Academic Articles & Research Papers

Incorporating Feature Interactions and Contrastive Learning for Credit Prediction

The efficacy of credit risk assessment models is pivotal to the risk management capacity of financial institutions. Traditional credit risk models often suffer from inadequate predictive accuracy due to overlooked feature combinations and w...

Feature Augmenting Networks for Improving Depression Severity Estimation From Speech Signals

Depression disorder has become one of the major psychological diseases endangering human health. Researcher in the affective computing community is supporting the development of reliable depression severity estimation system, from multiple ...

Deep learning and radiomics in precision medicine

INTRODUCTION: The radiological reading room is undergoing a paradigm shift to a symbiosis of computer science and radiology using artificial intelligence integrated with machine and deep learning with radiomics to better define tissue chara...

A deep-reinforcement learning approach for optimizing homogeneous droplet routing in digital microfluidic biochips

Over the past two decades, digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increasingly important in point-of-care analysis, drug discovery, and immunoassays, among other areas. How...

Deep Learning Methods and Applications

A deep learning approach to predict blood-brain barrier permeability

The blood–brain barrier plays a crucial role in regulating the passage of 98% of the compounds that enter the central nervous system (CNS). Compounds with high permeability must be identified to enable the synthesis of brain medications for...

Image-based cotton leaf disease diagnosis using YOLO and faster R-CNN techniques

Abstract Cotton has, in recent years, become one of the most important cash crops worldwide while being impacted in yield from leaf disease which generally goes unnoticed in the early stage. Detection methods depend on manual efforts produc...

IntelliScheduler: an edge-cloud computing environment hybrid deep learning framework for task scheduling based on learning

Abstract Edge–cloud computing has emerged as an important paradigm for modern Internet of Things (IoT) workflow applications, enabling low latency and on-demand resource allocation. In scenarios with heterogeneous deadlines and varying work...

Rider Optimization With Deep Learning Based Image Encryption for Secure Drone Communication

In recent years, drones or Unmanned Aerial Vehicles (UAVs) got significant attention among researchers because of their extensive application in commercial applications, border surveillance, etc. As the conventional terrestrial communicatio...

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

Steering angle prediction is critical in the control of Autonomous Vehicles (AVs) and has attracted the attention of researchers, manufacturers, and insurance companies in the automotive industry. Different Deep Learning (DL) architectures ...

Deep Learning for the Earth Sciences

Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only re...

A deep learning-driven pipeline for differentiating hypertrophic cardiomyopathy from cardiac amyloidosis using 2D multi-view echocardiography

Abstract Hypertrophic Cardiomyopathy (HCM) and Cardiac Amyloidosis (CA) are two cardiac conditions that can advance to heart failure if left untreated, which present considerable diagnostic challenges due to their overlapping echocardiograp...

TDLearning: Trusted Distributed Collaborative Learning Based on Blockchain Smart Contracts

Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their ...

Deep Learning of Representations: Looking Forward

Explainable and reliable kidney CT image classification using self-supervised DeiT-Tiny transformer

IntroductionKidney-related disorders are one of the global health concerns that require timely detection to prevent severe health complications. The use of computed tomography (CT) images for accurate classification of kidney diseases is im...

<i>H²A²</i>Net: A Hybrid Convolution and Hybrid Resolution Network with Double Attention for Hyperspectral Image Classification

Deep learning (DL) has recently been a core ingredient in modern computer vision tasks, triggering a wave of revolutions in various fields. The hyperspectral image (HSI) classification task is no exception. A wide range of DL-based methods ...

Application of Deep Learning for Object Detection

The ubiquitous and wide applications like scene understanding, video surveillance, robotics, and self-driving systems triggered vast research in the domain of computer vision in the most recent decade. Being the core of all these applicatio...

MWG-UNet: Hybrid Deep Learning Framework for Lung Fields and Heart Segmentation in Chest X-ray Images

Deep learning technology has achieved breakthrough research results in the fields of medical computer vision and image processing. Generative adversarial networks (GANs) have demonstrated a capacity for image generation and expression abili...

Recurrent Neural Networks Based Online Behavioural Malware Detection Techniques for Cloud Infrastructure

Several organizations are utilizing cloud technologies and resources to run a range of applications. These services help businesses save on hardware management, scalability and maintainability concerns of underlying infrastructure. Key clou...

A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images

Abstract Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imba...