https://journals.tultech.eu/index.php/qr <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> TULTECH en-US Quanta Research 2806-3279 https://journals.tultech.eu/index.php/qr/article/view/196 <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> Mansi Munde Ganesh Jadhav Sushma Gunjal Kamlesh Mahale Aditya Kale Copyright (c) 2024 Authors https://creativecommons.org/licenses/by/4.0 2024-08-06 2024-08-06 2 1 7 13 10.15157/QR.2024.2.1.7-13 https://journals.tultech.eu/index.php/qr/article/view/195 <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> Krishna Tilwane Aditya Savale Satchal Patil Prafull Satle Sanket Shinde Amruta More Copyright (c) 2024 Authors https://creativecommons.org/licenses/by/4.0 2024-08-02 2024-08-02 2 1 1 6 10.15157/QR.2024.2.1.1-6