Category Archives: Uncategorized

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

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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.

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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.

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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.

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Authors: S. Santhosh Kumar, Dr. R. Senthamil Selvi

Abstract: The construction site is considered a risky place for employees, and the risks are associated with falling objects, machines, and exposure to harmful substances. Monitoring the implementation of Personal Protective Equipment (PPE) standards, including helmets, vests, gloves, boots, and masks, is of critical importance in preventing accidents and injuries. The conventional approach to monitoring the implementation of these standards is through manual observation, which is associated with time delays and human error. This study proposes an intelligent framework for the implementation of PPE standards and safety monitoring using an improved YOLOv11 deep learning model for the detection and classification of different types of PPE in real-time construction site video feeds. The model is trained on a diverse dataset to cater to complex backgrounds, lighting, occlusion, and multiple PPE pose angles, ensuring the model performs well in diverse site environments. The framework helps improve workplace safety by ensuring compliance, reducing the probability of accidents caused by negligence, and promoting regulatory compliance, thereby creating a culture of consistent PPE usage and safe work practices across the construction industry.

DOI: http://doi.org/10.5281/zenodo.20325549

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Authors: Mrs. Parul Gupta, Safiya Naaz, Priya Upadhyay, Mohd. Arshad, Tanu

Abstract: The rapid degradation of environmental quality driven by industrialization and urbanization demands continuous, real-time monitoring of key atmospheric and ecological parameters. This paper presents the design and implementation of a low-power, solar-powered IoT-based environmental monitoring system built around the STM32 microcontroller. The proposed system integrates a suite of sensors to measure temperature, humidity, atmospheric pressure, air quality, UV radiation, and soil moisture. Data is transmitted wirelessly over Wi-Fi and LoRa protocols to a cloud-based dashboard for real-time visualization and historical analysis. The system is entirely powered by a solar photovoltaic panel coupled with a lithium-ion battery and a power management unit, ensuring uninterrupted autonomous operation in remote locations without access to the electrical grid. Experimental results demonstrate reliable data acquisition with a sampling accuracy exceeding 97%, an end-to-end data transmission latency of less than 2 seconds, and continuous operation exceeding 72 hours on battery backup under cloudy conditions. The proposed system offers a cost-effective, scalable, and energy-autonomous alternative to conventional environmental monitoring stations.

DOI: https://doi.org/10.5281/zenodo.20325332

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Authors: Mrs. Pragati Sharma, Aman Chandel, Harsh Sharma, Priya Upadhyay, Safiya Naaz, Sunny Kumar, Tanu Saini, Vashu Dhiman

Abstract: The escalating degradation of environmental quality in educational institutions and public spaces demands cost -effective, real-time monitoring solutions. Conventional systems rely on centralised infrastructure or mobile applications that fail to deliver localised, immediate feedback. This paper presents AgroNexus, an IoT-driven environmental monitoring and public display platform that integrates the ESP32 microcontroller with four sensing modules—DHT22 (temperature and humidity), MQ135 (air quality), a rain sensor (precipitation detection), and DS3231 (real-time clock)—to deliver continuous data acquisition, threshold-based alerting, and live display via a six-panel P10 LED matrix. Experiments conducted in a simulated campus environment demonstrate that AgroNexus achieves high sensor accuracy, low false-alert rates, and sub-three-second display refresh cycles, outperforming single-sensor baselines across all evaluation metrics. The framework is economical, scalable, and readily deployable in smart campuses, industrial zones, and public spaces, establishing a transparent and auditable pipeline for environmental awareness.

DOI: http://doi.org/10.5281/zenodo.20325235

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Authors: Sudarshan K, Sushmitha H Y, Varshanth Gowda M L, Vinay C N

Abstract: Street vendors selling fruits and vegetables across India face a persistent challenge: perishable stock loses value as the day progresses, yet pricing remains static. This paper presents Smart Vendor AI, a complete end-to-end system that combines inventory management, point-of-sale operations, analytics dashboards, sales forecasting, and AI-assisted product quality assessment within a unified web-based platform.. The pipeline consists of six sequential layers: a fine-tuned YOLOv8s model for ripeness classification, a signal engine that converts raw predictions into weighted freshness scores, a deterministic market con- text module, an XGBoost pricing model trained on 5,000 realistic scenarios, a rule-based decision engine, and a FAISS-backed retrieval-augmented generation module powered by LLaMA 3.3 70B. Experiments on banana and tomato datasets show classifi- cation accuracy of 99.3% and 98.6% respectively. The system delivers specific, actionable vendor instructions—including an exact discount percentage and an inventory action string—without requiring any technical knowledge from the user. Results indi- cate meaningful potential to reduce the 30–40% annual revenue loss that vendors typically incur through spoilage and mispricing.

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