LSTM ile EEG Tabanlı Kimliklendirme Sistemi Tasarımı

Balcı, Furkan; Oralhan, Zeki

doi:10.31590/ejosat.779526

European Journal of Science and Technology

2020

DOI: 10.31590/ejosat.779526

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LSTM ile EEG Tabanlı Kimliklendirme Sistemi Tasarımı

Furkan Balcı¹,

Zeki Oralhan²

Abstract: Öz Tanımlama sistemleri son derece güvenilir kişisel veriler kullanılarak tasarlanmaktadır. Doğruluk oranı ve güvenilirlik bu sistemlerin en temel parametreleridir. Elektroensefalografi (EEG) sinyali zamana, içsel ve çevresel faktörlere bağlı olarak değişir. Yapılan çalışmalar sonucunda EEG sinyalinin tanımlama sistemlerinde kullanılabilirliği teyit edilmiştir. Çevresel etkiler en aza indirildiğinde vücut tarafından üretilen sinyallerin kişiselleştirilmiş sinyaller olduğu anlaşılmaktadır. Uzun Kısa Süreli Bell… Show more

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Cited by 4 publications

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“…Academic Platform Journal of Engineering and Smart Systems 11(2),[47][48][49][50][51][52][53][54][55][56][57][58][59][60][61] 2023 …”

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confidence: 99%

Improving the Prediction Accuracy in Deep Learning-based Cryptocurrency Price Prediction

Balcı

2023

Academic Platform Journal of Engineering and Smart Systems

Self Cite

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Cryptocurrencies are popular today even though they do not have a physical form with their high profit rates and increasing usage day by day. However, the volatility of cryptocurrencies is higher than physical currencies. These volatilities change with the effect of social media rather than changes in exchange rates of physical currencies. For this reason, in this study, using Twitter data, one of the most widely used social media tools, real-time analysis on the values of four cryptocurrencies with the highest market value and the change in the estimated success compared to classical approaches were examined. The basic steps of this study: Obtaining Twitter data and financial data, performing sentiment analysis using Twitter data, making predictions on MM-LSTM architecture. The approach is aimed to be a predictive method open to online learning. Various filter steps were applied to remove the effect of bot users on Twitter that could prevent the prediction performance on the created data set, and the effect of the method on accuracy rate was tried to be reduced by eliminating the activity of bot accounts.

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“…Academic Platform Journal of Engineering and Smart Systems 11(2),[47][48][49][50][51][52][53][54][55][56][57][58][59][60][61] 2023 …”

mentioning

confidence: 99%

Improving the Prediction Accuracy in Deep Learning-based Cryptocurrency Price Prediction

Balcı

2023

Academic Platform Journal of Engineering and Smart Systems

Self Cite

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LieWaves: dataset for lie detection based on EEG signals and wavelets

Aslan,

Baykara,

Alakus

2024

Med Biol Eng Comput

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Nesnelerin interneti ortamlarında derin öğrenme ve makine öğrenmesi tabanlı anomali tespiti

GÖKDEMR

Çalhan

2022

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 Anomaly analysis using deep learning algorithm  Detection of duplication, interception and modification attacks in the IoT environment  Evaluation of LSTM algorithm in terms of performance metrics compared to SVM and NB algorithms Classical machine learning and deep learning were compared in detecting attacks on IoT environments. Due to its success in anomaly detection in the literature, Support Vector Machines (SVM) and Naive Bayes (NB) algorithms from classical machine learning algorithms were preferred. As a deep learning algorithm, the Long Short-Term Memory (LSTM) algorithm, which is mostly used in fields such as natural language processing and text processing, and which has very few studies in anomaly detection, has been chosen. With the LSTM algorithm, higher values were obtained in accuracy and f1 scores.Figure A. Proposed system model for anomaly detection in IoT environments with LSTM-SVM-NB algorithmsPurpose: As the use of Internet of Things (IoT) systems has become widespread, cyber-attacks against these systems have also increased. Cyber-attacks occurring in IoT environments can include different types of attacks, such as the inability of their devices to serve, corruption, data capture, modification, or deletion. In this study, it is tried to predict duplication, interception, and modification attacks in Message Queuing Telemetry Transport (MQTT) messages using an IoT dataset with artificial intelligence techniques. Theory and Methods:In this study, compared to the performance metrics of SVM and NB, which are machine learning algorithms, and LSTM, which is a deep learning algorithm. Results:Experimental results show that the LSTM algorithm can be used in anomaly detection in the cyber security area, apart from natural language processing and text processing, which are the areas widely used in the literature. Besides, it was concluded that the LSTM algorithm achieved higher accuracy than the classical machine learning algorithms. Conclusion:In this paper, a comparison of deep learning and machine learning algorithms for anomaly detection in IoT environments is made. The results show that the LSTM algorithm, gives more effective results in anomaly detection than classical machine learning algorithms, but has some disadvantages in terms of working time.

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LSTM ile EEG Tabanlı Kimliklendirme Sistemi Tasarımı

Cited by 4 publications

References 14 publications

Improving the Prediction Accuracy in Deep Learning-based Cryptocurrency Price Prediction

Improving the Prediction Accuracy in Deep Learning-based Cryptocurrency Price Prediction

LieWaves: dataset for lie detection based on EEG signals and wavelets

Nesnelerin interneti ortamlarında derin öğrenme ve makine öğrenmesi tabanlı anomali tespiti

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