The primary objective of this study is to evaluate the structural and durability performance of fibre-reinforced concrete incorporating zeolite and nano-alumina as partial cement replacement materials. The use of 10% zeolite and 1% nano-alumina aims to enhance mechanical strength, durability, and microstructural properties while reducing cement consumption for sustainable construction. Polypropylene (PP) fibres were added at 0.1%, 0.2%, and 0.3% volume fractions to further improve performance. A comprehensive experimental program included casting cubes for compressive strength, cylinders for split tensile strength, prisms for flexural strength, and disc specimens for sorptivity tests. Six concrete mixes were developed, including control, blended, and fibre-reinforced variants. Results indicate that the combined use of zeolite, nano-alumina, and PP fibres significantly improves strength, crack resistance, and durability. The study confirms that optimized nano-modified fibre-reinforced concrete is a promising solution for sustainable, high-performance construction applications.

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These instructions give you basic guidelines for preparing your journal quality paper for the Conference Proceedings. Rapid industrialization and large-scale infrastructure development have led to increasing scarcity of construction materials and serious environmental concerns related to waste disposal. This study investigates the feasibility of utilizing waste tyre crumb rubber as a modifier in VG-30 grade bitumen for flexible pavement construction. Crumb rubber obtained from waste tyres was blended with bitumen at 10% by weight, and the modified binder was evaluated through four standard tests: penetration, softening point, ductility, and viscosity, per relevant IS standards. Results indicate that Crumb Rubber Modified Bitumen (CRMB) reduces penetration from 61.8 to 56.3 (0.1 mm), increases softening point from 58.75°C to 62.25°C, decreases ductility from 82 to 61 cm, and substantially increases viscosity from 55.27 to 141.0 seconds. These findings confirm that CRMB offers improved stiffness, better high-temperature resistance, and superior rutting resistance, making it a promising, sustainable, and cost-effective material for road construction in India.

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This study investigates the seismic performance of reinforced concrete structures and evaluates the effectiveness of seismic retrofitting techniques combined with advanced material modifications. Many existing buildings are vulnerable to earthquake forces due to inadequate design provisions, making retrofitting a necessary solution for structural safety. In this research, a three-storey building model was analyzed using STAAD.Pro to compare structural behavior before and after retrofitting. Key parameters such as base shear, nodal displacement, storey displacement, support reactions, bending moment, and shear force were evaluated. The retrofitting techniques considered include shear walls and base isolation, while material enhancement was explored using rubberized concrete and graphene oxide. Experimental results indicated that rubberized concrete improves ductility and energy absorption, whereas graphene oxide enhances strength and durability. However, analytical results showed an increase in displacement and internal forces after retrofitting due to increased seismic mass. The study concludes that while retrofitting improves structural capacity, improper balance between stiffness and mass can lead to increased seismic demand. Optimized design strategies are essential for achieving effective earthquake-resistant structures.

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This paper presents a comparative study of intent-based and retrieval-augmented generation (RAG)-based chatbots, two widely used approaches in modern conversational AI systems. Chatbots are increasingly deployed across domains such as customer support, education, and enterprise services to automate query resolution and improve user experience. Intent-based chat-bots rely on predefined intents and structured responses, making them efficient for handling repetitive and well-defined queries. In contrast, RAG-based chatbots retrieve relevant information from external knowledge sources and generate context-aware responses, enabling them to handle dynamic and unseen queries more effectively. This study evaluates both approaches using a common dataset of queries categorized as known, rephrased, and unseen. The comparison is based on response accuracy, response time, flexibility, maintenance effort, and user satisfaction. An educational query dataset is used as a case study to simulate a real-world deployment scenario. The results show that intent-based chatbots perform well for structured and repetitive queries due to their speed and simplicity. However, their performance declines for rephrased and unseen inputs. RAG-based chatbots demonstrate higher accuracy and adaptability, although they require greater computational resources. The study concludes that while RAG-based systems are more suitable for dynamic environments, a hybrid approach combining both methods offers the most effective solution.

