Accuracy Of Support Vector Machine Research Articles

Prediction of RUL of lubricating oil based on Deep Learning Technique

This study uses condition monitoring (CM) to determine the lubricating oil's remaining usable life (RUL). First, Atomic emission spectroscopy (AES) was used to determine the lubricating oil's element makeup and content. Under use were quantitatively examined. The precision and efficacy of the RUL prediction model might be diminished in light of the wide range of oil data acquired using AES. A thorough it is recommended to screen oil data using a method for choosing parameters based on correlation analysis, lubricant degradation analysis, and information entropy. In order to address this issue. The lubricant's RUL prediction model was then developed using a support vector machine (SVM). It is shown that the SVM prediction model's efficacy and accuracy by contrasting the experimental findings with the prediction model's output data.

Machine learning-aided algorithm design for prediction of severity from clinical, demographic, biochemical and immunological parameters: Our COVID-19 experience from the pandemic.

The severity of laboratory and imaging finding was found to be inconsistent with clinical symptoms in COVID-19 patients, thereby increasing casualties. As compared to conventional biomarkers, machine learning algorithms can learn nonlinear and complex interactions and thus improve prediction accuracy. This study aimed at evaluating role of biochemical and immunological parameters-based machine learning algorithms for severity indexing in COVID-19. Laboratory biochemical results of 5715 COVID-19 patients were mined from electronic records including 509 admitted in COVID-19 ICU. Random Forest Classifier (RFC), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and K-Nearest Neighbours (KNN) classifier models were used. Lasso regression helped in identifying the most influential parameter. A decision tree was made for subdivided data set, based on randomization. Accuracy of SVM was highest with 94.18% and RFC with 94.04%. SVM had highest PPV (1.00), and NBC had highest NPV (0.95). QUEST modelling ignored age, urea and total protein, and only C-reactive protein and lactate dehydrogenase were considered to be a part of decision-tree algorithm. The overall percentage of correct classification was 78.31% in the overall algorithm with a sensitivity of 87.95% and an AUC of 0.747. C-reactive protein and lactate dehydrogenase being routinely performed tests in clinical laboratories in peripheral setups, this algorithm could be an effective predictive tool. SVM and RFC models showed significant accuracy in predicting COVID-19 severity and could be useful for future pandemics.

A Method for Identifying External Short-Circuit Faults in Power Transformers Based on Support Vector Machines

Being a vital component of electrical power systems, transformers significantly influence the system stability and reliability of power supplies. Damage to transformers may lead to significant economic losses. The efficient identification of transformer faults holds paramount importance for the stability and security of power grids. The existing methods for identifying transformer faults include oil chromatography analysis, temperature assessment, frequency response analysis, vibration characteristic examination, and leakage magnetic field analysis. These methods suffer from limitations such as limited sensitivity, complexity in operation, and a high demand for specialized skills. In this paper, we propose a method to identify external short-circuit faults of power transformers based on fault recording data on short-circuit currents. It involves analyzing the current signals of various windings during faults, extracting appropriate features, and utilizing a classification algorithm based on a support vector machine (SVM) to determine fault types and locations. The influence of different kernel functions on the classification accuracy of SVM is discussed. The results indicate that this method can proficiently identify the type and location of external short-circuit faults in transformers, achieving an accuracy rate of 98.3%.

A Fault Diagnosis Method for Ultrasonic Flow Meters Based on KPCA-CLSSA-SVM

To enhance the fault diagnosis capability for ultrasonic liquid flow meters and refine the fault diagnosis accuracy of support vector machines, we employ Levy flight to augment the global search proficiency. By utilizing circle chaotic mapping to establish the starting locations of sparrows and refining the sparrow position with the highest fitness value, we propose an enhanced sparrow search algorithm termed CLSSA. Subsequently, we optimize the parameters of support vector machines using this algorithm. A support vector machine classifier based on CLSSA has been constructed. Given the intricate data collected from ultrasonic liquid flow meters for diagnostic purposes, the approach of employing KPCA to decrease data dimensionality is implemented, and a KPCA-CLSSA-SVM algorithm is proposed to achieve fault diagnosis in ultrasonic flow meters. By using UCI datasets, the findings indicate that KPCA-CLSSA-SVM achieves fault diagnosis accuracies of 94.12%, 100.00%, 97.30%, and 100% in the four flow meters, respectively. Compared with the Bayesian classifier diagnostic algorithm, this has been increased by 4.18%. And compared with support vector machine diagnostic algorithms improved by the SSA, it has increased by 2.28%.

