AIMC Topic: Smartphone

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Smartphone Sensor-Based Human Motion Characterization with Neural Stochastic Differential Equations and Transformer Model.

Sensors (Basel, Switzerland)
With many conveniences afforded by advances in smartphone technology, developing advanced data analysis methods for health-related information from smartphone users has become a fast-growing research topic in the healthcare field. Along these lines, ...

Visual body composition assessment methods: A 4-compartment model comparison of smartphone-based artificial intelligence for body composition estimation in healthy adults.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: Visual body composition (VBC) estimates produced from smartphone-based artificial intelligence represent a user-friendly and convenient way to automate body composition remotely and without the inherent geographical and monetary re...

Practical and Accurate Indoor Localization System Using Deep Learning.

Sensors (Basel, Switzerland)
Indoor localization is an important technology for providing various location-based services to smartphones. Among the various indoor localization technologies, pedestrian dead reckoning using inertial measurement units is a simple and highly practic...

Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects.

Computers in biology and medicine
Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has b...

Lightweight On-Device Detection of Android Malware Based on the Koodous Platform and Machine Learning.

Sensors (Basel, Switzerland)
Currently, Android is the most popular operating system among mobile devices. However, as the number of devices with the Android operating system increases, so does the danger of using them. This is especially important as smartphones increasingly au...

Transportation Mode Detection Combining CNN and Vision Transformer with Sensors Recalibration Using Smartphone Built-In Sensors.

Sensors (Basel, Switzerland)
Transportation Mode Detection (TMD) is an important task for the Intelligent Transportation System (ITS) and Lifelog. TMD, using smartphone built-in sensors, can be a low-cost and effective solution. In recent years, many studies have focused on TMD,...

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.