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Chang, Chih-Chung, and Chih-Jen Lin. "Training v-Support Vector Regression: Theory and Algorithms." Neural Computation 14, no. 8 (2002): 1959–77. http://dx.doi.org/10.1162/089976602760128081.
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We discuss the relation betweenɛ-support vector regression (ɛ-SVR) and v-support vector regression (v-SVR). In particular, we focus on properties that are different from those of C-support vector classification (C-SVC) andv-support vector classification (v-SVC). We then discuss some issues that do not occur in the case of classification: the possible range of ɛ and the scaling of target values. A practical decomposition method forv-SVR is implemented, and computational experiments are conducted. We show some interesting numerical observations specific to regression.
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Flower, Dr K. Little. "Text Classification from positive and unlabeled examples using Support Vector Machine (SVM)." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27391.
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Support Vector Machines (SVMs) are a powerful machine learning algorithm that can be used for text classification. Traditional SVMs require both positive and negative examples to train the model. However, in many real-world scenarios, it can be difficult or expensive to obtain negative examples. This study explores the application of SVMs in textclassification when only positive and unlabeled examples are available. Theresults showed that the proposed approach achieved competitive performance compared to traditional supervised methods, even when trained on limited labeled examples. The utilization of SVC in the proposed approach is twofold. First, the SVC model is used to classify theunlabeled examples as positive or negative. Second, the SVC model is used to select the positive examples that are added to the training set. Thisiterative process of training and selecting examples helps to improve the classification accuracy of the SVM model. The proposed approach is a promising method for text classification when only positive and unlabeled examples are available. Theapproach is effective in achieving competitive performance compared to traditional supervised methods, even when trained on limited labeled examples. This work contributes to enhancing text classification techniques, particularly in situationswith resource constraints and challenging label acquisition. Keywords: Support Vector Machine(S VM), Text Classifications ,Text Mining, SVC, Supervised Methods
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Gu, Tian Hong, Wei Lv, Xia Shao, and Wen Cong Lu. "Detection of High Energy Materials Using Support Vector Classification." Advanced Materials Research 554-556 (July 2012): 1628–31. http://dx.doi.org/10.4028/www.scientific.net/amr.554-556.1628.
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Based on the element contents of N, O, H and C of objects detected by γ-ray resonance, support vector classification (SVC) method was used to construct the model for distinguishing high energy materials (HEMs) from ordinary ones. It was found that the accuracy of prediction was 95.9% based on the leave-one-out cross validation (LOOCV) test. The results indicated that the performance of SVC model is good enough to detect HEMs in the presence of ordinary materials for the purpose of security checking.
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Zhang, Bowen. "Using Logistic Regression and Support Vector Classification to Predict Cancer." Highlights in Science, Engineering and Technology 92 (April 10, 2024): 288–94. http://dx.doi.org/10.54097/bkvnxg90.
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This study investigates the application of machine learning (ML) algorithms in the early diagnosis of breast cancer, focusing on logistic regression and Support Vector Classification (SVC). Utilizing a dataset from Kaggle, which includes diverse clinical features from breast mass samples, the research conducts a comparative analysis of these models in terms of accuracy and interpretability. Our findings reveal that both logistic regression and SVC demonstrate high precision in distinguishing between benign and malignant tumors, with SVC showing a marginally superior performance due to its higher sensitivity and lower rate of false negatives. The study emphasizes the potential of ML in enhancing cancer diagnostic processes, highlighting the importance of non-invasive, cost-effective, and accurate diagnostic alternatives. It also addresses the challenges of model interpretability and the need for more transparent ML applications in clinical settings. This research paves the way for future advancements in medical diagnostics, offering promising directions for integrating ML algorithms into clinical decision-making and patient care.
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Xia, Ding, Huiming Tang, Sixuan Sun, Chunyan Tang, and Bocheng Zhang. "Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification." Remote Sensing 14, no. 11 (2022): 2707. http://dx.doi.org/10.3390/rs14112707.
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A landslide susceptibility model based on a metaheuristic optimization algorithm (germinal center optimization (GCO)) and support vector classification (SVC) is proposed and applied to landslide susceptibility mapping in the Three Gorges Reservoir area in this paper. The proposed GCO-SVC model was constructed via the following steps: First, data on 11 influencing factors and 292 landslide polygons were collected to establish the spatial database. Then, after the influencing factors were subjected to multicollinearity analysis, the data were randomly divided into training and testing sets at a ratio of 7:3. Next, the SVC model with 5-fold cross-validation was optimized by hyperparameter space search using GCO to obtain the optimal hyperparameters, and then the best model was constructed based on the optimal hyperparameters and training set. Finally, the best model acquired by GCO-SVC was applied for landslide susceptibility mapping (LSM), and its performance was compared with that of 6 popular models. The proposed GCO-SVC model achieved better performance (0.9425) than the genetic algorithm support vector classification (GA-SVC; 0.9371), grid search optimized support vector classification (GRID-SVC; 0.9198), random forest (RF; 0.9085), artificial neural network (ANN; 0.9075), K-nearest neighbor (KNN; 0.8976), and decision tree (DT; 0.8914) models in terms of the area under the receiver operating characteristic curve (AUC), and the trends of the other metrics were consistent with that of the AUC. Therefore, the proposed GCO-SVC model has some advantages in LSM and may be worth promoting for wide use.
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Zhang, Chunhua, Xiaojian Shao, and Dewei Li. "Knowledge-based Support Vector Classification Based on C-SVC." Procedia Computer Science 17 (2013): 1083–90. http://dx.doi.org/10.1016/j.procs.2013.05.137.
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Chen, Yang, Lei Sun, Yangwen Huang, Lin Ou, and Ying Su. "Raman spectral statistical classification of nasopharyngeal carcinoma and nasopharyngeal normal cell lines based on support vector classification." Spectroscopy 26, no. 4-5 (2011): 231–36. http://dx.doi.org/10.1155/2011/672430.
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Raman spectroscopy (RS) has been used in the discrimination of normal and tumor cells for years. It is very important to validate an existing classification model using different algorithms. In this work, two algorithms of support vector classification (SVC) are utilized to validate our previous work about a LDA classification model of nasopharyngeal carcinoma (NPC) cell lines C666-1, CNE2 and nasopharyngeal normal cell line NP69. All of these two SVC algorithms use the same data set as the previous LDA model and, achieve great sensitivity and specificity. The final results show that our previous LDA classification model could be supported by different SVC algorithms and this demonstrates our classification model is reliable and may be helpful to the realization of RS to be one of diagnostic techniques of NPC.
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Li, Pengfei, Yongying Jiang, and Jiawei Xiang. "Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/145807.
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To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples.
