Linear Discriminant Analysis Technique Research Articles

Nanozyme-Inhibited SERS Multichannel Paper-Based Sensor Array for the Quantification and Identification of Biothiols and Cancer Cells Based on Three Ag-Based Nanomaterials.

Biothiols play essential roles in maintaining normal physiological functions, resisting oxidative stress, and protecting cell health. Establishing an effective and reliable sensor array for the accurate quantification and discrimination of diverse biothiols is extremely meaningful. In this work, Ag/Mn3O4, Ag3PO4, and Ag3Cit with excellent oxidase-mimetic activity and surface-enhanced Raman scattering (SERS)-enhanced features have been prepared and loaded onto Whatman filter paper (WFP) to build SERS paper chips as three sensing channels, which can induce 3,3',5,5'-tetramethylbenzidine (TMB) oxidation to SERS-active reporters (TMBox) and concurrently generate prominent SERS signals. Nevertheless, the addition of biothiols can suppress conversion from TMB to TMBox, which can cause the reduction of the SERS signal from TMBox. Interestingly, each SERS sensing channel can generate different TMBox signals' variations due to differences in the oxidative inhibition abilities of diverse biothiols and exclusive properties of each paper chip, which can be plotted as specific fingerprint patterns of each biothiol and further translated into intuitive two-dimensional (2D) clustering profiles through linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA) techniques for precise identification of these six biothiols with the minimum concentration of 1 μM. More importantly, this SERS sensor array is exploited for the precise quantification of intracellular glutathione (GSH), and can differentiate between normal and cancer cells based on different intracellular GSH contents and even identify different types of tumor cells, demonstrating its powerful application prospects in disease diagnosis.

Prediction and Evaluation of Accidents within Oilfields of Arabian Gulf Company Using Discriminant Analysis

The dynamic nature of oil and gas production is one of the major causes for various types of accidents resulting in injuries and fatalities in oil fields. The main purpose of this paper is to identify and classify accidents in Arabian Gulf Oil Company (AGOCO) through application of the Linear Discriminant Analysis (LDA) technique. Data collected were for 8 years spanning from 2005 to 2012, from four oil fields (Sarir, Nafoora, Messla, and Byda).The LDA is used to classify an accident into one of the accident groups; “Oil and gas Leak”, “Fire”, “Accident”, and “Damage”.The developed discriminant functions revealed significant association between groups 60.1%, 22.75%, and 6.97% of between groups variability. However, the structure matrix revealed two significant predictors only of the first function, namely production area and camp with scores of 0.791 and 0.766 respectively. For the second function revealed also two significant predictions; the oil well and the transition station with scores of 0.806 and 0.740 respectively.The third function has no significant predictors. The cross-validated method showed that 65.1% of the grouped cases are correctly classified.

Comparison of Matrix Decomposition in Null Space-Based LDA Method

Problems with small sample sizes and high dimensionality are common in pattern recognition. Almost all machine learning algorithms degrade in high-dimensional data, so that singularities in the scatter matrices, the main problem of the Linear Discriminant Analysis (LDA) technique, might result. A null space-based LDA (NLDA) has been conceived to address the singularity issue. NLDA aims to maximize the distance between classes in the null space of the within-class scatter matrix. In the first research, the NLDA method was performed by computing the eigenvalue decomposition and singular value decomposition (SVD). This research led to several new implementations of the NLDA method that use other matrix decompositions. The new implementations include NLDA using Cholesky decomposition and NLDA using QR decomposition. This paper compares the performance of three NLDA methods that use different matrix decompositions, namely, SVD, Cholesky decomposition, and QR decomposition. Two sets of data were used in the experiments that used three different NLDA algorithms. To determine the performance of the NLDA methods, the classification accuracy of the three methods was measured using the confusion matrix. The results show that the NLDA method using SVD has the best performance when compared to the other two methods, achieving a precision of 77.8% accuracy for the Colon dataset and a precision of 98.8% accuracy for the TKI-resistance dataset.

