SVM Classifier Research Articles

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.

AGRICULTURAL LAND IMAGE CLASSIFICATION USING KNN AND COMPARE WITH RECURRENT NEURAL NETWORK

Over the last few years, the research into agriculture has gained momentum, showing signs of rapid growth. The latest to appear on the scene is bringing convenience in how agriculture can be done by employing various computational technologies. To implement this project we have used LAND satellite images which contains images of FOREST, AGRICULTURE LAND, URBAN AREA and Range LAND. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km2 within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RNN and kNN. In relation to the sample size, all three classifiers showed a similar and high OA when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets.

Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography.

Regional Wall Motion Abnormality (RWMA) serves as an early indicator of myocardial infarction (MI), the global leader in mortality. Accurate and early detection of RWMA is vital for the successful treatment of MI. Current automated echocardiography analyses typically concentrate on peak values from left ventricular (LV) displacement curves, based on LV contour annotations or key frames during the heart's systolic or diastolic phases within a single echocardiographic cycle. This approach may overlook the rich motion field features available in multi-cycle cardiac data, which could enhance RWMA detection. In this research, we put forward an innovative approach to detect RWMA by harnessing motion information across multiple echocardiographic cycles and multi-views. Our methodology synergizes U-Net-based segmentation with optical flow algorithms for detailed cardiac structure delineation, and Temporal Convolutional Networks (ConvNet) to extract nuanced motion features. We utilize a variety of machine learning and deep learning classifiers on both A2C and A4C views echocardiograms to enhance detection accuracy. A three-phase algorithm-originating from the HMC-QU dataset-incorporates U-Net for segmentation, followed by optical flow for cardiac wall motion field features. Temporal ConvNet, inspired by the Temporal Segment Network (TSN), is then applied to interpret these motion field features, independent of traditional cardiac parameter curves or specific key phase frame inputs. Employing five-fold cross-validation, our SVM classifier demonstrated high performance, with a sensitivity of 93.13%, specificity of 83.61%, precision of 88.52%, and an F1 score of 90.39%. When compared with other studies using the HMC-QU datasets, these Fig s stand out, underlining our method's effectiveness. The classifier also attained an overall accuracy of 89.25% and Area Under the Curve (AUC) of 95%, reinforcing its potential for reliable RWMA detection in echocardiographic analysis. This research not only demonstrates a novel technique but also contributes a more comprehensive and precise tool for early myocardial infarction diagnosis.

A novel method for vegetable and fruit classification based on using diffusion maps and machine learning

Vegetable and fruit classification can help all links of agricultural product circulation to better carry out inventory management, logistics planning and supply chain coordination, and improve the efficiency and response speed of the supply chain. However, the current classification of vegetables and fruits mainly relies on manual classification, which inevitably introduces the influence of human subjective factors, resulting in errors and misjudgments in the classification of vegetables and fruits. In response to this serious problem, this research proposes an efficient and reproducible novel model to classify multiple vegetables and fruits using handcrafted features. In the proposed model, preprocessing operations such as Gaussian filtering, grayscale and binarization are performed on the pictures of vegetables and fruits to improve the quality of the pictures; statistical texture features representing vegetable and fruit categories, wavelet transform features and shape features are extracted from the preprocessed images; the feature dimension reduction method of diffusion maps is used to reduce the redundant information of the combined features composed of statistical texture features, wavelet transform features and shape features; five effective machine learning methods were used to classify the types of vegetables and fruits. In this research, the proposed method was rigorously verified experimentally and the results show that the SVM classifier achieves 96.25% classification accuracy of vegetables and fruits, which proves that the proposed method is helpful to improve the quality and management level of vegetables and fruits, and provide strong support for agricultural production and supply chain.

