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All Journal International Journal of Electrical and Computer Engineering Aptisi Transactions on Technopreneurship (ATT) Bulletin of Social Informatics Theory and Application
Zainuddin, Ahmad Anwar
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences

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Empowering smart city governance through decentralized blockchain solutions for security and privacy in IoT communications Zainuddin, Ahmad Anwar; Muhammad, Nurul Fadhilah Ahmad; Hasli, Amir Husaini Mohd; Zaini, Nor Ani Jannah Ahmad; Karimudin, Nureen Batrisyia Balqis; Bahri, Hannah Sabrina Saiful; Nizam, Muhamad Khaleesh Mirza Khairul; Kamarudin, Saidatul Izyanie; Ghazalli, Nasyitah
Bulletin of Social Informatics Theory and Application Vol. 7 No. 2 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v7i2.642

Abstract

This paper highlights the benefits and challenges of smart cities, which leverage technology to offer a wide range of services to citizens. While these services have the potential to greatly improve the quality of life in metropolitan areas, they also raise significant privacy and security concerns. The study emphasizes the need to employ "privacy by design" principles to ensure that personal data is protected throughout the entire lifecycle of data, and data owners have the self-control to manage their data according to their preferences. Smart contracts, built on blockchain technology, offer a secure and transparent way of conducting transactions, particularly in e-governance, and automating processes. By leveraging these technologies, smart cities can address the privacy and security challenges they face while continuing to offer cutting-edge services to their inhabitants. Ultimately, the study emphasizes the importance of a proactive approach to privacy and security in the development and implementation of smart cities.
A comprehensive analysis of IoT security and privacy in smart city applications Zainuddin, Ahmad Anwar; Othman , Adam; Adlin Muhammad Zahid , Nur; Anis Sofea Kamarul Zaman , Nur; Nur Maisarah Ahmad Razmi , Alin; Haiqal Azreen Kamarul Zaman , Mohammad
Bulletin of Social Informatics Theory and Application Vol. 8 No. 1 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i1.645

Abstract

With the increasing growth of smart cities, the Internet of Things (IoT) has emerged as a key enabler, integrating various systems and devices to create intelligent urban environments. However, increased interconnectedness raises significant concerns regarding security and for IoT communications privacy. This work addresses the problems with and remedies for IoT connectivity in smart cities that are related to security and privacy. The problems will be addressed by examining the potential risks and vulnerabilities associated with IoT networks, considering device authentication, data encryption, access control, and intrusion detection. Furthermore, an investigation on privacy issues and provide ways to protect sensitive data, such as differential privacy and data anonymization will be conducted. This work further provides a rundown of current safeguarding and discretion practises in IoT communications and highlight new technologies that hold promise for addressing these challenges, based on a detailed assessment of existing research and case studies. Moreover this work outlines important avenues for future study and offer suggestions for strengthening the privacy preservation of IoT communications in the context of tech cities. By solving these fundamental issues, the dependability, sustainability, and trustworthiness of IoT deployments in smart cities can be guaranteed, enabling them to reach their full potential for enhancing the quality of life for urban people.
Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact Zainuddin, Ahmad Anwar; Nor, Rizal Mohd; Handayani, Dini; Tamrin, Mohd. Izzuddin Mohd.; Subramaniam, Krishnan; Sadikan, Siti Fairuz Nurr
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.497

Abstract

Current attendance methods, though simple, are prone to manipulation and can be time consuming for both educators and students. For instance, manual systems and QR code based methods allow students to register attendance on behalf of others due to the lack of unique identification. While calling names individually improves security, it disrupts the learning process by consuming significant time. This study addresses these issues by developing an autonomous robot, CObot, equipped with a facial recognition system powered by a Raspberry Pi microcontroller. CObot navigates classrooms autonomously, avoiding obstacles, and efficiently records attendance without requiring movement from students or educators. The use of facial recognition ensures that only registered individuals can mark attendance, creating a secure and tamper-proof system. Additionally, the integration of Internet of Things (IoT) technology enables real-time data transfer to Google Sheets, simplifying record-keeping and reducing educators administrative workload. A 3D-printed, customizable car structure enhances the robot design, while the Raspberry Pi 5 was selected over alternatives like the ESP32-S3 for its superior processing power and faster data transfer speeds, ensuring smoother operations. In testing with 60 participants, the Raspberry Pi 5 demonstrated a 99% accuracy rate in facial recognition, outperforming the ESP32-S3 90% accuracy. By saving time, improving security, and reducing manual effort, CObot enhances the classroom environment, benefiting both students and educators. While the improvement in attendance systems may appear incremental, CObot represents a meaningful step toward fostering a more efficient and effective learning environment.
Optimizing radial basis function networks for harmful algal bloom prediction: a hybrid machine learning approach Kamal, Nik Nor Muhammad Saifudin Nik Mohd; Zainuddin, Ahmad Anwar; Hussin, Amir ‘Aatieff Amir; Annas, Ammar Haziq; Mohammad-Noor, Normawaty; Razali, Roziawati Mohd
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5647-5654

Abstract

The deployment of artificial intelligence in environmental monitoring demands models balancing efficiency, interpretability, and computational cost. This study proposes a hybrid radial basis function network (RBFN) framework integrated with fuzzy c-means (FCM) clustering for predicting harmful algal blooms (HABs) using water quality parameters. Unlike conventional approaches, our model leverages localized activation functions to capture non-linear relationships while maintaining computational efficiency. Experimental results demonstrate that the RBFN-FCM hybrid achieved high accuracy (F1-score: 1.00) on test data and identified Chlorophyll-a as the strongest predictor (r = 0.94). However, real-world validation revealed critical limitations: the model failed to generalize datasets with incomplete features or distribution shifts, predicting zero HAB outbreaks in an unlabeled 11,701-record dataset. Comparative analysis with Random Forests confirmed the RBFN-FCM's advantages in training speed and interpretability but highlighted its sensitivity to input completeness. This work underscores the potential of RBFNs as lightweight, explainable tools for environmental forecasting while emphasizing the need for robustness against data variability. The framework offers a foundation for real-time decision support in ecological conservation, pending further refinement for field deployment.
Co-Authors Adlin Muhammad Zahid , Nur Anis Sofea Kamarul Zaman , Nur Annas, Ammar Haziq Bahri, Hannah Sabrina Saiful Dini Handayani, Dini Ghazalli, Nasyitah Haiqal Azreen Kamarul Zaman , Mohammad Hasli, Amir Husaini Mohd Hussin, Amir ‘Aatieff Amir Kamal, Nik Nor Muhammad Saifudin Nik Mohd Kamarudin, Saidatul Izyanie Karimudin, Nureen Batrisyia Balqis Mohammad-Noor, Normawaty Muhammad, Nurul Fadhilah Ahmad Nizam, Muhamad Khaleesh Mirza Khairul Nor, Rizal Mohd Nur Maisarah Ahmad Razmi , Alin Othman , Adam Razali, Roziawati Mohd Sadikan, Siti Fairuz Nurr Subramaniam, Krishnan Tamrin, Mohd. Izzuddin Mohd. Zaini, Nor Ani Jannah Ahmad