Journal of Intelligent Data Analysis and Computational Statistics (p-ISSN: 3049-3056 e-ISSN: 3048-7080)

MARS: A Multi-Modal Wartime Distress Signal Detection System Using NLP, Machine Learning, and Automatic Speech Recognition

Abhiram Rudra — 2026-06-06

In today’s digital era, a large amount of textual communication takes place through social media platforms, messaging applications, and online support systems. Identifying distress signals from such textual data is crucial for enabling timely intervention and emergency response. Manual monitoring of distress messages is inefficient, time-consuming, and prone to human error. Therefore, there is a need for an automated system capable of detecting distress signals accurately and quickly. This project presents a Distress Signal Detection System using natural language processing (NLP) and machine learning (ML) techniques. The system processes user-entered text and performs preprocessing steps such as tokenization, stop-word removal, and normalization. Relevant features are extracted using TF-IDF vectorization, and classification algorithms such as Naïve Bayes, Support Vector Machine (SVM), and logistic regression are used to determine whether the input text indicates distress or not. If distress is detected, the system generates an alert notification through an email-based alert mechanism. The proposed system aims to improve response time, enhance monitoring efficiency, and provide a scalable solution for detecting emergency-related textual communication. This system can be applied in areas such as social media monitoring, emergency helplines, mental health support platforms, and public safety applications.

AI-based Cattle Disease Detection System Using Modified CNN Architecture for Blood Smear Image Analysis

Sravani P — 2026-05-05

Tick-borne diseases such as babesiosis and anaplasmosis pose significant threats to cattle health, causing substantial economic losses in livestock farming. Traditional microscopic diagnosis of these diseases through blood smear analysis is time-consuming, labor-intensive, and prone to human error. This study presents an AI-based cattle disease detection system utilizing a modified convolutional neural network (CNN) architecture for automated blood smear image analysis. The proposed system implements a three-tier architecture comprising farmer, lab technician, and veterinary doctor modules, enabling seamless coordination in disease diagnosis. This modified CNN architecture incorporates optimized convolutional layers with ReLU activation, max-pooling strategies, and dropout regularization to enhance feature extraction from microscopic blood smear images. The system achieved 98.0% accuracy for babesiosis detection and 97.7% accuracy for anaplasmosis detection, with precision and recall exceeding 95%. Experimental results demonstrate that the proposed approach significantly outperforms traditional diagnostic methods and baseline CNN models, providing rapid, accurate, and cost-effective disease detection for improved cattle health management.

DT-SVM and Hybrid Approaches for Missing Data Imputation and Classification: A Comprehensive Survey

Satish Kumar Kalagotla — 2026-05-01

Missing data represents a pervasive challenge in real-world datasets, particularly within medical research and clinical applications, where its presence can substantially degrade the performance of machine learning classifiers and compromise the validity of analytical conclusions. This comprehensive survey paper systematically examines hybrid approaches that integrate decision trees (DT) and support vector machines (SVM) for missing value imputation and subsequent classification, with particular emphasis on the DT-SVM framework and its algorithmic variants. The study provides a thorough exploration of missing data mechanisms, evaluates traditional and machine learning-based imputation techniques, and delineates the theoretical foundations of decision trees and support vector machines. Through critical analysis of existing hybrid methodologies and comparative evaluation against conventional approaches, this review synthesizes current literature to reveal that DT-based imputation, which leverages enhanced attribute correlations within homogeneous data segments identified through recursive partitioning, consistently outperforms simple imputation methods when combined with SVM classification. The survey further examines recent advancements, including approximated k-nearest neighbor (A-kNN) variants that address computational efficiency concerns while maintaining classification accuracy. Key research gaps are identified, including challenges in high-dimensional settings, handling of missing not at random mechanisms, and integration with deep learning architectures. The findings collectively suggest that integrated frameworks such as DT-SVM represent a promising trajectory for achieving robust classification performance in the presence of missing data, with particular relevance to medical diagnosis applications where data quality issues are prevalent and prediction accuracy is paramount.

Blockchain Model for Secure Digital Identity Management and Healthcare Data Sharing

Olaoye N. S — 2026-06-01

As digital ecosystems evolve, the limitations of centralized identity management systems have become increasingly evident, particularly in healthcare environments where sensitive patient information is frequently exchanged across multiple institutions. Traditional identity models and federated single sign-on (SSO) systems create single points of failure and increase vulnerability to cyberattacks, identity theft, and unauthorized access. This study proposes a blockchain-based self-sovereign identity (SSI) framework for secure healthcare data sharing. The model integrates decentralized identifiers (DIDs), smart contracts, and zero-knowledge proofs (ZKPs) to enable secure identity verification and selective disclosure of sensitive information without exposing raw patient data. A layered architecture is adopted to separate trust management from data storage, combining user-centric identity portals, blockchain-based identity services, and off-chain storage using the InterPlanetary File System (IPFS). The framework ensures that healthcare records remain encrypted and under patient control while maintaining transparency and tamper resistance through blockchain audit mechanisms. A lightweight prototype was developed using Python and Streamlit to simulate healthcare access workflows and contextual risk evaluation. Experimental findings demonstrate that integrating decentralized identity, consent enforcement, and contextual access control enhances anomaly detection and improves healthcare data security. The proposed framework provides a scalable and privacy-preserving foundation for secure digital identity management in healthcare systems.

Correlation Analysis in Multidisciplinary Research: A Systematic Review of Theoretical Foundations, Methodological Frameworks, and Empirical Applications

Dhanashree Pawgi — 2026-05-01

Correlation analysis is a method researchers use to determine whether two variables are related. In this review, eight studies from various fields, including education, healthcare, finance, and social sciences, were examined. The goal was to understand how correlation is used and what kind of results it produces. Most researchers used Pearson’s correlation to measure relationships. They also used tests such as the t-test and Fisher’s z, often with SPSS software, to see if the results were statistically meaningful. In one education study, there was a moderate positive relationship (r = +0.34) between students’ awareness of nature and their science performance. This suggests there may be some link. However, studies related to online learning did not always show a significant connection. It is important to remember that correlation does not prove cause and effect. Two variables may be related without one directly causing the other. Overall, correlation is useful, but it must be interpreted carefully.