Support Vector Machine Technique Research Articles

Monitoring and predicting land use/land cover dynamics in Djelfa city, Algeria, using Google Earth Engine and a Multi Layer Perceptron Markov Chain model

Understanding the historical and projected changes in land use and land cover (LULC) in Djelfa city is crucial for sustainable land management, considering both natural and human influences. This study employs Landsat images from the Google Earth Engine and the support vector machine (SVM) technique for LULC classification in 1990, 2005, and 2020, achieving over 90% accuracy and kappa coefficients above 88%. The Land Change Modeler (LCM) was used for detecting changes and predicting future LULC patterns, with Markov Chain (MC) and Multi Layer Perceptron (MLP) techniques applied for 2035 projections, showing an average accuracy of 83.96%. Key findings indicate a substantial urban expansion in Djelfa city, from 924.09 hectares in 1990 to 2742.30 hectares in 2020, with a projected increase leading to 1.6% of nonurban areas transitioning to urban by 2035. There has been significant growth in steppe areas, while forested, agricultural, and barren lands have seen annual declines. Projections suggest continued degradation of bare land and a slight reduction in steppe areas by 2035. These insights underscore the need for reinforced policies and measures to enhance land management practices within the region to cater to its evolving landscape and promote sustainable development.

Deciphering the impact of microenvironmental factors on cuticular hydrocarbon degradation in Lucilia sericata empty Puparia: Bridging ecological and forensic entomological perspectives using machine learning models

Blow flies (Calliphoridae) play essential ecological roles in nutrient recycling by consuming decaying organic matter. They serve as valuable bioindicators in ecosystem management and forensic entomology, with their unique feeding behavior leading to the accumulation of environmental pollutants in their cuticular hydrocarbons (CHCs), making them potential indicators of exposure history. This study focuses on CHC degradation dynamics in empty puparia of Lucilia sericata under different environmental conditions for up to 90 days. The three distinct conditions were considered: outdoor-buried, outdoor-above-ground, and indoor environments. Five predominant CHCs, n-Pentacosane (n-C25), n-Hexacosane (n-C26), n-Heptacosane (n-C27), n-Octacosane (n-C28), and n-Nonacosane (n-C29), were analyzed using Gas Chromatography-Mass Spectrometry (GC–MS). The findings revealed variations in CHC concentrations over time, influenced by environmental factors, with significant differences at different time points. Correlation heatmap analysis indicated negative correlations between weathering time and certain CHCs, suggesting decreasing concentrations over time. Machine learning techniques Support Vector Machine (SVM), Multilayer Perceptron (MLP), and eXtreme Gradient Boosting (XGBoost) models explored the potential of CHCs as age indicators. SVM achieved an R-squared value of 0.991, demonstrating high accuracy in age estimation based on CHC concentrations. MLP also exhibited satisfactory performance in outdoor conditions, while SVM and MLP yielded unsatisfactory results indoors due to the lack of significant CHC variations. After comprehensive model selection and performance evaluations, it was found that the XGBoost model excelled in capturing the patterns in all three datasets. This study bridges the gap between baseline and ecological/forensic use of empty puparia, offering valuable insights into the potential of CHCs in environmental monitoring and investigations. Understanding CHCs' stability and degradation enhances blow flies' utility as bioindicators for pollutants and exposure history, benefiting environmental monitoring and forensic entomology.

Determining Prospective Recipients of The Indonesia Smart Scholarship Program Using The Support Vector Machine Algorithm and Simple Additive Weighting

