Accuracy Of Support Vector Machine Research Articles

The Effect of Data Types’ on the Performance of Machine Learning Algorithms for Cryptocurrency Prediction

Abstract Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A slight improvement in forecasting performance can lead to increased profitability; Therefore, obtaining a realistic forecast is very important for investors. Bitcoin, frequently mentioned in recent due to its volatility and chaotic behavior, has become an investment tool, especially during and after the COVID-19 pandemic. In this study, selected ML techniques were investigated for predicting cryptocurrency movements by using technical indicator-based data sets and measuring the applicability of the techniques to cryptocurrencies that do not have sufficient historical data. In order to measure the effect of data size, Bitcoin’s last 1 year and 7 years of data were used. Following the related literature, Google trends and the number of tweets were used as input features, in addition to the most commonly used twelve technical indicators. Random Forest, K-Nearest Neighbors, Extreme Gradient Boosting (XGBoost-XGB), Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Networks (ANN), and Long-Short-Term Memory (LSTM) network were optimized for best results. Accuracy, F1, and area under the ROC curve values were used to compare the model performance. For continuous data, ANN and SVM performed the best with the highest accuracy and outperformed the other ML models for complete and reduced sets. LSTM reached the best accuracy for trend data, but SVM, NB, and XGB models showed similar performance. The research shows that some indicators significantly affect prediction performance, and the data discretization process also improved the model’s accuracy. While the number of samples affects the results of many ML models, correctly optimized and fine-tuned models may also give excellent results even with less data.

Rapid detection and classification of PC + ABS meter box materials based on laser-induced breakdown spectroscopy

Polycarbonate + Acrylonitrile-Butadiene-Styrene (PC + ABS) is a typical non-metallic material used for electric meter enclosures, characterized by its corrosion resistance, excellent insulation properties, and lightweight nature, making it widely applicable in power supply systems. However, prolonged exposure to outdoor environments often leads to performance degradation, which can affect normal usage. Therefore, developing rapid detection technologies to timely identify and rectify potential faults is particularly important. This not only ensures the stable operation of the power grid but also enhances the overall performance and efficiency of the power system. This study employs Laser-Induced Breakdown Spectroscopy (LIBS) technology and proposes a method for the rapid detection of PC + ABS materials by optimizing testing system parameters. The research utilizes LIBS data to classify different formulations of PC + ABS based on Support Vector Machine (SVM) and Self-Organizing Map (SOM) neural networks. The results indicate that the classification accuracy of SVM reached 98.4%, while the unsupervised classification through the SOM neural network maintained approximately 90% consistency among the three different samples. This method achieves rapid detection and classification of PC + ABS electric meter enclosures, providing new theoretical and practical foundations for enhancing the safety and stable operation of the power grid system.

Advancements in Predictive Maintenance: A Bibliometric Review of Diagnostic Models Using Machine Learning Techniques

This bibliometric review investigates the advancements in machine learning techniques for predictive maintenance, focusing on the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for fault detection in wheelset axle bearings. Using data from Scopus and Web of Science, the review analyses key trends, influential publications, and significant contributions to the field from 2000 to 2024. The findings highlight the performance of ANNs in handling large datasets and modelling complex, non-linear relationships, as well as the high accuracy of SVMs in fault classification tasks, particularly with small-to-medium-sized datasets. However, the study also identifies several limitations, including the dependency on high-quality data, significant computational resource requirements, limited model adaptability, interpretability challenges, and practical implementation complexities. This review provides valuable insights for researchers and engineers, guiding the selection of appropriate diagnostic models and highlighting opportunities for future research. Addressing the identified limitations is crucial for the broader adoption and effectiveness of machine learning-based predictive maintenance strategies across various industrial contexts.

The Support Vector Machine (SVM) And Random Forest Methods For Classification Graduation Rate

Efforts towards an independent nation with high competitiveness can’t be separated from educational programs. Therefore, education must be able to produce quality graduates who have knowledge, master technology, and have technical skills, and adequate life skills. The timeliness of students in completing their studies is one of the supports for assessing the quality of higher education. Classification analysis can be used to predict whether a student is said to pass on time or not. Support Vector Machine (SVM) and Random Forest methods are part of the classification method. SVM and Random Forest classification analysis is done by using historical data alumni from FMIPA UNM of the graduation year 2019-2020 which come from the Administration, Academic and Student Affair Bureau of UNM. SVM accuracy level of RBF kernel with optimum value C = 1 and gamma = 1 is 68% and Random Forest accuracy with optimum value m = 2 and k = 500 is 72%. Therefore, the best method for determining the accuracy of the study duration of FMIPA UNM students is Random Forest..

Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops

Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses.

