FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

BackgroundForests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset.ResultsThe test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with the given data.ConclusionThe integration of the robust feature extraction of FireXnet with the transparency of explainable AI using SHAP enhances the model’s interpretability and allows for the identification of key characteristics triggering wildfire detections. Extensive experimentation reveals that in addition to being accurate, FireXnet has reduced computational complexity due to considerably fewer training and non-training parameters and has significantly fewer training and testing times

Machine Learning Approaches for Detecting Driver Drowsiness: A Critical Review

Driver drowsiness is a serious issue that poses a significant threat to road safety, as it can lead to accidents and injuries. In response to this problem, a thorough review of machine learning techniques for detecting driver drowsiness was conducted. The review examined a range of techniques, including more recent approaches that use machine learning and deep learning algorithms as well as different types of data sources driver behaviours, physiological signals, and vehicle behaviours. The primary objective of this paper was to critically analyse and provide a comprehensive overview of the current state-of-the-art in detecting driver drowsiness, evaluate the effectiveness of each technique in terms of accuracy and reliability, and identify potential areas for future research and improvement. In order to achieve this, a systematic review of relevant research studies was undertaken. The review determined that machine learning-based techniques can improve the accuracy and reliability of driver drowsiness detection systems. However, certain limitations, such as the need for large amounts of data, feature extraction, and model structure, must be addressed. By overcoming these limitations, machine learning-based systems have the potential to enhance road safety and prevent accidents. In conclusion, this paper provides a thorough review of machine learning techniques for driver drowsiness detection, evaluates their effectiveness, identifies potential research directions, and highlights their significance and contribution to road safety. The insights gained from this study can be used to guide the development of more effective driver drowsiness detection systems and improve road safety for the community

Effect of silica particle loading on shape distortion in glass/vinyl ester-laminated composite plates

International audienc

Enhanced Detection Technique for Driver Drowsiness Using Vehicle On-Board Diagnostics (OBD-II)

In this research on driver drowsiness detection employs OBD-II sensor data (speed, RPM, throttle position, and steering torque) and a camera with a pretrained model for data labeling. After preprocessing, which involves converting time series data into image windows, a CNN model achieves an 86.75% accuracy in identifying drowsiness and normal patterns. This integrated approach demonstrates promising results for enhancing road safety through effective driver drowsiness detection

Effect of silica particle loading on shape distortion in glass/vinyl ester-laminated composite plates

International audienc

Simultaneous Optimization of Woven Fabric Properties Using Principal Component Analysis

The yarn structure and fabric interlacing pattern are determining parameters for fabric properties. The current study focusses on the multi-response optimization of certain fabric properties like shrinkage, areal density, thickness, flexural rigidity, and bending modulus using principal component analysis for optimum properties. Yarn twist (four different levels), fabric weave design (plain and twill), and yarn type (carded and combed) were the variables of the study. The Taguchi approach of the orthogonal array was sued for designing the experiments, and eight different samples were produced. The yarn twist and fabric weave design were found to have significant effect on these properties of the fabric. Furthermore, using analysis of the variance method, contribution% of parameters to these properties was determined

A Statistical Approach for Obtaining the Controlled Woven Fabric Width

A common problem faced in fabric manufacturing is the production of inconsistent fabric width on shuttleless looms in spite of the same fabric specifications. Weft-wise crimp controls the fabric width and it depends on a number of factors, including warp tension, temple type, fabric take-up pressing tension and loom working width. The aim of this study is to investigate the effect of these parameters on the fabric width produced. Taguchi’s orthogonal design was used to optimise the weaving parameters for obtaining controlled fabric width. On the basis of signal to noise ratios, it could be concluded that controlled fabric width could be produced using medium temple type and intense take-up pressing tension at relatively lower warp tension and smaller loom working width. The analysis of variance revealed that temple needle size was the most significant factor affecting the fabric width, followed by loom working width and warp tension, whereas take-up pressing tension was least significant of all the factors investigated in the study