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Dr. Nabeena Ameen is an Assistant Professor (Selection Grade) in the Department of Information Technology at B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India. Her research interests include Big Data Analytics, Cloud Computing, Wireless Sensor Networks, Artificial Intelligence, and intelligent information systems. She is actively engaged in teaching, research, and interdisciplinary projects involving emerging computing technologies.
Deep Learning has become a pivotal sector of Artificial Intelligence (AI), facilitating astute forecasting and decision-making assistance in various practical scenarios. The capacity of deep neural networks to autonomously acquire hierarchical representations from extensive structured and unstructured datasets has markedly enhanced predictive accuracy across healthcare, finance, agriculture, transportation, manufacturing, smart cities, education, and industrial automation. The amalgamation of Deep Learning with the Internet of Things (IoT), cloud computing, edge computing, and Big Data Analytics has significantly augmented the proficiency of intelligent systems in processing real-time data, uncovering concealed patterns, predicting future occurrences, and facilitating automated decision-making. Architectural frameworks like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Autoencoders, Generative Adversarial Networks (GANs), Graph Neural Networks (GNNs), and Transformer models are essential technologies for predictive analytics. This chapter delineates the principles, frameworks, applications, obstacles, and prospective research avenues of deep learning for astute forecasting and decision-making assistance.