Template-Type: ReDIF-Article 1.0 Author-Name: B.G. Kodge Author-X-Name-First: B.G. Author-X-Name-Last: Kodge Title: Recognition of critical built-up areas located on high-hill slope regions using decision tree technique Abstract: In mountainous places, structures are being built for residential or commercial uses without the necessary safety precautions. Every year, landslides, torrential downpours, severe snowfall, earthquakes, volcanic eruptions, and floods cause buildings to collapse. The bulk of them are found in high-hill slope areas with loose soil types, close to river flows and other sorts of water sources. Therefore, these incidents have claimed thousands of lives. This paper deals with the process of automatic identification of critical buildings (residential/commercial) located in mountainous area which are on high-hill-slope, close to river flows, having loose soil type and high variations in land elevation contours. This study uses the primary data like, built-up/residential area and water body areas which are extracted from sample land use and land cover (LULC) using image classification techniques, and another important data like slope map and land elevation contour maps which are generated from digital elevation model (DEM). In addition, the supplementary data like, river maps, soil maps and other base maps, are also collected. All the data are integrated and taken into consideration for the identification and extraction of critical residential/build-up areas using spatial data mining technique. Journal: Int. J. of Data Mining, Modelling and Management Pages: 82-90 Issue: 1 Volume: 18 Year: 2026 Keywords: critical residential area identification; LUCL; DEM; image segmentation; decision tree; spatial data mining; SDM. File-URL: http://www.inderscience.com/link.php?id=151835 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdmmm:v:18:y:2026:i:1:p:82-90 Template-Type: ReDIF-Article 1.0 Author-Name: Rebeh Imane Ammar Aouchiche Author-X-Name-First: Rebeh Imane Ammar Author-X-Name-Last: Aouchiche Author-Name: Fatima Boumahdi Author-X-Name-First: Fatima Author-X-Name-Last: Boumahdi Author-Name: Mohamed Abdelkarim Remmide Author-X-Name-First: Mohamed Abdelkarim Author-X-Name-Last: Remmide Author-Name: Amina Guendouz Author-X-Name-First: Amina Author-X-Name-Last: Guendouz Title: Profiling cryptocurrency influencers on social media: a comparative study using SetFit and DistilBERT Abstract: Nowadays, in a world dominated by social media, the content people share can have significant effects, particularly in the domain of cryptocurrency, where investors often turn to online advice. The instability of the cryptocurrency market is well known, and some social media individuals wield considerable influence over this market through their posts. Our study focuses on categorising these influential cryptocurrency influencers based on their English tweets, with the challenge of limited data availability. Two transformer-based models: sentence transformer fine-tuning (SetFit) and distilled BERT (DistilBERT), were used to classify cryptocurrency influencers into three subtasks: profile authors based on their degree of influence, main interests, and message intent. These models were evaluated on a Twitter-based dataset from PAN2023. The results show that SetFit achieved the best performance with a 0.82 F1-score, followed closely by DistilBERT with a 0.80 F1-score. Journal: Int. J. of Data Mining, Modelling and Management Pages: 1-14 Issue: 1 Volume: 18 Year: 2026 Keywords: social media; author profiling; cryptocurrency influencers; DistilBert; SetFit; few-shot-learning. File-URL: http://www.inderscience.com/link.php?id=151836 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdmmm:v:18:y:2026:i:1:p:1-14 Template-Type: ReDIF-Article 1.0 Author-Name: Pranali Kosamkar Author-X-Name-First: Pranali Author-X-Name-Last: Kosamkar Author-Name: Vrushali Kulkarni Author-X-Name-First: Vrushali Author-X-Name-Last: Kulkarni Author-Name: Abdulrahim Shaikh Author-X-Name-First: Abdulrahim Author-X-Name-Last: Shaikh Author-Name: Geetika Agarwal Author-X-Name-First: Geetika Author-X-Name-Last: Agarwal Author-Name: Inderjeet Balotia Author-X-Name-First: Inderjeet Author-X-Name-Last: Balotia Title: Satellite image classification using deep learning model-ResNet Abstract: Data mining framework and artificial intelligence (AI) have played a key part in all decision-making scenarios. Due to the significant expenses associated with creating training and testing datasets, we need to deal with a number of issues, object recognition, classification, and semantic segmentation in images of low spatial resolution. In this paper we first reviewed the