scholarly journals A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jordan J. Bird ◽  
Diego R. Faria ◽  
Luis J. Manso ◽  
Anikó Ekárt ◽  
Christopher D. Buckingham
Keyword(s):  
Short Term Memory ◽  
Evolutionary Approach ◽  
Statistical Features ◽  
Short Term ◽  
Adaptive Boosting ◽  
New Approach ◽  
State Classification ◽  
Eeg Data ◽  
Long Short Term Memory ◽  
Machine Interaction

This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron (MLP) is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long Short-Term Memory (LSTM) are also explored, and Adaptive Boosting of the two types of models is tested for each problem. Three experiments are provided for comparison using different classifiers: one for attention state classification, one for emotional sentiment classification, and a third experiment in which the goal is to guess the number a subject is thinking of. The obtained results show that an Adaptive Boosted LSTM can achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional, and number datasets, respectively. An evolutionary-optimised MLP achieves results close to the Adaptive Boosted LSTM for the two first experiments and significantly higher for the number-guessing experiment with an Adaptive Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train and classify. In particular, the accuracy of the nonboosted DEvo MLP was of 79.81%, 96.11%, and 27.07% in the same benchmarks. Two datasets for the experiments were gathered using a Muse EEG headband with four electrodes corresponding to TP9, AF7, AF8, and TP10 locations of the international EEG placement standard. The EEG MindBigData digits dataset was gathered from the TP9, FP1, FP2, and TP10 locations.

scholarly journals BreakNet: detecting deletions using long reads and a deep learning approach

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Junwei Luo ◽  
Hongyu Ding ◽  
Jiquan Shen ◽  
Haixia Zhai ◽  
Zhengjiang Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Genetic Diseases ◽  
Structural Variations ◽  
Short Term ◽  
New Approach ◽  
Feature Vectors ◽  
Long Reads ◽  
Long Read ◽  
Long Short Term Memory

Abstract Background Structural variations (SVs) occupy a prominent position in human genetic diversity, and deletions form an important type of SV that has been suggested to be associated with genetic diseases. Although various deletion calling methods based on long reads have been proposed, a new approach is still needed to mine features in long-read alignment information. Recently, deep learning has attracted much attention in genome analysis, and it is a promising technique for calling SVs. Results In this paper, we propose BreakNet, a deep learning method that detects deletions by using long reads. BreakNet first extracts feature matrices from long-read alignments. Second, it uses a time-distributed convolutional neural network (CNN) to integrate and map the feature matrices to feature vectors. Third, BreakNet employs a bidirectional long short-term memory (BLSTM) model to analyse the produced set of continuous feature vectors in both the forward and backward directions. Finally, a classification module determines whether a region refers to a deletion. On real long-read sequencing datasets, we demonstrate that BreakNet outperforms Sniffles, SVIM and cuteSV in terms of their F1 scores. The source code for the proposed method is available from GitHub at https://github.com/luojunwei/BreakNet. Conclusions Our work shows that deep learning can be combined with long reads to call deletions more effectively than existing methods.

scholarly journals RECONHECIMENTO DE ESTADOS DOS OLHOS UTILIZANDO MÁQUINAS DE APRENDIZADO PROFUNDO A PARTIR DE ONDAS CEREBRAIS

2019 ◽  
Vol 9 (1) ◽  
pp. 41-57 ◽  
Keyword(s):  
Short Term Memory ◽  
Human Cognition ◽  
Short Term ◽  
Data Set ◽  
Eeg Data ◽  
Eyes Open ◽  
Long Short Term Memory ◽  
Base Data ◽  
The Brain

The brain-computer interface is one of the emerging fields of human-computer interaction due to its broad spectrum of applications, especially those that deal with human cognition. In this work, electroencephalography (EEG) is used as base data for classifying the state of the eyes (open or closed) by applying Long Short-Term Memory (LSTM) networks and variants. For benchmarking purposes, the EEG data set with the eye state record was used, available in the Machine Learning repository at UCI. The results obtained indicated that the LSTM and GRU bidirectional cells models are applicable to the classification of the data, presenting an accuracy greater than 95%, and that its performance is good compared to the more expensive models computationally.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

The capacity of short term memory from an evolutionary perspective

2019 ◽  
Author(s):  
Majid Manoochehri
Keyword(s):  
Short Term Memory ◽  
Memory Span ◽  
Critical Role ◽  
Experimental Studies ◽  
Cognitive System ◽  
Evolutionary Approach ◽  
Evolutionary Perspective ◽  
Short Term ◽  
Term Memory

Memory span in humans has been intensely studied for more than a century. In spite of the critical role of memory span in our cognitive system, which intensifies the importance of fundamental determinants of its evolution, few studies have investigated it by taking an evolutionary approach. Overall, we know hardly anything about the evolution of memory components. In the present study, I briefly review the experimental studies of memory span in humans and non-human animals and shortly discuss some of the relevant evolutionary hypotheses.

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory
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