International Journal of Business Intelligence and Data Mining (26 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
   
  
• 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 the rumour spread more rapidly among society and influences 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
   
  
• 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
   
  
• 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, Ramasamy 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 utilized 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 10 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.
  Keywords: Stock price prediction; Sectoral stock indices; Financial news sentiments; Hybrid TFIDF-Doc2Vec; Hybrid LSTM-GRU.
  
  

Special Issue on: Deep Learning Technology and Big Data Method for Business Intelligence and Management

• Enhancing multiple document summarisation with DNETCNN and BCHOA techniques  
  by Mamatha Mandava, Surendra Reddy Vinta 
  Abstract: Multi-document summarising (MDS) is a helpful method for information aggregation that creates a clear and informative summary from a collection of papers linked to the same subject. Due to the significant number of information available online, it might be challenging to extract the needed information from an internet source these days. To generate the summary, we propose the binary chimp optimisation algorithm (BChOA) in this research. Several preprocessing techniques utilised to remove unwanted terms from the content. Then, for word embedding, FastText is used. The semantic and synthetic features are extracted using the DarkNet-53 and ConvNeXt methods. Using a darknet convolutional neural network (DNetCNN), the features derived from the syntactic and semantic features are concatenated. The Movie review dataset contains 2000 review files, and the BBC news dataset has 50 unique documents. Finally, the outcome demonstrates that our model compares to cutting-edge solutions in terms of semantics and syntactic structure.
  Keywords: multi-document summarisation; MDS; binary chimp optimisation algorithm; BChOA; ConvNeXt approach; darknet convolutional neural network; DNetCNN.
  DOI: 10.1504/IJBIDM.2025.10067365
   
  
• Learning from high-dimensional unlabelled data with outliers: a novel robust approach  
  by Abdul Wahid 
  Abstract: This paper investigates the problem of feature selection and classification under the presence of multivariate outliers in high-dimensional unlabelled data. The research question is how to identify outliers and deal with them in unsupervised learning to improve the clustering accuracy compared with the state-of-the-art non-robust learning techniques. For this purpose, a robust method is proposed by utilise the Mahalanobis distance for outlier identification based on the minimum regularised covariance determinants approach. Furthermore, a new weighting scheme based on Mahalanobis distance is developed for dealing with outlying data points. Finally, it is suggested to combine the proposed weight function and least squared loss function along with the graph and sparsity constraints for achieving the robustness. This new procedure is named robust self-representation sparse reconstruction and manifold regularisation (RSSRMR). The novel technique is compared with previously proposed unsupervised feature selection techniques in simulation and real-world data experiments and exhibits better performance.
  Keywords: clustering; high-dimensional data; feature selection; Mahalanobis distance; multivariate outliers.
  DOI: 10.1504/IJBIDM.2025.10066878
   
  
• Data mining method for English classroom teaching quality based on hierarchical clustering  
  by Yue Zhang 
  Abstract: English classroom teaching involves multiple types of data, and effectively collecting and organising these data is a challenging task. Therefore, a hierarchical clustering based data mining method for English classroom teaching quality is proposed. Firstly, use dynamic layered distributed data collection algorithms to collect data; Secondly, use a moving average filter to smooth the data, transform the data through Fourier transform, and calculate the threshold for outliers based on normal distribution to achieve data outlier handling. Then, the recursive feature elimination method is used to perform feature selection on the data, and linear discriminant analysis is used to perform feature dimensionality reduction. Finally, use hierarchical clustering algorithm for data mining. The experimental results show that the recall rate of this method is high, the mean square error is low, the data storage space occupied is low, indicating that this method can effectively improve the effectiveness of teaching mining.
  Keywords: hierarchical clustering; data mining; recursive feature elimination; normal distribution.
  DOI: 10.1504/IJBIDM.2025.10067363
   
  
• A sentiment classification method for Weibo sensitive topic text based on multimodal features  
  by Manlin Li 
  Abstract: Due to the problem of reduced classification accuracy in traditional text sentiment classification methods, this paper proposes a Weibo sensitive topic text sentiment classification method based on multimodal features. Firstly, the bidirectional loop structure is introduced to improve the GRU model, and a BiGRU model is constructed for multimodal feature extraction and fusion of sensitive topics on Weibo. Secondly, by combining seed features, similar features, and residual features, a multimodal feature cluster is constructed to improve the accuracy of classification. Finally, the constructed multimodal feature clusters are input into the support vector machine model to complete sentiment classification of Weibo sensitive topic text. The experimental results show that compared with traditional methods, our method achieves higher accuracy in all emotion categories.
  Keywords: multimodal features; Weibo sensitive topics; text sentiment classification; BiGRU model; multimodal feature clusters.
  DOI: 10.1504/IJBIDM.2025.10066994
   
