Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Bewoor, Laxmi
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A multimodal machine learning approach to generate news articles from geo-tagged images Gotmare, Abhay; Thite, Gandharva; Bewoor, Laxmi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3434-3442

Abstract

Classical machine learning algorithms typically operate on unimodal data and hence it can analyze and make predictions based on data from a single source (modality). Whereas multimodal machine learning algorithm, learns from information across multiple modalities, such as text, images, audio, and sensor data. The paper leverages the functionalities of multimodal machine learning (ML) application for generating text from images. The proposed work presents an innovative multimodal algorithm that automates the creation of news articles from geo-tagged images by leveraging cutting-edge developments in machine learning, image captioning, and advanced text generation technologies. Employing a multimodal approach that integrates machine learning and transformer algorithms, such as visual geometry group network16 (VGGNet16), convolutional neural network (CNN) and a long short-term memory (LSTM) based system, the algorithm initiates by extracting the location from exchangeable image file format (Exif) data from the image. The features are extracted from the image and corresponding news headline is generated. The headlines are used for generating a comprehensive article with contemporary large language model (LLM). Further, the algorithm generates the news article big-science large open-science open-access multilingual language model (BLOOM). The algorithm was tested on real time photographs as well as images from the internet. In both the cases the news articles generated were validated with ROUGE and BULE score. The proposed work is found to be successful attempt in journalism field.
Deep learning-based classifier for geometric dimensioning and tolerancing symbols Bewoor, Laxmi; Bewoor, Anand; P. Hujare, Pravin; Rathod, Praveen; Yetekar, Vedant; Dollin, Shrish
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1345-1354

Abstract

This research investigates the recognition of geometric dimensioning and tolerancing (GD&T) symbols using a deep learning model for object detection. GD&T, playing a pivotal role in engineering and manufacturing, provides essential specifications for product design and production. Manual processes for GD&T are often time-consuming and error prone. The study demonstrates outstanding accuracy in automating GD&T symbol recognition in engineering applications using YOLOv8. A carefully curated dataset, encompassing a wide range of GD&T symbols, was employed for training and evaluating the model. The YOLOv8 architecture, renowned for its robust performance, was meticulously fine-tuned to cater to the specific requirements of GD&T symbol detection. This research not only addresses the challenges in manual GD&T processes but also showcases practical implications for improved quality control and streamlined engineering workflows. By automating GD&T symbol recognition, this study contributes to the efficiency and precision crucial in the engineering and manufacturing domains.
Co-Authors Bewoor, Anand Dollin, Shrish Gotmare, Abhay P. Hujare, Pravin Rathod, Praveen Thite, Gandharva Yetekar, Vedant