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AI-Powered Frameworks for the Detection and Prevention of Cyberbullying Across Social Media Ecosystems Mondol, Md. Anas; Uddaula, Md. Ashaf; Hossain, Md. Safaet; Siddika, Mst. Ayesha
TechComp Innovations: Journal of Computer Science and Technology Vol. 2 No. 1 (2025): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v2i1.59

Abstract

This study presents an advanced AI-powered framework to detect and prevent cyberbullying across diverse social media platforms using a multiclass classification approach. Addressing the growing complexity and linguistic diversity of online abuse, the research integrates various machine learning (RF, LR, SVM) and deep learning (Bi-LSTM, BERT) models trained on a balanced dataset covering bullying categories based on religion, age, ethnicity, gender, and neutral content. Data preprocessing, tokenization, feature extraction via TF-IDF and CountVectorizer, and class balancing using SMOTE were applied to enhance model accuracy. The proposed system further supports real-time detection through social media APIs, offering dynamic monitoring and intervention capabilities. Among the tested models, Random Forest and BERT achieved the highest classification performance with 94% accuracy. Despite its robust architecture, limitations include dependence on English-language datasets, exclusion of multimodal data (e.g., memes, audio), and API restrictions that challenge scalability. Future development will focus on incorporating vision-language models and optimizing the system for real-time, multilingual, and multimodal environments. This study contributes to digital safety efforts by proposing a scalable and adaptive detection system suitable for safeguarding users from evolving forms of online harassment.
IQAC’s Strategic Role in Enhancing OBE, Teaching Quality, Learning Practices, and Curriculum Innovation Hossain, Md. Safaet
Edu Spectrum: Journal of Multidimensional Education Vol. 2 No. 2 (2025): Edu Spectrum: Journal of Multidimensional Education
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/eduspectrum.v2i2.112

Abstract

Higher education institutions are experiencing accelerated transformation driven by global competition, technological advancements, and shifting learner expectations. In this context, Internal Quality Assurance Cells (IQACs) serve a strategic role in enhancing teaching quality, strengthening learning processes, and ensuring curriculum relevance through the implementation of Outcome-Based Education (OBE) and pedagogical innovation. This study employs qualitative content analysis by synthesizing theoretical frameworks and recent empirical literature to develop an integrated model of IQAC-led academic transformation. The analysis highlights three key mechanisms: curriculum intelligence systems, pedagogical innovation ecosystems, and comprehensive faculty empowerment. Findings indicate that effective IQACs function not merely as compliance bodies but as catalysts of academic excellence by aligning curriculum with industry needs, fostering continuous faculty development, and promoting technology-enhanced learning and learning analytics. Moreover, phased OBE implementation—comprising capacity building, curriculum redesign, instructional support, and continuous evaluation—significantly strengthens learning outcomes and cultivates reflective academic culture. The proposed framework underscores the interconnected role of curriculum, pedagogy, and faculty capabilities as foundations for institutional excellence. Ultimately, the study affirms that IQACs’ strategic leadership in embedding OBE, leveraging educational technologies, and empowering faculty positions them as pivotal agents in driving sustainable educational transformation.