Linear Discriminant Algorithm Research Articles

Humoral immune responses primed by the alteration of gut microbiota were associated with galactose-deficient IgA1 production in IgA nephropathy.

Galactose-deficient IgA1 (GdIgA1) is critical in the formation of immunodeposits in IgA nephropathy (IgAN), whereas the origin of GdIgA1 is unknown. We focused on the immune response to fecal microbiota in patients with IgAN. By running 16S ribosomal RNA gene sequencing, we compared IgAN samples to the control samples from household-matched or non-related individuals. Levels of plasma GdIgA1 and poly-IgA complexes were measured, and candidate microbes that can either incite IgA-directed antibody response or degrade IgA through specific IgA protease activities were identified. The IgAN group showed a distinct composition of fecal microbiota as compared to healthy controls. Particularly, high abundance of Escherichia-Shigella was associated with the disease group based on analyses using receiver operating characteristic (area under curve, 0.837; 95% CI, 0.738-0.914), principle coordinates, and the linear discriminant analysis effect size algorithm (linear discriminant analysis score, 4.56; p < 0.001). Accordingly, the bacterial levels directly correlated with high titers of plasma GdIgA1(r = 0.36, p < 0.001), and patients had higher IgA1 against stx2(2.88 ± 0.46 IU/mL vs. 1.34 ± 0.35 IU/mL, p = 0.03), the main antigen of Escherichia-Shigella. Conversely, the healthy controls showed relatively higher abundance of the commensal bacteria that produce IgA-degrading proteases. Particularly, the abundance of some intestinal bacteria expressing IgA proteases showed an inverse correlation with the levels of plasma GdIgA1 in IgAN. Our data suggest that mucosal IgA production, including those of GdIgA1, is potentially linked to the humoral response to gut Escherichia-Shigella as one of the sources of plasma GdIgA1. Conversely, the IgA protease-producing microbiota in the gut are suppressed in patients with IgAN.

Linear discriminant analysis-based deep learning algorithms for numerical character handwriting recognition

Linear discriminant analysis-based deep learning algorithms for numerical character handwriting recognition

Identification of KRAS mutation-associated gut microbiota in colorectal cancer and construction of predictive machine learning model.

Gut microbiota has demonstrated an increasingly important role in the onset and development of colorectal cancer (CRC). Nonetheless, the association between gut microbiota and KRAS mutation in CRC remains enigmatic. We conducted 16S rRNA sequencing on stool samples from 94 CRC patients and employed the linear discriminant analysis effect size algorithm to identify distinct gut microbiota between KRAS mutant and KRAS wild-type CRC patients. Transcriptome sequencing data from nine CRC patients were transformed into a matrix of immune infiltrating cells, which was then utilized to explore KRAS mutation-associated biological functions, including Gene Ontology items and Kyoto Encyclopedia of Genes and Genomes pathways. Subsequently, we analyzed the correlations among these KRAS mutation-associated gut microbiota, host immunity, and KRAS mutation-associated biological functions. At last, we developed a predictive random forest (RF) machine learning model to predict the KRAS mutation status in CRC patients, based on the gut microbiota associated with KRAS mutation. We identified a total of 26 differential gut microbiota between both groups. Intriguingly, a significant positive correlation was observed between Bifidobacterium spp. and mast cells, as well as between Bifidobacterium longum and chemokine receptor CX3CR1. Additionally, we also observed a notable negative correlation between Bifidobacterium and GOMF:proteasome binding. The RF model constructed using the KRAS mutation-associated gut microbiota demonstrated qualified efficacy in predicting the KRAS phenotype in CRC. Our study ascertained the presence of 26 KRAS mutation-associated gut microbiota in CRC and speculated that Bifidobacterium may exert an essential role in preventing CRC progression, which appeared to correlate with the upregulation of mast cells and CX3CR1 expression, as well as the downregulation of GOMF:proteasome binding. Furthermore, the RF model constructed on the basis of KRAS mutation-associated gut microbiota exhibited substantial potential in predicting KRAS mutation status in CRC patients.IMPORTANCEGut microbiota has emerged as an essential player in the onset and development of colorectal cancer (CRC). However, the relationship between gut microbiota and KRAS mutation in CRC remains elusive. Our study not only identified a total of 26 gut microbiota associated with KRAS mutation in CRC but also unveiled their significant correlations with tumor-infiltrating immune cells, immune-related genes, and biological pathways (Gene Ontology items and Kyoto Encyclopedia of Genes and Genomes pathways). We speculated that Bifidobacterium may play a crucial role in impeding CRC progression, potentially linked to the upregulation of mast cells and CX3CR1 expression, as well as the downregulation of GOMF:Proteasome binding. Furthermore, based on the KRAS mutation-associated gut microbiota, the RF model exhibited promising potential in the prediction of KRAS mutation status for CRC patients. Overall, the findings of our study offered fresh insights into microbiological research and clinical prediction of KRAS mutation status for CRC patients.

