Support Vector Machine Method Research Articles

Hyperspectral reflectance and machine learning for multi-site monitoring of cotton growth

Hyperspectral measurements can help with rapid decision-making and collecting data across multiple locations. However, there are multiple data processing methods (Savisky-Golay [SG], first derivative [FD], and normalization) and analyses (partial least squares regression [PLS], weighted k-nearest neighbor [KKNN], support vector machine [SVM], and random forest [RF]) that can be used to determine the best relationship between physical measurements and hyperspectral data. In the current study, FD was the best method for data processing and SVM was the best model for predicting average cotton (Gossypium spp. Malvaceae) height and nodes. However, the combination of FD and RF were best at predicting cotton leaf area index, canopy cover, and chlorophyll content across the growing season. Additionally, results from models developed by both SVM and RF were closely related to pseudo-CHIME satellite wavebands, where in-situ hyperspectral data were matched to the spectral resolutions of a future hyperspectral satellite. The information and results presented will aid producers and other members of the cotton industry to make rapid and meaningful decisions that could result in greater yield and sustainable intensification.

SENTIMENT ANALYSIS OF ONLINE LOANS ON TWITTER USING LEXICON BASED METHODS AND SUPPORT VECTOR MACHINE (SVM)

Technological developments are increasingly rapid and moving towards digital, which in the end technology can also help people who are experiencing economic problems, namely with online loan services. Even though there are many conveniences provided by online loan services, of course not all people give positive comments because there are quite a few negative comments about this service.One of the social media that is widely used by the public to provide comments about online loans is Twitter. Sentiment analysis is a data processing process to obtain information about whether an opinion sentence tends to be positive, negative or even neutral. This research contains sentiment analysis towards Online Loans on Twitter using the Lexicon Based and Support Vector Machine methods. From the results of this research, the accuracy for SVM was 82.36%. From these results it can be concluded that the use of the Lexicon Based and Support Vector Machine methods is considered quite good and effective for classifying sentiment

Facial-Based Autism Classification Using Support Vector Machine Method

Facial-Based Autism Classification Using Support Vector Machine Method

Performance of data-driven models based on seasonal-trend decomposition for streamflow forecasting in different climate regions of Türkiye

This study examines the ability of different methods such as machine learning, ensemble models, and metaheuristic algorithms to predict streamflow. For this purpose, five different methods were used: Artificial Neural Networks (ANN), Support Vector Machines (SVM), Adaptive Boosting, Particle Swarm Optimization (PSO), and BSPSO hybridized with Band Similarity (BS), a relatively new method. Additionally, the impact of seasonality and trend components obtained through Seasonal-Trend decomposition using LOESS (locally weighted regression and scatterplot smoothing) (STL) data decomposition technique on prediction success was investigated. Models were developed in three basins with three different climate characteristics: continental, temperate, and arid. The results showed higher prediction success in input structures including seasonality and trend components. While higher prediction successes were achieved at Karasu in the continental climate class and Körkün in the temperate climate class, model performances were lower at Küçük Muhsine in the arid climate class. While the most successful modeling for Küçük Muhsine (NSE = 0.696) and Karasu stations (NSE = 0.811) was obtained with the BSPSO method, the SVM method produced the best results for Körkün station (NSE = 0.818). Moreover, BSPSO models outperformed the prediction successes obtained by using PSO alone for each scenario at all three stations. The percentages of the BSPSO method improving the prediction success according to the NSE metric ranged from 3.53% to 17.40% at Küçük Muhsine, 0.49%–3.72% at Karasu, and 1.24%–7.24% at Körkün. The competitive results achieved by the BSPSO approach compared to ANN and SVM in flow prediction constitute the innovative aspect of this study.

Driving Attention State Detection Based on GRU-EEGNet.

The present study utilizes the significant differences in θ, α, and β band power spectra observed in electroencephalograms (EEGs) during distracted versus focused driving. Three subtasks, visual distraction, auditory distraction, and cognitive distraction, were designed to appear randomly during driving simulations. The θ, α, and β band power spectra of the EEG signals of the four driving attention states were extracted, and SVM, EEGNet, and GRU-EEGNet models were employed for the detection of the driving attention states, respectively. Online experiments were conducted. The extraction of the θ, α, and β band power spectrum features of the EEG signals was found to be a more effective method than the extraction of the power spectrum features of the whole EEG signals for the detection of driving attention states. The driving attention state detection accuracy of the proposed GRU-EEGNet model is improved by 6.3% and 12.8% over the EEGNet model and PSD_SVM method, respectively. The EEG decoding method combining EEG features and an improved deep learning algorithm, which effectively improves the driving attention state detection accuracy, was manually and preliminarily selected based on the results of existing studies.

