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Abstract To address the limitations of existing oil-well production forecasting models, particularly suboptimal hyperparameter selection, difficulties in fine-tuning, insufficient global search capability, and slow conve...

Abstract Lung cancer remains the primary cause of cancer-related deaths throughout the world. The main reason behind this is late diagnosis and the restrictions in the manual interpretation of imaging data. In these days...

Abstract As reported by the World Health Organization (WHO), Skin Cancer (SC) is a growing medical concern with millions of cases diagnosed each year. Deep Learning methods show promising results in early detection and c...

Abstract Although UNet has proven its success in various tasks involving medical image segmentation, its capacity to capture global context is restricted by the finite receptive field inherent to convolutional operations...

Abstract Underwater imaging suffers from various challenges such as color distortion, low contrast, and reduced visibility caused by light attenuation. Addressing these issues is crucial for improving the performance of ...

Abstract Medical picture categorization has been greatly enhanced by the use of deep learning, especially in the timely identification of skin lesions. Still, predictions from a single model remain unreliable due to thei...

Abstract Timely and accurate detection of COVID-19 is essential for rapid intervention and preventing the spread of the disease, especially among vulnerable populations. This study introduces a new two-stage methodology ...

Abstract The integration of the sixth-generation (6G) communication technology and the Industrial Internet of Things (IIoT) has realized the intelligence and automation of industrial applications. However, due to the com...

Abstract Medical image segmentation is fundamental for delineating lesion and organ boundaries in clinical workflows. While UNet-based models remain widely used, CNN-dominant designs are limited in modeling long-range co...

Abstract In the world of the Internet of Things (IoT), wireless sensor networks (WSNs) play a vital role in enabling seamless communication between connected devices. In a WSN data prediction system based, clustering and...

Abstract We developed and evaluated a deep learning system integrating segmentation and classification for automated detection of Weiss rings on fundus photographs (FPs). A U-Net segmenter and two EfficientNet-B0 classif...

Abstract Lameness in cattle is a significant welfare and economic concern. To address this, we developed an end-to-end deep learning framework for 24/7 lameness monitoring using top-down depth images of cattle. The frame...

Abstract The explosion of patient data, the demand for real-time insights, and the critical importance of data security drive healthcare innovation. Medical Internet of Things (IoMT) offers a promising solution, connecti...

Abstract Classifying exam questions using Bloom’s Taxonomy is a critical task in education. However, performing this classification manually is often inefficient, prone to subjective errors and demands significant time...

Abstract Facial Expression Recognition (FER) demonstrates significant value in practical scenarios such as intelligent human–computer interaction. However, conventional FER methods often struggle to balance performance...

Abstract Vision Transformers have demonstrated strong performance across diverse visual tasks; however, their high computational cost due to long token sequences remains a critical challenge. Sequence reduction via pooli...

Abstract Accurate monitoring and prediction of Chlorophyll-a (Chl-a) concentrations are critical for protecting desalination systems from algal blooms. This study presents an advanced framework employing the Graph Neural...