Authors: B. M. Promod Kumar, Bhavana N. S., C. Chinmayi, Deepthi C. Shekar, Deenadayal B. K.

Abstract: Radiology reports generated from imaging modalities such as X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound scans are critical clinical documents. However, these reports are authored in complex medical terminology intended for radiologists and specialist physicians, rendering them largely inaccessible to patients and non-medical users. This communication gap results in confusion, anxiety, and increased dependency on healthcare professionals for basic explanations. This paper presents MedLens, an AI- powered radiology report simplification system that bridges this gap by leveraging Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG). The system extracts text from uploaded PDF reports using PyMuPDF, processes clinical content using Google Gemini AI models, and generates accurate, context-aware patient-friendly summaries. It further classifies the urgency of findings into levels (Low, Moderate, High, Critical), and integrates multilingual translation, text-to-speech functionality, and an AI-powered contextual chatbot. The platform is deployed using FastAPI on the backend and React.js with Tailwind CSS on the frontend. Experimental results demonstrate that MedLens successfully simplifies complex medical terminology, detects critical conditions, provides multilingual support, and enables interactive report-based queries, thereby empowering patients with better health awareness and facilitating informed discussions with healthcare providers.

DOI: https://doi.org/10.5281/zenodo.20351715

Published by: vikaspatanker

Authors: Alexander Whitmore, Benjamin Clarke, Daniel Harrington, Ethan Montgomery, Naveen Kumar

Abstract: Autonomous Infrastructure Management using LLM-augmented platform engineering frameworks represents a transformative approach to modern cloud operations, combining large language models (LLMs), artificial intelligence, and platform engineering principles to automate infrastructure provisioning, monitoring, optimization, security enforcement, and lifecycle management across hybrid and multi-cloud environments. This research paper explores how LLM-driven automation frameworks enhance Infrastructure as Code (IaC), intelligent orchestration, self-healing systems, predictive analytics, and policy-driven governance to reduce operational complexity and improve infrastructure reliability. The study highlights the integration of natural language processing, machine learning-based anomaly detection, and autonomous decision-making mechanisms that enable adaptive infrastructure management with minimal human intervention. Furthermore, the paper examines the role of AI-powered observability, automated incident response, resource optimization, and compliance validation in accelerating DevOps and AIOps workflows while improving scalability, cost efficiency, and cybersecurity resilience. The proposed framework demonstrates how LLM-augmented platform engineering can streamline enterprise cloud operations through intelligent automation, contextual infrastructure recommendations, and continuous optimization strategies. Finally, the research discusses implementation challenges, ethical considerations, governance requirements, and future advancements in autonomous infrastructure ecosystems, emphasizing the growing significance of generative AI in next-generation cloud-native platform engineering and enterprise infrastructure transformation.

DOI: http://doi.org/10.5281/zenodo.20351661

Published by: vikaspatanker

Authors: Dr. Victoria S. Turner, Dr. Isabella N. Hughes, Dr. Christopher J. Walker, Prof. Daniel T. Harrison, Naveen Kumar

Abstract: Machine learning-driven infrastructure blueprinting and cloud architecture optimization represent a transformative approach to modern enterprise computing environments by integrating intelligent automation, predictive analytics, and adaptive resource management into cloud infrastructure design and deployment processes. Traditional infrastructure planning methods often require extensive manual intervention, static configuration models, and continuous monitoring efforts, which can lead to inefficiencies, increased operational costs, and scalability limitations in dynamic cloud ecosystems. This research explores the application of machine learning techniques in automating infrastructure blueprint generation, workload prediction, resource allocation, performance optimization, and fault detection across multi-cloud and hybrid cloud environments. By leveraging supervised learning, reinforcement learning, and deep neural networks, intelligent systems can analyze historical operational data, identify optimal architectural patterns, and generate scalable infrastructure configurations that align with business requirements, security policies, and compliance standards. The study further examines how AI-driven optimization improves cloud elasticity, reduces energy consumption, enhances infrastructure reliability, and accelerates Infrastructure as Code (IaC) deployment workflows through automated decision-making and self-healing capabilities. Additionally, the research highlights the integration of predictive analytics for proactive capacity planning, anomaly detection, and cost-aware cloud orchestration to improve operational resilience and service availability. The findings demonstrate that machine learning-enabled cloud architecture optimization significantly enhances deployment efficiency, reduces human error, strengthens infrastructure governance, and supports intelligent digital transformation initiatives in modern enterprises.