  
• Intelligent evaluation method for multimedia network public opinion decline period based on multi-divisional optimisation  
  by Xuefang Zhou 
  Abstract: In order to overcome the long data collection time, low accuracy in extracting features of public opinion decline, and low precision rate associated with traditional methods, a new intelligent evaluation method for multimedia network public opinion decline period based on multi-divisional optimisation is proposed. An evaluation index system for intelligent evaluation of public opinion decline period is constructed, and index data is collected and processed. The multiple fractal dimensions of the index data are determined, and multi-divisional optimisation is performed in conjunction with nonlinear support vector machines to extract features of public opinion decline. Public opinion decline period intelligent evaluation is achieved based on these features and the BiLSTM model. The experimental results show that the average data collection time of the proposed method is 0.72 s, the average accuracy of feature extraction of public opinion decline is 97.66%, and the precision rate is consistently above 95%.
  Keywords: multi-divisional optimisation; multimedia network; public opinion decline period; intelligent evaluation; nonlinear support vector machine; BiLSTM model.
  DOI: 10.1504/IJBIDM.2025.10066999
   
  
• A precision marketing method for e-commerce considering the hidden behavioural characteristics of user online shopping  
  by Zheng Xu, Hengzhi Nie, Wanqing Chen 
  Abstract: In order to improve user order rate and achieve high marketing satisfaction, this article considers the hidden behaviour characteristics of user online shopping and designs an e-commerce precision marketing method. Firstly, use web crawler technology to collect and preprocess hidden behaviour data of e-commerce platform users during online shopping. Then, calculate the level of interest of e-commerce platform users in different labelled products during the online shopping process, and use natural language processing algorithms to identify the hidden behaviour characteristics of e-commerce platform users during online shopping. Based on the K-means clustering algorithm, perform fuzzy clustering on the hidden behaviour characteristics of online shopping. Finally, the Pearson similarity algorithm is used to calculate the similarity between feature data and target product data, and to construct an e-commerce platform's online shopping product push matrix. Based on the ranking results of product push, precise e-commerce marketing is achieved. The experimental results show that using the proposed method, user satisfaction with product marketing recommendations remains above 85%, and user order rates remain above 90%. E-commerce marketing has high accuracy and good marketing effects.
  Keywords: e-commerce; electronic commerce; invisible behaviour; precision marketing; behavioural characteristics; fuzzy clustering.
  DOI: 10.1504/IJBIDM.2025.10067855
   
  
• Research on engineering cost prediction based on GA-BP neural network  
  by Yan Wu, Sha Lan, Tingting Liu 
  Abstract: In order to improve the accuracy of engineering cost prediction and reduce prediction errors, an engineering cost prediction method based on GA-BP neural network is proposed in this paper. Comprehensive index system for engineering cost prediction is constructed, and qualitative indicators are discretised using the equal interval method. The qualitative indicators are transformed into quantitative indicators through scale assignment. The BP neural network error is obtained through gradient descent, and the GA algorithm is used to adjust the weights from the output layer to the hidden layer. Using the discretised qualitative indicators as input vectors and engineering cost as the output vector, a prediction model for engineering cost based on GA-BP neural network is built to obtain prediction results. Experimental results show that the proposed method has a prediction range of 2.41%, a residual mean range of 0.005~0.219, a recall rate fluctuating between 96.9% and 99.7%, and high prediction accuracy.
  Keywords: GA algorithm; BP neural network; engineering cost prediction; gradient descent.
  DOI: 10.1504/IJBIDM.2025.10066998
   
  
• A method for merging and classifying higher mathematics teaching resources based on density clustering algorithm  
  by Hejie Chang, Xing Lv 
  Abstract: To enhance the recall and accuracy of resource merging classification, this study introduces a merging classification technique rooted in density clustering algorithms. Initially, we gather data pertaining to higher mathematics teaching resources. Subsequently, we convert textual sentences into word-level representations, eliminating stop words and unnecessary high-frequency vocabulary. Leveraging LDA, we extract mathematical resource features, transforming words into computer- and model-recognisable vectorised forms. Next, we calculate the density and distance between samples to categorise them into distinct groups, employing density clustering algorithms for merging and classifying teaching resources. Experimental findings reveal that our method achieves a classification recall rate of 99.6% and an accuracy of 99.9%, thereby enhancing the quality and efficacy of higher mathematics education.
  Keywords: density clustering; merge and classify; advanced mathematics; teaching resources; resource allocation.
  DOI: 10.1504/IJBIDM.2025.10066997
   
  
• An accurate and rapid pushing of marketing information based on multidimensional data mining  
  by Zhisheng Zhou, Bin Li 
  Abstract: In order to address the accuracy and recall issues in marketing information push, this study proposes a strategy based on multidimensional data mining to achieve accurate and efficient marketing information push. First of all, collect marketing information and build a push probability index system; secondly, the analytic hierarchy process is used to calculate the weight of the marketing information push index; finally, considering the product life cycle, data mining technology is used to obtain the stable and random purchase interest of active and inactive users, and marketing information is accurately and rapidly pushed through the above four dimensions. The research results show that after adopting this method, the accuracy of marketing information push reached 98.1%, the recall rate reached 96.9%, and user satisfaction also increased to 98.5%, significantly improving the overall effect of marketing information push and user satisfaction.
  Keywords: analytic hierarchy process; purchase interest; data mining techniques; multidimensional data mining; indicator weight.
  DOI: 10.1504/IJBIDM.2025.10066996
   
