IJAITE- International Journal of Advanced Innovative Technology in Engineering

Archives of International Journal of Advanced Innovative Technology in Engineering(IJAITE)

Volume 10 Issue 4 July 2025

1. Walkability Of Market Pathways

AUTHOR NAME : Vaishnavi C. Deshmukh, Prof. Snehal Vidhale

2. Adaptive E-Commerce Platform with Real-Time Price Negotiation Based on User Sentiment

AUTHOR NAME : Prof. P. A. Nandagawali, Prof. R. D. Thakare, Prof. R. S. Deshpande, Prof. V. S. Wadhwani, Prof. P. S. Sherekar

ABSTRACT : E-commerce has revolutionized global trade by offering unprecedented convenience and product access. However, conventional platforms often rely on rigid pricing models, limiting user interaction and pricing flexibility. This research introduces a novel e-commerce system integrating a real-time bid and counter-bid mechanism designed to enhance user-admin negotiation dynamics. Developed using the Flutter framework, the system allows users to submit personalized price proposals, while administrators can accept, reject, or counter these offers, enabling a multi-round negotiation process. This interactive pricing model aims to boost user satisfaction and platform profitability by offering a customizable shopping experience. The paper details the applications architecture, including the negotiation algorithm and system design. Comparative analysis with traditional models shows improved user engagement, conversion rates, and pricing flexibility. Performance metrics such as system responsiveness, user feedback, and revenue impact further validate the models effectiveness in creating a more personalized and efficient online shopping experience.

Download

3. Traditional and Modular Furniture

AUTHOR NAME : Jayesh Malasane, Palash Agrwal

ABSTRACT : This study explores the key differences between traditional furniture and modular furniture, focusing on design, functionality, material usage, and adaptability. Traditional furniture is characterized by its craftsmanship, fixed structure, and often ornate, timeless design rooted in cultural or historical styles. It emphasizes durability and aesthetic appeal but lacks flexibility in terms of space utilization and customization. In contrast, modular furniture is designed for modern living, offering versatility, ease of assembly, and space-saving benefits. It typically features standardized units that can be rearranged or expanded according to the users needs, making it ideal for compact and evolving environments. The comparison highlights how evolving lifestyles and urbanization are driving a shift towards modular solutions while still preserving the value and artistry of traditional furniture.

Download

4. Automated Lumpy Skin Diseases Detection Using Machine Learning

AUTHOR NAME : Sanjay Mate, Vikas Somani, Prashant Dahiwale

ABSTRACT : Lumpy Skin Diseases (LSD) has history of almost century now, the first recorded LSD outbreak was in Northern Rhodesia of Zambia (Africa) in 1929, later during 1989 in the Middle East. Recently South Asia comes under expansion LSD as of in 2019 and 2022 India faces large breakout of LSD. LSD is expanding from Africa to Middle East to South Asia and now can be seen across Eurasia. Algorithms were available to process images of lumpy portions of skin of livestock which results in prediction at early stage, it saves livestock life and financial losses for dairy and allied farmers. Precision of various algorithms has a limitation of valid datasets, image classification, and connected layers. To address this limitation, we developed a Random Forest to address large dataset. The model was trained on a dataset comprising 819 training and 205 validation images across eight classes, utilizing data augmentation techniques to enhance generalization. Through iterative optimization, including dropout regularization and increased model depth, we achieved more classification accuracy on the validation set. Our results indicate that deep learning, specifically Random Forest outperforms traditional SVM-based approaches in image classification tasks.

Download