Journal of Knowledge in Data Science and Information Management

Explainable Artificial Intelligence for Tuberculosis Detection: A Comprehensive Review of Techniques, Challenges, and Future Directions

Kangana Soni, Nitika Singhi — Sat, 02 May 2026 00:00:00 +0000

Explainable artificial intelligence (XAI) has appeared as an important study domain in healthcare, resolving the constraints of black-box machine learning and deep learning frameworks. In the field of tuberculosis recognition, AI methods have proven precision in evaluating chest X-ray scans and medical data; however, the absence of explainability restricts their implementation in healthcare settings. This study shows an extensive analysis of 50 research papers emphasizing on the utilization of XAI in tuberculosis recognition. The chosen papers are evaluated based on techniques, datasets, interpretability methods and assessment parameters. Frequently applied XAI procedures, such as LIME, SHAP, and Grad-CAM, are studied in depth. The analysis shows that although these methods increase transparency, they are frequently restricted to certain kinds of descriptions, such as local-global or spatial, causing partial awareness of system performance. Additionally, this paper detects important problems, including the lack of consistent analysis parameters, restricted application of hybrid XAI systems and absence of medical confirmation. The results indicate that combining multiple XAI methods and implementing measurable assessment procedures can considerably improve the trustworthiness and reliability of AI-driven TB identification frameworks. Ultimately, upcoming study areas are described to lead the advancement of clear, understandable and medically usable AI algorithms in healthcare.

AI-based Intelligent Smart City Complaint Management System

Narige Jyoshna, Shyam Sunder Pabboj, N. Ramakrishna, P. Harsha Sree Gayatri — Mon, 01 Jun 2026 00:00:00 +0000

As urbanization increases, the complexities faced while handling citizen problems and complaints increase as well. Complaint management systems in smart cities rely on artificial intelligence (AI). This project revolves around a complaint management system for smart cities that aims to streamline the process by reducing human intervention while handling complaints effectively and accurately. The system is fully automated; complaints are raised by citizens, and then AI processes them automatically by prioritizing, deduplicating, classifying and tracking them. The system was coded in Python language using Tkinter library for its user interface design, whereas for record-keeping and storage of information, SQLite database is used. Complaints management involves the implementation of natural language processing techniques to analyze complaints received in the form of texts. For classification of complaints, the text-to-vector conversion technique called term frequency-inverse document frequency (TF-IDF) and Naive Bayesian classifier were applied. Furthermore, cosine similarity technique was implemented to detect duplicate complaints. Moreover, a priority system was developed that ranks complaints in the order of urgency, so complaints with high priority come first and get processed. When a complaint is raised, a tracking ID is provided to the complainant so he/she can track its progress. All this will ensure that the reaction time is reduced while improving accuracy, thus making the entire process easier and better scalable. Ultimately, it can be argued that this is an intelligent solution to problems of smart cities.

Edge versus Cloud: Evaluating Big Data Processing Paradigms for IoT Applications

Shivang Mishra, Shikha Tiwari — Wed, 13 May 2026 00:00:00 +0000

The explosive growth of Internet of Things (IoT) ecosystems across industries, from factory floors and hospital wards to smart farms and urban infrastructure, has fundamentally changed how data processing is perceived. Billions of connected devices now generate continuous streams of sensor data, telemetry, images, and events, creating a need for computing architectures that are fast, scalable, secure, and cost-effective. Two paradigms dominate today's deployments: edge computing, which processes data close to where it is generated, and cloud computing, which centralises massive computational power in global data centres. This study offers a structured comparison of both paradigms in the context of IoT big data. The study introduce a two-category taxonomy: Type 1 workloads requiring millisecond-level responses, and Type 2 workloads suited for large-scale batch analytics and argue that the optimal architecture depends heavily on the workload’s latency profile, privacy requirements, scale, and operational context. The analysis covers architecture, performance, security, cost, and governance dimensions, and is grounded in real-world case studies across industrial automation, smart cities, precision agriculture, and healthcare. The study concludes that a thoughtfully designed hybrid architecture combining edge autonomy with cloud depth is the most effective path forward for most production IoT systems.

WombCare AI: An Integrated Machine Learning Framework for Fetal Health Classification and Birth Weight Prediction Using CTG Signal Analysis and Maternal Clinical Data

Rajshekar Gaithond, Namratha Murthy — Wed, 17 Jun 2026 00:00:00 +0000

WombCare AI focuses on improving maternal and fetal healthcare through a structured data-driven approach that supports clinical decision-making and early risk identification. The system integrates maternal clinical parameters and CTG signal data to provide a unified understanding of fetal condition and birth outcomes. Traditional monitoring approaches often remain subjective and inconsistent, which creates variability in diagnosis and delays in intervention. The framework of WombCare AI is conceptualized as a machine learning-driven construct that enables predictive analysis and classification by operationalizing clinical data patterns across layered computational processes, thereby facilitating accurate fetal assessment and birth weight estimation while contributing to improved obstetric care outcomes. The system performs two primary tasks, including birth weight prediction and fetal health classification. Regression models such as Random Forest and AdaBoost are utilized to estimate birth weight based on maternal attributes like age, weight, gestation, and lifestyle factors. Classification models, including Logistic Regression and an ensemble of support vector classifiers, Random Forest, decision tree, and AdaBoost, are applied to CTG data to categorize fetal health into Normal, Suspect, and Pathological conditions. The proposed multi-model framework achieves a macro recall of 0.921941, significantly outperforming the Logistic Regression baseline (0.757944), and achieves a test set RMSE of 0.441 in birth weight prediction, demonstrating superior generalization. This dual pipeline approach enables early detection of complications such as fetal distress and low birth weight risk, thereby enhancing diagnostic precision and supporting timely obstetric intervention.

Digital Preservation of Ethno-medicinal Knowledge: Oral Traditions of the Tharu Tribe in Lakhimpur Kheri, Uttar Pradesh—A Library and Information Science (LIS) Framework

Karan Jaiswal — Thu, 14 May 2026 00:00:00 +0000

The Ethno-medicinal knowledge of the Tharu tribe in Lakhimpur Kheri is threatened by rapid urbanization. This study proposes an LIS-based digital institutional repository (DIR) to document their oral traditions through field surveys and interviews. By leveraging this framework, the research aims to preserve indigenous heritage for future pharmacological study and community identity. Key components of the framework include the use of multimedia documentation (audio/video), the implementation of knowledge management (KM) techniques, and addressing ethical concerns regarding intellectual property rights (IPR). The findings suggest that a structured LIS intervention can transform oral “hidden” knowledge into a “visible” digital resource, fostering community identity and providing a valuable database for pharmacological research.