machine learning and deep learning-based model for satellite health monitoring systems. We built the deep learning model - for satellite image classification. The dataset used is Satellite Image Classification Dataset-RSI-CB256. Two variants, ResNet-12 and ResNet-18 were tested on the dataset. The ResNet-18 showed over 0.94 accuracy for 5 number of epochs and the ResNet-12 showed 0.92 accuracy for training over 10 number of epochs. The result shows that the choice of employing the ResNet CNN architecture for Satellite Image Classification is certainly better than employing other available models such as FCNN, RCNN (with F-RCNN). Journal: Int. J. of Data Mining, Modelling and Management Pages: 15-33 Issue: 1 Volume: 18 Year: 2026 Keywords: deep learning; ResNet; data mining; DM; artificial intelligence; AI; machine learning; ML; satellite image; remote sensing. File-URL: http://www.inderscience.com/link.php?id=151838 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdmmm:v:18:y:2026:i:1:p:15-33 Template-Type: ReDIF-Article 1.0 Author-Name: Sushovon Jana Author-X-Name-First: Sushovon Author-X-Name-Last: Jana Author-Name: Chandranath Pal Author-X-Name-First: Chandranath Author-X-Name-Last: Pal Title: Exploring solar activity dynamics: nonparametric change point analysis of sunspot and umbra areas Abstract: Solar observational studies are crucial for understanding the sun's behaviour, its impact on space weather, and its influence on Earth's climate. Central to this research is sunspot data analysis, a key indicator of solar activity and magnetic field variations. The study of solar differential rotation has been fundamental, with pioneering work revealing that faster equatorial rotation influences the sun's magnetic field and activity cycle. Sunspot areas, meticulously documented by observatories like the Royal Greenwich Observatory and KoSO, have been critical for analysing long-term solar activity trends. The integration of machine learning has significantly advanced sunspot data analysis, enhancing space weather forecasting and the understanding of solar phenomena. This paper employs change point analysis on KoSO sunspot and umbra area data to detect significant shifts over time, utilising nonparametric methods for their computational efficiency. Results show deviations from normality, positive trends, and significant autocorrelation in the data. The PELT algorithm reveals several significant shifts, dividing the period into distinct segments with varying statistical characteristics. These findings align with known solar cycles and highlight the importance of advanced statistical techniques in understanding solar activity. Journal: Int. J. of Data Mining, Modelling and Management Pages: 56-81 Issue: 1 Volume: 18 Year: 2026 Keywords: sunspot; summary statistics; change point analysis; nonparametric. File-URL: http://www.inderscience.com/link.php?id=151839 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdmmm:v:18:y:2026:i:1:p:56-81 Template-Type: ReDIF-Article 1.0 Author-Name: Shirina Samreen Author-X-Name-First: Shirina Author-X-Name-Last: Samreen Title: Machine learning pipeline with an optimal feature set in the stage-wise diagnosis of hepatitis C virus Abstract: Timely and accurate diagnosis of hepatitis C Virus is aimed in the proposed research using a novel dataset. For this purpose, numerous experiments are conducted using various machine learning models employing preprocessing techniques like feature engineering and data augmentation along with multiple heterogeneous classifiers. In addition, to detecting the onset of the disease, the proposed method also detects the stage of the disease to comprehend the severity for an appropriate follow-up treatment to prevent further damage to the health of the patient. Each experiment comprises various combinations of feature engineering approaches along with multiple heterogeneous classifiers. It was found that the machine learning pipeline employing the feature engineering approach of recursive feature elimination with support vector classifier as the estimator and a stacking ensemble classifier provides the best score for all performance metrics with a F1-score of 0.95, accuracy of 95.2 and mean square error of 0.06. Journal: Int. J. of Data Mining, Modelling and Management Pages: 34-55 Issue: 1 Volume: 18 Year: 2026 Keywords: machine learning; ML: multi-class classification; feature engineering; imbalanced dataset; synthetic minority oversampling technique; SMOTE; recursive feature elimination; RFE; F1-score; mean square error; MSE. File-URL: http://www.inderscience.com/link.php?id=151842 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdmmm:v:18:y:2026:i:1:p:34-55