scholarly journals Deep learning and machine learning to recognizing the disease of alcoholism by EEG signal processing

2021 ◽  
Author(s):  
ildar Rakhmatulin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Private Information ◽  
Wavelet Transforms ◽  
Physical Dependence ◽  
Personal Data ◽  
Diagnosis Method ◽  
The Subject ◽  
The One ◽  
And Control

Alcoholism is one of the most common diseases in the world. This type of substance abuse leads to mental and physical dependence on ethanol-containing drinks. Alcoholism is accompanied by progressive degradation of the personality and damage to the internal organs. Today still not exists a quick diagnosis method to detect this disease. This article presents the method for the quick and anonymous alcoholism diagnosis by neural networks. For this method, don't need any private information about the subject. For the implementation, we considered various algorithms of machine learning and deep neural networks. In detail analyzed the correlation of the signals from electrodes by neural networks. The wavelet transforms and the fast Fourier transform was considered. The manuscript demonstrates that the deep neural network which operates only with a dataset of EEG correlation signals can anonymously classify the alcoholic and control groups with high accuracy. On the one hand, this method will allow subjects to be tested for alcoholism without any personal data, which will not cause inconvenience or shame in the subject, and on the other hand, the subject will not be able to deceive specialists who diagnose the subject for the presence of the disease.

scholarly journals Analysis of Risk Factors in Dementia Through Machine Learning

2020 ◽  
pp. 1-17
Author(s):  
Francisco Javier Balea-Fernandez ◽  
Beatriz Martinez-Vega ◽  
Samuel Ortega ◽  
Himar Fabelo ◽  
Raquel Leon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optimization Algorithms ◽  
Progressive Increase ◽  
Control Group ◽  
Data Sets ◽  
Modifiable Factors ◽  
Validation Set ◽  
The One ◽  
And Control ◽  
Potential Tool

Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer’s disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable. Objective: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD). Methods: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD. Results: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier. Conclusion: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.

scholarly journals Is There a Predisposition towards the Use of New Technologies within the Traffic Field of Emerging Countries? The Case of the Dominican Republic

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1208
Author(s):  
Francisco Alonso ◽  
Mireia Faus ◽  
Cristina Esteban ◽  
Sergio A. Useche
Keyword(s):  
Dominican Republic ◽  
Road Safety ◽  
New Technologies ◽  
Technological Development ◽  
Personal Data ◽  
Emerging Countries ◽  
Level Of Knowledge ◽  
One Year ◽  
The One ◽  
And Control

Technological devices are becoming more and more integrated in the management and control of traffic in big cities. The population perceives the benefits provided by these systems, and, therefore, citizens usually have a favorable opinion of them. However, emerging countries, which have fewer available infrastructures, could present a certain lack of trust. The objective of this work is to detect the level of knowledge and predisposition towards the use of new technologies in the transportation field of the Dominican Republic. For this study, the National Survey on Mobility was administered to a sample of Dominican citizens, proportional to the ONE census and to sex, age and province. The knowledge of ITS topics, as well as the use of mobile applications for mobility, are scarce; however, there was a significant increase that can be observed in only one year. Moreover, technology is, in general, positively assessed for what concerns the improvement of the traffic field, even though there is a lack of predisposition to provide one’s personal data, which is necessary for these devices. The process of technological development in the country must be backed up by laws that protect the citizens’ privacy. Thus, technologies that can improve road safety, mobility and sustainability can be implemented in the country.

scholarly journals Competences of teachers of natural-physical sciences. An analysis from the perception of the students

2020 ◽  
Vol 1674 (1) ◽  
pp. 012020
Author(s):  
R Prada-Núñez ◽  
E T Ayala ◽  
W R Avendaño-Castro
Keyword(s):  
Educational Institution ◽  
Evaluation Process ◽  
Future Research ◽  
Physical Sciences ◽  
Four Dimensions ◽  
State Tests ◽  
Need To Evaluate ◽  
The Subject ◽  
The One ◽  
And Control

Abstract This article arises as a proposal in view of the need to evaluate the scientific competences promoted by teachers of the subject of physics at the level of basic secondary and secondary technical education. A valid questionnaire was designed from the application of scalar analysis, factorial analysis and content analysis, which is composed of 49 items evaluated by means of a Likert scale with five levels of response. It was applied in a sample of 249 students enrolled in a public educational institution during 2019, characterized by their good results in the area of physics in state tests. The results allowed the identification of strengths in the four dimensions proposed by the Ministerio de Educación Nacional, Colombia (pedagogical, didactic, disciplinary and behavioural), in contrast with some weaknesses within which the evaluation process stands out as the one with the greatest impact, since the students state that this process is assumed by the teacher as a mechanism of pressure and control. When investigating the teachers in a complementary way, positions were determined that were totally opposite to those held by the students, then it is suggested for future research to consider both the students and the teachers as informants and a supervision of the students’ notes as the end of triangulating the results to refine the conclusions, on which future improvement plans will depend.

