Supervised machine learning research papers. e. Feb 19, 2025 · This research emp...

Supervised machine learning research papers. e. Feb 19, 2025 · This research employs a supervised machine-learning approach using Neural Networks (FFNN and LSTM) and Sparse Identification of Nonlinear Dynamics (SINDy) to model the dynamics of a turboshaft engine under real flight conditions. 5 days ago · Abstract: This study examines the overhead transmission line ampacity prediction performance of a supervised multiple linear regression machine learning algorithm integrated with the IEEE-738 heat balance equation, using ten years of historical data from the Nigerian Meteorological Agency (NiMet) and operational data from the Transmission 3 days ago · This paper presents a comparative study of supervised and unsupervised machine learning techniques for sleep disorder identification using the Sleep Health and Lifestyle dataset, which consists of approximately 400 records with demographic, lifestyle, and health-related attributes. The focus is on neural networks (NN), with an in-depth exploration of its key components and learning methods. We present an automated approach for identifying magnetospheric regions using supervised machine learning techniques applied to Magnetospheric MultiSc Explore with advanced AI tools for machine learning research. Aug 11, 2025 · AI-powered analysis of 'Optimising supervised machine learning algorithms predicting cigarette cravings and lapses for a smoking cessation just-in-time adaptive intervention (JITAI)'. ABSTRACT This paper serves as an introductory guide to supervised learning within the field of machine learning (ML), aimed at readers with a foundational understanding of mathematics, primarily calculus and statistics. We begin with an overview of NNs, detailing the architecture and Oct 14, 2024 · Supervised Learning is a way of developing Artificial Intelligence systems in which a computer algorithm is trained on labeled data inputs. The models were fine-tuned on specific downstream tasks to evaluate their performance. Sep 30, 2025 · AI-powered analysis of 'Automatic Identification of Magnetospheric Regions using Supervised Machine Learning Models'. The model learns to map inputs to outputs, enabling it to make predictions or classifications on new, unseen data. Mar 6, 2025 · AI Quick Summary The paper introduces WeakSupCon, a weakly supervised contrastive learning method for encoder pre-training that leverages bag-level labels to improve feature representation. 3 days ago · Latest 6 papers on semi-supervised learning: Mar. Jan 9, 2026 · The research employed a combination of masked language modeling and self-supervised learning techniques to pre-train language models on large-scale speech and text data. The comparative experimental design aimed to identify high-performance systems for addressing the growing challenge of deceptive information spreading through social media platforms. This study aimed to optimise the balance between participant burden and performance of algorithms predicting high-risk moments for a smoking cessation Explore with advanced AI tools for machine learning research. Effectiveness of a Supervised Learning algorithm is determined by its performance on a given dataset for a particular problem. Abstract This study evaluated seven supervised machine learning algorithms for detecting deception in online social networks using two different datasets and four evaluation metrics. This is where semi-supervised . In case of Supervised Learning problems, Stacking Ensembles usually perform better than individual classifiers due to their Machine Learning (ML) algorithms are a subset of Artificial Intelligence that are applied to data with a primary focus of improving its accuracy over time by replicating and imitating the learning styles of human beings. 21, 2026 In the exciting world of AI and Machine Learning, the quest for robust models often hits a roadblock: data scarcity. The goal of this paper is to provide a primer in supervised machine learning (i. Jul 22, 2022 · To enhance the security of in-vehicle networks and promote the research in this area, based upon a large scale of CAN network traffic data with the extracted valuable features, this study comprehensively compared fully-supervised machine learning with semi-supervised machine learning methods for CAN message anomaly detection. Jun 8, 2017 · This paper describes various Supervised Machine Learning (ML) classification techniques, compares various supervised learning algorithms as well as determines the most efficient classification Supervised machine learning is a subset of machine learning where an algorithm is trained on labeled data, meaning that each training example is paired with an output label. , machine learning for prediction) including commonly used terminology, algorithms, and modeling building, validation, and evaluation procedures. Apr 7, 2025 · Significance This research is important as it advances box-supervised instance segmentation, reducing reliance on expensive pixel-level annotations and improving model performance with quality-aware and peer-assisted learning mechanisms. This approach enhances multiple instance learning (MIL) performance by generating better features compared to self-supervised methods, as demonstrated across three datasets. Within this framework, several supervised and unsupervised learning algorithms are studied through different scenarios. The advantages and disadvantages of these algorithms Feb 24, 2026 · Nature Machine Intelligence is an online-only journal publishing research and perspectives from the fast-moving fields of artificial intelligence, machine learning and robotics. Acquiring and meticulously labeling large datasets is a resource-intensive endeavor, especially in specialized domains like medical imaging or sensitive areas like blockchain analysis. ybnpft svfh cxa ogzon kgir vuen eilefy tohr vgq vrklv