A machine learning framework is developed to interpret vehicle subsystem status from sensor data, providing actionable insights for adaptive control systems. Using the vehicle’s suspension as a case study, inertial data are collected from driving maneuvers, including braking and cornering, to seed a prototype XGBoost classifier. The classifier then pseudo-labels a larger exemplar dataset acquired from street and racetrack sessions, which is used to train an inference model capable of robust generalization across both regular and performance driving. An overlapping sliding-window grading approach with reverse exponential weighting smooths transient fluctuations while preserving responsiveness. The resulting real-time semantic mode predictions accurately describe the vehicle’s current dynamics and can inform a model predictive control system that can adjust suspension parameters and update internal constraints for improved performance, ride comfort, and component longevity. The methodology extends to other components, such as braking systems, offering a scalable path toward fully self-optimizing vehicle control in both conventional and autonomous platforms.
Skin diseases in melanin-rich skin often present diagnostic challenges due to the unique characteristics of darker skin tones, which can lead to misdiagnosis or delayed treatment. This disparity impacts millions within diverse communities, highlighting the need for accurate, AI-b…
Recently, emerging technologies have assisted the healthcare system in the treatment of a wide range of diseases so considerably that the development of such methods has been regarded as a practical solution to cure many diseases. Accordingly, underestimating the importance of su…
Urban environments increasingly rely on interconnected digital infrastructures like IoT devices, SDN-enabled networks, and cloud platforms to support essential municipal services. Ensuring the resilience of these systems requires advanced, data-driven mechanisms capable of detect…
Real estate price prediction is crucial for informed decision making in the dynamic real estate sector. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing prediction accuracy and data-driven decision making. However, the existing litera…
Electric vehicles (EVs) are alternatives to traditional combustion engine-powered vehicles. This work focuses on a thermal management system for battery EVs using liquid cooling and a machine learning (ML) model to predict their thermal-related health. Real-world data of EV opera…
With the rapid expansion of social media, detecting offensive language has become critically important for healthy online interactions. This poses a considerable challenge for low-resource languages such as Roman Urdu which are widely spoken on platforms like Facebook. In this pa…