Decision Tree Support Vector Machine Research Articles

Fabric tearing performance state perception and classification driven by multi-source data.

The tear strength of textiles is a crucial characteristic of product quality. However, during the laboratory testing of this indicator, factors such as equipment operation, human intervention, and test environment can significantly influence the results. Currently, there is a lack of traceable records for the influencing factors during the testing process, and effective classification of testing activities is not achieved. Therefore, this study proposes a state-awareness and classification approach for fabric tear performance testing based on multi-source data. A systematic design is employed for fabric tear performance testing activities, which can real-time monitor electrical parameters, operational environment, and operator behavior. The data are collected, preprocessed, and a Decision Tree Support Vector Machine (DTSVM) is utilized for classifying various working states, and introducing ten-fold cross-validation to enhance the performance of the classifier, forming a comprehensive awareness of the testing activities. Experimental results demonstrate that the system effectively perceives fabric tear performance testing processes, exhibiting high accuracy in the classification of different fabric testing states, surpassing 98.73%. The widespread application of this system contributes to continuous improvement in the workflow and traceability of fabric tear performance testing processes.

Fuel composition forecasting for waste tires pyrolysis process based on machine learning methods

Waste tire pyrolysis oil holds significant potential as an energy source and chemical feedstock, playing a crucial role in enhancing resource efficiency and promoting environmental sustainability. This study aims to develop a reliable forecasting model for determining the composition proportions of waste tire pyrolysis oil and assessing its performance. This study employed the gradient boosting decision tree (GBDT) method to develop the model that accurately forecasts the composition proportions of aromatic, aliphatic, and other compounds present in waste tire pyrolysis oil. Importance in XGboost and Cramer's V analysis in correlation analysis were used to select the input variables for the forecasting mode. Additionally, this study normalized the percentages of the three components to ensure consistency. To evaluate the performance of the proposed models, we compared them with 13 models from the Decision Tree series, BPNN series, SVM series, and HDMR series, thereby verifying the accuracy of our models. Furthermore, this study conducted random sampling to examine the stability and generalization ability of our proposed models. The experimental results demonstrate that the forecasting model exhibits excellent performance in estimating the proportions of aromatic compounds, aliphatic compounds, and other compounds. Over 60% of the samples yielded relative error percentage (REP) values below 30%. This indicates that this proposed model is capable of accurately forecasting the composition proportions of waste tire pyrolysis oil. The proposed model holds the potential to facilitate dynamic optimization of the waste tire resource reuse process and contribute to the sustainable development of waste tires.

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.

Crop Yield Prediction in Nigeria Using Machine Learning Techniques: A Case Study of Southern Part Nigeria

A key tool for digitalizing the agriculture sector and other industries is using big data and machine learning to predict farm produce. The inability of farmers to accurately predict yield is a great problem using previous farming experience. This study adopted three (3) machine learning approaches, including a decision tree classifier, random forest, and support vector machine, to model data from different zones and make predictions. The techniques adopted were tested using root mean square error to ensure the right prediction algorithm is adopted and the right values are obtained. Results show prediction from the South East is the best in terms of yields and accuracy when tested and evaluated, with 138.9 %.

Inlet setting strategy via machine learning algorithm for thermal management of container-type battery energy-storage systems (BESS)

This research enhances the safety and efficiency of the container-type battery energy storage systems (BESS) through the utilization of machine learning algorithms. The decision tree algorithm and support vector machine (SVM) are employed to clarify the influence of cooling air on temperature distribution and predict the safety of battery modules. The results indicate that the flow rate of cooling air has significant impact on both the maximum temperature and temperature difference of the batteries, while the inlet temperature only affects the maximum temperature. Additionally, the distributed inlet setting strategy is introduced for the container-type BESS. The strategy effectively reduces the temperature of the target battery by 8.6 % and decreases the highest temperature in the BESS by 3.2 % without creating new hotspots. Comparing with the uniform inlet setting strategy, this strategy reduces the power consumption by 40 %, providing great efficiency for cost consideration. In conclusion, this research clarifies the relationship between the conditions of cooling air and the temperature distribution of batteries using the machine learning algorithms, and introduces the distributed inlet setting strategy to prevent thermal runaway in the BESS with less power consumption.