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In this study, a comparison of two open-source large language models (LLMs), tiiuae/falcon-7b-instruct and GPT-2, was performed by comparing their performance using three prompt engineering techniques: Zero-Shot, Few-Shot, and Chain-of-Thought prompting. This research employed qualitative and quantitative methods of assessment through factual queries, summarization tasks, and reasoning-based questions. The results of this study show that Falcon-7b-instruct consistently produced higher clarity, conciseness, and accuracy than GPT-2 for all prompting styles. In the quantitative evaluation, it was found that, in general, Falcon-7b-instruct produced less lengthy and focused outputs. Falcon demonstrated the most significant improvement in reasoning ability, particularly with Chain-of-Thought prompting. Lastly, Zero-Shot prompting yielded the most effective results for simple factual questions. These findings will be useful for practitioners in selecting the most appropriate LLM for tasks requiring both high accuracy and computational efficiency, and they will contribute to the continued academic research on prompt engineering and LLM evaluation in the field of Natural Language Processing (NLP).

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The healthcare industry faces significant challenges in efficient resource management and timely disease diagnosis. Traditional hospital management systems often lack intelligent automation, leading to inefficiencies such as high appointment no-show rates, delayed diagnosis of critical conditions, and suboptimal resource utilization. This project presents an AI-powered Hospital Management System that addresses these challenges through the integration of machine learning and deep learning technologies. The system comprises two primary modules: an Intelligent Appointment Management System and an AI-based Chest XRay Analysis System for lung disease detection. The appointment module leverages machine learning algorithms to predict patient no-show probability, optimize scheduling through intelligent time-slot recommendations, and provide conversational appointment booking via an AI chatbot. This reduces waiting times, improves resource allocation, and enhances patient experience. The X-Ray analysis module employs deep learning techniques, specifically Convolutional Neural Networks (CNNs) with transfer learning approaches using architectures such as DenseNet121 and ResNet50. The system can detect multiple lung conditions including pneumonia, tuberculosis, COVID-19, pleural effusion, cardiomegaly, and other pulmonary abnormalities with high accuracy. It generates automated preliminary reports with confidence scores, provides visual explanations This integrated approach demonstrates the potential of artificial intelligence in transforming healthcare delivery by reducing diagnostic delays, minimizing human error, optimizing hospital operations, and ultimately improving patient outcomes. The system serves as a proof-of-concept for how modern AI technologies can be effectively deployed in realworld clinical settings while maintaining explainability and reliability standards required in medical applications.

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The rapid increase in cybercrime incidents worldwide has created an urgent need for efficient, secure, and accessible reporting systems. However, many cybercrime victims hesitate to report incidents due to privacy concerns, fear of identity exposure, and the complexity of traditional text-based reporting portals. This paper proposes ReACT_OCRS, an AI-driven anonymous online cybercrime reporting system that enables users to submit complaints through voice input without revealing their identity. The system integrates OpenAI Whisper for multilingual speech-to-text conversion, a hybrid LSTM and BERT model for intelligent complaint classification as genuine or false, and Federated Learning for privacy-preserving model updates. The backend is built using Java Spring Boot, providing a secure and scalable web application. Users are assigned unique anonymous IDs during registration, ensuring complete privacy. Encryption techniques protect all stored complaint data. Experimental results demonstrate that the proposed system achieves 91.4% complaint classification accuracy while significantly improving accessibility, efficiency, and user trust compared to conventional reporting methods. ReACT_OCRS represents a significant advancement in AI-powered E-Governance solutions for cybercrime reporting.

