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  • Open Access


    Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries

    Vani Haridasan*, Kavitha Muthukumaran, K. Hariharanath

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3531-3544, 2023, DOI:10.32604/iasc.2023.030628

    Abstract Customer retention is one of the challenging issues in different business sectors, and various firms utilize customer churn prediction (CCP) process to retain existing customers. Because of the direct impact on the company revenues, particularly in the telecommunication sector, firms are needed to design effective CCP models. The recent advances in machine learning (ML) and deep learning (DL) models enable researchers to introduce accurate CCP models in the telecommunication sector. CCP can be considered as a classification problem, which aims to classify the customer into churners and non-churners. With this motivation, this article focuses on designing an arithmetic optimization algorithm… More >

  • Open Access


    Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

    Naeem Ali1, Taher M. Ghazal2,3, Alia Ahmed1, Sagheer Abbas4, M. A. Khan5, Haitham M. Alzoubi6, Umar Farooq7, Munir Ahmad4, Muhammad Adnan Khan8,*

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1671-1687, 2022, DOI:10.32604/iasc.2022.019892

    Abstract Supply Chain Collaboration is the network of various entities that work cohesively to make up the entire process. The supply chain organizations’ success is dependent on integration, teamwork, and the communication of information. Every day, supply chain and business players work in a dynamic setting. They must balance competing goals such as process robustness, risk reduction, vulnerability reduction, real financial risks, and resilience against just-in-time and cost-efficiency. Decision-making based on shared information in Supply Chain Collaboration constitutes the recital and competitiveness of the collective process. Supply Chain Collaboration has prompted companies to implement the perfect data analytics functions (e.g., data… More >

  • Open Access


    Implementation of Artificial Intelligence Based Analyzer Using Multi-Agent System Approach

    Norah S. Farooqi1, Mohamed O. Khozium2,*

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 297-309, 2022, DOI:10.32604/iasc.2022.019060

    Abstract Using Business Intelligence (BI) applications is a critical factor for modern enterprises’ success. BI is one of the key components that persistently required for the modern high-tech companies and industries were used to handle huge amounts of data in every minute of the operations. The existing literature suggested that the lack of dynamic decision making, accuracy, and the degree of flexibility are the key limitations for handling the operational data. Many industries and companies adopted the software-based solution; however, the intelligence is there due to the dependence of the operational engagement for each of the sectors. Therefore, artificial intelligence business… More >

  • Open Access


    An Intelligent Business Model for Product Price Prediction Using Machine Learning Approach

    Naeem Ahmed Mahoto1, Rabia Iftikhar1, Asadullah Shaikh2,*, Yousef Asiri2, Abdullah Alghamdi2, Khairan Rajab2,3

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 147-159, 2021, DOI:10.32604/iasc.2021.018944

    Abstract The price of a product plays a vital role in its market share. Customers usually buy a product when it fits their needs and budget. Therefore, it is an essential area in the business to make decisions about prices for each product. The major portion of the business profit is directly connected with the percentage of the sale, which relies on certain factors of customers including customers’ behavior and market competitors. It has been observed in the past that machine learning algorithms have made the decision-making process more effective and profitable in businesses. The fusion of machine learning with business… More >

  • Open Access


    A Novel Integrated Machine & Business Intelligence Framework for Sensor Data Analysis

    S. Kalyani*, A. Mary Sowjanya, K. Venkat Rao

    Journal on Internet of Things, Vol.3, No.1, pp. 27-38, 2021, DOI:DOI:10.32604/jiot.2021.013163

    Abstract Increased smart devices in various industries are creating numerous sensors in each of the equipment prompting the need for methods and models for sensor data. Current research proposes a systematic approach to analyze the data generated from sensors attached to industrial equipment. The methodology involves data cleaning, preprocessing, basics statistics, outlier, and anomaly detection. Present study presents the prediction of RUL by using various Machine Learning models like Regression, Polynomial Regression, Random Forest, Decision Tree, XG Boost. Hyper Parameter Optimization is performed to find the optimal parameters for each variable. In each of the model for RUL prediction RMSE, MAE… More >

  • Open Access


    Web Application Commercial Design for Financial Entities Based on Business Intelligence

    Carlos Andrés Tavera Romero1,*, Jesus Hamilton Ortiz2, Osamah Ibrahim Khalaf3, Andrea Ríos Prado4

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3177-3188, 2021, DOI:10.32604/cmc.2021.014738

    Abstract Multiple customer data management has become a focus of attention in big organizations. Although much information is available, it does not translate into significant profitable value-added services. We present a design of a commercial web application based on business intelligence that generates information on social and financial behavior of clients in an organization; with the purpose of obtain additional information that allows to get more profits. This app will provide a broader perspective for making strategic decisions to increase profits and reduce internal investment costs. A case in point is the financial sector, a group of financial entities were used… More >

  • Open Access


    An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

    Ayesha Jabeen1, Sitara Afzal1, Muazzam Maqsood1, Irfan Mehmood2, Sadaf Yasmin1, Muhammad Tabish Niaz3, Yunyoung Nam4,*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1191-1206, 2021, DOI:10.32604/cmc.2021.014598

    Abstract Stock market forecasting is an important research area, especially for better business decision making. Efficient stock predictions continue to be significant for business intelligence. Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns. However, major events’ news also contains significant information regarding market drivers. An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market. This research proposes an efficient model for stock market prediction. The current proposed study explores the positive and negative effects of… More >

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