Approaches of Combining Machine Learning with NMR-Based Pore Structure Characterization for Reservoir Evaluation

Tight gas, a category of unconventional natural gas, relies on advanced intelligent monitoring methods for their extraction. Conventional logging for reservoir evaluation relies on logging data and the manual setting of evaluation criteria to classify reservoirs. However, the complexity and heterogeneity of tight reservoirs pose challenges in accurately identifying target layers by using traditional well-logging techniques. Machine learning may hold the key to solving this problem, as it enables computers to learn without being explicitly programmed and manually adding rules. Therefore, it is possible to make reservoir evaluations using machine learning methods. In this paper, the reservoir quality index (RQI) and porous geometric parameters obtained from the optimized inversion of the spherical–tubular model are adopted to evaluate the reservoir. Then, three different machine learning approaches, the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm, are utilized for reservoir classification. The selected dataset covers more than 7000 samples from five wells. The data from four wells are arranged as the training dataset, and the data of the remaining one well is designed as the testing dataset to calculate the prediction accuracies of different machine learning algorithms. Among them, accuracies of RF, SVM, and XGB are all higher than 90%, and XGB owns the highest result by reaching 97%. Machine learning based approaches can greatly assist reservoir prediction by implementing the well-logging data. The research highlights the application of reservoir classification with a higher prediction accuracy by combining machine learning algorithms with NMR-logging-based pore structure characterization, which can provide a guideline for sweet spot identification within the tight formation. This not only optimizes resource extraction but also aligns with the global shift towards clean and renewable energy sources, promoting sustainability and reducing the carbon footprint associated with conventional energy production. In summary, the fusion of machine learning and NMR-logging-based reservoir evaluation plays a crucial role in advancing both energy efficiency and the transition to cleaner energy sources.

Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data.

(1) Background: Numerous variables could influence the risk of rectal cancer recurrence or metastasis, and machine learning (ML)-based algorithms can help us refine the risk stratification process of these patients and choose the best therapeutic approach. The aim of this study was to assess the predictive performance of 4 ML-based models for the prediction of local recurrence or distant metastasis in patients with locally advanced low rectal adenocarcinomas who underwent neoadjuvant chemoradiotherapy and surgical treatment; (2) Methods: Patients who were admitted at the first Oncologic Surgical Clinic from the Regional Institute of Oncology, Iasi, Romania were retrospectively included in this study between November 2019 and July 2023. Decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF) were used to analyze imagistic, surgical, and pathological data retrieved from the medical files, and their predictive performance was assessed; (3) Results: The best predictive performance was achieved by RF when used to predict disease recurrence (accuracy: 90.85%) or distant metastasis (accuracy: 89.63%). RF was closely followed by SVM (accuracy for recurrence 87.8%; accuracy for metastasis: 87.2%) in terms of predictive performance. NB and DT achieved moderate predictive power for the evaluated outcomes; (4) Conclusions: Complex algorithms such as RF and SVM could be useful for improving the prediction of adverse oncological outcomes in patients with low rectal adenocarcinoma.

Classification of flood disaster level news articles using Machine Learning

Floods have a significant socio-economic impact on Indonesian society. Much of this information is sourced from online news articles and social media. This research investigates whether the Support Vector Machine (SVM) method can be used for flood disaster level classification (low, medium, and high). Our methodology involves preparing data extracted from textual news articles on the National Disaster Management Agency (BNPB) website on the topic of flooding. We then labeled the data according to Regulation No. 02/2012 on general guidelines for disaster assessment and used the Support Vector Machine (SVM) method. Training and testing were conducted using different datasets, followed by accuracy and error evaluation. In addition, we considered the performance comparison of SVM with other classification methods, including Decision Tree, Naive Bayes, Adaboost, Random Forest, and Xgboost. The experimental results show that SVM still does not get good accuracy results for flood disaster level classification. The SVM accuracy level result of (52%) is still low compared to Random Forest (78%), and Xgboost (68%). Further research is expected to increase the accuracy of SVM for flood level classification.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Machine learning is currently one of the research hotspots in the field of landslide prediction. To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models, Conghua District, which is the most prone to landslide disasters in Guangzhou, was selected for landslide susceptibility evaluation. The evaluation factors were selected by using correlation analysis and variance expansion factor method. Applying four machine learning methods namely Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB), landslide models were constructed. Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic (ROC) curves. The results showed that LR, RF, SVM, and XGB models have good predictive performance for landslide susceptibility, with the area under curve (AUC) values of 0.752, 0.965, 0.996, and 0.998, respectively. XGB model had the highest predictive ability, followed by RF model, SVM model, and LR model. The frequency ratio (FR) accuracy of LR, RF, SVM, and XGB models was 0.775, 0.842, 0.759, and 0.822, respectively. RF and XGB models were superior to LR and SVM models, indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.©2024 China Geology Editorial Office.

Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics.

This study aimed to develop an automated classification framework for distinguishing between cervical cancer tumor and normal uterine tissue, leveraging CT images for radiomics feature extraction. We retrospectively analyzed CT images from 117 cervical cancer patients. To distinguish between cancerous and healthy tissue, we segmented gross tumor volume and normal uterine tissue as distinct regions of interest (ROIs) using manual segmentation techniques. Key radiomic parameters were extracted from these ROIs. To bolster model's predictive capability, the data was stratified into train data (70%) and validation data (30%). During feature selection phase, we applied Least Absolute Shrinkage and Selection Operator regression algorithm to identify most relevant features. Subsequently, we built classification models using five state-of-the-art machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Decision Tree (DT). Ultimately, the performance of each model was evaluated. Through stringent feature selection process, we identified 18 pivotal radiomic features for classification of cervical cancer and normal uterine tissue. When applied to test data, all five models achieved excellent performance, with area under the curve (AUC) values ranging from 0.8866 to 0.9190 (SVM: 0.9144, RF: 0.9078, KNN: 0.9051, DT: 0.8866, XGBoost: 0.9190), all surpassing threshold of 0.8. In terms of test data, all five models had high sensitivity; accuracy of SVM, RF, and XGBoost models was comparable; and specificity of five models was similar. XGBoost model outperformed the others in terms of diagnostic accuracy, achieving an AUC of 0.8737 (95% CI: 0.8198-0.9277) for train data and 0.9190 (95% CI: 0.8525-0.9854) for test data. Our findings underscore the potential of CT radiomics combined with machine learning algorithms for accurately classifying cervical cancer tumors and normal uterine tissue with high recognition capabilities. This approach holds significant promise for clinical diagnostics.

Dataset meta-level and statistical features affect machine learning performance

What dataset features affect machine learning (ML) performance has primarily been unknown in the current literature. This study examines the impact of tabular datasets' different meta-level and statistical features on the performance of various ML algorithms. The three meta-level features this study considered are the dataset size, the number of attributes and the ratio between the positive (class 1) and negative (class 0) class instances. It considered four statistical features for each dataset: mean, standard deviation, skewness and kurtosis. After applying the required scaling, this study averaged (uniform and weighted) each dataset’s different attributes to quantify its four statistical features. We analysed 200 open-access tabular datasets from the Kaggle (147) and UCI Machine Learning Repository (53) and developed ML classification models (through classification implementation and hyperparameter tuning) for each dataset. Then, this study developed multiple regression models to explore the impact of dataset features on ML performance. We found that kurtosis has a statistically significant negative effect on the accuracy of the three non-tree-based ML algorithms of the Support vector machine (SVM), Logistic regression (LR) and K-nearest neighbour (KNN) for their classical implementation with both uniform and weighted aggregations. This study observed similar findings in most cases for ML implementations through hyperparameter tuning, except for SVM with weighted aggregation. Meta-level and statistical features barely show any statistically significant impact on the accuracy of the two tree-based ML algorithms (Decision tree and Random forest), except for implementation through hyperparameter tuning for the weighted aggregation. When we excluded some datasets based on the imbalanced statistics and a significantly higher contribution of one attribute compared to others to the classification performance, we found a significant effect of the meta-level ratio feature and statistical mean and standard deviation features on SVM, LR and KNN accuracy in many cases. Our findings open a new research direction in understanding how dataset characteristics affect ML performance and will help researchers select appropriate ML algorithms for a possible optimal accuracy outcome.

Advanced Cybercrime Detection: A Comprehensive Study on Supervised and Unsupervised Machine Learning Approaches Using Real-world Datasets

In the ever-evolving field of cybersecurity, sophisticated methods—which combine supervised and unsupervised approaches—are used to tackle cybercrime. Strong supervised tools include Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), while well-known unsupervised methods include the K-means clustering model. These techniques are used on the publicly available StatLine dataset from CBS, which is a large dataset that includes the individual attributes of one thousand crime victims. Performance analysis shows the remarkable 91% accuracy of SVM in supervised classification by examining the differences between training and testing data. K-Nearest Neighbors (KNN) models are quite good in the unsupervised arena; their accuracy in detecting criminal activity is impressive, at 79.56%. Strong assessment metrics, such as False Positive (FP), True Negative (TN), False Negative (FN), False Positive (TP), and False Alarm Rate (FAR), Detection Rate (DR), Accuracy (ACC), Recall, Precision, Specificity, Sensitivity, and Fowlkes–Mallow's scores, provide a comprehensive assessment.

