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.

考虑到现代消费者越来越倾向于光顾能够迅速满足其多样化需求的大型零售连锁店，当代购物中心的普及率显着激增。订阅服务、以客户为中心的策略和高效的供应管理正在推动这些广阔的零售平台内智能商务的发展。物联网 (IoT) 为“智能”零售商提供了基础，可以监控库存水平、减少设备故障并提供更好的客户体验。许多模型，作为该领域广泛使用的方法之一，马尔可夫逻辑网络（MLN），可以通过统一概率和逻辑来同时使用活动知识和数据。在这项研究中，我们确定了一个智能交易系统（SDS），考虑改进的机器学习算法以满足性能，并开发安全的以人为中心的消费者应用程序以使系统可行。从结果来看，根据效率百分比，大约 10% 的客户端是随机连接的，这对 LR（逻辑回归）的结果影响较小。当 30-40% 范围内的客户数量通过 NB（朴素贝叶斯）连接时，会产生类似的结果。因此，预期的购物销售额将随着其运营效率和速度的提高而增加。