Discriminant Analysis Techniques 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.

Productivity and Firm Size in the Construction Sector in Ecuador: A Discriminant Analysis

The research addresses the relationship between the business size of the construction sector in Ecuador and productivity indicators, seeking to identify the group with the greatest influence on productive growth. The methodology employed begins with a descriptive-explanatory analysis, using the Discriminant Analysis Technique (ADM). The results reveal that small companies have suffered more impact during the health crisis. In addition, Wilks' Lambda test was applied, confirming that the size of organizations is linked to productive performance, highlighting specific factors. These findings offer valuable insights for improving efficiency in the sector. Received: 5 March 2024 / Accepted: 15 June 2024 / Published: 02 July 2024

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980–81 to 2020–21 have been taken from various issues of Statistical s of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

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.

Preliminary study on the optical diagnosis of orbital rhabdomyosarcoma by Raman spectroscopy

To investigate the Raman spectral features of orbital rhabdomyosarcoma (ORMS) tissue and normal orbital tissue in vitro, and to explore the feasibility of Raman spectroscopy for the optical diagnosis of ORMS. 23 specimens of ORMS and 27 specimens of normal orbital tissue were obtained from resection surgery and measured in vitro using Raman spectroscopy coupled to a fiber optic probe. The important spectral differences between the tissue categories were exploited for tissue classification with the multivariate statistical techniques of principal component analysis (PCA) and linear discriminant analysis (LDA). Compared to normal tissue, the Raman peak intensities located at 1450 and 1655 cm−1 were significantly lower for ORMS (p < 0.05), while the peak intensities located at 721, 758, 1002, 1088, 1156, 1206, 1340, 1526 cm−1 were significantly higher (p < 0.05). Raman spectra differences between normal tissue and ORMS could be attributed to the changes in the relative amounts of biochemical components, such as nucleic acids, tryptophan, phenylalanine, carotenoid and lipids. The Raman spectroscopy technique together with PCA-LDA modeling provides a diagnostic accuracy of 90.0%, sensitivity of 91.3%, and specificity of 88.9% for ORMS identification. Significant differences in Raman peak intensities exist between normal orbital tissue and ORMS. This work demonstrated for the first time that the Raman spectroscopy associated with PCA-LDA diagnostic algorithms has promising potential for accurate, rapid and noninvasive optical diagnosis of ORMS at the molecular level.

Differentiation of blue gel pens using visible spectroscopy coupled with chemometric techniques

Ink analysis is one of the most important and keydimension of forensic document examination (FDE). It is performed to compare, identify, characterize and discriminate different kinds of inks present on questioned document. It becomes key evidence in forensic document examination. In the present work, thirty-eight blue gel pens are discriminated using visible spectroscopy coupled with chemometric techniques. The recorded spectra were visually compared followed by chemometric techniques of principal component analysis (PCA) and linear discriminant analysis (LDA). Discriminating power for visual comparison, PCA and LDA are 58.18%, 100% and 100% respectively. Developed PCA model is validated and 100% accuracy, precision, sensitivity and specificity are achieved. Blue gel pen inks were 100% correctly differentiated on the basis of their brands using PCA followed by LDA. The utilized approach is fast and provides reproducible results. The results of present work demonstrate that potential utility of conventional and cost-effective visible spectroscopy can be improved by coupling it with these chemometric techniques. Major advantage of present research is the minimum damage caused to document. This study establishes a method which provides proof of concept discrimination of the gel pen samples. The methodology adopted in present work can be applied in the alteration cases in examination of suspected documents.

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

109 Animal Behavior Analysis Using Unsupervised Machine Learning Techniques

Abstract With recent technological advances, Global Positioning Systems (GPS) and other tracking sensors can collect large amounts of data and transfer information to the cloud and then to producers to remotely monitor livestock health and well-being. Currently, supervised machine learning, such as random forest and linear or quadratic discriminant analysis techniques are used to develop algorithms to identify changes in animal behavior that may be associated with well-being concerns. Supervised machine learning requires behavior observation of monitored animals and may hinder normal expression. However, recording behavioral observations is time consuming and expensive. Our goal was to design a new unsupervised machine learning framework to identify animal behavior without the utilization of human observations. The framework contains two steps. The first step is to segment the tracking data of the animal using time series segmentation, and the second step is to group the segments into clusters where each cluster represents one type of behavior. To validate the applicability of our proposed framework, we utilize GPS tracking data collected at 2-minute intervals from eight cows from May 28 to June 22, 2018, in a 1096 ha rangeland pasture near Prescott, Arizona. After extensive experiments, our framework can partition the movement of the cow using the speed, direction, and distance of the cow from water. These segments were grouped into meaningful behavior clusters using clustering analysis. Speed was the most successful feature for clustering into behaviors. Results are similar to approaches based only on expert knowledge, that rely on speed for classification. Our study demonstrated that we can directly use unlabeled data to group animal behaviors from GPS tracking data. The proposed unsupervised two-step framework allows the analysis of cattle tracking data without direct human observation of behaviors. It is applicable for analyzing the immense amount of data that can be obtained from real-time tracking and sensor devices.

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.