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Du, Qing Zhi. "Support Vector Linearly Inseparable Algorithm and its Optimizing Microwave Calcining Technology of Ammonium Uranyl Carbonate." Advanced Materials Research 739 (August 2013): 177–82. http://dx.doi.org/10.4028/www.scientific.net/amr.739.177.
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Support vector machines (SVMs) are a promising type of learning machine based on structural risk minimization and statistical learning theory, which can be divided into two categories: support vector classification (SVC) machines and support vector regression machines (SVR). The basic elements and algorithms of SVC machines are discussed. As modeling and prediction methods are introduced into the experiment of microwave calcining AUC, the better prediction accuracy and the better fitting results are compare with back propagation (BP) neural network method. This is conducted to elucidate the good generalization performance of SVMs, especially good for dealing with the data of some nonlinearity.
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Pham, Quoc, Tao-Chang Yang, Chen-Min Kuo, Hung-Wei Tseng, and Pao-Shan Yu. "Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling." Water 11, no. 3 (2019): 451. http://dx.doi.org/10.3390/w11030451.
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A statistical downscaling approach for improving extreme rainfall simulation was proposed to predict the daily rainfalls at Shih-Men Reservoir catchment in northern Taiwan. The structure of the proposed downscaling approach is composed of two parts: the rainfall-state classification and the regression for rainfall-amount prediction. Predictors of classification and regression methods were selected from the large-scale climate variables of the NCEP reanalysis data based on statistical tests. The data during 1964–1999 and 2000–2013 were used for calibration and validation, respectively. Three classification methods, including linear discriminant analysis (LDA), random forest (RF), and support vector classification (SVC), were adopted for rainfall-state classification and their performances were compared. After rainfall-state classification, the least square support vector regression (LS-SVR) was used for rainfall-amount prediction for different rainfall states. Two rainfall states (i.e., dry day and wet day) and three rainfall states (dry day, non-extreme-rainfall day, and extreme-rainfall day) were defined and compared for judging their downscaling performances. The results show that RF outperforms LDA and SVC for rainfall-state classification. Using RF for three-rainfall-states classification and LS-SVR for rainfall-amount prediction can improve the extreme rainfall downscaling.
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Li, Peng Fei, and Jia Wei Xiang. "Fault Diagnosis of Gearbox in Wind Turbine Based on Wavelet Transform and Support Vector Machine." Applied Mechanics and Materials 536-537 (April 2014): 18–21. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.18.
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To deal with the lack of effective experimental data under the current condition for gearbox fault pattern recognition, the Wind Turbine Drivetrain Diagnostics Simulator (WTDS) was used for experimental investigation and gained large number of gear fault samples. The wavelet transform is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the support vector classification (SVC). The experimental results show that the hybrid approach is robust to noise and has high classification accuracy.
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Oktaviani, Gracelya, and Christine Dewi. "Sentimen Analisis Penggunaan Aplikasi Canva Menggunakan Support Vector Classification." Jurnal Indonesia : Manajemen Informatika dan Komunikasi 6, no. 1 (2025): 499–510. https://doi.org/10.35870/jimik.v6i1.1240.
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This Canva graphic design application is very popular among many people including students, where this application is very helpful in developing graphic ideas with features that are easy to use by users to create an attractive design and make time efficient. This study discusses the classification of sentiment towards Canva applications using the Naïve Bayes and Support Vector Machine methods. In this study, the data used are user reviews taken from the Google Play Store application, then the reviews are analyzed and classified into three categories, namely, positive, negative, neutral and after that the data will be processed in several stages; data collection to research results. And the result of this study is that the Support Vector Machine (SVM) method which has the best performance with SVC parameters gets an accuracy of 77.48%, followed by Support Vector Machines (SVM) with LinearSVC parameters with an average accuracy value of 71.80%.
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Umam, Chaerul, Lekso Budi Handoko, and Folasade Olubusola Isinkaye. "Performance Analysis of Support Vector Classification and Random Forest in Phishing Email Classification." Scientific Journal of Informatics 11, no. 2 (2024): 367–74. https://doi.org/10.15294/sji.v11i2.3301.
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Purpose: This study aims to conduct a performance analysis of phishing email classification system using machine learning algorithms, specifically Random Forest and Support Vector Classification (SVC). Methods/Study design/approach: The study employed a systematic approach to develop a phishing email classification system utilizing machine learning algorithms. Implementation of the system was conducted within the Jupyter Notebook IDE using the Python programming language. The dataset, sourced from kaggle.com, comprised 18,650 email samples categorized into secure and phishing emails. Prior to model training, the dataset was divided into training and testing sets using three distinct split percentages: 60:40, 70:30, and 80:20. Subsequently, parameters for both the Random Forest and Support Vector Classification models were carefully selected to optimize performance. The TF-IDF Vectorizer method was employed to convert text data into vector form, facilitating structured data processing. Result/Findings: The study's findings reveal notable performance accuracies for both the Random Forest model and Support Vector Classification across varying data split percentages. Specifically, the Support Vector Classification consistently outperforms the Random Forest model, achieving higher accuracy rates. At a 70:30 split percentage, the Support Vector Classification attains the highest accuracy of 97.52%, followed closely by 97.37% at a 60:40 split percentage. Novelty/Originality/Value: Comparisons with previous studies underscored the superiority of the Support Vector Classification model. Therefore, this research contributes novel insights into the effectiveness of this machine learning algorithms in phishing email classification, emphasizing its potential in enhancing cybersecurity measures.
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Ernawati, Siti, Risa Wati, and Nuzuliarini Nuris. "Support Vector Classification with Hyperparameters for Analysis of Public Sentiment on Data Security in Indonesia." Jurnal Riset Informatika 5, no. 1 (2022): 529–36. http://dx.doi.org/10.34288/jri.v5i1.481.
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The development of Information Technology makes increasing use of the internet. This raises the vulnerability of data security. Cyber attacks in Indonesia caused many tweets on social media Twitter. Some are positive, and some are negative. The problem of this study is to determine the public sentiment towards data security in Indonesia, while the purpose of this study is how the response or evaluation of the government of Indonesia to the many perceptions of people who lack confidence in data security in Indonesia. Data obtained from twitter with as much as 706 data was processed using python with a percentage of 10% test data and 90% training data. Weighting is done using TF-IDF, and then the Data is processed using the Support Vector Machine algorithm using the SVC (Support Vector Classification) library. Support Vector Classification with RBF kernel classifies Text well to obtain AUC value with good classification category. Utilizing one of the hyperparameter techniques, which is a grid search technique that can compare the accuracy of test results. The test results using SVC with RBF kernel obtained an accuracy value of 0.87, Precision of 0.82, recall of 0.94, and F1_Score of 0.87. This study is expected to be used by decision-makers related to public confidence in data security in Indonesia
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Cho, Byeong-Hyo, Yong-Hyun Kim, Ki-Beom Lee, Young-Ki Hong, and Kyoung-Chul Kim. "Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity." Sensors 22, no. 12 (2022): 4378. http://dx.doi.org/10.3390/s22124378.
