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All Journal International Journal of Electrical and Computer Engineering JOIV : International Journal on Informatics Visualization
Megat Syahirul Amin Megat Ali
Universiti Teknologi MARA
Computer Science & IT Electrical & Electronics Engineering

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Human movement detection and classification capabilities using passive Wi-Fi based radar Razali, Hidayatusherlina; Abd Rashid, Nur Emileen; Nasarudin, Muhammad Nazrin Farhan; Ismail, Nor Najwa; Ismail Khan, Zuhani; Enche Ab Rahim, Siti Amalina; Megat Ali, Megat Syahirul Amin; Zakaria, Nor Ayu Zalina
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3545-3556

Abstract

Human detection and classification via Wi-Fi transmission have received a lot of attention in recent years as crucial facilitators in security and human-computer interaction (HCI). The passive Wi-Fi radar (PWR) system used by previous researchers applied cross-ambiguity function (CAF) and CLEAN algorithms to process the detected signals. This paper explores the feasibility and viability of a PWR system in detecting and classifying human movements without utilizing CAF and CLEAN algorithms. The movements are performed by four participants but with comparable body sizes and heights. Three daily human movements are investigated namely walking, bending, and sitting, with each participant performing each movement 24 times, providing a total of 96 samples per activity. The system is evaluated based on the consistency of the signal pattern in a frequency domain and the percentage accuracy is assessed using an artificial neural network (ANN) classifier and trained using a leave-one-out cross-validation (LOOCV) method. The frequency domain results reveal that the signals are consistent, with no noticeable variations or changes in the voltage intensity or shape of the main lobe. The classification of the movements shows that the classifier has an overall accuracy of 97.6%.
MLP-NARX Bitcoin Price Prediction Model Integrating System Identification Modelling Principles Farhan Nasarudin, Muhammad Nazrin; Yassin, Ihsan Mohd; Megat Ali, Megat Syahirul Amin; Adzhar Mahmood, Mohd Khairil; Baharom, Rahimi; Rizman, Zairi Ismael
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.943

Abstract

Bitcoin is a decentralized digital currency that enables people to exchange value without requiring a third-party intermediary. Due to its many advantages, it has received much interest from institutional and individual investors. Despite its meteoric increase, the price of Bitcoin extremely volatile asset class as it purely relies on supply and demand. This presents an interesting opportunity to create a forecasting model. However, many research papers in this area does not analyse the residuals as part of the forecasting resulting in potentially biased models. In this paper, we demonstrate System Identification (SI) residual analysis techniques to the analysis of our forecasting model. The Multi-Layer Perceptron (MLP) Nonlinear Autoregressive with Exogeneous Inputs (NARX) uses historical price data and several technical indicators to predict the future price movements of Bitcoin. The Particle Swarm Optimization (PSO) algorithm was used to find optimal parameters for the model. The model was able to predict one day ahead price in the prediction test. The model has successfully captured the dynamics of the data through the tests performed on residuals. It is also proving the randomness of residuals, albeit some minor violations.
Co-Authors Abd Rashid, Nur Emileen Adzhar Mahmood, Mohd Khairil Baharom, Rahimi Enche Ab Rahim, Siti Amalina Farhan Nasarudin, Muhammad Nazrin Ismail Khan, Zuhani Ismail, Nor Najwa Nasarudin, Muhammad Nazrin Farhan Razali, Hidayatusherlina Rizman, Zairi Ismael Yassin, Ihsan Mohd Zakaria, Nor Ayu Zalina