Between the period from 2018 to the first semester of 2020, a total of 2.4 million students who owned the Kartu Indonesia Pintar (Indonesia Smart Card) were at risk of not being able to access the Program Indonesia Pintar due to delays in entering recipient data, misdirected targeting, and fund disbursement obstacles. During its implementation, there were issues with inaccurate data of Kartu Indonesia Pintar recipients, resulting in the beneficiaries not being entirely targeted correctly. According to Article 12 of Minister of Education and Culture Regulation No. 10 of 2020, schools, as educational units, are assigned the responsibility of managing the Smart Indonesia Program at the school level. This includes tasks such as proposing eligible student candidates for the Program based on criteria, monitoring and assisting the smooth process of receiving the benefits, and accommodating students who hold the Kartu Indonesia Pintar (KIP). However, schools faced difficulties in obtaining the eligibility data of potential Indonesia Pintar Program recipients through the school proposal format. The objective of this research is to determine the eligibility of potential Indonesia Pintar Program recipients using a combination of Support Vector Machine (SVM) and Simple Additive Weighting (SAW) techniques. The SVM model was trained using kernel tricks and parameter selection (C and gamma) based on data sourced from the DAPODIK application of students in SMK Negeri 1 Kota Bekasi for the academic years 2020/2021 to 2022/2023, with a dataset containing 2499 rows . This dataset was used to determine the eligibility of potential Indonesia Pintar Program recipients. The SAW method was then employed to calculate scores based on the weighting of attributes possessed by the students, which were subsequently ranked from the highest score. The Linear Kernel with parameter values C=0.1 and gamma=0.1 achieved an accuracy of 88.8% during the classification of test data, using 10% of the total dataset, which amounted to 250 rows of data. The combination of SVM and SAW methods resulted in the classification of eligibility for potential Indonesia Pintar Program recipients, ranked based on their scores using the weighted assessment with the SAW method.

Predicting financial distress using supervised machine learning algorithms: an application on Borsa Istanbul

Purpose- The main purpose of this study is to identify the most significant variables to detect financial distress earlier and to find the best machine learning algorithm model. Methodology-This study has used Support Vector Machine, Logistic Regression, Random Forest and K-nearest neighbors method techniques to predict the financial distress prediction for the companies of Turkey between 2012 and 2021. Findings- As a result of the study, it has been determined that Random Forest provides the best results in terms of precision, accuracy, and recall. Further, this study has found the most important five independent variables to determine the financial distress status of the firms. In this way, it has been found that Current Assets/ Current Liabilities, Working Capital / Total Assets, Gross profit / Revenue, Retained Earnings / Total Assets and Sales growth rate are the most useful variables to determine financial distress status of Turkish firms earlier. Conclusion- This study has concluded that cash ratios and profitability ratios and sales growth are the most important independent variables to determine financial distress one-year ahead. Furthermore, it has been found that random forest is the best machine learning method among other supervised machine learning methods used in this study. Keywords: Financial distress, support vector machine, logistic regression, random forest, k-nearest neighbors JEL Codes: G32, G33, C52

FAULT DETECTION AND DIAGNOSIS BY SUPPORT VECTOR MACHINES: APPLICATION TO VINYL-CHLORIDE-MONOMER PROCESS

Monitoring process status and identifying process operational faults are essential for improving the process safety in petrochemical plants that interactions between various process streams and units are associated. This paper presents a deployment of a support vector machine technique for detecting and identifying operational fault cases with a case study of a vinyl chloride monomer plant. An integrated simulation environment between MATLAB and UniSim Design dynamic simulator is utilized for evaluating the performance of the proposed fault detection and identification framework. Under the real-time software-in-the-loop simulation, the confusion matrix results and receiver operating characteristics supported that the proposed framework provides high accuracy of fault classification.

Performance Evaluation Model of Land port and Seaport Collaboration Using the Support Vector Machine

This study delves into the pivotal role of shipper satisfaction in cultivating efficacious cooperation between dry ports and seaports. The research endeavors herein present a comprehensive indicator system tailored to evaluate the efficacy of the cooperation of the sea ports and dry ports. This system encompasses 14 key indicators, thoughtfully categorized into four dimensions, offering a holistic perspective on the multifaceted factors that underscore the synergy between dry ports and seaports. The establishment of a performance evaluation model for this collaborative nexus draws upon the support vector machine (SVM) technique, a choice substantiated by its suitability for the available dataset and its relevance within the research context. Leveraging the indicator data as a training dataset, the SVM approach culminates in the construction of a cooperative matrix, ultimately facilitating the discernment of performance rankings within dry port and seaport collaboration. This research methodology not only yields valuable insights into the evaluation of this distinct collaboration but also presents a versatile framework with potential application for addressing various challenges encountered within the cooperative milieu of dry ports and seaports.

