Academic Articles & Research Papers

Deep Learning for Computer Vision: A Brief Review

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief ...

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and m...

Application of Deep Learning in Food: A Review

Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food sci...

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,...

Bridge Node Detection between Communities Based on GNN

In a complex network, some nodes are relatively concentrated in topological structure, thus forming a relatively independent node group, which we call a community. Usually, there are multiple communities on a network, and these communities ...

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 for neuroimaging: a validation study

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neu...

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 ...

Text Data Augmentation for Deep Learning

Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augme...

Synthetic Data for Deep Learning

Deep Learning and Its Applications in Biomedicine

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitat...

Deep reinforcement learning to enhance the energy-efficient performance of UAV-enabled F-RAN

Fog radio access network (F-RAN) is suitable for Internet of things applications of national important industries, such as pipeline network monitoring in wide area.However, the performance of the F-RAN based on the territorial fog access po...

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 ...

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...

Deep learning in optical metrology: a review

With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, n...

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....

A Review of Machine Learning and Deep Learning Applications

Machine learning is one of the fields in the modern computing world. A plenty of research has been undertaken to make machines intelligent. Learning is a natural human behavior which has been made an essential aspect of the machines as well...

The rise of deep learning in drug discovery

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other...