Academic Articles & Research Papers

An overview of deep learning in medical imaging focusing on MRI

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when...

Hyperspectral Image Classification With Deep Learning Models

Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision task...

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent...

Seeing ourselves in anatomy: Embedding equity, diversity, and inclusivity to transform learning in a UK BSc curriculum.

A learning‐based energy management strategy for hybrid energy storage systems with compressed air and solid oxide fuel cells

Abstract The intermittency and volatility of renewable energy have been major challenges in modern power systems. This paper proposes a self‐adaptive energy management strategy based on deep reinforcement learning (DRL) to integrate renewab...

Contour-Based Wild Animal Instance Segmentation Using a Few-Shot Detector

Camera traps are widely used in wildlife research, conservation, and management, and abundant images are acquired every day. Efficient real-time instance segmentation networks can help ecologists label and study wild animals. However, exist...

Deep learning approaches to biomedical image segmentation

The review covers automatic segmentation of images by means of deep learning approaches in the area of medical imaging. Current developments in machine learning, particularly related to deep learning, are proving instrumental in identificat...

Deep learning: a statistical viewpoint

The remarkable practical success of deep learning has revealed some major surprises from a theoretical perspective. In particular, simple gradient methods easily find near-optimal solutions to non-convex optimization problems, and despite g...

Deep Learning (CNN) and Transfer Learning: A Review

Abstract Deep Learning is a machine learning area that has recently been used in a variety of industries. Unsupervised, semi-supervised, and supervised-learning are only a few of the strategies that have been developed to accommodate differ...

Database Meets Deep Learning

Deep learning has recently become very popular on account of its incredible success in many complex datadriven applications, including image classification and speech recognition. The database community has worked on data-driven application...

Deep Learning for Encrypted Traffic Classification: An Overview

Traffic classification has been studied for two decades and applied to a wide range of applications from QoS provisioning and billing in ISPs to security-related applications in firewalls and intrusion detection systems. Port-based, data pa...

EmoRepLKNet: Facial Emotion Recognition Neural Network Based on UniRepLKNet

This study presents a facial emotion recognition network based on UniRepLKNet to address the difficulty in effectively capturing feature information and preventing key facial information from occupying a more prominent position in the facia...

An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles

In the rapidly evolving domain of Unmanned Aerial Vehicles (UAVs), precise altitude estimation remains a significant challenge, particularly for lightweight UAVs. This research presents an innovative approach to enhance altitude estimation ...

Rapid profiling of carcinogenic types of Helicobacter pylori infection via deep learning analysis of label-free SERS spectra of human serum

WHO classified Helicobacter pylori as a Group I carcinogen for gastric cancer as early as 1994. However, despite the high prevalence of H. pylori infection, only about 3 % of infected individuals eventually develop gastric cancer, with the ...

Applications of Deep Learning and Reinforcement Learning to Biological Data

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body...

A review on deep learning in medical image analysis

Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is ef...

A novel agricultural commodity price prediction model integrating deep learning and enhanced swarm intelligence algorithm.

The volatility of agricultural commodity prices significantly affects market stability and financial market dynamics, especially during periods of economic uncertainty and global shocks. Accurate price prediction, however, remains challengi...

A longitudinal study on students’ self-regulated listening during transition to an English-medium transnational university in China

Listening to academic content in English medium instruction (EMI) classrooms at university can be a demanding task for students who transition from first language (L1) instructed secondary schools. This longitudinal mixed methods study anal...

Deep Learning-Driven Predictive Modelling for Optimizing Stingless Beekeeping Yields

Environmental factors like temperature, solar irradiance, and rain may influence the health and productivity of stingless bees. This paper aims to investigate the best approaches applied in meliponiculture to predict beehive health and prod...

Machine and deep learning methods for radiomics

Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has...