Raeed Alsabri

• PhD
• Researcher at Central South University

Globally, diabetic retinopathy (DR) is the primary cause of blindness, affecting millions of people worldwide. This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment. Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early d...

Background Drug response prediction is critical in precision medicine to determine the most effective and safe treatments for individual patients. Traditional prediction methods relying on demographic and genetic data often fall short in accuracy and robustness. Recent graph-based models, while promising, frequently neglect the critical role of ato...

Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and exper...

Drug response prediction is critical in personalized medicine, aiming todetermine the most effective and safe treatments for individual patients.Traditional prediction methods relying on demographic and genetic dataoften fall short in accuracy and robustness. Recent graph-based models,while promising, frequently neglect the critical role of atomic...

The prototypical network effectively classifies skin diseases in few-shot learning but faces challenges with prototype accuracy due to limited data and insufficient long-term knowledge retention, affecting generalization to new classes. However, the prototypical network faces challenges such as inaccurate prototype estimations due to limited data a...

Accurate and timely diagnosis of pulmonary diseases is critical in the field of medical imaging. While deep learning models have shown promise in this regard, the current methods for developing such models often require extensive computing resources and complex procedures, rendering them impractical. This study focuses on the development of a light...

The rapid outbreak of COVID-19 has proven to be a dangerous virus with catastrophic effects on large populations and health systems worldwide. Therefore, in order to limit the rapid spread of this virus, artificial intelligence (AI) combined with radiological images such as chest X-rays (CXRs) has recently become a worthwhile option for screening C...

Drug–drug interaction (DDI) has attracted widespread attention because when incompatible drugs are taken together, DDI will lead to adverse effects on the body, such as drug poisoning or reduced drug efficacy. The adverse effects of DDI are closely determined by the molecular structures of the drugs involved. To represent drug data effectively, res...

Text-graph representation learning is a critical and important area of research with extensive applications in natural language processing (NLP). Recently, graph learning models based on graph neural networks (GNNs) have been effectively utilized for encoding text-graph representation for various tasks due to their ability to handle complex structu...

Graph contrastive learning (GCL) has been successfully used to solve the problem of the huge cost of graph data annotation, such as labor cost, time cost, and professional knowledge cost. Recent works have focused on improving the generalization performance of GCL with automated data augmentation. However, GCL methods with automated data augmentati...

Graph neural networks (GNNs) have shown their superiority in the modeling of graph data. Recently, increasing attention has been paid to automatic graph neural architecture search, aiming to overcome the shortcomings of manually constructing GNN architectures that requires a lot of expert experience. However, existing graph neural architecture sear...

Many people wonder, when they look at fashion models on social media or on television, whether they could look like them by wearing similar products. Furthermore, many people suffer when they sometimes find fashion models in e-commerce, and they want to obtain similar products, but after clicking on the fashion model, they receive unwanted products...

Motivation: Understanding drug response differences in cancer treatments is one of the most challenging aspects of personalized medicine. Recently, graph neural networks (GNNs) have become state-of-the-art methods in many graph representation learning scenarios in bioinformatics. However, building an optimal handcrafted GNN model for a particular...

Extracting Aspect Sentiment Triplets is a relatively new method for conducting sentiment analysis. The triplet extraction is carried out in an end-to-end way by more recent models; however, they focus a significant amount of emphasis on the interactions between each target word and opinion word. Thus, they are not capable of achieving their goals o...

Graph neural network-based multitask learning models on multiview graphs have achieved acceptable results in different real-world applications. However, constructing and fine-tuning artificially designed architectures for various multiview graphs are time-consuming and require expert knowledge. To address this challenge, we propose a multitask mult...

Graph neural architecture search (GNAS) has been successful in many supervised learning tasks, such as node classification, graph classification, and link prediction. GNAS uses a search algorithm to sample graph neural network (GNN) architectures from the search space and evaluates sampled GNN architectures based on estimation strategies to generat...

Graph Neural Architecture Search (Graph-NAS) methods have shown great potential in finding better graph neural network designs compared to handcrafted designs. However, existing Graph-NAS frameworks are based on complex algorithms and fail to maintain low costs for high scalability with high performance. They require full training of thousands of g...

Recently, graph neural architecture search (GNAS) frameworks have been successfully used to automatically design the optimal neural architectures for many problems such as node classification and graph classification. In the existing GNAS frameworks, the designed graph neural network (GNN) architectures learn the representation of homogenous graphs...

In academia and industries, graph neural networks (GNNs) have emerged as a powerful approach to graph data processing ranging from node classification and link prediction tasks to graph clustering tasks. GNN models are usually handcrafted. However, building handcrafted GNN models is difficult and requires expert experience because GNN model compone...

Pneumonia is one of the main causes of child mortality in the world and has been reported by the World Health Organization (WHO) to be the cause of one-third of child deaths in India. Designing an automated classification system to detect pneumonia has become a worthwhile research topic. Numerous deep learning models have attempted to detect pneumo...

Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years. Recently, deep neural network (DNN) models have been used for sentiment analysis tasks, achieving promising results. Although these models can analyze sequences of arbitrary l...

In academia and industries, graph neural networks (GNNs) have emerged as a powerful approach to graph data processing ranging from node classification and link prediction tasks to graph clustering tasks. GNN models are usually handcrafted. However, building handcrafted GNN models is difficult and requires expert experience because GNN model compone...

Due to the increasing growth of social media content on websites such as Twitter and Facebook, analyzing textual sentiment has become a challenging task. Therefore, many studies have focused on textual sentiment analysis. Recently, deep learning models, such as convolutional neural networks and long short-term memory, have achieved promising perfor...

In this article, first a comprehensive study of the impact of term weighting schemes on the topic modeling performance (i.e., LDA and DMM) on Arabic long and short texts is presented. We investigate six term weighting methods including Word count method (standard topic models), TFIDF, PMI, BDC, CLPB, and CEW. Moreover, we propose a novel combinatio...

The current baseline architectures in the field of the Internet of Things (IoT) strongly recommends the use of edge computing in the design of the solution applications instead of the traditional approach which solely uses the cloud/core for analysis and data storage. This research, therefore, focuses on formulating an edge-centric IoT architecture...