AutoML accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke.

Automated machine learning or autoML has been widely deployed in various industries. However, their adoption in healthcare, especially in clinical settings is constrained due to a lack of clear understanding and explainability. The aim of this study is to utilize autoML for the prediction of functional outcomes in patients who underwent mechanical thrombectomy and compare it with traditional ML models with a focus on the explainability of the trained models. A total of 156 patients of acute ischemic stroke with Large Vessel Occlusion (LVO) who underwent mechanical thrombectomy within 24 h of stroke onset were included in the study. A total of 34 treatment variables including clinical, demographic, imaging, and procedure-related data were extracted. Various conventional machine learning models such as decision tree classifier, logistic regression, random forest, kNN, and SVM as well as various autoML models such as AutoGluon, MLJAR, Auto-Sklearn, TPOT, and H2O were used to predict the modified Rankin score (mRS) at the time of patient discharge and 3 months follow-up. The sensitivity, specificity, accuracy, and AUC for traditional ML and autoML models were compared. The autoML models outperformed the traditional ML models. For the prediction of mRS at discharge, the highest testing accuracy obtained by traditional ML models for the decision tree classifier was 74.11%, whereas for autoML which was obtained through AutoGluon, it showed an accuracy of 88.23%. Similarly, for mRS at 3 months, the highest testing accuracy of traditional ML was that of the SVM classifier at 76.5%, whereas that of autoML was 85.18% obtained through MLJAR. The 24-h ASPECTS score was the most important predictor for mRS at discharge whereas for prediction of mRS at 3 months, the most important factor was mRS at discharge. Automated machine learning models based on multiple treatment variables can predict the functional outcome in patients more accurately than traditional ML models. The ease of clinical coding and deployment can assist clinicians in the critical decision-making process. We have developed a demo application which can be accessed at https://mrs-score-calculator.onrender.com/.

Electric powered wheelchair control using user-independent classification methods based on surface electromyography signals.

Wheelchairs are one of the most popular assistive technology (AT) among individuals with motor impairments due to their comfort and mobility. People with finger problems may find it difficult to operate wheelchairs using the conventional joystick control method. Therefore, in this research study, a hand gesture-based control method is developed for operating an electric-powered wheelchair (EPW). This study selected a comfort-based hand position to determine the stop maneuver. An additional exploration was undertaken to investigate four gesture recognition methods: linear regression (LR), regularized linear regression (RLR), decision tree (DT), and multi-class support vector machine (MC-SVM). The first two methods, LR and RLR, have promising accuracy values of 94.85% and 95.88%, respectively, but each new user must be trained. To overcome this limitation, this study explored two user-independent classification methods: MC-SVM and DT. These methods effectively addressed the finger dependency issue and demonstrated remarkable success in recognizing gestures across different users. MC-SVM has about 99.05% of both precision and accuracy, and the DT has about 97.77% accuracy and precision. All six participants were successful in controlling the EPW without any collisions. According to the experimental results, the proposed approach has high accuracy and can address finger dependency issues.

Towards a semantic structure for classifying IoT agriculture sensor datasets : An approach based on machine learning and web semantic technologies

With the increase in the number of IoT farming datasets, it has become so difficult to identify the right data for IoT agriculture applications. Therefore, a meaningful structure is needed to well understand, interpret and index IoT farming datasets. This paper proposes a new IoT farming ontology that allows the organization, the understanding, and the classification of IoT agriculture datasets knowledge as well as meta-data storage. For this, we have developed a new IoT agriculture taxonomy that helps to identify an IoT agriculture application based on the combination of various IoT agriculture sensors. The evaluation of the semantic IoT agriculture datasets classification, based on the background knowledge provided by the proposed ontology, was achieved using Machine Learning algorithms, including Logistic Regression, Decision Tree Classifier, K-Neighbors Classifier, Linear Discriminant Analysis, Gaussian NB, SVM, and Random Forest Regressor. The obtained results clearly show the effectiveness of the proposed ontology to classify IoT agriculture datasets with high performances and accuracy (0.98), (0.99) using Decision tree classifier and SVM respectively.

