A review on large Language Models: Architectures, applications, taxonomies, open issues and challenges
Large Language Models (LLMs) recently demonstrated extraordinary capability in various
natural language processing (NLP) tasks including language translation, text generation …
natural language processing (NLP) tasks including language translation, text generation …
Deep learning in automatic diabetic retinopathy detection and grading systems: a comprehensive survey and comparison of methods
Diabetic Retinopathy is one of the leading global causes of vision impairment and blindness
in humans. It has seen a rise in prevalence, necessitating the development of advanced …
in humans. It has seen a rise in prevalence, necessitating the development of advanced …
[HTML][HTML] Enhancing smart grid load forecasting: An attention-based deep learning model integrated with federated learning and XAI for security and interpretability
Smart grid is a transformative advancement that modernized the traditional power system for
effective electricity management, and involves optimized energy distribution by load …
effective electricity management, and involves optimized energy distribution by load …
A computer-aided diagnostic system to identify diabetic retinopathy, utilizing a modified compact convolutional transformer and low-resolution images to reduce …
Diabetic retinopathy (DR) is the foremost cause of blindness in people with diabetes
worldwide, and early diagnosis is essential for effective treatment. Unfortunately, the present …
worldwide, and early diagnosis is essential for effective treatment. Unfortunately, the present …
Enhancement of diabetic retinopathy prognostication using deep learning, CLAHE, and ESRGAN
One of the primary causes of blindness in the diabetic population is diabetic retinopathy
(DR). Many people could have their sight saved if only DR were detected and treated in …
(DR). Many people could have their sight saved if only DR were detected and treated in …
A Systematic Review on Recent Advancements in Deep Learning and Mathematical Modeling for Efficient Detection of Glioblastoma
In medical facilities, the glioblastoma detection and growth patterns are critical yet
challenging tasks. It is important for early diagnosis and therapy planning to save lives …
challenging tasks. It is important for early diagnosis and therapy planning to save lives …
NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data
In the realm of precision medicine, the potential of deep learning is progressively harnessed
to facilitate intricate clinical decision-making, especially when navigating multifaceted …
to facilitate intricate clinical decision-making, especially when navigating multifaceted …
Deep learning-enhanced diabetic retinopathy image classification
Objective Diabetic retinopathy (DR) can sometimes be treated and prevented from causing
irreversible vision loss if caught and treated properly. In this work, a deep learning (DL) …
irreversible vision loss if caught and treated properly. In this work, a deep learning (DL) …
IoT-Based Object-Detection System to Safeguard Endangered Animals and Bolster Agricultural Farm Security
Significant threats to ecological equilibrium and sustainable agriculture are posed by the
extinction of animal species and the subsequent effects on farms. Farmers face difficult …
extinction of animal species and the subsequent effects on farms. Farmers face difficult …
[HTML][HTML] Artificial intelligence for diabetic retinopathy detection: a systematic review
The incidence of diabetic retinopathy (DR) has increased at a rapid pace in recent years all
over the world. Diabetic eye illness is identified as one of the most common reasons for …
over the world. Diabetic eye illness is identified as one of the most common reasons for …