Authors: Shaunak Das, Saumyajit Maulik, Subhodeep Sengupta, Shubhajit Mridha, Sagnik Banerjee, Supriyo Mondal, Anurima Majumdar, Antara Ghosal, Koushik Pal
Abstract: Explainable Artificial Intelligence (XAI) is now quickly becoming the game changer which makes it easier to get the answer of why the complex engineering models do what they do. Explainable Artificial Intelligence (XAI) refers to the AI systems which are designed to provide clear and understandable outcomes of their decisions, predictions and actions. XAI aims to make the Artificial Intelligence system more transparent and trustworthy for the users. This study aims to dive into how using XAI can be a better and efficient option for designing and implementing the microstrip patch antenna. Microstrip patch antennas are good options for wireless network design and implementation because they are small in size and cost effective. The traditional methods of designing those antennas tend to depend on the guess and round after round of try and check which produces delays and keeps us detached from what’s going on with the trade-offs in the designing process. By using the XAI into the design of microstrip patch antennas can make a huge impact on the optimization of design by providing us clear and interpretable insights into the design parameters and their impact on the working performance of the antenna. By applying XAI techniques such as feature importance analysis, feature selection, rule based systems designers can get a deeper understanding about the design parameters, rules and regulations
DOI: http://doi.org/