Machine Learning-based Distributed Big data Analytics Framework for IoT Applications

The emerging trend of the Internet of Things (IoT) leads to more real-time applications and it raises the accumulation of more structured and unstructured data. The processing of unstructured and structured data for different distributed applications becomes arduous. Moreover, the accuracy, load balancing, and latency of the services are also challenging. Some of the state-of-art works failed to achieve those parameters. In context with these, we proposed a machine learning-based novel approach that utilizes an SVM classifier. The SVM classifier can be used for the classification and data analytic purposes of features extracted from the data processing step. Data processing employs two steps such as data extraction and data scaling. The data extraction is performed by the adoption of the Principal Component Discriminant power-based Linear Discriminant analysis (PCDP-LDA) technique. The big data framework of the proposed model is evaluated using a tool called Weka. Meanwhile, the data scaling is performed by the Naïve Bayes approach and divides the extracted features into blocks. Experimental analysis is performed and compared with existing approaches. Our proposed approach provides more effective classification and data analytic accuracy than the other approaches. Our approach also provides better latency, and load balancing of data in the distributed big data analysis.

Machine Learning-based Distributed Big data Analytics Framework for IoT Applications

The emerging trend of the Internet of Things (IoT) leads to more real-time applications and it raises the accumulation of more structured and unstructured data. The processing of unstructured and structured data for different distributed applications becomes arduous. Moreover, the accuracy, load balancing, and latency of the services are also challenging. Some of the state-of-art works failed to achieve those parameters. In context with these, we proposed a machine learning-based novel approach that utilizes an SVM classifier. The SVM classifier can be used for the classification and data analytic purposes of features extracted from the data processing step. Data processing employs two steps such as data extraction and data scaling. The data extraction is performed by the adoption of the Principal Component Discriminant power-based Linear Discriminant analysis (PCDP-LDA) technique. The big data framework of the proposed model is evaluated using a tool called Weka. Meanwhile, the data scaling is performed by the Naïve Bayes approach and divides the extracted features into blocks. Experimental analysis is performed and compared with existing approaches. Our proposed approach provides more effective classification and data analytic accuracy than the other approaches. Our approach also provides better latency, and load balancing of data in the distributed big data analysis.

Enhancing Breast Cancer Classification: A Deep Learning Approach with Outlier Detection With Egret Swarm Optimization Algorithm and Feature Selection Integration – A Systematic Review

Cancer, the second leading cause of death worldwide, encompasses a spectrum of diseases characterized by uncontrolled cell proliferation. Effective therapy relies on screening, early diagnosis, and recurrence prediction. Timely identification of lesions is critical for improving patient survival rates, and accurate classification of benign cancers can prevent unnecessary treatment. Breast cancer (BC) classification into benign or malignant categories is a subject of extensive review. This review discusses a novel approach using deep neural networks (DNN) and genetic algorithms (GA) based on Egret Swarm Optimization (ESO) to address this challenge. Initially, an Enhanced Linear Discriminant Analysis (ILDA) technique is applied for data dimensionality reduction. Subsequently, DNN controls outlier detection to identify anomalies in the cancer dataset. GA is then employed for feature selection, efficiently locating optimal features in complex search spaces, crucial for cancer diagnosis models, especially non-invasive ones. Finally, ESO algorithm, integrating the aggressive and sit-and-wait tactics of Great Egret and Snowy Egret, respectively, is utilized to classify malignancy. In ths review, The proposed method is evaluated using BC datasets, with trials conducted before and after outlier removal. Experimental results demonstrate the effectiveness and accuracy of the proposed strategy. The findings of this study hold promise for enhancing BC diagnosis and guiding patient treatment decisions.

Application of Linear Discriminant Analysis and k-Nearest Neighbors Techniques to Recommendation Systems

Application of Linear Discriminant Analysis and k-Nearest Neighbors Techniques to Recommendation Systems

Efficient machine learning models for the detection of coconut milk adulteration

Coconut milk adulteration detection involves the use of analytical methods, including machine learning, to detect the presence of impurities or additives in coconut milk. Adulteration can occur when substances such as water or other cheaper ingredients are added to coconut milk, compromising its quality, nutritional value, and authenticity. Identifying adulteration is crucial for ensuring consumer safety, maintaining product quality, and upholding industry standards. The aim of this study was to propose a machine learning model to detect adulteration in coconut milk. To implement the proposed work, a coconut milk adulteration dataset is collected from a standard source. The amount of available data is limited; hence, synthetic data is generated by applying the CTGAN algorithm. The proposed framework employs three different Feature Extraction (FE) strategies, i.e., Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Autoencoder (AE). Then, the feature-extracted dataset is classified through four effective machine learning algorithms, such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The machine learning algorithms outcomes are evaluated using four performance metrics, i.e., Accuracy, Precision, Recall, and F1-score. RF provides the highest accuracy of 99.17%, precision value of 97.29%, recall value of 96.71%, and F1 Score of 97% for the LDA techniques.