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It is necessary to convert to automation in a tomato hydroponic greenhouse because of the aging of farmers, the reduction in agricultural workers as a proportion of the population, COVID-19, and so on. In particular, agricultural robots are attractive as one of the ways for automation conversion in a hydroponic greenhouse. However, to develop agricultural robots, crop monitoring techniques will be necessary. In this study, therefore, we aimed to develop a maturity classification model for tomatoes using both support vector classifier (SVC) and snapshot-type hyperspectral imaging (VIS: 460–600 nm (16 bands) and Red-NIR: 600–860 nm (15 bands)). The spectral data, a total of 258 tomatoes harvested in January and February 2022, was obtained from the tomatoes’ surfaces. Spectral data that has a relationship with the maturity stages of tomatoes was selected by correlation analysis. In addition, the four different spectral data were prepared, such as VIS data (16 bands), Red-NIR data (15 bands), combination data of VIS and Red-NIR (31 bands), and selected spectral data (6 bands). These data were trained by SVC, respectively, and we evaluated the performance of trained classification models. As a result, the SVC based on VIS data achieved a classification accuracy of 79% and an F1-score of 88% to classify the tomato maturity into six stages (Green, Breaker, Turning, Pink, Light-red, and Red). In addition, the developed model was tested in a hydroponic greenhouse and was able to classify the maturity stages with a classification accuracy of 75% and an F1-score of 86%.
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Jiang, Lai, and Runming Yao. "Modelling personal thermal sensations using C-Support Vector Classification (C-SVC) algorithm." Building and Environment 99 (April 2016): 98–106. http://dx.doi.org/10.1016/j.buildenv.2016.01.022.
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Hu, Ting-Chen, Jason C. H. Chen, Gino K. Yang, and Cheng-Wei Chen. "Development of a Military Uniform Size System Using Hybrid Support Vector Clustering with a Genetic Algorithm." Symmetry 11, no. 5 (2019): 665. http://dx.doi.org/10.3390/sym11050665.
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Military uniforms serve as an essential symbol for servicemen and an important image of national and military dignity. The current military uniform size system in Taiwan, which features various types of military uniforms based on the body sizes of servicemen, was formulated in 1986. This size classification system includes numerous groups and is too complex, leading to inventory overstock, increased inventory cost and warehouse staff workload, and a waste of national defense resources. This study used support vector clustering (SVC) with genetic algorithm (GA) models to improve the upper garment size system for uniforms. The SVC technique was employed to classify sizes, and the GA technique was used to determine optimal parameter values for the SVC model. This paper developed an upper garment size system that can increase the fit of uniforms to servicemen’s body sizes and reduce the number of size groups, thereby alleviating warehouse staff workload and inventory cost.
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Yang, Biao, Wei Li, Li Jun Liu, et al. "Support Vector Machine and its Predicting Stability of Partially Stabilized Zirconia by Microwave Heating Preparation." Advanced Materials Research 382 (November 2011): 281–88. http://dx.doi.org/10.4028/www.scientific.net/amr.382.281.
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Support vector machines (SVMs) are a promising type of learning machine based on structural risk minimization and statistical learning theory, which can be divided into two categories: support vector classification (SVC) machines and support vector regression machines (SVR). The basic elements and algorithms of SVR machines are discussed. As modeling and prediction methods are introduced into the experiment of microwave preparing partially stabilized zirconia (PSZ) and built the stability prediction model, the better prediction accuracy and the better fitting results are verified and analyzed. This is conducted to elucidate the good generalization performance of SVMs, specially good for dealing with nonlinear data.
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Sudianto, Sudianto, Juan Arton Arton Masheli, Nursatio Nugroho, Rafi Wika Ananda Rumpoko, and Zarkasih Akhmad. "Comparison of Support Vector Machines and K-Nearest Neighbor Algorithm Analysis of Spam Comments on Youtube Covid Omicron." JURNAL TEKNIK INFORMATIKA 15, no. 2 (2022): 110–18. http://dx.doi.org/10.15408/jti.v15i2.24996.
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Every time a new variant of Coronavirus (Covid-19) appears, themedia or news platforms review it to find out whether the new variantis more dangerous or contagious than before. One of the media orplatforms that is fast in presenting news in videos is YouTube.YouTube is a social media that can upload videos, watch videos, andcomment on the video. The comment field on YouTube videos cannotbe separated from spam comments that annoy other users who want tofollow or participate in the comment column. Indication of spamcomments is still done by observing one by one; this is very inefficientand time-consuming. This study aims to create a model that canclassify spam on YouTube comments. The classification method uses the SVM (Support Vector Machines) algorithm and the KNN (K-Nearest Neighbor) algorithm to identify spam comments or not with comment data taken from Omicron's Covid-19 news video on national news channels. The classification results show that the SVM method is superior inaccuracy with the Linear SVC algorithm of 75.12%, SVC of 76.11%, and Nu-SVC of 77.11%. While the KNN algorithm with k=2 is 65.67%, k=4 is 64.51%, k=6 is 62.35%.
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Chen, Wei Dong, Ping Jia, Xian De Wu, Yan Chun Yu, Feng Chao Zhang, and Sheng Zhuo Lu. "The SVC Based AFOSM Method for the Structure Reliability Sensitivity Analysis." Applied Mechanics and Materials 477-478 (December 2013): 146–49. http://dx.doi.org/10.4028/www.scientific.net/amm.477-478.146.
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The limit state function (LSF) is implicit to many structure reliability analysis problems, which may make some classical reliability method complicated to be applied. One of the surrogate methods-support vector classification (SVC) was applied in the structural reliability analysis herein which has not been applied to structure reliability analysis until recent years. Then the advanced first order second moment method (AFOSM) can be applied. The expressions of structure system reliability sensitivity to basic variable were deduced. The flow of how to call the SVC program was presented. An example was shown to compare the SVC based method with some other classical reliability analysis methods. The results are accurately accepted and the advantages of SVC are analyzed.
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Islam, Kazi Samiul, Gourab Roy, Nafiz Nahid, et al. "Advancing Bangla typography: machine learning and transfer learning based font detection and classification approach using the ‘Bang-laFont45’ dataset." Journal of Computer Sciences Institute 35 (June 30, 2025): 166–74. https://doi.org/10.35784/jcsi.7120.
