Publications by authors named "M K Nallakaruppan"
Article Synopsis
- The health of living species heavily relies on the quality of water, making its purity management essential to prevent adverse health and environmental impacts.
- The study focuses on automating water quality assessment using Explainable Artificial Intelligence (XAI), which clarifies how certain factors affect water potability by utilizing various machine learning models like Random Forest, SVM, and Decision Trees.
- The Random Forest classifier showed exceptional performance, achieving high accuracy and F1-Score, highlighting the research's aim to improve water quality monitoring for future needs.
Fake face identity is a serious, potentially fatal issue that affects every industry from the banking and finance industry to the military and mission-critical applications. This is where the proposed system offers artificial intelligence (AI)-based supported fake face detection. The models were trained on an extensive dataset of real and fake face images, incorporating steps like sampling, preprocessing, pooling, normalization, vectorization, batch processing and model training, testing-, and classification via output activation.
View Article and Find Full Text PDFRecently, research into Wireless Body-Area Sensor Networks (WBASN) or Wireless Body-Area Networks (WBAN) has gained much importance in medical applications, and now plays a significant role in patient monitoring. Among the various operations, routing is still recognized as a resource-intensive activity. As a result, designing an energy-efficient routing system for WBAN is critical.
View Article and Find Full Text PDFThe impact analysis of deep learning models for COVID-19-infected X-ray images is an extremely challenging task. Every model has unique capabilities that can provide suitable solutions for some given problem. The prescribed work analyzes various deep learning models that are used for capturing the chest X-ray images.
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