AIMC Topic: Neural Networks, Computer

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Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot.

Sensors (Basel, Switzerland)
This paper describes the development of a laser-based people detection and obstacle avoidance algorithm for a differential-drive robot, which is used for transporting materials along a reference path in hospital domains. Detecting humans from laser d...

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal.

Sensors (Basel, Switzerland)
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal ...

Comprehensive Review of Vision-Based Fall Detection Systems.

Sensors (Basel, Switzerland)
Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the m...

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks.

BMC bioinformatics
BACKGROUND: Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from paralle...

SAR and QSAR research on tyrosinase inhibitors using machine learning methods.

SAR and QSAR in environmental research
Tyrosinase is a key rate-limiting enzyme in the process of melanin synthesis, which is closely related to human pigmentation disorders. Tyrosinase inhibitors can down-regulate tyrosinase to effectively reduce melanin synthesis. In this work, we condu...

CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization.

IEEE transactions on visualization and computer graphics
Deep learning's great success motivates many practitioners and students to learn about this exciting technology. However, it is often challenging for beginners to take their first step due to the complexity of understanding and applying deep learning...

ES-ARCNN: Predicting enhancer strength by using data augmentation and residual convolutional neural network.

Analytical biochemistry
Enhancers are non-coding DNA sequences bound by proteins called transcription factors. They function as distant regulators of gene transcription and participate in the development and maintenance of cell types and tissues. Since experimental validati...

A Systematic Comparison of Depth Map Representations for Face Recognition.

Sensors (Basel, Switzerland)
Nowadays, we are witnessing the wide diffusion of active depth sensors. However, the generalization capabilities and performance of the deep face recognition approaches that are based on depth data are hindered by the different sensor technologies an...

Enzyme Promiscuity Prediction Using Hierarchy-Informed Multi-Label Classification.

Bioinformatics (Oxford, England)
MOTIVATION: As experimental efforts are costly and time consuming, computational characterization of enzyme capabilities is an attractive alternative. We present and evaluate several machine-learning models to predict which of 983 distinct enzymes, a...

Multidisease Deep Learning Neural Network for the Diagnosis of Corneal Diseases.

American journal of ophthalmology
PURPOSE: To report a multidisease deep learning diagnostic network (MDDN) of common corneal diseases: dry eye syndrome (DES), Fuchs endothelial dystrophy (FED), and keratoconus (KCN) using anterior segment optical coherence tomography (AS-OCT) images...