independent component analysis provides optimal performance over the considered dimensionality reduction techniques. Therefore, we arrive at a new combination of the post-processing algorithm and dimensionality reduction (independent component analysis) techniques, which has not been investigated before. The latter was applied to both contextual and non-contextual word embeddings to reduce the size of the vectors while achieving a better translation quality than the original ones.

Teleconnections between ocean-atmosphere circulations and historical integrated drought in the Middle East and North Africa.

In the Middle East and North Africa (MENA) with extreme water scarcity worldwide, drought significantly impacts various aspects of the local people. This study uses the empirical multivariate standardized drought index (MSDI), wavelet power spectrum, and a blind source septation technique of independent component analysis (ICA) to unravel linkages between Atlantic- and Pacific-based climate indices and MENA's drought between Jan 1979 and Dec 2016. It is found that drought in the eastern MENA is more than its western linked to climate indices. PDO, ENSO, and AMO, on the whole, have statistically stronger correlations with the MENA's drought than NAO (absolute R2 is less than 0.17, 0.22, and 0.21 for interannual, decadal, and interdecadal modes, respectively). The blind sources extracted by ICA confirm the higher impacts of PDO (R2 = 0.39), ENSO (R2 = 0.30), and AMO (R2 = 0.38) on the Middle East's drought. PDO's modulation linkage to MENA's drought occurs from interannual to decadal, and interdecadal variability cycles. ENSO largely impacts the 2-7-year interannual (R2 = 0.40-0.48 for the Middle East) and 8-12-year decadal (R2 = 0.18-0.30) drought cycles, while the PDO more affects 8-12-year decadal (R2 = 0.29-0.58) modes. The interdecadal (30-50-year) variability of MENA's drought mostly correlates with AMO and PDO. Overall, the interannual droughts are mostly controlled by ENSO while the decadal by PDO. For the interdecadal droughts, PDO and AMO are the most important in the Middle East and the western MENA, respectively. This study highlights that an integrated drought signal is not solely derived from a single climate index since each index may modulate a specific drought variability mode that may differ from other indices. The physical mechanisms behind these observations are further described.

Sparse Bayesian modeling of hierarchical independent component analysis: Reliable estimation of individual differences in brain networks.

Independent component analysis (ICA) is one of the leading approaches for studying brain functional networks. There is increasing interest in neuroscience studies to investigate individual differences in brain networks and their association with demographic characteristics and clinical outcomes. In this work, we develop a sparse Bayesian group hierarchical ICA model that offers significant improvements over existing ICA techniques for identifying covariate effects on the brain network. Specifically, we model the population-level ICA source signals for brain networks using a Dirichlet process mixture. To reliably capture individual differences on brain networks, we propose sparse estimation of the covariate effects in the hierarchical ICA model via a horseshoe prior. Through extensive simulation studies, we show that our approach performs considerably better in detecting covariate effects in comparison with the leading group ICA methods. We then perform an ICA decomposition of a between-subject meditation study. Our method is able to identify significant effects related to meditative practice in brain regions that are consistent with previous research into the default mode network, whereas other group ICA approaches find few to noeffects.

IoT-Based Voice-Controlled Smart Homes with Source Separation Based on Deep Learning