WEKA-based machine learning for traffic congestion prediction in Amman City

<p>Traffic congestion leads to wasted time, pollution, and increased fuel consumption. Traffic congestion prediction has become a developing research topic in recent years, particularly in the field of machine learning (ML). The evaluation of various traffic parameters is used to predict traffic congestion by relying on historical data. In this study, we will predict traffic congestion in Amman City, specifically at the 8th circle, using different ML classifiers. The 8th circle links four main streets: Westbound, Northbound, Eastbound, and Southbound. Datasets were collected from the greater Amman municipality hourly. The logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) classifiers have been chosen to predict traffic congestion at each street linked with the 8th circle. The waikato environment for knowledge analysis (WEKA) data mining tool is used to evaluate chosen classifiers by determining accuracy, F-measure, sensitivity, and precision evaluation metrics. The results obtained from all experiments have demonstrated that SVM is the best classifier to predict traffic congestion. The accuracy of SVM to predict traffic congestion at Westbound Street, Northbound Street, Eastbound Street, and Southbound Street was 99.4%, 99.7%, 99.6%, and 99.1%, respectively.</p>

Machine Learning, Analytical, and Numerical Techniques for Vibration Analysis of Submerged Porous Functional Gradient Piezoelectric Microbeams with Movable Supports

This research numerically and analytically examines the dynamics of underwater movable porous functional gradient (FG) microsize beams integrated with a piezoelectric layer. Also, the efficiency and accuracy of support vector machine (SVM) techniques in the vibration prediction of the microbeam are assessed. The dynamical simulation is conducted by considering the assumptions of Rayleigh beam theory, different porosity distribution models, nonlinear and linear stress–temperature relationships, and modified couple stress theory (MCST). The eigenvalues of the system, critical temperature increment, and critical speed of the microbeam are computed. Comparative studies are carried out, frequency analyses are performed, and stability diagrams are drawn. The impacts of microbeam geometry, fluid mass ratio, piezoelectric voltage, and axial force on stability behavior in variable complex environmental conditions are parametrically inspected. The outcomes revealed that the smaller the contribution of voids on the outer surface of the microbeam, the better the microbeam stability. It is understood that among the utilized regression-based SVMs, the quadratic polynomial and medium Gaussian kernel models have the highest performance and speed, respectively. These research outcomes will be advantageous for designing the next generation of targeted drug delivery devices.

Near infrared spectroscopy for the characterisation of bovine teeth in terms of sex, diet, tooth type and place of origin

Near infrared spectroscopy was used in discrimination of intact bovine teeth in terms of animal sex, diet, tooth type and place of origin. Discriminant Analysis (DA) models were developed and tested using a stratified random 70:30 data split to calibration and test sets. Of the discriminant techniques of Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares – DA, Artificial Neural Networks (ANN) and Support Vector Machine (SVM), SVM and PLS-DA models performed best in most instances, with pretreatment choice impacting technique success. For test set prediction of animal sex, an accuracy of 95% was achieved using a PLS-DA model, and similar performance achieved using a SVM model. SVM, ANN and SIMCA models predicted three categories of tooth type (deciduous, permanent unerupted and erupted) with similar accuracies (89, 79 and 82%, respectively), however, permanent unerupted teeth were conflated with permanent erupted teeth. Prediction accuracy for ANN, SVM and PLS-DA models were also similar for discrimination of teeth from animals on a grain versus grass diet (PLS-DA 82%). There was considerable variation between models in prediction of geographic origin. Of the seven locations, two were discriminated with true positive rate around 70% using SVM and ANN models. Potential applications of this technology are discussed.

UNLEASHING THE POWER OF SVM AND KNN: ENHANCED EARLY DETECTION OF HEART DISEASE

Heart disease is a fatal illness responsible for approximately 36% of deaths in 2020. Therefore, it is important to pay attention to and better anticipate the risk of heart disease. One technological contribution that can be made is through information related to the risk of heart disease. Classification techniques in data mining can be used to diagnose and identify the risk of heart disease earlier by processing medical data and making predictions. This study compares the effectiveness of two classification algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), in predicting the risk of heart disease using a Kaggle dataset consisting of 303 records with 14 attribute columns. The data is divided into 70% for training and 30% for testing. The software used in this study is Orange Data Mining to build the SVM and KNN models. The results show that the SVM accuracy is 85.6%, while KNN achieves 81.1%. Based on the confusion matrix, the SVM algorithm has a lower error rate compared to KNN. In conclusion, the SVM algorithm is superior to KNN in predicting the risk of heart disease. These findings indicate that SVM has a better potential in identifying individuals at high risk of experiencing a heart attack. This research can contribute to the development of a more accurate medical decision support system for early detection of heart disease.

