scholarly journals Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5283 ◽  
Author(s):  
Muhammad Tariq Sadiq ◽  
Xiaojun Yu ◽  
Zhaohui Yuan ◽  
Muhammad Zulkifal Aziz
Keyword(s):  
Neural Network ◽  
Mental Imagery ◽  
Practical Applications ◽  
The Public ◽  
Series Of Experiments ◽  
Neural Network Classifiers ◽  
Public Datasets ◽  
Dependent Tasks ◽  
First Time

The development of fast and robust brain–computer interface (BCI) systems requires non-complex and efficient computational tools. The modern procedures adopted for this purpose are complex which limits their use in practical applications. In this study, for the first time, and to the best of our knowledge, a successive decomposition index (SDI)-based feature extraction approach is utilized for the classification of motor and mental imagery electroencephalography (EEG) tasks. First of all, the public datasets IVa, IVb, and V from BCI competition III were denoised using multiscale principal analysis (MSPCA), and then a SDI feature was calculated corresponding to each trial of the data. Finally, six benchmark machine learning and neural network classifiers were used to evaluate the performance of the proposed method. All the experiments were performed for motor and mental imagery datasets in binary and multiclass applications using a 10-fold cross-validation method. Furthermore, computerized automatic detection of motor and mental imagery using SDI (CADMMI-SDI) is developed to describe the proposed approach practically. The experimental results suggest that the highest classification accuracy of 97.46% (Dataset IVa), 99.52% (Dataset IVb), and 99.33% (Dataset V) was obtained using feedforward neural network classifier. Moreover, a series of experiments, namely, statistical analysis, channels variation, classifier parameters variation, processed and unprocessed data, and computational complexity, were performed and it was concluded that SDI is robust for noise, and a non-complex and efficient biomarker for the development of fast and accurate motor and mental imagery BCI systems.

scholarly journals Detection of Fake Doctored Images used to Circulate Fake News

2020 ◽  
Vol 9 (7) ◽  
pp. 1031-1036
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Neural Network Models ◽  
The Public ◽  
Learning Techniques ◽  
The World ◽  
Classification Of Images

Over the few years the world has seen a surge in fake news and some people are even calling it an epidemic. Misleading false articles are sold as news items over social media, whatsapp etc where no proper barrier is set to check the authenticity of posts. And not only articles but news items also contain images which are doctored to mislead the public or cause sabotage. Hence a proper barrier to check for authenticity of images related to news items is absolutely necessary. And hence classification of images(related to news items) on the basis of authenticity is imminent. This paper discusses the possibilities of identifying fake images using machine learning techniques. This is an introduction into fake news detection using the latest evolving neural network models

An Approach of Anomaly Detection and Neural Network Classifiers to Measure Cellulolytic Activity

2019 ◽  
Vol 21 (9) ◽  
pp. 681-692
Author(s):  
Luis Francisco Barbosa-Santillán ◽  
María de los Angeles Calixto-Romo ◽  
Juan Jaime Sánchez-Escobar ◽  
Liliana Ibeth Barbosa-Santillán
Keyword(s):  
Neural Network ◽  
Enzymatic Activity ◽  
Real Time ◽  
High Throughput Screening ◽  
Cellulolytic Activity ◽  
Color Patterns ◽  
Cellulolytic Microorganisms ◽  
The Neural Network ◽  
Neural Network Classifiers

Aim and Objective: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work was to develop a method of computational analysis of the visual patterns created by cellulolytic activity through artificial neural networks description. Materials and Methods: Our method learns by an adaptive prediction model and automatically determines when enzymatic activity on a chromogenic indicator such as the hydrolysis halo occurs. To achieve this goal, we generated a data library with absorbance readings and RGB values of enzymatic hydrolysis, obtained by spectrophotometry and a prototype camera-based equipment (Enzyme Vision), respectively. We used the first part of the library to generate a linear regression model, which was able to predict theoretical absorbances using the RGB color patterns, which agreed with values obtained by spectrophotometry. The second part was used to train, validate, and test the neural network model in order to predict cellulolytic activity based on color patterns. Results: As a result of our model, we were able to establish six new descriptors useful for the prediction of the temporal changes in the enzymatic activity. Finally, our model was evaluated on one halo from cellulolytic microorganisms, achieving the regional classification of the generated halo in three of the six classes learned by our model. Conclusion: We assume that our approach can be a viable alternative for high throughput screening of enzymatic activity in real time.