The widespread availability of cutting-edge computer technologies has shed light on the relevance of artificial intelligence (AI) applications in almost all sectors of the economy. As a result of the incorporation of voice control processing into many Internet of Things (IoT) devices, many of these IoT devices may be operated using spoken commands. The environment that is controlled by speech may include several devices, each of which may be used for a separate activity; yet, all of the devices may collect and process the same command at the same time. This may be the case if the devices can communicate with one another. Because other devices may choose to ignore orders that are intended for particular devices if those devices are not equipped to deal with those orders, only the device that is designed to carry out the activity and process the command will be able to carry out the activity. This is because only the device that is designed to carry out the activity and process the command will be able to carry out the activity. On the other hand, when all of the voice-controlled devices capture the command through the microphone, there is a greater chance that it will mix with other sounds coming from a variety of sources. This is because the microphone is being used to capture the command from all of the voice-controlled devices. These noises may include those that are emanating from the television, music systems, and other sounds that are created by activities taking on inside the family, among other things. During the identification of instructions via processing, any blending of other sounds that are not the primary command is regarded as noise and has to be deleted. This is because any such blending is deemed to be noise. The direction of arrival (also known as DOA) of the sound waves is given primary consideration by this approach. This is done at the same time as the performance of the system, and the proposal for it are being evaluated. Based on the angle of arrival estimate, a specific room impulse response (RIR) from a collection of defined RIR is identified as a room acoustic characteristic, and source separation is carried out using the technique of independent component analysis (ICA). Following the completion of the analysis of the signals produced by the split command speech, the characteristics of the speech are retrieved from the signals. The Mel-frequency cepstral coefficients (MFCC) approach is used so that the operation of feature extraction may be carried out. This is the goal of the technique. Following that, a support vector machine classifier is used to the data in order to further split these characteristics into a large range of distinct groups. Comparisons are made between the performance of the SVM classifier and the performance of a large number of different classifiers, including decision trees, which are often used in applications that incorporate machine learning (DT). After analyzing its performance, the multiclass SVM classifier is found to have an accuracy of 91%, according to the conclusions of the study. Utilizing a classifier that is based on a probabilistic neural network, which is sometimes referred to as a PNN, is one way in which the accuracy of future classifications may be enhanced. This particular classifier is made up of three layers: one layer of gated recurrent units (GRU), one layer of long short-term memory (LSTM), and one layer that integrates the two of those different kinds of memory. This classification seems to have obtained an accuracy of 94.5 percent, which is higher than the classification accuracy attained by the multiclass SVM classifier.

UNRAVELING INDEPENDENT COMPONENT ANALYSIS FOR TENSOR-VALUED DATA

In the realm of data analysis, the exploration of independent component analysis (ICA) for tensor-valued data represents a burgeoning area of research. Unlike traditional scalar or vector data, tensor-valued data capture complex relationships and structures across multiple dimensions. Independent component analysis offers a powerful framework for decomposing tensor-valued data into statistically independent components, revealing underlying patterns and dependencies that may remain obscured in raw data representations. This paper delves into the application of ICA techniques specifically tailored for tensor-valued data, exploring theoretical foundations, algorithmic implementations, and practical considerations. Through a comprehensive review and analysis, we elucidate the potential of ICA in uncovering hidden structures and sources of variability within tensor-valued datasets across diverse domains.

A stage-wise non-Gaussian statistical process control strategy

The article delves into the development of a Non-Gaussian Process Monitoring Strategy for a Copper Cathode Manufacturing Unit (CCMU). The monitoring strategy being devised highlighted the issue of multi-stage process monitoring via the usage of Multi-block Independent Component Analysis (MBICA) techniques. MBICA is the multi-block variant of ICA technique which is prevalently used for process laden with non-Gaussian or non-normal data. Development of the monitoring strategy involved detection of fault(s) and their subsequent diagnosis. Detection of fault(s) was carried out by employment of I2 control chart whose control limit was established via Bootstrap procedure. The diagnosis of the detected fault was carried out by employment of fault diagnostic statistic. An amalgamation of MBICA and Multivariate Exponentially Weighted Moving Average (MEWMA) are also known as MBICA-MEWMA approach was also proposed for detection of incipient fault(s). The monitoring strategy thus developed was showcased for a CCMU which specialised in the manufacture of copper cathode which has got varied practical applications. The monitoring strategy thus devised was able to detect and diagnose the faults with appreciable accuracy.

Sulfide disseminations and hydrothermal alteration haloes in the Gabal Monqul area, Egypt: Field, mineralogical, and remote sensing studies

Field and mineralogical studies are integrated with multispectral and radar satellite data to unravel the occurrence of sulfide dissemination in the Gabal Monqul area (Eastern Desert, Egypt). This study was motivated by the strong belief that the widespread hydrothermal alteration haloes and sulfide disseminations in this area were ambiguously deciphered as a porphyry-style mineralisation. NE-Elongate bodies of silicified and sulfidized rocks frequently occur at the contacts between andesite-dacite, granodiorite, and porphyritic biotite granite intrusions.Hydrothermal alteration zones are best identified and outlined by employing band ratios (BR) and independent component analysis (ICA) techniques for the distinction between ferric and ferrous iron minerals. If fused with the clay and OH-bearing minerals maps, ferric iron oxides may characterise epithermal and porphyry mineralisation in which ore formation occurs under oxidising conditions. Iron-hydroxide, copper hydro-carbonate (malachite), and hydrous copper phosphate (pseudomalachite) are supergene mineral phases.The resultant processed images show that alteration extends beyond unit boundaries and overlaps different crosscutting lithologies. This observation implies that hydrothermal alteration of the host rock represents a secondary overprint, yet gypsum (±anhydrite) and barite in association with iron oxides and pyrite disseminations pinpoint a porphyry-related alteration assemblage. The poor metal endowment in the potassic alteration zones goes against the supergene enrichment typical of the porphyry mineralisation system and is coupled with the lack of hydrothermal breccia bodies, implying a rather epithermal sulfidation genesis. However, a thorough assay program is required to outline the potential metal-rich lithological unit(s) using XRD and remote sensing investigations that point to zonal alteration anomalies.

Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings

In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built.

Portable Electromagnetic Device for Steatotic Liver Detection Using Blind Source Separation and Shannon Wavelet Entropy

Hepatic steatosis is the most common cause of chronic liver disease worldwide. Symmetric placement of sensors is the main challenge of previous electromagnetic hepatic steatosis techniques as they require signals from the left side of the torso as a healthy reference. To tackle this issue, this study proposes an electromagnetic technique to assist in observation and detection of hepatic steatosis only by measuring electromagnetic signals from the right side of the torso where liver is located. The combination of blind source separation and wavelet transform techniques is used for detection. Firstly, the principal component analysis is applied to the time-domain converted electromagnetic signals to sphere the measured data. Secondly, the sphered signals are modelled as a linear mixture of different components and separated utilizing independent component analysis technique. Thirdly, the liver response signal is isolated based on its lowest cross-correlation with the average of input signals. Finally, Shannon wavelet entropy and wavelet energy of the estimated liver signal is evaluated by considering the excitation electromagnetic Gaussian pulse as the mother wavelet. Healthy livers can be differentiated from the steatotic ones based on their higher level of wavelet entropy. The method is validated by realistic numerical models and human measurements. In both scenarios, the wavelet entropy of estimated liver response is significantly lower and distinguishable in steatotic livers. The proposed method can detect HS cases with more than 16% diffused fat. Compared to previous techniques, it requires significantly less scanning time, is independent of antennas symmetrical placement, and is thus more reliable.

An Approach for Determining Voltage Imbalance Contributions Based on Complex Independent Component Analysis

In the context of power quality problems, the voltage imbalance index is relevant, given its harmful impacts on the networks and loads. Thus, reliable and viable methodologies for practical use are necessary to determine agents’ contributions. This article presents a noninvasive method for sharing responsibility for imbalances based on the principle of superposing the individual voltage imbalance produced by the parties. A procedure based on the Complex Independent Component Analysis (CICA) technique is proposed to meet the parameters required by the superposition method. Based on the measurements of voltages and currents carried out on the point of common coupling (PCC), the negative sequence impedances are determined using the CICA method under the terms needed by the superposition principle. The methodology’s effectiveness is evaluated through performance comparisons carried out over the process, in light of the response from the methods for sharing responsibilities currently published in the literature of this domain. The results obtained through the proposed approach show good adherence to the procedures presented with solid conceptual bases. However, unlike these, this article’s methodology offers practical perspectives for application in the field.

Predicting the prevalence of lung cancer using feature transformation techniques

Healthcare sector is one of the most important sectors of any country as a big part of the country’s economy is associated with it. The research is about to contribute to the health sector by minimizing the expenses of a lung cancer diagnosis. The study tries to devise an efficient method for initial screening of the patients with symptoms through their demographic and clinical data. The study seeks appropriate feature transformation techniques from dimensionality reduction techniques in combination with an apposite regression model that can perform this task robustly using the lung cancer dataset for early carcinoma diagnosis. To equip the health sector with state-of-the-art technology and for the betterment of humankind, the most beneficial tool of today is machine learning. Lungs play a vivacious role in the human body, oxygen is circulated in the body, and the air is taken from the atmosphere and sends it into the bloodstream. Several people decease every year because of lung carcinoma as lung cancer is normally identified at the latter phase due to lack of awareness. It stays unidentified because people have pneumonia often, which converts into lung cancer later. The projected research seeks to enable health professionals to the rationalization of primary diagnosis and treatment of lung carcinoma in developing countries. The proposed methodology has selected the optimized combination of regression-based machine learning technique and feature transformation technique for the available patterns based on demographic and clinical features of lung cancer patients. Based on the gathered results during training (RMSE = 0.1324, R2-Score = 0.7428) and testing (RMSE = 0.1273, R2-Score = 0.7405) it can be concluded that the fast independent component analysis and elastic net regression technique provided optimized results and outperform the other aspirant techniques.

