Sheikh Shanawaz Mostafa | Computer Science | Best Researcher Award

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Dr. Sheikh Shanawaz Mostafa | Computer Science | Best Researcher Award

PostDoc at Instituto Superior Técnico, Portugal

Dr. Sheikh Shanawaz Mostafa is a dynamic researcher with over 12 years of experience in artificial intelligence, biomedical engineering, and computer science. He has an impressive track record of over 60 publications with a cumulative impact factor exceeding 144 and has contributed to impactful projects in healthcare, agriculture, energy, and smart systems. With academic credentials spanning Bangladesh and Portugal, including a Ph.D. from Instituto Superior Técnico, his work bridges interdisciplinary fields and real-world applications. He has led and contributed to high-profile projects such as Sleep Revolution, BASE, and AHEAD, demonstrating expertise in deep learning, explainable AI, and human-in-the-loop systems. Dr. Mostafa has supervised Ph.D., M.Sc., and B.Sc. theses, secured international research funding, and collaborated with institutions like Carnegie Mellon University and companies such as Zomato. Known for his mentorship, cross-cultural adaptability, and innovative thinking, he is a highly suitable candidate for the Best Researcher Award.

Professional Profile 

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Scopus Profile
ORCID Profile

Education

Dr. Sheikh Shanawaz Mostafa holds a Ph.D. in Electrical and Computer Engineering from Instituto Superior Técnico, University of Lisbon, Portugal, where he specialized in Networked Interactive Cyber-Physical Systems, a joint program with Carnegie Mellon University. He completed his M.Sc. in Biomedical Engineering and B.Sc. in Electronics and Communication Engineering from Khulna University of Engineering & Technology (KUET), Bangladesh. Throughout his academic journey, he has maintained a strong interdisciplinary focus, integrating electrical engineering, biomedical systems, and artificial intelligence. His Ph.D. thesis on sleep apnea detection was awarded “Pass with Distinction,” reflecting his academic excellence and research impact. With a consistent academic record across diverse technical disciplines, Dr. Mostafa’s educational background has provided a solid foundation for innovative research at the intersection of AI and health technologies.

Professional Experience

Dr. Mostafa brings over 12 years of international academic and experience, having worked with renowned institutions such as Instituto Superior Técnico, Madeira Interactive Technologies Institute, ARDITI, and KUET. Currently serving as a Postdoctoral Researcher (R3) at Instituto Superior Técnico, he has held multiple roles as an AI consultant, assistant professor, and principal researcher across EU-funded and industry-partnered projects. His key projects include Sleep Revolution, BASE (banana harvesting optimization), AHEAD (EV infrastructure planning), and RRSO (restaurant sentiment analytics). Dr. Mostafa has demonstrated excellence in leading interdisciplinary teams, securing competitive research grants, and collaborating with industry partners like Zomato and Asseco PST. He has also mentored numerous Ph.D., M.Sc., and undergraduate students and actively contributes to curriculum development. His ability to bridge academic research with practical, high-impact applications highlights his value as both a researcher and educator.

Research Interest

Dr. Mostafa’s research interests lie at the intersection of artificial intelligence and real-world problem-solving. His primary focus includes deep learning, explainable AI, machine learning, and human-in-the-loop systems, particularly in applications related to healthcare, biomedical signal analysis, smart agriculture, energy systems, and natural language processing. He has contributed significantly to advancing AI-driven diagnostics, such as in sleep disorder analysis, and to building predictive models for fields ranging from sports performance to restaurant sentiment analysis. He is also interested in the integration of AI into smart cities and infrastructure, including EV charging optimization and real-time decision systems. His interdisciplinary approach allows him to explore novel AI applications in medicine, agritech, and environmental systems. Combining theoretical modeling with applied innovation, Dr. Mostafa’s work seeks to create intelligent systems that are not only technically robust but also socially and economically impactful.

