International Journal of Data Mining, Modelling and Management (10 papers in press)

Regular Issues

• Hybrid Kernel Support Vector Penalised Regression Model for Forewarning Pest Incidence using Weather Variables  
  by Naranammal Narayanasamy, Krishna S. R. Priya 
  Abstract: Crop pest incidence and development are impacted by environmental factors. Therefore, weather-based machine learning model will be an effective scientific measure for forewarning pests. But in many cases, the raw data is complex and has the problems of nonlinearity and multicollinearity. So, development of robust model is much needed to forecast complex data. The present study is an attempt to develop hybrid models such as kernel support vector ridge and kernel support vector elastic net regression (KSVENR) to forewarn crop pests of Cotton. Weekly pest incidence data of sucking pests such as aphids, jassid, thrips and whitefly from year 2015-16 to 2022-23 has been used for the study. The results reveal that the KSVENR model outperformed other penalised models by 43%, 42%, 40% and 33% for forewarning pest incidence of aphids, jassid, thrips and whitefly respectively. The proposed model would be a good tool for forecasting nonlinear data with multicollinearity.
  Keywords: Time series; Modelling; Forecasting; Nonlinear; Multicollinearity; Data Analysis; Machine Learning; Hybrid Model.
  DOI: 10.1504/IJDMMM.2026.10070953
   
  
• Advancements in Mental Health Diagnosis: Leveraging Delta Feature Extraction Framework and PWSA Ensemble for Motion Data Analysis  
  by S. Annapoorani, Lakshmi M. 
  Abstract: Depression affects over 350 million people globally and can become a serious health issue, especially when prolonged and ranging from mild to severe. Physical activity data offers a cost-effective and accessible approach to aid in diagnosing mental illnesses. This study introduces the Delta feature extraction framework (D-FEF), which extracts delta series and relevant features from original time series data, subsequently selecting a significant feature set. A probabilistic weighted selection algorithm (PSWA) with SMOTE generates multiple hypotheses using training data based on modified distributions, creating an ensemble of classifiers to predict healthy controls, depressive disorder, and schizophrenia. The PSWA classifier, utilising the D-FEF feature selection process, achieved 92.94% accuracy, outperforming all other tested methods. The techniques performance was evaluated on mental health datasets, including Depresjon and Psykose, and compared against state-of-the-art approaches. The proposed D-FEF and PSWA methodology demonstrates promising results for the classification of mental health conditions using physical activity data.
  Keywords: Actigraphy data; mental health; feature engineering; feature selection; ensemble machine learning algorithm.
  DOI: 10.1504/IJDMMM.2026.10072023
   
  
• D-HUP Tree: Distributed HUP Tree for Scalable High Utility Itemset Mining  
  by Chintan Rajput, Mathe John Kenny Kumar, Dipti Rana 
  Abstract: High utility itemset mining (HUIM) is useful for extracting useful information from datasets. As volume, velocity and variety increases, the traditional methods struggle with computational efficiency with respect to runtime and memory utilisation. The proposed work introduces a new approach called distributed-high utility pattern tree (D-HUP Tree) by combining a HUP Tree data structure with the Hadoop distributed computing framework thereby improving runtime, memory management and enabling parallel processing. Experimental results clearly illustrate that the proposed methodology reduces computation complexity without compromising the quality of discovered high utility itemsets, providing a substantial contribution to the high utility itemset mining field.
  Keywords: High Utility Itemset Mining; HUP Tree; Distributed Itemset Mining; Map Reduce.
  DOI: 10.1504/IJDMMM.2026.10072676
   
  
• Mining Maximal Empty Rectangles  
  by Dwipen Laskar, Irani Hazarika, Farha Naznin, Anjana Kakoti Mahanta 
  Abstract: An interval data with k-dimensions can be represented as a hyperrectangle. All the domains of an interval dataset can be represented as a bounded hyperrectangle, which can be treated as the universe or bounding region. Empty hyperrectangles within this bounding hyperrectangle are regions having no intersections with any other hyperrectangle represented by any data in the dataset. A maximal empty hyperrectangle is an empty hyperrectangle that is not properly contained in any other empty hyperrectangle. In a 2D interval dataset, the problem of mining all maximal empty hyperrectangles can be reduced to mining all maximal empty rectangles within the bounding rectangle of the dataset. In this paper, a two-steps dynamic algorithm called AMER-Miner has been proposed for mining all maximal empty rectangles contained in bounding rectangle of a 2D interval dataset. The proposed method has been tested on two real life datasets, one synthetic dataset and experimental results have reported.
  Keywords: Interval data; Empty interval; Empty rectangle; Hyperrectangle.
  DOI: 10.1504/IJDMMM.2026.10072741
   
