Biography

is an expert in Civil Engineering with over 17 years of experience. He holds a Ph.D. in Structural Engineering. He has published numerous research papers in reputed journals. He has guided several research scholars. He has worked on infrastructure development projects. His research interests include structural analysis and sustainable construction. He has received awards for academic excellence. He contributes to curriculum development. He collaborates with industries on engineering projects. He is a member of professional bodies. He actively mentors students and researchers. He is dedicated to sustainable engineering practices.constraints. The subsequent structure of the paper is outlined as follows. Section 2 examines the pertinent literature, Section 3 introduces the fuzzy c-means-PSO algorithm for clustering, and Section 4 outlines the experimental results. This job is ultimately completed in section 5. II. LITERATURE SURVEY The analysis of consumer behavior delves into the history and influence of CNN in the banking industry specifically looking at how they have helped improve consumer behavior satisfaction and enable data-driven decision-making. Because DL and CNN in particular have just emerged, digital marketing tactics have been utterly transformed. This is because can now glean deeper insights from massive diverse information. CNNs have been modified to handle various types of data in the industry such as consumer behavior social media interactions online behavior patterns transaction histories and more [6]. In order to gain a better grasp of the subject at hand it have employed a ML strategy that is fuelled by DL and makes use of supervised and unsupervised ML approaches. The offered predicted information and key performance indicators of the various ML approaches. Data mining ML encompasses both supervised and unsupervised learning [7]. The goal of DL a classification approach is to uncover rules and insights by mapping input and output layers with transformation functions. This area of ML typically makes use of a stochastic BP technique. Class labels which are the output layer's dependent variables are mapped to independent multivariate consumer behavior variables during the mapping process. In addition it evaluated our DL method's predictive power against that of other popular ML prediction methods such as DT, RF, SVM and ANN. Using the same dataset it discovered that DL performed better than ML [8]. By mining massive volumes of data for patterns and insights ML can foretell what's to come and lend a hand with decision-making. The strategic decision-making process of organizations is significantly improved by this functionality [9]. The digital marketers' attitudes towards and awareness of ML and DL tools as well as their adoption and utilization of these tools to assist strategic and operational management according to the ML research gap analysis. In order to better plan for the future these advanced analytical tools employ ML to learn from past data. There are numerous industries that can benefit from the ML and DL tools [10]. This article presents research that focuses on their usage in consumer behavior marketing analysis specifically in relation to enhancing marketing management's strategic DL and operational decisions. To address the requests of to discuss the methodological issues consumer behavior researchers face when dealing with text and to evaluate the various NLP methods used in marketing to analyze consumer review text it will undertake a review of these methods [11]. While topic modeling and other supervised ML technologies based on NN models have seen extensive use in marketing research they have only lately emerged from consumer behavior and are as prevalent in the field [12]. With this study it looks at how ML algorithms can be used to optimize consumer behavior analytics. The finally popular ML models like DT, SVM and NN with optimization techniques like GA gradient descent and bayesian optimization is something it looks into [13]. It may tackle a variety of function optimisation problems, or problems that can be changed to function optimisation problems, with ease by implementing and applying PSO, a population-based optimisation method. A variant of particle swarm optimisation, known as particle swarm optimisation for TSP, was suggested. This study proposes FCM-FPSO, a fuzzy clustering approach that combines FCM and FPSO. In comparison to the FCM and FPSO algorithms, the Fuzzy C means-PSO method outperforms them in six real-world data sets. PROPOSED SYSTEM III. In contemporary society, virtually everyone is online. Digital marketers predominantly utilise the internet as a strategy for selling products and services. This is due to its capacity to conserve considerable time, costs, and other resources. Products and services promoted through digital platforms are known as digital marketers. They intend to market the brands using a variety of digital media platforms. As an example, brands can learn which media platforms are most effective for reaching their target audience by utilising social media as a marketing strategy. Market research and targeted advertising rely on the extensive consumer data found in the Consumer Behaviour and Shopping Habits Dataset. It details the demographic information (age and gender), the identity information (customer ID), and the transaction value (purchase amount in USD). Information such as the purchased item, category, and location can provide light on consumer tastes and patterns by area. Customers have distinct preferences, which are met by the Size, Colour, and Season data [14].