Intelligent Models to Predict the Prognosis of Premature Neonates According to Their EEG Signals

2020 ◽  
pp. 830-840
Author(s):  
Yasser Al Hajjar ◽  
Abd El Salam Ahmad Al Hajjar ◽  
Bassam Daya ◽  
Pierre Chauvet
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Linear Discriminant ◽  
Premature Newborns ◽  
Intelligent Models ◽  
Intelligent Model ◽  
Electroencephalogram Eeg ◽  
The Brain

The aim of this paper is to find the best intelligent model that allows predicting the future of premature newborns according to their electroencephalogram (EEG). EEG is a signal that measures the electrical activity of the brain. In this paper, the authors used a dataset of 397 EEG records detected at birth of premature newborns and their classification by doctors two years later: normal, sick or risky. They executed machine learning on this dataset using several intelligent models such as multiple linear regression, linear discriminant analysis, artificial neural network and decision tree. They used 14 parameters concerning characteristics extracted from EEG records that affect the prognosis of the newborn. Then, they presented a complete comparative study between these models in order to find who gives best results. Finally, they found that decision tree gave best result with performance of 100% for sick records, 76.9% for risky and 69.1% for normal ones.

Intelligent Models to Predict the Prognosis of Premature Neonates According to Their EEG Signals

2017 ◽  
Vol 6 (1) ◽  
pp. 57-66 ◽  
Author(s):  
Yasser Al Hajjar ◽  
Abd El Salam Ahmad Al Hajjar ◽  
Bassam Daya ◽  
Pierre Chauvet
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Tree ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Premature Newborns ◽  
Intelligent Models ◽  
Intelligent Model ◽  
Electroencephalogram Eeg ◽  
The Brain

The aim of this paper is to find the best intelligent model that allows predicting the future of premature newborns according to their electroencephalogram (EEG). EEG is a signal that measures the electrical activity of the brain. In this paper, the authors used a dataset of 397 EEG records detected at birth of premature newborns and their classification by doctors two years later: normal, sick or risky. They executed machine learning on this dataset using several intelligent models such as multiple linear regression, linear discriminant analysis, artificial neural network and decision tree. They used 14 parameters concerning characteristics extracted from EEG records that affect the prognosis of the newborn. Then, they presented a complete comparative study between these models in order to find who gives best results. Finally, they found that decision tree gave best result with performance of 100% for sick records, 76.9% for risky and 69.1% for normal ones.

scholarly journals An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6928
Author(s):  
Łukasz Wojtecki ◽  
Sebastian Iwaszenko ◽  
Derek B. Apel ◽  
Tomasz Cichy
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Rock Mass ◽  
Decision Tree ◽  
Risk Prediction ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Hard Coal ◽  
Underground Excavations

Rockburst is a dynamic rock mass failure occurring during underground mining under unfavorable stress conditions. The rockburst phenomenon concerns openings in different rocks and is generally correlated with high stress in the rock mass. As a result of rockburst, underground excavations lose their functionality, the infrastructure is damaged, and the working conditions become unsafe. Assessing rockburst hazards in underground excavations becomes particularly important with the increasing mining depth and the mining-induced stresses. Nowadays, rockburst risk prediction is based mainly on various indicators. However, some attempts have been made to apply machine learning algorithms for this purpose. For this article, we employed an extensive range of machine learning algorithms, e.g., an artificial neural network, decision tree, random forest, and gradient boosting, to estimate the rockburst risk in galleries in one of the deep hard coal mines in the Upper Silesian Coal Basin, Poland. With the use of these algorithms, we proposed rockburst risk prediction models. Neural network and decision tree models were most effective in assessing whether a rockburst occurred in an analyzed case, taking into account the average value of the recall parameter. In three randomly selected datasets, the artificial neural network models were able to identify all of the rockbursts.

scholarly journals Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2499 ◽  
Author(s):  
Yue Gu ◽  
Zhenhu Liang ◽  
Satoshi Hagihira
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Brain Activity ◽  
Permutation Entropy ◽  
Depth Of Anesthesia ◽  
Spectral Edge Frequency ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Electroencephalogram Eeg ◽  
The Brain

The electroencephalogram (EEG) can reflect brain activity and contains abundant information of different anesthetic states of the brain. It has been widely used for monitoring depth of anesthesia (DoA). In this study, we propose a method that combines multiple EEG-based features with artificial neural network (ANN) to assess the DoA. Multiple EEG-based features can express the states of the brain more comprehensively during anesthesia. First, four parameters including permutation entropy, 95% spectral edge frequency, BetaRatio and SynchFastSlow were extracted from the EEG signal. Then, the four parameters were set as the inputs to an ANN which used bispectral index (BIS) as the reference output. 16 patient datasets during propofol anesthesia were used to evaluate this method. The results indicated that the accuracies of detecting each state were 86.4% (awake), 73.6% (light anesthesia), 84.4% (general anesthesia), and 14% (deep anesthesia). The correlation coefficient between BIS and the index of this method was 0.892 ( p < 0.001 ). The results showed that the proposed method could well distinguish between awake and other anesthesia states. This method is promising and feasible for a monitoring system to assess the DoA.

scholarly journals A new ML-based approach to enhance student engagement in online environment

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258788
Author(s):  
Sarra Ayouni ◽  
Fahima Hajjej ◽  
Mohamed Maddeh ◽  
Shaha Al-Otaibi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Student Engagement ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Online Environment

The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting.

scholarly journals Prediksi Hipertensi menggunakan Decision Tree, Naïve Bayes dan Artificial Neural Network pada software KNIME

