A Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification or regression tasks. It works by finding the hyperplane that best separates data points of different classes, maximizing the margin between the closest points (support vectors) of each class.
A novel method combining infrared imaging and machine learning improves real-time heat management in metal 3D printing, enhancing part quality and process efficiency. The approach was experimentally validated, demonstrating robust performance across various geometries.
In a study published in Minerals, researchers used machine learning techniques to classify selenium (Se) and tellurium (Te) in pyrite, sphalerite, and chalcopyrite from various global deposits. By applying PCA, silhouette coefficients, and models like random forest (RF) and support vector machine (SVM), they accurately distinguished ore genetic types.
Researchers developed an advanced automated system for early plant disease detection using an ensemble of deep-learning models, achieving superior accuracy on the PlantVillage dataset. The study introduced novel image processing and data balancing techniques, significantly enhancing model performance and demonstrating the system's potential for real-world agricultural applications.
Researchers from China have integrated computer vision (CV) and LiDAR technologies to improve the safety and efficiency of autonomous navigation in port channels. This innovative approach utilizes advanced path-planning and collision prediction algorithms to create a comprehensive perception of the port environment, significantly enhancing navigation safety and reducing collision risks.
A study in Heliyon introduced a machine learning-based approach for predicting defects in BLDC motors used in UAVs. Researchers compared KNN, SVM, and Bayesian network models, with SVM demonstrating superior accuracy in fault classification, highlighting its potential for improving UAV operational safety and predictive maintenance.
Researchers introduce a novel electronic tongue (E-tongue), the multichannel triboelectric bioinspired E-tongue (TBIET), engineered with advanced triboelectric components on a single glass slide chip. Through comprehensive classification studies across medical, environmental, and beverage samples, the TBIET demonstrates exceptional taste classification accuracy, promising significant advancements in on-site liquid sample detection and analysis.
This study introduces an AI-driven approach to optimize tunnel boring machine (TBM) performance in soft ground conditions by predicting jack speed and torque settings. By synchronizing operator decisions with machine data and utilizing machine learning models, the research demonstrates significant improvements in TBM operational efficiency, paving the way for enhanced tunneling projects.
Researchers introduce a novel method for edge detection in color images by integrating Support Vector Machine (SVM) with Social Spider Optimization (SSO) algorithms. The two-stage approach demonstrates superior accuracy and quality compared to existing methods, offering potential applications in various domains such as object detection and medical image analysis.
Researchers proposed a fusion algorithm merging Lightning Search Algorithm (LSA) with Support Vector Machine (SVM) technology, forming an advanced Power Network Security Risk Evaluation Model (PNSREM), achieving high accuracy, low error rates, and rapid convergence. Empirical validation demonstrated its superiority, empowering preemptive threat identification, ensuring uninterrupted power system operation, and highlighting its potential for real-world application in enhancing power network security.
Researchers utilized machine learning algorithms to predict anemia prevalence among young girls in Ethiopia, analyzing data from the 2016 Ethiopian Demographic and Health Survey. The study identified socioeconomic and demographic predictors of anemia and highlighted the efficacy of advanced ML techniques, such as random forest and support vector machine, in forecasting anemia status.
Researchers introduced Deep5HMC, a machine learning model combining advanced feature extraction techniques and deep neural networks to accurately detect 5-hydroxymethylcytosine (5HMC) in RNA samples. Deep5HMC surpassed previous methods, offering promise for early disease diagnosis, particularly in conditions like cancer and cardiovascular disease, by efficiently identifying RNA modifications.
Researchers introduced a deep convolutional neural network (DCNN) model for accurately detecting and classifying grape leaf diseases. Leveraging a dataset of grape leaf images, the DCNN model outperformed conventional CNN models, demonstrating superior accuracy and reliability in identifying black rot, ESCA, leaf blight, and healthy specimens.
Through hyperspectral imaging (HSI) and multivariate analysis, researchers accurately predicted pH and carotenoid content in carrots, crucial for nutritional assessment. Models utilizing partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) showed LS-SVM's superiority in pH prediction and carotenoid content, offering a promising approach for internal quality evaluation in carrots.
Researchers evaluated 13 machine learning models to forecast compressive strength in preplaced aggregate concrete. Extreme gradient boosting (XGBoost) emerged as the most accurate, with sensitivity and SHAP analyses highlighting crucial factors like gravel and water-to-binder ratio.
Researchers propose an AI-driven approach for predicting and managing water quality, crucial for environmental sustainability. Utilizing explainable AI models, they showcase the significance of transparent decision-making in classifying drinkable water, emphasizing the potential of their methodology for real-time monitoring and proactive risk mitigation in water management practices.
Researchers leverage AI and earth observation techniques to predict citizen perceptions of deprivation in Nairobi's slums. Combining satellite imagery and citizen science, their methodology accurately forecasts deprivation, offering policymakers invaluable insights for targeted interventions aligned with Sustainable Development Goal 11, potentially benefiting millions worldwide.
Researchers explore the potential of artificial intelligence (AI) algorithms in enhancing glaucoma detection, aiming to address the significant challenge of undiagnosed cases globally, with a focus on Australia. By reviewing AI's performance in analyzing optic nerve images and structural data, they propose integrating AI into primary healthcare settings to improve diagnostic efficiency and accuracy, potentially reducing the burden of undetected glaucoma cases.
Researchers leverage robotics and machine learning in a pioneering approach to accelerate the discovery of biodegradable plastic alternatives. By combining automated experimentation with predictive modeling, they develop eco-friendly substitutes mimicking traditional plastics, paving the way for sustainable material innovation.
Researchers introduced an AI-driven anomaly detection system, outlined in Scientific Reports, to combat illegal gambling and uphold fairness in sports. By analyzing diverse machine learning models on sports betting odds data, they achieved significant accuracy rates, paving the way for a robust solution against match-fixing in real-time, thus safeguarding sports integrity.
In their study published in Scientific Reports, researchers introduced the IABC-MLP model for predicting concrete compressive strength. This innovative approach combines an improved artificial bee colony algorithm (IABC) with a multilayer perceptron (MLP) model, addressing issues like local optima and slow convergence. Comparative analyses demonstrated that IABC-MLP outperformed traditional methods and other heuristic algorithms in accuracy and convergence speed, showcasing its potential for real-world applications in concrete strength prediction.
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