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  • Open Access


    A Boosted Tree-Based Predictive Model for Business Analytics

    Mohammad Al-Omari1, Fadi Qutaishat1, Majdi Rawashdeh1, Samah H. Alajmani2, Mehedi Masud3,*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 515-527, 2023, DOI:10.32604/iasc.2023.030374

    Abstract Business Analytics is one of the vital processes that must be incorporated into any business. It supports decision-makers in analyzing and predicting future trends based on facts (Data-driven decisions), especially when dealing with a massive amount of business data. Decision Trees are essential for business analytics to predict business opportunities and future trends that can retain corporations’ competitive advantage and survival and improve their business value. This research proposes a tree-based predictive model for business analytics. The model is developed based on ranking business features and gradient-boosted trees. For validation purposes, the model is tested 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… More >

  • Open Access


    Social Media and Stock Market Prediction: A Big Data Approach

    Mazhar Javed Awan1,2,*, Mohd Shafry Mohd Rahim2, Haitham Nobanee3,4,5, Ashna Munawar2, Awais Yasin6, Azlan Mohd Zain 7

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2569-2583, 2021, DOI:10.32604/cmc.2021.014253

    Abstract Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns. The quantity and variety of computer data are growing exponentially for many reasons. For example, retailers are building vast databases of customer sales activity. Organizations are working on logistics financial services, and public social media are sharing a vast quantity of sentiments related to sales price and products. Challenges of big data include volume and variety in both structured and unstructured data. In this paper, we implemented several machine learning models through Spark MLlib using PySpark, which More >

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