  
• Predicting Building Fitness Ranks Using a Hybrid Evaluation Method on Earthquake Damage Data  
  by Moram Vishnu Vardhana Rao, Aparna Chaparala, Kusuma Kumari Katakam, G.Apparao Naidu, N. D. S. S. Kiran Relangi, Radhika Sajja 
  Abstract: Structural health monitoring system (SHMS) plays a crucial role in assessing the status of structures. This article presents a feature selection method and examines 5-machine-learning classifiers. In this paper, we proposed a fuzzy rough set features selection (FRSFS) methodology for identification of more correlated and removal of irrelevant attributes in datasets. FRSFS well reduces features by eliminating duplicates, null values, missing data, and other irrelevant features, facilitating the extraction of pertinent information. To enhance classification accuracy, novel modified-KNN (M-KNN) classifier is introduced, which is designed to address undesirable structural health characteristics. Comprehensive analysis, comparing various machine-learning classifiers and their growth rates using classification metrics like accuracy, mean squared error (MSE), precision, recall, and F1-score, demonstrates the effectiveness of FRSFS and M-KNN. The suggested methodology is slightly more accurate (99.76%) than other classifiers like naive Bayes (NB), stochastic gradient descent (SGD), K-nearest neighbours (KNN), logistic regression (LR), and random forest (RF).
  Keywords: Structural Health Monitoring Systems (SHMS); Fuzzy Rough Set Features Selection (FRSFS); Rough Set Theory (RST); Modified-KNN (M-KNN); Mean Squared Error (MSE); Engineering-Based Tools (EBT).
  DOI: 10.1504/IJDMMM.2026.10076121
   
  
• Predicting Stock Prices using state-of-art Machine Learning Models with Enhanced Feature Representation and Momentum Indicator Selection  
  by Haoyu Wang, Dejun Xie, Yujian Liu 
  Abstract: This study examines advanced machine learning approaches for predicting next-period prices of the CSI 300 Index, a key benchmark of the Chinese stock market. Momentum-based technical indicators, adapted from Yin and Yang (2016), which are subjected to a residual correction process and serve as continuous features for regression tasks? are utilized as the primary features for this analysis. A comparative study is conducted among Deep Forest (DF), Support Vector Regression (SVR), Ridge Regression (RR), Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Neural Networks (NN), Random Forests (RF), and XGBoost. Dimensionality reduction techniques, including Principal Component Analysis, Factor Analysis, and Autoencoders, are applied to further enhance model evaluation. Empirical findings demonstrate that Ridge Regression, GRU, and LSTM, particularly when combined with dimensionality reduction, outperform other popular machine learning algorithms in stock price prediction.
  Keywords: Stock price prediction; quantitative finance; applied machine learning models; deep learning.
  DOI: 10.1504/IJDMMM.2026.10076902
   
  
• Modeling the Combined Effect of Node Centrality Measures in Predicting Links in Complex Networks  
  by Nurmeen Basharat, Irfan Yousuf 
  Abstract: Using a variety of centrality measures and machine learning models, we introduce a novel generic strategy for link prediction in this work. We use some of the most important node centrality measures to collect the network's global structure, local structure and quasi-local structure. To create a dataset for machine learning models, the values of centrality score act as the features of a node in the network where as the presence or absence of an edge produce positive or negative samples respectively. We test our approach on twelve publicly available graphs and use multiple performance metrics to figure out that Light Gradient Boosting Machine (LGBM) outperforms many other machine learning models. We achieve a maximum of 97\% and a minimum of 75\% ROC-AUC. We also find that there is no single machine learning model that can perform well on all types of graphs or networks.
  Keywords: Complex Network; Social Networks; Link Prediction; Node Centrality Measures; Link Prediction Models.
  DOI: 10.1504/IJDMMM.2027.10076947
   