Award and Honor

Dr. Sheikh Shanawaz Mostafa has earned recognition throughout his academic and professional career for his contributions to research and education. While formal award listings are not detailed, his achievements—such as securing over $25,000 in competitive research funding, earning a Ph.D. with Distinction, publishing in high-impact journals, and successfully leading EU and industry-sponsored research projects—reflect significant professional recognition. His selection for collaborative international programs like the CMU-Portugal partnership, and his roles in innovative projects supported by organizations like the Portuguese Foundation for Science and Technology (FCT), further highlight his esteemed standing in the academic and research community. His work has also been showcased in prestigious venues, such as the Electronic Imaging conference in San Francisco. The impact of his research and mentorship, as well as the trust placed in him by academic and industrial collaborators, is a testament to his excellence and potential for future honors.

Conclusion

Dr. Sheikh Shanawaz Mostafa exemplifies the qualities of a leading interdisciplinary researcher, combining deep technical expertise with a commitment to solving real-world challenges through AI and engineering. His strong academic foundation, international experience, and impressive publication record reflect a sustained dedication to research excellence. He has not only led cutting-edge projects across healthcare, smart systems, and agriculture, but also mentored the next generation of scholars, thereby extending his impact. His adaptability across cultural and institutional contexts, successful collaborations with industry, and ability to secure research funding mark him as a forward-thinking and versatile contributor to the global scientific community. With a clear trajectory of growth, innovation, and leadership, Dr. Mostafa is highly deserving of recognition such as the Best Researcher Award and stands poised to make even greater contributions in the future.

Publications Top Notes

  • Title: A review of obstructive sleep apnea detection approaches
    Authors: F. Mendonca, S.S. Mostafa, A.G. Ravelo-Garcia, F. Morgado-Dias, T. Penzel
    Year: 2018
    Citations: 235

  • Title: An adaptive level dependent wavelet thresholding for ECG denoising
    Authors: M.A. Awal, S.S. Mostafa, M. Ahmad, M.A. Rashid
    Year: 2014
    Citations: 198

  • Title: A systematic review of detecting sleep apnea using deep learning
    Authors: S.S. Mostafa, F. Mendonça, A.G. Ravelo-García, F. Morgado-Dias
    Year: 2019
    Citations: 175

  • Title: Devices for home detection of obstructive sleep apnea: A review
    Authors: F. Mendonça, S.S. Mostafa, A.G. Ravelo-García, F. Morgado-Dias, T. Penzel
    Year: 2018
    Citations: 137

  • Title: A review of approaches for sleep quality analysis
    Authors: F. Mendonça, S.S. Mostafa, F. Morgado-Dias, A.G. Ravelo-Garcia, T. Penzel
    Year: 2019
    Citations: 119

  • Title: SpO2 based Sleep Apnea Detection using Deep Learning
    Authors: S.S. Mostafa, F. Mendonça, F. Morgado-Dias, A. Ravelo-García
    Year: 2017
    Citations: 86

  • Title: Performance analysis of Savitzky-Golay smoothing filter using ECG signal
    Authors: M.A. Awal, S.S. Mostafa, M. Ahmad
    Year: 2011
    Citations: 79

  • Title: XGB-RF: A hybrid machine learning approach for IoT intrusion detection
    Authors: J.A. Faysal, S.T. Mostafa, J.S. Tamanna, K.M. Mumenin, M.M. Arifin, M.A. Awal, …
    Year: 2022
    Citations: 66

  • Title: Multi-objective hyperparameter optimization of CNN for obstructive sleep apnea detection
    Authors: S.S. Mostafa, F. Mendonca, A.G. Ravelo-Garcia, G.G. Juliá-Serdá, …
    Year: 2020
    Citations: 56

  • Title: Human emotion recognition using frequency & statistical measures of EEG signal
    Authors: M. Islam, T. Ahmed, S.S. Mostafa, M.S.U. Yusuf, M. Ahmad
    Year: 2013
    Citations: 50

  • Title: Implementation strategy of CNNs on FPGAs for appliance classification using VI trajectory
    Authors: D. Baptista, S.S. Mostafa, L. Pereira, L. Sousa, F. Morgado-Dias
    Year: 2018
    Citations: 47

