Due to the increasing of traffic accidents, there is an urgent need to control and reduce driving mistakes. Driver fatigue or drowsiness is one of these major mistakes. Many algorithms have been developed to address this issue by detecting fatigue and alerting the driver to this dangerous condition. The major problem of the developed algorithms is their detection accuracy, as well as the time required to detect fatigue status and alert the driver. The accuracy and the time represent a critical condition that affects the reduction of traffic accidents. Several datasets have been used in the development of fatigue or drowsy detection techniques. These data are gathered from the deriver’s brain Electroencephalogram (EEG) signals or from video streaming recordings of the driver behavior. This paper develops two distinct approaches, the first based on the use of machine learning classifiers and the second depends on the use of deep learning models to produce a high-performance fatigue detection system. The machine learning approach is used to process EEG signals, whereas the deep learning approach is used to process video streams. In machine learning classifiers, Support Vector Machine (SVM) provides up to 98% of detection accuracy, which is the highest accuracy among the other five deployed classifiers. In deep learning models, Convolutional Neural Network (CNN) provides up to 99% detection accuracy, which is the highest accuracy among the other two deployed models. The experimental results demonstrate that the two proposed algorithms provide the highest detection accuracy with the shortest Testing Time ( TT ) when compared to all other recent and efficient fatigue detection algorithms.

The basic idea of this paper is concerned with the Peak-to-Average Power Ratio (PAPR) problem in multicarrier underwater acoustic (UWA) communication systems that implement Orthogonal Frequency Division Multiplexing (OFDM). A Tangent Rooting (TanhR) companding technique is presented in this paper to achieve this target. This technique achieves a noticeable reduction of the PAPR in UWA systems, while maintain a good Bit Error Rate (BER) performance on UWA channel models. Channel equalization with Linear Minimum Mean Square Error (LMMSE) and Zero-Forcing (ZF) equalizers is considered in this paper. Simulation results ensure superiority of the proposed technique compared to the Selected Mapping (SLM) and the Partial Transmit Sequence (PTS) techniques from both PAPR and BER perspectives.

The rapid advancement of technology has transformed the automotive sector through intelligent systems for safety, control, and infotainment. This study reviews key networking protocols controller area network (CAN), local interconnect network (LIN), FlexRay, MOST, Ethernet, and Master-Slave used in electric vehicles (EVs) in India and worldwide, providing insights into their application trends across different regions. CAN provides reliable low-latency communication for safety-critical functions (1 Mbps), while CAN FD extends support up to 12 Mbps. LIN and Master-Slave topologies enable cost-effective low-speed operations (2–20 kbps). FlexRay ensures real-time communication (10–100 Mbps), and MOST supports 150 Mbps for multimedia applications. Ethernet offers superior bandwidth up to 10 Gbps for advanced driver assistance and autonomous systems, but it involves higher complexity and cost. The review identifies key challenges in interoperability, scalability, and cybersecurity and evaluates protocol suitability for next-generation EV architectures. It also integrates Industry 5.0 principles and SDGs 7, 9, and 13, emphasizing human-centric, sustainable, and resilient mobility.