Hierarchical, Multi-Sensor Based Classification of Daily Life Activities: Comparison with State-of-the-Art Algorithms Using a Benchmark Dataset

BACKGROUND Dementia is a general term for various disorders characterized by memory impairment and loss of at least one cognitive domain. People with dementia are faced with different difficulties in their daily life activities (DLA). With the use of modern technologies, such as mobile phone apps – often called health apps, their difficulties can be alleviated. OBJECTIVE The aim of this paper was to systematically search, analyze and synthetize mobile phone apps designed to support people with mild dementia in daily life activities in two apps bases: Apple App Store and Google Play Store. METHODS A search was conducted in May 2019 following PRISMA recommendations. Results were analyzed and displayed as tables and graphs. Results were synthetized using thematic analysis which was conducted from 14 components, based on human needs for categorized nursing activities. Mobile phone apps were assessed for quality using the System Usability Scale. RESULTS A total of 15 mobile phone apps were identified applying inclusion and exclusion criteria. Five major themes were identified with thematic analysis: multi-component DLA, communication and feelings, recreation, eating and drinking, and movement. Most of the apps (73%) of the apps were not mentioned in scientific literature. CONCLUSIONS There are many mobile phone apps available in mobile phone markets for the support for people with mild dementia; yet only a few of them are focused on challenges in daily life activities. Most of the available apps were not evaluated nor assessed for quality.

Download Full-text

Improving Land Cover Classification Using Genetic Programming for Feature Construction

Remote Sensing ◽

10.3390/rs13091623 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1623

Author(s):

João E. Batista ◽

Ana I. R. Cabral ◽

Maria J. P. Vasconcelos ◽

Leonardo Vanneschi ◽

Sara Silva

Keyword(s):

Land Cover ◽

Genetic Programming ◽

Satellite Images ◽

State Of The Art ◽

Binary Classification ◽

Feature Construction ◽

Classification Problems ◽

Construction Methods ◽

Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

Download Full-text

Review of Control and Energy Management Approaches in Micro-Grid Systems

Energies ◽

10.3390/en14010168 ◽

2020 ◽

Vol 14 (1) ◽

pp. 168

Author(s):

Abdellatif Elmouatamid ◽

Radouane Ouladsine ◽

Mohamed Bakhouya ◽

Najib El Kamoun ◽

Mohammed Khaidar ◽

...

Keyword(s):

Energy Management ◽

Predictive Control ◽

State Of The Art ◽

Household Demand ◽

Grid Systems ◽

Control Approach ◽

Micro Grid ◽

Management Approaches ◽

Air Conditioning Systems

The demand for electricity is increased due to the development of the industry, the electrification of transport, the rise of household demand, and the increase in demand for digitally connected devices and air conditioning systems. For that, solutions and actions should be developed for greater consumers of electricity. For instance, MG (Micro-grid) buildings are one of the main consumers of electricity, and if they are correctly constructed, controlled, and operated, a significant energy saving can be attained. As a solution, hybrid RES (renewable energy source) systems are proposed, offering the possibility for simple consumers to be producers of electricity. This hybrid system contains different renewable generators connected to energy storage systems, making it possible to locally produce a part of energy in order to minimize the consumption from the utility grid. This work gives a concise state-of-the-art overview of the main control approaches for energy management in MG systems. Principally, this study is carried out in order to define the suitable control approach for MGs for energy management in buildings. A classification of approaches is also given in order to shed more light on the need for predictive control for energy management in MGs.

Download Full-text

A Machine Vision Approach for Bioreactor Foam Sensing

SLAS TECHNOLOGY Translating Life Sciences Innovation ◽

10.1177/24726303211008861 ◽

2021 ◽

pp. 247263032110088

Author(s):

Jonas Austerjost ◽

Robert Söldner ◽

Christoffer Edlund ◽

Johan Trygg ◽

David Pollard ◽

...

Keyword(s):

Machine Learning ◽

Machine Vision ◽

State Of The Art ◽

Low Cost ◽

High Accuracy ◽

Consumer Electronics ◽

Learning System ◽

Automotive Applications ◽

Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

Download Full-text