SVM Classifier Research Articles

Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes.

Background: Endometriosis (EM) is a long-lasting inflammatory disease that is difficult to treat and prevent. Existing research indicates the significance of immune infiltration in the progression of EM. Efferocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs) in EM is sparse. Methods: The EFRDEGs (differentially expressed efferocytosis-related genes) linked to datasets associated with endometriosis were thoroughly examined utilizing the Gene Expression Omnibus (GEO) and GeneCards databases. The construction of the protein-protein interaction (PPI) and transcription factor (TF) regulatory network of EFRDEGs ensued. Subsequently, machine learning techniques including Univariate logistic regression, LASSO, and SVM classification were applied to filter and pinpoint diagnostic biomarkers. To establish and assess the diagnostic model, ROC analysis, multivariate regression analysis, nomogram, and calibration curve were employed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infiltration, while the Comparative Toxicogenomics Database (CTD) was utilized for the identification of potential therapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were utilized to quantify the expression levels of biomarkers in clinical samples of endometriosis. Results: Our findings revealed 13 EFRDEGs associated with EM, and the LASSO and SVM regression model identified six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2). Among these, ARG2, GAS6, and C3 were confirmed as diagnostic biomarkers through multivariate logistic regression analysis. The ROC curve analysis of GSE37837 (AUC = 0.627) and GSE6374 (AUC = 0.635), along with calibration and DCA curve assessments, demonstrated that the nomogram built on these three biomarkers exhibited a commendable predictive capacity for the disease. Notably, the ratio of nine immune cell types exhibited significant differences between eutopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, and CD8 Tex cells as the cell populations undergoing the most substantial changes in the three biomarkers. Additionally, our study predicted seven potential medications for EM. Finally, the expression levels of the three biomarkers in clinical samples were validated through RT-qPCR and IHC, consistently aligning with the results obtained from the public database. Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings provide valuable insights for subsequent mechanistic research and clinical applications in the field of endometriosis.

Histogram-based features track Alzheimer's progression in brain MRI.

Alzheimer's disease is a form of general dementia marked by amyloid plaques, neurofibrillary tangles, and neuron degeneration. The disease has no cure, and early detection is critical in improving patient outcomes. Magnetic resonance imaging (MRI) is important in measuring neurodegeneration during the disease. Computer-aided image processing tools have been used to aid medical professionals in ascertaining a diagnosis of Alzheimer's in its early stages. As characteristics of non and very-mild dementia stages overlap, tracking the progression is challenging. Our work developed an adaptive multi-thresholding algorithm based on the morphology of the smoothed histogram to define features identifying neurodegeneration and track its progression as non, very mild, mild, and moderate. Gray and white matter volume, statistical moments, multi-thresholds, shrinkage, gray-to-white matter ratio, and three distance and angle values are mathematically derived. Decision tree, discriminant analysis, Naïve Bayes, SVM, KNN, ensemble, and neural network classifiers are designed to evaluate the proposed methodology with the performance metrics accuracy, recall, specificity, precision, F1 score, Matthew's correlation coefficient, and Kappa values. Experimental results showed that the proposed features successfully label the neurodegeneration stages.

Causal Brain Network Predicts Surgical Outcomes in Patients With Drug-Resistant Epilepsy: A Retrospective Comparative Study.

Network neuroscience, especially causal brain network, has facilitated drug-resistant epilepsy (DRE) studies, while surgical success rate in patients with DRE is still limited, varying from 30% ∼ 70 %. Predicting surgical outcomes can provide additional guidance to adjust treatment plans in time for poorly predicted curative effects. In this retrospective study, we aim to systematically explore biomarkers for surgical outcomes by causal brain network methods and multicenter datasets. Electrocorticogram (ECoG) recordings from 17 DRE patients with 58 seizures were included. Ictal ECoG within clinically annotated epileptogenic zone (EZ) and non-epileptogenic zone (NEZ) were separately computed using six different algorithms to construct causal brain networks. All the brain network results were divided into two groups, successful and failed surgeries. Statistical results based on the Mann-Whitney-U-test show that: causal connectivity of α -frequency band ( 8 ∼ 13 Hz) in EZ calculated by convergent cross mapping (CCM) gains the most significant differences between the surgical success and failure groups, with a P value of 7.85e-08 and Cohen's d effect size of 0.77. CCM-defined EZ brain network can also distinguish the successful and failed surgeries considering clinical covariates (clinical centers, DRE types) with [Formula: see text]. Based on the brain network features, machine learning models were developed to predict the surgical outcomes. Among them, the SVM classifier with Gaussian kernel function and Bayesian optimization demonstrates the highest average accuracy of 84.48% by 5-fold cross-validation, further indicating that the CCM-defined EZ brain network is a reliable biomarker for predicting DRE surgical outcomes.

