Forthcoming Articles

International Journal of Business Intelligence and Data Mining

International Journal of Business Intelligence and Data Mining (IJBIDM)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Business Intelligence and Data Mining (2 papers in press)

Special Issue on: Knowledge Discovery from Big Data to Spur Social Development Part 3

  • A lightweight photovoltaic fault diagnosis model based on infrared images and texture features   Order a copy of this article
    by Zhuohang Wei , Xiaoyang Lu 
    Abstract: Fault diagnosis is crucial for the stability and safety of photovoltaic (PV) arrays' power generation. However, infrared image-based PV fault diagnosis faces challenges such as feature sparsity, scale differences, and sample imbalance. Thus, this paper proposes a lightweight PV fault diagnosis model based on infrared images to identify different fault types and severities. Firstly, an edge intensity embedding module is applied PV fault detection to enhance distinguishing faults of varying severities. Secondly, the proposed method adopts an improved MicroNet as the backbone to efficiently capture global thermal distribution features via sparse connection and dynamic cross-channel fusion. Subsequently, it combines PANet and multi-detection heads to improve detection robustness at different shooting distances. Finally, it uses a weighted loss function to alleviate sample imbalance. Experimental results show that with only 1.3 GFLOPs, the model achieves a mAP of 91.8%, outperforming other mainstream object detection algorithms.
    Keywords: PV fault detection; thermal imaging; MicroNet.
    DOI: 10.1504/IJBIDM.2027.10080247
     
  • Deep churn dive: unveiling customer defection in retail banking   Order a copy of this article
    by Hani Ibrahim Younis, Lubna Fayez Eliyan, Mohammad Alshraideh, Qasem M. Kharma, Imad Salah 
    Abstract: Retail banking is highly competitive, and customer retention is crucial to revenue. This research focuses on developing an advanced churn prediction model to address this challenge. It proposes a model that employs deep neural network methods and incorporates state-of-the-art performance tuning techniques. We have demonstrated the effectiveness of this model by evaluating its best-tuned hyperparameters, achieving an impressive area under the curve (AUC) of 95.0% on a dataset of 10,000 banking customers. Our model outperformed five other machine learning techniques, including logistic regression, support vector machines, decision trees, random forests, and extreme gradient boosting, based on its superior AUC result. This forecasting tool can assist retail bank decision-makers in making informed decisions regarding their customer retention strategy.
    Keywords: churn prediction; retail banking; deep forward neural network; deep learning; feature engineering; artificial neural network; ANN.
    DOI: 10.1504/IJBIDM.2027.10080761
     

 

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