Driver drowsiness is a major contributor to road accidents, often resulting from delayed reaction times and impaired cognitive performance. This study introduces EffRes-DrowsyNet, a hybrid deep learning model that integrates the architectural efficiencies of EfficientNetB0 with the deep representational capabilities of ResNet50. The model is designed to detect early signs of driver fatigue through advanced video-based analytics by leveraging both computational scalability and deep feature learning. Extensive experiments were conducted on three benchmark datasets—SUST-DDD, YawDD, and NTHU-DDD—to validate the model’s performance across a range of environmental and demographic variations. EffRes-DrowsyNet achieved 97.71% accuracy, 98.07% precision, and 97.33% recall on the SUST-DDD dataset. On the YawDD dataset, it sustained a high accuracy of 92.73%, while on the NTHU-DDD dataset, it reached 95.14% accuracy, 94.09% precision, and 95.39% recall. These results affirm the model’s superior generalization and classification performance in both controlled and real-world-like settings. The findings underscore the effectiveness of hybrid deep learning models in real-time, safety-critical applications, particularly for automotive driver monitoring systems. Furthermore, EffRes-DrowsyNet’s architecture provides a scalable and adaptable solution that could extend to other attention-critical domains such as industrial machinery operation, aviation, and public safety systems.

In the precision viticulture domain, data recorded by monitoring devices are large-scale processed to improve solutions for grapes’ quality and global production and to offer various recommendations to achieve these goals. Soil-related parameters (soil moisture, structure, etc.) and atmospheric parameters (precipitation, cumulative amount of heat) may facilitate crop diseases occurrence; thus, following predictive analysis, their estimation in vineyards can offer an early-stage warning for farmers and, therefore, suggestions for their prevention and treatment are of particular importance. Using remote sensing devices (e.g., satellites, unmanned vehicles) and proximal sensing methods (e.g., wireless sensor networks (WSNs)), we developed an efficient precision agriculture telemetry platform to provide reliable assessments of atmospheric phenomena periodicity and crop diseases estimation in a vineyard near Bucharest, Romania. The novelty of the materials and methods of this work relies on providing comprehensive preliminary references about monitored parameters to enable efficient, sustainable agriculture. Comparative analyses for two consecutive years illustrate an excellent correlation between cumulative and daily heat, precipitation quantity, and daily evapotranspiration (ET). In addition, the platform proved viable for wine-grapes disease estimation (powdery mildew, grape bunch rot, and grape downy mildew) and treatment recommendations based on the elaborated phenological calendar. Our results, together with continuous monitoring for the upcoming years, may be used as a reference to perform productive, sustainable smart agriculture in terms of yield and crop quality in Romania. In the Conclusion section, we show that farmers and personnel from cooperatives can use this information to make assessments based on the correlation of the available data to avoid critical damage to the wine-grape.

Recently, driver inattention has become the leading cause of automobile accidents. As a result, the driver's perception and decision-making abilities are diminished, and the driver can lose control of the car. To prevent accidents caused by driver inattention, it’s vital to continuously monitor the driver and his driving behaviour and inform him if he becomes distracted or sleepy. This topic has been the subject of study for decades. Whenever feasible to recognise unsafe driving in advance, accidents could be avoided. This document presents an overview of the existing driver alertness system and the various techniques for detecting driver attentiveness.

Driver fatigue is a key factor in road accidents worldwide, requiring effective real-time detection mechanisms. Traditional deep neural network (DNN)-based solutions have shown promising results in detecting drowsiness; however, they are often less suitable for real-time applications due to their high computational complexity, risk of overfitting, and reliance on large datasets. Hence, this paper introduces an innovative approach that integrates fast neighbourhood component analysis (FNCA) with a deep neural network (DNN) to enhance the detection of driver drowsiness using electroencephalogram (EEG) data. FNCA is employed to optimize feature representation, effectively highlighting critical features for drowsiness detection, which are then analysed using a DNN to achieve high accuracy in recognizing signs of driver fatigue. Our model has been evaluated on the SEED-VIG dataset and achieves state-of-the-art accuracy: 94.29% when trained on 12 subjects and 90.386% with 21 subjects, surpassing existing methods such as TSception, ConvNeXt LMDA-Net, and CNN + LSTM.

The Kalman filter represents a very popular signal processing tool, with a wide range of applications within many fields. Following a Bayesian framework, the Kalman filter recursively provides an optimal estimate of a set of unknown variables based on a set of noisy observations. Therefore, it fits system identification problems very well. Nevertheless, such scenarios become more challenging (in terms of the convergence and accuracy of the solution) when the parameter space becomes larger. In this context, the identification of linearly separable systems can be efficiently addressed by exploiting tensor-based decomposition techniques. Such multilinear forms can be modeled as rank-1 tensors, while the final solution is obtained by solving and combining low-dimension system identification problems related to the individual components of the tensor. Recently, the identification of multilinear forms was addressed based on the Wiener filter and most well-known adaptive algorithms. In this work, we propose a tensorial Kalman filter tailored to the identification of multilinear forms. Furthermore, we also show the connection between the proposed algorithm and other tensor-based adaptive filters. Simulation results support the theoretical findings and show the appealing performance features of the proposed Kalman filter for multilinear forms.