Machine Learning Algorithms for Lithological Mapping Using Landsat 9 Data in Central Western Highlands of Yemen

This research designed the lithological units of the Central Western Highlands of Yemen (encompassing parts of Dhamar, Raymah, Sana’a, and northern Ibb) using Landsat 9 imagery. The area's complex geological features, characterized by units of the Yemen Volcanic Group from the Tertiary and Quaternary eras, Tertiary granite intrusions, and limestone, sandstone, metamorphic rocks, and Quaternary deposits, pose challenges for traditional field mapping techniques. By leveraging the spectral resolution of Landsat 9, this study aims to achieve accurate classification and mapping of lithological units. ENVI 5.6 software was used for image processing, applying a supervised classification approach represented by the two most common methods: Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC), based on training samples for each lithological class. The accuracy assessment of the classification was validated through an error matrix. The overall accuracy of SVM reached 85.3% with a Kappa coefficient of 0.8, while the overall accuracy of MLC reached 83.3% with a Kappa coefficient of 0.8, indicating a high degree of consistency and reliability in the classification process. This signifies a highly reliable classification outcome. The findings of this study highlight the significant advantages of utilizing Landsat 9 for detailed geological mapping of complex terrains, demonstrating a notable improvement in efficiency and accuracy over traditional methodologies. It can be relied upon to classify lithological units in other areas.

Optimal inversion model for cultivated land soil salinity based on UAV hyperspectral data.

Soil salinization is a common factor constraining agricultural production safety, achieving rapid and accurate acquisition of cultivated land soil salinity information is of paramount importance for ameliorating and resolving soil salinization problems. In this study, with unmanned aerial vehicle (UAV) hyperspectral remote sensing data as the data source, we selected feature band subsets using various spectral transformation data based on different land use statuses of cultivated land, to compare the model accuracies of Support Vector Machine (SVR), Back Propagation Neural Network (BPNN) and Random Forest regression (RFR), and propose the optimal inversion model for regional cultivated land soil salinity. The results showed that the inversion model combining first-order differential spectral transformation data with RFR achieved the highest accuracy. Extracting feature bands separately for cultivated land with different land use statuses would ensure a higher overall model accuracy, with a coefficient of determination of 0.885, a root mean square error of 0.413, and a ratio of performance to deviation of 4.208. Our results could provide a reference for achieving high-precision inversion of soil salinity in cultivated land by UAV hyperspectral technology, and offer scientific support for the prevention and control of soil salinization in cultivated land.

Analysis of Big Data Network Security Defense Mechanism Application of Artificial Intelligence

In the context of the close integration of computer network technology and daily life, big data network security faces many challenges. With its powerful information processing ability, this AI can effectively identify and handle potential threats in the network, thereby improving network security. This paper discusses the application of artificial intelligence (AI) in enhancing big data network security defense, especially in the threat detection link. This paper proposes a detection algorithm that combines support vector machine (SVM) and K nearest neighbor (KNN), namely SVM-KNN algorithm, for network attack detection and defense. When the hazard data is 60 under the test sample, the detection accuracy of KNN, SVM, and SVM-KNN is 86.88%, 89.51%, and 96.75% respectively; when the hazard data is 60 under the experimental sample, the detection accuracy of KNN, SVM, and SVM-KNN is 82.70%, 83.88%, and 89.26% respectively.response.

Confidential outsourced support vector machine learning based on well-separated structure

Confidential outsourced support vector machine learning based on well-separated structure

Noise-robust automated sudden damage detection using blind source separation enhanced by variational mode decomposition and support vector machine based on shapelet transform

Noise-robust automated sudden damage detection using blind source separation enhanced by variational mode decomposition and support vector machine based on shapelet transform

Mapping individual cortico-basal ganglia-thalamo-cortical circuits integrating structural and functional connectome: implications for upper limb motor impairment poststroke.

This study investigated alterations in functional connectivity (FC) within cortico-basal ganglia-thalamo-cortical (CBTC) circuits and identified critical connections influencing poststroke motor recovery, offering insights into optimizing brain modulation strategies to address the limitations of traditional single-target stimulation. We delineated individual-specific parallel loops of CBTC through probabilistic tracking and voxel connectivity profiles-based segmentation and calculated FC values in poststroke patients and healthy controls, comparing with conventional atlas-based FC calculation. Support vector machine (SVM) analysis distinguished poststroke patients from controls. Connectome-based predictive modeling (CPM) used FC values within CBTC circuits to predict upper limb motor function. Poststroke patients exhibited decreased ipsilesional connectivity within the individual-specific CBTC circuits. SVM analysis achieved 82.8% accuracy, 76.6% sensitivity, and 89.1% specificity using individual-specific parallel loops. Additionally, CPM featuring positive connections/all connections significantly predicted Fugl-Meyer assessment of upper extremity scores. There were no significant differences in the group comparisons of conventional atlas-based FC values, and the FC values resulted in SVM accuracy of 75.0%, sensitivity of 67.2%, and specificity of 82.8%, with no significant CPM capability. Individual-specific parallel loops show superior predictive power for assessing upper limb motor function in poststroke patients. Precise mapping of the disease-related circuits is essential for understanding poststroke brain reorganization.