Criteria for comparing artificial intelligence systems

2021 ◽  
pp. 21-27
Author(s):  
В.А. Пятакович ◽  
В.Ф. Рычкова ◽  
Н.Г. Левченко
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Model ◽  
Neural Network Models ◽  
Actual Problem ◽  
Neuro Fuzzy ◽  
Classification Of Images ◽  
Fuzzy Network ◽  
First Time

Модели нейронных и нейро-нечетких сетевых критериев сравнения в задачах диагностики и классификации образов. Предложен комплекс критериев для оценки свойств искусственных нейронных и нейро-нечетких сетей. Он включает в себя критерии разнообразия, подгонки, эластичности, равнозначности, устойчивости к шуму, аварийной ситуации, а также заданную монотонность для построения нейронной модели. Применение предложенных критериев на практике позволяет автоматизировать процесс построения, анализа и сравнения нейронных моделей для решения задач диагностики и классификации паттернов. Предложено решение задачи повышения эффективности параметрического синтеза нейросетевых моделей сложных систем для обоснованного принятия решений о классификации подводных целей. Научная новизна работы заключается в том, что впервые предложен комплекс моделей критериев, характеризующих такие свойства нейронных и нейро-нечетких сетей как разнообразие, переобученность, эластичность, эквифинальность, устойчивость к шуму, эмерджентность, что позволяет автоматизировать решение задачи анализа свойств и сравнения нейросетевых и нейро-нечетких моделей при решении задач диагностики и классификации образов. В работе решена актуальная задача автоматизации анализа свойств и сравнения нейросетевых моделей. Models of neural and neuro-fuzzy network comparison criterions in the tasks of diagnostics and pattern classification. The complex of criterions for an estimation of properties artificial neural and neuro-fuzzy networks is proposed. It includes criterions of variety, overfitting, elasticity, equifinality, stability to a noise, emergency, and also set monotonicity for a neural model construction. The application of offered criterions in practice allows to automatize the process of a construction, analysis and comparison of neural models for problem solving of diagnostics and patternt classification. The solution of the problem of increasing the efficiency of parametric synthesis of neural network models of complex systems for informed decision-making on the classification of underwater targets is proposed. The scientific novelty of the work lies in the fact that for the first time a set of models of criteria characterizing such properties of neural and neuro-fuzzy networks as diversity, retraining, elasticity, equifinality, noise resistance, emergence is proposed, which allows automating the solution of the problem of analyzing the properties and comparing neural network and neuro-fuzzy models when solving problems of diagnostics and classification of images. The paper solves the actual problem of automating the analysis of properties and comparison of neural network models.

Artificial neural network ensembles for fatigue damage detection in aircraft

2016 ◽  
Vol 28 (7) ◽  
pp. 851-861 ◽  
Author(s):  
Ziemowit Dworakowski ◽  
Krzysztof Dragan ◽  
Tadeusz Stepinski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fatigue Cracks ◽  
Guided Wave ◽  
Multidimensional Data ◽  
Neural Network Ensemble ◽  
Practical Applications ◽  
The Neural Network ◽  
Neural Network Ensembles

Neural networks are commonly recognized tools for the classification of multidimensional data obtained in structural health monitoring (SHM) systems. Their configuration for a given scenario is, however, a challenging task, which limits the possibilities of their practical applications. In this article the authors propose using the neural network ensemble approach for the classification of SHM data generated by guided wave sensor networks. The overproduce and choose strategy is used for designing ensembles containing different types and sizes of neural networks. The proposed method allows for a significant increase of the state assessment reliability, which is illustrated by the results obtained from the practical industrial case of a full-scale aircraft test. The method is verified in the process of detecting fatigue cracks propagating in the aircraft load-carrying structure. The long-term experiments are performed in variable environmental conditions with a net of structure-embedded piezoelectric sensors.

A New Approach Hybrid Based in Artificial Neural Networks to Detection and Classification of Failures in Aeronautical Structures

2015 ◽  
Vol 763 ◽  
pp. 175-181
Author(s):  
Simone Silva Frutuoso Souza ◽  
Fernando Parra dos Anjos Lima ◽  
Fábio Roberto Chavarette
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
New Approach ◽  
Inspection Process ◽  
Artificial Neural ◽  
Aluminum Beam ◽  
First Time ◽  
Hybrid Methodology

In this paper presents a new hybrid methodology to perform fault detection and classification of aircraft structures using the tool as ARTMAP-Fuzzyand Perceptron multi-layer artificial neural networks. This method is divided into two steps, the first step performed by the multi-layer Perceptron neural network, which consists in the detection of abnormalities in the structure. The second step is performed by ARTMAP-Fuzzyneural network and consists of the classification of faults structural detected in the first time. The main application of this hybrid methodology is to assist in the inspection process of aeronautical structures in order to identify and characterize flaws as well, make decision-making in order to avoid accidents or air crashes. To evaluate this method, the modeling and simulation was carried out signals from a numerical model of an aluminum beam. The results obtained by the methodology demonstrating robustness and accuracy structural flaws.

scholarly journals EEG BASED COGNITIVE WORKLOAD CLASSIFICATION DURING NASA MATB-II MULTITASKING

2015 ◽  
Vol 3 (1) ◽  
pp. 35-41
Author(s):  
Sushil Chandra ◽  
Kundan Lal Verma ◽  
Greeshma Sharma ◽  
Alok Mittal ◽  
Devendra Jha
Keyword(s):  
Neural Network ◽  
Load Balancing ◽  
Cognitive Workload ◽  
Spectral Features ◽  
Scoring Method ◽  
Neural Network Classification ◽  
Neural Network Classifiers