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Healthcare information is highly sensitive because it includes details such as patient prescriptions, medical history, treatments, and clinical activities. This data is often shared among different stakeholders within the healthcare system, including doctors, laboratories, and other medical professionals. Since patient information is critical, it must be kept accurate, up-to-date, confidential, and accessible only to authorized individuals. Traditional centralized systems used for storing healthcare records can increase security risks, as they create a single point of vulnerability. Therefore, ensuring the privacy and security of medical data while sharing it across multiple entities has become a major concern. This study proposes a privacy-preserving access control framework based on blockchain technology. The framework uses a decentralized approach, where data is managed across distributed systems through consensus mechanisms. This ensures better security, transparency, and integrity of healthcare data. One of the key advantages of blockchain is its immutability, which prevents unauthorized modification of records and protects transactions from tampering. Furthermore, the proposed system considers different participants in the healthcare ecosystem, such as patients, doctors, researchers, and pathology labs, allowing them to share information securely through authorized channels. The framework is implemented and evaluated using Laravel and Hyperledger Fabric, and the results demonstrate improvements in security, regulatory compliance, reliability, scalability, and data accuracy.

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The rapid growth of digital image manipulation tools has significantly increased the prevalence of image forgeries, posing serious challenges to media credibility, digital forensics, and information security. This project presents a deep learning–based image forgery detection framework designed to identify and classify tampered images with high reliability. The proposed approach employs convolutional neural network (CNN) architectures to automatically learn discriminative features from forged and authentic images, eliminating the need for handcrafted feature extraction. The model is trained and evaluated on a labelled dataset containing both genuine and manipulated images, including common forgery types such as copy–move and splicing attacks. Experimental results demonstrate that the deep learning–based method achieves superior accuracy and robustness compared to traditional image forensics techniques. Additionally, visual explanation methods are used to highlight manipulated regions, improving interpretability and trust in model decisions. The proposed system shows strong generalisation capability and offers an effective, scalable solution for real-world image forgery detection applications.

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In today’s time, digital solutions are becoming common in many different fields, especially in healthcare where handling information properly and maintaining clear communication is very important. Even then, many dental clinics still rely on older ways to communicate with laboratories, such as phone calls, messaging apps, and written records. These methods are not very dependable and often lead to issues like missing information, delays in communication, incorrect order details, and no reliable way to track work progress. Because of this, the overall performance of dental services is affected. To address these problems, we developed DentLink, a web-based Dental Lab Management Platform. It provides a single system where dentists and laboratories can work together in a more organized way. Dentists can store patient data, create lab orders with proper details like tooth selection and materials, and check the progress of their orders. At the same time, laboratories can receive orders, update their work status, manage pricing, generate invoices, and handle delivery updates in a structured manner. This application is created with tools like React.js for the user interface, Node.js with Express for backend processing, and PostgreSQL for storing data securely. User access is controlled based on their role, so each user can only use the features allowed to them. The system follows a client–server model, which helps in smooth data flow and allows multiple users to work on the system at the same time without affecting performance. After testing the system in different practical situations, it was found that the platform improves overall system performance, reduces chances of manual mistakes, and makes communication between dentists and laboratories clearer. It also keeps data secure and allows users to access it whenever needed, saving both time and effort. Overall, DentLink is a reliable and scalable solution for managing dental clinic and laboratory activities. It helps move from manual work to a digital system, making operations faster and better organized. In the future, the system can be improved further by adding features like AI-based analysis, automatic report generation, and support through a mobile-based app to enhance usability.

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Plastic waste has become one of the most pressing environmental challenges of modern society, contributing to pollution and resource depletion. At the same time, additive manufacturing technologies such as 3D printing are rapidly expanding, creating demand for affordable and sustainable filament materials. This research focuses on the design and development of a machine capable of converting post-consumer plastic waste into usable 3D printer filament. The proposed system integrates mechanical shredding, extrusion, and filament calibration processes to transform discarded plastic into high-quality filament with consistent diameter and strength. By utilizing locally available waste plastics, the machine reduces environmental impact while lowering the cost of 3D printing materials. Experimental trials demonstrate that the recycled filament maintains adequate mechanical properties for prototyping and functional applications. The project highlights the dual benefits of waste management and resource recovery, offering a practical solution for sustainable manufacturing. Future scope includes optimizing the machine for different types of plastics, improving filament quality control, and scaling the system for community-level adoption.

