International Journal of Business Intelligence and Data Mining (13 papers in press)

Regular Issues

• Improving brain MRI segmentation of multiple sclerosis using an advanced CNN approach  
  by V. Biksham , Sampath Korra, B. Pradeep Kumar , Salar Mohammad 
  Abstract: Multiple sclerosis (MS) can be detected early by looking for lesions in brain magnetic resonance imaging (MRI). Recently, unsupervised anomaly detection algorithms based on autoencoders were presented for the automatic identification of MS lesions. However, because these autoencoder-based approaches were created exclusively for 2D MRI pictures (e.g., 2D cross-sectional slices), they do not take use of the complete 3D information of MRI. In this research work, a novel 3D autoencoder-based methodological solution for detecting MS lesion volume in MRI is offered. We begin by defining a 3D convolutional neural network (CNN) for complete MRI volumes and then construct each encoder and decoder layer of the 3D autoencoder using 3D CNN. For optimal data reconstruction, we additionally include a skip link between the encoder and decoder layers. In the experimental results, we compare the 3D autoencoder-based method to the 2D autoencoder models using training datasets from the Human Connectome Project (HCP) and testing datasets from the Longitudinal MS Lesion Segmentation Challenge, and show that the proposed method outperforms the 2D autoencoder models by up to 20% in MS lesion prediction.
  Keywords: multiple sclerosis; brain MRI; image segmentation; CNN; chronic disease; healthcare.
  DOI: 10.1504/IJBIDM.2025.10068668
   
  
• Analysing the effectiveness of financial news sentiments on stock price prediction of twelve Indian sectoral stock indices using a hybrid LSTM-GRU model  
  by Meera George, R. Murugesan 
  Abstract: Despite the growing interest in combining news sentiments with historical data to improve stock price prediction, a considerable gap exists in predicting the sectoral stock indices using this methodology. This study addresses this gap by predicting the closing price of 12 Indian sectoral stock indices through a hybrid deep-learning architecture. It employs a hybrid TFIDF-Doc2Vec feature extraction technique and an SVM classifier to extract the financial news sentiments. These sentiments are utilised to create a sentiment Index, combined with historical stock data to predict each sectoral stock index using a hybrid LSTM-GRU model. The study evaluates the effectiveness of financial news sentiments in sectoral stock prediction by comparing models with and without sentiments. Results demonstrate a notable influence of sentiments on the stock price prediction of ten sectoral stock indices with a pronounced impact on the NSEBANK index. This study offers valuable insights for investors in formulating sector-specific trading strategies. It also aids policymakers in market regulation and helps financial analysts improve forecasting models by incorporating financial news sentiments. Future research could explore the integration of multi-source investor sentiments with advanced deep-learning models for even more accurate stock price predictions across diverse sectors.
  Keywords: stock price prediction; sectoral stock indices; financial news sentiments; FNSs; hybrid TFIDF-Doc2Vec; hybrid LSTM-GRU.
  DOI: 10.1504/IJBIDM.2025.10070322
   
  
• Presenting a model to reduce students' academic drop by using analytical comparison of machine learning algorithms in data mining (case study of Shahed University)  
  by Mozhdeh Salari, Reza Radfar, Mahdi Faghihi 
  Abstract: This research aims to find factors that predict undergraduate student educational performance. To achieve this goal, the study follows the CRISP-DM method. This study used various classification algorithms to predict the total GPA. The data used in this research are records of undergraduate students from 2012 in Shahed University. We used 1,468 data records in data mining. We used the Rapidminer 9.9 tool for modelling. This study also considers four feature selection techniques. This study used K-fold cross-validation to split the data. This study introduced the best model for predicting students' academic performance. In two-class modelling, we get better results and higher accuracy than four-class modelling. This research found the random forest algorithm best for predicting students' performance. It achieved 94.17% accuracy with two classes. The random forest results show a higher chance of success in students with a higher 1st semester GPA.
  Keywords: student performance prediction; data mining; machine learning; data science applications in education.
  DOI: 10.1504/IJBIDM.2025.10067362
   
