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p-Index From 2021 - 2026
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This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal Education and Computer Studies
Anjali Sharma
Postgraduate Institute of Medical Sciences, No. 1447, Sector-1. Urban State, Rohtak - 124001
Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Other

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Advancing Science Education through Project-Based Learning: An Analytical Framework of Practical Applications for Indian Secondary Schools in Resource-Constrained Settings Sharma, Anjali
International Journal Education and Computer Studies (IJECS) Vol. 5 No. 3 (2025): NOVEMBER
Publisher : Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijecs.v5i3.4444

Abstract

This study looks at how Project-Based Learning (PBL) can be used in real life to improve science education in Indian secondary schools with limited resources. By qualitatively analyzing 111 Hindi-language science projects that follow the National Curriculum Framework, it reviewed three projects that are representative: biodegradable waste decomposition, plant morphology, and rust formation for their design pedagogical content scientific and feasibility. Results show that these inexpensive investigations relevant to the context help develop core competencies effectively such as observation data analysis collaborative inquiry and reasoning based on evidence. By merging learning through experience with relevance to the community, PBL closes the gap between instruction in theory and real scientific practice, even under conditions of resource constraint. In addition, PBL has shown potential for issues of gender and social equity by fostering inclusive participation and contextualized engagement. The study ends with the statement that scaling PBL across schools would need coherent policy alignment, teacher professional development, and reform in assessment practices to take science learning out of rote memorization into reflective inquiry-driven socially responsive education.
Transformer-based Hindi image description and storytelling using enhanced attention and FastText embeddings Sharma, Anjali; Aggrwal, Mayank; Khanna, Jitin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1771-1782

Abstract

This work presents a novel image description generation framework that combines a Transformer-based encoder-decoder architecture with a custom squeeze-and-excitation (SE) attention block integrated into an EfficientNet feature extractor. The decoder uses FastText embeddings specifically trained for Hindi and is evaluated on the Microsoft common objects in context (MS-COCO) dataset. To improve the captioning process, the model incorporates a generative pre-trained transformer (GPT) module to generate narrative descriptions based on the initial captions and applies multiple similarity metrics to assess output quality. The proposed system significantly outperforms existing methods, achieving high bilingual evaluation understudy (BLEU) scores (BLEU-1 to BLEU-4: 83.24, 73.17, 64.56, and 58.22), a consensus-based image description evaluation (CIDEr) score of 81.41, an F1 score of 90.29, and a metric for evaluation of translation with explicit ordering (METEOR) score of 81.18, indicating strong caption accuracy. Furthermore, the model achieves low error rates, with a word error rate (WER) of 15% and a character error rate (CER) of 11%. This work highlights the challenges of applying large-scale datasets like MS-COCO to resource-limited languages and demonstrates the effectiveness of integrating FastText embeddings with transformer-based models for Hindi image captioning.
Co-Authors Aggrwal, Mayank Khanna, Jitin