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Maidin, Siti Sarah
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Predictive Data Analytics for Fault Diagnosis and Energy Optimization in Industrial IoT Environments Fallah, Dina; Abdul-Kareem, Bushra Jabbar; Murad, Nada Mohammed; Mahdi, Ammar Falih; Janan, Ola; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1392

Abstract

The fusion of predictive maintenance with energy optimization represents a critical advance for intelligent Industrial Internet of Things (IIoT) systems. In response to the growing industrial demand for highly reliable and efficient operations, this study introduces and validates a unified framework that couples fault diagnosis via deep learning with energy management via reinforcement learning. We utilize a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture for multivariate fault detection, which demonstrates superior classification accuracy and robustness against data incompleteness. Simultaneously, a Deep Q-Network (DQN) performs dynamic energy scheduling based on predicted system health, achieving substantial energy reductions without compromising task deadlines. Extensive experimental results from real-world industrial datasets and simulations confirm the integrated framework's superiority over conventional approaches in both diagnostic precision and energy efficiency. Key performance indicators, including inference speed and cross-validation, affirm its suitability for real-time industrial applications. This work demonstrates that integrating predictive analytics into intelligent control paradigms is crucial for improving the reliability and sustainability of modern IIoT systems and offers a replicable blueprint for developing next-generation smart manufacturing solutions.
Edge Computing Frameworks for Real-Time Optimisation in Autonomous Electric Vehicle Networks Ismail, Laith S.; Jamil, Abeer Salim; Ali, Taghreed Alaa Mohammed; Al-Dosari, Ibraheem Hatem Mohammed; Salman, Khdier; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1397

Abstract

Autonomous electric vehicles (AEVs) require real-time decision-making, low-latency computation, and energy-aware coordination to operate effectively. Traditional centralised cloud computing struggles to meet these demands due to inherent delays and scalability issues in large-scale AEV networks. This paper proposes a novel hybrid edge–fog computing architecture to address these challenges. Our framework utilises a three-tier system (vehicle-edge, roadside-fog, and cloud) governed by a deep reinforcement learning agent that manages energy-aware task offloading. Extensive simulations demonstrate the framework's effectiveness, achieving significant end-to-end latency reductions of up to 56% during urban peak hours and decreasing energy consumption by 20% under high-load conditions. The deep reinforcement learning agent successfully adapts control policies to dynamic road conditions, while the architecture proves highly scalable and resilient, maintaining high task success rates and recovering from node failures in seconds. These findings confirm that a hybrid edge–fog architecture, guided by reinforcement learning, is a highly effective solution for scalable, adaptive, and energy-efficient AEV operations. This study's primary contribution is an empirically validated framework that uniquely integrates predictive control and energy-aware scheduling at the edge, providing a deployable model for next-generation intelligent transportation systems.
Design and Deployment of a Secure Cyber-Physical System for Energy Monitoring in Smart Agriculture Fallah, Dina; Abbas, Elaf Sabah; Ahmed, Mohsen Ali; Sajid, Wafaa Adnan; Al Hilfi, Thamer Kadum Yousif; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1398

Abstract

The growing need for sustainable agricultural practices has spurred the integration of cyber-physical systems (CPS) into modern farming. This paper presents the design, deployment, and evaluation of a modular CPS architecture for adaptive energy monitoring and control in smart agriculture. The system integrates environmental sensing, predictive modelling, and optimisation-guided actuation to enhance energy efficiency and operational resilience. Field tests on a 3-hectare site across six crop environments demonstrated significant performance gains, achieving energy savings of up to 25.8% and peak demand reductions of up to 19.8%. Our multi-layer architecture, featuring STM32 microcontrollers, LoRaWAN communication, and a cloud analytics dashboard, enables proactive control by anticipating energy demand using an LSTM-NARX predictive model. This approach reduced control actuation delay to 1.8 seconds and proved robust against cyber-physical faults, recovering from communication failures and data anomalies in under 15 seconds. The results validate that embedding energy-aware, predictive logic into CPS infrastructure creates scalable, efficient, and reliable agricultural solutions. We acknowledge limitations related to predictive model complexity and communication latency, and we propose future work focused on distributed CPS coordination, federated learning, and full lifecycle sustainability analysis to further advance intelligent, resource-efficient agriculture.
Swarm Intelligence Algorithms for Resource Allocation in Renewable-Powered Smart City Infrastructures Nazar, Mustafa; Majeed, Adil Abbas; Abdul Radhi, Rafah Hassan; Jafar, Qusay Mohammed; Khalil, Baker Mohammed; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1355