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The growing demand for energy and increasing environmental concerns have led India to explore alternative and sustainable fuel sources. Ethanol produced from sugarcane has emerged as a significant solution to reduce dependence on fossil fuels and enhance energy security. This study examines the economic and environmental impact of sugarcane-based ethanol in India. The research highlights how ethanol blending in petrol contributes to substantial foreign exchange savings by reducing crude oil imports. It also supports the agricultural sector by ensuring stable demand and better income for sugarcane farmers, thereby strengthening rural livelihoods. From an environmental perspective, ethanol is a cleaner fuel that helps in reducing greenhouse gas emissions and air pollution. The study also discusses challenges such as water consumption, infrastructure limitations, and policy constraints. Despite these challenges, sugarcane-based ethanol presents a promising pathway toward sustainable development and a greener economy. The paper concludes that with appropriate policy support and technological advancements, ethanol production can play a crucial role in India's economic growth and environmental protection.

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Cloud computing has become a core component of modern software development, particularly in DevOps environments that emphasize automation, scalability, and continuous delivery. However, the dynamic nature of cloud resources often leads to uncontrolled and excessive costs, especially in Amazon Web Services (AWS) infrastructures. This paper presents a systematic review of cloud cost optimization strategies by analyzing existing research, industry practices, and commonly used techniques. The study focuses on key approaches such as resource rightsizing, auto-scaling, spot and reserved instances, storage lifecycle management, and monitoring-driven cost control, while also examining the impact of DevOps practices like CI/CD, infrastructure as code (IaC), and containerization on cloud expenditure. The findings reveal that although automation improves efficiency, poor configuration can significantly increase costs. The paper highlights the need for integrating costaware practices into DevOps workflows and identifies future directions for more intelligent and automated cost optimization solutions.

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The rising concern over fossil-fuel dependence, greenhouse-gas emissions, and the high operational cost of conventional lawn-maintenance equipment has created a strong demand for clean and affordable alternatives. This review paper presents a detailed study of the student project titled "Design and Fabrication of a Solar Powered Grass Cutter", which replaces the petrol engine of a traditional grass cutter with a photovoltaic (PV) powered DC-motor drive. The machine uses a 12–24 V solar panel, a charge-controller circuit, a 12 V rechargeable battery (2200 mAh), and a high-RPM 775 DC motor coupled to a cutting blade. A lightweight PVC-pipe frame and MFD-sheet body make the unit portable, corrosion-free, and low-cost (approximately 4,000). The paper reviews the working principle, component selection, electrical circuit, frame design, cost analysis, advantages, limitations, and future scope of the system. Comparative observations with published literature on solar grass cutters are also discussed. The study concludes that solar-powered grass cutters are a feasible, eco-friendly, and economically viable alternative for small-scale lawn and garden maintenance, with significant potential for further enhancement through IoT-based control and height-adjustment mechanisms.

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A metro system is a railway transport system in an urban area with a high capacity, frequency and the grade separation from other traffic. Metro System is used in cities, agglomerations, and metropolitan areas to transport large numbers of people. An elevated metro system is more preferred type of metro system due to ease of construction and also it makes urban areas more accessible without any construction difficulty. An elevated metro system has two major elements pier and box girder. The present study focuses on two major elements, pier and box girder, of an elevated metro structural system. The parametric study on behaviour of box girder bridges showed that, as curvature decreases, responses such as longitudinal stresses at the top and bottom, shear, torsion, moment and deflection decreases for three types of box girder bridges and it shows not much variation for fundamental frequency of three types of box girder bridges due to the constant span length. It is observed that as the span length increases, longitudinal stresses at the top and bottom, shear, torsion, moment and deflection increases for three types of box girder bridges. As the span length increases, fundamental frequency decreases for three types of box girder bridges. Also, it is noted that as the span length to the radius of curvature ratio increases responses parameter longitudinal stresses at the top and bottom, shear, torsion, moment and deflection are increases for three types of box girder bridges. As the span length to the radius of curvature ratio increases fundamental frequency decreases for three types of box girder bridges.

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