Francisco Javier Lima Florido | Artificial Intelligence | Best Researcher Award

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Mr Francisco Javier Lima Florido | Artificial Intelligence | Best Researcher Award

Researcher in training , University of Malaga , Spain

Francisco Javier Lima Florido is an accomplished researcher whose work in Machine Learning, Deep Learning, and Natural Language Processing has significant practical and academic merit. His focus on multilingual dialogue systems, health applications, and automatic interpretation solutions speaks to his expertise and potential to impact society through technology. As a PhD student, he is still developing his academic career but has already made noteworthy contributions to the field through participation in significant projects.

Publication Profile
scopus

Education :

Francisco Javier Lima Florido holds a Bachelor’s degree in Software Engineering from the University of Málaga (2016). He also earned a Master’s degree in Software Engineering and Artificial Intelligence from the same institution in 2019. Currently, Francisco is pursuing a PhD in the Translation and Interpreting Department at the University of Málaga, where his research is primarily focused on the intersection of technology and language.

Experience:

Francisco has actively participated in various research projects throughout his academic career. Notably, he was involved in the VIP: Integrated Voice-Text System for Interpreters project. This project explored the integration of voice and text systems for interpreters. Presently, he is contributing to cutting-edge projects like the Neural-based multilingual dialogue systems for the development of health apps (focusing on triage in Spanish, English, and Arabic) and the MI4ALL – Automatic Interpretation for All Using a Deep Learning-based API transfer project. These initiatives demonstrate his extensive experience in developing machine learning models for natural language processing (NLP).

Research Focus:

His primary research interests lie in the application of Machine Learning and Deep Learning techniques to Natural Language Processing (NLP). He is particularly focused on the development of multilingual dialogue systems and automatic interpretation technologies. His work aims to enhance the functionality and accessibility of tools for interpreters and healthcare applications, with a special interest in bridging communication gaps in multilingual settings.

Skills:

Francisco is highly skilled in several areas within Software Engineering and Artificial Intelligence, with a strong emphasis on Machine Learning and Deep Learning. His technical expertise spans:

    • Natural Language Processing (NLP)
    • Multilingual Dialogue Systems
    • Deep Learning Algorithms
    • Machine Learning Model Development
    • Speech-to-Text Technologies
    • Python Programming and related frameworks (e.g., TensorFlow, PyTorch)

 

Publication :

Francisco Javier Lima Florido has contributed to several research projects and publications in the fields of Machine Learning, Deep Learning, and Natural Language Processing. Notably:​

  1. “Mapping tillage direction and contour farming by object-based analysis of UAV images” (2021): This study, co-authored by Francisco J. Lima-Cueto, Rafael Blanco-Sepúlveda, María L. Gómez-Moreno, José Dorado, and José M. Peña, was published in Computers and Electronics in Agriculture. ​

  2. “Using Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain” (2019): Authored by Francisco J. Lima-Cueto, Rafael Blanco-Sepúlveda, María L. Gómez-Moreno, and Federico B. Galacho-Jiménez, this paper appeared in Remote Sensing. ​

Additionally, Francisco Javier Lima Florido has been involved in research projects such as “VIP: Integrated Voice-Text System for Interpreters” and is currently participating in “Neural-based multilingual dialogue systems for the development of health apps: triage (Spanish – English/Arabic)” and the transfer project “MI4ALL – Automatic Interpretation For All Using a Deep Learning-based API”.

conclusion:

Francisco is highly deserving of consideration for the “Best Researcher Award.” His expertise in cutting-edge AI technologies, especially in the context of language translation and interpretation, holds immense potential for positive social impact. While there are areas for improvement, such as enhancing his publication record and broadening his collaborative network, his current research trajectory shows great promise. His ongoing contributions to AI research and application indicate that he is on a path to becoming a leading figure in the field.