Convolutional Neural Network-Driven Impedance Flow Cytometry for Accurate Bacterial Differentiation.

Impedance flow cytometry (IFC) has been demonstrated to be an efficient tool for label-free bacterial investigation to obtain the electrical properties in real time. However, the accurate differentiation of different species of bacteria by IFC technology remains a challenge owing to the insignificant differences in data. Here, we developed a convolutional neural networks (ConvNet) deep learning approach to enhance the accuracy and efficiency of the IFC toward distinguishing various species of bacteria. First, more than 1 million sets of impedance data (comprising 42 characteristic features for each set) of various groups of bacteria were trained by the ConvNet model. To improve the efficiency for data analysis, the Spearman correlation coefficient and the mean decrease accuracy of the random forest algorithm were introduced to eliminate feature interaction and extract the opacity of impedance related to the bacterial wall and membrane structure as the predominant features in bacterial differentiation. Moreover, the 25 optimized features were selected with differentiation accuracies of >96% for three groups of bacteria (bacilli, cocci, and vibrio) and >95% for two species of bacilli (Escherichia coli and Salmonella enteritidis), compared to machine learning algorithms (complex tree, linear discriminant, and K-nearest neighbor algorithms) with a maximum accuracy of 76.4%. Furthermore, bacterial differentiation was achieved on spiked samples of different species with different mixing ratios. The proposed ConvNet deep learning-assisted data analysis method of IFC exhibits advantages in analyzing a huge number of data sets with capacity for extracting predominant features within multicomponent information and will bring about progress and advances in the fields of both biosensing and data analysis.

Characterization and Identification of Cracks on Perforated Embryo Eggs Based on Fractal Theory

Highlights A fractal matrix model was established to characterize the cracks on the surfaces of perforated embryo eggs. A function for classifying perforated embryo eggs was constructed using the Fisher linear discriminant algorithm. An experiment to identify the qualification of perforated embryo eggs was performed. Abstract. In response to the problems of low accuracy and unsatisfactory consistency in manual identification, a method for characterizing and identifying cracks on perforated embryo eggs is proposed based on fractal theory. Due to the intersection of the fractal dimensions of a few qualified and cracked embryo eggs, an improved fractal box-counting algorithm was used to characterize the overall characteristics of the cracks and construct an 8 × 8 fractal matrix using image segmentation technology. Based on the morphology and structure, the eggshell was divided into three regions and five characteristic variables expressing perforation cracks were extracted. Two significant variables were screened using the stepwise method to construct a linear discriminant function. Ten-day-old embryonated eggs of white phoexix black-bone chickens were selected as experimental materials. Embryo egg perforation and image acquisition devices were established. Then, crack recognition experiments were carried out. The accuracy of crack determination was 98.89%, and the identification time for a single egg was less than 1.24 s. The research results can lay a solid theoretical and practical foundation for the development of automatic recognition systems for perforated embryo eggs. Keywords: Characterization analysis, Crack, Fractal theory, Image processing, Linear discrimination.

