Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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This Author published in this journals
All Journal TechComp Innovations: Journal of Computer Science and Technology Global Science: Journal of Information Technology and Computer Science
Umna iftikhar
Iqra University
Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : techcomp innovations journal of computer science and technology

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Federated Intelligence Architectures for Secure, Data-Driven Innovation Across AI, IoT, and Cloud Ecosystems Ravi Kumar Saidala; Umna Iftikhar; Tofig Hasanov; Vüqar Ahmad Mammadli
TechComp Innovations: Journal of Computer Science and Technology Vol. 2 No. 2 (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.v2i2.124

Abstract

This study examines the emerging paradigm of federated intelligence architectures as a secure, privacy-preserving, and scalable foundation for data-driven innovation across AI, IoT, and cloud ecosystems. With billions of interconnected devices generating massive heterogeneous data, traditional centralized machine-learning models face critical limitations, including privacy risks, regulatory constraints, latency, and single points of failure. Through a qualitative content-analysis approach, this paper synthesizes contemporary research on federated learning, blockchain integration, zero-trust governance, and edge intelligence to formulate a comprehensive understanding of distributed AI infrastructures. The findings highlight that federated learning enables collaborative model training without exposing raw data, significantly enhancing privacy, security, and compliance. Moreover, combining blockchain with federated learning strengthens auditability, model integrity, and trust, while zero-trust principles provide continuous verification and adaptive security enforcement across devices. Edge-AI integration further reduces latency and bandwidth consumption, enabling real-time analytics in resource-constrained IoT environments. Collectively, these elements contribute to the formation of cognitive ecosystems capable of autonomous, interoperable, and context-aware operations. The study underscores the transformative potential of federated intelligence while identifying critical gaps that inform future research trajectories.
Machine Learning Approaches for Detecting Political Disinformation in Social Media Ecosystems Ahmad Nur Ihsan Purwanto; Nur Hazwani Dzulkefly; Umna Iftikhar
TechComp Innovations: Journal of Computer Science and Technology Vol. 3 No. 1 (2026): 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.v3i1.190

Abstract

Political disinformation has become one of the most critical challenges in contemporary digital democracies due to the rapid expansion of social media ecosystems. This study investigates the effectiveness of machine learning approaches in detecting political disinformation across online platforms such as Twitter, Facebook, and political discussion forums. Using a qualitative research design with a content analysis approach, the study examines linguistic manipulation, emotional narratives, sentiment polarity, and behavioral communication patterns embedded in misleading political content. The findings indicate that deep learning models, particularly Long Short-Term Memory (LSTM) architectures, demonstrate superior performance in identifying contextual and semantic inconsistencies compared to traditional machine learning algorithms. The study also reveals that algorithmic amplification, echo chambers, and coordinated bot activities significantly contribute to the rapid spread of political misinformation. Furthermore, the research highlights the importance of ethical artificial intelligence governance, transparency, and digital literacy in strengthening democratic resilience and protecting information integrity within digital communication environments
Co-Authors Agustinus Suradi Ahmad Nur Ihsan Purwanto Muhamad Aris Sunandar Nur Hazwani Dzulkefly Ravi Kumar Saidala Tofig Hasanov Vüqar Ahmad Mammadli