Deep neural network-based approach for processing sequential data

Increasing volumes of offline/online data has thrown diverse challenges. Availability of the data has made the researchers to urge for new paradigms to analyze data to gain useful insights from it. Such analysis can be helpful for predictions in various domains such as medical informatics, cyber security, fraud detection etc. Further, problem of overloaded information is that it leads to over-choice, especially for users in web applications. Machine learning algorithms construct models that can learn from the data without being explicitly programmed, to perform predictions/decisions. However, the limitations of machine learning have paved a way to deep learning. Unlike the task specific algorithms of machine learning, deep learning learns feature representations from data itself. These traits enabled deep learning to achieve futuristic results for exceptionally complex tasks of various domains. Particularly in health care, natural language processing, information retrieval, computer vision etc. Area of research in this work focused on processing sequential data in the domains of ambient air quality management and recommender systems. Air quality data analysis is one of the domains where deep learning models have outperformed the traditional machine learning models. Hence, this research aims to build a model to predict ambient air quality using Amplified Recurrent Neural Networks for a particular geographical area. Recommender Systems (RS) have become a part of all the web applications these days. The main objective of RS is to asist users by avoiding information overload through generating personalized suggestions. Deep learning-based RS models have gained more prominence these days. Consequently, this research aims to develop deep learning-based recommenders/personal assistance/advisory systems to exploit the information systems and to capture its user’s preferences. The primary goals of the objectives were successfully accomplished with promising results.