Academic Articles & Research Papers

Deep Learning for Time Series Anomaly Detection: A Survey

Time series anomaly detection is important for a wide range of research fields and applications, including financial markets, economics, earth sciences, manufacturing, and healthcare. The presence of anomalies can indicate novel or unexpect...

Applications of deep learning to MRI images: A survey

Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, and is seen as a key method for various future applications. On account of its non-invasive and good soft tiss...

Deep learning for image inpainting: A survey

Deep learning for digital holography: a review

Recent years have witnessed the unprecedented progress of deep learning applications in digital holography (DH). Nevertheless, there remain huge potentials in how deep learning can further improve performance and enable new functionalities ...

A Preliminary Study on the Auto-Segmentation of Targets and Organs at Risk in Pediatric Total Marrow and Lymphoid Irradiation

Introduction Leukemia is one of the most prevalent cancers in children. The use of total marrow and lymphoid irradiation (TMLI) via helical tomotherapy (TOMO) as a conditioning regimen prior to bone marrow transplant (BMT) has been widely a...

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cance...

A Deep Learning-Based Approach for Automated Coarse Registration (ACR) of Image-Guided Surgical Navigation

Coarse registration is the first step in determining the accuracy of surgical navigation. The purpose of this study was to present an automated coarse registration (ACR) methodology to improve the convenience and accuracy. For this purpose,...

A Space-Time Partial Differential Equation Based Physics-Guided Neural Network for Sea Surface Temperature Prediction

Sea surface temperature (SST) prediction has attracted increasing attention, due to its crucial role in understanding the Earth’s climate and ocean system. Existing SST prediction methods are typically based on either physics-based numerica...

Detection of short-term peaks in VLF/LF signals around the time of gamma-ray burst detections using deep learning

Gamma-ray bursts (GRBs) are recognized as the most energetic and luminous astrophysical phenomena whose high-energy photons can ionize atmospheric particles and thus affect electrical conductivity. Consequently, electromagnetic waves ...

A Soybean Classification Method Based on Data Balance and Deep Learning

Soybean is a type of food crop with economic benefits. Whether they are damaged or not directly affects the survival and nutritional value of soybean plants. In machine learning, unbalanced data represent a major factor affecting machine le...

A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence

Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily li...

A Review of Deep Learning Applications in Intrusion Detection Systems: Overcoming Challenges in Spatiotemporal Feature Extraction and Data Imbalance

In the rapid development of the Internet of Things (IoT) and large-scale distributed networks, Intrusion Detection Systems (IDS) face significant challenges in handling complex spatiotemporal features and addressing data imbalance issues. T...

Applications of deep learning in fundus images: A review

A Comprehensive Survey on Graph Anomaly Detection With Deep Learning

Anomalies are rare observations (e.g., data records or events) that deviate significantly from the others in the sample. Over the past few decades, research on anomaly mining has received increasing interests due to the implications of thes...

Fundamentals of Deep Learning

This chapter deals with the basic understanding of the deep learning model. It starts with a view of artificial intelligence, machine learning, and deep learning. It also represents how these three concepts are related to each other. The en...

Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance...

Deep learning: new computational modelling techniques for genomics

Deep Learning

A structure for online discussion within a framework for deep learning provides three strategies for dynamic online discussion; flexible peer, structured topic, and collaborative task discussion. This article examines the discussion structu...

Deep Learning--based Text Classification

Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this art...

Predicting Parameters in Deep Learning

We demonstrate that there is significant redundancy in the parameterization of several deep learning models. Given only a few weight values for each feature it is possible to accurately predict the remaining values. Moreover, we show that n...