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This paper presents a dataset for detecting and classifying Bangla fonts, consisting of 28,000 images across 45 classes, aimed at supporting font users and typography researchers. Four traditional machine learning models— Support Vector Classifier (SVC), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Random Forest—achieved accuracies of 93.43%, 92.37%, 84.71%, and 81.48%, respectively, with SVC performing best. Six transfer learning models—VGG-16, VGG-19, ResNet-50, MobileNet-v3, Xception, and Inception—were trained, yielding accuracies of 87.74%, 80.00%, 87.26%, 80.55%, 82.30%, and 80.11%, respectively. The results highlight the effectiveness of both traditional and transfer learning models in font detection, with SVC and VGG-16 emerging as top performers.
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Hoang, Nhat-Duc, Thanh-Canh Huynh, and Van-Duc Tran. "Computer Vision-Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic-Based Investigation Metaheuristic." Complexity 2021 (September 4, 2021): 1–17. http://dx.doi.org/10.1155/2021/3511375.
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During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. This study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. Using two-dimensional images, patched and unpatched potholes may have similar shapes. Therefore, this study relies on image texture descriptors to delineate these two objects of interest. The texture descriptors of statistical measurement of color channels, the gray-level cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads. To construct a classification model based on the extracted texture-based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic-Based Investigation (FBI) metaheuristic. The SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes. To optimize the SVC performance, the FBI algorithm is utilized to fine-tune the SVC hyperparameters. To establish the hybrid FBI-SVC framework, an image dataset consisting of 600 samples has been collected. The experiment supported by the Wilcoxon signed-rank test demonstrates that the proposed computer vision is highly suitable for the task of interest with a classification accuracy rate = 94.833%.
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Feng, Yifan. "Support Vector Machine for Stroke Risk Prediction." Highlights in Science, Engineering and Technology 38 (March 16, 2023): 917–23. http://dx.doi.org/10.54097/hset.v38i.5977.
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Nowadays, there are many paralytic patients sent to the hospital, and it is impossible for hospitals to search out those particular patients at once because these patients share the same symptoms such as unconsciousness as those hypoglycemic patients. Nowadays, most patients need to conduct blood tests to ensure whether they have a stroke or not. However, this will take a lot of time and resources. The investigation aims at stroke prediction. By using machine learning, the model will be used to train and test data. Support vector machine (SVM) will be used in this project. By using a support vector classifier (SVC), the model will be trained to learn from data. Then it will react to another data set to find out if it is fitted. As a classification problem, the accuracy is 0.77. It shows that certain model performs well after training which reflects that the prediction is successful. What’s more, its high recall which is 0.83 means that the model of stroke prediction can surely offer some help to patient classification to some extent because it can find most of the paralytic patients among all the samplers. Stroke prediction trained by the SVM model can help make the first division among all patients which can help save a lot of time and energy. However, since there is still a little deviation, it is still need to keep pace with modern medical technology to improve it.
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Yaseen, Muhammad Waseem, Muhammad Awais, Khuram Riaz, Muhammad Babar Rasheed, Muhammad Waqar, and Sajid Rasheed. "Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan." Archives of Hydro-Engineering and Environmental Mechanics 69, no. 1 (2022): 59–77. http://dx.doi.org/10.2478/heem-2022-0005.
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Abstract Floods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested for classification: classical algorithms (Random Forest, RF and Support Vector Classifier, SVC) and deep learning algorithms (Multi-Layer Perceptron, MLP). For the regression problem, the result of MLP and Support Vector Regression (SVR) algorithms were compared based on their mean square, root mean square and mean absolute errors. The results obtained show that the accuracy of the RF classifier is 0.99, while the accuracies of the SVC and MLP methods are 0.98; moreover, in the case of flood prediction, the SVR algorithm outperforms the MLP approach.
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Shaik, Riyaaz Uddien, Aiswarya Unni, and Weiping Zeng. "Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers." Remote Sensing 14, no. 22 (2022): 5774. http://dx.doi.org/10.3390/rs14225774.
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A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique.
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G. Deena, K. Raja, M. Azhagiri, W. A. Breen, and S. Prema. "Application of support vector classifier for mango leaf disease classification." Scientific Temper 14, no. 04 (2023): 1163–69. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.16.
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In India, Mango is the fruit of high economic and ecological importance as it exports in large quantities. 1000 varieties of mangoes are cultivated and mostly supported commercially. Among all the Indian fruits, mangoes are highly demand. In majority of the Indian region, mango crops are suffering from several diseases that reduce both the production and the quality and parallel reduces its value on the international market. Mangoes are highly affected by number of diseases, which hamper its appearance, taste and has huge impact on the economy the Indian commercial growth rate has not raised. Manually identifying those disease is a complex task and time consuming, since lack of knowledge, poverty, infrastructure and the facilities the identification of the disease in earlier stages are not done by the farmers. In recent years, the plant pathologists apply different techniques to identify the diseases but then again these techniques are time consuming and relatively expensive for mango growers and the solutions proposed are often not very accurate and sometimes biased. The disease has to diagnosed in order to provide solution to the farmers to increase the productivity with high quality. Currently, researchers have proposed several solutions to diagnosis of mango diseases automatically to gain high returns. The use of machine learning algorithms to identify diseases of plants from leaf photos is a very exciting field for advancement and research has carried in the proposed system using Support vector machine. Using non-linear SVC, achieved the accuracy of 88% for the dataset.
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Hashemi, H., D. M. J. Tax, R. P. W. Duin, A. Javaherian, and P. de Groot. "Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier." Nonlinear Processes in Geophysics 15, no. 6 (2008): 863–71. http://dx.doi.org/10.5194/npg-15-863-2008.
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Abstract. Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a statistical feature ranking technique and combining different classifiers. The method, which has general applicability, is demonstrated here on a gas chimney detection problem. First, we evaluate a set of input seismic attributes extracted at locations labeled by a human expert using regularized discriminant analysis (RDA). In order to find the RDA score for each seismic attribute, forward and backward search strategies are used. Subsequently, two non-linear classifiers: multilayer perceptron (MLP) and support vector classifier (SVC) are run on the ranked seismic attributes. Finally, to capitalize on the intrinsic differences between both classifiers, the MLP and SVC results are combined using logical rules of maximum, minimum and mean. The proposed method optimizes the ranked feature space size and yields the lowest classification error in the final combined result. We will show that the logical minimum reveals gas chimneys that exhibit both the softness of MLP and the resolution of SVC classifiers.
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Cizmic, Dea, Dominik Hoelbling, René Baranyi, Roland Breiteneder та Thomas Grechenig. "Smart Boxing Glove “RD α”: IMU Combined with Force Sensor for Highly Accurate Technique and Target Recognition Using Machine Learning". Applied Sciences 13, № 16 (2023): 9073. http://dx.doi.org/10.3390/app13169073.