Toxic Fake News Detection and Classification for Combating COVID-19 Misinformation

The emergence of COVID-19 has led to a surge in fake news on social media, with toxic fake news having adverse effects on individuals, society, and governments. Detecting toxic fake news is crucial, but little prior research has been done in this area. This study aims to address this gap and identify toxic fake news to save time spent on examining nontoxic fake news. To achieve this, multiple datasets were collected from different online social networking platforms such as Facebook and Twitter. The latest samples were obtained by collecting data based on the topmost keywords extracted from the existing datasets. The instances were then labeled as toxic/nontoxic using toxicity analysis, and traditional machine-learning (ML) techniques such as linear support vector machine (SVM), conventional random forest (RF), and transformer-based ML techniques such as bidirectional encoder representations from transformers (BERT) were employed to design a toxic-fake news detection (FND) and classification system. As per the experiments, the linear SVM method outperformed BERT SVM, RF, and BERT RF with an accuracy of 92% and -score, -score, and -score of 95%, 85%, and 87%, respectively. Upon comparison, the proposed approach has either suppressed or achieved results very close to the state-of-the-art techniques in the literature by recording the best values on performance metrics such as accuracy, F1-score, precision, and recall for linear SVM. Overall, the proposed methods have shown promising results and urge further research to restrain toxic fake news. In contrast to prior research, the presented methodology leverages toxicity-oriented attributes and BERT-based sequence representations to discern toxic counterfeit news articles from nontoxic ones across social media platforms.

Analysis of Hybrid Feature Optimization Techniques Based on the Classification Accuracy of Brain Tumor Regions Using Machine Learning and Further Evaluation Based on the Institute Test Data.

The goal of this study was to get optimal brain tumor features from magnetic resonance imaging (MRI) images and classify them based on the three groups of the tumor region: Peritumoral edema, enhancing-core, and necrotic tumor core, using machine learning classification models. This study's dataset was obtained from the multimodal brain tumor segmentation challenge. A total of 599 brain MRI studies were employed, all in neuroimaging informatics technology initiative format. The dataset was divided into training, validation, and testing subsets online test dataset (OTD). The dataset includes four types of MRI series, which were combined together and processed for intensity normalization using contrast limited adaptive histogram equalization methodology. To extract radiomics features, a python-based library called pyRadiomics was employed. Particle-swarm optimization (PSO) with varying inertia weights was used for feature optimization. Inertia weight with a linearly decreasing strategy (W1), inertia weight with a nonlinear coefficient decreasing strategy (W2), and inertia weight with a logarithmic strategy (W3) were different strategies used to vary the inertia weight for feature optimization in PSO. These selected features were further optimized using the principal component analysis (PCA) method to further reducing the dimensionality and removing the noise and improve the performance and efficiency of subsequent algorithms. Support vector machine (SVM), light gradient boosting (LGB), and extreme gradient boosting (XGB) machine learning classification algorithms were utilized for the classification of images into different tumor regions using optimized features. The proposed method was also tested on institute test data (ITD) for a total of 30 patient images. For OTD test dataset, the classification accuracy of SVM was 0.989, for the LGB model (LGBM) was 0.992, and for the XGB model (XGBM) was 0.994, using the varying inertia weight-PSO optimization method and the classification accuracy of SVM was 0.996 for the LGBM was 0.998, and for the XGBM was 0.994, using PSO and PCA-a hybrid optimization technique. For ITD test dataset, the classification accuracy of SVM was 0.994 for the LGBM was 0.993, and for the XGBM was 0.997, using the hybrid optimization technique. The results suggest that the proposed method can be used to classify a brain tumor as used in this study to classify the tumor region into three groups: Peritumoral edema, enhancing-core, and necrotic tumor core. This was done by extracting the different features of the tumor, such as its shape, grey level, gray-level co-occurrence matrix, etc., and then choosing the best features using hybrid optimal feature selection techniques. This was done without much human expertise and in much less time than it would take a person.

