https://journals.tultech.eu/index.php/qr/issue/feed 2024-08-16T09:07:41+02:00 Mr. Mahad Uzairu Mahad.Uzairu@tultech.eu Open Journal Systems <p><strong>Quanta Research (QR)</strong> is an open-access journal that promotes cutting-edge research and innovation in the interdisciplinary field of social sciences, with a specific emphasis on psychology, evaluation, and education. QR is a platform that focuses on new methods and the latest advancements for the exploration of innovative ideas where the social sciences and artificial intelligence (AI) intersect.</p> https://journals.tultech.eu/index.php/qr/article/view/196 2024-08-16T09:07:41+02:00 Mansi Munde mansi.munde@dypic.in Ganesh Jadhav ganesh.jadhav@dypic.in Sushma Gunjal sushmagunjal@dypic.in Kamlesh Mahale kamlesh.mahale@dypic.in Aditya Kale aditya.kale@dypic.in

<p>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.</p>

2024-08-06T00:00:00+02:00 Copyright (c) 2024 Authors https://journals.tultech.eu/index.php/qr/article/view/195 2024-08-14T14:07:45+02:00 Krishna Tilwane krishna.tilwane@dypic.in Aditya Savale aditya.savle@dypic.in Satchal Patil satchal.patil@dypic.in Prafull Satle prafull.satle@dypic.in Sanket Shinde sanket.shinde@dypic.in Amruta More amrutamore@dypic.in

<p>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.</p>

2024-08-02T00:00:00+02:00 Copyright (c) 2024 Authors