Automated Speed and Lane Change decision-making Model using Support Vector Machine

One of the major obstacles that the auto industry must overcome is the rise of autonomous vehicles. The study of lane-changing is an important part of this problem. Previous studies on autonomous vehicle lane changes have predominantly focused on lane change path planning and path monitoring, with limited attention given to the autonomous vehicle's lane change decision-making process. This paper introduces a novel Lane Change Decision-Making Model for autonomous vehicles using the Support Vector Machine (SVM) method. The suggested model employs real-time sensor data to assess whether or not a lane change is possible, taking into account the proximity of other vehicles (cars, buses, motorbikes), and adjusting speed as necessary to ensure a seamless transition. Researching the various facets of lane changes in autonomous vehicles allows for decision-making that is grounded in utility, safety, and tolerance. The implementation of a support vector machine (SVM) technique with Bayesian parameter optimization is used to deal with the non-linearity and complexity of the process of autonomous lane change decision-making. Finally, we compare the suggested SVM model against a rule-based lane change model using the test data. The SVM-based strategy is shown to improve lane change decision-making in a comprehensive simulation exercise, which in turn improves the safety and efficiency of autonomous driving systems. The experiment also use a real vehicle to gauge the efficacy of the underlying decision-making model.

Research on Maize Acreage Extraction and Growth Monitoring Based on a Machine Learning Algorithm and Multi-Source Remote Sensing Data

Getting accurate and up-to-date information on the cultivated land area and spatial arrangement of maize, an important staple crop in the Ningxia Hui Autonomous Region, is very important for planning agricultural development in the region and judging crop yields. This work proposes a machine-learning methodology to extract corn from medium-resolution photos obtained from the Sentinel-2 satellite. The Google Earth Engine (GEE) cloud platform is utilized to facilitate the process. The identification of maize cultivation regions in Huinong District in the year 2021 was performed through the utilization of support vector machine (SVM) and random forest (RF) classification techniques. After obtaining the results, they were compared to see if using the random forest classification method to find planting areas for maize was possible and useful. Subsequently, the regions where maize was cultivated were combined with image data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which has a high temporal resolution. The Normalized Difference Vegetation Index (NDVI) contemporaneous difference method, which gives regular updates, was then used to track the growth of maize during its whole growth phase. The study’s results show that using the GEE cloud platform made it easier to quickly map out data about where to plant maize in Huinong District. Furthermore, the implementation of the random forest method resulted in enhanced accuracy in extracting maize planting areas. The confusion matrix’s evaluation of the classification performance produced an average overall accuracy of 98.9% and an average Kappa coefficient of 0.966. In comparison to the statistics yearbook of the Ningxia Hui Autonomous Region, the method employed in this study consistently yielded maize-planted area estimates in Huinong District with relative errors below 4% throughout the period spanning 2017 to 2021. The average relative error was found to be 2.04%. By combining MODIS image data with the NDVI difference model in the year 2021, the high-frequency monitoring of maize growth in Huinong District was successful. The growth of maize in Huinong District in 2021 exhibited comparable or improved performance in the seedling stage, nodulation stage, and the early stage of staminate pulling and spitting, possibly attributed to the impact of climate and other relevant elements. After that, the growth slowed down in August, and the percentage of regions with slower growth rates than in previous years gradually increased. However, overall, the growth of maize in Huinong District during the year 2021 showed improvement relative to the preceding years. The present study introduces a novel approach that demonstrates the capability to accurately extract corn crops in the Huinong District while simultaneously monitoring their growth at a high frequency.