DOI: http://doi.org/10.5281/zenodo.20351605

Published by: vikaspatanker

Authors: Shital Vishwanath Ban, Shankar Sanjay Rathod, Prerana Prakash Malgave, Mrs. M.R, Raste

Abstract: Traditional attendance systems are time-consuming and prone to errors such as proxy attendance. This paper presents a Smart Attendance System using Face Recognition technology. The system automatically detects and recognizes faces to mark attendance. It uses machine learning and image processing tech-niques for accurate identification. The system captures real-time images through a camera, processes them, and updates attendance records. It reduces manual effort and improves accuracy. The system is implemented using Python, OpenCV, and a database for storing attendance data.

Published by: vikaspatanker

Authors: Ethan Cole Harrison, Prof. Daniel Reeves Walker, Prof. Emily Carter Hayes, Dr. Christopher Liam Foster, Naveen Kumar

Abstract: Infrastructure as Code (IaC) has emerged as a foundational paradigm for automating cloud infrastructure provisioning, configuration management, and deployment orchestration across modern enterprise environments. However, the growing complexity of multi-cloud architectures, dynamic scaling requirements, and heterogeneous deployment policies has increased the difficulty of maintaining reliable and secure declarative infrastructure templates. This research explores the integration of Generative Artificial Intelligence and neural modeling techniques into Infrastructure as Code workflows to enable intelligent, adaptive, and automated cloud infrastructure engineering. The proposed framework leverages large language models, transformer-based neural architectures, and AI-assisted configuration synthesis to generate, validate, optimize, and remediate declarative infrastructure definitions across cloud platforms. The study investigates how generative models can enhance infrastructure provisioning accuracy, reduce manual scripting complexity, improve deployment consistency, and accelerate DevOps and platform engineering operations. Furthermore, the research examines AI-driven policy validation, anomaly detection, infrastructure drift correction, security compliance automation, and predictive resource optimization within declarative cloud ecosystems. Experimental analysis demonstrates that neural-assisted IaC generation significantly improves deployment efficiency, operational scalability, infrastructure resilience, and automation intelligence while minimizing configuration errors and deployment failures. The findings highlight the transformative potential of generative AI in enabling autonomous cloud operations, intelligent infrastructure orchestration, and next-generation cloud-native automation frameworks for enterprise-scale digital transformation initiatives.

DOI: http://doi.org/10.5281/zenodo.20351381

Published by: vikaspatanker

Authors: Manoj S, Chaitra B P, Nandan J M, Nehal Eldho Binu

Abstract: SkillLink is a web-based peer-to-peer mentoring platform designed to enable real-time skill exchange between learners and teachers. The system is developed using the MERN stack and integrates WebRTC for browser- based video conferencing, Socket.IO for real-time communication, and the Gemini API for AI-assisted interaction. Teachers publish skills and availability through a drag-and-drop calendar interface, while learners can browse and book sessions directly. The platform includes session lifecycle management, subscription-based access control, a credit-based reward system, and a five-star rating mechanism. Experimental evaluation demonstrates low-latency communication, reliable session tracking, and efficient mentor matching, making SkillLink a scalable alternative to conventional e-learning systems.

Published by: vikaspatanker

Authors: Murmylo Yulia

Abstract: Children's happiness” is the principal vector of any society and the foundation on which the Sustainable Development Goals rest today, tomorrow and for the next generation. This multidimensional term encompasses complex components, without each of which it remains incomplete. We examine the interrelation between the phenomenon of children's happiness and nature-based practices (in the context of an environment in which, through neurobiological, sensory and interpersonal mechanisms, qualitative changes take place in the child's personality, emotional repertoire, cognitive strategies and immune profile). We review existing methodologies, the international studies that have been conducted on this topic, and their results, and draw a conclusion about the most effective practices contributing to the enhancement of children's happiness. This article is unique in that it identifies a set of aspects of children's well-being, presents concrete methodologies for analysing this multifaceted concept, lays out natural factors of influence, summarises a research base on the impact of nature on the younger generation across individual components, and describes working programmes that demonstrate the action of the natural environment on children as transformative. The author argues that, from the standpoint of sustainable development, nature-oriented programmes possess a unique property: they are simultaneously a tool for achieving goals (improving children's health and well-being) and a means of forming agents of sustainable development in the next generation. Adapting the principles of the Stanford course “Interpersonal Dynamics” to nature-based programmes for children opens up the possibility of creating a new class of pedagogical products.