Indo-Aryan Dialect Identification Using Deep Learning Ensemble Model

Language identification has become a critical challenge in NLP, particularly in multilingual countries like India. This study addresses the identification of closely related Indo-Aryan languages, proposing a robust deep-learning ensemble model with data augmentation. Pre-trained models (Indic BERT, Indic Transformer, DevBERT, L3Cube-HindBERT) and various embedding techniques (word2vec, Indic transformer, Keras embedding) are explored with LSTM, Bi-LSTM, GRU, Bi-GRU, and CNN-Bi-LSTM deep learning models. Keras embedding performs better as compared to other embedding methods. The four best deep learning models are selected, and ensemble methods (soft voting, hard voting, stacking with meta models Random Forest and SVM classifier) are employed. When utilizing data augmentation with the training data and fitting the models Bi-LSTM, GRU, Bi-GRU, and CNN-Bi-LSTM with Keras embedding, the best performance, a 93% F1-score, was achieved by their soft voting classifier as compared to the other models. The performance of the proposed model was compared with the existing baseline models on the same dataset. For the comparison of model performance, the macro F1 score, accuracy, recall, and precision were used.

Indian Cultural event detection using GBVS boosted deep convolutional neural networks

Cultural heritage plays a significant role in shaping the identity of a region but recognizing cultural events poses a notable challenge in the field of computer vision. In this paper, a novel framework is proposed that blends convolutional neural networks (CNNs) and spatial feature learning for identifying Indian cultural events from image samples. The proposed method first starts by extracting spatial characteristics that capture important information about cultural events using graph-based visual saliency. These extracted images were then fed to a DenseNet201 model for event recognition. This model had an SVM classifier as the last model layer instead of the Softmax layer. The work uses a comprehensive dataset for training and model evaluation which achieves a significant accuracy of 82.50%. The proposed methodology shows efficient results when measured against state-of-the-art methods for cultural event recognition over other datasets.

A Study on the Reproduction of Traditional Chinese Painting Techniques and Their Innovation under Digital Transformation

Abstract After a long period of development and evolution, Chinese painting has formed a set of regular artistic language, which highlights the national characteristics of Chinese painting and is an important factor distinguishing it from other works of art. As the main characteristic of Chinese painting, this paper analyzes the traditional techniques of Chinese painting according to the three characteristics of Chinese painting: line, ink, and color, and analyzes the language of programed techniques. Then the method of digital reproduction of traditional techniques of Chinese painting is proposed and elaborated with the example of “The Western Garden of Elegant Gathering”. Subsequently, for the complexity of hand movement recognition of Chinese painting techniques, this paper proposes a sample splitting and re-fusion algorithm RCF based on sEMG to optimize the hand movement signals and construct a hand movement recognition model based on surface EMG signals. Through empirical analysis, the SVM classifier used in this paper has an average recognition correctness rate of 98%, which is 6% and 3% higher than BP and LDA, respectively. Meanwhile, the recognition accuracy of the hand movements of the eight techniques is all above 95%, with an average recognition accuracy of 98.5%. The mean value of all five indicators of the experience evaluation of Chinese painting techniques reproduction and innovation exceeded 4 points, and the overall average score was 4.542, indicating that the experiencers were very satisfied with the experience of Chinese painting techniques reproduction to innovation in this paper. The research of this paper provides theoretical and technical guidance for the reproduction and innovation of traditional Chinese painting techniques, as well as ideas for the organic combination of digital technology and traditional Chinese paintings.

Improving sentiment analysis of financial news headlines using hybrid Word2Vec-TFIDF feature extraction technique

With the evolution of big data and information technology, sentiment analysis has become a research hotspot in the financial market. Researchers are increasingly focused on improving the efficiency of sentiment analysis using different machine learning and deep learning architectures. Feature extraction is a fundamental process in sentiment analysis that enhances text representation and classification. Though various feature extraction techniques are present in the field, limited attention has been given to hybrid feature extraction techniques. The primary objective of this study is to improve the sentiment analysis of financial news headlines using a hybrid Word2Vec-TFIDF feature extraction technique. The study evaluates the performance of Word2Vec, Doc2Vec, TFIDF, Word2Vec-TFIDF, and Doc2Vec-TFIDF with six machine learning classifiers. The results find that the hybrid feature extraction technique, Word2vec-TFIDF with SVM classifier, outperforms all other sentiment analysis models with an accuracy of 82 percent.