Evaluating different methods for ranking inputs in the context of the performance assessment of decision making units: A machine learning approach

In the context of assessing the performance of decision-making units (companies, institutions, etc.), it is important to know the contribution or importance of each input to the generation of products and services in the production process. Identifying the degree of relevance of each input is a challenge from both an applied and a methodological point of view, especially within the field of non-parametric techniques, such as Data Envelopment Analysis (DEA), where the mathematical expression of the production function associated with the data generating process is not specified. This means that there is no specific coefficient to be estimated for each input, which makes it difficult to determine a ranking of importance of this type of variable compared to parametric methods, where a target function dependent on some parameters must be previously specified. Within this challenging context associated with the non-parametric approach to estimating technical efficiency, in this paper, we adapt several methods for identifying the importance of features used together with the Support Vector Machine technique in order to determine an importance ranking of the inputs in a productive process. The different adaptations developed in this article are computationally checked through a simulated experiment.

A simplified machine learning model utilizing platelet-related genes for predicting poor prognosis in sepsis.

Thrombocytopenia is a known prognostic factor in sepsis, yet the relationship between platelet-related genes and sepsis outcomes remains elusive. We developed a machine learning (ML) model based on platelet-related genes to predict poor prognosis in sepsis. The model underwent rigorous evaluation on six diverse platforms, ensuring reliable and versatile findings. A retrospective analysis of platelet data from 365 sepsis patients confirmed the predictive role of platelet count in prognosis. We employed COX analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) techniques to identify platelet-related genes from the GSE65682 dataset. Subsequently, these genes were trained and validated on six distinct platforms comprising 719 patients, and compared against the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ-Failure Assessment (SOFA) score. A PLT count <100×109/L independently increased the risk of death in sepsis patients (OR = 2.523; 95% CI: 1.084-5.872). The ML model, based on five platelet-related genes, demonstrated impressive area under the curve (AUC) values ranging from 0.5 to 0.795 across various validation platforms. On the GPL6947 platform, our ML model outperformed the APACHE II score with an AUC of 0.795 compared to 0.761. Additionally, by incorporating age, the model's performance was further improved to an AUC of 0.812. On the GPL4133 platform, the initial AUC of the machine learning model based on five platelet-related genes was 0.5. However, after including age, the AUC increased to 0.583. In comparison, the AUC of the APACHE II score was 0.604, and the AUC of the SOFA score was 0.542. Our findings highlight the broad applicability of this ML model, based on platelet-related genes, in facilitating early treatment decisions for sepsis patients with poor outcomes. Our study paves the way for advancements in personalized medicine and improved patient care.

Anomaly Diagnosis Method and Condition Assessment of Power Metering Device Based on SSD Algorithm

The advancement of anomaly diagnosis methods plays a crucial role in classifying and analyzing data, particularly in distinguishing between normal and abnormal patterns. This study explores the utilization of Support Vector Machine (SVM) techniques to facilitate the selection of pertinent data, thus enhancing the accuracy of anomaly detection. Furthermore, this research delves into the condition assessment process, which offers valuable insights into the real-time state of entities passing through power metering devices. Drawing upon a wealth of secondary data sources, this study employs the SSD algorithm to gain a comprehensive understanding of power metering devices and their interrelated aspects. The SSD algorithm, with its diverse anchor points, is revealed as a powerful tool for quantifying the energy flow passing through the power metering device. This approach not only aids in precise energy measurement but also provides essential insights into the functioning of power metering systems. By combining anomaly diagnosis, SVM techniques, and the SSD algorithm, this study contributes to a deeper comprehension of power metering devices' performance and their capacity to accurately measure energy. These insights have significant implications for improving the overall reliability and efficiency of power metering in various applications.

