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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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Research Paper

Automated Brain Tumor Segmentation Using a UNet3D-Based Deep Learning Model

A crucial task in medical imaging is brain tumor segmentation, which allows for accurate diagnosis and treatment planning for patients with brain tumors. Magnetic Resonance Imaging (MRI) provides detailed volumetric data, but manual segmentation is time-consuming and prone to variability. Deep learning, particularly convolutional neural networks such as UNet3D, has emerged as a powerful tool for automating and enhancing segmentation accuracy. Accurate and efficient segmentation of brain tumors from multi-modal MRI scans remains challenging due to the heterogeneity of tumor appearances, varying MRI modalities (e.g., T1, FLAIR), and the need for robust models that generalize across diverse datasets. This study aims to develop and evaluate a UNet3D-based deep learning model for automated brain tumor segmentation, leveraging the BraTS2020 dataset to achieve high-precision delineation of tumor regions in MRI scans. We developed and trained a UNet3D-based model tailored for brain tumor segmentation, utilizing PyTorch and nibabel to process 3D MRI data from the BraTS2020 dataset. The model was comprehensively evaluated on standard datasets, demonstrating robust performance across multiple MRI modalities. We conducted a thorough comparison with baseline segmentation techniques, including traditional methods and other deep learning approaches, analyzing metrics such as Dice scores and segmentation accuracy. Our results highlight the model’s superior ability to delineate tumor boundaries, offering improved precision and efficiency over baselines, thus advancing the application of artificial intelligence in medical imaging for brain tumor diagnosis.

Published by: Sidhartha Tadala, Angad Singh Chopra

Author: Sidhartha Tadala

Paper ID: V11I5-1178

Paper Status: published

Published: October 14, 2025

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Research Paper

Fortifying AI Infrastructure: Securing Code, Configuration, and Integrity in National Systems

The rapid adoption of artificial intelligence (AI) on cloud platforms, such as AWS and Azure, has introduced critical security vulnerabilities across various national sectors, including defense, healthcare, and energy. While these environments deliver scalable intelligence, they also expand the attack surface, exposing misconfigured resources, unverified code, and weak identity controls. Recent breaches, including Capital One’s AWS data exposure, Tesla’s compromised Kubernetes console, and Microsoft’s AI dataset leak, demonstrate how cloud-hosted AI pipelines can be weaponized through insecure defaults, leaked credentials, and permissive access roles. This study analyzes prominent security incidents alongside current research on cloud and AI threats to identify recurring weaknesses in configuration management, secret handling, and model integrity. The findings highlight how attackers exploit these gaps to steal data, engage in cryptojacking, and gain unauthorized access to AI models. To address these risks, the paper proposes a framework for fortifying AI infrastructure that emphasizes: (1) zero-trust identity and access management, (2) secure coding and model lifecycle practices, (3) automated configuration scanning, and (4) continuous policy enforcement. The results underscore that AI infrastructure should be treated as national critical infrastructure, warranting rigorous standards and proactive defense measures. Without systematic hardening, AI pipelines are high-value targets for cybercriminals and nation-state actors, posing a threat to public safety and national security.

Published by: Ifeoma Eleweke

Author: Ifeoma Eleweke

Paper ID: V11I5-1190

Paper Status: published

Published: October 14, 2025

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