Perbandingan Metode Machine Learning dalam Analisis Sentimen Komentar Pengguna Aplikasi InDriver pada Dataset Tidak Seimbang

The InDriver service is an online transportation service that has more flexibility in price and driver choice by consumers. Various comments from InDriver service users can affect people's views, so it is necessary to carry out a sentiment analysis of these comments. The purpose of this study was to identify positive, negative and neutral sentiments in user comments and to compare the performance of classification methods. The results of analysis with unbalanced datasets show that the Support Vector Machine (SVM) and Logistic Regression methods have the highest accuracy, reaching 89%. However, quality assessment is not only based on accuracy alone. In terms of the balance between precision and recall in the minority (neutral) class, the Random Forest method shows a more balanced performance with an F1-score of 55%. After balancing the dataset with the SMOTE method, performance increases significantly for the Naïve Bayes Classifier method, especially in the neutral class for recall and F1-score metrics of 57% and 52%. In conclusion, SVM and Logistic Regression have high accuracy, but to consider the balance of precision and recall in the minority class, the Random Forest method is recommended.

Hybrid intelligent and numerical methods to estimate the transmission coefficients of rectangular floating breakwaters

ABSTRACT Breakwaters are used to reduce incoming wave energy at harbors and shorelines. This paper presents a comparison of novel two-dimensional hybrid intelligent models for the idealization of the effects of waves on the performance of moored rectangular floating breakwaters (FBs). Fluid structure interactions (FSIs) were idealized by airy-type monochromatic regular waves generated in a numerical wave tank. The coupled Volume of Fluid-Fast Fictitious Domain (VOF-FFD) interpolation method was used to evaluate FB motions. Different forms of Least Squares Support Vector Machine Methods (LSSVMs) that utilized 183 data streams were used to model FB performance for different wave height-to-water depth ratios, dimensional aspect ratios, and specific length-to-water depth ratios. Of those, 80% were used to train the model and 20% to test it. Parametric studies have shown that during training a Least Squares Support Vector Machine Method-Bat Algorithm (LSSVM-BA) with R2 = 0.8725, MAE = 0.0276, and RMSE = 0.0488 presents the most appropriate model for the evaluation of FB performance. Notwithstanding this, during testing a Least Squares Support Vector Machine Method-Cuckoo Search (LSSVM-CS) Algorithm with corresponding values of 0.6841, 0.0519, and 0.0708 performs better.

Predicting Prices of Airbnb Accommodations in Thailand by SVM and XGBoost Methods

Predicting Prices of Airbnb Accommodations in Thailand by SVM and XGBoost Methods

Performance and Clinical Impact of Radiomics and 3D-CNN Models for the Diagnosis of Neurodegenerative Parkinsonian Syndromes on 18 F-FDOPA PET.

The aim of this study was to compare the performance and added clinical value of a semiautomated radiomics model and an automated 3-dimensinal convolutional neural network (3D-CNN) model for diagnosing neurodegenerative parkinsonian syndromes on 18 F-FDOPA PET images. This 2-center retrospective study included 687 patients with motor symptoms consistent with parkinsonian syndrome. All patients underwent 18 F-FDOPA brain PET scans, acquired on 3 PET systems from 2 different hospitals, and classified as pathological or nonpathological (by an expert nuclear physician). Artificial intelligence models were trained to replicate this medical expert's classification using 2 pipelines. The radiomics pipeline was semiautomated and involved manually segmenting the bilateral caudate and putamen nuclei; 43 radiomic features were extracted and combined using the support vector machine method. The deep learning pipeline was fully automatic and used a 3D-CNN model. Both models were trained on 417 patients and tested on an internal (n = 100) and an external (n = 170) test set. The final models' performance was evaluated using balanced accuracy and compared with that of a junior medical expert and nonexpert nuclear physician. On the internal test set, the 3D-CNN model outperformed the radiomic model with a balanced accuracy of 99% (vs 96%). It led to diagnostic performance similar to that of a junior medical expert (only 1 in 100 patients misclassified by both). On the external test set from a less experienced hospital, the 3D-CNN model allowed physicians to correctly reclassify the diagnosis of 10 out 170 patients (6%). The developed 3D-CNN model can automatically diagnose neurodegenerative parkinsonian syndromes, also reducing diagnostic errors by 6% in less-experienced hospitals.