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Emerging smart devices have gathered increasing popularity within the sports community, presenting a promising avenue for enhancing athletic performance. Among these, the Rise Dynamics Alpha (RD α) smart gloves exemplify a system designed to quantify boxing techniques. The objective of this study is to expand upon the existing RD α system by integrating machine-learning models for striking technique and target object classification, subsequently validating the outcomes through empirical analysis. For the implementation, a data-acquisition experiment is conducted based on which the most common supervised ML models are trained: decision tree, random forest, support vector machine, k-nearest neighbor, naive Bayes, perceptron, multi-layer perceptron, and logistic regression. Using model optimization and significance testing, the best-performing classifier, i.e., support vector classifier (SVC), is selected. For an independent evaluation, a final experiment is conducted with participants unknown to the developed models. The accuracy results of the data-acquisition group are 93.03% (striking technique) and 98.26% (target object) and for the independent evaluation group 89.55% (striking technique) and 75.97% (target object). Therefore, it is concluded that the system based on SVC is suitable for target object and technique classification.
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Shao, Yan Ming, Feng Ji, Shu An Zhao, Mu Chun Zhou, Yan Ru Chen, and Qi Zhao. "Applying Flame Spectrum on SVC-RVM Modeling for BOF Endpoint Prediction." Advanced Materials Research 631-632 (January 2013): 870–74. http://dx.doi.org/10.4028/www.scientific.net/amr.631-632.870.
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A new non-contact method for predicting the basic oxygen furnace(BOF) end point carbon content is proposed in this study. A model applying the flame spectrum of the converter vessel mouth is constructed to carry out the prediction. This model consists two parts, viz. a classifier based on support vector classification to classify the whole period of one BOF heat into two main phases, and a relevance vector machine working at the posterior phase to predict the carbon content. Compared with current non-contact methods of end point carbon content prediction, the proposed method can make better use of the information of the flame of the converter mouth. Simulations on industrial data show that this method yields good results on the classification as well as end point carbon content prediction.
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Iparraguirre-Villanueva, Orlando, and Michael Cabanillas-Carbonell. "Predictive Analysis of Vector-Borne Diseases through Tabular Classification of Epidemiological Data." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 13 (2024): 103–17. http://dx.doi.org/10.3991/ijoe.v20i13.50437.
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Vector-borne diseases (VBDs) are major threats to human health. They are estimated to cause more than 700,000 deaths each year. This presents serious health problems for CBD. In recent years, the incidence of VBDs has increased globally, affecting one billion people approximately and accounting for 17% of all infectious diseases. Globally, disease rates have risen at an alarming rate, with more than 3.9 billion people at risk of infection. Therefore, it is essential to find approaches to detect these diseases; this is where machine learning (ML) models come into play. The purpose of this study was to predict VBDs using tabular epidemiological data. For this purpose, a set of ML models was used, such as support vector classifier (SVC), extreme gradient boosting (XGBoost), LightGBM, CatBoost, random forest (RF), and balanced random forest (BRF). A dataset consisting of 65 features and 1262 records was used during the training stage. The results highlighted the successful integration of the different models, such as SVC, XGBoost, LightGBM, CatBoost, BRF, and RF, with weights of 0.49959 ± 0.27112, 0.58496 ± 0.22619, 0.48482 ± 0.29971, 0.54992 ± 0.27982, 0.24924 ± 0.22654, and 0.45592 ± 0.25849. In addition, the BRF model stood out for having the lowest log loss, evaluated through the ensemble log-loss metric, with an average of 0.24924 and a standard deviation of 0.22654.
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Bhargav, P., MVL Kathyayani, K. Raviteja, PTV Aditya Ram, and K. Pavan Kumar. "Enhancing Disease Prediction Accuracy Using Random Forest." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42828.
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MultiDisease prediction system” uses advanced machine learning techniques to facilitate identification Multiple diseases based on user -provided symptoms. The The system integrates classification algorithms, including random Forest, Support Vector Machine (SVM), K-Ner Closer Neighbors (KNN), support vector classifier (SVC) and logistics regression, to diagnose health conditions such as diabetes, gastroesophageal reflux disease (GERD), dengue, pneumonia and more than 20 other diseases. The proposed methodology is followed by a structured pipe involving data collection, function extraction, Pre -workment, model training, disease predictions, performance Evaluation and optimal selection of the model. It uses extensive Savets of medical data, extract relevant clinical traits, applies data Cleaning and normalization techniques and train machine learning models to increase diagnostic accuracy. During training The system predicts the likelihood of a disease -based disease and user input and evaluates the power of the model using metrics of key rating as accuracy, accuracy, appeal and f1-score to determine The most effective predictive model. This approach makes it easier for Disease detection, increases diagnostic reliability, supports personalized medical strategies and provides data -based data Help healthcare workers in clinical decision -making. According to Integration of machine learning into medical diagnostics, system It contributes to effective and accurate identification of diseases, permits Early medical intervention and finally improved patient results Key Words: Machine learning, disease prediction, medical diagnostics, classification algorithms, timely detection, health care analysis, clinical decision support, Random Forest, Logistic Regression, KNN, SVC.
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Darapureddy, Nagadevi, Nagaprakash Karatapu, and Tirumala Krishna Battula. "Comparative Analysis of Texture Patterns on Mammograms for Classification." Traitement du Signal 38, no. 2 (2021): 379–86. http://dx.doi.org/10.18280/ts.380215.
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Breast cancer is a cancerous tumor that arrives within the tissues of the breast. Women are mostly attacked than men. To detect early cancer medical specialists, suggest mammography for screening. Algorithms in Machine learning were executed on mammogram images to classify whether the tissues are deleterious or not. An analysis is done based on the texture feature extraction using different techniques like Frequency decoded local binary pattern (FDLBP), Local Bit-plane Decoded Pattern (LBDP), Local Diagonal Extrema Pattern (LDEP), Local Directional Order Pattern (LDOP), Local Wavelet Pattern (LWP). The features extracted are tested on 322 images from MIA’s database of three different classes. The algorithms in Machine learning like K-Nearest Neighbor classifier (KNN), Support vector classifier (SVC), Decision Tree classifier (DTC), Random Forest classifier (RFC), AdaBoost classifier (AC), Gradient Boosting classifier (GBC), Gaussian Naive Bayes classifier (GNB), Linear Discriminant Analysis classifier (LDA), Quadratic Discriminant Analysis classifier (QDA) were used to evaluate the accuracy of classification. This paper examines the comparison of accuracy using different texture features. KNN algorithm with LDEP for texture feature extraction gives classification accuracy of 64.61%, SVC with all the texture patterns mentioned gives classification accuracy of 63.07%, DTC with FDLBP, LBDP gives classification accuracy of 47.69, RFC with LBDP and AC with LDOP and GBC with FDLBP gives 61.53% classification accuracy, GNB and LDA with FDLBP gives 60% and 63.07% classification accuracy respectively, QDA with LBDP gives 64.61 classification accuracy. Of all the Algorithms support vector classifier gives good accuracy results with all the texture patterns mentioned.