The Association between Meteorological Drought and the State of the Groundwater Level in Bursa, Turkey

This study addressed the intricate interplay between meteorological droughts and groundwater level fluctuations in the vicinity of Mount Uludag in Bursa, Turkey. To achieve this, an exhaustive analysis encompassing monthly precipitation records and groundwater level data sourced from three meteorological stations and eight groundwater observation points spanning the period from 2007 to 2018 was performed. Subsequently, this study employed the Standard Precipitation Index (SPI) and Standard Groundwater Level (SGL) metrics, meticulously calculating the temporal extents of drought events for each respective time series. Following this, a judicious application of both the Thiessen and Support Vector Machine (SVM) methodologies was undertaken to ascertain the optimal groundwater observation wells and their corresponding SGL durations, aligning them with SPI durations tied to the selected meteorological stations. The SVM technique, in particular, excelled in the identification of the most pertinent observation wells. Additionally, the Elman Neural Network (ENN) and its optimized version through the Firefly Algorithm (ENN-FA), demonstrated their prowess in accurately predicting SPI durations based on SGL durations. The results were favorable, as evidenced by the commendable performance metrics of the Normalized Root Mean Square Error (NRMSE), the Nash–Sutcliffe Efficiency (NSE), the product of the coefficient of determination and the slope of the regression line (bR2), and the Kling–Gupta Efficiency (KGE). Consequently, the favorable simulation results were construed as evidence supporting the presence of a discernible association between SGL and the duration of the SPI. As we substantiate the concordance between the temporal extent of meteorological droughts and the perturbations in groundwater levels, this unmistakably underscores the fact that the historical fluctuations in groundwater levels within the region were predominantly attributable to climatic influences, rather than being instigated by anthropogenic activities. Nevertheless, it is imperative to underscore that this revelation should not be misconstrued as an endorsement of future heedless exploitation of groundwater resources.

AI Driven Innovation in early Detection and Diagnosis of Brain Cancer

The continuous advancement of artificial intelligence (AI) has brought about a significant transformation in the healthcare sector, namely in the domain of early identification and diagnosis of intricate medical ailments. The present study investigates the use of artificial intelligence (AI) in the identification and diagnosis of brain tumors at an early stage. This research capitalizes on a combination of advanced methodologies such as Genetic Algorithms, Local Binary Patterns (LBP), Deep Learning-Based Segmentation, and Support Vector Machines (SVM) to achieve its objectives. Genetic algorithms are utilized in the context of feature selection to optimize the discriminative capability of input data. The use of Local Binary Patterns (LBP) presents a reliable method for doing texture analysis, hence improving the characterization of diseased areas in brain imaging. Deep learning-based segmentation approaches have demonstrated high efficiency in extracting tumor boundaries and accurately distinguishing them from healthy brain tissue, hence enabling precise localization. The Support Vector Machine (SVM) technique, which is highly effective in classification tasks, plays a crucial role in the diagnostic process by accurately distinguishing between benign and malignant tumour cases. The utilisation of an interdisciplinary method not only enhances the precision and dependability of brain tumor diagnosis, but also accelerates the procedure, therefore facilitating prompt intervention and potentially life-preserving therapeutic alternatives for individuals. The research highlights the significant capacity of AI-based approaches in revolutionizing the field of neuroimaging, emphasizing their crucial contribution to augmenting the abilities of healthcare practitioners in the essential undertaking of identifying and diagnosing brain tumors.

Heart sound classification using wavelet scattering transform and support vector machine

