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AI Bias in Data Training

This research paper talks about AI bias in data training and how it creates age, gender and cultural discrimination. This paper also talks about how spreading awareness about AI bias can help mitigate the issue. It examines how biased training data distorts decision-making in various fields like hiring, healthcare and law enforcement. This paper shows us the need for transparency, accountability and awareness in AI systems and how mitigating data bias is essential for creating an AI system that is fair, responsible, and that can be held accountable in case of any biased decisions and output.

Published by: Sarjas Gauhar Singh

Author: Sarjas Gauhar Singh

Paper ID: V11I6-1287

Paper Status: published

Published: December 12, 2025

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Research Paper

Ergonomic Mechanical Tools for Reducing Repetitive Strain Injuries among Workers in Small-Scale Manufacturing Units

Repetitive strain injuries (RSIs) have emerged as a major challenge within small-scale manufacturing environments, damaging worker health and productivity. Ergonomically designed mechanical tools are increasingly recognized as a pivotal solution to this chronic problem. This paper undertakes a thorough examination of RSI etiology, analyzes ergonomic design methodologies relevant for mechanical tools, and synthesizes practical recommendations for designing interventions targeted at small enterprises. Emphasizing both technical and organizational dimensions, the discussion highlights evidence-based strategies and implementation barriers, providing a comprehensive roadmap for sustained ergonomic improvement.

Published by: Nevaan Aggarwal

Author: Nevaan Aggarwal

Paper ID: V11I6-1277

Paper Status: published

Published: December 11, 2025

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Research Paper

Echo Scan – AI Detection of Synthetic and Fake Voices

In a world where voice-based technologies are rapidly evolving, the rise of AI-generated synthetic voices poses serious concerns for authenticity, privacy, and security. Echo Scan is a machine learning-based system designed to differentiate between real human voices and artificially generated ones. The system leverages acoustic features, waveform analysis, and deep learning techniques to identify subtle inconsistencies that are often overlooked by the human ear. Through extensive training on diverse datasets and voice patterns, Echo Scan aims to act as a reliable shield against voice cloning, fraud, and misinformation. This project not only strengthens digital trust but also sets the foundation for future advancements in audio forensics and secure voice authentication.

Published by: Soham Nimse, Aakshaj Nadpurohit, Gayatri Narwade, Rugved Nigade, Nikhil Nagargoje, Priti Nikam, Vikas Nandeshwar

Author: Soham Nimse

Paper ID: V11I5-1298

Paper Status: published

Published: December 11, 2025

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Research Paper

Efficiency of Carbon Fibre for the Automotive Industry

This paper explores the role of carbon fibre reinforced plastic (CFRP) in improving the efficiency, safety and sustainability of modern automobiles. The aim is to determine how beneficial carbon fibre is in making vehicles lighter, cleaner and more energy efficient. It examines CFRP’s advantages over traditional materials such as steel and aluminium, its applications in vehicle design and the manufacturing challenges that limit large-scale use. The paper also discusses recycling methods and environmental improvements that make CFRP more sustainable, along with future prospects for its wider use in the automotive industry.

Published by: Ritvik Pahwa

Author: Ritvik Pahwa

Paper ID: V11I5-1244

Paper Status: published

Published: December 11, 2025

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Research Paper

A Study of Clustering Analysis in Identification of Butterfly Species

This study investigates the use of clustering analysis techniques for identifying butterfly species based on their morphological characteristics. Butterflies exhibit substantial variation in wing patterns, colors and body size which makes traditional taxonomic identification both time-consuming and error-prone. Clustering analysis provides a data-driven strategy to group individuals into putative species based on similarities in measurable features. By applying multiple clustering algorithms together with appropriate validation methods, this work evaluates the effectiveness of clustering analysis for butterfly species identification and highlights its potential applications in biodiversity research and conservation. Accurate identification of butterfly species is fundamental to biodiversity conservation, ecological monitoring, and environmental impact assessment. This study examines the efficacy of clustering methods for species identification using butterfly image data. Several algorithms, including K-means, hierarchical clustering, spectral clustering, Gaussian mixture models, and DBSCAN, are employed to partition images into species clusters. To represent discriminative visual information, feature extraction techniques such as Histogram of Oriented Gradients (HOG), Gray Level Co-Occurrence Matrix (GLCM), and Local Binary Patterns (LBP) are used to encode wing textures and shape characteristics. The quality of the resulting clusters is assessed by comparing them with known species labels, enabling a systematic evaluation of each method. The results indicate that clustering analysis offers a scalable and promising approach for automated butterfly species identification and biodiversity monitoring, while also clarifying the strengths and limitations of different clustering techniques for image-based species classification.

Published by: Ajaykumar R

Author: Ajaykumar R

Paper ID: V11I6-1279

Paper Status: published

Published: December 11, 2025

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Research Paper

Data-Driven Crop Recommendation for Rajasthan Using Linear and Ensemble Models

The agricultural sector is a vital part of the Indian economy, comprising 18.2% of India’s GDP and representing approximately 44% of the total labour force. However, one of the biggest problems faced is the loss of crop yield, especially among farms using traditional methods of farming that lack the technological means to predict and maximise their potential yield. The problem is further compounded by farmers often being unaware of which crops are suitable, given conditions that are specific to individual farmers or parcels of land. This research paper focuses on maximising crop yield by helping farmers choose a suitable crop in Rajasthan, one of the largest Indian states by land mass and population, where over 54% of citizens depend on agriculture as a primary source of income. The data used throughout this paper are publicly accessible and are taken from multiple official Indian government sources. Using these data, the paper incorporates exploratory data analysis to identify key variables such as soil nutrient levels, rainfall, and temperature that influence crop performance. Furthermore, the paper aims to lay out the groundwork for building a crop yield prediction and, primarily, a crop recommendation model that is easily accessible and simple to understand. This is implemented using a transparent linear regression baseline and a decision-tree-based ensemble approach, specifically Random Forest.

Published by: Aryaveer Jain

Author: Aryaveer Jain

Paper ID: V11I6-1263

Paper Status: published

Published: December 8, 2025

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