  • Title: Automatic detection of cyclic alternating pattern
    Authors: F. Mendonça, A. Fred, S.S. Mostafa, F. Morgado-Dias, A.G. Ravelo-García
    Year: 2022
    Citations: 39

  • Title: Optimization of sleep apnea detection using SpO2 and ANN
    Authors: S.S. Mostafa, J.P. Carvalho, F. Morgado-Dias, A. Ravelo-García
    Year: 2017
    Citations: 37

  • Title: An oximetry based wireless device for sleep apnea detection
    Authors: F. Mendonça, S.S. Mostafa, F. Morgado-Dias, A.G. Ravelo-García
    Year: 2020
    Citations: 30

  • Title: Design and optimization of ECG modeling for generating different cardiac dysrhythmias
    Authors: M.A. Awal, S.S. Mostafa, M. Ahmad, M.A. Alahe, M.A. Rashid, A.Z. Kouzani, …
    Year: 2021
    Citations: 26

Eduardo Coronel | Computer Science | Best Researcher Award

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Dr. Eduardo Coronel | Computer Science | Best Researcher Award

M.Sc. Eng. at Facultad Politécnica,  Paraguay

Eduardo Damián Coronel Torales, born on March 5, 1991, in Asunción, Paraguay, is a distinguished researcher and engineer specializing in electrical engineering, automation, and artificial intelligence applications. He has actively contributed to academia, industry, and international conferences, earning recognition for his innovative work in energy distribution and automation systems. His professional journey has taken him from academic research to practical implementations in one of the world’s largest hydroelectric plants, Itaipu Binacional. With a strong foundation in engineering and computational intelligence, Coronel Torales has made significant contributions to optimizing power distribution and developing automation solutions. His research extends beyond Paraguay, reaching international platforms and collaborations. He continues to push the boundaries of technology by integrating advanced optimization techniques, machine learning, and smart grid systems, positioning himself as a leader in his field.

Professional Profile

Education

Coronel Torales holds a Master’s degree in Electrical Engineering with an emphasis on Energy Systems Planning from the Facultad Politécnica of the Universidad Nacional del Este, obtained in 2021. His postgraduate research focused on optimizing power distribution using computational intelligence. He completed his undergraduate degree in Electronics Engineering with a specialization in Mechatronics at the Universidad Nacional de Asunción in 2017. During his academic career, he demonstrated exceptional analytical and problem-solving skills, engaging in multiple research projects related to automation, robotics, and energy systems. His academic journey reflects a strong commitment to technological advancements and interdisciplinary research. The combination of these degrees has provided him with a robust foundation in both theoretical and practical aspects of energy optimization, artificial intelligence, and industrial automation, equipping him with the expertise to tackle complex engineering challenges at both research and industrial levels.

Professional Experience

With extensive experience in academia and industry, Coronel Torales has worked as a research engineer at Itaipu Binacional, contributing to the modernization of automation systems. His expertise in failure analysis using PI tools and machine learning models has been instrumental in enhancing the reliability of large-scale energy infrastructure. He has also served as a postgraduate lecturer at the Universidad Nacional del Este, teaching heuristic optimization methods. Additionally, he has worked as an instructor at the Paraguay-Korea Advanced Technology Center (SNPP-KOICA), where he trained professionals in digital electronics and industrial automation. His work experience blends research, teaching, and industry applications, allowing him to bridge the gap between theory and practice. Through his diverse roles, he has been actively involved in developing intelligent systems, optimizing automation processes, and mentoring students and professionals in engineering disciplines.

Research Interests

Coronel Torales’ research interests lie at the intersection of power systems optimization, automation, and artificial intelligence. He has extensively explored the use of metaheuristic and multi-objective optimization techniques for enhancing the efficiency of electrical power distribution systems. His research also focuses on computer vision, machine learning, and control systems, particularly for applications in autonomous vehicles, industrial automation, and smart grids. Additionally, he is interested in the integration of AI-driven fault detection and predictive maintenance in large-scale energy infrastructures. His work contributes to improving the reliability and efficiency of energy management systems through data-driven solutions. By combining engineering principles with computational intelligence, he aims to develop sustainable and intelligent solutions for modern energy challenges. His forward-thinking research aligns with global trends in smart energy systems, IoT-enabled automation, and digital transformation in power distribution networks.