Fuzzy Logic Prediction of Hypertensive Disorders in Pregnancy Using the Takagi–Sugeno and C-Means Algorithms

Hypertensive disorders in pregnancy, which include preeclampsia, eclampsia, and chronic hypertension, complicate approximately 10% of all pregnancies in the world, constituting one of the most serious causes of mortality and morbidity in gestation. To help predict the occurrence of hypertensive disorders, a study based on algorithms that help model this health problem using mathematical tools is proposed. This study proposes a fuzzy c-means (FCM) model based on the Takagi–Sugeno (T-S) type of fuzzy rule to predict hypertensive disorders in pregnancy. To test different modeling methodologies, cross-validation comparisons were made between random forest, decision tree, support vector machine, and T-S and FCM methods, which achieved 80.00%, 66.25%, 70.00%, and 90.00%, respectively. The evaluation consisted of calculating the true positive rate (TPR) over the true negative rate (TNR), with equal error rate (EER) curves achieving a percentage of 20%. The learning dataset consisted of a total of 371 pregnant women, of which 13.2% were diagnosed with a condition related to gestational hypertension. The dataset for this study was obtained from the Secretaría de Salud del Estado de Hidalgo (SSEH), México. A random sub-sampling technique was used to adjust the class distribution of the data set, and to eliminate the problem of unbalanced classes. The models were trained using a total of 98 samples. The modeling results indicate that the T-S and FCM method has a higher predictive ability than the other three models in this research.

Identifying diseases symptoms and general rules using supervised and unsupervised machine learning

The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of varying severity. Machine learning has made major advances in recent years, proving its effectiveness in various healthcare applications. This study aims to identify patterns of symptoms and general rules regarding symptoms among patients using supervised and unsupervised machine learning. The integration of a rule-based machine learning technique and classification methods is utilized to extend a prediction model. This study analyzes patient data that was available online through the Kaggle repository. After preprocessing the data and exploring descriptive statistics, the Apriori algorithm was applied to identify frequent symptoms and patterns in the discovered rules. Additionally, the study applied several machine learning models for predicting diseases, including stepwise regression, support vector machine, bootstrap forest, boosted trees, and neural-boosted methods. Several predictive machine learning models were applied to the dataset to predict diseases. It was discovered that the stepwise method for fitting outperformed all competitors in this study, as determined through cross-validation conducted for each model based on established criteria. Moreover, numerous significant decision rules were extracted in the study, which can streamline clinical applications without the need for additional expertise. These rules enable the prediction of relationships between symptoms and diseases, as well as between different diseases. Therefore, the results obtained in this study have the potential to improve the performance of prediction models. We can discover diseases symptoms and general rules using supervised and unsupervised machine learning for the dataset. Overall, the proposed algorithm can support not only healthcare professionals but also patients who face cost and time constraints in diagnosing and treating these diseases.

Developing a crash severity model based on multi objective evolutionary feature selection approaches

The main purpose of this article is assessing the capability of multiobjective optimisation evolutionary algorithms (MOEAs) along with machine learning method on predicting the crash injury severity with the approach of reducing input variables. Dataset of crashes which were occurred in Iran was analysed over the period of 2016–2020. The data were classified into two classes in terms of injury severity: fatal and non-fatal datasets. At first, general prediction models were created by inserting 31 available input variables into support vector machine (SVM) method based on two imbalanced and obtained balanced datasets. A cluster based under sampling technique was used to obtained balanced data. Then evolutionary algorithms of two objectives optimisation algorithms namely non-dominated sorting genetic algorithm (NSGA-II), multi-objective evolutionary algorithm based on decomposition (MOEA/D), and pareto envelope-based selection algorithm (PESA-II) along with SVM were used to select features that significantly affect the severity of crashes and developed the simplified models. Then the simplified developed model was compared to the model which was prepared with random forest (RF) method in feature selection. It is shown that the simplified model developed by combined MOEAs and SVM is more appropriate to accurately predict the crash severity, considering the number of input variables in comparison to RF.

