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IJARI
IJARI
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International Journal of Advanced Research & Innovation

A multidisciplinary, peer-reviewed, open access journal committed to publishing innovative research across science, engineering, technology, and management.

ISSN: XXXX-XXXXE-ISSN: XXXX-XXXXFrequency: Quarterly
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Current Issue

Volume 1, Issue 1, January–March 2026

Computer ScienceVol. 1, Issue 1, 2026

Deep Learning Approaches for Natural Language Understanding in Low-Resource Languages

Dr. Priya Sharma, Prof. Rajesh Kumar

This paper presents a comprehensive study of deep learning techniques applied to natural language understanding tasks in low-resource languages. We propose a novel transfer learning framework that leverages multilingual pre-trained models and demonstrates significant improvements over existing baselines across multiple Indian languages. Our approach achieves state-of-the-art results on sentiment analysis, named entity recognition, and text classification tasks with limited training data.

DOI: 10.XXXXX/ijari.2026.001
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Environmental ScienceVol. 1, Issue 1, 2026

Sustainable Urban Water Management: A Framework for Developing Nations

Dr. Ananya Patel, Dr. Mohammed Hussain, Prof. Lakshmi Rao

Water scarcity in urban areas of developing countries demands innovative management strategies. This research proposes a comprehensive framework integrating IoT-based monitoring systems with traditional water harvesting techniques. Field studies conducted across three major Indian cities demonstrate a 35% improvement in water use efficiency. The framework addresses both supply-side and demand-side management, offering scalable solutions for municipalities.

DOI: 10.XXXXX/ijari.2026.002
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Information TechnologyVol. 1, Issue 1, 2026

Blockchain-Based Supply Chain Transparency in Pharmaceutical Industry

Dr. Vikram Singh, Dr. Sarah Chen

The pharmaceutical supply chain faces persistent challenges of counterfeit drugs and lack of transparency. This paper proposes a blockchain-based solution using Hyperledger Fabric that ensures end-to-end traceability of drug products. Our prototype implementation demonstrates real-time verification capabilities with sub-second latency, while maintaining regulatory compliance with FDA and WHO guidelines.

DOI: 10.XXXXX/ijari.2026.003
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Medical ScienceVol. 1, Issue 1, 2026

Machine Learning Models for Early Detection of Cardiovascular Disease Risk

Dr. Meera Joshi, Prof. David Williams, Dr. Arjun Nair

Cardiovascular diseases remain the leading cause of mortality worldwide. This study develops an ensemble machine learning model combining gradient boosting, random forest, and neural network classifiers for early risk prediction. Using a dataset of 50,000 patient records, our model achieves 94.7% accuracy in identifying high-risk patients, outperforming traditional risk scoring systems by 18%.

DOI: 10.XXXXX/ijari.2026.004
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