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All Journal International Journal of Engineering, Science and Information Technology
Ashfaq, Farzeen
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Empirical Performance Analysis of Hyperledger Fabric Blockchain Network for Healthcare Das, Shampa Rani; Jhanjhi, NZ; Ashfaq, Farzeen; Ahmed, Husham M.; Khan, Azeem
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.928

Abstract

The most prevalent blockchain-enabled systems have several apparent advantages, but scaling remains a technical difficulty that results in performance deficiencies in latency and throughput.  The foremost concern is that a thorough performance analysis is required to determine their viability and efficiency. The prospective impact of transaction delay on blockchain networks is an acute issue for e-healthcare-related services since it can jeopardize the patient’s life safety. A benchmarking tool Hyperledger Caliper is utilized to measure performance parameters in the Hyperledger Fabric network. The effects of workload fluctuation in 6 rounds with up to 3000 Transactions Per Second (TPS) are demonstrated when four organizations are put up in the network. Significant findings include a noteworthy decrease of 27.11% in open latency and 26.27% in query latency, and an increase of 3.13% in query throughput and 3.44% in open throughput demonstrating enhancements in adaptability and operational efficiency over the recent existing approaches proposed. It demonstrates an ongoing increase in CPU and memory consumption, peaking at 5.49% and 528.23 MB for 3000 TPS, respectively. Inbound and outbound traffic indicate relatively even utilization, with variations falling within a moderate range.
AI-Driven Framework for Location-Aware Sentiment Analysis and Topic Classification of Public Social Media Data in West Malaysia Faisal, Amna; Jhanjhi, NZ; Ashfaq, Farzeen; M. Ahmed, Husham; Khan, Azeem
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.926

Abstract

While social media has facilitated communication, it has also amplified collective attitudes, often leading to polarized opinions and negative emotional expressions that can disrupt social harmony. Consequently, monitoring public sentiments on social media, and identifying thematic trends across regions has become crucial for understanding collective emotions and opinions. Despite advancements in sentiment analysis and topic classification, very little research has been done to integrate geospatial analysis with these techniques, limiting their ability to provide location-aware insights into public sentiments and discussion trends. This study develops an AI-driven framework that leverages social media data to analyze public sentiments and classify discussions into relevant topics. Specifically, this research focuses on understanding the emotions and conversations of Peninsular Malaysia citizens using a self-collected dataset of public Facebook posts, analyzed at the state level to provide location-aware insights. Using VADER for sentiment analysis and zero-shot transformer for topic classification, this study categorizes posts into five predefined topics: politics, religion, tragedy, tourism, and food. The proposed architecture achieves a sentiment classification accuracy of 97% and a topic classification accuracy of 89%. Findings reveal that the Peninsular Malaysian population generally maintains a positive online environment, though some states showed a dominant negative sentiment. Patterns of dissatisfaction were largely related to political issues and local incidents, while positive emotions were primarily associated with tourism, religious festivities, and food-related news. This research not only identifies areas with dissatisfied publics but also explores the topics contributing to this sentiment. By emphasizing location-aware sentiment and topic trends, this framework offers insights to help policymakers and sociologists address region-specific issues, potentially reducing dissatisfaction and fostering a more harmonious society.
Co-Authors Ahmed, Husham M. Das, Shampa Rani Faisal, Amna Jhanjhi, NZ Khan, Azeem M. Ahmed, Husham