Mahmoud Marhamati |Artificial | Best Researcher Award

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Mr.Mahmoud Marhamati |Artificial | Best Researcher Award

Mr. Mahmoud Marhamati ,PhD candidate in Tehran University of Medical Science, Iran

Mr. Mahmoud Marhamati is a PhD candidate at Tehran University of Medical Sciences in Iran. His research focuses on advancing medical science through innovative studies in his field. He is dedicated to contributing to healthcare improvements and academic excellence, and is actively involved in both research and academic pursuits at the university.

Summary:

Strengths: Innovation in noisy data augmentation, high-impact publications, interdisciplinary collaboration, and significant contributions to COVID-19 research.,Areas for Improvement: Diversifying AI research topics and enhancing recent paper visibility.

Professional Profiles:

Google Scholar

🎓 Education :

M. Marhamati holds an advanced degree in medical sciences, specifically focusing on computational medicine and biomedical research. This background includes a strong foundation in medical imaging, computational biology, and clinical research, preparing them to contribute significantly to the application of artificial intelligence in healthcare. The educational path also demonstrates a blend of medical knowledge with a deep understanding of technological advancements in disease detection, management, and clinical trials.

🏢 Experience:

M. Marhamati has a rich experience in the intersection of healthcare and technology, particularly in the development and enhancement of machine learning and deep learning algorithms for medical image analysis. Their work spans from the detection of COVID-19 using X-ray and CT images to research in chronic disease management, leveraging the Internet of Things (IoT). They have also contributed to clinical research, including trials related to the efficacy of various medical interventions, such as intravenous catheter patency, airway monitoring during CPR, and pain management in medical procedures.

🛠️Skills:

Deep Learning & Machine Learning: Extensive experience in applying deep convolutional neural networks (CNNs) for medical image analysis, including the detection of COVID-19 and tuberculosis.,Medical Imaging Analysis: Expertise in X-ray and CT image processing, particularly for respiratory diseases like COVID-19.,Clinical Research & Trials: Proven track record in designing and conducting clinical trials, focusing on novel therapeutic interventions and medical devices.,Biomedical Research: Ability to bridge clinical practice with cutting-edge research, with publications in both medical and technical fields.,Data Augmentation & Noise Handling: Experience in developing noise-robust deep learning models and augmentation strategies to improve model generalization.,Internet of Things (IoT) in Healthcare: Knowledge in integrating IoT technologies for chronic disease management, particularly during the COVID-19 pandemic.

🔬Awards:

Throughout their career, M. Marhamati has received recognition for their innovative work in applying deep learning to medical image analysis. Their research has been published in top-tier journals, and they have been acknowledged for their contributions to improving the detection of diseases like COVID-19 and tuberculosis through AI-based models. Additionally, their clinical research has garnered attention for improving patient care practices.

Research Focus:

M. Marhamati’s research is primarily focused on the application of deep learning and AI in medical imaging and healthcare. One key area is the development of noise-robust deep convolutional neural networks (CNNs) for the detection of COVID-19 and tuberculosis from X-ray and CT images. They have also pioneered strategies for learning-to-augment methods to enhance the generalizability of CNN models in noisy environments. Moreover, their work extends to the integration of IoT in healthcare, exploring its role in managing chronic diseases, especially during pandemics.

Conclusion:

Mr. Mahmoud Marhamati is a highly suitable candidate for the Best Researcher Award due to his innovative contributions to AI in medical imaging, particularly in the detection and management of COVID-19. His interdisciplinary approach, impactful publications, and focus on real-world healthcare applications position him as a forward-thinking researcher who exemplifies excellence in combining AI with healthcare innovation. To further strengthen his candidacy, expanding into other AI applications beyond COVID-19 and seeking leadership opportunities would broaden his impact

Publications :

  • Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images
    • Authors: M. Momeny, A.A. Neshat, M.A. Hussain, S. Kia, M. Marhamati, et al.
    • Journal: Computers in Biology and Medicine
    • Year: 2021
    • Citations: 67

 

  • Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images
    • Authors: A. Akbarimajd, N. Hoertel, M.A. Hussain, A.A. Neshat, M. Marhamati, et al.
    • Journal: Journal of Computational Science
    • Year: 2022
    • Citations: 36

 

  • Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy
    • Authors: M. Momeny, A.A. Neshat, A. Gholizadeh, A. Jafarnezhad, E. Rahmanzadeh, et al.
    • Journal: Computers in Biology and Medicine
    • Year: 2022
    • Citations: 32

 

  • Retracted: Internet of things in the management of chronic diseases during the COVID‐19 pandemic: A systematic review
    • Authors: A. Shamsabadi, Z. Pashaei, A. Karimi, P. Mirzapour, K. Qaderi, M. Marhamati, et al.
    • Journal: Health Science Reports
    • Year: 2022
    • Citations: 27

 

  • LAIU-Net: a learning-to-augment incorporated robust U-Net for depressed humans’ tongue segmentation
    • Authors: M. Marhamati, A.A.L. Zadeh, M.M. Fard, M.A. Hussain, K. Jafarnezhad, et al.
    • Journal: Displays
    • Year: 2023
    • Citations: 18

 

  • Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT
    • Authors: M.A. Hussain, Z. Mirikharaji, M. Momeny, M. Marhamati, A.A. Neshat, R. Garbi, et al.
    • Journal: Computerized Medical Imaging and Graphics
    • Year: 2022
    • Citations: 11

 

  • Comparing Serum Levels of Vitamin D and Zinc in Novel Coronavirus–Infected Patients and Healthy Individuals in Northeastern Iran, 2020
    • Authors: S.J. Hosseini, B. Moradi, M. Marhamati, A.A. Firouzian, E. Ildarabadi, A. Abedi, et al.
    • Journal: Infectious Diseases in Clinical Practice
    • Year: 2021
    • Citations: 6

 

  • Comparing the effects of pulsatile and continuous flushing on time and type of peripheral intravenous catheters patency: a randomized clinical trial
    • Authors: S.J. Hosseini, F. Eidy, M. Kianmehr, A.A. Firouzian, F. Hajiabadi, M. Marhamati, et al.
    • Journal: Journal of Caring Sciences
    • Year: 2021
    • Citations: 4

 

  • Emergency Medical Service Personnel Satisfaction Regarding Ambulance Service Facilities and Welfare
    • Authors: A. Jesmi, H.M. Ziyarat, M. Marhamati, T. Mollaei, H. Chenari
    • Journal: Iranian Journal of Emergency Medicine
    • Year: 2015
    • Citations: 2

 

  • Patient’s airway monitoring during cardiopulmonary resuscitation using deep networks
    • Authors: M. Marhamati, B. Dorry, S. Imannezhad, M.A. Hussain, A.A. Neshat, et al.
    • Journal: Medical Engineering & Physics
    • Year: 2024
    • Citations: 1

 

  • Comparison of using cold versus regular temperature tube on successful nasogastric intubation for patients in toxicology emergency department: a randomized clinical trial
    • Authors: S.R. Mazlom, A.A. Firouzian, H.M. Norozi, A.G. Toussi, M. Marhamati
    • Journal: Journal of Caring Sciences
    • Year: 2020
    • Citations: 1

 

  • Comparison of using cooled and regular-temperature nasogastric tubes on the success of nasogastric intubation
    • Authors: S. Mazlom, M. Marhamati, H. Norozi, A. Ghasemi Toosi
    • Journal: Evidence Based Care
    • Year: 2015
    • Citations: 1

 

 

Assoc Prof Dr.Yong Liang |edge intelligence| Best Researcher Award

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Assoc. Prof Dr.Yong Liang |edge intelligence| Best Researcher Award

Assoc Prof Dr.Yong Liang , College of Mechanical and Control Engineering, Guilin University of TechnologyChina

Assoc Prof Dr. Yong Liang is a distinguished academic at the College of Mechanical and Control Engineering, Guilin University of Technology, China. With a robust background in mechanical engineering, Dr. Liang’s research interests encompass advanced manufacturing technologies, robotics, and control systems.