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Sulaiman, Nurul Ainina Filza, Shazlyn Milleana Shaharudin, Shuhaida Ismail, Nurul Hila Zainuddin, Mou Leong Tan, and Yusri Abd Jalil. "Predictive Modelling of Statistical Downscaling Based on Hybrid Machine Learning Model for Daily Rainfall in East-Coast Peninsular Malaysia." Symmetry 14, no. 5 (2022): 927. http://dx.doi.org/10.3390/sym14050927.
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In recent years, climate change has demonstrated the volatility of unexpected events such as typhoons, flooding, and tsunamis that affect people, ecosystems and economies. As a result, the importance of predicting future climate has become even direr. The statistical downscaling approach was introduced as a solution to provide high-resolution climate projections. An effective statistical downscaling scheme aimed to be developed in this study is a two-phase machine learning technique for daily rainfall projection in the east coast of Peninsular Malaysia. The proposed approaches will counter the emerging issues. First, Principal Component Analysis (PCA) based on a symmetric correlation matrix is applied in order to rectify the issue of selecting predictors for a two-phase supervised model and help reduce the dimension of the supervised model. Secondly, two-phase machine learning techniques are introduced with a predictor selection mechanism. The first phase is a classification using Support Vector Classification (SVC) that determines dry and wet days. Subsequently, regression estimates the amount of rainfall based on the frequency of wet days using Support Vector Regression (SVR), Artificial Neural Networks (ANNs) and Relevant Vector Machines (RVMs). The comparison between hybridization models’ outcomes reveals that the hybrid of SVC and RVM reproduces the most reasonable daily rainfall prediction and considers high-precipitation extremes. The hybridization model indicates an improvement in predicting climate change predictions by establishing a relationship between the predictand and predictors.
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Rahimi, Nouf, Fathy Eassa, and Lamiaa Elrefaei. "An Ensemble Machine Learning Technique for Functional Requirement Classification." Symmetry 12, no. 10 (2020): 1601. http://dx.doi.org/10.3390/sym12101601.
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In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifying FR statements to improve their accuracy and availability. This technique combines different ML models and uses enhanced accuracy as a weight in the weighted ensemble voting approach. The five combined models are Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and Support Vector Classification (SVC). The technique was implemented, trained, and tested using a collected dataset. The accuracy of classifying FR was 99.45%, and the required time was 0.7 s.
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Alves, Julio Cesar L., Claudete B. Henriques та Ronei J. Poppi. "Classification of diesel pool refinery streams through near infrared spectroscopy and support vector machines using C-SVC and ν-SVC". Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 117 (січень 2014): 389–96. http://dx.doi.org/10.1016/j.saa.2013.08.018.
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Mutaz Rasmi, Abu Sara, Khaled Sabarna, and Jawad H. Alkhateeb. "The Analysis of Breast Cancer Classification Involves Utilizing Machine Learning (Ml) Techniques and Hyperparameter Adjustment - A Comparative Study." Ahliya Journal of Allied Medico-Technology Science 1, no. 2 (2024): 10–15. https://doi.org/10.59994/ajamts.2024.2.10.
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This study aims to analyze and classify breast cancer (BC) cases using machine learning (ML) techniques and hyperparameter tuning. The BC dataset from the University of California (UCI) was utilized, which comprises 569 cases classified as malignant (M) and benign (B), with 32 features. The algorithms employed in the study included Logistic Regression (LR), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Decision Tree (DT), and Gaussian Naive Bayes (NB). The results indicated that the SVC algorithm performed the best, achieving an accuracy of 98% on the test set, along with a precision of 100%. Furthermore, all algorithms demonstrated high performance, reflecting the effectiveness of machine learning techniques in classifying breast cancer cases.
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Dangi, Dr Abhilasha. "Educational Data Classification using Different Classifiers for Real Time Student Applications." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 5667–71. https://doi.org/10.22214/ijraset.2025.71502.
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The application of data mining techniques to educational datasets is gaining increasing attention due to the growing availability of student-related information. However, organizing and interpreting this data effectively poses a significant challenge because of its high dimensional and complexity. This study explores the use of the Linear Support Vector Classifier (Linear - SVC), SVM, and naive bias known for its computational efficiency and robustness, in categorizing educational data. The model's output can reveal actionable insights into student performance, offering valuable support for real-time academic assessments. Additionally, it holds potential for informing future strategies related to student admissions and selection processes in higher education institutions.
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Hudon, Alexandre, Kingsada Phraxayavong, Stéphane Potvin, and Alexandre Dumais. "Comparing the Performance of Machine Learning Algorithms in the Automatic Classification of Psychotherapeutic Interactions in Avatar Therapy." Machine Learning and Knowledge Extraction 5, no. 3 (2023): 1119–30. http://dx.doi.org/10.3390/make5030057.
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(1) Background: Avatar Therapy (AT) is currently being studied to help patients suffering from treatment-resistant schizophrenia. Facilitating annotations of immersive verbatims in AT by using classification algorithms could be an interesting avenue to reduce the time and cost of conducting such analysis and adding objective quantitative data in the classification of the different interactions taking place during the therapy. The aim of this study is to compare the performance of machine learning algorithms in the automatic annotation of immersive session verbatims of AT. (2) Methods: Five machine learning algorithms were implemented over a dataset as per the Scikit-Learn library: Support vector classifier, Linear support vector classifier, Multinomial Naïve Bayes, Decision Tree, and Multi-layer perceptron classifier. The dataset consisted of the 27 different types of interactions taking place in AT for the Avatar and the patient for 35 patients who underwent eight immersive sessions as part of their treatment in AT. (3) Results: The Linear SVC performed best over the dataset as compared with the other algorithms with the highest accuracy score, recall score, and F1-Score. The regular SVC performed best for precision. (4) Conclusions: This study presented an objective method for classifying textual interactions based on immersive session verbatims and gave a first comparison of multiple machine learning algorithms on AT.
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May, Zazilah, M. K. Alam, Nazrul Anuar Nayan, Noor A’in A. Rahman, and Muhammad Shazwan Mahmud. "Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier." PLOS ONE 16, no. 12 (2021): e0261040. http://dx.doi.org/10.1371/journal.pone.0261040.