OBJECTIVE: A representation of the sound recordings that are associated with the movement of the entire cardiac structure is termed the Phonocardiogram (PCG) signal. In diagnosing such diverse diseases of the heart, PCG signals are helpful. Nevertheless, as recording PCG signals are prone to several surrounding noises and other disturbing signals, it is a complex task. Thus, prior to being wielded for advanced processing, the PCG signal needs to be denoised. This work proposes an improved heart sound classification by utilizing two-stage Low pass filtering and Wavelet Threshold (WT) technique with subsequent Feature Extraction (FE) using Wavelet Scatter Transform and further classification utilizing the Cubic Polynomial Support Vector Machine (SVM) technique for CVD. METHOD: A computer-aided diagnosis system for CVD detection centered on PCG signal analysis is offered in this work. Initially, by heavily filtering the signal, the raw PCG signals obtained using the database were pre-processed. Then, to remove redundant information and noise, it is denoised via the WT technique. From the denoised PCG, wavelet time scattering features were extracted. After that, by employing SVMs, these features were classified for pathology. RESULTS: For the analysis, the PCG signal obtained from the Physionet dataset was considered. Heavy low-pass filtering utilizing a Low-Pass Butterworth Filter (LPBF) is entailed in the pre-processing step. This removed 98% of the noise inherently present in the signal. Further, the signal strength was ameliorated by denoising it utilizing the WT technique. Promising results with maximum noise removal of up to 99% are exhibited by the method. From the PCG, Wavelet Scattering (WS) features were extracted, which were later wielded to categorize the PCG utilizing SVMs with 99.72% accuracy for different sounds. DISCUSSION: The Classification accuracies are analogized with other classification techniques present in the literature. This technique exhibited propitious outcomes with a 3% improvement in the F1 score when weighed against the top-notch techniques. The improvement in the metrics is attributed to the usage of the pre-processing stage comprising of Low-pass filter and WT method, WS Transform (WST), and SVMs. CONCLUSION: The superiority of the proposed technique is advocated by the comparative investigation with prevailing methodologies. The system revealed that Coronary Artery Disease (CAD) can be implemented with superior methods to achieve high accuracy.

Realistic Approach for Capacity Estimation of U-Turns Under Heterogeneous Traffic Condition

The main purpose of the present study is to estimate U-turn capacity at mid-block median openings (MBMO). Estimation of U-turn capacity requires three key parameters, namely: conflicting traffic volume, critical gap, and follow-up time. Conflicting traffic volume has been estimated by introducing a novel parameter, namely, spatiotemporal conflicting factor. Critical gap has been estimated using five different methods: modified Raff method, Ashworth method, logit model, occupancy time method, and support vector machine technique. The follow-up time has been estimated directly from recorded videos. Subsequently, capacity has been estimated for different types of vehicles along with the U-turning stream capacity. The estimated capacity has been validated using field capacity, using Kyte’s method. Further, a suitable method for the estimation of critical gap in the Indian context has been determined. This study could be used as a guideline for the U-turn capacity estimation for efficient design and management of traffic at MBMO.

Brain Tumor Classification, Segmentation, and Detection using Deep Learning - A Review

V.Vapnik in 1965 proposed Vector methods. Kimeldorf presented a technique for creating kernel space based on support vectors in 1971. Support Vector Machine (SVM) techniques were initially presented in the 1990s by V. Vapnik in the field of statistical learning. Since then, pattern recognition, natural language processing, image processing and other areas have seen extensive use of SVM. By converting non-linear sample space into linear space via a kernel approach, the algorithm's complexity is reduced. Image classification is a well-known issue in image processing. Predicting the input image categories using the features is the main objective of image classification. There are several different classifiers, including Artificial Neural Networks, Support Vector Machines, and Random Forests, Decision Forests, k-NNs (k Nearest Neighbors), and Adaptive Boost. SVM is one of the best techniques for categorizing any image or pattern. A common non-invasive technique used in the medical sector for the analysis, diagnosis, treatment of brain tissues is magnetic resonance imaging. When a brain tumor is discovered early, the patient's life can be saved by receiving the appropriate care. It becomes difficult to accurately identify tumors in the MRI slices, which requires fussy work..

A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps.

The real-time diagnostic monitoring of self-priming centrifugal pumps is essential to ensure their safe operation. Nevertheless, owing to the intricate structure and complex operational conditions inherent in such pumps, existing fault diagnosis methods encounter challenges in effectively extracting crucial fault feature information and accurately identifying fault types. Consequently, this paper introduces an intelligent fault diagnosis method tailored for self-priming centrifugal pumps. The approach amalgamates refined time-shift multiscale fluctuation dispersion entropy, cosine pairwise-constrained supervised manifold mapping, and adaptive chaotic Aquila optimization support vector machine techniques. To begin with, refined time-shift multiscale fluctuation dispersion entropy is employed to extract fault-related features, adeptly mitigating concerns related to entropy domain deviations and instability. Subsequently, the application of cosine pairwise-constrained supervised manifold mapping serves to reduce the dimensionality of the extracted fault features, thereby bolstering the efficiency and precision of the ensuing identification process. Ultimately, the utilization of an adaptive chaotic Aquila optimization support vector machine facilitates intelligent fault classification, leading to enhanced accuracy in fault identification. The experimental findings unequivocally affirm the efficacy of the proposed method in accurately discerning among various fault types in self-priming centrifugal pumps, achieving an exceptional recognition rate of 100%. Moreover, it is noteworthy that the average correct recognition rate achieved by the proposed method surpasses that of five existing intelligent fault diagnosis techniques by a significant margin, registering a notable increase of 15.97%.

