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Research Paper

Implementation of reflective programming in .NET

Programming languages are evolving very fast, but their principles are almost the same. There are two main concepts about the writing process of software, Closed Code and Open-Source Software. Most developers prefer to have the source code of the software developed by other developers so they can understand, read, and change the product very easily. Even when a product is an open source, the number of developers who modify the product is very low compared to the number of users who use the product. In this paper we will present an implementation of reflective programming in .NET to allow other developers to extend the capabilities of a product with their contributions on writing source code even if the project is not open source.

Published by: Hakik Paci, Dorian Minarolli, Nelda Kote

Author: Hakik Paci

Paper ID: V11I1-1518

Paper Status: published

Published: April 12, 2025

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Review Paper

LogiX: AI-Driven Secure Login System with Quantum-Resistant Algorithms and Multi-Factor Authentication

LogiX is a next-generation authentication and security framework designed to withstand emerging cybersecurity threats, including those posed by quantum computing. The system integrates Post-Quantum Cryptographic Algorithms such as Kyber for Key Exchange, Dilithium for Digital Signatures, and NTRU For Encryption To Ensure Robust Data Protection. Additionally, Advanced AI-driven techniques are Employed for Anomaly Detection, Credential Sharing Analysis, Adaptive Multi-Factor Authentication (MFA), and Phishing De-Detection. By leveraging secure authentication protocols, quantum-resistant cryptographic standards, and intelligent monitoring systems, LogiX provides a comprehensive security solution that mitigates risks associated with traditional and quantum-era cyber threats. This documentation outlines the foundational principles, methodologies, and implementation strategies used to fortify authentication mechanisms and protect sensitive user data in cloud-based environments.

Published by: Dr. M.K. Jayanthi Kannan, Abir Barman, Anjali Yadav, Samikshya Pruseth, Santosini Sahu

Author: Dr. M.K. Jayanthi Kannan

Paper ID: V11I1-1558

Paper Status: published

Published: April 12, 2025

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Review Paper

Advancing Sentiment Analysis: A Comprehensive Review of Data Augmentation Strategies and Deep Learning Techniques

Sentiment analysis has gained significant attention in natural language processing (NLP) due to its applications in social media monitoring, customer feedback analysis, and opinion mining. However, a major chall enge in sentiment classification is class imbalance, where certain sentiment categories are underrepresented, leading to biased models and reduced accuracy. This research addresses the issue by leveraging data augmentation techniques, specifically synonym replacement and back-translation, to balance the dataset and enhance model performance. Unlike conventional deep learning approaches that rely on complex architectures, this study proposes a simplified yet more effective model by utilizing a Decision-based Recurrent Neural Network (D-RNN) trained on an augmented dataset. Additionally, Aspect-based and Priority-based augmentation techniques are introduced to ensure semantic consistency and emphasize critical contextual information during augmentation. Experimental results demonstrate that the proposed approach effectively reduces class skewness and improves sentiment classification accuracy, surpassing traditional models in performance while maintaining lower computational complexity. This research highlights the significance of data augmentation as a powerful strategy to enhance sentiment analysis, offering a cost-effective and scalable solution without the need for more complex deep learning models.

Published by: Rashidkhan R Pathan, Dr. Pradip Patel

Author: Rashidkhan R Pathan

Paper ID: V11I1-1559

Paper Status: published

Published: April 12, 2025

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Research Paper

Emotions as a Marketing Strategy in the Social Media World

Emotional branding is widely used in building a marketing strategy. In this study, we will discuss various factors involved in creating emotions as benefits of advertising. How emotions as a main tool can create a brand’s identity and uniqueness for consumers to have brand loyalty. This study examines both for-profit and non-profit sectors to show how empathy-driven marketing is becoming important for establishing genuine connections with consumers. The use of emotional appeals in advertising The most important factor of having a strong social media appearance is for consumers to connect with the organization's uniqueness and have a true sight and information about the brand values and ethics towards creating a brand shows the importance to do collaborative campaigns with influencers and consumers to get connected with the brand. The use of social media marketing as a main tool in creating awareness through promoting video advertising on brands' Social media handles and, finally, talking about the future trends that will be used in emotional marketing strategy.

Published by: Simran Juneja

Author: Simran Juneja

Paper ID: V11I1-1517

Paper Status: published

Published: April 12, 2025

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Research Paper

Design and Development of a Semi-Automated System for Food Waste Disposal and Management

Waste accumulation in multi-tap sink systems causes clogging that disturbs operations in commercial kitchens, hospitals, schools, and industrial buildings. Waste particles gather in sloped disposals, resulting in water stagnation and manual cleaning needed, which inefficiencies and hygienic issues follow from. Constant operation is guaranteed by an automated waste separation system comprising a perforated conveyor belt with a wind-up mechanism and torsional spring. As water runs over the conveyor, solid waste gets caught and allows seamless drainage. By guiding collected waste into a disposal tank, a tapped sheet keeps sink operation free from blockages. By means of a more affordable and low-maintenance substitute for hand waste disposal, this method lowers human effort, improves hygiene, and minimizes operating downtime. Good waste management guarantees continuous sink use in high demand, reduces food waste, and guarantees hygiene environments. Automated operation offers a logical replacement for conventional waste disposal methods in commercial and industrial settings, promoting sustainability and operational effectiveness.

Published by: Jai Harish D, Tamilarasu K, Vinayaga Moorthi M A

Author: Jai Harish D

Paper ID: V11I1-1553

Paper Status: published

Published: April 12, 2025

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Research Paper

CropVion: A VGG16-based Convolutional Approach for Plant Disease Detection

This project focuses on developing a machine learning-based system for detecting plant diseases, providing valuable support to farmers, botanists, and researchers. The aim is to improve agricultural productivity and research efforts through automated plant health monitoring. The solution includes a user-friendly and responsive interface built with Streamlit, a Python framework that facilitates the creation of web applications, enabling efficient interaction for users, whether they are in farming or research. The system leverages Convolutional Neural Networks (CNN) with a fine-tuned VGG16 pre-trained model, utilizing transfer learning to accurately classify plant diseases based on leaf imagery. A diverse dataset of plant diseases is used for training, with advanced image preprocessing techniques applied to improve classification accuracy. This solution ensures a scalable, precise, and user-friendly method for disease detection, facilitating seamless adoption into modern agricultural workflows.

Published by: Rishika Gazula, Asiya Jamadar, Avinash Shinde, Gauri Bilaye

Author: Rishika Gazula

Paper ID: V11I2-1138

Paper Status: published

Published: April 12, 2025

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