  
• Method for mining students' online English learning intention based on user portrait and big data  
  by Yanli Li, Lili Wang, Haitao Gao, Bin Zhang 
  Abstract: To overcome the problems of low recall, low accuracy, and long time in traditional methods, a new method for mining students' online English learning intention based on user portrait and big data is proposed. With the support of big data technology, the maximum mean difference algorithm is used to determine the distance between student online English learning data sample points, and the K-means algorithm is used to implement student online English learning data collection. The collected data is used to construct user personas, and the attention mechanism is used to extract students' online English learning characteristics. A student's online English learning willingness mining model based on extreme learning machine network is established to obtain relevant mining results. Experimental tests have shown that the recall rate of the proposed method is always above 97.3%, the maximum mining accuracy is 98.1%, and the average mining time is 79.15 ms.
  Keywords: user portrait; big data; students; online English; learning intention; maximum mean difference algorithm; attention mechanism; extreme learning machine.
  DOI: 10.1504/IJBIDM.2025.10066988
   
  
• Intelligent retrieval method for power grid dispatching information based on knowledge graph  
  by Baoyu Hou, Qichao Wang, Zhiguo Zhou 
  Abstract: To improve the retrieval efficiency of power grid dispatch information, the paper proposes an intelligent retrieval method based on knowledge graph. Firstly, after mining the terminology of power grid dispatch information, the entities and relationships in the power grid dispatch information are extracted to obtain a string of entity names in the terminology dictionary, achieving the design of the knowledge graph pattern layer for power grid dispatch information. Finally, the power grid dispatch information is embedded into a discrete Hamming space, and the nearest neighbour retrieval method is used in the embedded space to achieve intelligent retrieval of power grid dispatch information. The experimental results show that the intelligent retrieval accuracy of our method can reach 98.51%, the recall rate of our method can reach 98.32%, and the time consumption of our method is only 6.6 seconds. The retrieval efficiency of power grid dispatch information is relatively high.
  Keywords: knowledge graph; Hamming space; nearest neighbour retrieval; term dictionary tree.
  DOI: 10.1504/IJBIDM.2025.10066989
   
  
• Personalised recommendation method for smart library literature based on user behaviour feature perception  
  by Yina Liu 
  Abstract: To solve the problem that existing library literature recommendation methods cannot achieve diversity and personalisation, this study proposes a personalised recommendation method for smart library literature based on user behaviour feature perception technology. Firstly, based on network coding technology, the collection of user behaviour data for smart libraries is completed, and the collected data is reduced in dimensionality through information entropy to remove redundant features. Then, a user behaviour feature model is constructed through Bayesian networks to perceive and analyse user behaviour, obtain user behaviour features, and finally, based on the feature perception results, a collaborative filtering algorithm is used to complete personalised recommendation of literature materials in the smart library. The experimental results show that this method can fully utilise the behavioural characteristics of users, accurately understand their interests and needs, and provide more accurate literature recommendation results.
  Keywords: perception of user behaviour characteristics; smart library; literature materials; personalised recommendation; network coding; information entropy.
  DOI: 10.1504/IJBIDM.2025.10066987
   
  
• A refined pushing method for financial product marketing data based on user interest mining  
  by Huijun Wang 
  Abstract: To improve the marketing effectiveness of financial products, the article designs a precise push method based on user interest mining. This method divides user groups using the K-means clustering algorithm and uses density parameters to reflect the level of user activity. Comprehensively mining user interests and preferences in financial products, and calculating the transition probability of user browsing message categories to construct a marketing data model. Accurate push is achieved by calculating the similarity between users, user interests, and candidate data. The experimental results show that after applying this method, the click through rate of financial products is between 93.58% and 96.69%, and the conversion rate of push results is between 0.88% and 0.95%. The activity level of users participating in activities has always remained above 95%, which verifies the effectiveness of this method.
  Keywords: financial product marketing; marketing data; user interests; data push; K-means clustering; category model.
  DOI: 10.1504/IJBIDM.2025.10067364
   
  
• Analysis of online marketing user participation preference attribute based on social network text mining  
  by Lu Zhang, Wanqing Chen, Hengzhi Nie 
  Abstract: In order to improve the accuracy of online marketing users' participation preference feature attribute recognition, an analysis method of online marketing users' participation preference feature attribute based on social network text mining is proposed. Firstly, the TF-IDF algorithm is used to calculate the weight value of keywords in the tag, and then the user portrait of the social network platform is constructed after sorting. Then, the collaborative filtering algorithm is used to determine the user's preference characteristics for products containing keywords, and the K-L feature compressor is used to extract the user's participation preference characteristics of online marketing. Finally, the online marketing user participation preference characteristic attributes are classified to realise the analysis of online marketing user participation preference characteristic attributes. The experimental results show that the accuracy of this method is always above 90% and the average time is 3.88s.
  Keywords: social networks; text mining; online marketing; preferential features; TF-IDF algorithm.
  DOI: 10.1504/IJBIDM.2025.10067625
   
  

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: Empowering Business Intelligence with AI Data Analytics and IoT for Efficient in Digital Era

• 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 1468 data records in data mining. We used the Rapidminer9.9 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