Hydrological evaluation of conservation bench terrace system in outer foothills of Himalayas using soft computing techniques

Soil erosion has been problematic in the outer foothills of the Himalayas. Therefore, it is necessary to implement suitable conservation measures to check erosion issues. The present study experimented on conservation bench terraces (CBT) as a conservation measure in the outer foot Himalayas in Dehradun, India. The study evaluates the effectiveness of different CBTs of embankment heights 10 cm (CBT-10), 15 cm (CBT-15), and 20 cm (CBT-20) to runoff and soil loss under different rainfall storms. Further, the estimation of Soil loss (Sloss) was evaluated using soft computing techniques (machine learning models). The soft computing techniques were applied to the data obtained from CBT plots for hydrological assessment. The methods used for analysis were support vector machine (SVM), Bagging, and Random Forest (RF) models. The performance of developed models was evaluated using mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson's correlation coefficient (PCC), and Wilmott Index (WI) along with graphical representation (Scatter plot and Taylor diagram). The results showed that the CBT techniques were observed to reduce runoff and soil loss. Among CBT, CBT-20 was the most significant as the number of runoff events was less than others. Considering the storm size, the maximum percentage of runoff generated from the storm size is more important than 100 mm. Moreover, soil loss (Sloss) estimation showed that the SVM models perform better in all three scenarios, which are results of three scenarios such as R2 = 0.50, 0.52 and 0.80; MAE = 0.03, 0.03 and 0.10; RMSE = 0.08, 0.07 and 0.27; NSE = 0.39, 0.12 and 0.21; PCC = 0.71, 0.725 and 0.89; and WI = 0.67, 0.794, and 0.49, respectively. Therefore, SVM techniques can be applied to the estimation of Sloss. Results can be recommended for precision farming under climate changes in the hilly and plain areas and similar models can be applied to other areas.

Assessment of Land Use Land Cover Classification using Support Vector Machine and Random Forest Techniques in the Agusan River Basin through Geospatial Techniques

Assessment of Land Use Land Cover Classification using Support Vector Machine and Random Forest Techniques in the Agusan River Basin through Geospatial Techniques

Classification of the equatorial plasma bubbles using convolutional neural network and support vector machine techniques

Equatorial plasma bubble (EPB) is a phenomenon characterized by depletions in ionospheric plasma density being formed during post-sunset hours. The ionospheric irregularities can lead to disruptions in trans-ionospheric radio systems, navigation systems and satellite communications. Real-time detection and classification of EPBs are crucial for the space weather community. Since 2020, the Prachomklao radar station, a very high frequency (VHF) radar station, has been installed at Chumphon station (Geographic: 10.72° N, 99.73° E and Geomagnetic: 1.33° N) and started to produce radar images ever since. In this work, we propose two real-time plasma bubble detection systems based on support vector machine techniques. Two designs are made with the convolutional neural network (CNN) and singular value decomposition (SVD) used for feature extraction, the connected to the support vector machine (SVM) for EPB classification. The proposed models are trained using quick look (QL) plot images from the VHF radar system at the Chumphon station, Thailand, in 2017. The experimental results show that the combined CNN-SVM model, using the RBF kernel, achieves the highest accuracy of 93.08% while the model using the polynomial kernel achieved an accuracy of 92.14%. On the other hand, the combined SVD-SVM models yield the accuracies of 88.37% and 85.00% for RBF and polynomial kernels of SVM, respectively.Graphical