Browsing by Author "Zaker, M."

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Citation - Scopus: 0
Building Damage Assessment To Facilitate Post-Earthquake Search and Rescue Missions by Leveraging a Machine Learning Algorithm
(Institute of Electrical and Electronics Engineers Inc., 2024) Arsan, Taner; Alsan, H.F.; Arsan, T.
Earthquakes have a severe impact on people's lives and infrastructure. Many emergency institutes and search and rescue missions need accurate post-earthquake response strategies, particularly in building damage assessment. Traditional methods, relying on manual inspections, are inefficient compared to Machine Learning (ML) algorithms. Thus, Random Forest (RF) algorithms stand out because they handle diverse datasets effectively and minimize overfitting. The study outlines the methodology encompassing data preparation, exploratory analysis, feature engineering, and model building, employing a preprocessing pipeline integrating numerical and categorical features. Additionally, Principal Component Analysis (PCA) is applied to reduce dimensionality. The results of the RF model showed an accuracy of 94% and the highest F1-score of 97% among all the grades, demonstrating its efficacy in predicting damage grades post-earthquake. The results can help support better disaster management plans by helping to prioritize rescue operations and allocate resources wisely. © 2024 IEEE.
A Nano-Design of a Quantum-Based Arithmetic and Logic Unit for Enhancing the Efficiency of the Future Iot Applications
(American Institute of Physics, 2025) Ahmadpour, S.S.; Zaker, M.; Navimipour, N.J.; Misra, N.K.; Zohaib, M.; Kassa, S.; Hakimi, M.
The Internet of Things (IoT) is an infrastructure of interconnected devices that gather, monitor, analyze, and distribute data. IoT is an inevitable technology for smart city infrastructure to ensure seamless communication across multiple nodes. IoT, with its ubiquitous application in every sector, ranging from health-care to transportation, energy, education, and agriculture, comes with serious challenges as well. Among the most significant ones is security since the majority of IoT devices do not encrypt normal data transmissions, making it easier for the network to breach and leak data. Traditional technologies such as CMOS and VLSI have the added disadvantage of consuming high energy, further creating avenues for security threats for IoT systems. To counter such problems, we require a new solution to replace traditional technologies with a secure IoT. In contrast to traditional solutions, quantum-based approaches offer promising solutions by significantly reducing the energy footprint of IoT systems. Quantum-dot Cellular Automata (QCA) is one such approach and is an advanced nano-technology that exploits quantum principles to achieve complex computations with the advantages of high speed, less occupied area, and low power consumption. By reducing the energy requirements to a minimum, QCA technology makes IoT devices secure. This paper presents a QCA-based Arithmetic Logic Unit (ALU) as a solution to IoT security problems. The proposed ALU includes more than 12 logical and arithmetic operations and is designed using majority gates, XOR gates, multiplexers, and full adders. The proposed architecture, simulated in QCADesigner 2.0.3, achieves an improvement of 60.45% and 66.66% in cell count and total occupied area, respectively, compared to the best of the existing designs, proving to be effective and efficient. © 2025 Author(s).