Advancements in Artificial Intelligence (AI), such as machine learning, natural language processing, computer vision, and cloud computing, are presenting new possibilities in information systems technology by improving the management of data, decision-making processes, interaction interfaces, and cybersecurity. With a focus on an optimised AI information system, this monograph studies the evolution of AI information systems. It addresses data processing, decision-making, and cybersecurity AI optimisations. AI-powered personalisation interfaces, AI-powered trends, AI-infused IoT, quantum computing, and the ethics surrounding AI are addressed in the book as well. Profound case studies make the content relatable for numerous industries to aid researchers, academics, and specialists in
understanding information systems intertwined with the intelligence of AI.

Keywords:

Artificial Intelligence, Information Systems, Machine Learning, Data Management, Decision Support Systems, Cybersecurity, Natural Language Processing, Cloud Computing, AI-driven Innovations, Emerging Technologies

How to Cite this Book:

Kanya, N., Rajavarman, V. N., & Pavan, S. (2025). AI-driven innovations in information systems. Jupiter Publications Consortium. https://doi.org/10.47715/978-93-86388-55-1

This monograph provides a comprehensive exploration of deep learning, a transformative technology in the field of artificial intelligence. Beginning with an introduction to the background of machine learning and the rise of deep learning, the text delves into the historical evolution of artificial intelligence and neural networks. It offers a detailed examination of the foundations of deep learning, including neural network basics, architecture, activation functions, and essential techniques like backpropagation and gradient descent. The work then progresses to advanced neural network architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms, including transformers. A significant focus is placed on practical aspects, like training deep models, regularization techniques, optimization strategies, and the utilization of transfer learning with pre-trained models. The monograph also explores the diverse applications of deep learning in fields such as image recognition, natural language processing, and autonomous systems, while addressing the challenges of overfitting, interpretability, and ethical considerations. Looking forward, it examines emerging trends like quantum neural networks, neural architecture search, and the intersection of neuroscience and deep learning. The text concludes with insightful case studies, reflecting on real-world implementations and milestones in deep learning. This work serves as a valuable resource for understanding the current state of deep learning, its practical applications, and future directions.

Keywords: Deep Learning, Machine Learning, Artificial Intelligence, Neural Networks, Backpropagation, Gradient Descent, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Training Models, Regularization, Optimization, Transfer Learning, Image Recognition, Natural Language Processing, Autonomous Systems, Overfitting, Interpretability, Ethical Considerations, Quantum Neural Networks, Neural Architecture Search, Neuroscience.