Identification of Structural VAR Models via Independent Component Analysis: A Performance Evaluation Study

Independent Component Analysis (ICA) is a statistical method that linearly transforms a random vector. Under the assumption that the observed data are mixtures of non-Gaussian and independent processes, ICA is able to recover the underlying components, but with a scale and order indeterminacy. Its application to structural vector autoregressive (SVAR) models allows the researcher to recover the impact of independent structural shocks on the observed series from estimated residuals. We analyze different ICA estimators, recently proposed within the field of SVAR analysis, and compare their performance in recovering structural coefficients. Moreover, we assess the size distortions of the estimators in hypothesis testing. We conduct our analysis by focusing on non-Gaussian distributional scenarios that get gradually close to the Gaussian case. The latter is the case where ICA methods fail to recover the independent components. Although the ICA estimators that we analyze show similar pattern of performance, two of them — the fastICA algorithm and the pseudo-maximum likelihood estimator — tend to perform relatively better in terms of variability, stability across sub- and super-Gaussian settings, and size distortion. We finally present an empirical illustration using US data to identify the effects of government spending and tax cuts on economic activity, thus providing an example where ICA techniques can be used for hypothesis testing.

A Stock Price Forecasting Model Integrating Complementary Ensemble Empirical Mode Decomposition and Independent Component Analysis

In recent years, due to the non-stationary behavior of data samples, modeling and forecasting the stock price has been challenging for the business community and researchers. In order to address these mentioned issues, enhanced machine learning algorithms can be employed to establish stock forecasting algorithms. Accordingly, introducing the idea of “decomposition and ensemble” and the theory of “granular computing”, a hybrid model in this paper is established by incorporating the complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SE), independent component analysis (ICA), particle swarm optimization (PSO), and long short-term memory (LSTM). First, aiming at reducing the complexity of the original data of stock price, the CEEMD approach decomposes the data into different intrinsic mode functions (IMFs). To alleviate the cumulative error of IMFs, SE is performed to restructure the IMFs. Second, the ICA technique separates IMFs, describing the internal foundation structure. Finally, the LSTM model is adopted for forecasting the stock price results, in which the LSTM hyperparameters are optimized by synchronously utilizing the PSO algorithm. The experimental results on four stock prices from China stock market reveal the accuracy and robustness of the established model from the aspect of statistical efficiency measures. In theory, a useful attempt is made by integrating the idea of “granular computing” with “decomposition and ensemble” to construct the forecasting model of non-stationary data. In practice, the research results will provide scientific reference for the business community and researchers.

Discrete wavelet transform based on EEG signal analysis for diagnosing neurological disorder

ElectroEncephaloGram (EEG) signal analysis is critical since it is a reliable approach for detecting neurological brain diseases. In this work, the artifacts in the EEG dataset are removed using the Independent Component Analysis (ICA) technique. The EEG dataset was then filtered with a band-pass filter to eliminate noise. In this work using a Discrete Wavelet Transform (DWT) to deconstruct the filtered data, the EEG signal features are recovered. The features are also supplied into four separate classifiers. Five statistical techniques are utilised to extract characteristics from EEG sub bands: Local Binary Pattern (LBP), Standard Deviation (SD), Variance, Kurtosis, and Shannon Entropy (SE). To classify the features related to their classes Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Artificial Neural Network (ANN) are four classifiers that use the features.The overall classification accuracy approaches 90.5% using SVM, 99% using ANN, 87.5% using LDA and 97% using KNN respectively. In this work ANN gives better performance accuracy than other classifers.

Performance Evaluation of SWT and ICA Techniques for Automatic Separation between Oscillations and Epileptic Spikes by SWT and ICA Techniques

In this work, we studied two approaches for separating the signal mixtures observed by FO electrodes, which are filtered techniques, independent component analysis (ICA) and transformed into wavelets. Precisely, we will study two methods to solve the problem of overlap between the spikes and the oscillation. To do this, we must first study the technique of analysis in independent components and implement it for the separation between these two biomarkers of epilepsy. Next, we will evaluate the performance of the ICA method and the Stationary Wavelet Transform (SWT) filtering method.

