Bshair Alharthi

In today's digital landscape, chatbots have become widely used across various applications, particularly in systems that provide intelligent assistance to users. To enhance user experience, these systems are often equipped with chatbots capable of processing user inquiries and delivering accurate, timely responses. This study proposes a structured framework for evaluating the impact of social media on customer buying behavior through a case study focused on selecting the most suitable Online Chatbot Platform (OCP). The selection of an optimal OCP is a complex multi-criteria decision-making (MCDM) problem, involving multiple interdependent and often conflicting factors. One of the key challenges in this process is the potential loss of critical decision information and underestimation of engagement complexity in a neutrosophic environment. To address this challenge, this paper introduces a hybrid MCDM framework that integrates Interval-Valued Neutrosophic Sets Analytical Hierarchy Process (IVNSs-AHP) with Weighted Aggregated Sum Product Assessment (WASPAS). The IVNSs-AHP method is employed to determine the relative importance of the evaluation criteria, while WASPAS is used to rank different OCP options. A comprehensive set of assessment criteria, grounded in the principles of sustainable development, was established through an extensive literature review and expert consultations to ensure the practical applicability of the proposed MCDM model.

Credit scoring models serve as pivotal instruments for lenders and financial institutions, facilitating the assessment of creditworthiness. Traditional models, while instrumental, grapple with challenges related to efficiency and subjectivity. The advent of machine learning heralds a transformative era, offering data-driven solutions that transcend these limitations. This research delves into a comprehensive analysis of various machine learning algorithms, emphasizing their mathematical underpinnings and their applicability in credit score classification. A comprehensive evaluation is conducted on a range of algorithms, including logistic regression, decision trees, support vector machines, and neural networks, using publicly available credit datasets. Within the research, a unified mathematical framework is introduced, which encompasses preprocessing techniques and critical algorithms such as Particle Swarm Optimization (PSO), the Light Gradient Boosting Model, and Extreme Gradient Boosting (XGB), among others. The focal point of the investigation is the LIME (Local Interpretable Model-agnostic Explanations) explainer. This study offers a comprehensive mathematical model using the LIME explainer, shedding light on its pivotal role in elucidating the intricacies of complex machine learning models. This study’s empirical findings offer compelling evidence of the efficacy of these methodologies in credit scoring, with notable accuracies of 88.84%, 78.30%, and 77.80% for the Australian, German, and South German datasets, respectively. In summation, this research not only amplifies the significance of machine learning in credit scoring but also accentuates the importance of mathematical modeling and the LIME explainer, providing a roadmap for practitioners to navigate the evolving landscape of credit assessment.