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Research Paper

QiML Framework for Anomaly Detection in NFV-Clouds

Network Function Virtualization (NFV) transforms traditional network infrastructures by replacing hardware components with software-based Virtual Network Functions (VNFs). While NFV improves flexibility, scalability, and cost efficiency, it also introduces significant cybersecurity challenges due to vulnerabilities in virtualization layers, orchestration tools, and multi-tenant environments. Conventional intrusion detection systems and classical machine learning (ML) models such as Support Vector Machines, Random Forests, and traditional neural networks often fail to cope with evolving threats, leading to high false positives, computational overhead, and limited effectiveness against zero-day attacks. To address these limitations, this paper proposes a Quantum-Inspired Machine Learning (QiML) framework specifically designed for anomaly detection in NFV-cloud security. The framework integrates multiple modules: Quantum-inspired Feature Encoding (QiFE) for compact data representation, a Quantum-inspired Evolutionary Algorithm (QiEA) for feature selection, Quantum-inspired Neural Networks (QiNN) for accurate anomaly detection, an Adaptive Quantum-Inspired Cybersecurity Strategy for real-time mitigation, and Quantum-inspired Explainable AI (QiXAI) for interpretability. Experimental evaluations using CIC-IDS2018, UNSW-NB15, and NFV-specific synthetic datasets demonstrate the superior performance of the proposed framework. The QiEA + QiNN model achieved an accuracy of 98.20%, precision of 97.70%, recall of 97.40%, and F1-score of 97.55% on CIC-IDS2018, outperforming classical ML baselines. Furthermore, the framework reduced feature dimensionality and training time, enhancing efficiency for real-world NFV-cloud deployments. Overall, the QiML framework demonstrates strong potential for advancing secure, adaptive, and interpretable anomaly detection in NFV-cloud environments.

Published by: Mr M. Jayababu, Dr J. Kejiya Rani

Author: Mr M. Jayababu

Paper ID: V11I5-1206

Paper Status: published

Published: October 24, 2025

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Survey Report

IOT-Based Drainage Block Detection with Control-Based Drainage Unit Cleaner

This Paper presents an IoT-based drainage block detection and cleaning system designed to address frequent drainage blockages that cause waterlogging, foul odors, and health risks. The system uses sensors such as ultrasonic, flow, and gas detectors to monitor water levels and detect blockages in real time. Data is transmitted to a control room dashboard via IoT modules (ESP8266/ESP32), where alerts are generated when abnormal conditions occur. A mechanized cleaning unit, controlled remotely from the control room, removes solid waste using motorized arms or brushes, reducing manual intervention and ensuring worker safety. The proposed system provides an efficient, low-cost, and smart solution for real-time monitoring and automated drainage maintenance, contributing to safer and cleaner urban environments.

Published by: Kshama N Pendse, Shrinivas R Vaidya, Preetam R Joshi, Prof G M Patil

Author: Kshama N Pendse

Paper ID: V11I5-1203

Paper Status: published

Published: October 23, 2025

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Research Paper

Explainable Deep Learning for Satellite-Based Natural Disaster Detection and Prediction

Over Earth’s 4.54 billion-year history, natural disasters have reshaped its topography countless times. Earthquakes, storms, floods, and droughts are among the most destructive and unpredictable natural disasters. However, satellite data combined with machine learning algorithms now offer new ways to detect early warning signs of these disasters and mitigate their effects. By leveraging Geographic Information System (GIS) data, NASA’s Global Precipitation Measurement (GPM), and other satellite technologies, researchers can analyze massive geospatial datasets to identify subtle patterns imperceptible to humans. This paper explores the role of machine learning and satellite data in predicting natural disasters. It highlights the technological advancements that could significantly reduce the human and environmental toll of these events.

Published by: Hruday Shreyas Rachapudi

Author: Hruday Shreyas Rachapudi

Paper ID: V11I5-1202

Paper Status: published

Published: October 22, 2025

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Research Paper

AI in Healthcare- A Global Perspective

Despite their initial seeming incompatibility, research shows that AI and conventional medicine may work effectively together. 'Mapping the use of artificial intelligence in traditional medicine' is a new brief from the World Health Organization (WHO) and its partners that demonstrates how AI may support TCIM (traditional, complementary, and integrative medicine) while preserving cultural heritage. By raising the standard of patient care, artificial intelligence (AI) is predicted to enhance long-term health outcomes. AI makes it possible for extremely accurate diagnoses, individualized treatment plans, quicker recovery times, and fewer problems by rapidly and correctly analyzing patient data. In addition to helping patients, these advancements lower the expenses associated with incorrect diagnoses and inefficient therapies. AI is useful in public health management. It can alleviate the strain on healthcare systems by forecasting health trends and enhancing outcomes for entire populations. By providing more individualized and affordable services, increasing patient alternatives, and promoting better treatment, AI strengthens competition.

Published by: Rishaan Sanjay Lulla

Author: Rishaan Sanjay Lulla

Paper ID: V11I5-1198

Paper Status: published

Published: October 17, 2025

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Research Paper

A Detailed Analysis of Biosensors Used to Combat Antibiotic Resistance

Antibiotic resistance presents a global health crisis, where bacteria evolve to withstand antimicrobial treatments, increasing mortality rates. This escalating threat, even though a natural evolutionary process, has been significantly accelerated by the pervasive misuse and overuse of antibiotics in both human and veterinary medicine. The staggering statistics, including millions of infections and thousands of deaths annually in the United States alone, underscore the urgent need for innovative solutions. Among the most promising advancements are biosensors, analytical devices comprising a biorecognition element and a transducer. These instruments offer rapid, sensitive, and precise detection of pathogens and antibiotic residues. Various biosensors are being developed and deployed to identify resistant microbial strains. Biosensors are a pivotal tool in mitigating the deadly impact of antimicrobial resistance and safeguarding public health.

Published by: Aryav Parikh

Author: Aryav Parikh

Paper ID: V11I5-1174

Paper Status: published

Published: October 15, 2025

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Research Paper

The Integration of AI in Cybersecurity

This paper examines the integration of AI in cybersecurity, highlighting its implications for everyday life and its role in preventing cyberattacks. It analyses key protective measures, including SIEM and SOAR, and evaluates the emerging field of Agentic AI as both a potential solution and a risk. Finally, it explores the relationship between AI, IT, and IOT, emphasising AI’s capacity to advance technological progress while simultaneously expanding potential vulnerabilities.

Published by: Abhinav Singh

Author: Abhinav Singh

Paper ID: V11I5-1187

Paper Status: published

Published: October 15, 2025

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