Research on Building a New Mode of Civic and Political Education by Image Recognition Technology for Popularizing Socialist Core Values

Abstract Given the current state of ideological education in colleges and universities, we propose leveraging image recognition technology to foster innovation and digital advancement in this mode of education. The weighted average method is used to greyscale the video images of students’ classrooms, and after binarizing the images using the OTSU algorithm, interference factors are avoided by denoising and angle correction. We combine the motion history image (MHI) and HOG features to create a new feature that depicts Civics classroom behavior. The SVM classifier then performs the classification of classroom behavior, ultimately constructing the Civics classroom behavior recognition model based on HOC+MHII+SVM. This paper’s model and research data are combined to conduct an example analysis of Civics education in colleges and universities. It can be concluded from the analysis that, in the case of simultaneously adopting SVM for recognition and classification, it is found that HOC+MHI feature fusion has a more significant effect on classroom behavior recognition than FPN+Multi-layer CBAM, and the difference of its various indexes is maintained within the range of 0.01~0.08. In addition, the experimental group has a significant difference relationship in the four dimensions of demonstrating the concept of honor and shame, the concept of national development, the value of development, and the value of society, P<0.05. This study provides theoretical references for research on the innovation of the ideological and political education model and is conducive to the promotion of cultivating the formation of students’ socialist core values.

The Evaluation of Electronic Human Resources (eHR) Management based Internet of Things using Machine Learning Techniques

The eHR solutions are frequently employed in large organizations and sectors. For the company, such eHR is extremely competent, congruent, affordable, and committed. These days, eHR is greatly impacted by the Internet of Things (IoT), which provides eHR functions like standards, privacy, and security with a variety of facilities and supports. There are several uses for eHR and IoT together to execute plans, rules, and practices inside the company. The five essential components of an eHR are e-Selection, e-Recruitment, e-Performance, eCompensation, and e-Learning. The suggested system in this study consists of two components. The first section covered a discussion and detailed explanation of the different eHR tasks using examples. The second section describes and provides examples of analytics of data using IoT for every eHR task. The four parts of the data analytics section are as follows: (a) preparing the data; (b) choosing features; (c) classifying the data; and (d) assessing performance. Four HR analytic datasets gathered at the Kaggle site were used to conduct extensive experimentation for each eHR activity. Ultimately, each dataset was used to perform performance with appropriate reasons for each eHR activity. CART outperformed kNN and SVM classifiers in terms of performance.

Integrating Deep Learning, Grey Wolf Optimization, and SVM for Precise Plant Seedling Classification.

Integrating Deep Learning, Grey Wolf Optimization, and SVM for Precise Plant Seedling Classification.

Oral Cancer Stage Classification Using Machine Learning

Oral cancer (OC)is frequently occurring cancer that affects various areas within the oral cavity. Despite the advancement of sophisticated diagnostic and therapeutic techniques, the morbidity and mortality rates associated with oral cancer continue to be concerning. OC can affect a person’s quality of life. Beforehand identification of oral cancer can greatly reduce the need for aggressive treatment. In recent times machine learning ways has shown promising results in the identification of oral cancer. In this study, we have applied machine learning techniques such as SVM, XG Boost, and Random Forest (RF) in the stage classification of oral cancer. Our dataset contains a collection of oral images obtained from different levels of oral cancer patients from SMS hospital. We extracted various features from these images, such as color, texture, and shape, and used them as input data in our machine-learning model. We have calculated various measures like accuracy, precision, and F1 score and found that accuracy Random Forest have outperformed than SVM and XG Boost by achieving accuracy as 87.1%.

Comparative Analysis of SVM and CNN for Sonar Signal Classification Using Sparse Arrays

Comparative Analysis of SVM and CNN for Sonar Signal Classification Using Sparse Arrays

Express method of biometric person authentication based on one cycle of the ECG signal

The article is devoted to an express method of biometric authentication of a person based on an electrocardiogram (ECG). The method is characterized by high accuracy (efficiency) of authentication of a person based on only one cycle of its ECG. Such characteristics as Accuracy, Balanced Accuracy and F1-score on average are not lower than 96.1% for such binary classifiers as k-Nearest Neighbors, Linear SVM, Decision Tree, Random Forest, Multilayer Perceptron, Adaptive Boosting, Naive Bayes and Statistical Interval Classifier. The research utilized the Combined Measurement of ECG, Breathing, and Seismocardiograms database, which features data from 20 healthy people. A method of constructing confidence intervals for ECG cycles has been developed, which is based on the rhythm-adaptive statistical estimation of the mathematical expectation and the standard deviation of the ECG signal. The method of constructing confidence intervals is based on the functioning of the Statistical Interval Classifier in the system of biometric authentication of a person. The Statistical Interval Classifier has the lowest time computational complexity among the 8 studied classifiers, which justifies its use in portable biometric authentication systems that have negligible computing resources.