Improvement Path of Legal System Related to ChatGPT Application Combined with Decision Tree Algorithm

Abstract Artificial Intelligence (AI) has been widely used in the social and legal fields, and ChatGPT, after AI painting, has once again set off a wave of discussion on whether AI and its generated works can obtain legal protection. Starting from the theoretical orientation of the origin of ChatGPT legal governance, this paper proposes the legal positioning and layered governance framework of ChatGPT application. It explores the role mechanism of ChatGPT empowering legal modernization and combs through the realistic dilemmas of ChatGPT-generated content data compliance legalization. To effectively analyze legal risks in the process of the ChatGPT application, data crawling technology and SMOTE oversampling technology are utilized to obtain ChatGPT application data and produce datasets. The Stacking integration strategy is introduced to combine the Random Forest in the Decision Tree Algorithm, GBDT algorithm, and Support Vector Machine to construct the legal risk prediction model of the ChatGPT application. For the effectiveness of the model, the ChatGPT application dataset is used to analyze the accuracy, ROC curve, and AUC value, which provides a reference for improving the legal system related to the ChatGPT application. The results show that the accuracy of the SVM classifier reaches 0.839, the correctness of the GBDT model is 0.947, and the AUC value of ChatGPT legal risk prediction based on the Stacking integration strategy is 0.947. Based on the inspiration of the decision tree algorithm, the improvement of the legal system related to the ChatGPT application should be improved in terms of generating content and allocating risk. Based on the insights of the decision tree algorithm, the improvement of the legal system related to the ChatGPT application should be made based on the dimensions of generation content and risk allocation.

Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital

Efforts have been made to address the adverse impact of heart disease on society by improving its treatment and diagnosis. This study uses the Jordan University Hospital (JUH) Heart Dataset to develop and evaluate machine‐learning models for predicting heart disease. The primary objective of this study is to enhance prediction accuracy by utilizing a comprehensive approach that includes data preprocessing, feature selection, and model development. Various artificial intelligence techniques, namely, random forest, SVM, decision tree, naive Bayes, and K‐nearest neighbours (KNN) were explored with particle swarm optimization (PSO) for feature selection. These results have substantial implications for early disease detection, diagnosis, and tailored treatment, potentially aiding medical professionals in making well‐informed decisions and improving patient outcomes. The PSO is used to select the most compelling features out of 58 features. Experiments on a dataset comprising 486 heart disease patients at JUH yielded a commendable classification accuracy of 94.3% using our proposed system, aligning with state‐of‐the‐art performance. Notably, our research utilized a distinct dataset provided by the corresponding author, while alternative algorithms in our study achieved accuracies ranging from 85% to 90%. These results emphasize the superior accuracy of our proposed system compared to other algorithms considered, particularly highlighting the SVM classifier with PSO as the most accurate, contributing significantly to improving heart disease diagnosis in regions like Jordan, where cardiovascular diseases are a leading cause of mortality.

Image Processing-based Performance Evaluation of KNN and SVM Classifiers for Lung Cancer Diagnosis

Image Processing-based Performance Evaluation of KNN and SVM Classifiers for Lung Cancer Diagnosis

Design of Basketball Teaching Aid System Based on Artificial Intelligence

Abstract Artificial intelligence has penetrated every aspect of people’s lives with the rapid development of science and technology. In basketball, how to effectively utilize artificial intelligence technology to improve the effectiveness of training has become a problem worth studying. A Mediapipe-based 3D human posture domain adaptive detection method is proposed in the study by combining statistics and human limb proportion simulation. The modified SVM classification algorithm is used to compare and classify the newly acquired key point data information of the system with the standard basketball action key point data information preset by the system to judge whether the basketball action is up to the standard or not, so as to design the basketball teaching aid system based on artificial intelligence. Based on this basis, after testing the recognition performance of the system, its practical application effect is explored. The total basketball scores improved after the experiment (86.26±2.228 for the experimental group and 81.34±9.672 for the control group), but there was no significant difference (P>0.05). The control group was slower than the experimental group by 0.64 seconds and 0.77 seconds overall in the cross-step breakout time with the ball and the same-side step breakout time with the ball, respectively, indicating that the experimental group was more proficient in basketball technology. The system has been tested in various ways to meet the multiple needs of the users, meet the expected standards, and be able to be applied in real-world scenarios.