A linear discriminant analysis-based algorithm for identifying anomalous traffic in large-scale networks

Abstract To protect network security, this paper develops a large-scale network anomalous traffic identification algorithm that utilizes the linear discriminant analysis method to intercept network anomalous traffic. Firstly, the classification of large-scale network anomalous traffic is explored, and the SSAE algorithm is combined with the feature selection of large-scale network traffic on the basis of network flow feature extraction. Secondly, data dimensionality reduction of network anomalous traffic using linear discriminant analysis and feature selection of large-scale network traffic based on SSAE to identify network anomalous traffic. Finally, the CICIDS2017 dataset and the NSL-KDD dataset are used to experimentally analyze the effect and performance of feature selection and anomaly identification algorithms. The results show that the classification accuracy of the feature selection algorithm is 0.989, the 10-dimensional optimal features selected are (12,6,5,38,29,3,33,35,36,40), and the recognition result is 0.803 for normal network traffic and 0.197 for anomalous traffic, with an overall recognition error of 0.003, and a performance of more than 0.988.

Substantiation of Sequential Trait with Independent Correlation

Abstract: The expanding aquaculture sector can greatly benefit from having access to comprehensive fish information, which includes data on ecosystems, food requirements, and related details. Relational databases play a crucial role in storing the diverse range of fish species data in the aquaculture industry. However, due to the extensive variety of species and their associated data, organizing and categorizing this information can be a challenging task. Nonetheless, fishermen can derive significant advantages from visual representations of the data and a recommendation system that suggests the most suitable fish species and optimal ecological conditions. By leveraging data on fish species, names, taxonomy, and survival characteristics, a connected user can receive personalized recommendations based on their interests. The proposed system also offers appropriate food variety suggestions for the fish, ensuring scalability and effective generation of recommendations. Through the integration of information from multiple sources, a database system can efficiently process, manage, store, and retrieve relational data. Furthermore, implementing the linear discriminant algorithm enhances the accuracy and speed of food recommendations for fish.

Classification of cancer cells and gene selection based on microarray data using MOPSO algorithm.

Microarray information is crucial for the identification and categorisation of malignant tissues. The very limited sample size in the microarray has always been a challenge for classification design in cancer research. As a result, by pre-processing gene selection approaches and genes lacking their information, the microarray data are deleted prior to categorisation. In essence, an appropriate gene selection technique can significantly increase the accuracy of illness (cancer) classification. For the classification of high-dimensional microarray data, a novel approach based on the hybrid model of multi-objective particle swarm optimisation (MOPSO) is proposed in this research. First, a binary vector representing each particle's position is presented at random. A gene is represented by each bit. Bit 0 denotes the absence of selection of the characteristic (gene) corresponding to it, while bit 1 denotes the selection of the gene. Therefore, the position of each particle represents a set of genes, and the linear Bayesian discriminant analysis classification algorithm calculates each particle's degree of fitness to assess the quality of the gene set that particle has chosen. The suggested methodology is applied to four different cancer database sets, and the results are contrasted with those of other approaches currently in use. The proposed algorithm has been applied on four sets of cancer database and its results have been compared with other existing methods. The results of the implementation show that the improvement of classification accuracy in the proposed algorithm compared to other methods for four sets of databases is 25.84% on average. So that it has improved by 18.63% in the blood cancer database, 24.25% in the lung cancer database, 27.73% in the breast cancer database, and 32.80% in the prostate cancer database. Therefore, the proposed algorithm is able to identify a small set of genes containing information in a way choose to increase the classification accuracy. Our proposed solution is used for data classification, which also improves classification accuracy. This is possible because the MOPSO model removes redundancy and reduces the number of redundant and redundant genes by considering how genes are correlated with each other.

Retracted: Multimedia Digital Signal Processing of Infrared Chemical Remote Sensing Based on Piecewise Linear Discriminant Algorithm

Retracted: Multimedia Digital Signal Processing of Infrared Chemical Remote Sensing Based on Piecewise Linear Discriminant Algorithm

Multiobjective Wrapper Sampling Design for Leak Detection of Pipe Networks Based on Machine Learning and Transient Methods