Dealing with redundancies among criteria in multicriteria decision making through independent component analysis

Redundancies among criteria are frequently observed in multicriteria decision making problems. This aspect may introduce bias in the achieved ranking, since alternatives that have a good performance only in redundant criteria will be favored in comparison with other ones. Aiming at dealing with this inconvenience, several sophisticated approaches were developed in the literature. Most of them require subjective information provided by the decision maker in order to define the set of parameters. In this paper, we consider one of the simplest and most used method, called weighted arithmetic mean, and propose some approaches to automatically adjust the weights in order to deal with redundant criteria. For this purpose, we formulate the problem as a blind source separation one and apply independent component analysis techniques to extract the information used to perform the adjustments. Numerical experiments in both synthetic and real data attest our proposal. Moreover, we extend the proposed approaches to deal with group multicriteria decision making problems.

Detection of Multiple Drones in a Time-Varying Scenario Using Acoustic Signals

Detection of unauthorized drones is mandatory for defense organizations and also for human life protection. Currently, detection methods based on thermal, video, radio frequency (RF) and acoustic signals exist. In previous research, we presented an acoustic signals-based multiple drones detection technique utilizing independent component analysis (ICA) in the presence of interfering sources. In this paper, a method is proposed in which the mixed signals are first separated taking the ICA technique into account. After extracting the features, the support vector machines (SVM) and the k-nearest neighbors (KNN) are used to identify multiple drones in the field. This technique can detect multiple drones in static and quasi-static mixing scenarios, while failing in time-varying scenarios. In this paper, a time-varying drone detection technique (TVDDT) is proposed that first stores a data set of the mixed signals in a time-varying scenario, where time variations occur within the processing data blocks. After estimating the mixing matrices, we developed a technique to track variations in the channel. This technique is based on variations in the mixing coefficients. The proposed channel tracking technique performs classification and detection based on minimum variation criteria in the channel. The proposed TVDDT technique is evaluated through simulations and its superior performance is observed.

Dimensionality reduction of the attributes using fuzzy optimized independent component analysis for a Big Data Intrusion Detection System

Big data cybersecurity has garnered more attraction in recent years with the development of advanced machine learning and deep learning classifiers. These new classifier algorithms have significantly improved Intrusion Detection Systems (IDS). In these classifiers, the performance is positively influenced by high relevant features while less relevant features negatively influence the performance. However, considering all the attributes, especially the high dimensional attributes, increases computational complications. Hence it is essential to diminish the dimensionality of the attributes to improve the classifier performance. To achieve this objective, an efficient dimensionality reduction approach is presented through the development of the Fuzzy Optimized Independent Component Analysis (FOICA) technique. The standard Independent Component Analysis (ICA) is coupled with the fuzzy entropy to transform the high dimension attributes into low dimension attributes and helps in selecting high informative low-dimensional attributes. These selected features are fed to efficient hybrid classifiers namely Hyper-heuristic Support Vector Machines (HH-SVM), Hyper-Heuristic Improved Particle Swarm Optimization based Support Vector Machines (HHIPSO-SVM) and Hyper-Heuristic Firefly Algorithm based Convolutional Neural Networks (HHFA-CNN) to classify the cybersecurity data to identify the intrusions. Experiments are conducted over two cybersecurity datasets and real-time laboratory data whose outcomes specify the supremacy of the suggested IDS model based on FOICA dimensionality reduction.

Recognition of Ocular Artifacts in EEG Signal through a Hybrid Optimized Scheme.

Brain computer interface (BCI) requires an online and real-time processing of EEG signals. Hence, the accuracy of the recording system is improved by nullifying the developed artifacts. The goal of this proposal is to develop a hybrid model for recognizing and minimizing ocular artifacts through an improved deep learning scheme. The discrete wavelet transform (DWT) and Pisarenko harmonic decomposition are used for decomposing the signals. Then, the features are extracted by principal component analysis (PCA) and independent component analysis (ICA) techniques. After collecting the features, an optimized deformable convolutional network (ODCN) is used for the recognition of ocular artifacts from EEG input signals. When artifacts are sensed, the moderation method is executed by applying the empirical mean curve decomposition (EMCD) followed by ODCN for noise optimization in EEG signals. Conclusively, the spotless signal is reconstructed by an application of inverse EMCD. The proposed method has achieved a higher performance than that of conventional methods, which demonstrates a better ocular artifact reduction by the proposed method.