Improving smart deals system to secure human-centric consumer applications: Internet of things and Markov logic network approaches

Considering the increasing inclination of modern consumers to frequent large retail chains capable of promptly fulfilling their diverse needs, there is a noticeable surge in the prevalence of contemporary shopping complexes. Subscription services, customer-focused strategies, and efficient supply management are driving the progression of intelligent commerce within these expansive retail platforms. The Internet of Things (IoT) presents the foundation for “smart” retailers that can monitor inventory levels, diminish equipment failures, and provide better customer experience. Many models, as one of the widely used methods in this domain, Markov Logic Network (MLN), can simultaneously use activity knowledge and data by unifying probability and logic. In this research, we determine a smart deals system (SDS), consider the improved machine learning algorithms to meet performance, and develop secure human-centric consumer applications to render the system workable. From the results, and based on the percentage of efficiency, around 10% of clients are connected randomly, which has a minor impact on the outcomes from LR (logistic regression). Similar outcomes are delivered when the number of customers in the scope of 30–40% is connected for NB (Naive Bayes). Hence, prospective shopping sales will increase along with the efficiency and speed at which it operates.