Machine Learning-Based Gait Phase Detection for Semi-Active Prosthetic Knee

Human knee plays a vital role in performing day-to-day activities. For healthy person it is easy to perform locomotion activities but for people with transfemoral amputation, it is very difficult task. To overcome this issue prosthetic knees are developed. These prosthetic knees provide necessary function of gait cycle. In order to mimic gait cycle of human knee, it is very crucial to detect different gait phases in gait cycle. Mechanical sensors such as force and angle sensors are used to collect kinematic data and then with heuristic rule base system, the gait phases are detected. Rule-based system performs well but as number of gait phases are increased then it is difficult to identify them. This paper proposed machine learning based gait phase detection. Decision Trees, Liner discriminant analysis and Support Vector Machine, are applied to the kinematics data obtained from strain gauges and angle encoder. These algorithms are easy to implement on embedded hardware as they use low computational power. The Linear Discriminant analysis has highest validation accuracy of 95.6% and test accuracy of 95.40% while both Support Vector Machine and Decision Trees algorithm has 95.2% validation accuracy. The test accuracy of Support Vector Machine is 95.10% and for Decision Tree it is 95.05%.

An integrated data mining algorithms and meta-heuristic technique to predict the readmission risk of diabetic patients

Reducing hospital readmission rate is a significant challenge in the healthcare industry for managers and policymakers seeking to improve healthcare and lower costs. This study integrates data mining and meta-heuristic techniques to predict the early readmission probability of diabetic patients within 30 days of discharge. The research dataset was obtained from the UC Irvine Machine Learning Repository, including 101765 instances with 50 features representing patient and hospital outcomes, collected from 130 US hospitals. After data preprocessing, including cleansing, sampling, and normalization, a Chi-square analysis is done to confirm and rank the 20 identified factors affecting the readmission risk. As the algorithms' performance could vary based on the features’ characteristics, several classification algorithms, including a Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM), are employed. Moreover, the Genetic Algorithm (GA) is integrated into the SVM algorithm, called GA-SVM, for hyper-parameter tuning and increasing the prediction accuracy. The performance of the models was evaluated using accuracy, recall, precision, and f-measure metrics. The results indicate that the accuracy of RF, GA-SVM, SVM, and NN are calculated respectively as 74.04 %, 73.52 %, 72.40 %, and 70.44 %. Using GA to adjust c and gamma hyper-parameters led to a 1.12 % increase in SVM prediction accuracy. In response to increasing demand and considering poor hospital conditions, particularly during epidemics, these findings point out the potential benefits of a more tailored methodology in managing diabetic patients.

A Fault Identification Method for Metal Oxide Arresters Combining Suppression of Environmental Temperature and Humidity Interference with a Stacked Autoencoder

Most existing methods aiming to solve the fault identification problem of metal oxide arresters (MOAs) are limited by strong subjectivity in judgment, the significant impact of environmental temperature and humidity on the online monitoring of the resistance current, and poor generalization ability. Therefore, in this article, we propose an MOA fault identification method that combines suppressing environmental temperature and humidity interference with a stacked autoencoder (SAE). Firstly, a functional relationship model between resistance current and environmental temperature and humidity is established. Then, a temperature and humidity interference suppression method based on weighted nonlinear surface modeling is proposed to normalize the resistance current to the same reference temperature and humidity conditions. Finally, an MOA fault identification method combining the suppression of environmental temperature and humidity interference with an SAE is proposed. Furthermore, a comprehensive comparison is conducted on the recall, accuracy, F1-score, and average accuracy of support vector machine, random forest, logistic regression, and SAE classification algorithms in three different scenarios to demonstrate the effectiveness of the proposed method. The results indicate that environmental temperature and humidity interference suppression for resistive current prior to MOA fault classification significantly reduce the number of false alarms. Compared with other methods, the MOA fault identification method, which combines environmental temperature and humidity interference suppression with an SAE, has the highest average accuracy of 99.7%.

Random forest and support vector machine classifiers for coastal wetland characterization using the combination of features derived from optical data and synthetic aperture radar dataset

Abstract Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random forest (RF) and support vector machine (SVM) algorithms, employing Sentinel-1 dual polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests. The study also incorporates various derived parameters. The Jeffries–Matusita distance and Spearman’s rank correlation are used to evaluate the significance of commonly used spectral indices and SAR parameters in wetland classification. Only significant parameters are retained, reducing data dimensionality from 63 initial features to 23–33 essential features, resulting in an 18% improvement in classification accuracy. The combination of SAR and optical data yields a substantial 33% increase in the overall accuracy for both SVM and RF classification. Consistently, the fusion of SAR and optical data produces higher classification accuracy in both RF and SVM algorithms. This research provides an effective approach for monitoring changes in Pichavaram wetlands and offers a valuable framework for future wetland monitoring, supporting the planning and sustainable management of this critical area.