Framework for the Development of an Enhanced Machine Learning Algorithm for Non-Cognitive Variables Influencing Students’ Performance using Feature Extraction

Machine learning is a powerful tool for creating computational models in scientific analysis in areas where there is need to extract hidden data such as educational data. In order to make planning easier and identify at-risk students who may be in danger of failing or dropping out of school due to their academic performance, Educational Data Mining (EDM) uses computational tools. In this paper, a framework using machine learning approach was proposed to develop an enhanced algorithm for non-cognitive variables influencing students’ performance using feature extraction. In the framework, the Decision Tree (DT) and Linear Support Vector Machine (SVM) are proposed as base classifiers, and Random Forest (RF) and Gradient Boosting (GB) as ensemble classifiers. The DT classifier allows the classification process to be modelled as a series of hierarchical decisions on the features, forming a tree-like structure. Using this technique, planning and predicting students who might be at-risk of dropping out would have been made easier.

Study on small leaks in submarine liquid pipelines based on passive acoustic internal detection method

ABSTRACT In a submarine liquid pipeline, the timely detection of tiny amounts of leakage (less than 1 L/min) can have a critical impact on pipeline safety. Hence, a method based on a passive acoustic internal detection for pipeline small leakages was established. Firstly, the variation laws between leakage rates and sound power levels with different leakage apertures and internal pressures were simulated using Ansys Fluent 19.0 software. Furthermore, an experimental platform for pipeline small leakage detection was constructed. The acoustic experiments of small leakage detection were carried out under different leakage apertures and internal pressures using a high-sensitivity acoustic sensor placed into the pipeline. It was found that consistency between the experimental results and the finite element simulation results was established. A novel improved empirical mode decomposition (IEMD) was used to denoise the leakage signals. The characteristic parameters of different signal processing fields were extracted. A novel decision tree support vector machine (DTSVM), based on parameter optimization, was used to construct a classification model to identify small leakages through different apertures. The results show that compared with three commonly used models, the accuracy of this novel model of pipeline small leakage detection was 92.2%. This model was found to improve the small leakages detection rate of different apertures and also provided a theoretical basis for developing an acoustic internal detector for submarine liquid pipelines.

A balanced communication-avoiding support vector machine decision tree method for smart intrusion detection systems

The Internet of Things field has created many challenges for network architectures. Ensuring cyberspace security is the primary goal of intrusion detection systems (IDSs). Due to the increases in the number and types of attacks, researchers have sought to improve intrusion detection systems by efficiently protecting the data and devices connected in cyberspace. IDS performance is essentially tied to the amount of data, data dimensionality, and security features. This paper proposes a novel IDS model to improve computational complexity by providing accurate detection in less processing time than other related works. The Gini index method is used to compute the impurity of the security features and refine the selection process. A balanced communication-avoiding support vector machine decision tree method is performed to enhance intrusion detection accuracy. The evaluation is conducted using the UNSW-NB 15 dataset, which is a real dataset and is available publicly. The proposed model achieves high attack detection performance, with an accuracy of approximately 98.5%.

