Published Paper Details:

The term anomaly detection refers to methods and procedures that help in seeking the differences in the sample data that deviate from generally expected patterns. Depending on the availability of data labels, the types of abnormalities, and the applications, many anomaly detection models are developed. This study aims to give a well-organized and thorough review of anomaly detection research. We think it will aid in a superior thoughtful of the various areas in which study has been conducted on this issue, as well as how approaches created in one field can be utilized in domains where they were not originally intended. We've divided the anomaly detection research methodologies into distinct categories. We describe the fundamental anomaly detection approach, as well as its modifications and important assumptions, for distinguishing between normal and abnormal behavior in each category. In addition, we highlight the merits and limits of each category, as well as examine the computational complexity of the approaches in real-world application areas. Deep learning can be enhanced by integrating insights from the Indian Knowledge System, which includes ancient texts like the Vedas, Upanishads, and various philosophical and scientific treatises. By incorporating concepts such as holistic thinking, consciousness, and the interconnectedness of all things, deep learning algorithms may gain a more nuanced understanding of complex systems and patterns. Additionally, principles from disciplines like Ayurveda, yoga, and Jyotish (Vedic astrology) could inspire novel approaches to optimization, adaptation, and self-learning within deep learning frameworks. Integrating these insights could lead to more culturally inclusive and globally relevant AI systems.Finally, we discuss research gaps and limitations encountered when using deep anomaly detection algorithms to solve real-world problems