Behbod Ghalamkari | Sensor | Research Excellence Award

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Assist. Prof. Dr. Behbod Ghalamkari | Sensor | Research Excellence Award

Assistant Professor | Islamic Azad University | Iran

Assist. Prof. Dr. Behbod Ghalamkari is an active researcher in the field of electrical and telecommunication engineering, with a strong focus on electromagnetic wave propagation, analytical and semi-analytical scattering techniques, antennas, and RF/microwave systems. His research contributions span rigorous solutions to complex electromagnetic scattering problems using methods such as the Kobayashi Potential, Fourier transform techniques, and modal analysis, addressing advanced media including chiral, anisotropic, plasma, and topological insulator environments. He has also made significant contributions to antenna engineering, including reconfigurable, wideband, terahertz, and energy-harvesting antenna systems, as well as microwave and millimeter-wave circuit design. His work integrates theoretical analysis with practical design, simulation, fabrication, and measurement of high-frequency components. With a substantial publication record in leading international journals and conferences, his research has influenced both fundamental electromagnetic theory and applied wireless technologies, contributing to advancements in communication systems, sensing, and high-frequency device engineering.

Citation Metrics (Scopus)

400

300

200

100

0

Citations 385

Documents 50

h-index
12


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Top 5 Featured Publications


Dual-Band Slim Microstrip Patch Antennas

– IEEE Transactions on Antennas and Propagation, 2018 · Cited by 48

A Fast Semianalytical Solution of a 2-D Dielectric-Filled and Coated Rectangular Groove

– IEEE Transactions on Antennas and Propagation, 2014 · Cited by 31

A Dual-Mode Tunable Bandpass Filter for GSM, UMTS, WiFi, and WiMAX Standards Applications

– International Journal of Circuit Theory and Applications, 2019 · Cited by 23

Ahmed Y. Hassebo | Smart Cities IoT Applications | Best Researcher Award

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Dr. Ahmed Y. Hassebo | Smart Cities IoT Applications | Best Researcher Award

Doctoral Lecturer | NYC College of Technology | United States

Dr. Ahmed Y. Hassebo, Ph.D., P.E., is an accomplished electrical engineering educator and researcher with over fourteen years of teaching experience across leading U.S. institutions, including the City University of New York (CUNY), Purdue University Northwest (PNW), and Wentworth Institute of Technology (WIT). Currently serving as a Doctoral Lecturer at NYC College of Technology–CUNY, Dr. Hassebo has made significant contributions in the domains of telecommunications, signal processing, smart cities, and Internet of Things (IoT)-based systems. His academic foundation is rooted in a Ph.D. and M.Phil. in Electrical Engineering from the City College of New York, supported by a strong research portfolio emphasizing the integration of 4G/5G communication infrastructures with mission-critical IoT and smart grid applications. He has authored a book, multiple book chapters, and several peer-reviewed journal and conference papers, earning multiple Best Paper and Best Presentation Awards from IEEE and ASEE conferences. His published works in Urban Science and IoT journals underscore his global perspective on smart city transformation and sustainable urban connectivity. Beyond research, Dr. Hassebo has mentored undergraduate and high school students in projects funded by NSF, NASA, and CUNY initiatives, fostering interdisciplinary learning in AI, robotics, and embedded systems. His active role as an IEEE reviewer, ASEE session chair, and committee member at CityTech demonstrates his leadership in both academic governance and scholarly service. Holding a Professional Engineer (PE) license in Electrical and Computer Engineering, he continues to pursue research excellence through grant proposals such as NSF Engineering Research Initiation (ERI) and CUNY GRTI initiatives. With over 8 years of research experience and a growing academic impact—reflected in multiple citations and collaborations—Dr. Hassebo exemplifies a commitment to advancing smart technologies that enhance urban sustainability, education, and global digital transformation.

Profile: Google Scholar
Publications:

Hassebo, A., Mohamed, A. A., Dorsinville, R., & Ali, M. A. (2018). 5G-based converged electric power grid and ICT infrastructure. 2018 IEEE 5G World Forum (5GWF), 33–37. 
(Cited by: 27)

Hassebo, A., Obaidat, M., & Ali, M. A. (2018). Commercial 4G LTE cellular networks for supporting emerging IoT applications. 2018 Advances in Science and Engineering Technology International Conferences (ASET). 
(Cited by: 24)

Hassebo, A. Y. (2018). Commercial 4G LTE cellular networks for supporting emerging mission-critical IoT applications [Master’s thesis, The City College of New York]. CUNY Academic Works.
(Cited by: 9)

Tealab, M., Hassebo, A., Dabour, A., & AbdelAziz, M. (2020). Smart cities digital transformation and 5G–ICT architecture. 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). 
(Cited by: 13)

Hassebo, A. (2022). The road to 6G: Vision, drivers, trends, and challenges. 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC). 
(Cited by: 12)

