Abstract: Machine learning models (MLMs) are used in industry to automate complicated activities, minimize human error, and improve decision-making by evaluating large volumes of data in real time. To managing inventory and quality control in the apparel and auto industries, they provide predictive capabilities such as predicting equipment breakdowns, maintenance and detecting fraud in the finance sector and the major key advantages include cost reduction, higher productivity, better product quality, and tailored client experiences. MLM helps the industries to reduce downtime, prevent errors, and gain a competitive edge through data-driven strategies and processing massive volumes of data in real time. So, there is a need to select the best MLMs for industrial robotics and by considering it, this paper addresses this problem as multiple criteria decision-making (MCDM) by exploiting hesitant bipolar fuzzy information, which takes into account both hesitation and bipolarity in decision-maker preferences. This paper introduced the new aggregation operators (AO) based on geometric and arithmetic procedures to efficiently aggregate the data including the hesitant bipolar fuzzy weighted geometric operator (HBFWGO), which is appropriate for multiplicative relationships, and the hesitant bipolar fuzzy weighted average operator (HBFWAO), which gives weighted importance to qualities. Further, the dual operators including the dual hesitant bipolar fuzzy weighted geometric operator (DHBFWGO) and the dual hesitant bipolar fuzzy weighted average operator (DHBFWAO) have been presented that are further applied to create novel strategies for resolving MCDM issues and offering a methodical manner to assess and combine features. Moreover, the example of selecting the optimal MLMs to show the robustness and efficiency of the suggested methodology has been presented which illustrates the applicability and strength of the proposed methodology in actual decision-making situations.

Chan Gu and Bo Tang. “Selecting the Best Machine Learning Models for Industrial Robotics with Hesitant Bipolar Fuzzy MCDM”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.11 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0151151

@article{Gu2024,
title = {Selecting the Best Machine Learning Models for Industrial Robotics with Hesitant Bipolar Fuzzy MCDM},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151151},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151151},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Chan Gu and Bo Tang}
}