  
• AI advancements - scary or hand holding for employees? A systematic literature review  
  by Remya Lathabhavan, Kottuvada M.S.V.D. Akshar 
  Abstract: The knowledge gained from a thorough literature analysis that was carried out to identify, categorise, and analyse recent developments in artificial intelligence (AI), its business applications, and its effects on the labour force is presented in this paper. Ninety-four papers are analysed and categorised as AI-related, business-related and domain-specific. AI developments and their applications in different functions of business and sectors of industry, and the possible impact on workforce are discussed. Robotic process automation, machine learning and natural language processing, along with their recent features that find use in business functions are presented. This study contributes to both technical and managerial literature. Future studies irrespective of their discipline can use this study as a roadmap from both technical and business perspectives. The paper also discusses the impact of AI on workforce in a futuristic and optimistic perspective. This study's practical implications include illuminating the path towards individual self-evaluation and skill acquisition, organisational skill development and forecasting, and societal welfare policy framing.
  Keywords: AI advancements; AI and business applications; AI and workforce impact; AI and employees; Systematic literature review.
  DOI: 10.1504/IJBIDM.2025.10068800
   
  
• Future trend of rumour detection by using net-map analysis: a bibliometric review  
  by Neetu Rani, Prasenjit Das 
  Abstract: Through the enhancement of numerous social media sites rumours spread more rapidly among society and influence people in a very negative way. In the last decade more attention is given by the researchers to mitigate the threats produced by rumour. The present study provides bibliometric analysis by using various tools and discusses research advancement in the area of rumour detection. The present study uses VOSviewer software to implement the bibliometric analysis of 1,907 records related to rumour dissemination by collecting the data from the Web of Science from 1989 to 2019. The bibliometric results have shown publications trends, main journals, most cited articles, most productive country, prominent authors and institutions. Further net-map analysis illustrates the growth of rumour detection in past, present and future as well. This study illustrates the publication evolution during the time, identifies the area of present investigation and probable guidelines for forthcoming research. Bibliometric and network analysis outcomes from this research will significantly facilitate understanding the progress and trends in rumour detection.
  Keywords: rumour; bibliometric analysis; fake news; social media.
  DOI: 10.1504/IJBIDM.2025.10069196
   
  

Special Issue on: Data Analysis and Mining in Business Domains New Techniques and Applications

• A method for mining comment text data on e-commerce platforms for enterprise digital transformation  
  by Yang Wang 
  Abstract: In order to solve the problems of poor data mining performance, high RMSE value, and low stability variance in traditional e-commerce platform comment text data mining methods, a new method for mining comment text data on e-commerce platforms for enterprise digital transformation is proposed. Firstly, LDA is used for dimensionality reduction of comment text data; Secondly, the information gain method is applied to screen the key features of the comment text data based on the dimensionality reduction results; Finally, based on the selected key features, the K-means algorithm is used to cluster and mine the comment text data. The experimental results show that this method can effectively identify and classify different types of comment data, and obtain more accurate mining results. After multiple iterations, its RMSE index remained stable at around 0.2, and the highest stability variance reached 98.3, indicating that its data mining results were more accurate and stable.
  Keywords: enterprise digital transformation; e-commerce platform; text data mining; LDA; key features; k-means algorithm.
  DOI: 10.1504/IJBIDM.2025.10069901
   