This study introduces a novel sampling design (SD) method for hybrid Machine Learning/Transient-Based (ML/TB) leak detection of pipe networks. The proposed technique exploits the hydraulic responses of the network in the frequency domain and the concept of Filter and Wrapper feature selection as a machine learning approach. It also utilizes multiobjective optimization to handle the trade-off between leak detection error and number of sampling nodes. To apply this method, a transient hydraulic simulation model of the network is developed in the time domain. Then, considering a wide range of leak scenarios, the hydraulic responses of the network are calculated at candidate measurement sites. The responses are then transferred into the frequency domain using the Fast Fourier Transform (FFT) and stored as the train and test datasets. To reduce the dimensions of the initial feature vector, a threshold is applied to the responses to filter the very high frequencies. Finally, a classifier based on a Linear Discriminant Algorithm (LDA) coupled to a binary-coded Non-dominated Sorting Genetic Algorithm (NSGA-II) is applied to preprocessed datasets. Four sampling design methods are adopted from the literature and modified in the frequency domain for more investigations and comparisons. Solving two example pipe networks showed that the proposed method outperforms the existing approaches with respect to higher accuracy leak detection with fewer sampling sites.

Isometric mapping algorithm based GNSS-R sea ice detection

&lt;abstract&gt; &lt;p&gt;Global navigation satellite system reflectometry (GNSS-R) is based on satellite signals' multipath interference effect and has developed as one of the important remote sensing technologies in sea ice detection. An isometric mapping (ISOMAP)-based method is proposed in this paper as a development in sea ice detection approaches. The integral delay waveforms (IDWs), selected from February to April in 2018, derived from TechDemoSat-1 (TDS-1) Delay-Doppler maps (DDMs) are applied to open water and sea ice classification. In the first, the model for extracting low-dimensional coordinates of IDWs employs the randomly selected 187666 IDW samples, which are 30% of the whole IDW dataset. Then, low-dimensional coordinates of IDWs are used to train three different classifiers of support vector machine (SVM) and gradient boosting decision tree (GBDT), linear discriminant algorithm (LDA) and K-nearest neighbors (KNN) for determining the sea ice and sea water. The remaining 437889 samples, about 70% of the whole datasets, are used to validate with the ground surface type from the National Snow and Ice Data Center (NSIDC) data provided by the National Oceanic and Atmospheric Administration (NOAA). The algorithm performance is evaluated, and the overall accuracy of SVM, GBDT, LDA and KNN are 99.44%, 85.58%, 91.88% and 98.82%, respectively. The sea ice detection methods are developed, and the accuracy of detection is improved in this paper.&lt;/p&gt; &lt;/abstract&gt;

PaleoWood: A machine learning approach for determining the affinity of Paleozoic gymnosperm woods

Fossil plant remains are commonly found in fragments in the sediment, thus complicating the reconstruction and classification of fossil plants into a higher taxonomic group. Particularly for stem anatomy, some described features repeat among the proposed lineages due to environmental pressures that induce anatomical convergence. Other characteristics cannot always be seen because of the fossil's state of preservation, as often happens with the bark and the arrangement of axes and leaves. Given these difficulties, we developed PaleoWood, an unprecedented affinity classifier for Paleozoic gymnosperm woods based on 16 variables collected from 42 consistent genera that have the central core, primary xylem, and secondary xylem described. Similarities among samples were analyzed by principal coordinates, and models were trained through logistic regression, linear discriminant, and k-nearest neighbors algorithms. Models' performance was estimated by cross-validation and testing of the affinity of 20 previously known samples. Results agreed with some hypotheses previously discussed in the literature, such as the linkage of Eristophyton, Megaloxylon, and Tetrastichia with Lyginopteridales. Some other predictions were interpreted to be a result of convergent evolution or the models' limitations, especially those predictions relating to the samples of simple protostele or pycnoxylic pteridosperms (but these models are not definitive and may be improved as new data are collected). Therefore, they could assist in future comparisons and discussions about the taxonomy, evolution, and paleobotanical affinities of the basal seed plants, especially for the woods from Gondwana, in which affinities are obscure for several genera.