COMPARISON OF SUPPORT VECTOR MACHINE BASED ON FASTTEXT WITHOUT AND WITH FIREFLY OPTIMIZATION PARAMETERS FOR DISASTER SENTIMENT ANALYSIS IN INDONESIA

Sentiment analysis is a process for analyzing opinions, sentiments, assessments, and emotions from someone's statements regarding a domain or is also a process for entering and processing data in the form of text. Support vector machine (SVM) is a supervised machine learning technique that functions as a separator of two classes of data. SVM aims to obtain numerical vectors using fasttext. SVM cannot choose appropriate parameters so the use of parameters is not optimal. To obtain optimal parameters with better classification results, firefly optimization was carried out. This research compares the fasttext-based SVM method without and with firefly optimization parameters using data from tweets with the keyword "Indonesian disaster" which was crawled using the Twitter application. The results of this research obtained 128 dimensions that form the weight of each word. This means that each word is represented in a 128-dimensional vector space. The evaluation of the SVM classification model with and without firefly optimization provides an accuracy of 89.1% and 61.3% respectively. This shows that the SVM classification method with firefly optimization provides quite good classification performance compared to the SVM model without optimization.

Implementation Of The Support Vector Machine Method In Predicting Student Graduation

Student graduation is an important indicator of the quality of education at a higher education institution. A high graduation rate not only indicates success in the learning process but also has a positive impact on the reputation of the institution. Conversely, a low graduation rate can be a signal of problems that require special attention. Various factors, both academic and non-academic, influence higher education institutions in ensuring timely student graduation. Therefore, we need a method that can accurately predict student graduation to carry out early intervention. This study aims to apply the support vector machine method in predicting student graduation. We chose this method due to its capacity to classify complex data. We use historical student data, such as Semester Achievement Index scores, as input variables to build a prediction model. We evaluate the model using precision, recall, and f1-score metrics. According to the study's findings, the support vector machine model's accuracy level is 71.20%. This method is good at predicting students who graduate with a precision of 95%, recall of 72%, and f1-score of 82%. However, the model's performance in predicting students who failed was less than satisfactory, with a precision of only 17%, a recall of 62%, and an f1-score of 26%. The imbalance in data between passed and failed students contributed to this result. The Support Vector Machine method effectively predicts student graduation for the majority class (passed), but requires special handling of the data imbalance to enhance the accuracy of predictions for the minority class (failed). Universities expect to use the results of this study to carry out early intervention and increase student graduation rates.

Sentiment analysis on twitter about the death penalty using the support vector machine method

It is estimated that 175 million people in Indonesia utilize the Internet, according to the most recent We Are Social survey. 160 million of them are internet users who utilize social media, according to this data. It is estimated that 19.5 million Indonesians use Twitter. This is consistent with the numerous tweets that users have posted on Twitter about a variety of topics, including politics, music, health, and education. The death penalty is still one of the most popular subjects that is addressed on Twitter. When a judge rules that someone will be executed as retribution for a crime they have committed, this is referred to as the death penalty. As a result, sentiment analysis utilizing the Support Vector Machine technique with linear kernel features and Python programming was used to study public opinions on the death sentence. To improve the accuracy of the results obtained, data labeling on 848 data that were received through the scraping process was done manually in this study. Positive data is categorized as belonging to the class that supports the death sentence, while negative data is categorized as belonging to the class that opposes it. The study that was done shows an 8:2 difference between the training and test data. After preprocessing a dataset containing 758 data points, of which 606 will be utilized for training and 152 for testing, we obtain 91% accuracy, 91% precision, 100% recall, and 95% f1-score

Machine learning-based real time identification of driver posture during driving

The detection of driver distractions is exceptionally important for driving safety. Driver distraction can originate from various sources such as external tasks (e.g., texting or eating) or mental states (e.g., sleepiness, tiredness, anger, and tension). To detect these conditions, most of the previous studies were based on vision-based techniques. These techniques are affected by environmental factors (e.g., day, night, and facial accessories such as glasses and hats). However, the steering wheel is an interface that provides a direct relationship between the driver and vehicle. The driver’s interaction can effectively reflect this behavior and mental state. This study introduced a new method for detecting driver distractions by utilizing force/torque (F/T) sensor data extracted from the steering wheel. An experimental setup was designed and developed to measure the accuracy of the proposed method. To validate the strategy, a machine learning-based algorithm was developed. It demonstrated remarkable performance in determining the position of the driver’s hand on the steering wheel and in inferring with high precision the hand the driver uses to operate the vehicle. The method produced accurate results in all the grip ranges that could be held by the driver within the range of 0°–360°. The support vector machine (SVM) method was used in machine learning. It predicted with a 91.1% accuracy rate.

A hybrid learning approach to better classify exhaled breath's infrared spectra: A noninvasive optical diagnosis for socially significant diseases.

