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Author

Kiran B. M.

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Jul 2026

Human Stress Detection using Machine Learning

Abstract—Stress, which is a more and more common part of contemporary life, can have a serious negative effect on a person's physical and mental health. Determining and tracking stress levels is therefore essential to improving general health and quality of life. The "Human Stress Detection Based on Sleeping Habits Usi...

C. Prathyusha, Kiran B. M., D. Reddy · 0 citations
Jul 2026

Enhanced SAC For Text Privacy

An enhanced SAC algorithm in corporating embedding techniques, semantic generalization, and clustering to efficiently process and anonymize text data is introduced to ensure privacy preservation while retaining the utility of the transformed data, making it suitable for various privacy-sensitive applications.

K. G, Kiran B. M., G. Prasad · 0 citations
Jul 2026

Enhancing Cloud Data Security with Machine Learning through the Analysis of Random Forest, Deep Neural Network, and Q-Learning Approaches

The research outlines the cloud data security when using machine learning techniques – Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches.

Bharda Priya Dutt, Kiran B. M., G. Prasad · 0 citations
Review Jul 2026

Truth Identification by Discarding Rumor and Vulgar Posts

The review identifies key challenges and open research questions in the field of rumour detection using ML, including handling evolving rumour patterns, addressing adversarial attacks, and enhancing the interpretability and explain ability of ML models.

C.Sandeep Reddy, Kiran B. M., P. Rani · 0 citations
Jul 2026

Machine Learning-Based Real-Time UPI Fraud Detection System

This project presents an ML-Based Real-Time UPI Fraud Detection System that uses machine learning algorithms to identify fraudulent transactions efficiently and shows that the Random Forest algorithm achieves the highest accuracy, making it the most effective model for fraud detection.

Yekkirala Suvarcha, K. M., G. Prasad · 0 citations
Jul 2026

An Intelligent Prediction Model for Air Quality Monitoring Using GA-ELM

An optimized machine learning-based Air Quality Forecasting System that integrates Extreme Learning Machines (ELM) and Genetic Algorithms (GA) to predict short-term variations in air quality and demonstrates a robust, scalable, and practical solution for short-term air quality prediction.

Shivatejaswini B, Kiran B. M., G. Prasad · 0 citations

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