Study on Music Emotion Recognition Based on the Machine Learning Model Clustering Algorithm

In recent years, the explosive growth of online music resources makes it difficult to retrieve and manage music information. To efficiently retrieve and classify music information has become a hot research topic. Thayer’s two-dimensional emotion plane is selected as the basis for establishing the music emotion database. Music is divided into five categories, the concept of continuous emotion perception is introduced, and music emotion is regarded as a point on a two-dimensional emotional plane, together with the two sentiment variables to determine its location. The artificial labeling method is used to determine the position range of the five types of emotions on the emotional plane, and the regression method is used to obtain the relationship between the VA value and the music features so that the music emotion classification problem is transformed into a regression problem. A regression-based music emotion classification system is designed and implemented, which mainly includes a training part and a testing part. In the training part, three algorithms, namely, polynomial regression, support vector regression, and k-plane piecewise regression, are used to obtain the regression model. In the test part, the input music data is regressed and predicted to obtain its VA value and then classified, and the system performance is considered by classification accuracy. Results show that the combined method of support vector regression and k-plane piecewise regression improves the accuracy by 3 to 4 percentage points compared to using one algorithm alone; compared with the traditional classification method based on a support vector machine, the accuracy improves by 6 percentage points. Music emotion is classified by algorithms such as support vector machine classification, K-neighborhood classification, fuzzy neural network classification, fuzzy K-neighborhood classification, Bayesian classification, and Fisher linear discrimination, among which the support vector machine, fuzzy K-neighborhood, and the accuracy rate of music emotion classification realized by Fisher linear discriminant algorithm are more than 80%; a new algorithm “mixed classifier” is proposed, and the music emotion recognition rate based on this algorithm reaches 84.9%.

低场核磁共振技术结合机器学习判别水稻种子活力方法研究

Physiological index data and low-field nuclear magnetic resonance (LF-NMR) spectral data of rice seed samples from three varieties harvested in different years were collected through a combination of the standard germination test and an LF-NMR test. Three parameters of seed vigor: germination energy, germination percentage, and germination index, were calculated based on the physiological index data of the rice seed samples to determine their vigor over the years after harvest. LF-NMR Carr-Purcell-Meiboom-Gill (CPMG) sequence echo-peak data were used as the input, and rice seed vigor was used as the output to establish discriminative models using principal component analysis, support vector machine, logistic regression, K-nearest neighbor, artificial neural network, and Fisher’s linear discriminant. The results showed that models constructed using any algorithm, except for principal components analysis-algorithm distinguished between seeds with high and low vigor, while models constructed using Fisher’s linear discriminant algorithm gave the best results. This study provided a rapid, accurate, and non-destructive method to test rice seed vigor, offering theoretical support and a reference for rice seed-sorting and storage research.

Seasonal Aspects of Radiative and Advective Air Temperature Populations: A Canadian Perspective

Canadian high-frequency temperature time series exhibit physical heterogeneity in the coexistence of radiative and advective populations in the total air temperature sample. This work examines forty-five Canadian hourly air temperature records to study seasonal characteristics and variability of radiative and advective population counts and their corresponding temperature biases and trends. The Linear Pattern Discrimination algorithm, conceptualized in a previous study, was adjusted to seasonal analysis on the equinox-to-equinox time scale. Count analysis of radiative and advective days supports the existence of two distinct thermal regimes, Spring–Summer and Fall–Winter. Further, seasonal advective counts for the majority of examined stations typically decrease in numbers. The consistently warmer winter radiative temperature extrema points to the critical role of the advective population in control of the overall temperature magnitude. Canadian northwest warming trends are found to be the highest, indicating the amplifying effect of decreasing advective counts with rapidly increasing temperatures that weaken the advective population’s moderating ability to control the magnitude of the total temperature population.

An efficient and portable solar cell defect detection system

The photovoltaic (PV) system industry is continuously developing around the world due to the high energy demand, even though the primary current energy source is fossil fuels, which are a limited source and other sources are very expensive. Solar cell defects are a major reason for PV system efficiency degradation, which causes disturbance or interruption of the generated electric current. In this study, a novel system for discovering solar cell defects is proposed, which is compatible with portable and low computational power devices. It is based on K-means, MobileNetV2 and linear discriminant algorithms to cluster solar cell images and develop a detection model for each constructed cluster. It can extract the distinct features between defective and nondefective solar cell clusters and overcome the confusion between different cell shapes. The proposed system was assessed using a benchmark dataset of electroluminescence images. The results demonstrate that it achieved the highest accuracy at a substantial rate compared to recent studies.