Abstract

The increasing integration of renewable energy sources into urban systems necessitates the development of intelligent resource management strategies to ensure optimal and reliable power distribution. Swarm Intelligence (SI) algorithms have emerged as a promising solution for addressing the complex energy management challenges inherent in smart cities, such as generation variability, distributed loads, and the need for real-time decision-making. This paper conducts a rigorous comparative analysis of three prominent SI algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC)—within a simulated, renewable-powered smart city environment. Our model incorporates edge computing nodes, solar and wind generation systems, and heterogeneous urban load profiles, including residential, municipal, and electric vehicle charging demands. The study evaluates each algorithm against key performance metrics, including energy efficiency, task latency, convergence behavior, load balancing, and system fault tolerance. The results unequivocally demonstrate that PSO outperforms both ACO and ABC across most performance dimensions, exhibiting faster convergence, superior energy utilization, more effective latency management, and enhanced fault recovery capabilities. While ABC demonstrates competitive performance in flexibility and fairness, ACO shows significant limitations in time-sensitive and failure-prone scenarios. This research contributes a modular simulation framework suitable for real-time edge computing applications and offers practical guidance for deploying adaptive optimization strategies in urban energy systems. Ultimately, our findings underscore the critical importance of algorithm selection in smart city energy infrastructure and highlight the potential of swarm-based intelligence to enable scalable, resilient, and efficient resource management in the sustainable cities of the future.
Energy-Aware Multimodal Biometric Authentication Systems for Mobile Hamodi Aljanabi, Yaser Issam; Mahdi, Mohammed Fadhil; Hadi, Shahd Imad; Shnain, Saif Kamil; Abbas, Intesar; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1356

Abstract

As smartphones become central to personal identity verification, the need for secure, efficient, and power-conscious authentication methods is paramount. While multimodal biometric systems, combining features like face and fingerprint recognition, offer superior accuracy over unimodal approaches, their adoption on mobile platforms is severely hindered by high energy consumption and hardware variability. This paper introduces an energy-aware multimodal biometric authentication framework designed for Android smartphones that directly confronts this challenge. Our system features a novel adaptive fusion mechanism that intelligently balances recognition accuracy with power consumption by dynamically adjusting the weights of biometric modalities in real-time based on battery level and ambient environmental conditions. To validate our framework, we conducted an extensive experimental study involving 46 participants across 460 authentication sessions on five different smartphone models. The results demonstrate that our adaptive system significantly outperforms both unimodal and static fusion baselines. It achieves a high True Acceptance Rate (TAR) and a low Equal Error Rate (EER) while substantially reducing the Energy-Delay Product (EDP). A key feature is the system's ability to gracefully degrade to a secure, fingerprint-only mode when the battery is critically low, ensuring continuous availability without compromising security. This research proves that intelligent, context-aware modality adaptation is a viable strategy for creating robust, efficient, and sustainable biometric authentication solutions suitable for long-term use in consumer electronics.
A Multiple Linear Regression Approach to Predicting AI Professionals’ Salaries from Location and Skill Data Maidin, Siti Sarah; Yi, Ding; Ayyasy, Yahya
International Journal of Informatics and Information Systems Vol 7, No 3: September 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i3.213