Perbandingan Analisis Sentimen Aplikasi Traveloka dan Tiket.com pada Twitter dengan Metode Support Vector Machine

The emergence of the COVID-19 pandemic in Indonesia resulted in an economic crisis, including in the world of tourism, which caused a decline in the national economy. With the existence of Online Travel Agencies (OTA) such as Traveloka and Tiket.com, it is hoped that they can help improve the tourism sector for the Indonesian economy. As a popular OTA and to see the opinion of the Indonesian people, it can be seen from public opinion in the form of tweets on the Twitter application. The tweets data will be taken and sentiment analysis will be carried out on the OTA Traveloka and Tiket.com applications which will be classified into certain classes based on opinions and modeling will be carried out using the Support Vector Machine (SVM) algorithm method. This research aims to determine the level of accuracy of the SVM algorithm and find out how sentiment analysis compares between Traveloka and Tiket.com. In the sentiment analysis comparison, in terms of price, Traveloka is superior and in terms of service, Tiket.com is superior. After modeling by comparing splitting data and handling imbalanced data using Synthetic Minority Oversampling Technique (SMOTE), the best SVM accuracy results for the Tiket.com price dataset were 68%, for Traveloka prices it was 97%, for Tiket.com services it was 92%, and for Traveloka services it is 89%.

Optimizing Seagrass Detection in Lampung Selatan: Leveraging Machine Learning Algorithms and Sentinel-2A Imagery

Seagrass beds are one of the coastal ecosystems that play an important role in maintaining the stability of blue carbon. However, high community activities threaten the existence of seagrass beds themselves. South Lampung Regency is one of the areas with considerable seagrass potential but the availability of distribution and density data is still minimal. This research aims to identify and map seagrass density as a first step for seagrass management. The data used in this research is Sentinel 2A multispectral image. Machine learning-based classification methods used are random forest (RF) and support vector machine (SVM) because these algorithms have a good ability to distinguish objects based on their features. This study uses a 2-level classification scheme, where level 1 consists of land, shallow sea, and deep-sea classes. Level 2 is the shallow marine bottom benthic habitat class. The type of seagrass found in this area is Enhalus acoroides. Based on the results of the analysis, low, medium, and high-density classes were obtained with an area of low around 20.12 ha, medium around 34.67 ha and high around 320.12ha with a total area of 374.91ha. RF has a higher overall accuracy of 88.00% while SVM accuracy is 84.00% so it can be concluded that Sentinel 2A images can be used to detect seagrass meadows.

Relationship Between BE4DBE2 and Variables n and z: A Comprehensive Analysis Using Linear Regression, Nonparametric Regression, Naive Bayes Classification, Decision Tree Analysis, SVM Analysis, K-Means Clustering, and Bayesian Regression

This research employed various statistical techniques, including linear regression, nonparametric regression, Naive Bayes classification, decision tree analysis, Support Vector Machine (SVM) analysis, k-means clustering, and Bayesian regression, to analyze nuclear data. The research aims to explore the relationships between variables, predict binding energy, classify nuclear data, and identify similar groups. The research results revealed that linear regression indicated a significant influence of the intercept and predictor variable 'n' on the variable 'BE4DBE2,' while the variable 'z' was not significant. However, the overall model had limited explanatory power. Nonparametric regression with smoothing functions effectively modeled the relationship between 'BE4DBE2' and variables 'n' and 'z,' explaining approximately 11% of the variability in the response variable. Classification using Naive Bayes successfully categorized nuclear data based on 'n' and 'z,' revealing their relationship. Decision tree analysis evaluated the performance of this classification model and provided insights into accuracy, agreement, sensitivity, specificity, precision, and negative predictive value. SVM analysis successfully built an accurate SVM model with a linear kernel, classifying nuclear data while depicting decision boundaries and support vectors. K-means clustering grouped nuclear data based on 'n' and 'z,' revealing distinct characteristics and enabling the identification of similar clusters. The Bayesian regression model predicted binding energy using 'n' and 'z' as independent variables, capturing the Gaussian distribution of 'BE4DBE2' and providing statistical measures for parameter estimation. Ccomprehensives nuclear data analysis using various statistical approaches provides valuable insights into relationships, predictions, classification, and clustering, contributing to the advancement of nuclear science and facilitating further research in this field.