The objective of this experiment was to determine the best possible input EEG feature for classification of the workload while designing load balancing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjective scoring. Method utilizes to identify best EEG feature as an input in Neural Network Classifiers for workload classification, to identify channels which could provide classification with the highest accuracy and for identification of EEG feature which could give discrimination among workload level without adding any classifiers. The result had shown Engagement Index is the best feature for neural network classification.

scholarly journals Public Policy Analysis of Health System in Conditions of the Greek Economic Crisis

2017 ◽  
Vol 13 (7) ◽  
pp. 41
Author(s):  
Georgios I. Farantos ◽  
Nikitas Spiros Koutsoukis
Keyword(s):  
Public Policy ◽  
Policy Analysis ◽  
Health System ◽  
Economic Crisis ◽  
Doctoral Studies ◽  
Practical Applications ◽  
The Public ◽  
Literature Overview ◽  
Public Policy Analysis

The aim of the study is to provide a literature overview of the Public Policy Analysis of the Greek Health System during the Economic Crisis, so that the generated knowledge can be used in Doctoral studies. The research methodology extends to the literature overview of valid Greek and international literature. The Findings of the study include summarising and categorising information related to the categories of public policies and those involved in policy making. Studies on policy changes in conditions of an economic crisis are reviewed. The Policy Change in the Greek Health System during the economic crisis is studied and placed within a pre-existing framework for classification of health system policies. Study limitation. The study specialises in the policy of the Greek Health System during the Economic Crisis and cannot be generalised as such for health systems of other countries. The studys practical applications are that it may constitute a useful literature review for students who want to study the Policy of the Greek Health System during the Economic Crisis and it also leaves room for further future study of this field. Value. The present study is an innovative research, it is a source of collected information and offers practical help in the study of the field.

scholarly journals Cholera as a present-day concern in perception of the dwellers of the county town of Tsaritsyn (based on the materials of the newspaper “Volzhsko-Donskoy Listok”)

2021 ◽  
pp. 151-168
Author(s):  
Irina Vasil'evna Maksimova
Keyword(s):  
Traditional Approach ◽  
Interdisciplinary Approach ◽  
The Public ◽  
Social Nature ◽  
Cholera Epidemic ◽  
History Of ◽  
Xix Century ◽  
First Time ◽  
County Town

The object of this research is the regional press as a source on the history of cholera epidemics of the late XIX century. The subject is the public moods and behavioral practices of the dwellers of the county town of Saratov governorate – Tsaritsyn, which was affected by cholera epidemic of 1892, in coverage of the regional press. The historiographical framework consists on the versatile and unique materials of the municipal newspaper "Volzhsko-Donskoy Listok", which are introduced into the scientific discourse for the first time. A detailed analysis of the annual publications of local press allowed tracing the chronology of the traditional approach towards perception of the poorly studied in literature cholera epidemics of 1892 in Tsaritsyn in the context of studying the history of everyday life. The author reveals this topic up until September 1893. Special attention is turned to examination of different stereotypical models of behavior that appeared to be widespread among the population, and became a somewhat response to the emerged situation of biological and social nature. The research is based on the interdisciplinary approach that implies comprehensive examination of the problem, involving the achievements in the history of medicine, historiography, psychology, sociology, etc. This article is the first attempt within the regional historiography to holistically examine the cholera epidemic of 1892 in Tsaritsyn. The conducted analysis of the newspaper “Volzhsko-Donskoy Listok” allowed to highly rate the informative capabilities of the regional press for studying cholera epidemics and their impact on life of the population in the post-reform period. The author offers the original classification of the whole variety of materials on the topic, as well as outlines the ways for their further usage.

scholarly journals Lesion Detection and Classification techniques for Diabetic Retinopathy

2020 ◽  
Vol 9 (3) ◽  
pp. 3436-3441
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Lesion Detection ◽  
Classification Techniques ◽  
The Public ◽  
Detection Systems ◽  
Regular Screening ◽  
Public Datasets

Diabetes is a worldwide spread disease which is increasing rapidly and found in all age people. Diabetic Retinopathy is a retinal abnormality caused by diabetes. Which can lead to permanent vision loss or blindness. As Diabetic Retinopathy pathology damages retina without any early symptoms, it is very important to do the regular screening of retina and detection of Retinopathy. Ophthalmologist does the identification of Retinopathy manually which is time consuming and error prone. Hence, there is a need for early and correct automatic detection of Diabetic Retinopathy. Many researches have done for detection using Image Processing, Artificial Intelligence, Neural Network and Machine Learning. This paper presents a review on Diabetic Retinopathy Detection systems. This review highlights the public datasets available for the evaluation of the detection systems with different segmentation and classification techniques. We have discussed the analysis of different classification and segmentation techniques used in DR detection.

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