Machine learning technology in biodiesel research: A review

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO), deep learning (DL)-based superimposed channel state information (CSI) feedback has presented promising performance. However, it is still facing many challenges, such as the high complexity of parameter tuning, large number of training parameters, and long training time, etc. To overcome these challenges, an extreme learning machine (ELM)-based superimposed CSI feedback is proposed in this paper, in which the downlink CSI is spread and then superimposed on uplink user data sequence (UL-US) to feed back to base station (BS). At BS, an ELM-based network is constructed to recover both downlink CSI and UL-US. In the constructed ELM-based network, we employ the simplified versions of ELM-based subnets to replace the subnets of DL-based superimposed feedback, yielding less training parameters. Besides, the input weights and hidden biases of each ELM-based subnet are loaded from the same matrix by using its full or partial entries, which significantly reduces the memory requirement. With similar or better recovery performances of downlink CSI and UL-US, the proposed ELM-based method has less training parameters, storage space, offline training and online running time than those of DL-based superimposed CSI feedback.

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ELM-Based Superimposed CSI Feedback for FDD Massive MIMO System

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

/pdf/artificial-intelligence-approach-for-target-classification-a-1as55tr51k.pdf

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

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.

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

The main source for the operation of Brain Computer Interface (BCI) technique is EEG signals BCI is used as a direct communication between the brain and the external device It is mainly used for supporting, augmenting, or repairing human cognitive or sensory motor function The proposed work is the classification of EEG signals measured under eye opening and closing state using advanced learning classifiers Eye Opening and closing (motor imagery) dataset is a benchmark data obtained from UCI (University of California Irvine) The advance learning classifiers considered are ELM (Extreme Learning Machine), PE-CELM (Phase Encoded Extreme Learning Machine and FC-FLC (Fully Complex Valued fast learning classifiers) ELM is a fast learning real valued classifier which selects input weights randomly and computes weight of the output layer analytically PE-CELM and FC-FLC are recently developed complex valued neural classifiers and they are used in the EEG signal classification task Generally real valued networks have less computational ability when compared to complex valued neural networks Hence, PE-CELM and FC-FLC performs better than ELM classifier due to the orthogonal decision boundary, natural property of complex valued signal

M. Sivachitra

Papers

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

Eye state EEG signal classification using complex valued neural classifiers