In this day and age, a considerable number of fatalities are caused by colon and lung cancer. Although their appearance simultaneously is rare, the likelihood of cancer cells spreading between these two organs in the ...
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This study investigates the effectiveness of virtual rehabilitation using a 3D animated virtual therapist delivering the LSVT-BIG program for people with Parkinson's disease (PwPD). Integrating an animated virtual...
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Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water le...
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In real-world scenarios mirroring fine-grained datasets, labeling may result in noisy labels due to ambiguous data characteristics. This study introduces a new classification approach that integrates active and contra...
In real-world scenarios mirroring fine-grained datasets, labeling may result in noisy labels due to ambiguous data characteristics. This study introduces a new classification approach that integrates active and contrastive learning to address the issue of highly noisy labels in fine-grained datasets. We strategically employ active learning to select boundary points near class-dependent decision boundaries, effectively expanding the distances between classes in the data space. By combining contrastive learning and active learning, we successfully extract robust features from the data. Our experiments consistently achieve high average performance across various levels of label noise, highlighting the potential of our approach in fine-grained datasets. This underscores the method's capability to maintain reliable performance even in the presence of label noise, contributing to the field of fine-grained classification and noisy label mitigation. This innovative approach provides promising insights for enhancing the performance of machine learning models in fine-grained classification tasks.
Cloud computing has already gained vast momentum in various fields like healthcare, finance, defense, education, and businesses. With the increasing amount of data generated, most firms have migrated to cloud platform...
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This study analyses road traffic accident data recorded over a period of time to gain insights to the underlying pain points in the infrastructure and policies. Such insight allows us to focus our efforts in the right...
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The integration of machine learning into predictive maintenance strategies has revolutionized how industries manage equipment reliability & performance. This abstract examines the role of machine learning algorith...
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Creating media content suited to many users is crucial from the viewpoint of business and a situation with multiple users' participation. Interactive Evolutionary Computation (IEC) is known as a search method that...
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A microgrid consists of distributed energy resources (DERs) and loads. Due to the intermittent power generation of DERs and the changeable operation mode of microgrid, the magnitude and direction of fault currents are...
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With the development of the Internet of Things, applications of the wireless sensor network are more and more widespread. In wireless sensor networks, the sensor is basically stationary and performs sensing tasks at t...
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