DEPRESSION DETECTION MODEL USINGWORD AND SENTENCE EMBEDDING WITH DIFFERENT CLASSIFIERS

Determining depression is hugely important because it affects people's wellness. The common practice of diagnosing depression usually involves a physical session with a practitioner, which reduces the possibility of finding patients who are depressed. Instead, they investigate the possibility of detecting depression solely from textual data based on the posts people post on social media profiles. This research work consists of how Reddit users' text messages can be processed to perform a classification task to identify depression. In order to detect depression, we used Word and Sentence Embeddings. Different embeddings are tested with algorithms such as SVM classifiers, KNN classifiers, and decision tree classifiers, and the results are compared to those from various datasets. Evaluation metrics such as accuracy and precision are used to evaluate the performance of classifiers. USE embedding outperformed Word embeddings with a maximum accuracy of 92%, precision of 95%, recall of 96%, and F1 score of 92%. Results from the suggested method are compared to those from other methods and are found to be satisfactory.

Designing new student performance prediction model using ensemble machine learning

Academic success for students in any educational institute is the primary requirement for all stakeholders, i.e., students, teachers, parents, administrators and management, industry, and the environment. Regular feedback from all stakeholders helps higher education institutions (HEIs) rise professionally and academically, yet they must use emerging technologies that can help institutions to grow at a faster pace. Early prediction of students’ success using trending artificial intelligence technologies like machine learning, early finding of at-risk students, and predicting a suitable branch or course can help both management and students improve their academics. In our work, we have proposed a new student performance prediction model in which we have used ensemble machine learning with stacking of four multi-class classifiers, decision tree, k-nearest neighbor, Naïve Bayes, and One vs. Rest support vector machine classifiers. The proposed model predicts the final grade of a student at the earliest possible time and the suitable stream for a new student. A student dataset of over a thousand students from five different branches of an engineering institute has been taken to test the results. The proposed model compares the four-machine learning (ML) techniques being used and predicts the final grade with an accuracy of 93%.

Efficient Machine Learning-based Approach for Brain Tumor Detection Using the CAD System

Medical research has focused on improving diagnosis through medical imaging in recent decades. Computer Assisted Diagnosis (CAD) systems have been developed to help doctors identify suspicious areas of interest, particularly those with cancer-like characteristics. CAD systems employ various algorithms and techniques to extract important numerical measurements from medical images that clinicians can use to evaluate patient conditions. This study proposes a statistical classification-based approach to efficient brain cancer detection. The proposed approach operates in three stages: first, Gradient Vector Flow (GVF) Snake models and mathematical morphology techniques retrieve regions of interest. The second stage characterizes these regions using morphological and textural parameters. Finally, a Bayesian network uses this description as input to identify malignant and benign cancer classes. We also compared the performance of the Bayesian network with other popular classification algorithms, including SVM, MLP, KNN, Random Forest, Decision Tree, XGBoost, LGBM, Gaussian Process, and RBF SVM. The results showed the superiority of the Bayesian network for the task of brain tumor classification. The proposed approach has been experimentally validated, with a sensitivity of 100% and a classification accuracy of over 98% for tumors, demonstrating the high efficiency of cancer cell segmentation.

Risk prediction model of early-onset preeclampsia based on risk factors and routine laboratory indicators

