Machine learning technology in biodiesel research: A review

Rapid advances in technology have enabled print media to be published online and the emergence of Facebook, Twitter, YouTube and other social networks. Social networks have become an important way for people to communicate with each other and share their ideas. The most important feature of social networks is the rapid information sharing. In this context, the accuracy of the news or information published is very important. The spread of fake news in social networks has recently become one of the biggest problems. Fake news affects people’s daily life and social order and may cause some negativity. In this study, the most comprehensive and prestigious electronic databases have been examined in order to find the latest articles about the detection of fake news in social networks by systematic literature review method. The main aim of the study is to reveal the benefits of artificial intelligence tools used in the detection of fake news and their success levels in different applications. As a result of the study, it was concluded that the success levels of artificial intelligence tools are over 90%. This study is thought to be a guide both researchers and individuals related to this field.

Fake News Detection on Social Networks with Artificial Intelligence Tools: Systematic Literature Review

The invention relates to an electroencephalogram (EEG) signal online identification method with data structure information being fused. The method comprises the following steps: S1) establishing a classification model based on an online sequential extreme learning machine (OS-ELM) algorithm by utilizing a small training set formed by a small number of labeled EEG samples to serve as an initial classification model in semi-supervised learning; S2) establishing a structure learning model by utilizing an on-line fuzzy clustering method, and estimating a global structure of data distribution afterbatch increase of EEG samples collected online based on prior information of the labeled EEG samples; S3)carrying out labeling on the EEG samples collected online by utilizing the classification model, and through a batch learning mode and based on the structure information estimated by a structural learning model, selecting a batch of EEG samples collected online and meeting a certain conditionsto add to a training set, and re-training the classification model by utilizing the updated training set; and S4) carrying out online identification on the collected EEG signals through the updated classification model.

Electroencephalogram(EEG) signal online identification method with data structure information being fused

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Artificial Intelligence Approach for Target Classification: A State of The Art

This paper presents development of a robotic arm that can mimic the mobility of human arm with the recorded EEG (electroencephalogram) signals from the brain. This is a project designed for people who have lost the ability to control their arms. The robot arm will work with the signals that will be produced by the EEG instrument simultaneously when the user thinks that he or she will perform certain movements. These movements can be classified using Deep Multilayer Perceptron (Deep MLP) classifier designed in Python with Keras library. Deep MLP network is trained using Backpropagation algorithm for each patient by using own data. Experimental results demonstrate the proposed method can classify different imaginary movements with high accuracy.

Patient-Specific Imaginary Motor Movement Classification of EEG Signals and Control of Robotic Arm

Brain computer interface (BCI) aims at providing a brand new communication approach without brain's traditional output through nerve and muscle. “Electroencephalography” has been widely used for BCI system as it is a non-invasive approach. Recently, various classifiers have been used for the analysis of EEG signals measured under the planning and relaxed state. The major work addressed in the paper is the classification of EEG signals (motor imagery) measured under planning and relaxed state using advanced learning classifiers. The dataset of planning and relaxed state is a benchmark data and it is taken from UCI (University of California, Irvine) repository. Semi supervised ELM (SS-ELM) and unsupervised ELM (US-ELM) are recently developed networks and used for the EEG signal classification task. Both of these algorithms can be fit in to a unified framework and handle multi-class classification or multi-cluster clustering. SS-ELM algorithm performs better than US-ELM and other real valued algorithms in classifying planning and relaxed states. The improvement is due to the use of spectral techniques in embedding and clustering.

M. Dharani

Papers

Motor imagery signal classification using semi supervised and unsupervised extreme learning machines