AIMC Topic: Cell Phone

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Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions.

Addictive behaviors
BACKGROUND: Real-time detection of drinking could improve timely delivery of interventions aimed at reducing alcohol consumption and alcohol-related injury, but existing detection methods are burdensome or impractical.

Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting.

Journal of medical Internet research
BACKGROUND: Smartphones contain sensors that measure movement-related data, making them promising tools for monitoring physical activity after a stroke. Activity recognition (AR) systems are typically trained on movement data from healthy individuals...

Deep Learning for Plant Identification in Natural Environment.

Computational intelligence and neuroscience
Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental pla...

Dermatologist-level classification of skin cancer with deep neural networks.

Nature
Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of ski...

Ubiquitous Multicriteria Clinic Recommendation System.

Journal of medical systems
Advancements in information, communication, and sensor technologies have led to new opportunities in medical care and education. Patients in general prefer visiting the nearest clinic, attempt to avoid waiting for treatment, and have unequal preferen...

Prediction of telephone calls load using Echo State Network with exogenous variables.

Neural networks : the official journal of the International Neural Network Society
We approach the problem of forecasting the load of incoming calls in a cell of a mobile network using Echo State Networks. With respect to previous approaches to the problem, we consider the inclusion of additional telephone records regarding the act...

A Machine Learning Method for Power Prediction on the Mobile Devices.

Journal of medical systems
Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption o...

Daytime Data and LSTM can Forecast Tomorrow's Stress, Health, and Happiness.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Accurately forecasting well-being may enable people to make desirable behavioral changes that could improve their future well-being. In this paper, we evaluate how well an automated model can forecast the next-day's well-being (specifically focusing ...

Post hoc support vector machine learning for impedimetric biosensors based on weak protein-ligand interactions.

The Analyst
Impedimetric biosensors for measuring small molecules based on weak/transient interactions between bioreceptors and target analytes are a challenge for detection electronics, particularly in field studies or in the analysis of complex matrices. Prote...