Exploration of badminton teaching reform and development ideas in colleges and universities under the background of big data

Abstract Intelligent wearable devices effectively collect real-time data and physiological indicators of students performing badminton sports activities through the functions of collecting, organizing, and analyzing mobile big data. Meanwhile, the SVM classification algorithm based on data fusion theory is proposed to realize the collection and monitoring of human posture and other signals. The smart wearable device is used as an auxiliary teaching means to help students quickly master basic movement skills, and a 32-credit-hour teaching experiment is conducted to compare and analyze the effect of its teaching impact. It was analyzed that the physical quality and basic skills of hairball of the two groups of students before the experiment were basically similar (P>0.05). The mean values of badminton long-throw movement of the experimental group before the experiment and after the experiment were 4.72 and 5.73, respectively, and showed a significant difference (P<0.01), and the four movements such as forehand serve high long ball were also significantly improved (P<0.01). 43.75% of the students felt that the smart wearable device badminton special learning was of interest. The reform and development of badminton teaching in colleges and universities in the era of big data can be promoted by this paper’s method.

Machine Learning Based Analysis and Prediction of Emotional Expressions in Dance Movements

Abstract Dance movement is a powerful way to convey human emotions, and analyzing and predicting the emotional expression of dance movement through machine learning has become a hotspot in the field of artificial intelligence research nowadays. This paper employs the Seagull optimization algorithm to enhance the SVM classification model, laying the algorithmic groundwork for the research, and refines it to align with the research requirements. The classification of dance movements is accomplished by the nonlinear regression algorithm in the support vector machine regression algorithm, while the task of capturing dance movements is realized by means of Euler angles to describe the orientation, rotation matrices to transform vectors between different coordinate systems, and quaternions to optimize the Euler angles. Finally, this paper analyses and predicts the emotional expression of dance movements using a classification loss model (LSTMBO) and a W-RNN model that incorporates the weights of emotion words. In this paper, tests on the classification algorithm revealed that the algorithm's classification accuracy is above 90% for all datasets used in the research. Moreover, the performance and effectiveness of dance action capture are significantly better than other comparative algorithms. Simultaneously, this paper's algorithm achieves an accuracy of over 80% in predicting emotions expressed through dance movements. Numerous experiments have proven the effectiveness and superiority of the algorithm model in this paper, thereby promoting the research and development of the field.

Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs.

Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in severe cases, it may progress to hemorrhagic shock. Pelvic radiography can quickly diagnose pelvic fractures, and the Association for Osteosynthesis Foundation and Orthopedic Trauma Association (AO/OTA) classification system is useful for evaluating pelvic fracture instability. This study aimed to develop a radiomics-based machine-learning algorithm to quickly diagnose fractures on pelvic X-ray and classify their instability. data used were pelvic anteroposterior radiographs of 990 adults over 18 years of age diagnosed with pelvic fractures, and 200 normal subjects. A total of 93 features were extracted based on radiomics:18 first-order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM features. To improve the performance of machine learning, the feature selection methods RFE, SFS, LASSO, and Ridge were used, and the machine learning models used LR, SVM, RF, XGB, MLP, KNN, and LGBM. Performance measurement was evaluated by area under the curve (AUC) by analyzing the receiver operating characteristic curve. The machine learning model was trained based on the selected features using four feature-selection methods. When the RFE feature selection method was used, the average AUC was higher than that of the other methods. Among them, the combination with the machine learning model SVM showed the best performance, with an average AUC of 0.75±0.06. By obtaining a feature-importance graph for the combination of RFE and SVM, it is possible to identify features with high importance. The AO/OTA classification of normal pelvic rings and pelvic fractures on pelvic AP radiographs using a radiomics-based machine learning model showed the highest AUC when using the SVM classification combination. Further research on the radiomic features of each part of the pelvic bone constituting the pelvic ring is needed.

Review to unfold the role of Machine Learning Algorithms in Natural Language Processing