Abstract

The rapid growth of Artificial Intelligence (AI) industries worldwide has increased the demand for skilled professionals and highlighted the need to understand salary determinants in this sector. This study aims to analyze the factors influencing the compensation of AI professionals globally, with a particular focus on the effects of company location, experience level, and required technical skills. Using a dataset of 15,000 AI job postings collected from multiple countries, a Multiple Linear Regression (MLR) model was developed to identify predictive relationships between independent variables—location, experience, and skills—and the dependent variable, annual salary in U.S. dollars. Data preprocessing included one-hot encoding for categorical variables, standardization of numerical attributes, and vectorization of text-based skill descriptions. Model evaluation produced strong predictive results, with an R² of 0.82, a Mean Absolute Error (MAE) of 18,677 USD, and a Root Mean Squared Error (RMSE) of 25,704 USD. Statistical tests confirmed that company location and experience level significantly affected salary outcomes (p 0.05), while technical skills contributed only marginally. These findings suggest that structural factors such as geography and seniority play a more decisive role in determining AI salaries than specific technical competencies. The study concludes that MLR offers a transparent and interpretable analytical framework for exploring salary disparities in the global AI workforce. The results provide practical implications for organizations designing fair compensation policies, professionals assessing market value, and educators aligning training programs with evolving industry demands.
Algorithms and Modeling for Optimizing Sustainable Energy Systems Jaleel Maktoof, Mohammed Abdul; Shaker, Alhamza Abdulsatar; Nayef, Hamdi Abdullah; Taher, Nada Adnan; Yousif Al Hilfi, Thamer Kadum; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1457

Abstract

The global transition toward sustainable energy necessitates intelligent, integrated solutions to overcome the intermittency of renewable sources. This paper presents and validates a comprehensive framework for optimising Hybrid Solar-Wind Energy (HSWE) systems by integrating advanced simulation, machine learning-based forecasting, and metaheuristic optimisation. Using meteorological and operational data from three distinct climate zones, we modelled and analysed a PV-wind-lithium-ion hybrid system. A neural network was employed for precise load forecasting, while Particle Swarm Optimisation (PSO) managed real-time resource allocation and storage dispatch. Comparative analysis reveals that the optimised hybrid system significantly outperforms standalone units, increasing energy production by up to 32%, improving overall energy efficiency to 92.3%, and reducing operational costs by over 36%. The simulation models demonstrated high fidelity, with predictions matching experimental field data with less than 1% error. Furthermore, the integration of predictive fault handling and intelligent load balancing enhanced system reliability, increasing the mean time between failures (MTBF) by over 70% and achieving 97.6% system availability. This research provides a validated, replicable framework for engineers and policymakers, demonstrating a practical pathway to developing efficient, economically viable, and resilient decentralised renewable energy infrastructure to meet global sustainability goals.
The need for an enhanced IoT-based malware detection model using Artificial Intelligence (AI) algorithm: A Review Maidin, Siti Sarah; Yahya, Norzariyah
Data Science Insights Vol. 1 No. 1 (2023): Journal of Data Science Insights
Publisher : PT. Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v1i1.6

Abstract

The interconnected world using technology has opened the door for cyberattacks. For example, the utilization of Internet of Things (IoT) devices has increased the exposure to malware attacks. The massive amount of data generated by the IoT devices leads to the possibility of infections in the network. Due to the diverse nature of the IoT devices and the ever-evolving nature of their environment, it can be challenging to devise very comprehensive security measures. Therefore, the application of Artificial Intelligence (AI) in detecting malware has gained attention as a suitable tool for detecting malware due to its strength in malware classification. This research aims to review malware detection in IoT devices using AI and its challenges.
Decision Support System Application in Disaster Management Yilin, Li; Zhaoji, Fu; Kowthalam, Vijay Rathnam; Guangfa, Wu; Binti Abdul Rahim, Yusrina; Maidin, Siti Sarah; Yahya, Norzariyah
Data Science Insights Vol. 2 No. 1 (2024): Journal of Data Science Insights
Publisher : PT. Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i1.21

Abstract

Disasters such as earthquake, flood, fire, and tsunami result in catastrophic human suffering, loss of property and other negative consequences. The continues threats of future disasters enforce human to find best possible ways to detect and take premeasured actions based on calculated risks to reduce these negative impacts of disasters. 
Comparative Analysis of Data Visualization Techniques for Rainfall Data Wan Ishak, Wan Hussain; Yamin, Fadhilah; Maidin, Siti Sarah; Husin, Abdullah
Data Science Insights Vol. 3 No. 2 (2025): Journal of Data Science Insights
Publisher : PT. Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v3i2.204