Techno Com ◽  
2020 ◽  
Vol 19 (4) ◽  
pp. 353-363
Author(s):  
Mayanda Mega Santoni ◽  
Nurul Chamidah ◽  
Nurhafifah Matondang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Network Data ◽  
Artificial Neural ◽  
Fold Cross Validation

Hipertensi merupakan salah satu penyakit tidak menular yang dapat menyebabkan kematian karena meningkatkan resiko munculnya berbagai penyakit seperti gagal ginjal, gagal jantung, bahkan stroke. Resiko hipertensi disebabkan oleh beberapa faktor penyebab seperti usia, keturunan, pola makan dan olahraga, dan merokok. Teknologi artificial intelligence yakni machine learning dimanfaatkan di bidang kesehatan khususnya prediksi penyakit hipertensi. Pada penelitian ini diimplementasi tiga algoritma machine learning yakni decision tree, naïve bayes dan artificial neural networks. Data yang digunakan pada penelitian ini sebanyak 274 data yang diperoleh dari hasil kuesioner dengan 26 pertanyaan, dimana 25 pertanyaan adalah variabel faktor resiko dan satu pertanyaan merupakan kelas yang menyatakan responden memiliki riwayat hipertensi atau tidak. Data diolah menggunakan platform analisis data yakni KNIME. Sebelum data diolah untuk membangun model klasifikasi menggunakan decision tree, naïve bayes dan artificial neural network, data dipraproses terlebih dahulu dengan melakukan imputasi missing value, oversampling dan normalisasi data. Selanjutnya pembagian data menggunakan 5-fold cross validation. Model klasifikasi yang diperoleh dievaluasi menggunakan nilai akurasi, recall dan precision. Hasil evaluasi dari eksperimen yang dilakukan diperoleh bahwa algoritma artificial neural network memiliki tingkat performa lebih baik dibandingkan decision tree dan naïve bayes dengan nilai akurasi sebesar 94.7%, recall sebesar 91.5% dan precision sebesar 97.7%.

scholarly journals Performance Evaluation of Supervised Ensemble Cyber Situation Perception Models for Computer Network

2021 ◽  
Vol 12 (1) ◽  
pp. 1-14
Author(s):  
S.S. Olofintuyi ◽  
◽  
T.O. Omotehinwa ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Computer Network ◽  
Detection System ◽  
Cyber Threats ◽  
Ensemble Algorithms

The trend at which cyber threats are gaining access to companies, industries and other sectors of the economy is becoming alarming, and this is posting a serious challenge to network administrators, governments and other business owners. A formidable intrusion detection system is needed to outplay the activities of the cyberattacks. An ensemble system is believed to perform better than a single classifier. With this fact, five different Machine Learning (ML) ensemble algorithms are suggested at the perception phase of Situation Awareness (SA) model for threat detection and the algorithms include; Artificial Neural Network Based Decision Tree (ANN based DT), Bayesian Based Artificial Neural Network (BN based ANN), J48 Based Naïve Bayes Model (J48 based NB), Decision Tree based Bayesian Network (BN) and Random Forest based on Support Vector Machine (RF based SVM). The efficiency and effectiveness of all the aforementioned algorithms were evaluated based on precision, recall and accuracy. ANN based DT gave 98.87% accuracy, BN based ANN gave 99.72% accuracy, J48 based NB gave 98.90% accuracy, DT based BN gave 89.92% accuracy and FR based SVM gave 98.40% accuracy. The implication of these results is that BN based ANN is more suitable in the perception phase of SA for threats detection. Keywords- Cyber-threats, Ensemble Algorithms, Computer Network, Intrusion Detection System, Machine Learning

scholarly journals Betting models using AI: A review on ANN, SVM, and Markov Chain

2021 ◽  
Author(s):  
Aladár Kollár
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Markov Chain ◽  
Sports Betting ◽  
Modern World ◽  
Support Vector ◽  
Intelligent Technique ◽  
Vector Machines ◽  
The Brain

In today's modern world, sports generate a great deal of data about each athlete, team, event, andseason. Many people, from spectators to bettors, find it fascinating to predict the outcomes ofsporting events.With the available data, the sports betting industry is turning to Artificial Intelligence.Working with a great deal of data and information is needed in sports betting all over the world.Artificial intelligence and machine learning are assisting in the prediction of sporting trends. The trueinfluence of technology is felt as it offers these observations in real-time, which can have an impacton important factors in betting. An artificial neural network is made up of several small,interconnected processors called neurons, which are similar to the biological neurons in the brain. InANN framework, MLP, the most applicable NN algorithm, are generally selected as the best model forpredicting the outcomes of football matches. This review also discussed another common techniqueof modern intelligent technique, namely Support Vector Machines (SVM). Lastly, we also discussedthe Markov chain to predict the result of a sport. Markov chain is the sequence or chain from whichthe next sample from this state space is sampled.

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

scholarly journals Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

2020 ◽  
Vol 8 (10) ◽  
pp. 766
Author(s):  
Dohan Oh ◽  
Julia Race ◽  
Selda Oterkus ◽  
Bonguk Koo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Machine Learning Techniques ◽  
Burst Pressure ◽  
Comparison Results ◽  
Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

Determination of thermodynamic state variables of liquids from its microscopic structure using an artificial neural network

Soft Matter ◽  
2020 ◽  
Author(s):  
Ulices Que-Salinas ◽  
Pedro Ezequiel Ramirez-Gonzalez ◽  
Alexis Torres-Carbajal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Distribution Function ◽  
Microscopic Structure ◽  
Machine Learning Method ◽  
Learning Method ◽  
State Variables ◽  
Thermodynamic State

In this work we implement a machine learning method to predict the thermodynamic state of a liquid using only its microscopic structure provided by the radial distribution function (RDF). The...

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