Hassebo, A., & Tealab, M. (2023). Global models of smart cities and potential IoT applications: A review. IoT, 4(3), 366–411. 
(Cited by: 85)

Maloth Naresh | Structural Health Monitoring | Best Researcher Award

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Dr. Maloth Naresh | Structural Health Monitoring | Best Researcher Award

Assistant Professor | Sharad Institute of Technology College of Engineering Yadrav | India

Dr. Maloth Naresh is an accomplished researcher and academic in Structural Engineering, specializing in data-driven and machine learning applications for Structural Health Monitoring (SHM) of steel frame structures. He earned his Ph.D. from the National Institute of Technology Hamirpur in 2024, where his doctoral research focused on developing advanced machine learning algorithms to monitor, predict, and assess joint damages in steel structures under varying environmental and operational conditions. With a strong publication record in high-impact SCI and SCIE-indexed journals such as Smart Materials and Structures, Strain (Wiley), and Asian Journal of Civil Engineering, Dr. Naresh has significantly contributed to the intersection of civil engineering and computational intelligence. His works on CNN–LSTM-based hybrid models and optimized SVM frameworks have advanced precision in damage detection, enabling early prediction and maintenance optimization in complex infrastructure systems. Recognized among the top-cited authors by Strain for 2023, his research demonstrates both academic excellence and practical relevance. He has presented papers at reputed international conferences including NIT Silchar and IIT Hyderabad and has also submitted a national-level research proposal under the NSTMIS scheme. Currently serving as Head of the Civil Engineering Department at Sharad Institute of Technology College of Engineering, Maharashtra, he fosters research collaborations with scholars from NIT Hamirpur, NIT Sikkim, and the University of Huddersfield (UK), emphasizing global partnerships and interdisciplinary innovation. His technical expertise spans MATLAB programming, ANSYS modeling, AutoCAD design, and advanced data analysis in structural dynamics. Dr. Naresh’s academic journey is marked by consistent excellence, including qualifying GATE 2015 and earning an HRD Scholarship from the Government of India. With nine high-quality publications and growing recognition across the research community, he continues to expand his scholarly impact through innovative methodologies that enhance the reliability, sustainability, and safety of civil infrastructure systems worldwide.

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Featured Publications:

Dr. Dinesh G – Wirless Sensors – Excellence in Research

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Dr. Dinesh G | Wirless Sensors | Excellence in Research

Assistant Professor | SRM Institute of Science and Technology | India

Dr. Dinesh G has established a strong research profile in the field of computer science with notable expertise spanning cognitive radio networks, artificial intelligence, machine learning, wireless sensor networks, and Internet of Things applications. His contributions include optimization techniques for spectrum sharing, development of secure and efficient network protocols, and innovative applications of AI for healthcare, sustainable energy, agriculture, and environmental monitoring. His work integrates advanced optimization algorithms like Salp Swarm Optimization and Modified Spider Monkey Optimization for enhancing energy efficiency and scheduling in cognitive radio systems. Beyond wireless communication, Dr. Dinesh has expanded research into domains such as reinforcement learning for thermoplasmonic imaging, deep learning for subsurface hydrology, AI-driven coral reef monitoring, and real-time disease detection in agriculture. His multidisciplinary approach reflects a strong alignment with societal needs, focusing on healthcare prediction models, e-vehicle sustainability, smart farming, and secure data transmission frameworks. The publication record spans SCIE and Scopus-indexed journals, covering diverse areas such as cybersecurity, big data analytics, environmental health, and computational intelligence, demonstrating a balance between theoretical models and practical applications. In addition, patents on AI-based prediction, 5G-ORAN synchronization, and collaborative filtering systems highlight his innovative drive and industry relevance. His collaborative works with industries such as Renault Nissan and Mahindra further reinforce the applied impact of his research in automotive and computer vision domains. With contributions to book chapters, international conferences, and training initiatives, Dr. Dinesh has advanced both academic scholarship and industrial applications. His dedication to emerging technologies like blockchain, edge computing, and intelligent systems positions his research within the evolving global landscape of computing and communication. With 87 citations from 75 documents, 21 published works, and an h-index of 6.

Profile: Scopus | ORCID | Google Scholar
Publications:

  1. Unveiling hidden water resources: Deep learning and remote sensing for subsurface hydrology for environmental health. (2025).  available.

  2. Modified spider monkey optimization—An enhanced optimization of spectrum sharing in cognitive radio networks. (2021). Cited by 30.

  3. Analysis of classification and clustering techniques for ambient AQI using machine learning algorithms. (2022). Cited by 11.

  4. Comparative analysis of diverse classification algorithms of machine learning by using various quality metrics. (2023). Cited by 9.

  5. Demand based crop prediction using machine learning algorithm. (2020). Cited by 9.