Muhammad Javed¹, Zhaohui Zhang^1,*, Fida Hussain Dahri², Asif Ali Laghari^3,*, Martin Krajčík⁴, Ahmad Almadhor⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1457-1493, 2025, DOI:10.32604/cmc.2025.062954 - 29 August 2025

Abstract Recent advances in artificial intelligence and the availability of large-scale benchmarks have made deepfake video generation and manipulation easier. Therefore, developing reliable and robust deepfake video detection mechanisms is paramount. This research introduces a novel real-time deepfake video detection framework by analyzing gaze and blink patterns, addressing the spatial-temporal challenges unique to gaze and blink anomalies using the TimeSformer and hybrid Transformer-CNN models. The TimeSformer architecture leverages spatial-temporal attention mechanisms to capture fine-grained blinking intervals and gaze direction anomalies. Compared to state-of-the-art traditional convolutional models like MesoNet and EfficientNet, which primarily focus on global facial… More >

Nur Syazreen Ahmad^*, Jia Hui Teo, Patrick Goh

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 611-628, 2022, DOI:10.32604/cmc.2022.025823 - 18 May 2022

Abstract A single-channel electroencephalography (EEG) device, despite being widely accepted due to convenience, ease of deployment and suitability for use in complex environments, typically poses a great challenge for reactive brain-computer interface (BCI) applications particularly when a continuous command from users is desired to run a motorized actuator with different speed profiles. In this study, a combination of an inconspicuous visual stimulus and voluntary eyeblinks along with a machine learning-based decoder is considered as a new reactive BCI paradigm to increase the degree of freedom and minimize mismatches between the intended dynamic command and transmitted control… More >

Mohammad Monirujjaman Khan^1,*, Shamsun Nahar Safa¹, Minhazul Hoque Ashik¹, Mehedi Masud², Mohammed A. AlZain³

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 49-64, 2021, DOI:10.32604/iasc.2021.016077 - 26 July 2021

Abstract Smart wheelchairs play a significant role in supporting disabled people. Individuals with motor function impairments due to some disorders such as strokes or multiple sclerosis face frequent moving difficulties. Hence, they need constant support from an assistant. This paper presents a brain-controlled wheelchair model to assist disabled and paralyzed patients. The wheelchair is controlled by interpreting Electroencephalogram (EEG) signals, also known as brain waves. In the EEG technique, an electrode cap is positioned on the user’s scalp to receive EEG signals, which are detected and transformed by the Arduino microcontroller into motion commands, which drive More >