Background: 10% - 15% of maternal deaths are statistically attributable to preeclampsia. Compared with late-onset PE, the severity of early-onset PE remains greater harm, with higher morbidity and mortality. Objective: To establish an early-onset preeclampsia prediction model by clinical characteristics, risk factors and routine laboratory indicators from 6 to 10 gestational weeks of pregnant women. Methods: The clinical characteristics, risk factors and 38 routine laboratory indicators (6 - 10 weeks of gestation) including blood lipids, liver and kidney function, coagulation, blood count and other indicators of 91 early-onset preeclampsia patients and 709 normal controls without early-onset preeclampsia from January 2010 to May 2021 in Peking University Third Hospital (PUTH) were retrospectively analyzed. Logistic regression, Decision tree model and Support vector machine (SVM) model were applied for establishing prediction models, respectively. ROC curves were drawn, and the area under the curve (AUCROC), sensitivity and specificity was calculated and compared. Results: There were statistically significant differences in the rates of diabetes, Antiphospholipid Syndrome (APS), kidney disease, Obstructive Sleep Apnea (OSAHS), primipara, history of preeclampsia and Assisted Reproductive Technology (ART) (p < 0.05). Among the 38 routine laboratory indicators, there were no significant differences in the levels of PLT/LYM, NEU/LYM, TT, D-Dimer, FDP, TBA, ALP, TP, ALB, GLB, UREA, Cr, P, Cystatin C, HDL- C, Apo-A1, and Lp(a) between the two groups (p > 0.05). The levels of the rest indicators were all statistically different between the two groups (p < 0.05). If only 12 risk factors of PE were analyzed by logistic regression, decision tree model, and the Support Vector Machine (SVM), the AUCROC were 0.78, 0.74 and 0.66 respectively, while 12 risk factors of PE and 38 routine laboratory indicators were analyzed by logistic regression, decision tree model and the support vector machine(SVM), the AUCROC were 0.86, 0.77 and 0.93 respectively. Conclusion: The efficacy of clinical risk factors alone in predicting early-onset preeclampsia is not high, while the efficacy increased significantly when PE risk factors were combined with routine laboratory indicators. The SVM model was better than the logistic regression model and decision tree model in the early prediction of early-onset preeclampsia incidence.

Wireless Sensor Network Based Real-Time Pedestrian Detection and Classification for Intelligent Transportation System

Pedestrian safety has become a critical consideration in developing society especially road traffic, an intelligent transportation need of the hour is the solution left. India tops the world with 11% of global road accidents. With this data, we have moved in the direction of computer vision applications for efficient and accurate pedestrian detection for intelligent transportation systems (ITS). The important application of this research is robot development, traffic management and control, unmanned vehicle driving (UVD), intelligent monitoring and surveillance system, and automatic pedestrian detection system. Much research has focused on pedestrian detection, but sustainable solution-driven research must still be required to overcome road accidents. We have proposed a wireless sensor network-based pedestrian detection system that classifies the real-time set of pedestrian activity and samples the reciprocally received signal strength (RSS) from the sensor node. We applied a histogram of oriented gradient (HOG) descriptor algorithm K-nearest neighbor, decision tree and linear support vector machine to measure the performance and prediction of the target. Also, these algorithms have performed a comparative analysis under different aspects. The linear support vector machine algorithm was trained with 481 samples. The performance achieves the accuracy of 98.90%and has accomplished superior results with a maximum precision of 0.99, recall of 0.98, and F-score of 0.95 with 2% error rate. The model’s prediction indicates that it can be used in the intelligent transportation system. Finally, the limitation and the challenges discussed to provide an outlook for future research direction to perform effective pedestrian detection.

Root cause prediction for failures in semiconductor industry, a genetic algorithm–machine learning approach

Failure analysis has become an important part of guaranteeing good quality in the electronic component manufacturing process. The conclusions of a failure analysis can be used to identify a component’s flaws and to better understand the mechanisms and causes of failure, allowing for the implementation of remedial steps to improve the product’s quality and reliability. A failure reporting, analysis, and corrective action system is a method for organizations to report, classify, and evaluate failures, as well as plan corrective actions. These text feature datasets must first be preprocessed by Natural Language Processing techniques and converted to numeric by vectorization methods before starting the process of information extraction and building predictive models to predict failure conclusions of a given failure description. However, not all-textual information is useful for building predictive models suitable for failure analysis. Feature selection has been approached by several variable selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune and others are not applicable to textual data. This article aims to develop a predictive model able to predict the failure conclusions using the discriminating features of the failure descriptions. For this, we propose to combine a Genetic Algorithm with supervised learning methods for an optimal prediction of the conclusions of failure in terms of the discriminant features of failure descriptions. Since we have an unbalanced dataset, we propose to apply an F1 score as a fitness function of supervised classification methods such as Decision Tree Classifier and Support Vector Machine. The suggested algorithms are called GA-DT and GA-SVM. Experiments on failure analysis textual datasets demonstrate the effectiveness of the proposed GA-DT method in creating a better predictive model of failure conclusion compared to using the information of the entire textual features or limited features selected by a genetic algorithm based on a SVM. Quantitative performances such as BLEU score and cosine similarity are used to compare the prediction performance of the different approaches.

Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI.

The mortality of colorectal cancer patients with pelvic bone metastasis is imminent, and timely diagnosis and intervention to improve the prognosis is particularly important. Therefore, this study aimed to build a bone metastasis prediction model based on Gray level Co-occurrence Matrix (GLCM) - based Score to guide clinical diagnosis and treatment. We retrospectively included 614 patients with colorectal cancer who underwent pelvic multiparameter magnetic resonance image(MRI) from January 2015 to January 2022 in the gastrointestinal surgery department of Gezhouba Central Hospital of Sinopharm. GLCM-based Score and Machine learning algorithm, that is,artificial neural net7work model(ANNM), random forest model(RFM), decision tree model(DTM) and support vector machine model(SVMM) were used to build prediction model of bone metastasis in colorectal cancer patients. The effectiveness evaluation of each model mainly included decision curve analysis(DCA), area under the receiver operating characteristic (AUROC) curve and clinical influence curve(CIC). We captured fourteen categories of radiomics data based on GLCM for variable screening of bone metastasis prediction models. Among them, Haralick_90, IV_0, IG_90, Haralick_30, CSV, Entropy and Haralick_45 were significantly related to the risk of bone metastasis, and were listed as candidate variables of machine learning prediction models. Among them, the prediction efficiency of RFM in combination with Haralick_90, Haralick_all, IV_0, IG_90, IG_0, Haralick_30, CSV, Entropy and Haralick_45 in training set and internal verification set was [AUC: 0.926,95% CI: 0.873-0.979] and [AUC: 0.919,95% CI: 0.868-0.970] respectively. The prediction efficiency of the other four types of prediction models was between [AUC: 0.716,95% CI: 0.663-0.769] and [AUC: 0.912,95% CI: 0.859-0.965]. The automatic segmentation model based on diffusion-weighted imaging(DWI) using depth learning method can accurately segment the pelvic bone structure, and the subsequently established radiomics model can effectively detect bone metastases within the pelvic scope, especially the RFM algorithm, which can provide a new method for automatically evaluating the pelvic bone turnover of colorectal cancer patients.

Heart Plaque Detection with Improved Accuracy using Decision tree in comparison with Least Squares Support Vector Machine

Aim: The main aim of this research is to detect heart plaque using the Decision Tree algorithm with improved accuracy and comparing it with Least Squares Support Vector Machine. Materials and Methods: Decision tree and Least squares Support Vector Machine algorithms are two groups compared in this study. Each group has 20 samples and calculations utilized pretest power of 0.08 with 95% confidence interval. The G power is estimated for samples using clincalc, which has two groups: alpha, power, and enrollment ratio. These samples are split into two groups: training dataset (n = 489 [70%]) and test dataset (n = 277 [30%]). Results: The accuracy obtained for Decision Tree was 68.13 % and 67.3 % for the Least Squares Support Vector Machine technique. Since p (2-tailed) < 0.05, in SPSS statistical analysis, a significant difference exists between the two groups. Conclusion: It is found that the Decision Tree algorithm is significantly better than the Least Squares Support Vector Machine algorithm in Heart plaque disease detection for the dataset considered.

Alternate Support Vector Machine Decision Trees for Power Systems Rule Extractions

Increasing renewable energy penetrations bring complex feasibility and stability problems. Data-driven methods are applied in extracting and embedding these feasibility and stability rules in power system operations and planning. This paper presents a method of alternate support vector machine decision trees for rule extraction problems. The method significantly improves the classical decision-tree-based algorithms' efficiency, stability, and versatility. Finally, we apply the method to several power and energy system scenarios to show its effectiveness.