This research review has been written to explore the role of machine learning algorithms in natural language processing (NLP), a computer field that offers human-like comprehension of virtual text. The main motive of the article is to reveal the participation of machine learning (ML) in NLP and its scope in coherent fields. In this study, the researchers have used a systematic literature review approach to explore the role of ML algorithms in NLP. Highlighting the techniques of ML algorithms as supervised, unsupervised, semi-supervised, and reinforcement methods, this study discloses the connectivity with morphological, semantic, syntactic, pragmatic, and discourse analysis in NLP. The article describes that ML/NLP has immense applications in different fields where several tools of ML/NLP are utilized. The researchers have studied the use of chatbots, text summarization tools, web scrapping, sentimental analysis in the social media stock market, medical field disease detection, and fraud detection. ML’s role and contributions to the progressions are detained in current studies. A distinct comparison has been made between the past present and future of ML in NLP. ML algorithms and applications with examples are disclosed in the classification of logistic regression, SVM, Naive Bayes, K-Nearest Neighbor, and decision tree. Data distortion, interpretation of the research findings, and contextual ambiguity are visibly determined as challenges and discussed in the study. This writing opens up to the recent advancements and advantages in multiple areas correspondingly. The discussions and findings expose the role of ML in opening the gateway to the profound revolutionary search engines, algorithms, and multiple techniques through the development of technology and its evolution to adapt to new language differences, slang, and changes in language use.

COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES

Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods.

IMPLEMENTATION OF SUPPORT VECTOR MACHINE METHOD IN CLASSIFYING SCHOOL LIBRARY BOOKS WITH COMBINATION OF TF-IDF AND WORD2VEC

The development of technology in education is integral to enhancing its quality, such as implementing information technology in school libraries. Searching for books in school libraries is time-consuming due to conventional book classification, lacking organization based on classifications. Therefore, implementing information technology in school libraries is crucial to improve library management effectiveness. An innovative solution optimizing library management involves leveraging artificial intelligence, particularly machine learning. In applying machine learning to library book classification, Support Vector Machine acts as an algorithm understanding patterns and characteristics of book titles, categorizing them into Dewey Decimal Classification (DDC). The dataset comprises 10 classes aligned with DDC. Random data collection follows an 80:20 scale for training and testing data. Data preprocessing is an initial research stage, addressing imbalanced data through oversampling. Testing the SVM algorithm with a linear kernel and C = 1 parameter is conducted three times using different feature extraction methods: TF-IDF alone, Word2Vec alone, and a combination of TF-IDF and Word2Vec. Model performance evaluation employs K-Fold Cross-Validation. After the three objective tests, the most accurate book classification results were obtained using a combination of TF-IDF and Word2Vec feature extraction. It's concluded that SVM's book classification method can be applied, yielding the highest accuracy of 73% with the TF-IDF and Word2Vec feature extraction combination. This outperforms other feature extraction methods, with precision at 83%, recall at 72%, and an F1-Score of 76%.

A Comparative Study of Machine Learning Models For NCAA Men’s Basketball Tournament Games Outcome Prediction

The evolution of machine learning has produced many avant-garde prediction algorithms, some of which provide more accurate predictions than conventional statistical tools. For the last two decades, machine learning (ML) approaches have largely been employed to predict game results. The purpose of this study is to utilize data gathered from web sources on the performance of 354 NCAA basketball teams over five seasons from 2015 to 2019 to forecast the results of NCAA men’s basketball tournament games with an assortment of big data and machine learning classification models. Among these include Sweet Sixteen, Elite Eight, and Final Four. The prediction results of each model were analysed and compared, and Decision Tree showed the best prediction performance compared with KNN, Logistic Regression, and SVM classification models, with a prediction accuracy of 75.71%, but Decision Tree was prone to overfitting problems. However, the Decision Tree is prone to overfitting problems, while Random Forest can correct the overfitting problem of Decision Tree by bagging and reduce the variance of Decision Tree prediction. Therefore, this study hypothesized that Random Forest would outperform Decision Tree in predicting NCAA game results. The results showed that, after a comprehensiveanalysis and comparison of the evaluation metrics of the Decision Tree and Random Forest models, Random Forest was found to have better acceptable forecast performance than Decision Tree, with a prediction accuracy of 85.71%.

Automatic multiclass classification of laryngeal cancer using deep convolution neural networks

AbstractIn this work, the classification of laryngeal cancer is attempted using deeply learned features obtained using Inception V3, Squeezenet, and VGG‐16 embedders in the Orange toolbox. Machine learning algorithms such as KNN, SVM, random forest, decision tree, and neural network classifiers are employed to classify the stages or categories of laryngeal cancer. The ranking of deep learning feature values is carried out using state‐of‐the‐art metrics such as information gain, information gain ratio, chi‐square, and reliefF. It is observed that the performance of the algorithms is affected by the cross‐validation.