Abstract

Rainfall data is essential for applications such as climate monitoring, agricultural planning, flood forecasting, and water resource management. However, the interpretation of this data is often hindered by its high volume, variability, and multi-scale temporal nature. Effective visualization is critical not only for summarizing complex datasets but also for uncovering patterns, detecting anomalies, and facilitating informed decision-making. Despite the availability of numerous visualization techniques, selecting the most suitable method for rainfall data, especially across varying temporal resolutions is a challenging task. This study presents a comparative analysis of widely used data visualization techniques in the context of rainfall data. The methodology was structured into three phases: understanding the nature of rainfall data, reviewing relevant visualization techniques, and conducting a comparative content analysis. A SWOT (Strengths, Weaknesses, Opportunities, and Threats) evaluation was used to assess each technique’s analytical potential, while a temporal suitability comparison was performed across five time granularities: yearly, monthly, weekly, daily, and hourly. Findings show that no single technique is universally effective. Instead, each method demonstrates specific strengths and limitations depending on the temporal scale and analytical objective. Line charts and bar charts are well-suited for lower-frequency data, while heat maps and scatter plots are more effective for high-resolution, time-sensitive patterns. Box plots and histograms provide valuable insights into data distribution and variability, whereas map-based visualizations excel in spatial analysis but require enhancements for temporal exploration. The study concludes that visualization effectiveness depends on aligning method selection with data characteristics and analytical goals. A thoughtful combination of techniques is often necessary to achieve clarity, reduce misinterpretation, and enhance decision support in rainfall data analysis.
Co-Authors Abbas, Elaf Sabah Abbas, Intesar Abdul Radhi, Rafah Hassan Abdul-Kareem, Bushra Jabbar Abdullah Abdullah Adnan, Kahtan Mohammed Ahmed, Mohsen Ali Ahmed, Saif Saad Ajitha, Ajitha Al Hilfi, Thamer Kadum Yousif Al-Dosari, Ibraheem Hatem Mohammed Alfalahi, Saad.T.Y. Ali, Taghreed Alaa Mohammed Ali, Zaid Ghanim Arthi, R. Attarbashi, Zainab S. Ayi Abdurahman Ayyasy, Yahya Bakar, Normi Sham Awang Abu Binti Abdul Rahim, Yusrina Dhilipan, J. Fallah, Dina Faraj, Lydia Naseer Fauzi, Muhammad Ashraf bin Fauri Ge, Wu Gelar Budiman Govindaraju, S Guangfa, Wu Hadi, Shahd Imad Hafedh, Milad Abdullah Hammad, Qudama Khamis Hamodi Aljanabi, Yaser Issam Hao Wu Haodic, Gao Hemalatha, M. I Dewa Gede Budi Utama I Gede Astawan Indirani, M Indrarini Dyah Irawati Ishak, Wan Hussain Wan Ismail, Laith S. Jafar, Qusay Mohammed Jaleel Maktoof, Mohammed Abdul Jamil, Abeer Salim Janan, Ola Jaya, M. Izham Jeyaboopathiraja, J. Jing Sun Khalil, Baker Mohammed Khalil, Ibraheem Mohammed Kowthalam, Vijay Rathnam Kumar, B.L. Shiva Lie, Ye Luo Jun Mahdi, Ammar Falih Mahdi, Mohammed Fadhil Mahendiran, N Majeed, Adil Abbas Mariajohn, Princess Mohammed, Doaa Thamer Murad, Nada Mohammed Nassar, Waleed Nayef, Hamdi Abdullah Nazar, Mustafa Nivetha, N. Praneesh, M. Priscilla, G Maria Priscilla, G. Maria Rahmafadilla, Rahmafadilla Rizal, Mochammad Fahru Sajid, Wafaa Adnan Salman, Khdier Samson, A Sunil Selvaraj, Poovarasan Shaker, Alhamza Abdulsatar Shanmugam, D.B. Shing, Wong Ling Shivakumar, B L Shivakumar, B. L. Shnain, Saif Kamil Subramanian, Devibala Sumathi, N Sumathi, V. Taher, Nada Adnan Thavamani, S. Triasari, Biyantika Emili Varun, S. T. Vidhya, B. Vijayalakshmi, N. Wan Ishak, Wan Hussain Wayan Eka Paramartha Wei, Jingchuan Wider, Walton Yahya, Norzariyah Yamin, Fadhilah Yang, Qingxue Yi, Ding Yilin, Li Yousif Al Hilfi, Thamer Kadum YULI SUN HARIYANI Zhang Xing Zhao, Zhong Zhaoji, Fu