Towards driving-independent prediction of fuel consumption in heavy-duty trucks

Heavy-duty vehicles are among the major contributors to greenhouse gas emissions in addition to their high energy consumption. Thus, modeling their fuel consumption (FC) is of prime importance to the limitation of these environment-harmful emissions and energy saving. In this paper, we propose a data-driven model based on artificial neural networks (ANN) to predict the average FC in heavy-duty cloud-connected Ford trucks. In particular, we propose a driving-independent model based only on the vehicle weight and road grade. Owing to idling situations, the average FC includes some outliers; we propose to remove these outliers based on the weight-normalized average FC to take the changing vehicle weights into consideration. Initially, the model uses the percent torque, vehicle speed, vehicle weight, and road slope as predictors. In that case, our proposed model achieved an R2 of 0.96 outperforming the results in the literature by a significant margin. Next, we investigate the cases of excluding the torque and vehicle speed in order to assess the model's effectiveness when using only those predictors which are independent of the vehicle dynamics. In these challenging cases, our proposed model still maintains an R2 above 0.8

Fuel consumption classification for heavy-duty vehicles: a novel approach to identifying driver behavior and system anomalies

In this paper, we propose a fuel consumption classification system for heavy-duty vehicles (HDVs) based on two machine learning models that categorize sections of driving data as normal or high and inlier or outlier fuel consumption. A dataset of 606 naturalistic driving records collected from 57 different heavy-duty trucks with varying carry loads is generated and utilized. Proposed models are trained to categorize driving sections taking into consideration of vehicle weight and road slope, which are the two major factors affecting the fuel consumption of a heavy-duty truck. Results show an accuracy of 92.2% in high fuel consumption prediction and an F1 score of 0.78 in outlier prediction using the bagged decision trees models. The proposed approach provides an advanced categorization of driving data in terms of fuel economy. It has substantial potential to determine driving behavior anomalies or system faults that may cause excessive energy consumption and emissions in HDVs

FXAI: fusing XAI for predicting COVID-19 using diverse chest x - ray images

Fusing explainable artificial intelligence (FXAI) is currently a prominent research topic in medical imaging interpretations. The proposed FXAI has the capability to provide the following benefits. Firstly, it can extract strong and reliable high-level deep features by combining various standard AI networks. Secondly, it can simultaneously generate visual explainable saliency maps associated with each chest X-ray (CXR) scan. Such heat maps not only demonstrate the most relevant regions of the AI decision-making process but also offer advantages to radiologists and patients. Thirdly, it enhances prediction performance to deliver an optimal intelligent solution for communities worldwide. These advantages can support the development of an optimal treatment plan, reduce medical costs, and enhance the capabilities of health care systems. We have trained and evaluated the proposed FXAI using a diverse benchmark medical CXR dataset that has been collected from various public resources. Our findings encourage researchers and stakeholders in the medical industry to validate this proposed framework in a practical manner