  
• Book classification recommendation method for university libraries based on collaborative filtering algorithm  
  by Yina Liu 
  Abstract: The recommendation of book classification in university libraries is of great significance for improving information retrieval efficiency and optimising book resource management. In order to solve the problems of low accuracy, long time, and low user satisfaction in traditional book classification recommendation methods for university libraries, a book classification recommendation method for university libraries based on collaborative filtering algorithm is proposed. Firstly, the fuzzy C-means clustering algorithm is used to cluster the data of university library platforms, completing the collection of library platform data. Secondly, determine the user characteristics of university libraries based on the collected data. Finally, the collaborative filtering algorithm calculates the predicted scores of university library books based on user characteristics, and implements book classification recommendations for university libraries. Experimental results show that the maximum classification recommendation accuracy of the proposed method is 97.6%, the average recommendation time is 0.52 s, and the average user satisfaction is 94.71.
  Keywords: collaborative filtering algorithm; university library; book classification recommendation; fuzzy C-means clustering algorithm; user characteristics.
  DOI: 10.1504/IJBIDM.2025.10069902
   
  
• Rapid detection of abnormal sales data on e-commerce platforms under the digital transformation of enterprises  
  by Jing Wang, Hongmei Zhao 
  Abstract: In order to quickly and accurately detect outliers in sales data, a new e-commerce platform sales data abnormal rapid detection method is proposed under the digital transformation of enterprises. Firstly, analyse the impact of enterprise digital transformation on sales data of e-commerce platforms. Secondly, principal component analysis is used to extract key information through dimensionality reduction techniques, reducing computational complexity. Singular value decomposition is utilised to process data and effectively identify the main factors affecting sales. Again, by calculating the dispersion of sales data, quantitatively evaluate the fluctuation of sales data. Finally, optimize the grid partitioning and KNN algorithm parameters, and use the fast density peak algorithm to achieve efficient and real-time abnormal detection in e-commerce platform sales data. The experimental results show that the data abnormal detection accuracy of our method consistently remains above 91%, and the longest detection time does not exceed 10 seconds.
  Keywords: digital transformation of enterprises; e-commerce platform; sales data; rapid detection of anomalies.
  DOI: 10.1504/IJBIDM.2025.10069903
   
  
• Parsing and verification method of basic power grid data based on multi data source fusion  
  by Zhibin Zhou, Zhiguo Zhou, Xiongfeng Ye 
  Abstract: In order to solve the problems of low parsing accuracy, low verification accuracy, and long data parsing and verification time in traditional power grid basic data parsing and verification methods, a parsing and verification method of basic power grid data based on multi data source fusion is proposed. Using the D-S evidence theory to fuse multiple data sources in the power grid, smoothing the fusion results and inputting them into an RBF neural network to obtain the parsing results of the power grid basic data. Combining the five verification principle attributes and Bayes’ theorem, the power grid basic data verification is implemented. The experimental results show that the average data parsing accuracy of the proposed method is 96.69%, the average validation accuracy is 96.48%, and the time consumption varies between 0.23s and 0.55s, which is of great significance for improving data quality and management level.
  Keywords: multi data source fusion; basic power grid data; parsing verification; D-S evidence theory; RBF neural network; Bayes’ theorem.
  DOI: 10.1504/IJBIDM.2025.10069904
   
  
• Study on cloud resource scheduling in power multi service scenarios based on large language model technology framework  
  by Shuhong Wu 
  Abstract: Due to the complexity of various business scenarios in the power industry, it is difficult to achieve load balancing, resulting in long cloud resource scheduling and system execution times. Propose a cloud resource scheduling algorithm for power multi service scenarios based on the big language model technology framework. Using triangular fuzzy number analysis to determine the uncertainty of execution time, and using logarithmic method to unify the data scale, the optimisation objective of cloud resource scheduling is determined. Using the linear variation of sine functions in big language modelling techniques to determine scheduling order. By utilising multi head self attention and feedforward neural networks for internal transmission, a pre trained model is constructed, and combined with fine-tuning and implementation stages, cloud resource scheduling is achieved. Experiments have shown that this algorithm reduces the execution time and cost of cloud resource scheduling in multi service scenarios of electricity.
  Keywords: large language model; power multi service scenario; cloud resource scheduling; two level mode; internal transmission.
  DOI: 10.1504/IJBIDM.2025.10070230
   
