Academic Articles & Research Papers

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...

Examining the Consequences of School Bans on the Self-esteem of Afghan Adolescent Girls in Kabul, Afghanistan.

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 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...

Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An In vitro Study

Background: The integration of artificial intelligence in dentistry has seen remarkable advancements, especially in diagnostic imaging. This study evaluates and compares the accuracy of deep learning models with that of dental postgraduate ...

On the Origin of Deep Learning

This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep ...

A Dual-Segmentation Framework for the Automatic Detection and Size Estimation of Shrimp

In shrimp farming, determining the physical traits of shrimp is vital for assessing their health and growth. One of the critical traits is their size, as it serves as a key indicator of growth rates, biomass, and effective feed management. ...

Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma

Abstract Objective This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC)...

Deep Learning for Spatio-Temporal Data Mining: A Survey

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temp...

Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization

Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative ...

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves ...

Deep Learning in Mining Biological Data

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data...

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...

Teaching for Deep Learning

The authors have been engaged in research focused on students' depth of learning as well as teachers' efforts to foster deep learning. Findings from a study examining the teaching practices and student learning outcomes of sixtyfour teacher...

Integrating multi-source geoscience data with RF-weighted conditional variational autoencoders (CVAE) for porphyry copper prospectivity mapping: an application to the Ardestan district, Central Iran

Abstract Porphyry Cu deposits in the Urmia–Dokhtar magmatic belt constitute important exploration targets, yet the heterogeneous and multi-scale nature of geological, geochemical, and remote sensing data introduces substantial uncertainty i...

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...

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...

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 for sentiment analysis: A survey

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state‐of‐the‐art prediction results. Along with the success of deep learning in many appl...