Locally Recurrent Neural Networks and Their Applications

2010 ◽  
pp. 195-222
Author(s):  
Todor D. Ganchev
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Computational Models ◽  
Machine Learning Techniques ◽  
Spatial Correlations ◽  
Temporal Correlations ◽  
Learning Techniques ◽  
Locally Recurrent ◽  
The One ◽  
And Control

In this chapter we review various computational models of locally recurrent neurons and deliberate the architecture of some archetypal locally recurrent neural networks (LRNNs) that are based on them. Generalizations of these structures are discussed as well. Furthermore, we point at a number of realworld applications of LRNNs that have been reported in past and recent publications. These applications involve classification or prediction of temporal sequences, discovering and modeling of spatial and temporal correlations, process identification and control, etc. Validation experiments reported in these developments provide evidence that locally recurrent architectures are capable of identifying and exploiting temporal and spatial correlations (i.e., the context in which events occur), which is the main reason for their advantageous performance when compared with the one of their non-recurrent counterparts or other reasonable machine learning techniques.

Forensics: Assessing model goodness: A machine learning view

2018 ◽  
Vol 02 (02) ◽  
pp. 1850015 ◽  
Author(s):  
Joseph R. Barr ◽  
Joseph Cavanaugh
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Statistical Model ◽  
Statistical Learning ◽  
Statistical Models ◽  
The Subject ◽  
The Many ◽  
The One ◽  
Compare And Contrast ◽  
Learning Data

It is not unusual that efforts to validate a statistical model exceed those used to build the model. Multiple techniques are used to validate, compare and contrast among competing statistical models: Some are concerned with a model’s ability to predict new data while others are concerned with model descriptiveness of the data. Without claiming to provide a comprehensive view of the landscape, in this paper we will touch on both aspects of model validation. There is much more to the subject and the reader is referred to any of the many classical statistical texts including the revised two volumes of Bickel and Docksum (2016), the one by Hastie, Tibshirani, and Friedman [The Elements of Statistical Learning: Data Mining, Inference, and Predication, 2nd edn. (Springer, 2009)], and several others listed in the bibliography.

Some Suggestions respecting the Care of the Feeble-minded under the Mental Deficiency Bill, 1913

1913 ◽  
Vol 59 (246) ◽  
pp. 487-492 ◽  
Author(s):  
A. R. Douglas
Keyword(s):  
Public Opinion ◽  
Mental Deficiency ◽  
The Other ◽  
House Of Commons ◽  
Standing Committee ◽  
The Face ◽  
The Subject ◽  
The One ◽  
And Control

In dealing with any subject in connection with the burning question of the care and control of the feeble-minded, some reference will be expected to the second Mental Deficiency Bill recently introduced into the House of Commons by the Home Secretary. For the purposes of this paper it is unnecessary to do more than quote the Clause, which defines the classes of persons who are mentally defective and deemed to be defectives within the meaning of the Act. Taken all round, it is a much better Bill than its predecessor of last year, but it should be noted that in the present measure no allusion is made to the undesirability of procreation of children by defectives, or to any intention to penalise persons wittingly bringing about a marriage between defectives. These proposals, which were likely to arouse uncompromising disapproval, may be the less regretted, as their inclusion might doubtless have been instrumental in the blocking of the Bill as a whole. Their effacement, it is hoped, may do away with the opposition which is at present invariably evoked by any attempt to infringe upon the so-called liberty of the subject, and may also give opportunity for educating public opinon, so that in time it may be clear to all that the prevention of amentia can only be attained by life segregation on the one hand, and by the prohibition of marriage on the other. The promoters of the Bill have gone as far as they possibly could in the face of uneducated public opinion, and those of us who were present at the discussion of last year's measure in Standing Committee cannot but admire the courage and resourcefulness of Mr. McKenna in presenting the new Bill after the repeated discouragement which he had to face in connection with his first effort last year.

scholarly journals Network Anomaly Detection Using Machine Learning Techniques

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 8
Author(s):  
Julio J. Estévez-Pereira ◽  
Diego Fernández ◽  
Francisco J. Novoa
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Machine Learning Techniques ◽  
Security Threats ◽  
Learning Techniques ◽  
Distribution Phase ◽  
The One ◽  
Network Anomaly Detection ◽  
And Storage

