As our dependence on intelligent machines continues to grow, so does the demand for more transparent and interpretable models. In addition, the ability to explain the model generally is now the gold standard for building trust and deployment of Artificial Intelligence (AI) systems in critical domains. Explainable Artificial Intelligence (XAI) aims to provide a suite of machine learning (ML) techniques that enable human users to understand, appropriately trust, and produce more explainable models. Selecting an appropriate approach for building an XAI-enabled application requires a clear understanding of the core ideas within XAI and the associated programming frameworks. We survey state-of-the-art programming techniques for XAI and present the different phases of XAI in a typical ML development process. We classify the various XAI approaches and using this taxonomy, discuss the key differences among the existing XAI techniques. Furthermore, concrete examples are used to describe these techniques that are mapped to programming frameworks and software toolkits. It is the intention that this survey will help stakeholders in selecting the appropriate approaches, programming frameworks, and software toolkits by comparing them through the lens of the presented taxonomy.

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Big and small firms, organizations, hospitals, schools, and other commercial offices are generating moderate to huge amounts of data regularly and need to constantly update and manage these data. These data are not only used at that instance, but generally, the retrospective analysis of data helps tremendously to improve the business strategies and the marketing trends. With time, these data may grow and become unmanageable if handled conventionally, like the file system. These factors resulted in the introduction of the terms database and database management system. Hierarchical, network, relational, and object-oriented approaches of DBMS are discussed in this paper. A highlight of the new-generation database approach called NoSQL is also included in this paper along with an insight into augmented data management. A model based on the database design for the Study in India Program is discussed. It is followed by a graphical user interface developed in Java for the same which ensures the ease of access to the database.