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All Journal Bulletin of Electrical Engineering and Informatics Journal of Applied Pharmaceutical Research
Vivek Kumar Sharma, Vivek Kumar
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Electrical & Electronics Engineering Medicine & Pharmacology

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Design and in vitro evaluation of mouth dissolving tablets olanzapine Sharma, Vivek Kumar; Singh, Vikram; Juyal, Divya; Rawat, Geeta
Journal of Applied Pharmaceutical Research Vol 3 No 1 (2015)
Publisher : Creative Pharma Assent

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Abstract

The purpose of this research was to design and evaluate the olanzapine fast dissolving tablets.  The variable formulation of Olanzapine having challenging methodology. Olanzapine practically insoluble in water so used different polymers and superdisintigrant to make formulation. Direct compression are most desired method for preparation of mouth dissolving tablets. The tablets were evaluated for disintegration and dissolution properties of the formulation. In formulation of mouth dissolving tablet evaluate the precompression parameter and post compression parameter and after evaluation found satisfactory
Design and in vitro evaluation of mouth dissolving tablets olanzapine Sharma, Vivek Kumar; Singh, Vikram; Juyal, Divya; Rawat, Geeta
Journal of Applied Pharmaceutical Research Vol. 3 No. 1 (2015)
Publisher : Creative Pharma Assent

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (296.195 KB)

Abstract

The purpose of this research was to design and evaluate the olanzapine fast dissolving tablets. The variable formulation of Olanzapine having challenging methodology. Olanzapine practically insoluble in water so used different polymers and superdisintigrant to make formulation. Direct compression are most desired method for preparation of mouth dissolving tablets. The tablets were evaluated for disintegration and dissolution properties of the formulation. In formulation of mouth dissolving tablet evaluate the precompression parameter and post compression parameter and after evaluation found satisfactory
Hybrid 3D CNN–transformer model for early brain tumor detection with multi-modal magnetic resonance imaging Sharma, Vivek Kumar; Ameta, Gaurav Kumar
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.11082

Abstract

Accurate and early diagnosis of brain tumors using multi-modal magnetic resonance imaging (MRI) remains a critical challenge due to tumor heterogeneity and complex spatial representation. This study proposes a novel hybrid deep learning framework that integrates a 3D convolutional neural network (3D CNN) with swin transformer blocks and an attention-based feature fusion module (ABFFM). The model leverages multi-modal MRI inputs—T1, T1Gd, T2, and fluid-attenuated inversion recovery (FLAIR)—and features a dual-branch classification head for binary tumor detection and multi-label tumor sub-region classification: enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Experiments conducted on the BraTS2023-GLI dataset demonstrate that the proposed model achieves a superior classification accuracy of 96.51%, with precision of 97.98%, recall of 97.04%, and F1-score of 97.61%, outperforming state-of-the-art methods. Furthermore, intrinsic attention weights offer interpretability by highlighting modality-specific contributions. The proposed model establishes a clinically promising approach for brain tumor analysis, with strong implications for early diagnosis and treatment planning.
Co-Authors Ameta, Gaurav Kumar Divya Juyal, Divya Geeta Rawat, Geeta Vikram Singh, Vikram