Underwater exploration, environmental monitoring, and infrastructure inspection are just few
of the many applications where autonomous navigation of underwater robots is a tough and …

The image object recognition and detection technology are widely used in many scenarios.
In recent years, big data has become increasingly abundant, and big data-driven artificial …

In the context of big data analytics, this study examines the use of algorithms based on deep
learning for feature extraction. Traditional methods usually have trouble sifting through the …

Ensemble learning has emerged as a potent method for improving prediction accuracy in
Big Data classification tasks. This paper presents a comprehensive study of ensemble …

Collecting and annotating real-world data for the development of object detection models is
a time-consuming and expensive process. In the military domain in particular, data collection …

Pharmaceutical manufacturing is a critical industry that demands high precision and
efficiency while adhering to stringent quality standards. This is where artificial intelligence …

TinyML has made deploying deep learning models on low-power edge devices feasible,
creating new opportunities for real-time perception in constrained environments. However …

Neuro-symbolic artificial intelligence (AI) stands at the frontier of machine learning by
amalgamating the interpretability and structured knowledge representation of symbolic …

In this study, we investigate how AI and ML might revolutionize the agricultural industry,
particularly with regard to increasing crop output while decreasing input costs. Applying AI …

This study investigates the manner in which to diagnose and treat Parkinson's disease using
deep learning-based medical robotics. The study analyses the possibilities of these …