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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Education and Learning Studies
Sharma, Aryan
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Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Mathematics Other

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EmoVibe: AI-driven multimodal emotion analysis for mental health via social media dashboards Vora, Deepali; Sharma, Aryan; Garg, Mudit; Fransis, Steve
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4565-4578

Abstract

Monitoring mental health via social media often utilizes unimodal approaches, such as sentiment analysis on text or single-staged image categorization, or executes early feature fusion. However, in real-world contexts where emotions are conveyed via text, emojis, and images, unimodal approach leads to obscured decision-making pathways and overall diminished performance. To overcome these limitations, we propose EmoVibe, a hybrid multimodal AI framework for emotive analysis. EmoVibe uses attention-based late fusion strategy, where text embeddings are generated from bidirectional encoder representations from transformers (BERT) and visual features are extracted by vision transformer. Subsequently, emoticon vectors linked to avatars are processed independently. Later, these independent data features are integrated at higher levels, enhancing interpretability and performance. In contrast to early fusion methods and integrated multimodal large language models (LLMs) like CLIP, Flamingo, GPT-4V, MentaLLaMA, and domain-adapted models like EmoBERTa, EmoVibe preserves modality-specific contexts without premature fusion. This architecture saves processing cost, allowing for clearer, unambiguous rationalization and explanations. EmoVibe outperforms unimodal baselines and early fusion models, obtaining 89.7% accuracy on GoEmotions, FER, and AffectNet, compared to BERT's 87.4% and ResNet-50's 84.2%, respectively. Furthermore, a customizable, real time, privacy-aware dashboard is created, supporting physicians and end users. This technology enables scalable and proactive intervention options and fosters user self-awareness of mental health.
Kathak Dance–Based Cultural Values and Teaching Effectiveness among Secondary School Teachers Sharma, Aryan; Kumari, Priyanka; Singh, Aditya; Tyagi, Shalini
International Journal of Education and Learning Studies Vol. 2 No. 1 (2026): International Journal of Education and Learning Studies (IJELS)
Publisher : PT. Intelektiva Global Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64421/ijels.v2i1.69

Abstract

Cultural values embedded in traditional performing arts have long played an important role in shaping educational practices in India. Among these traditions, Kathak dance represents a classical art form rooted in storytelling, rhythm, discipline, and expressive communication, which are pedagogically relevant to classroom teaching. This study investigates the relationship between Kathak dance–based cultural values in teaching practices and teaching effectiveness among secondary school teachers. A cross-sectional correlational survey design was employed. Data were collected from secondary school teachers using a structured Likert-scale questionnaire measuring Kathak-based cultural values in teaching and perceived teaching effectiveness. Descriptive statistics, reliability analysis, Pearson correlation, and regression analysis were used to analyze the data. The findings indicate a significant positive relationship between Kathak dance–based cultural values and teaching effectiveness. The results suggest that integrating culturally grounded pedagogical values inspired by Kathak can enhance instructional effectiveness in secondary education. This study contributes to the growing literature on culture-responsive pedagogy by highlighting the educational relevance of classical dance traditions in contemporary school teaching.
Co-Authors Fransis, Steve Garg, Mudit Kumari, Priyanka Singh, Aditya Tyagi, Shalini Vora, Deepali