Artificial Neural Network: Architecture, Learning Algorithms, Applications and Challenges

Abstract

Artificial neural networks (ANNs) are computational models inspired by the way biological nervous systems process information. Over the past eight decades they have grown from simple threshold units into deep, billion-parameter systems that underpin modern computer vision, language processing, healthcare analytics, finance and autonomous systems. This paper presents a structured review of ANNs covering their basic building blocks, the major architectures (feedforward, convolutional, recurrent, attention-based, generative and self-organising networks), the principal learning algorithms (supervised, unsupervised, semi-supervised and reinforcement learning together with gradient-based optimisers and regularisation techniques), representative real-world applications, and the open challenges that limit their wider adoption. Comparative tables and schematic diagrams are provided to summarise design choices. The review concludes that, while ANNs deliver state-of-the-art accuracy in many domains, issues of data dependency, interpretability, robustness, energy consumption and ethics remain unresolved and define the most promising directions for future research.