AIMC Topic: Neural Networks, Computer

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An Experimental Review on Deep Learning Architectures for Time Series Forecasting.

International journal of neural systems
In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mi...

Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase.

Sensors (Basel, Switzerland)
Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a rec...

Thyroid gland delineation in noncontrast-enhanced CTs using deep convolutional neural networks.

Physics in medicine and biology
The purpose of this study is to develop a deep learning method for thyroid delineation with high accuracy, efficiency, and robustness in noncontrast-enhanced head and neck CTs. The cross-sectional analysis consisted of six tests, including randomized...

Comparison of diagnostic performance between convolutional neural networks and human endoscopists for diagnosis of colorectal polyp: A systematic review and meta-analysis.

PloS one
Prospective randomized trials and observational studies have revealed that early detection, classification, and removal of neoplastic colorectal polyp (CP) significantly improve the prevention of colorectal cancer (CRC). The current effectiveness of ...

Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment.

Environmental science and pollution research international
One of the most critical parameters in concrete design is compressive strength. As the compressive strength of concrete is correctly measured, time and cost can be decreased. Concrete strength is relatively resilient to impacts on the environment. Th...

Diabetes detection using deep learning techniques with oversampling and feature augmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Diabetes is a chronic pathology which is affecting more and more people over the years. It gives rise to a large number of deaths each year. Furthermore, many people living with the disease do not realize the seriousness of ...

GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification.

Journal of biomedical informatics
Exponential growth of biomedical literature and clinical data demands more robust yet precise computational methodologies to extract useful insights from biomedical literature and to perform accurate assignment of disease-specific codes. Such approac...

A method to screen left ventricular dysfunction through ECG based on convolutional neural network.

Journal of cardiovascular electrophysiology
OBJECTIVE: This study aims to develop an artificial intelligence-based method to screen patients with left ventricular ejection fraction (LVEF) of 50% or lesser using electrocardiogram (ECG) data alone.

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy.

Molecules (Basel, Switzerland)
The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This r...

Fast and precise single-cell data analysis using a hierarchical autoencoder.

Nature communications
A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. To address this challenge, we introduce an analysis framework, named single-cell Decomposition using Hierarchical ...