Intelligent Evaluation and Dynamic Prediction of Oyster Freshness with Electronic Nose Based on the Distribution of Volatile Compounds Using GC-MS Analysis.

The quality of oysters is reflected by volatile organic components. To rapidly assess the freshness level of oysters and elucidate the changes in flavor substances during storage, the volatile compounds of oysters stored at 4, 12, 20, and 28 °C over varying durations were analyzed using GC-MS and an electronic nose. Data from both GC-MS and electronic nose analyses revealed that alcohols, acids, and aldehydes are the primary contributors to the rancidity of oysters. Notably, the relative and absolute contents of Cis-2-(2-Pentenyl) furan and other heterocyclic compounds exhibited an upward trend. This observation suggests the potential for developing a simpler test for oyster freshness based on these compounds. Linear Discriminant Analysis (LDA) demonstrated superior performance compared to Principal Component Analysis (PCA) in differentiating oyster samples at various storage times. At 4 °C, the classification accuracy of the optimal support vector machine (SVM) and random forest (RF) models exceeded 90%. At 12 °C, 20 °C, and 28 °C, the classification accuracy of the best SVM and RF models surpassed 95%. Pearson correlation analysis of the concentrations of various volatile compounds and characteristic markers with the sensor response values indicated that the selected sensors were more aligned with the volatiles emitted by oysters. Consequently, the volatile compounds in oysters during storage can be predicted based on the response information from the sensors in the detection system. This study also demonstrates that the detection system serves as a viable alternative to GC-MS for evaluating oysters of varying freshness grades.

A Stand-Off Laser-Induced Breakdown Spectroscopy (LIBS) System for Remote Bacteria Identification.

Bacteria are the primary cause of infectious diseases, making rapid and accurate identification crucial for timely pathogen diagnosis and disease control. However, traditional identification techniques such as polymerase chain reaction and loop-mediated isothermal amplification are complex, time-consuming, and pose infection risks. This study explores remote (~3 m) bacterial identification using laser-induced breakdown spectroscopy (LIBS) with a Cassegrain reflective telescope. Principal component analysis (PCA) was employed to reduce the dimensionality of the LIBS spectral data, and the accuracy of support vector machine (SVM) and Random Forest (RF) algorithms was compared. Multiple repeated experiments showed that the RF model achieved a classification accuracy, recall, precision, and F1-score of 99.81%, 99.80%, 99.79%, and 0.9979, respectively, outperforming the SVM model and providing more accurate remote bacterial identification. The method based on laser-induced plasma spectroscopy and machine learning has broad application prospects, supporting noncontact disease diagnosis, improving public health, and advancing medical research and technological development.

Rice Yield Estimation Using Machine Learning and Feature Selection in Hilly and Mountainous Chongqing, China

To investigate effective techniques for estimating rice production in hilly and mountainous areas, in this study, we collected yield data at the field level, agro-meteorological data, and Sentinel-2/MSI remote sensing data in Chongqing, China, between 2020 and 2023. The integral values of vegetation indicators from the rice greening up to heading–filling stages were determined using the Newton–trapezoidal integration method. Using correlation analysis and importance analysis of permutation features, the effects of agro-meteorological variables and vegetation index integrals on rice yield were assessed. The chosen characteristics were then combined with three machine learning techniques—random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR)—to create six rice yield estimate models. The results showed that combined vegetation indices were more effective than indices used in separate development phases. Specifically, the correlation coefficients between the integral values of eight vegetation indices from rice greening up to heading–filling stages and rice yield were all above 0.65. By introducing agro-meteorological factors as new independent variables and combining them with vegetation indices as input parameters, the predictive capability of the model was evaluated. The results showed that the performance of PLSR remained stable, while the prediction accuracies of SVM and RF improved by 13% to 21.5%. After feature selection, the inversion performance of all three machine learning models improved, with the RF model coupled with variables selected during permutation feature importance analysis achieving the optimal inversion effect, which was characterized by a coefficient of determination of 0.85, a root mean square error of 529.1 kg/hm2, and a mean relative error of 5.63%. This study provides technical support for improving the accuracy of remote sensing-based crop yield estimation in hilly and mountainous regions, facilitating precise agricultural management and informing agrarian decision making.

Characterization of natural fracture development in coal reservoirs using logging machine learning inversion, well test data and simulated geostress analyses

Characterization of natural fracture development in coal reservoirs using logging machine learning inversion, well test data and simulated geostress analyses

Neodymium oxide concentration state recognition model in neodymium molten salt electrolysis process based on flame image features

Neodymium oxide concentration state recognition model in neodymium molten salt electrolysis process based on flame image features