Early diagnosis is crucial for effective treatment of socially significant diseases, such as type 1 diabetes mellitus (T1DM), pneumonia, and asthma. This study employs a diagnostic method based on infrared laser spectroscopy of human exhaled breath. The experimental setup comprises a quantum cascade laser, which emits in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3-12.8 μm (780-1890 cm-1), and a Herriott multipass gas cell with a specific optical path length of 76 m. Using this setup, spectra of exhaled breath in the mid-infrared range were obtained from 165 volunteers, including healthy individuals, patients with T1DM, asthma, and pneumonia. The study proposes a hybrid approach for classifying these spectra, utilizing a variational autoencoder for dimensionality reduction and a support vector machine method for classification. The results demonstrate that the proposed hybrid approach outperforms other machine learning method combinations.

Comparison of Binary Logistic Regression and SVM to Classify Diabetes Sufferers

Diabetes is a chronic metabolic disorder characterized by high levels of glucose in the blood due to disruption of the insulin hormone which functions as a regulator of the balance of blood sugar levels. This disease continues to increase in prevalence in various countries, making it a global health problem. Diabetes has trigger factors that contribute to the incidence of the disease, such as age, gender, smoking habits, healthy eating patterns, high blood pressure, and others. Diagnosis of diabetes can be done by carrying out a fasting blood sugar test, a 2-hour postprandial (PP) blood sugar test, and a random blood sugar test. However, it is very possible for diagnoses made by health workers to have errors due to subjectivity and different experiences, so a fast and precise classification method is needed to classify patients undergoing diabetes examination based on variables related to diabetes. The classification method used in this research is binary logistic regression and Support Vector Machine (SVM). A similar study carried out classification of diabetes sufferers using the Naive Bayes and KNN methods by comparing the results with SVM, so in this study the binary logistic regression method and SVM will be used to determine the performance of the classification method. The data used is secondary data. Next, the data is divided into training and testing data. The analysis results show that the SVM method is slightly superior in classification accuracy of testing data, namely 97.75%. With this research, it is hoped that decisions on patients undergoing diabetes examination will be faster, more precise and effective, and classification methods with better performance can be applied

Effect of Using GLCM and LBP+HOG Feature Extraction on SVM Method in Classification of Human Skin Disease Type

Skin diseases have been ranked third out of ten diseases suffered by outpatients in many hospitals in Indonesia. The public often underestimates these diseases because they are considered not to cause death. In general, dermatologists diagnose skin diseases using the biopsy process, but the biopsy process is quite expensive and can cause injury to the skin. Each skin disease has different texture and shape characteristics, so classification can be used to distinguish the type of skin disease. This study compares LBP+HOG and GLCM feature extraction with the SVM classification method to determine the best feature extraction in skin disease classification. This research compares GLCM and LBP+HOG feature extraction using the SVM method. GLCM is used to measure the relationship between pixel intensities in the image, while LBP+HOG combines information about the texture and shape of the image. The test results show that the GLCM feature extraction method with SVM classification achieves an accuracy of 74%. In this test, the parameters C=100 and the features used are homogeneity, contrast, energy, correlation, ASM, and dissimilarity. Meanwhile, extraction with LBP+HOG resulted in an accuracy of 68%.

Advancing architectural frameworks for vibration signature classification in rotating machinery

Advancements in data-driven predictive maintenance have significantly improved digital twin applications for rotating machinery, offering robust solutions for smart manufacturing challenges. These improvements are crucial since equipment failures can cause extensive and costly disruptions to both maintenance schedules and operations. As precision and reliability are critical in production processes, undetected fluctuations in operating frequencies can swiftly escalate to complete part failure, leading to prolonged repairs and productivity loss. This study explores an integrated dataflow pipeline, specifically through Siemens’ MindSphere, to enable continuous predictive maintenance and enhance data acquisition and management. Particularly, conditions such as normal operation, mass balance, rotating imbalance, and mechanical looseness are classified using support vector machine (SVM), neural network (NN), and K-Nearest Neighbor (KNN) methods for the purpose of comparing results. Our results highlight the efficacy of ensemble techniques in collecting and diagnosing vibration signatures, thereby enabling proactive maintenance. To classify various failure signatures, we have proposed a framework to interpret time-series and frequency-dependent data for determining failure types. This research exemplifies how merging data-driven methods with digital twin can improve the accuracy and reliability of condition monitoring. Additionally, we introduce a cloud-based architecture for the diagnosis of rotating machinery, utilizing Application Programming Interface (API) configurations, and develop a real-time dashboard for streaming and visualizing classified data, fostering immediate and informed decision-making.