Awards and Honors

Coronel Torales has received international recognition for his research contributions, including multiple conference presentations at IEEE and other prestigious platforms. His work on remote-controlled switch optimization in power distribution systems has been published in IEEE Latin America Transactions and presented at international computing and engineering conferences such as CLEI, ICDIM, and INTERCON. He has been acknowledged for his contributions to automation failure analysis at Itaipu Binacional, influencing modernization decisions in one of the world’s largest hydroelectric plants. Additionally, his early research in autonomous vehicle navigation and fuzzy logic control earned him invitations to research symposiums in Argentina, Peru, South Korea, and the United States. His ability to translate research into practical applications has cemented his reputation as an emerging leader in electrical engineering and computational intelligence. His continued contributions are setting a benchmark for innovation in energy systems and industrial automation.

Conclusion

Eduardo Damián Coronel Torales has a strong research background with impactful contributions in energy systems optimization, automation, and AI applications. His publications, international recognition, and industry collaboration make him a strong candidate for the Best Researcher Award. However, to further strengthen his candidacy, he should aim for higher-impact journal publications, more independent research leadership, and broader contributions in emerging fields.

Publications Top Noted

  • Coronel, E., Barán, B., & Gardel, P. (2025). A Survey on Data Mining for Data-Driven Industrial Assets Maintenance Technologies. Journal article. DOI: 10.3390/technologies13020067.
  • Coronel Torales, E. D. (2024). Leveraging Machine Learning for Multi-Step Failure Forecasting in RTU Analog Modules and Estimating Key Performance Indicators to Support Management Decision-Making. CIGRE Paris Session 2024, Conference poster.
  • Coronel, E., Barán, B., & Gardel, P. (2022). Optimal Placement of Remote Controlled Switches in Electric Power Distribution Systems with a Meta-heuristic Approach. IEEE Latin America Transactions. DOI: 10.1109/TLA.2022.9675464.
  • Coronel Torales, E. D. (2021). Optimal Placement of Remote Controlled Switches in Electric Power Distribution Systems with a Multi-Objective Approach. 2021 XLVII Latin American Computing Conference (CLEI). DOI: 10.1109/clei53233.2021.9639970.
  • Coronel Torales, E. D. (2020). Optimización en la Ubicación de Seccionadores Tele-comandados en Sistemas de Distribución de Energía Eléctrica con enfoque meta-heurístico y soporte de decisión multi-criterio. Edited book. DOI: 10.13140/RG.2.2.32305.92002.
  • Coronel Torales, E. D. (2017). Estimación de disponibilidad de energía eléctrica de la Central Hidroeléctrica Itaipú y del crecimiento de la energía cedida al Paraguay hasta el 2023. Facultad Politécnica – Universidad Nacional del Este. DOI: 10.13140/RG.2.2.11838.79685.
  • Coronel Torales, E. D. (2015). Reliable navigation-path extraction system for an autonomous mobile vehicle. 2015 Tenth International Conference on Digital Information Management (ICDIM). DOI: 10.1109/icdim.2015.7381882.
  • Coronel Torales, E. D. (2015). PROTOTIPO DE VEHÍCULO AUTÓNOMO CON RNA Y VISIÓN POR COMPUTADORA. Simposio Argentino de Sistema Embebidos (SASE), Conference poster.
  • Coronel Torales, E. D. (2015). SISTEMA DE ALGORITMOS DE VISIÓN POR COMPUTADOR, APRENDIZAJE DE MÁQUINA, LOCALIZACIÓN Y NAVEGACIÓN DESARROLLADOS EN MATLAB, CON IMPLEMENTACIÓN EN VEHÍCULOS TERRESTRES PARA AUTO-CONDUCCIÓN. XXII Congreso Internacional de Ingeniería Eléctrica, Electrónica, Computación y Afines INTERCON 2015, Conference paper.
  • Coronel Torales, E. D. (2014). STABILITY COMMAND OF A TILT-ROTOR VEHICLE WITH A FUZZY LOGIC CONTROLLER. 3rd Conference of Computational Interdisciplinary Sciences – CCIS 2014, Conference poster. ISBN: 978-85-68888-00-1.
  • Coronel Torales, E. D. (2014). BALANCEADOR AERODINÁMICO CON LÓGICA DIFUSA. XXI Congreso Internacional de Ingeniería Electrónica, Eléctrica y Computación INTERCON 2014, Conference poster.