  
• Personalised recommendation of English MOOC teaching resources based on multi dimensional user portrait  
  by Yingying Zhu, Jipeng Mao 
  Abstract: In order to improve the accuracy of personalised recommendation of English MOOC teaching resources, the research on personalised recommendation method of English MOOC teaching resources based on multi-dimensional user portrait was carried out. This paper first introduces the self attention mechanism, extracts the user attribute features and potential features, and integrates them to realise the construction of multi-dimensional user portraits, and then considers the basic attributes, interest attributes and social attributes to complete the calculation of user similarity. Finally, on this basis, combined with the attribute characteristics of teaching resources and user interest characteristics, it completes the personalised recommendation of English MOOC teaching resources. The experimental results show that the accuracy of the proposed method is higher than 96.3%, the recall rate is higher than 95.7%, and the F1 value is between 0.93~0.98002E.
  Keywords: user portrait; English courses; MOOC teaching resources; personalised recommendation.
  DOI: 10.1504/IJBIDM.2025.10070231
   
  

Special Issue on: Innovative AI driven 3D Modelling for Business Intelligence Tools

• Leveraging traditional business culture for business intelligence: a scalable parameter server architecture with distributed machine learning  
  by Chengcai Xing 
  Abstract: Yanan, a significant historical and cultural hub in China, is being revitalised and utilised to drive development in various spheres. The citys traditional commercial and cultural resources are being harnessed to contribute to its political, cultural, educational, and economic growth. Yanan models other historically significant regions, demonstrating how heritage can be leveraged for contemporary development. Advanced machine learning approaches are used to overcome scalability and robustness issues in large-scale data-driven systems. The parameter server architecture decentralises the training process of machine learning models, enabling efficient handling of vast datasets and high computational demands. This design enhances fault tolerance and ensures seamless operation under challenging conditions. Intelligent simulations and tests validate the efficacy of these machine-learning approaches in modelling the evolution and application of traditional commercial culture. These simulations provide a dynamic and accurate representation of how cultural and business practices can adapt and thrive in modern contexts. The reliability and precision of machine learning models in capturing complex patterns and trends inherent in cultural and economic transitions are underscored through rigorous testing. This exploration highlights the innovative intersection of technology and tradition, showcasing how machine learning can play a transformative role in preserving and advancing historical and cultural assets.
  Keywords: traditional commercial and cultural resources; Yan’an historical value; distributed machine learning; parametric server architecture.
  DOI: 10.1504/IJBIDM.2025.10069536
   
  

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

• Deep learning based DAVS-UNet for medical image segmentation  
  by Zihui Zhu 
  Abstract: Deep learning based Convolutional Neural Networks (CNNs) and transformers are widely used in medical image processing tasks, while the State Space Sequence Model (SMM) architecture is proposed to address its limitations in improving the scaling efficiency and solving the transformed quadratic scale problem. Inspired by the Mamba architecture, this paper proposes Dual-Attention Vision Scaled-UNet (DAVS-UNet) for medical image segmentation, in which Adaptive Multi-scale Selection (AMS) is applied to the input image for better capturing details at different scales and extracting input features. Furthermore, Atrous Space Pyramid Pooling (ASPP) is introduced to expand the sensory field by collecting global contextual information after the final encoder. The experiments on a large number of publicly available datasets illustrate that DAVs-UNet shows excellent performance on the ISIC2017, ISIC2018, Synapse datasets, and outperforms existing SSM-architecture networks employed in medical image segmentation tasks. The code is available at https://github.com/zhzhuac/DAVS.
  Keywords: attention mechanism; multi-scale information; state space models; convolutional neural networks; CNNs; adaptive multi-scale selection; AMS; atrous space pyramid pooling; ASPP.
  DOI: 10.1504/IJBIDM.2026.10070330