Low-velocity impact damage detection in CFRP composites by applying long pulsed thermography based on post-processing techniques

ABSTRACT Carbon fibre reinforced polymer (CFRP) composites are widely used in the aerospace industry; they are susceptible to impact damage from low-velocity impacts throughout their service life. Long pulse thermography (LPT) was proposed to detect defect in low-velocity impact test (LVIT). In this framework, the defects of matrix fractures, fibre cracks and interface delamination were produced by LVIT in the CFRP specimens. The halogen lamps LPT test system was applied to detect the low-velocity impact damage, and an A655sc infrared camera was applied to collect the surface temperature. In order to improve the efficiency of the impact damage detection, the infrared image sequences were processed by applying different post-processing techniques such as pulse phase thermography (PPT), thermal signal reconstruction (TSR), principal component analysis (PCA), total harmonic distortion (THD), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). The results show that the signal-to-noise ratio (SNR) was improved by PPT, TSR, PCA, THD, LDA and QDA post-processing techniques. The proposed supervised learning (SL) QDA algorithm achieves better defect identification results than the above post-processing techniques. The detectability of real defects can provide a realistic assessment by the above post-processing techniques.

Enhancing Attendance Management through Real-Time Face Recognition using combination of LBP Histogram and Linear Discriminant Analysis

Face recognition technology's excellent accuracy and non-intrusiveness have made it a major player in the attendance management space. This research study presents an advanced method of tracking attendance through the integration of a Face Recognition System (FRS) with Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP) histogram analysis. The architecture of the suggested attendance system is described in the study, with an emphasis on how LDA and LBP techniques are used to increase the accuracy of face feature extraction and recognition. Through the use of LDA, the system improves face data's discriminative strength, producing attendance records that are more trustworthy. Robust facial texture and pattern identification are simultaneously made possible by LBP histogram analysis, particularly in a variety of environmental settings. To sum up, this study advances biometric attendance systems by demonstrating how LDA and LBP histogram analysis may improve the precision and dependability of face recognition systems used for attendance tracking

EEG-Based Functional Connectivity Analysis for Cognitive Impairment Classification

Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mild cognitive impairment (MCI) and 16 healthy control (HC) participants. We conducted an examination using the visual oddball paradigm, focusing on electroencephalography signals with targeted stimuli. Our analysis employed functional connectivity utilizing the change point detection method. Additionally, we implemented training for linear discriminant analysis, K-nearest neighbor, and decision tree techniques to classify brain activity, distinguishing between subjects with mild cognitive impairment and those in the healthy control group. Our results demonstrate the efficacy of combining functional connectivity measurements derived from electroencephalography with machine learning for cognitive impairment classification. This research opens avenues for further exploration, including the potential for real-time detection of cognitive decline in complex real-world scenarios.

Impulsive Aggression Break, Based on Early Recognition Using Spatiotemporal Features

The study of human behaviors aims to gain a deeper perception of stimuli that control decision making. To describe, explain, predict, and control behavior, human behavior can be classified as either non-aggressive or anomalous behavior. Anomalous behavior is any unusual activity; impulsive aggressive, or violent behaviors are the most harmful. The detection of such behaviors at the initial spark is critical for guiding public safety decisions and a key to its security. This paper proposes an automatic aggressive-event recognition method based on effective feature representation and analysis. The proposed approach depends on a spatiotemporal discriminative feature that combines histograms of oriented gradients and dense optical flow features. In addition, the principal component analysis (PCA) and linear discriminant analysis (LDA) techniques are used for complexity reduction. The performance of the proposed approach is analyzed on three datasets: Hockey-Fight (HF), Stony Brook University (SBU)-Kinect, and Movie-Fight (MF), with accuracy rates of 96.5%, 97.8%, and 99.6%, respectively. Also, this paper assesses and contrasts the feature engineering and learned features for impulsive aggressive event recognition. Experiments show promising results of the proposed method compared to the state of the art. The implementation of the proposed work is available here.

Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis

Autism Spectrum Disorder (ASD) is a neurodevelopment syndrome decreasing sufferers' social interaction, communication skills, and emotional expression. Autism syndrome can be detected using an electroencephalogram (EEG). This study utilized the EEG of autistic people to support the classification study of machine learning schemes to produce the best accuracy. One of the best approaches to classify the EEG signal is The Linear Discriminant Analysis (LDA), a machine learning technique to classify autism and normal EEG signals. LDA was chosen because it can maximize the distance between classes and minimize the number of scatters by utilizing between and within-class functions. This method was combined with other methods: Independent Components Analysis (ICA) and Discrete Wavelet Transform (DWT), to improve the accuracy system. ICA removes artifacts or signals other than brain signals that can cause noise in the EEG signal, so the analyzed signal was a complete EEG signal without other factors. DWT can help increase noise suppression in the EEG signal and provide signal information through frequency and time representation. The EEG dataset was collated from 16 children (eight autistic and eight normal). The signals from the dataset were filtered by artifacts using ICA, decomposed by three levels through DWT, and classified using the Linear Discriminant Analysis (LDA) technique. Using the Confusion Matrix, the results reveal the best accuracy of 99%.

Species-Selective Detection of Volatile Organic Compounds by Ionic Liquid-Based Electrolyte Using Electrochemical Methods.

The detection of volatile organic compounds (VOCs) is an important topic for environmental safety and public health. However, the current commercial VOC detectors suffer from cross-sensitivity and low reproducibility. In this work, we present species-selective detection for VOCs using an electrochemical cell based on ionic liquid (IL) electrolytes with features of high selectivity and reliability. The voltammograms measured with the IL-based electrolyte absorbing different VOCs exhibited species-selective features that were extracted and classified by linear discriminant analysis (LDA). The detection system could identify as many as four types of VOCs, including methanol, ethanol, acetone, formaldehyde, and additional water. A mixture of methanol and formaldehyde was detected as well. The sample required for the VOCs classification system was 50 μL, or 1.164 mmol, on average. The response time for each VOC measurement is as fast as 24 s. The volume of VOCs such as formaldehyde in solution could also be quantified by LDA and electrochemical impedance spectroscopy techniques, respectively. The system showed a tunable detection range for 1.6 and 16% (w/v) CH2O solution by adjusting the composition of the electrolyte. The limit of detection was as low as 1 μL. For the 1.6% CH2O solution, the linearity calibration range was determined to be from 5.30 to 53.00 μmol with a limit of detection at 0.53 μmol. The mechanisms for VOCs determination and quantification are also thoroughly discussed. It is expected that this work could provide a new insight into the concept of electrochemical detection of VOCs with machine learning analysis and be applied to both VOCs gas monitoring and fluid detection.

An effective healthcare monitoring system in an IoMT environment for heart disease detection using the HANN model

The proposed work aims to develop an automated machine learning based network model for heart disease prediction with better accuracy. In the pre-processed data, the most significant features are selected using the White Shark Optimization based Linear Discriminant Analysis (WSO-LDA) technique, reducing computational complexity. Finally, the selected features are fed to the Hybrid Artificial Neural Network (HANN) with a Multi-Objective Spotted Hyena optimization (MOSHO) based classification stage. This stage classifies heart disease with minimized processing time.

G4-QuadScreen: A Computational Tool for Identifying Multi-Target-Directed Anticancer Leads against G-Quadruplex DNA.

The study presents 'G4-QuadScreen', a user-friendly computational tool for identifying MTDLs against G4s. Also, it offers a few hit MTDLs based on in silico and in vitro approaches. Multi-tasking QSAR models were developed using linear discriminant analysis and random forest machine learning techniques for predicting the responses of interest (G4 interaction, G4 stabilization, G4 selectivity, and cytotoxicity) considering the variations in the experimental conditions (e.g., G4 sequences, endpoints, cell lines, buffers, and assays). A virtual screening with G4-QuadScreen and molecular docking using YASARA (AutoDock-Vina) was performed. G4 activities were confirmed via FRET melting, FID, and cell viability assays. Validation metrics demonstrated the high discriminatory power and robustness of the models (the accuracy of all models is ~>90% for the training sets and ~>80% for the external sets). The experimental evaluations showed that ten screened MTDLs have the capacity to selectively stabilize multiple G4s. Three screened MTDLs induced a strong inhibitory effect on various human cancer cell lines. This pioneering computational study serves a tool to accelerate the search for new leads against G4s, reducing false positive outcomes in the early stages of drug discovery. The G4-QuadScreen tool is accessible on the ChemoPredictionSuite website.