Summary:

Assoc. Prof. Dr. Yong Liang is a dedicated researcher and educator with a robust academic background and a focused research agenda in emerging fields such as small sample learning, edge intelligence, and intelligent robots. His recent accolades, including the Chapek Award, reflect his significant contributions to integrating robotics education with industry needs. Dr. Liang’s work is both innovative and impactful, making him a strong candidate for the Best Researcher Award.

 

Professional Profiles:

Orcid

Education :

Mr. Yong Liang is a dedicated individual on a journey of continuous learning and academic achievement. He pursued his undergraduate degree at Wuhan Science and Technology Institute in 2005, laying a solid foundation in technology and engineering. Furthering his education, he earned a Master’s degree from Guilin University of Electronic and Technology in 2008, where he deepened his knowledge in electronic engineering. His thirst for knowledge did not stop there; in 2016, he received a Ph.D. degree from the College of Mechanical and Electronic Engineering, Northwest A&F University. His educational path reflects a commitment to the ever-evolving field of technology, showcasing a passion for electronic engineering and a determination to delve deeper into the world of intelligent robots.

Professional Skills:

Yong Liang not only excels academically but also possesses outstanding skills that have earned him recognition in the professional world. In 2024, he was awarded the prestigious Chapek Award, which honors excellence in integrating robotics education and the robotics industry, particularly as an Industry Education Integration Teacher. Over the past three years, he has published six research papers focusing on small sample learning, edge computing, FPGA, and related fields. His skills, awards, and achievements reflect a deep passion for learning and a commitment to excellence, setting him apart as a leader in his field.

Experience:

Mr. Yong Liang’s professional journey is marked by a continuous pursuit of growth and innovation. After earning his Master’s degree in 2008, he joined a college where he taught electronic courses such as Analog Electronics Technology, Digital Electronic Technology, Microcontroller Systems, and FPGA Principles and Applications. However, realizing that his technological perspective needed to be broader, he left academia to pursue a doctoral degree. Upon completing his Ph.D. in 2016, he joined Guilin University of Technology as both a teacher and a researcher. There, he formed an intelligent robot research team, which has made significant achievements in the field over the years. His experience highlights a blend of teaching and research, underpinned by a continuous drive to innovate and expand his expertise.

Research Focus:

Mr. Yong Liang is a tech enthusiast with a keen interest in cutting-edge advancements in small sample learning, intelligent robots, edge intelligence, and Green AI. His research explores the intersections of artificial intelligence and edge computing with a focus on low-power requirements. He is particularly interested in decentralized edge intelligence and collaborative deep learning, areas that promise to revolutionize how we think about and implement intelligent systems. His dedication to these fields reflects a commitment to shaping the future of technology, where seamless connectivity and intelligent systems pave the way for transformative innovations.

Conclusion:

Dr. Yong Liang is highly suitable for the Best Researcher Award. His strengths in cutting-edge research, educational contributions, and recent recognition align well with the award criteria. To further enhance his candidacy, he could focus on broadening his technological perspective through interdisciplinary research and increasing his publication volume and international collaborations. Overall, his achievements and commitment to advancing technology through research make him a deserving candidate for this prestigious award.

 

Publications :

 

  1. Research on Convolutional Neural Network Inference Acceleration and Performance Optimization for Edge Intelligence
    Journal: Sensors
    Published Year: 2024

 

  1. Research and Implementation of Adaptive Stereo Matching Algorithm Based on ZYNQ
    Journal: Journal of Real-Time Image Processing
    Published Year: 2024