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Abstract Proposed research presents a data-driven framework for forecasting municipal solid waste (MSW) generation and emission dynamics in Erode City, India, by employing supervised machine learning algorithms. Leveragi...

Weakly supervised semantic segmentation (WSSS), particularly using image-level labels, has emerged as a pivotal solution to alleviate the burden of costly and labor-intensive pixel-level annotations typically required fo...

Abstract Feature selection is a critical step in machine learning, as it helps minimize redundant features, thereby enhancing model performance and interpretability. In practice, partially labeled datasets are common. Wh...

Abstract Air pollution is a major environmental and public health challenge in Greater Cairo due to rapid urbanization, intense traffic activity, and recurrent regional dust intrusions. This study presents a data driven ...

Abstract Background Deep learning faces a significant bottleneck in medical image analysis due to its reliance on large-scale, expert-annotated datasets. This challenge is acute in ophthalmology, particularly for detecti...

Abstract Credit risk assessment is a critical function in financial analytics, requiring models that can adapt to diverse borrower profiles while providing clear and interpretable insights. Although a range of data drive...

Abstract BackgroundCardiovascular magnetic resonance (CMR) is widely used across various cardiac conditions and systematically assesses cardiac anatomical structures and functional dynamics. Machine learning ...

Abstract Learning to program is not just about writing code. It is a complex process involving trial and error, hesitation, corrections, and sometimes silence. And each student experiences it in their own way. Understand...

Abstract Collaborative filtering and neural recommendation models have advanced substantially over the past decade, yet they frequently struggle to capture the fine-grained structure of user preferences and the rich, mul...

Abstract Reliable interpretation of clinical imaging requires integrating complementary evidence across modalities, yet most AI systems remain limited by single-modality analysis and poor generalization across institutio...

Graph-based text classification has become an effective paradigm for modeling corpus-level structure, particularly under limited supervision. However, most existing approaches rely on document–word representations...

Options trading is a type of financial trading that can offer significant profit opportunities depending on market conditions, but its profit and loss structure is complex. Traditional trading decisions are often made ba...

Los requerimientos para capturar información sobre la forma del cuerpo humano están creciendo rápidamente y no solo se limitan a estudios ergonómicos o diseño, sino que también resultan de interés para aplicacione...

Abstract Financial institutions continue to face a significant challenge with money laundering and need intelligent, scalable ways to detect illegal financial activity. This paper introduces a machine learning-driven pla...

Mathematics readiness at entry remains critical for success in first-year engineering programs, yet many institutions lack transparent, reusable tools to diagnose risk and design levelling policies. This study develops a...

An accurate intraoperative assessment of tissue perfusion in digestive surgery is critical to prevent complications such as anastomotic leakage, an event in which a surgical connection between organs fails to heal proper...

Abstract Objective Acute heart failure (AHF) is a common but underrecognized cause of dyspnea. Chest computed tomography (CT) can accurately assess pulmonary congestion, but radiologist reporting capacity may limit clini...

IntroductionVision-based fall analysis has advanced rapidly, yet a key bottleneck remains: visually similar motions can correspond to markedly different physical outcomes, as subtle differences in contact mechanics and p...

This study seeks to enhance lead conversion for online professional education providers by using supervised machine learning algorithms for lead conversion targeting and lead scoring, including Logistic Regression, K-Nea...

Mandatory lane changes (MLCs) pose significant challenges to trajectory planning at intersections, where vehicles are required to change lanes mid-block to reach designated turn lanes before the stop bar. MLCs often gene...

Background Lupus nephritis (LN) is a leading cause of mortality in patients with systemic lupus erythematosus (SLE), and the accurate classification of renal pathological subtypes is crucial for reducing mortality rates ...

With the consolidation of deep learning in drug discovery, several novel algorithms for learning molecular representations have been proposed. Despite the interest of the community in developing new methods for learning ...

"Local Field Potentials (LFPs) are easy to access electrical signals of the brain that represent the summation in the extracellular space, of currents originated within the neurons. As such, LFPs could contain infor mati...

Abstract Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems have significantly advanced data modelling capabilities and improved opportunities for extracting knowledge from vast and heterogeneo...