AI Medical Compendium Topic

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A Smart Visual Sensing Concept Involving Deep Learning for a Robust Optical Character Recognition under Hard Real-World Conditions.

Sensors (Basel, Switzerland)
In this study, we propose a new model for optical character recognition (OCR) based on both CNNs (convolutional neural networks) and RNNs (recurrent neural networks). The distortions affecting the document image can take different forms, such as blur...

Android Spyware Detection Using Machine Learning: A Novel Dataset.

Sensors (Basel, Switzerland)
Smartphones are an essential part of all aspects of our lives. Socially, politically, and commercially, there is almost complete reliance on smartphones as a communication tool, a source of information, and for entertainment. Rapid developments in th...

Evaluation and comparison of smartphone application tracing, web based artificial intelligence tracing and conventional hand tracing methods.

Journal of stomatology, oral and maxillofacial surgery
AIM: The aim of this study was to compare and evaluate the reliability of three different cephalometric assessment methods: Smartphone Application Tracing Method CephNinja (SATM), Web Based Artificial Intelligence (AI) Driven Tracing Method WebCeph (...

We got nuts! use deep neural networks to classify images of common edible nuts.

Nutrition and health
BACKGROUND: Nuts are nutrient-dense foods that contribute to healthier eating. Food image datasets enable artificial intelligence (AI) powered diet-tracking apps to help people monitor daily eating patterns.

Exploring Orientation Invariant Heuristic Features with Variant Window Length of 1D-CNN-LSTM in Human Activity Recognition.

Biosensors
Many studies have explored divergent deep neural networks in human activity recognition (HAR) using a single accelerometer sensor. Multiple types of deep neural networks, such as convolutional neural networks (CNN), long short-term memory (LSTM), or ...

Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model.

Sensors (Basel, Switzerland)
To quantitatively assess pathological gait, we developed a novel smartphone application for full-body human motion tracking in real time from markerless video-based images using a smartphone monocular camera and deep learning. As training data for de...

Emerging Artificial Intelligence-Empowered mHealth: Scoping Review.

JMIR mHealth and uHealth
BACKGROUND: Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such...

Ensem-HAR: An Ensemble Deep Learning Model for Smartphone Sensor-Based Human Activity Recognition for Measurement of Elderly Health Monitoring.

Biosensors
Biomedical images contain a huge number of sensor measurements that can provide disease characteristics. Computer-assisted analysis of such parameters aids in the early detection of disease, and as a result aids medical professionals in quickly selec...

Evaluation of 1D and 2D Deep Convolutional Neural Networks for Driving Event Recognition.

Sensors (Basel, Switzerland)
Driving event detection and driver behavior recognition have been widely explored for many purposes, including detecting distractions, classifying driver actions, detecting kidnappings, pricing vehicle insurance, evaluating eco-driving, and managing ...

Nanocatalyst-Enabled Physically Unclonable Functions as Smart Anticounterfeiting Tags with AI-Aided Smartphone Authentication.

ACS applied materials & interfaces
Counterfeiting is a worldwide issue affecting many industrial sectors, ranging from specialized technologies to retail market, such as fashion brands, pharmaceutical products, and consumer electronics. Counterfeiting is not only a huge economic burde...