Adel Saad Assiri^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069826 - 10 November 2025

Abstract Customer churn is the rate at which customers discontinue doing business with a company over a given time period. It is an essential measure for businesses to monitor high churn rates, as they often indicate underlying issues with services, products, or customer experience, resulting in considerable income loss. Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth. Traditional machine learning (ML) models often struggle to capture complex temporal dependencies in client behavior data. To address this, an optimized deep learning (DL) approach using a Regularized Bidirectional Long Short-Term… More >

Ginanjar Suwasono Adi¹, Samsul Huda^2,*, Griffani Megiyanto Rahmatullah³, Dodit Suprianto¹, Dinda Qurrota Aini Al-Sefy³, Ivon Sandya Sari Putri⁴, Lalu Tri Wijaya Nata Kusuma⁵

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068619 - 10 November 2025

Abstract In the competitive retail industry of the digital era, data-driven insights into gender-specific customer behavior are essential. They support the optimization of store performance, layout design, product placement, and targeted marketing. However, existing computer vision solutions often rely on facial recognition to gather such insights, raising significant privacy and ethical concerns. To address these issues, this paper presents a privacy-preserving customer analytics system through two key strategies. First, we deploy a deep learning framework using YOLOv9s, trained on the RCA-TVGender dataset. Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate More >

Fan Deng^*

Journal of Psychology in Africa, Vol.35, No.6, pp. 797-805, 2025, DOI:10.32604/jpa.2025.068735 - 30 December 2025

Abstract This study investigated the relationship between customer-employee exchange (CEX) and employee work engagement (vigor, dedication, absorption), and the role of psychological safety and self-efficacy mediate that relationship. Survey data were collected from 329 Chinese hotel employees (females = 52.9%, tenure: 1–3 years = 50.5%). The results following ordinary least squares regression and the SPSS PROCESS macro indicate that higher customer-employee exchange is associated with employee vigor, dedication, and absorption. Psychological safety and self-efficacy mediate the relationship between customer-employee exchange and vigor and dedication to be stronger, while the work absorption mediating effect is not significant. More >

Muhammad Adeen Jamal¹, Bilal Khan^2,*, Sameed Ur Rehman¹, Wahab Khan¹

Journal on Artificial Intelligence, Vol.7, pp. 417-435, 2025, DOI:10.32604/jai.2025.070762 - 20 October 2025

Abstract The development of artificial intelligence (AI) has sparked the invention of chatbots, which are intelligent conversational agents. These chatbots have the potential to completely transform how people interact while enhancing user experience. This study explores the building along with its execution of a chatbot for customer service support at a university reception using recurrent neural networks (RNNs). To increase user requests, the accuracy of the information, and overall satisfaction with the service, it evaluates machine learning models including RNN, XLNet, and Bidirectional Encoder Representations from Transformers (BERT). In this research project, data were gathered from… More >

Phan-Anh-Huy Nguyen^*, Luu-Luyen Than

Intelligent Automation & Soft Computing, Vol.40, pp. 221-234, 2025, DOI:10.32604/iasc.2025.058976 - 14 April 2025

Abstract The burgeoning e-commerce industry has made online customer reviews a crucial source of feedback for businesses. Sentiment analysis, a technique used to extract subjective information from text, has become essential for understanding consumer sentiment and preferences. However, traditional sentiment analysis methods often struggle with the nuances and context of natural language. To address these issues, this study proposes a comparison of deep learning models that figure out the optimal method to accurately analyze consumer reviews on women's clothing. CNNs excel at capturing local features and semantic information, while LSTMs are adept at handling long-range dependencies… More >

Murat Cikan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 673-721, 2025, DOI:10.32604/cmes.2025.061699 - 11 April 2025

Abstract This study examines various issues arising in three-phase unbalanced power distribution networks (PDNs) using a comprehensive optimization approach. With the integration of renewable energy sources, increasing energy demands, and the adoption of smart grid technologies, power systems are undergoing a rapid transformation, making the need for efficient, reliable, and sustainable distribution networks increasingly critical. In this paper, the reconfiguration problem in a 37-bus unbalanced PDN test system is solved using five different popular metaheuristic algorithms. Among these advanced search algorithms, the Bonobo Optimizer (BO) has demonstrated superior performance in handling the complexities of unbalanced power… More >

Konstantinos I. Roumeliotis^1,*, Nikolaos D. Tselikas², Dimitrios K. Nasiopoulos³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2769-2792, 2025, DOI:10.32604/cmc.2025.059567 - 17 February 2025

Abstract In the rapidly evolving landscape of natural language processing (NLP) and sentiment analysis, improving the accuracy and efficiency of sentiment classification models is crucial. This paper investigates the performance of two advanced models, the Large Language Model (LLM) LLaMA model and NLP BERT model, in the context of airline review sentiment analysis. Through fine-tuning, domain adaptation, and the application of few-shot learning, the study addresses the subtleties of sentiment expressions in airline-related text data. Employing predictive modeling and comparative analysis, the research evaluates the effectiveness of Large Language Model Meta AI (LLaMA) and Bidirectional Encoder… More >

Santiago Bañales^1,2,*, Raquel Dormido¹, Natividad Duro¹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 869-907, 2025, DOI:10.32604/cmes.2024.054946 - 17 December 2024

Abstract Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’ participation in the energy transition. This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons. Smart meter data is split between daily and hourly normalized time series to assess monthly, weekly, daily, and hourly seasonality patterns separately. The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series… More > Graphic Abstract

Xin Zhang^1,2, Gege Jiang^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.3, pp. 1-5, 2023, DOI:10.32604/icces.2023.09222

Abstract With the booming development of the ride-sourcing (RS)industry, aggregation platforms that integrate RS companies have emerged in recent years, such as Gaode and Meituan. Aggregation platforms can consolidate resources and avoid fragmentation of the market. But the emergence of aggregation platforms has also changed the market structure and brought challenges. This paper explores the impact of aggregation platforms on the market with two models of companies: customer-to-customer (C2C) companies, and business-to-customer (B2C) companies. C2C companies adjust supply and demand to maximize revenue by determining travel fares and the cut taken from the travel fares, i.e., More >

Yaling Xu, Congjun Rao^*, Xinping Xiao, Fuyan Hu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2715-2742, 2023, DOI:10.32604/cmes.2023.029023 - 03 August 2023

Abstract As the banking industry gradually steps into the digital era of Bank 4.0, business competition is becoming increasingly fierce, and banks are also facing the problem of massive customer churn. To better maintain their customer resources, it is crucial for banks to accurately predict customers with a tendency to churn. Aiming at the typical binary classification problem like customer churn, this paper establishes an early-warning model for credit card customer churn. That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm (GSA) and an Improved Beetle Antennae Search (IBAS) is proposed to… More > Graphic Abstract