AIMC Topic: Smartphone

Clear Filters Showing 381 to 390 of 453 articles

A nanozyme colorimetric sensor combined with cloud-based machine learning algorithm-assisted WeChat mini program for intelligent identification of Chinese green tea.

Food research international (Ottawa, Ont.)
Green tea has become increasingly renowned among consumers by virtue of its exceptional flavor and high nutritional value. There is often a strong correlation between the varieties of green tea, quality and corresponding price. In this work, a simple...

Designing a Smartphone Application for Detection of Oral Bite Force Using Artificial Intelligence.

International dental journal
INTRODUCTION AND AIMS: The abnormal bite force plays a crucial role in triggering oral diseases. Currently, the field of bite force remains largely unexplored despite its immense potential. Previous studies have documented various devices for detecti...

Fluorescent Microneedle Sensors Based on Tb@Hydrogen-Bonded Organic Frameworks for Real-Time Food Freshness Monitoring.

Inorganic chemistry
As crucial biomarkers of food spoilage, portable and real-time monitoring of the biogenic amines (BAs) is essential to ensuring food safety. In light of this, a ratiometric fluorescent probe (Tb@HOF-BPTC) is developed, which exhibits distinct fluores...

Smartphone-Powered Automated Image Recognition Tool for Multianalyte Rapid Tests: Application to Infectious Diseases.

Analytical chemistry
Point-of-Care Testing (POCT) is rapidly increasing, providing quick, user-friendly, and portable diagnostic tools. Lateral flow assays (LFAs) have been central to POCT, administering fast and cost-effective diagnosis. However, traditional LFAs are li...

Establishment of an intelligent analysis system for clinical image features of melanonychia based on deep learning image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Melanonychia, a condition that can be indicative of malignant melanoma, presents a significant challenge in early diagnosis due to the invasive nature and equipment dependency of traditional diagnostic methods such as nail biopsy and dermatoscope ima...

Predictors of smartphone addiction in adolescents with depression: combing the machine learning and moderated mediation model approach.

Behaviour research and therapy
Smartphone addiction (SA) significantly impacts the physical and mental health of adolescents, and can further exacerbate existing mental health issues in those with depression. However, fewer studies have focused on the predictors of SA in adolescen...

Smartphone digital phenotyping in mental health disorders: A review of raw sensors utilized, machine learning processing pipelines, and derived behavioral features.

Psychiatry research
With increased access to digital technology, there has been a surge in the use of and interest in digital phenotyping as a tool to calculate various features from raw smart device data. However, the increased usage of digital phenotyping has created ...

Facial emotion based smartphone addiction detection and prevention using deep learning and video based learning.

Scientific reports
Smartphone addiction among students has emerged as a critical issue, negatively impacting their academic performance, emotional well-being, and social behavior. This paper introduces the Theory of Mind integrated with Video Modelling (TMVM) framework...

Ultrafast Ratiometric Fluorescent Probe and Deep Learning-Assisted On-Site Detection Platform for BAs and Meat Freshness Based on Molecular Engineering.

ACS sensors
As metabolic byproducts and representative indicators of food spoilage, the monitoring and detection for biogenic amines (BAs) are crucial but challenging for food quality assessment. Here, a strategy is proposed by combining fluorescent probe molecu...

Personalized prediction of negative affect in individuals with serious mental illness followed using long-term multimodal mobile phenotyping.

Translational psychiatry
Heightened negative affect is a core feature of serious mental illness. Over 90% of American adults own a smartphone, equipped with an array of sensors which can continuously and unobtrusively measure behaviors (e.g., activity levels, location, and p...