Impact of Key Bank Risk Components on Banking System Performance in Nigeria: A Discriminant Analysis Technique

Using discriminant analysis technique, this study evaluates the impact of key bank risk components on the banking system performance in Nigeria. The performance of the banking sector was evaluated to determine the extent to which each of the key risk components impacts on it. The findings revealed that credit risk, compliance risk and liquidity risk made outstanding contributions of 95.6 percent to the total discriminant score for the function, implying that successful performance of banking sector hinges mostly on the three risk factors. Credit risk

Investigating the impact of attenuated fluorescence spectra on protein discrimination.

The optical remote sensing techniques are promising for the real-time detection, and identification of different types of hazardous biological materials. However, the received fluorescent spectra from a remote distance suffer from the atmospheric attenuation effect upon the spectral shape. To investigate the influence of atmospheric attenuation on characterizing, and classifying biological agents, the laboratory-measured fluorescence data of fourteen proteins combined with the atmospheric transmission factors of the MODTRAN model were conducted with different detection ranges. The multivariate analysis techniques of principal component analysis (PCA) and linear discriminant analysis (LDA), and the predictors of Random Forest and XGBoost were employed to assess the separability and distinguishability of different spectra recorded. The results showed that the spectral-shift effect on attenuated spectra varied as a function of the detection range, the atmospheric visibility, and the spectral distribution. According to the PCA and LDA analysis, the distribution of decomposed factors changed in the spectral explanatory power with the increasing attenuation effect, which was consistent with the hierarchical clustering results. Random Forest exhibited higher performance in classifying protein samples than that of XGBoost, while the two methods performed similarly in identifying harmful and harmless subgroups of proteins. Fewer subgroups decreased the sensitivity of the classification accuracy to the attenuation effect. Our analysis demonstrated that combining atmospheric transport models to build a fluorescence spectral database is essential for fast identification between spectra, and reduced classification criteria could facilitate the compatibility of spectral database and classification algorithms.

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%.

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.

Discriminant Analysis and Optimization Applied to Vibration Signals for the Quality Control of Rotary Compressors in the Production Line

In this paper, the applications of the multivariate data analysis and optimization on vibration signals from compressors have been tested on the assembly line to identify nonconforming products. The multivariate analysis has wide applicability in the optimization of weather forecasting, agricultural experiments, or, as in this case study, in quality control. The techniques of discriminant analysis and linear program were used to solve the problem. The acceleration and velocity signals used in this work were measured in twenty-five rotating compressors, of which eleven were classified as good baseline compressors and fourteen with manufacturing defects by the specialists in the final acoustic test of the production line. The results obtained with the discriminant analysis separated the conforming and nonconforming groups with a significance level of 0.01, which validated the proposed methodology.

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.

Different statistical models based on weather parameters in Navsari district of Gujarat

Agriculture plays very important role in development of country. Rice is a staple food for more than half of world’s population. Timely and reliable forecasting provides vital and appropriate input, foresight and informed planning. The present investigation was carried out to forecast Kharif rice yield using two different statistical techniques, viz., discriminant function analysis and logistic regression analysis. The statistical models were developed using data from 1990 to 2012 and validation of developed models was done by using remaining data, i.e., 2013 to 2016. It was observed that value of adjusted R2 varied from 73.00 per cent to 93.30 per cent in different models. The best forecast model was selected based on high value of adjusted R2, Forecast error and RMSE. Based on obtained results in Navsari district, the discriminant function analysis technique (Model-5) was found better than logistic regression analysis (Model-12) for pre-harvest forecasting of rice crop yield. The results revealed that Model-5 showed comparatively low forecast error (%) along with highest value of Adj. R2 (93.30) and lowest value of RMSE (120.07). Also Model-5 is able to generate yield forecast a week earlier (39thSMW) than Model-12 (40thSMW).

Psychological Profiles of Positive Mental Health of Women in Situation of Confinement Due to COVID-19

Background: The world health organization has indicated that the problem of COVID-19 and confinement generated strong psychological impacts on the world population. Much of the research has focused on studying mental health in different population groups, leaving aside a positive mental health perspective. Objective: The present research intended to establish positive mental health profiles in confined women due to COVID-19 during 2020. Method: The factors of personal satisfaction, prosocial attitude, self-control, autonomy, problem- solving, self-actualization, and interpersonal relationships were assessed through the application of Lluch's positive mental health scale, in a sample of 202 confined women in the Department of Sucre, Colombia, selected by non-probabilistic convenience sampling. In addition, cluster analysis models were applied to identify psychological profiles of positive mental health and characterize sociodemographic variables, the selected model was evaluated and validated using the statistical technique of discriminant analysis using Minitab 18 software. Results and Discussion: a positive mental health differentiation in women is shown from which four psychological profiles of positive mental health could be identified, with scores of 14.10 in profile 1, 11.41 in profile 2, 9.15 in profile 3, and 7.56 in profile 4. The positive mental health factors used showed an ability to discriminate in 92.6% of the cases in the profiles. Conclusion: the identified profiles are significant and important to characterize psychometric profiles of positive mental health of confined women, which are important results for their diagnosis and the development of public policies for their treatment.