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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electrical Engineering and Computer Science
Hazra, Sumit
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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UniMSE: a unified approach for multimodal sentiment analysis leveraging the CMU-MOSI Dataset Basu, Miriyala Trinath; Saha, Mainak; Gupta, Arpita; Hazra, Sumit; Fatima, Shahin; Sumalakshmi, Chundakath House; Shanvi, Nallagopu; Reddy, Nyalapatla Anush; Abhinav, Nallamalli Venkat; Hemanth, Koganti
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp2032-2042

Abstract

This paper explores multimodal sentiment analysis using the CMU-MOSI dataset to enhance emotion detection through a unified approach called UniMSE. Traditional sentiment analysis, often reliant on single modalities such as text, faces limitations in capturing complex emotional nuances. UniMSE overcomes these challenges by integrating text, audio, and visual cues, significantly improving sentiment classification accuracy. The study reviews key datasets and compares leading models, showcasing the strengths of multimodal approaches. UniMSE leverages task formalization, pre-trained modality fusion, and multimodal contrastive learning, achieving superior performance on widely used benchmarks like MOSI and MOSEI. Additionally, the paper addresses the difficulties in effectively fusing diverse modalities and interpreting non-verbal signals, including sarcasm and tone. Future research directions are proposed to further advance multimodal sentiment analysis, with potential applications in areas like social media monitoring and mental health assessment. This work highlights UniMSE's contribution to developing more empathetic artificial intelligence (AI) systems capable of understanding complex emotional expressions.
BRU-SOAT: Brain Tissue Segmentation via Deep Learning based Sailfish Optimization and Dual Attention Segnet Athur Shaik Ali Gousia Banu; Hazra, Sumit
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.795

Abstract

Automated segmentation of brain tissue into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from magnetic resonance imaging (MRI) plays a crucial role in diagnosing neurological disorders such as Alzheimer’s disease, epilepsy, and multiple sclerosis. A key challenge in brain tissue segmentation (BTS) is accurately distinguishing boundaries between GM, WM, and CSF due to intensity overlaps and noise in the MRI image. To overcome these challenges, we propose a novel deep learning-based BRU-SOAT model for BTS using the BrainWeb dataset. Initially, brain MRI images are fed into skull stripping to remove skull regions, followed by preprocessing with a Contrast Stretching Adaptive Wiener (CSAW) filter to improve image quality and reduce noise. The pre-processed images are fed into ResEfficientNet for fine feature extraction. After extracting the features, the Sailfish Optimization (SFO) is employed to select the most related features while eliminating irrelevant features. A Dual Attention SegNet (DAS-Net) segments GM, CSF, and WM with high precision. The proposed BRU-SOAT model is assessed based on its precision, F1 score, specificity, recall, accuracy, Jaccard Index, and Dice Index. The proposed BRU-SOAT model achieved a segmentation accuracy of 99.17% for brain tissue segmentation. Moreover, the proposed DAS-Net outperformed fuzzy c-means clustering, fuzzy consensus clustering, and U-Net methods, achieving 98.50% (CSF), 98.63% (GM), and 99.15% (WM), indicating improved segmentation accuracy. In conclusion, the BRU-SOAT model provides a robust and highly accurate framework for automated brain tissue segmentation, supporting improved clinical diagnosis and neuroimaging analysis
Co-Authors Abhinav, Nallamalli Venkat Athur Shaik Ali Gousia Banu Basu, Miriyala Trinath Fatima, Shahin Gupta, Arpita Hemanth, Koganti Reddy, Nyalapatla Anush Saha, Mainak Shanvi, Nallagopu Sumalakshmi, Chundakath House