Whale Optimization-based Synthetic Minority Oversampling Technique for Binary Imbalanced Datasets

The problem of class imbalance has become a predominant area of research recently. Synthetic Minority Oversampling Technique (SMOTE) stands as a popular and widely adopted oversampling technique that effectively addresses the challenge of class imbalance. However, its performance relies on one of the critical parameters i.e., the number of nearest neighbors, k_neighbors, which is often arbitrarily chosen by the users due to which it may not yield optimal results. Furthermore, the varying imbalance ratios across datasets add complexity to the task of parameter selection in SMOTE. In an effort to address this issue, this paper proposes a hybrid rebalancing technique called Whale optimization algorithm based SMOTE (WOA-SMOTE) that combines a metaheuristic technique, WOA, with SMOTE. The algorithm utilizes the advantages of WOA in finding the optimal value of k_neighbors of SMOTE which is crucial in generating synthetic samples that represent the distribution of samples more appropriately, thereby improving the performance of classifiers on imbalanced datasets. The study evaluates the performance of WOA-SMOTE alongside 6 benchmark sampling techniques on 10 real-world imbalanced datasets from Keel repository. These belong to different domains with imbalance ratio (IR) ranging from 1.25 to 15.46. Four different classifiers are used and the evaluation is based on three performance measures: AUC, g-mean and F1 scores. The experimental results showcase WOA-SMOTE's superior performance over SMOTE in majority of the datasets. Notably, WOA-SMOTE outperforms existing techniques in terms of F1 scores when using SVM and XGBoost classifiers in 8 out of 10 datasets. Moreover, its performance remains impressive in 6 of these datasets with random forest and logistic regression classifiers.

A Pre-Voiding Alarm System Using Wearable Ultrasound and Machine Learning Algorithms for Children With Nocturnal Enuresis.

Nocturnal enuresis is a bothersome condition that affects many children and their caregivers. Post-voiding systems is of little value in training a child into a correct voiding routing while existing pre-voiding systems suffer from several practical limitations, such as cumbersome hardware, assuming individual bladder shapes being universal, and being sensitive to sensor placement error. Methods: A low-voltage ultrasound system with machine learning has been developed in estimating bladder filling status. A custom-made flexible 1D transducer array has been excited by low-voltage coded pulses with a pulse compression technique for an enhanced signal-to-noise ratio. In order to minimize the negative influence of possible transducer misplacement, a multiple-position training strategy using machine learning has been adopted in this work. Three popular classification methods, KNN, SVM and sparse coding, have been utilized to classify the acquired different volumes ranging from 100 ml to 300 ml into two categories: low volume and high volume. The low-volume category requires no further action while the high-volume category triggers an alarm to alert the child and caregiver. Results: When the sensor placement is ideal, i.e., the position of the practical sensor placement is on spot with the trained position, the precision and recall of the classification using sparse coding are [Formula: see text] and [Formula: see text], respectively. Even if the transducer array is misplaced by up to 4.5 mm away from the ideal location, the proposed system is able to maintain high classification accuracy (precision [Formula: see text] and recall [Formula: see text]). Category: Early/Pre-Clinical Research Clinical and Translational Impact: The proposed ultrasound sensor system for nocturnal enuresis is of significant clinical and translational value as it addresses two major issues that limit the wide adoption of similar devices. Firstly, it offers enhanced safety as the entire system has been implemented in the lowvoltage domain. Secondly, the system features ample tolerance to sensor misplacement while maintaining high classification accuracy. These features combined provide a much more user-friendly environment for children and their caregivers than existing devices.

Optimization model for mineral composition data analysis and its application in jade classification

Abstract The classification of jade grade has always been a very critical part of the jade industry, and improving the accuracy of jade grade classification is of great significance to the sustainable development of the jade industry. The study constructs a mineral identification classification model based on Raman spectroscopy + PCA through Raman spectroscopy and PCA principal component analysis and analyzes the data of jade grades and constituents. The actual performance of this paper’s model is explored by comparing its effectiveness with other algorithmic models in jade classification and the accuracy of classification parameters. The model in this paper is feasible in classifying the four grades of Hetian jade (seed material, gobi material, shanliushui material, and shanmu material). Green dense jade’s main minerals are <unk>-quartz and a few other minerals, including albite, hematite, graphite, and tourmaline. The main compositions of the sample jade are SiO2, Al2O3, and K2O. The overall accuracy of this paper’s model in classifying Xinjiang Hotan jade grades is 97.9%, which is significantly higher than that of the KNN classification algorithm and SVM classification algorithm. The total accuracy of this paper’s model on each parameter of jade grade is 85, which is higher than the 60 of the KNN algorithm and the 62 of the SVM algorithm, and the classification accuracy grade is high.

