Enhancing Cross Language for English-Telugu pairs through the Modified Transformer Model based Neural Machine Translation

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 16, 2025

Cross-Language Translation (CLT) refers to conventional automated systems that generate translations between natural languages without human involvement. As the most of resources are mostly available in English, multi-lingual translation is badly required for penetration essence education deep roots society. Neural machine (NMT) one such intelligent technique which usually deployed an efficient process from source language another language. But these NMT techniques substantially requires large corpus data achieve improved process. This bottleneck makes apply mid-resource compared its dominant English counterparts. Although some benefit established systems, creating low-resource a challenge due their intricate morphology and lack non-parallel data. To overcome this aforementioned problem, research article proposes modified transformer architecture improve efficiency NMT. The proposed framework, consist Encoder-Decoder enhanced version with multiple fast feed forward networks multi-headed soft attention networks. designed extracts word patterns parallel during training, forming English–Telugu vocabulary via Kaggle, effectiveness evaluated using measures like Bilingual Evaluation Understudy (BLEU), character-level F-score (chrF) Word Error Rate (WER). prove excellence model, extensive comparison existing architectures performance metrics analysed. Outcomes depict has shown improvised by achieving BLEU as 0.89 low WER when models. These experimental results promise strong hold further experimentation based

Language: Английский

Integrating Deep Learning and MRQy: A Comprehensive Framework for Early Detection and Quality Control of Brain Tumors in MRI Images using Python

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 15, 2025

The early detection of brain tumors is crucial for timely medical intervention and improved patient survival rates. Magnetic Resonance Imaging (MRI) the gold standard tumor diagnosis due to its superior soft-tissue contrast non-invasive nature. However, variations in MRI quality, including noise, artifacts, scanner inconsistencies, can impact diagnostic accuracy. This study aims de-velop a Python-based deep-learning model scans while integrating an automated quality control system using MRQy. MRQy, open-source tool, facilitates assessment by evaluating signal-to-noise ratios (SNR), contrast-to-noise (CNR), motion-related artifacts. deep learning will be trained on meticulously curated dataset, ensur-ing high-quality artifact-free images. By combining MRQy’s capabilities with techniques, expected en-hance accuracy reduce false-positive false-negative Furthermore, this research underscores significance standardized imaging protocols minimize variability across scanners institutions, ensuring repro-ducibility clinical AI applications. proposed approach leverages modern convolutional neural networks (CNNs) transfer incorpo-rating pre-trained architectures such as Res Net Efficient enhance fea-ture extraction. MRQy-based AI-driven classification, optimize MRI-based diagnostics, human error, improve outcomes. findings contribute ad-vancement AI-powered highlight importance

Language: Английский