While traditional network security methods have been proven useful until now, the flexibility of machine learning techniques makes them a solid candidate in the current scene of our networks. In this paper, we assess how well the latter are capable of detecting security threats in a corporative network. To that end, we configure and compare several models to find the one which fits better with our needs. Furthermore, we distribute the computational load and storage so we can handle extensive volumes of data. The algorithms that we use to create our models, Random Forest, Naive Bayes, and Deep Neural Networks (DNN), are both divergent and tested in other papers in order to make our comparison richer. For the distribution phase, we operate with Apache Structured Streaming, PySpark, and MLlib. As for the results, it is relevant to mention that our dataset has been found to be effectively modelable with just a reduced number of features. Finally, given the outcomes obtained, we find this line of research encouraging and, therefore, this approach worth pursuing.

scholarly journals An Adjective Selection Personality Assessment Method Using Gradient Boosting Machine Learning

Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 618 ◽  
Author(s):  
Bruno Fernandes ◽  
Alfonso González-Briones ◽  
Paulo Novais ◽  
Miguel Calafate ◽  
Cesar Analide ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Five ◽  
Personality Assessment ◽  
Rating Scale ◽  
Assessment Method ◽  
Gradient Boosting ◽  
Point Rating Scale ◽  
Gradient Boosting Machine ◽  
The Subject ◽  
The One

Goldberg’s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performed by presenting a set of markers, or adjectives, to the subject, requesting him to quantify each marker using a 9-point rating scale. Consequently, the goal of this study is to conduct experiments and propose a shorter alternative where the subject is only required to identify which adjectives describe him the most. Hence, a web platform was developed for data collection, requesting subjects to rate each adjective and select those describing him the most. Based on a Gradient Boosting approach, two distinct Machine Learning architectures were conceived, tuned and evaluated. The first makes use of regressors to provide an exact score of the Big Five while the second uses classifiers to provide a binned output. As input, both receive the one-hot encoded selection of adjectives. Both architectures performed well. The first is able to quantify the Big Five with an approximate error of 5 units of measure, while the second shows a micro-averaged f1-score of 83%. Since all adjectives are used to compute all traits, models are able to harness inter-trait relationships, being possible to further reduce the set of adjectives by removing those that have smaller importance.

scholarly journals Machine learning active-nematic hydrodynamics

2021 ◽  
Vol 118 (10) ◽  
pp. e2016708118
Author(s):  
Jonathan Colen ◽  
Ming Han ◽  
Rui Zhang ◽  
Steven A. Redford ◽  
Linnea M. Lemma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Initial Conditions ◽  
Molecular Motor ◽  
Machine Learning Algorithms ◽  
Many Body ◽  
Hydrodynamic Parameters ◽  
Many Body Systems ◽  
And Control ◽  
Macroscopic Parameters

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatiotemporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. We analyze biofilament/molecular-motor experiments with microtubule/kinesin and actin/myosin complexes as computer vision problems. Our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as adenosine triphosphate (ATP) or motor concentration. The only input needed is the orientation of the biofilaments and not the coupled velocity field which is harder to access in experiments. We can also forecast the evolution of these chaotic many-body systems solely from image sequences of their past using a combination of autoencoders and recurrent neural networks with residual architecture. In realistic experimental setups for which the initial conditions are not perfectly known, our physics-inspired machine-learning algorithms can surpass deterministic simulations. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems, even in the absence of knowledge of the underlying dynamics.

Abstraction Through the Lens of Neuroscience

2021 ◽  
Vol 58 (2) ◽  
pp. 6-18
Author(s):  
Valentin A. Bazhanov ◽  
Keyword(s):  
Neural Networks ◽  
Brain Networks ◽  
The Other ◽  
Activity Type ◽  
Other Hand ◽  
The Subject ◽  
The One ◽  
Concrete Concepts ◽  
The Brain ◽  
Different Levels

The interpretation of the abstraction process and the use of various abstractions are consistent with the trends associated with the naturalistic turn in modern cognitive and neural studies. Logic of dealing with abstractions presupposes not only acts of digress from the insignificant details of the object, but also the replenishment of the image due to idealization, endowing the object with properties that are absent from it. Thus, abstraction expresses not only the activity of the subject but the fact of “locking” this activity on a certain kind of ontology as well. The latter, in the spirit of I. Kant’s apriorism, is a function of epistemological attitudes and the nature of the subject's activity. Therefore, in the context of modern neuroscience, we can mean the transcendentalism of activity type. An effective tool for comprehension of abstractions making and development is a metaphor, which, on the one hand, allows submerge the object of analysis into a more or less familiar context, and on the other hand, it may produce new abstractions. Naturalistic tendencies manifested in the fact that empirically established abstractions activate certain neural brain networks, and abstract and concrete concepts are "processed" by various parts of the brain. If we keep in mind the presence of different levels abstractions then not only neural networks but even individual neurons (called “conceptual”) can be excited. The excitation of neural networks is associated with understanding the meaning of some concepts, but at the same time, the activity of these networks presupposes the "dissection" of reality due to a certain angle, determined in the general case by goals, attitudes and concrete practices of the subject.

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