  
• QSAR-Based Computational Screening and Molecular Docking of Natural Compounds for Drug Discovery against Bacterial Meningitis  
  by Saima Kashif, Amna Sethi, Samreen Feroz, Tasmia Anwar, Fatima Faryal, Nayab Mubashir, Fatima Parekh, Sabahat Rashid 
  Abstract: Bacterial meningitis poses a significant threat to public health, necessitating the exploration of novel therapeutic avenues. This study aimed to identify natural compound-based therapeutics for bacterial meningitis using quantitative structure-activity relationship (QSAR) and molecular docking. In this study, a train/active dataset comprising 49 existing drug antibiotics used to treat meningitis was trained on 6,000 inactive natural compounds. These 6,000 natural herbal compounds were selected from four different databases. Molecular descriptors were calculated to quantify the structural characteristics of active and inactive compounds. The random forest model predicted 291 out of 6,000 compounds as active. From these 291 compounds, four predicted active compounds, including xanthone, xenematide, sequirin C and formycin A, possessing different RF scores, were selected to perform molecular docking experiments with the bacterial protein receptor DD-transpeptidase. Docking studies revealed that xenematide exhibited the best binding energy to DD-transpeptidase. The identified molecules offer promising leads for further investigation.
  Keywords: Bacterial meningitis; Quantitative structure-activity relationship; Natural compounds; Random forest (RF) model; DD-Transpeptidase; molecular docking.
  DOI: 10.1504/IJDMMM.2026.10077075
   
  
• Twin-attention based masked Swin Transformer for anomaly detection  
  by Anilkumar Vishwanath Brahmane, Dipak Devchand Bage, Satpalsing Devising Rajput, Ritu Dudhmal, Dhanraj Somling Jadhav, Vijay Arun Kotkar 
  Abstract: This study suggests a novel deep learning method for visual anomaly detection. The initial step is gathering the input images from the Kaggle dataset. Next, the HSV colour channel conversion technique and image resizing and rescaling are used to pre-process the input images. The pre-processed images are then segmented using twin-attention-based masked Swin Transformer UNet (MSTUnet) methods. This method offers great advantages for anomaly detection, especially in commercial environments. Its superior ability to achieve high detection accuracy, quickly manage limited data, and capture a global environment provides a unique solution to protect commercial processes and maintain optimal functionality. The MVTecAD dataset is used to examine the findings of the proposed study, which favours the Python tool for simulation. In terms of accuracy (97.09%), sensitivity (96.96%), specificity (96%), F1-score (95.12), and precision (95.87%), the obtained simulation results demonstrate that the proposed model outperforms other current approaches.
  Keywords: anomaly detection; AD; image resizing; image rescaling; HSV colour channel conversion; masked Swin Transformer unet; MSTUnet; deep learning.
  DOI: 10.1504/IJDMMM.2026.10078181
   
  
• An Efficient Outlier Detection in High Dimensional Data with Ridge Regression  
  by Moram Vishnu Vardhana Rao, Patanala Srirama C. Murthy 
  Abstract: Due to the difficulty of finding patterns that deviate from the norm, the topic of outlier detection has received considerable attention in recent years. Unfortunately, there is currently no viable technique for detecting these patterns. Numerous algorithms have been created to address this issue, but they have yet to overcome the barriers effectively. To limit the adverse effects of extreme outliers in real-world applications, it is necessary to develop a novel strategy due to the difficulty of identifying rare patterns. Using a framework given by this research, outliers in high-dimensional data are recognized and analyzed. Rapid miner, a free and open-source software, has been upgraded to incorporate the newly proposed method. It is proven by taking into account the maximum f-measure parameters as well as the average precision, R-precision, and ROC-AUC. The Ridge Regression technique is the most promising method for detecting data points that are regarded as outliers, according to the studys findings.
  Keywords: Detecting Outlier; Ridge Regression; High Dimensional Data; Rapid miner; Page Blocks.
  DOI: 10.1504/IJDMMM.2027.10079560