State of the art and challenges for the classification of studies on electromechanical and robotic devices in neurorehabilitation: a scoping review

2021 ◽  
Vol 57 (5) ◽  
Author(s):  
Marialuisa GANDOLFI ◽  
Nicola VALÈ ◽  
Federico POSTERARO ◽  
Giovanni MORONE ◽  
Antonella DELL’ORCO ◽  
...  
Keyword(s):  
Scoping Review ◽  
State Of The Art ◽  
Robotic Devices

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

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.

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

Energies ◽  
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.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

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.

Hydrogel-Based Bioinks for Three-Dimensional Bioprinting: Patent Analysis

2021 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Ahmed Fatimi
Keyword(s):  
Tissue Engineering ◽  
Detailed Analysis ◽  
State Of The Art ◽  
Three Dimensional ◽  
Patent Analysis ◽  
The State ◽  
Driving Factors ◽  
3D Bioprinting ◽  
Preparation Methods

There are a variety of hydrogel-based bioinks commonly used in three-dimensional bioprinting. In this study, in the form of patent analysis, the state of the art has been reviewed by introducing what has been patented in relation to hydrogel-based bioinks. Furthermore, a detailed analysis of the patentability of the used hydrogels, their preparation methods and their formulations, as well as the 3D bioprinting process using hydrogels, have been provided by determining publication years, jurisdictions, inventors, applicants, owners, and classifications. The classification of patents reveals that most inventions intended for hydrogels used as materials for prostheses or for coating prostheses are characterized by their function or properties Knowledge clusters and expert driving factors show that biomaterials, tissue engineering, and biofabrication research is concentrated in the most patents.

scholarly journals An Analysis of State-of-the-art Activation Functions For Supervised Deep Neural Network

2021 ◽  
Author(s):  
Anh Nguyen ◽  
Khoa Pham ◽  
Dat Ngo ◽  
Thanh Ngo ◽  
Lam Pham
Keyword(s):  
Neural Network ◽  
Supervised Classification ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Network Architectures ◽  
Activation Functions ◽  
Scene Classification ◽  
Learning Network ◽  
Deep Learning Network

This paper provides an analysis of state-of-the-art activation functions with respect to supervised classification of deep neural network. These activation functions comprise of Rectified Linear Units (ReLU), Exponential Linear Unit (ELU), Scaled Exponential Linear Unit (SELU), Gaussian Error Linear Unit (GELU), and the Inverse Square Root Linear Unit (ISRLU). To evaluate, experiments over two deep learning network architectures integrating these activation functions are conducted. The first model, basing on Multilayer Perceptron (MLP), is evaluated with MNIST dataset to perform these activation functions.Meanwhile, the second model, likely VGGish-based architecture, is applied for Acoustic Scene Classification (ASC) Task 1A in DCASE 2018 challenge, thus evaluate whether these activation functions work well in different datasets as well as different network architectures.

scholarly journals nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift

2021 ◽  
Vol 15 ◽  
Author(s):  
Gianluca Susi ◽  
Luis F. Antón-Toro ◽  
Fernando Maestú ◽  
Ernesto Pereda ◽  
Claudio Mirasso
Keyword(s):  
State Of The Art ◽  
Classification Problem ◽  
Mathematical Framework ◽  
Recognition Mechanism ◽  
Specific Input ◽  
Biological Learning ◽  
Time And Energy ◽  
Spike Latency ◽  
Spike Sequence

The recent “multi-neuronal spike sequence detector” (MNSD) architecture integrates the weight- and delay-adjustment methods by combining heterosynaptic plasticity with the neurocomputational feature spike latency, representing a new opportunity to understand the mechanisms underlying biological learning. Unfortunately, the range of problems to which this topology can be applied is limited because of the low cardinality of the parallel spike trains that it can process, and the lack of a visualization mechanism to understand its internal operation. We present here the nMNSD structure, which is a generalization of the MNSD to any number of inputs. The mathematical framework of the structure is introduced, together with the “trapezoid method,” that is a reduced method to analyze the recognition mechanism operated by the nMNSD in response to a specific input parallel spike train. We apply the nMNSD to a classification problem previously faced with the classical MNSD from the same authors, showing the new possibilities the nMNSD opens, with associated improvement in classification performances. Finally, we benchmark the nMNSD on the classification of static inputs (MNIST database) obtaining state-of-the-art accuracies together with advantageous aspects in terms of time- and energy-efficiency if compared to similar classification methods.

scholarly journals State-of-the-art on monolingual lexicography for Croatia (Croatian)

2019 ◽  
Vol 7 (1) ◽  
pp. 65-77
Author(s):  
Kristina Štrkalj Despot ◽  
Lana Hudeček ◽  
Tomislav Stojanov ◽  
Nikola Ljubešić
Keyword(s):  
State Of The Art ◽  
The State ◽  
Writing Systems ◽  
Insight Into

In this minireview, the state of the art of the Croatian monolingual lexicography is presented. A brief overview and classification of all existing lexicographic resources is provided in the firts part of the minireview, followed by somewhat more detailed insight into the existing Croatian monolingual dictionaries and monolingual lexicographic projects, orthography dictionaries, and dictionary writing systems used.

scholarly journals ICF Personal Factors Strengthen Commitment to Person-Centered Rehabilitation – A Scoping Review

2021 ◽  
Vol 2 ◽  
Author(s):  
Maarit Karhula ◽  
Sari Saukkonen ◽  
Essi Xiong ◽  
Anu Kinnunen ◽  
Tuija Heiskanen ◽  
...  
Keyword(s):  
Scoping Review ◽  
International Classification Of Functioning ◽  
Personal Factors ◽  
Inclusion Criteria ◽  
Physical Context ◽  
Holistic Understanding ◽  
Socio Demographic Factors ◽  
And Behavior

Background: The International Classification of Functioning, Disability and Health (ICF) classification is a biopsychosocial frame of reference that contributes to a holistic understanding of the functioning of a client and the factors involved. Personal factors (PFs) are not currently classified in the ICF due to large societal and cultural diversity and lack of clarity in the scope of such factors.Aims: To ascertain which factors in the ICF classification have been defined as PFs in different studies and what conclusions have been drawn on their role in the ICF classification.Methods: The study was a scoping review. A systematic search for articles published in 2010–2020 was performed on the Cinahl, Pubmed, ScienceDirect, and Sport Discus databases. The PFs specified in the articles were classified according to the seven categories proposed by Geyh et al. socio-demographic factors; position in the immediate social and physical context; personal history and biography; feelings; thoughts and beliefs; motives; and general patterns of experience and behavior.Results: The search yielded 1,988 studies, of which 226 met the inclusion criteria. The studies had addressed a wide variety of PFs that were linked to all seven categories defined by Geyh et al. Some studies had also defined PFs that were linkable to other components of the ICF or that did not describe functioning. Approximately 22% (51) of the studies discussed the role of PFs in rehabilitation.Conclusions: The range of PFs in the ICF classification addressed in the reviewed studies is wide. PFs play an important role in rehabilitation. However, according to the reviewed studies, a more precise coding of PFs is not yet warranted.

scholarly journals Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

2018 ◽  
Author(s):  
Arghavan Bahadorinejad ◽  
Ivan Ivanov ◽  
Johanna W Lampe ◽  
Meredith AJ Hullar ◽  
Robert S Chapkin ◽  
...  
Keyword(s):  
16S Rrna ◽  
Sample Size ◽  
Microbial Communities ◽  
State Of The Art ◽  
Metagenomic Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Sample Data

AbstractWe propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.Author summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

Sign in / Sign up

Export Citation Format

Share Document