DOI: http://doi.org/10.5281/zenodo.20341762

Published by: vikaspatanker

Authors: Amrutha H, Chaithra HM, Chandana BM, Chethana GH, Mr. Santhosh Babu KC Assistant Professor

Abstract: Women's safety remains a critical global concern, with increasing incidents of harassment, assault, and emergencies requiring immediate intervention. Traditional safety devices such as panic buttons and mobile applications have limitations: they rely on cellular connectivity, which may be unavailable in remote areas, and they lack automatic fall detection for situations where the user cannot manually trigger an alert. This project presents a comprehensive LoRa based women safety device that combines manual panic activation, automatic fall detection, and dual communication channels for maximum reliability. The system consists of two units: a portable transmitter unit carried by the user and a stationary receiver unit placed at a trusted location such as home, workplace, or police station. The transmitter unit uses an ESP32 microcontroller with a panic button for manual emergency activation and an MPU6050 sensor for automatic fall detection. When an emergency is detected, the transmitter sends an alert via LoRa wireless communication (operating at 433MHz) over long distances (several kilometers). Simultaneously, a GSM800L module sends an SMS alert directly to authorities or emergency contacts. The receiver unit, comprising another ESP32 with a LoRa module, buzzer, and LCD display, receives the LoRa transmission, displays the alert message on the LCD, and activates an audible buzzer to notify personnel at the receiving location. This dual-path communication ensures that even if one channel fails (GSM network down or LoRa interference), the other channel may still deliver the alert. The system is designed to be wearable, low-power, and effective in both urban and remote areas where cellular coverage may be unreliable.

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Published by: vikaspatanker

Authors: Prof. V. U. Bansude, A. S. Yadav, D. D. Pawal, A. S. Yadav

Abstract: The robotic arm is one of the most significant innovations in the field of automation and robotics, capable of replicating human arm movements with high precision, accuracy, and repeatability. Over the past decades, researchers have developed robotic arms for various applications such as industrial manufacturing, medical surgery, agriculture, space exploration, and defense operations. Early robotic arm systems were limited to simple wired control and basic pick-and-place operations. However, recent advancements have integrated modern technologies including artificial intelligence (AI), computer vision, machine learning, and Internet of Things (IoT) to achieve intelligent and autonomous functionality. This paper presents a comprehensive survey of existing robotic arm systems with emphasis on their design methodologies, actuation techniques, control mechanisms, and practical applications. A comparative analysis of various research works has been conducted to understand the technological evolution and identify limitations in current robotic arm systems. The study also highlights future opportunities for developing intelligent robotic arms capable of performing complex real-world tasks with improved efficiency and reliability.

Published by: vikaspatanker

Authors: Shreya Shashikant Patil, Shital Nivrutti Sutar, Prachi Prasad Patil, Mrs . Meghana Khare

Abstract: Artificial intelligence has made it possible to generate highly realistic images, which can be mis used for misinformation, fraud and identity theft. Detecting such AI- generated images manually is difficult and time consuming. Detecting such AI-generated images has become very important to maintain the authenticity of digital content. This paper presents an AI Image Fraud Detector such that uses deep learning techniques to classify as real or fake. The system integrates YOLO (You Only Look Once) model with a web-based applications developed using Flask and JavaScript. Users can upload images through a user-friendly interface, and the system provides prediction result along with confidence scores. The model processes images in real time and ensures fast detection. Experimental results show that the system performs efficiently with good accuracy depending on the dataset quality. This research contributes to improving digital security by providing an automated solution for detecting AI-generated images. In this research, we developed an AI image fraud detection system using deep learning models such as VGG16, ResNet, and InceptionV3.Thesemodels are trained on a dataset containing both real and AI generated images. The system compares the performance of all three model to find which one give better accuracy. The model is trained on a dataset from Kaggle that contain both real and fake images of Aadhar- id photo and other documents. Image preprocessing techniques are used to improve performance of the model. The result show that deep learning models can effectively detect fake images, with one model performing better based on accuracy and efficiency. The study highlights that using multiple models improve reliability and provides a strong solution for detecting AI-generated images in real world applications. We also tested different settings of the model to understand what works best. Our study shows that it is a strong and reliable method for detecting AI-generated images and can be useful in real-world applications. Model is addressing the increasing challenge of AI-generated image detection, laying a foundation for future research in critical area.

Published by: vikaspatanker