 

 

Yanming Zhao | Computer Science | Best Researcher Award

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Prof. Yanming Zhao | Computer Science | Best Researcher Award

Professor at Hebei MINZU Normal University, China

Yanming Zhao is a distinguished Professor at Hebei University of Nationalities, specializing in visual computing and deep neural networks. With a commitment to advancing technology and innovation, he has made significant contributions to the field of computer application technology, evidenced by his extensive research and numerous publications. 🌟

Profile 

Scopus Profile

Education🎓

Yanming graduated with a Master’s degree in Computer Application Technology from the School of Information at Shenyang University of Technology in 2010. His academic background laid a solid foundation for his future research endeavors and leadership in academia.

Experience🏛️💼

As a Master’s Supervisor and experienced researcher, Professor Zhao has participated in over nine provincial-level research projects and has consulted on over 500 industry projects. His work not only showcases his expertise but also his dedication to bridging the gap between academia and industry.

Research Interests🔬📈

Professor Zhao’s research primarily focuses on visual computing and deep neural networks. He has developed innovative algorithms, including the visual selectivity-based 3D graph convolutional algorithm (VS-3DGCN), aimed at enhancing point cloud segmentation performance and addressing key challenges in 3D graph convolutional algorithms.

Awards 🏆

Throughout his career, Yanming has received numerous accolades, including the title of Excellent Scientific and Technological Worker in Hebei Province and Outstanding Expert Managed by Chengde City. These awards reflect his significant contributions to the scientific community and his leadership in research.

Publications

Professor Zhao has published more than 30 academic papers in esteemed journals, such as:

  • Multi-channel depth segmentation network based on 3D graph convolution algorithm and its application in point cloud segmentation
    • Authors: Zhao, Y.
    • Journal: Alexandria Engineering Journal
    • Year: 2024
    • Citations: 0
  • The Multi-View Deep Visual Adaptive Graph Convolution Network and Its Application in Point Cloud
    • Authors: Fan, H., Zhao, Y., Su, G., Zhao, T., Jin, S.
    • Journal: Traitement du Signal
    • Year: 2023
    • Citations: 4
  • Graph Convolution Algorithm Based on Visual Selectivity and Point Cloud Analysis Application
    • Authors: Zhao, Y., Su, G., Yang, H., Jin, S., Yang, J.
    • Journal: Traitement du Signal
    • Year: 2022
    • Citations: 2
  • Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application
    • Authors: Yang, H., Zhao, Y., Su, G., Fan, H., Shang, Y.
    • Journal: Traitement du Signal
    • Year: 2021
    • Citations: 0
  • Design and application of a slow feature algorithm coupling visual selectivity and multiple long short-term memory networks
    • Authors: Zhao, Y., Yang, H., Su, G.
    • Journal: Traitement du Signal
    • Year: 2021
    • Citations: 1

These contributions have garnered a total citation index of 102 times, illustrating the impact of his work on the research community. 📚🔗

Conclusion🌍✨

In summary, Professor Yanming Zhao stands out as a leading figure in the fields of visual computing and deep learning. His extensive research, numerous publications, and accolades make him a deserving candidate for the Best Researcher Award. His ongoing commitment to innovation and excellence continues to inspire colleagues and students alike.