Design and Application of Virtual Simulation Teaching System under Artificial Intelligence Orientation

Abstract This paper builds a virtual simulation teaching system based on the Unity simulation platform, which contains a network connection module, resource loading module, and so on. Using the combination of natural interaction and virtual simulation teaching system, the design of Kinect depth information and SVM classifier for gesture recognition classification is integrated with the virtual simulation teaching scene to realize cross-platform collaborative interaction. The virtual simulation teaching process involves a large number of gesture interaction processes, using mf-score multi-feature selection of features that play a vital role in the classification of the data set, eliminating the raw noise of the customized gesture data and improving the contrast of the gesture image. The results of the practical application of the virtual simulation teaching system show that the discipline to which the virtual simulation course belongs (0.008) and the time of identification (0.047) have a significant effect on its application activity. The multisensory, immersive, and interactive nature of virtual simulation teaching (P < 0.001) all have a highly significant positive effect on learners’ foreign language useability. The foreign language assessment score at the end of the virtual simulation course is (92.74±3.32), which is significantly different from the traditional teaching method (P<0.01). The teaching system in this paper meets the requirements of most learners in terms of interactive interface, function, and content.

ANALYSIS ON CREATING MINIMUM CLASSIFICATION GUIDELINES FOR BREAST CANCER DIAGNOSIS THROUGH THE APPLICATION OF SUPPORT VECTOR MACHINE ALGORITHMS

The Support Vector Machine, or SVM, is a linear model that can be used to solve classification and regression issues. It can solve both linear and nonlinear problems and is useful for a wide range of applications. SVM is a basic concept: The method divides the data into classes by drawing a line or hyperplane. The goal of this study is to see if putting past knowledge into an SVM classifier for Breast Cancer Diagnosis can increase its accuracy. In the diagnosis of breast cancer, a rule-based classifier is extensively utilized. In recent decades, a classifier with good illness classification performance has been developed, and it is in high demand. Because classification rules are derived from previous diagnostics with a large number of features, it is difficult to create a minimum number of high performance rules while retaining all diagnostic information. In this paper, we proposed a SVM model in order to provide the best performance classifier. Based on the experimental results of the SVM model, we can achieve an accuracy of 1.0.

Optimal SVM Classifier based Cross-layer Design in Ad-hoc Wireless Network

Optimal SVM Classifier based Cross-layer Design in Ad-hoc Wireless Network

Development of an Approach for Image Forgery Detection Using Machine Learning Algorithms

Digital picture fraud detection is an increasing societal necessity due to the importance of verified images. The detection of picture copying, splicing, retouching, and re-sampling forgeries is included. In the absence of digital signatures or watermarks, passive picture authentication may serve as an alternative to active authentication. Passive techniques, every so often recognized as blind techniques, could take place without preceding knowledge of the picture or its reference. Identifying counterfeiting picture or tampering was a research field for long a period of time, triggered via the Internet, online platforms, social messaging platforms, and extensive digital image usage. The rate of failure could be a key factor for examining the alteration of picture or forgery, among other existing methods. The research applies almost six common algorithms related to machine learning in order to extract features from Lightweight, Spatial Exploitation, and Residual deep learning models on benchmark datasets MICC-F220, Columbia, and CoMoFoD. The models of incorporated deep learning could consist of AlexNet, GoogleNet, VGG16, VGG19, SqueezeNet, MobileNetV2, ShuffleNet, ResNet-18, ResNet-50, and ResNet-101 for spatial exploitation. Fine-tuning is applied to the top three deep learning models, optimizing hyperparameters centered on indicators of performance for every single benchmark dataset. Tweaked SqueezeNet, MobileNetV2, and ShuffleNet deep learning models with SGDM Optimizer and SVM classifier yielded the best results for MICC-F220 dataset. Fine-tuned VGG19, MobileNetV2, and ResNet-50 deep learning models with SGDM Optimizer and SVM v classifier yielded the best results for Columbia dataset. In CoMoFoD dataset, fine-tuned AlexNet, MobileNetV2, and ShuffleNet deep learning models with SGDM Optimizer and SVM classifier yielded the best results. The proposed approach, utilizing machine learning algorithms and deep learning features, enhanced forgery detection and reduced false positives. Results were validated on benchmark image forgery datasets and compared to current methods.