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Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.
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Shabbeer Ahmad, Dr Syed, and Dr Krishna Prasad K. "Forecasting Nasdaq stock progressions using classification and deep learning techniques." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 5 (2023): 40–49. http://dx.doi.org/10.54660/.ijmrge.2023.4.5.40-49.
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Stock Market prices have always been unpredictable resulting in a lot of risk for its investors. This proposal uses machine learning techniques (Decision Tree, Random Forest, Adaptive Boosting (Adaboost), eXtreme Support Vector Classifier (SVC), Logistic Regression and deep learning methods such as Long short-term memory (LSTM) to build modules that can be used to predict accurate stock prices reducing the chances of risk and increasing in gains. In this proposal the National Association of Securities Dealers Automatic Quotation System (NASDAQ) stock data is being used which has been extracted from Yahoo Finance to predict and analyze various Stock Progressions.
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Fahri Reza Pahlawan, Muhammad, Yena Kim, Rudiati Evi Masithoh, and Byoung-Kwan Cho. "VNIR and SWIR Hyperspectral Imaging for Microplastic detection on Soil." BIO Web of Conferences 167 (2025): 05006. https://doi.org/10.1051/bioconf/202516705006.
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Microplastics in soil significantly threatens ecology, impacting plant growth, soil, and humans health through the food chain. Conventional methods to detect microplastic in soil usually require complicated and time-consuming steps. This study used non-destructive hyperspectral imaging techniques in visible-near infrared (VNIR, 400-1000 nm) and short-wave-infrared (SWIR, 1000-2000) to identify microplastic in the soil surface. Seven cryo-milled microplastic polymer were used. Partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector classification (SVC) with linear, polynomial, and radial basis function kernels were used to develop the calibration model. The result shows that in both VNIR and SWIR regions, models with linear kernel (PLS-DA, LDA, and SVC-linear) were superior to the non-linear model (SVC-poly and SVC-RBF). The masked image of SVC-linear model using VNIR SNV spectra was superior to the other VNIR model but could only differentiate microplastic from soil. The LDA model yield using the original SWIR spectra was performed perfectly, outperforming the other model with a clear classification of soil and each polymer in the masked validation image. This study provides initial insights into soil microplastic detection by hyperspectral imaging (HSI), presenting a practical, non-destructive method for the efficient identification of microplastic polymers without complicated sample preparation.
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Mahalakshmi, K. V., P. Vishnu, R. Venkateshkumar, Harshini Raja, and Kaustubh Lakshmi Narayanan. "Data Analysis in Healthcare Automation Using Computer Vision." Indian Journal Of Science And Technology 18, Sp1 (2025): 37–44. https://doi.org/10.17485/ijst/v18si1.icamada27.
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Objectives: To apply computer vision techniques in healthcare automation and analysis and assist medical professionals. Methods: The proposed work encapsulates computer vision techniques; such as traditional computer vision technique using contour detection, Machine Learning-Based Computer vision techniques and clustering techniques. The Machine Learning (ML)-based Computer vision techniques include; Support Vector Machine (SVM) and Logistic Regression (LR). The methodology uses Magnetic Resonance imaging scans of brain, Ovarian and hepatic tumors, focusing on parameters like region of interest, accuracy of classification to determine the behavior of the algorithm. The traditional method has successfully detected the Tumor region and created a bounding box to showcase the region. The method uses concept of thresholding and contour detection. Findings: The Machine learning (ML)-based computer vision uses Classification algorithm such as Support Vector Classifier (SVC) and Logistic Regression (LR), which classifies the testing image data into its respective tumor subtype. After classification, by leveraging thresholding and contour detection techniques, our Region of Interest (ROI) which is, the tumor region is detected with a bounding box. SVC attains an accuracy of 86.3% and Logistic regression attains an accuracy of 82%. The clustering algorithms such as K-Means, successfully detected the glioma tumor regions and cancer image datasets including Hepatic and Ovarian cancer. Novelty: The proposed methodology classifies the tumor images into their respective tumor sub-groups and the tumor affected area can be highlighted with the computer vision techniques like bounding boxes simultaneously.
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Omkar, Singh1 Sunali Bhattacherji*2. "Research on Sentimental Analysis on Veganism." International Journal of Scientific Research and Technology 2, no. 3 (2025): 450–57. https://doi.org/10.5281/zenodo.15082266.
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This project introduces a sentiment analysis system developed to gauge public opinions on veganism as a social justice movement. By employing machine learning algorithms—Support Vector Classification (SVC), Logistic Regression, and K-Nearest Neighbors (KNN)—the system categorizes text data into positive, negative, or neutral sentiments. A dataset of 50,000 text entries from Kaggle was preprocessed and converted using Term Frequency-Inverse Document Frequency (TF-IDF). Comparative analysis using accuracy, precision, recall, and F1-score determined the most effective model. The scalable system supports social media monitoring and public perception research, providing insights into societal views on veganism.
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Chen, Kelvin, R. A. Fattah Adriansyah, Carles Juliandy, Frans Mikael Sinaga, Frederick Liko, and Aswin Angkasa. "Classification of Big Data Stunting Using Support Vector Regression Method at Stella Maris Medan Maternity Hospital." Indonesian Journal of Artificial Intelligence and Data Mining 7, no. 2 (2024): 497. http://dx.doi.org/10.24014/ijaidm.v7i2.31112.
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This study aims to classify big data related to stunting using the Support Vector Regression (SVR) method at Stella Maris Maternity Hospital, Medan. Stunting, a condition of impaired growth in children due to chronic malnutrition and repeated infections, affects physical and cognitive development. With increasing health data, big data processing methods are essential for accurate information. SVR was chosen for handling high-dimensional and non-linear data, providing precise results. The study uses medical information, nutritional history, and socio-economic factors collected from hospital patients. The research process includes data collection, pre-processing to address missing values and outliers, normalization, and SVR application. Final results use SVR with Voting Classifier combining Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB), achieving an accuracy of 91.67%. This approach effectively identifies main stunting factors, aiding clinical decision-making and intervention programs. The study showcases big data and machine learning's potential in healthcare, serving as a model for improving health services and monitoring children's health conditions.
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Nofie Prasetiyo, Kiki Ahmad Baihaqi, Santi Arum Puspita Lestari, and Yana Cahyana. "CLASSIFICATION OF RICE PLANTS AFFECTED BY RATS USING THE SUPPORT VECTOR MACHINE (SVM) ALGORITHM." Jurnal Teknik Informatika (Jutif) 5, no. 2 (2024): 637–43. https://doi.org/10.52436/1.jutif.2024.5.2.1949.
