scholarly journals Big Data Analysis of Facebook Users Personality Reconition using Map Reduce Back Propagation Neural Networks

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Solomon Akinboro ◽  
Isaac K. Ogundoyin ◽  
Ayobami T. Olusesi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Back Propagation ◽  
Threshold Value ◽  
Back Propagation Neural Network ◽  
Classification Model ◽  
Map Reduce ◽  
Support Vector ◽  
Computationally Efficient ◽  
Personality Recognition

Machine learning has been an effective tool to connect networks of enormous information for predicting personality. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in most research efforts. This research modeled user personality based on set of features extracted from the Facebook data using Map-Reduce Back Propagation Neural Network (MRBPNN). The performance of the MRBPNN classification model was evaluated in terms of five basic personality dimensions: Extraversion (EXT), Agreeableness (AGR), Conscientiousness (CON), Neuroticism (NEU), and Openness to Experience (OPN) using True positive, False Positive, accuracy, precision and F-measure as metrics at the threshold value of 0.32. The experimental results reveal that MRBPNN model has accuracy of 91.40%, 93.89%, 91.33%, 90.43% and 89.13% CON, OPN, EXT, NEU and AGR respectively for personality recognition which is more computationally efficient than Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). Therefore, personality recognition based on MRBPNN would produce a reliable prediction system for various personality traits with data having a very large instance.  Keywords— Machine learning, Facebook, MRBPNN, Personality Recognition, Neuroticism, Agreeableness.

scholarly journals EEG-Based Emotion Classification for Alzheimer’s Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7212
Author(s):  
Jungryul Seo ◽  
Teemu H. Laine ◽  
Gyuhwan Oh ◽  
Kyung-Ah Sohn
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Recurrent Neural Network ◽  
Classification Model ◽  
Support Vector ◽  
Number Of Patients ◽  
Study Results ◽  
Conventional Machine

As the number of patients with Alzheimer’s disease (AD) increases, the effort needed to care for these patients increases as well. At the same time, advances in information and sensor technologies have reduced caring costs, providing a potential pathway for developing healthcare services for AD patients. For instance, if a virtual reality (VR) system can provide emotion-adaptive content, the time that AD patients spend interacting with VR content is expected to be extended, allowing caregivers to focus on other tasks. As the first step towards this goal, in this study, we develop a classification model that detects AD patients’ emotions (e.g., happy, peaceful, or bored). We first collected electroencephalography (EEG) data from 30 Korean female AD patients who watched emotion-evoking videos at a medical rehabilitation center. We applied conventional machine learning algorithms, such as a multilayer perceptron (MLP) and support vector machine, along with deep learning models of recurrent neural network (RNN) architectures. The best performance was obtained from MLP, which achieved an average accuracy of 70.97%; the RNN model’s accuracy reached only 48.18%. Our study results open a new stream of research in the field of EEG-based emotion detection for patients with neurological disorders.

Brain Signal Classification Based on Deep CNN

2020 ◽  
Vol 12 (2) ◽  
pp. 17-29
Author(s):  
Terry Gao ◽  
Grace Ying Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mental Status ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Imaging Features ◽  
Learning Approaches ◽  
Computationally Efficient ◽  
Set Up ◽  
Brain Data

It is essential to increase the accuracy and robustness of classification of brain data, including EEG, in order to facilitate a direct communication between the human brain and computerized devices. Different machine learning approaches, such as support vector machine (SVM), neural network, and linear discrimination analysis (LDA), have been applied to set up automatic subjective-classifier, and the findings for their capacities in this regard have been inconclusive. The present study developed an effective classifier for human mental status using deep learning in a convolutional neural network. In contrast to most previous studies commonly using EEG waveform or numeric value of brain signals for classification, the authors utilised imaging features generated from EEG data at alpha frequency band. A new model proposed in this study provides a simple and computationally efficient approach to distinguish mental status during resting. With training, this model could predict new 2D EEG images with above 90% accuracy, while traditional machine learning techniques failed to achieve this accuracy.

ARRHYTHMIA DISEASE DIAGNOSIS USING NEURAL NETWORK, SVM, AND GENETIC ALGORITHM-OPTIMIZED k-MEANS CLUSTERING

2011 ◽  
Vol 11 (04) ◽  
pp. 897-915 ◽  
Author(s):  
ROSHAN JOY MARTIS ◽  
CHANDAN CHAKRABORTY
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Normal Sinus Rhythm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Principal Component ◽  
Disease Diagnosis ◽  
Back Propagation Neural Network ◽  
Polynomial Kernel ◽  
Support Vector

This work aims at presenting a methodology for electrocardiogram (ECG)-based arrhythmia disease detection using genetic algorithm (GA)-optimized k-means clustering. The open-source ECG data from MIT-BIH arrhythmia database and MIT-BIH normal sinus rhythm database are subjected to a sequence of steps including segmentation using R-point detection, extraction of features using principal component analysis (PCA), and pattern classification. Here, the classical classifiers viz., k-means clustering, error back propagation neural network (EBPNN), and support vector machine (SVM) have been initially attempted and subsequently m-fold (m = 3) cross validation is used to reduce the bias during training of the classifier. The average classification accuracy is computed as the average over all the three folds. It is observed that EBPNN and SVM with different order polynomial kernel provide significant accuracies in comparison with k-means one. In fact, the parameters (centroids) of k-means algorithm are locally optimized by minimizing its objective function. In order to overcome this limitation, a global optimization technique viz., GA is suggested here and implemented to find more robust parameters of k-means clustering. Finally, it is shown that GA-optimized k-means algorithm enhances its accuracy to those of other classifiers. The results are discussed and compared. It is concluded that the GA-optimized k-means algorithm is an alternate approach for classification whose accuracy will be near to that of supervised (viz., EBPNN and SVM) classifiers.

scholarly journals Computational method for predicting Self-Interactions Protein using Recurrent Neural Network from Protein Evolutionary Information

2020 ◽  
Author(s):  
Ji-Yong An
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Biological Activities ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Computational Method ◽  
Evolutionary Information ◽  
Support Vector ◽  
Scale Invariant ◽  
Average Accuracy

Abstract Self-interactions Protein (SIPs) play crucial roles in biological activities of organisms. Many high-throughput methods can be used to identify SIPs. However, these methods are both time-consuming and expensive. How to develop effective computational approaches for identifying SIPs is a challenging task. In the paper, we presented a novelty computational method called RRN-SIFT, which combines the Recurrent Neural Network (RNN) with Scale Invariant Feature Transform (SIFT) to predict SIPs based on protein evolutionary information. The main advantage of the proposed RNN-SIFT model is that it used SIFT for extracting key feature by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM) and employed RNN classifier to carry out classification based on extracted features. Extensive experiments show that the RRN-SIFT obtained average accuracy of 94.34% and 97.12% on yeast and human dataset. We also compared our performance with the Back Propagation Neural Network (BPNN), the state-of-the-art support vector machine (SVM) and other exiting methods. By comparing with experimental results, the performance of RNN-SIFT is significantly better than those of the BPNN, SVM and other previous methods in the domain. Therefore, we can come to the conclusion that the proposed RNN-SIFT model is useful tools and can execute incredibly well for predicting SIPs, as well as other bioinformatics tasks. In order to facilitate widely studies and encourage future proteomics research, a freely available web server called RNN-SIFT-SIPs was developed, and is available at http://219.219.62.123:8888/RNNSIFT/ and includes source code and SIPs datasets.

Sign in / Sign up

Export Citation Format

Share Document