Academic Articles & Research Papers

PharmaNet Deep: Real-Time Pharmaceutical Defect Detection Using Defect-Guided Feature Fusion and Uncertainty-Driven Inspection

Abstract Oral dosage forms are the most widely employed method of drug delivery in therapeutic treatments. However, the presence of visual defects in blister packages can adversely affect the drug's bioavailability and therapeutic efficacy,...

TransImg: A Translation Algorithm of Visible-to-Infrared Image Based on Generative Adversarial Network

Abstract Infrared images of sensitive targets are difficult to obtain and cannot meet the design and training needs of target detection and tracking algorithms for mobile platforms such as aircraft. This paper proposes an image translation ...

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...

Deep Learning in Proteomics

Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS)...

Deep Learning in Ultrasound Imaging

In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on ...

Review of Deep Learning Algorithms and Architectures

Deep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. The pain...

Deep learning, reinforcement learning, and world models

Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and w...

Ship Detection With SAR C-Band Satellite Images: A Systematic Review

Detecting and tracking ships remotely is now required in a wide range of contexts, from military security to illegal immigration control, as well as the management of fisheries and marine protected areas. Among the available methods, radar ...

Development of an automated two pronuclei detection system on time‐lapse embryo images using deep learning techniques

Abstract Purpose To establish an automated pronuclei determination system by analysis using deep learning technology which is able to effectively learn with limited amount of supervised data. Methods An algorithm was developed by explicitly...

Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images

Abstract Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability...

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...

Review of Deep Learning for Language Modeling

[Purpose/Significance] Deep learning for language modeling is one of the major methods and advanced technologies to enhance language intelligence of machines at present, which has become an indispensable important technical means for automa...

Research Progress on Distributed Fiber Optic Event Recognition Methods Under Data Constraints

Due to the dynamic monitoring capability, Distributed Acoustic Sensing (DAS) has been widely deployed in various industrial scenarios. Deep learning greatly boosts the event detection accuracy of fiber sensing systems. Nevertheless, practic...

A comprehensive construction of deep neural network‐based encoder–decoder framework for automatic image captioning systems

Abstract This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory”...

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data....

Deep Learning Techniques: An Overview

Object Detection With Deep Learning: A Review

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable...

Deep Learning With Edge Computing: A Review

Deep learning is currently widely used in a variety of applications, including computer vision and natural language processing. End devices, such as smartphones and Internet-of-Things sensors, are generating data that need to be analyzed in...

IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT.

Early and real-time detection of rice leaf diseases (RLD) poses a significant challenge for farmers, especially in rural regions with limited access to advanced technology. Conventional deep learning models often require substantial computa...

Intelligent Distribution-Network Fault Handling via Cross-Modal Semantic Fusion and Knowledge-Graph Reasoning

Power distribution network fault handling imposes stringent requirements on both response timeliness and decision reliability. The high penetration of distributed renewable generation, the widespread deployment of power-electronic devices, ...