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In the era of Indonesia's agrarian economy which is supported by the agricultural sector, rice plants play an important role in meeting food needs. However, pest attacks, especially field mice, can cause significant losses in rice production. To overcome this, this research proposes the use of the Support Vector Machine (SVM) algorithm with the Particle Swarm Optimization method in predicting rat pest attacks on rice plants. This research involves the process of collecting data from drone photos to identify affected agricultural land. The preprocessing stage involves changing colors from RGB to GRAY and zoom augmentation. Feature extraction is carried out using Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP). Testing was carried out involving the SVM/SVC model and performance evaluation was carried out using accuracy, precision and recall metrics. The preprocessing test results showed an increase in performance with training accuracy of 68.33%. However, the actual prediction on the original image results in a low accuracy of around 25%. However, image testing after involving the entire process, including preprocessing and model prediction, shows a higher level of accuracy, reaching around 90%.
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Zhao, Peng, Zhengyang Dong, Jianfeng Zhang, et al. "Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem." Advances in Polymer Technology 2020 (March 26, 2020): 1–9. http://dx.doi.org/10.1155/2020/7654249.
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Product weight is one of the most important properties for an injection-molded part. The determination of process parameters for obtaining an accurate weight is therefore essential. This study proposed a new optimization strategy for the injection-molding process in which the parameter optimization problem is converted to a weight classification problem. Injection-molded parts are produced under varying parameters and labeled as positive or negative compared with the standard weight, and the weight error of each sample is calculated. A support vector classifier (SVC) method is applied to construct a classification hyperplane in which the weight error is supposed to be zero. A particle swarm optimization (PSO) algorithm contributes to the tuning of the hyperparameters of the SVC model in order to minimize the error between the SVC prediction results and the experimental results. The proposed method is verified to be highly accurate, and its average weight error is 0.0212%. This method only requires a small amount of experiment samples and thus can reduce cost and time. This method has the potential to be widely promoted in the optimization of injection-molding process parameters.
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Martínez-Trespalacios, José A., Daniel E. Polo-Herrera, Tamara Y. Félix-Massa, et al. "QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand." Molecules 29, no. 15 (2024): 3562. http://dx.doi.org/10.3390/molecules29153562.
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The development of new methods of identification of active pharmaceutical ingredients (API) is a subject of paramount importance for research centers, the pharmaceutical industry, and law enforcement agencies. Here, a system for identifying and classifying pharmaceutical tablets containing acetaminophen (AAP) by brand has been developed. In total, 15 tablets of 11 brands for a total of 165 samples were analyzed. Mid-infrared vibrational spectroscopy with multivariate analysis was employed. Quantum cascade lasers (QCLs) were used as mid-infrared sources. IR spectra in the spectral range 980–1600 cm−1 were recorded. Five different classification methods were used. First, a spectral search through correlation indices. Second, machine learning algorithms such as principal component analysis (PCA), support vector classification (SVC), decision tree classifier (DTC), and artificial neural network (ANN) were employed to classify tablets by brands. SNV and first derivative were used as preprocessing to improve the spectral information. Precision, recall, specificity, F1-score, and accuracy were used as criteria to evaluate the best SVC, DEE, and ANN classification models obtained. The IR spectra of the tablets show characteristic vibrational signals of AAP and other APIs present. Spectral classification by spectral search and PCA showed limitations in differentiating between brands, particularly for tablets containing AAP as the only API. Machine learning models, specifically SVC, achieved high accuracy in classifying AAP tablets according to their brand, even for brands containing only AAP.
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Astuti, Erna Zuni, Christy Atika Sari, Eko Hari Rachmawanto, and Rabei Raad Ali. "Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS." Scientific Journal of Informatics 11, no. 1 (2024): 177–86. http://dx.doi.org/10.15294/sji.v11i1.47364.
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Purpose: Fraud is rampant in the current era, especially in the era of technology where there is now easy access to a lot of information. Therefore, everyone needs to be able to sort out whether the information received is the right information or information that is fraudulent. In this research, the process of classifying messages including ham or spam has been carried out. The purpose of this research is to be able to build a model that can help classify messages. The purpose of this research is also to determine which machine learning method can accurately and efficiently perform the ham or spam classification process on messages.Methods: In this research, the ham or spam classification process has been using machine learning methods. The machine learning methods used are the classification process with Random Forest, Logistic Regression, Support Vector Classification, Gradient Boosting, and XGBoost Classifier algorithms. Results: The results obtained after testing in this study are the classification process using the Random Forest algorithm getting an accuracy of 97.28%, Logistic Regression getting an accuracy of 94.67%, with Support Vector Classification getting an accuracy of 97.93%, and using XGBoost Classifier getting an accuracy of 96.47%. The best precision value obtained in this study is 98% when using the random forest algorithm. The best recall value is 94% when using the SVC algorithm. While the best f1-score value is 95% when using the SVC algorithm.Novelty: This research has been compared with several algorithms. In previous research, it is still very rarely done using XGBoost to classify the ham or spam in messages. We focus on giving brief information based con comparison algorithm and show the best algorithm to classify classify the ham or spam in messages. And for the novelty that exists from this research, the machine learning model built gets better accuracy when compared to previous research.
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Liu, Tingzhang, Linyi Jin, Chujun Zhong, and Fan Xue. "Study of thermal sensation prediction model based on support vector classification (SVC) algorithm with data preprocessing." Journal of Building Engineering 48 (May 2022): 103919. http://dx.doi.org/10.1016/j.jobe.2021.103919.
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Reshma, M., Paul T. Pristy, and Mariam Varghese Surekha. "Support Vector Machine Based Route Classification and Description." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 14–18. https://doi.org/10.35940/ijeat.C6286.049420.
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Traveling is very much important in one's life. Location-based services have developed a lot due to the development of communication technologies. It confines services that execute programs that use geographical data. Map services authorize travelers to look for the information surrounding them and organize an outing to his/her best-loved spot. Google Maps API is convenient for locating the shortest route information. However, the map system doesn't supply any illustration about air quality or congestion in a path. At times, a substitute route with less congestion can take you quickly to your spot than a shorter route. Numerous crucial health concerns for human beings are caused due to pollution. The motive is to develop a system that offers the textual explanation of routes utilizing the sub-routes information from Google map and BreezoMeter. The end-user can choose the starting and ending points of his/her travel and the route map showing various routes from source to destination is exhibited along with a small description of each route. The illustration of the routes is obtained depending on three factors such as air quality, congestion and distance gathered from BreezoMeter, Google map traffic API and distance matrix API respectively. Multicategory Support Vector Machine (SVM) is an organized and guided categorization technique and is used here to classify factors into various levels. Since the textual illustration of the route is accessible, the end-user can effortlessly understand the details about the route and they can choose a particular route.