Abstract: Data annotation in medical image segmentation is time-consuming and expensive. Semi-supervised learning (SSL) presents a viable solution. However, unlike organ segmentation, current ...
Abstract: Landslides inflict substantial societal and economic damage, underscoring their global significance as recurrent and destructive natural disasters. Recent landslides in northern parts of ...
Abstract: This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning ...
Abstract: Parkinson's disease is a neurological disorder hat effects the movements including shaking, stiffness, difficulty while walking and speaking. This condition will occur when the nerve cells ...
Abstract: Wildfires are a growing concern due to their catastrophic impacts on the environment, ecosystem services, biodiversity, and human settlements. Different fire detection systems face ...
Abstract: Skin cancer ranks among ubiquitous malignancies, its prevalence escalating due to ecological shifts and protracted ultraviolet (UV)exposure. This study aims to address the pressing need for ...
Abstract: Most Smartphone users prefer their phones to read news via various social platforms on the Internet. The news site publishes news and provides the source of identity verification. Humans are ...
Abstract: Strawberry production is globally significant because it contains high nutrients. Strawberry leaf disease shapes a significant barrier to strawberry cultivation worldwide. Numerous ...
Abstract: This study proposes a robust and efficient two-stage deep learning framework aimed at the accurate classification of Chest X-ray images into NORMAL and PNEUMONIA categories. The methodology ...
Abstract: Over the past few years especially in the context of communication and information processing the importance of Natural language processing which demands efficient deep learning models has ...
Abstract: Time series data analysis is essential in a variety of fields, such as finance, meteorology, and healthcare. Traditional methods often struggle to capture the complex temporal correlations ...
Abstract: Data stream learning is an emerging machine learning paradigm designed for environments where data arrive continuously and must be processed in real time. Unlike traditional batch learning, ...
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