AIMC Topic: Machine Learning

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A Method for Sensor-Based Activity Recognition in Missing Data Scenario.

Sensors (Basel, Switzerland)
Sensor-based human activity recognition has various applications in the arena of healthcare, elderly smart-home, sports, etc. There are numerous works in this field-to recognize various human activities from sensor data. However, those works are base...

Using diffusion tensor imaging to detect cortical changes in fronto-temporal dementia subtypes.

Scientific reports
Fronto-temporal dementia (FTD) is a common type of presenile dementia, characterized by a heterogeneous clinical presentation that includes three main subtypes: behavioural-variant FTD, non-fluent/agrammatic variant primary progressive aphasia and se...

A machine learning workflow for raw food spectroscopic classification in a future industry.

Scientific reports
Over the years, technology has changed the way we produce and have access to our food through the development of applications, robotics, data analysis, and processing techniques. The implementation of these approaches by the food industry ensure qual...

Molecular docking and machine learning analysis of Abemaciclib in colon cancer.

BMC molecular and cell biology
BACKGROUND: The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design ...

Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations.

Science advances
Inefficient injection of microparticles through conventional hypodermic needles can impose serious challenges on clinical translation of biopharmaceutical drugs and microparticle-based drug formulations. This study aims to determine the important fac...

Probing the characteristics and biofunctional effects of disease-affected cells and drug response via machine learning applications.

Critical reviews in biotechnology
Drug-induced transformations in disease characteristics at the cellular and molecular level offers the opportunity to predict and evaluate the efficacy of pharmaceutical ingredients whilst enabling the optimal design of new and improved drugs with en...

Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet.

Biochemical and biophysical research communications
We propose an image based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wrinkles when cells grab the substrate and contract, and the wrinkles can be used to visualize the for...

Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers.

ACS sensors
Extracellular vesicles (EVs) are a frontier class of circulating biomarkers for the diagnosis and prognosis of different diseases. These lipid structures afford various biomarkers such as the concentrations of the EVs () themselves and carried protei...

Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set.

Proceedings of the National Academy of Sciences of the United States of America
Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed...