Quanta Research

A Real-Time Sign Language to Text Conversion System for Enhanced Communication Accessibility

2024-08-16T09:07:41+02:00

The research addresses the problem of converting American Sign Language (ASL) finger spelling into text in real-time, enhancing communication for the deaf and hard of hearing. A convolutional neural network (CNN) is utilized to recognize hand gestures from camera images, focusing on the position and orientation of the hand to create accurate training and testing data. The methodology involves filtering hand images, followed by classification to predict the corresponding sign language characters. The calibrated images are then used to train the CNN model. Key findings demonstrate that the proposed system effectively recognizes ASL finger spelling with high accuracy, offering a valuable tool for improving accessibility in communication. These findings suggest significant potential for further applications in real-time sign language interpretation.

Machine Learning-Based College Admission Predictor: A Telegram Bot for Indian Engineering Colleges

2024-08-14T14:07:45+02:00

This study addresses the challenge of accurately predicting college admissions in India, where students often struggle to identify suitable colleges based on their entrance exam scores. The research explores the development of a College Predictor Bot that leverages key factors, specifically JEE and CET scores, to estimate the likelihood of admission to various Indian colleges. The model is trained on historical admissions data from multiple institutions, encompassing a wide range of student profiles and performance levels. Methodologically, the study employs machine learning algorithms, including random forest and decision tree models, to analyze the entrance exam scores and generate predictions. The model’s accuracy is evaluated through rigorous statistical analysis, with significant correlations observed between entrance exam scores and admission outcomes. The findings indicate that the College Predictor Bot can effectively predict admissions, providing students with valuable insights into their college options. The broader implications suggest that this tool could simplify the college selection process, offering a more transparent and informed approach to admissions in the Indian education system.

Assessing Strategic Agricultural Policies for Sustainable Development in Iran: A Retrospective and Delphi-Based Evaluation on Progress in Economic, Social, and Environmental Dimensions

2024-11-18T10:37:49+01:00

Sustainable development in agriculture requires strategies that align with economic, social, and political priorities while also adhering to global standards such as the Sustainable Development Goals (SDGs). This study employs a retrospective approach and the Delphi method to assess strategic agricultural policies in Iran as a developing country, evaluating past policy effectiveness and proposing future directions. A combination of library surveys and expert opinions reveals that, in the short term, prioritizing cultural programs for food security and comprehensive free-market agriculture hold the highest significance. In the mid-term, initiatives such as knowledge-based agriculture (political), village-based entrepreneurial efforts, and industrial agricultural research (economic) emerge as critical areas of focus. Long-term strategies emphasize justice-oriented progress, effective water resource management, and transformational industries. The study's findings contribute to developing standards and metrics by which agricultural policies can be assessed for sustainable efficiency, performance, and progress.