Academic Articles & Research Papers

An Efficient Distributed Reinforcement Learning Architecture for Long-Haul Communication Between Actors and Learner

A computing cluster that interconnects multiple compute nodes is used to accelerate distributed reinforcement learning that uses DQN (Deep Q-Network). In distributed reinforcement learning, actor nodes acquire experiences by interacting wit...

Deep learning in cancer diagnosis, prognosis and treatment selection

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning acr...

Deep learning for power quality

This paper aims to introduce deep learning to the power quality community by reviewing the latest applications and discussing the open challenges of this technology. Publications covering deep learning to power quality are stratified in ter...

Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm

Ceramic image shape 3D image modeling focuses on of ceramic that was obtained from the camera imaging equipment such as 2D images, by normalization, gray, filtering denoising, wavelet image sharpening edge enhancement, binarization, and sha...

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...

Learning to decode linear codes using deep learning

A novel deep learning method for improving the belief propagation algorithm is proposed. The method generalizes the standard belief propagation algorithm by assigning weights to the edges of the Tanner graph. These edges are then trained us...

Deep learning in ECG diagnosis: A review

Deep Learning on Graphs: A Survey

Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characte...

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...

Combining the YOLOv4 Deep Learning Model with UAV Imagery Processing Technology in the Extraction and Quantization of Cracks in Bridges

Bridges are often at risk due to the effects of natural disasters, such as earthquakes and typhoons. Bridge inspection assessments normally focus on cracks. However, numerous concrete structures with cracked surfaces are highly elevated or ...

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...

Controlled Signal Technique in VL‐NOMA Communication Under Interference‐Controlled Environment With Intelligent Reflecting Surfaces

ABSTRACT This paper proposes a controlled signal technique for visible light non‐orthogonal multiple access (VL‐NOMA) communication in an interference‐controlled environment with intelligent reflecting surfaces (IRS) for beyond 5G (B5G) and...

Book Review: Deep Learning

This book offers a solution to more intuitive problems in these areas. These solutions allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined in terms of its relation...

SPAF-SegNeXt: Prior-Guided Attention and Frequency-Spatial Hybrid Fusion for Multimodal Sugarcane Segmentation in Remote Sensing

Accurate monitoring of the spatial extent of sugarcane cultivation is critical for optimizing land-use efficiency and regulating the sugar industry. However, sugarcane cultivation areas are characterized by complex topography and fragmented...

A Survey on Deep Learning for Named Entity Recognition

Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language ...

Deep Learning-Based Channel Estimation

In this letter, we present a deep learning algorithm for channel estimation in communication systems. We consider the time-frequency response of a fast fading communication channel as a 2D image. The aim is to find the unknown values of the...

Semantic-Aware Guided Low-Light Image Super-Resolution

The single image super-resolution based on deep learning has achieved extraordinary performance. However, due to inevitable environmental or technological limitations, some images not only have low resolution but also low brightness. The ex...

DeepTox: Toxicity Prediction using Deep Learning

The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 differen...

Deep Learning for Time-Series Analysis

In many real-world application, e.g., speech recognition or sleep stage classification, data are captured over the course of time, constituting a Time-Series. Time-Series often contain temporal dependencies that cause two otherwise identica...

Privacy-preserving task offloading strategy for digital twin system in satellite edge computing

Satellite edge computing faces a trade-off between privacy protection and system overhead, strict task-dependency constraints, and the limited ability of conventional algorithms to capture complex dependency topologies in digital twin task ...