A Novel Functional Machine Learning Approaches on Prostate Cancer

Abstract: Cancer registries are collections of curated data about malignant tumor diseases. The amount of data processed by cancer registries increases every year, making manual registration more and more tedious.This research work finds Bayes Net classifier gives an optimal results. The Sequential Minimal Optimization of functional machine learning approach is having highest accuracy level which is 85% of accuracy level. The Sequential Minimal Optimization of functional machine learning approach is having highest precision level which is 0.85 of precision level. The least precision value is 0.80 of precision value which is having Quadratic Discriminant Analysis of functional machine learning classifier approach. The Sequential Minimal Optimization of functional machine learning approach is having highest recall level which is 0.85 of recall level. The least recall value is 0.79 which is produced by Quadratic Discriminant Analysis functional machine learning classification approach. The Sequential Minimal Optimization of functional machine learning approach is having highest FMeasure level which is 0.85 of F-Measure level. The Fisher’s Discriminant Analysis algorithm of functional machine learning classifier and Linear Discriminant Analysis classification algorithm of functional machine learning classifier are having same receiver operating characteristic curve value which is 0.90 of receiver operating characteristic curve value.The maximum precision recall curve value is 0.90 of precision recall curve value which is produced by Linear Discriminant Analysis of functional machine learning classifier. This system recommends that the Sequential Minimal Optimization of functional machine learning approach produces optimal results compare with other models. Keywords: SMO, functional learning, LDA, QDA, and SDG

A fisher linear discriminant analysis classifier fused with naïve Bayes for simultaneous detection in an asynchronous brain-computer interface

BackgroundAn asynchronous event-related potential-based brain computer interface (ERP-BCI) permits the subjects to output intentions at their own pace, which provides a more free and practical communication pathways without the need for muscle activity. The core of constructing this type of system is to discriminate both the intentions and brain states. New methodsThis study proposes a fisher linear discriminant analysis classification algorithm fused with naïve Bayes (B-FLDA) for the ERP-BCI to simultaneous recognize the subjects’ intentions, working and idle states. This method uses the spectral characteristics of visual-evoked potential and the time-domain characteristics of ERP to simultaneously detect brain states and target stimulus, and obtain the final discrimination result through probability fusion. ResultsThe accuracy and the information transfer rate increase to 98.61% and 62.80 bits/min under 10 repetitions and 1 repetition, respectively. The three parameters of receiver operator characteristic curve have achieved better performance. Comparison with existing methodsTen subjects participate in this study with the proposed algorithms and two other control methods. The accuracy and information transfer rate of this algorithm are better than the other methods. ConclusionsIt indicates that the naïve Bayes-FLDA algorithm is able to improve the performance of an asynchronous BCI system by detecting the intentions and states simultaneously.

A Machine-Learning Model for Lung Age Forecasting by Analyzing Exhalations.

Spirometers are important devices for following up patients with respiratory diseases. These are mainly located only at hospitals, with all the disadvantages that this can entail. This limits their use and consequently, the supervision of patients. Research efforts focus on providing digital alternatives to spirometers. Although less accurate, the authors claim they are cheaper and usable by many more people worldwide at any given time and place. In order to further popularize the use of spirometers even more, we are interested in also providing user-friendly lung-capacity metrics instead of the traditional-spirometry ones. The main objective, which is also the main contribution of this research, is to obtain a person’s lung age by analyzing the properties of their exhalation by means of a machine-learning method. To perform this study, 188 samples of blowing sounds were used. These were taken from 91 males (48.4%) and 97 females (51.6%) aged between 17 and 67. A total of 42 spirometer and frequency-like features, including gender, were used. Traditional machine-learning algorithms used in voice recognition applied to the most significant features were used. We found that the best classification algorithm was the Quadratic Linear Discriminant algorithm when no distinction was made between gender. By splitting the corpus into age groups of 5 consecutive years, accuracy, sensitivity and specificity of, respectively, 94.69%, 94.45% and 99.45% were found. Features in the audio of users’ expiration that allowed them to be classified by their corresponding lung age group of 5 years were successfully detected. Our methodology can become a reliable tool for use with mobile devices to detect lung abnormalities or diseases.

Effective face recognition using dual linear collaborative discriminant regression classification algorithm

Effective face recognition using dual linear collaborative discriminant regression classification algorithm