Statistical Machine Learning Strategy and Data Fusion for Detecting Incipient ITSC Faults in IM

The new technological developments have allowed the evolution of the industrial process to this new concept called Industry 4.0, which integrates power machines, robotics, smart sensors, communication systems, and the Internet of Things to have more reliable automation systems. However, electrical rotating machines like the Induction Motor (IM) are still widely used in several industrial applications because of their robust elements, high efficiency, and versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions; hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs, and its appearance is due to electrical stresses leading to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology capable of performing the assessment and automatic detection of incipient electric faults like ITSC in IMs; the proposed method is supported through the processing of different physical magnitudes such as vibration, stator currents and magnetic stray-flux and their fusion of information. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as their fusion following a feature-level fusion approach and their reduction through the Linear discriminant Analysis technique. Furthermore, the fusion and reduction of information from different physical magnitudes lead to performing automatic fault detection and identification by a simple Neural-Network (NN) structure since all considered conditions can be represented in a 2D plane. The proposed method is evaluated under a complete set of experimental data, and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) can lead to achieving a global classification ratio of up to 99.4% during the detection of ITSC in IMs and an improvement higher than 30% in comparison with classical approaches that consider the analysis of a unique physical magnitude. Additionally, the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

Encryption‐Decryption Strategy of High Security Constructed via Encoded Fluorescence Sensing and Linear Discriminant Analysis Technique

AbstractInnovative fluorescence materials combining the special reagent stimuli response have considerable potential to provide a convenient and highly secure communication strategy for encoding messages. With the unique decryption method, encrypted information that is previously unidentifiable can now be accurately parsed without the risk of interception or decipherment. Herein, white‐emitting light semiconducting polymer dots for advanced secret communication are developed, along with a machine learning‐based encryption‐decryption strategy. The encrypted data are readily hidden by the fluorescent polymer‐stimulus system, which can be identified by fluorescence imaging equipment and only be decrypted via the special linear discriminant analysis route pre‐transferred to the recipient. The results presented here suggest that chemical‐stimulated fluorescence information storage and the unique decoding strategy can be developed as prospective methods for advanced data communication and security protection.

A spatial-temporal linear feature learning algorithm for P300-based brain-computer interfaces

Speller brain-computer interface (BCI) systems can help neuromuscular disorders patients write their thoughts by using the electroencephalogram (EEG) signals by just focusing on the speller tasks. For practical speller-based BCI systems, the P300 event-related brain potential is measured by using the EEG signal. In this paper, we design a robust machine-learning algorithm for P300 target detection. The novel spatial-temporal linear feature learning (STLFL) algorithm is proposed to extract high-level P300 features. The STLFL method is a modified linear discriminant analysis technique focusing on the spatial-temporal aspects of information extraction. A new P300 detection structure is then proposed based on the combination of the novel STLFL feature extraction and discriminative restricted Boltzmann machine (DRBM) for the classification approach (STLFL + DRBM). The effectiveness of the proposed technique is evaluated using two state-of-the-art P300 BCI datasets. Across the two available databases, we show that in terms of average target recognition accuracy and standard deviation values, the proposed STLFL + DRBM method outperforms traditional methods by 33.5, 78.5, 93.5, and 98.5% for 1, 5, 10, and 15 repetitions, respectively, in BCI competition III datasets II and by 71.3, 100, 100, and 100% for 1, 5, 10, and 15 repetitions, respectively, in BCI competition II datasets II and by 67.5 ± 4, 84.2 ± 2.5, 93.5 ± 1, 96.3 ± 1, and 98.4 ± 0.5% for rapid serial visual presentation (RSVP) based dataset in repetitions 1–5. The method has some advantages over the existing variants including its efficiency, robustness with a small number of training samples, and a high ability to create discriminative features between classes.

An ion-coordination hydrogel based sensor array for point-of-care identification and removal of multiple tetracyclines

Misuse and overuse of tetracycline antibiotics (TCs) brings serious issues to ecological environment, food safety and human health. It is urgent to develop unique platform for high efficient identification and removal of TCs. In the present investigation, an effective and simple fluorescence sensor array was constructed based on the interaction between metal ions (Eu3+ and Al3+) and antibiotics. Benefiting from the different affinities between the ions and TCs, the sensor array can identify TCs from other antibiotics, which also can further differentiating four kinds of TCs (OTC, CTC, TC and DOX) from each other via linear discriminant analysis (LDA) technique. Meanwhile, the sensor array performed well in quantitative analysis of single TC antibiotic and differentiation of TCs mixtures. More interestingly, Eu3+ and Al3+-doped sodium alginate/polyvinyl alcohol hydrogel beads (SA/Eu/PVA and SA/Al/PVA) were further constructed, which can not only identify the TCs but simultaneously remove the antibiotics with high efficiency. The investigation provided an instructive way for rapid detection and environment protection.