Jaehwan Jeong | Generative AI models | Best Researcher Award

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Mr. Jaehwan Jeong | Generative AI models | Best Researcher Award

Ph.D Student,Korea University,South Korea

Jaehwan Jeong is an emerging researcher in AI and computer vision, with a strong academic background and collaborations with top institutions. His work in deepfake defense and generative models positions him well for awards in AI safety and multi-modal learning. However, securing additional accepted publications and leading independent research could further bolster his case for the Best Researcher Award.

Publication Profile
Google Scholar

Education :

Jaehwan Jeong is currently pursuing a Ph.D. in Artificial Intelligence at Korea University, Seoul, South Korea (2024–2029, expected). He completed his Bachelor of Engineering (B.E.) in Electrical & Electronic Engineering from Chung-Ang University, Seoul, in 2021. His academic journey has been focused on artificial intelligence, deep learning, and computer vision.

Experience:

Research Focus:

Skills:

Jaehwan possesses strong expertise in:,Programming: Python, Shell Scripting, Git, LaTeX,Deep Learning Frameworks: PyTorch, PyTorch Lightning, TensorFlow,AI & ML Libraries: Hugging Face, Scikit-Learn

Publication :

  • MTVG: Multi-text Video Generation with Text-to-Video Models

    • Authors: Gyeongrok Oh, Jaehwan Jeong, Sieun Kim, Wonmin Byeon, Jinkyu Kim, Sungwoong Kim, Hyeokmin Kwon, Sangpil Kim
    • Publication: arXiv preprint arXiv:2312.04086
    • Year: 2023
    • Citation: Oh, G., Jeong, J., Kim, S., Byeon, W., Kim, J., Kim, S., Kwon, H., & Kim, S. (2023). MTVG: Multi-text Video Generation with Text-to-Video Models. arXiv preprint arXiv:2312.04086.
      kuai-lab.github.io

  • MEVG: Multi-event Video Generation with Text-to-Video Models

    • Authors: Gyeongrok Oh, Jaehwan Jeong, Sieun Kim, Wonmin Byeon, Jinkyu Kim, Sungwoong Kim, Sangpil Kim
    • Publication: European Conference on Computer Vision (ECCV), pages 401–418
    • Year: 2025
    • Citation: Oh, G., Jeong, J., Kim, S., Byeon, W., Kim, J., Kim, S., & Kim, S. (2025). MEVG: Multi-event Video Generation with Text-to-Video Models. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Computer Vision – ECCV 2024 (pp. 401–418). Springer.
      pure.korea.ac.kr

  • FaceShield: Defending Facial Image against Deepfake Threats

    • Authors: Jaehwan Jeong, Seungmin In, Sieun Kim, Hyojin Shin, Jaeho Jeong, Seunghyun Yoon, Jaewon Chung, Sungwoong Kim
    • Publication: arXiv preprint arXiv:2412.09921
    • Year: 2024
    • Citation: Jeong, J., In, S., Kim, S., Shin, H., Jeong, J., Yoon, S., Chung, J., & Kim, S. (2024). FaceShield: Defending Facial Image against Deepfake Threats. arXiv preprint arXiv:2412.09921.
Conclusion:

Jeong is a strong candidate for the award but would benefit from more accepted publications and demonstrated leadership in independent research. His ongoing Ph.D. work and collaborations with Samsung, NVIDIA, and Google make him a promising researcher with significant potential.

Saba Inam | machine learning | Women Researcher Award

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Dr.Saba Inam |machine learning| Women Researcher Award

Dr Saba InamFatima Jinnah women university, The Mall, Rawalpindi, Pakistan.

Dr. Saba Inam is a lecturer in the Department of Mathematical Sciences at Fatima Jinnah Women University in Rawalpindi, Pakistan. She earned her PhD in Algebraic Cryptography from the Capital University of Science and Technology, completing her studies between February 2014 and January 2019. Her research interests include Algebraic Number Theory, Algebraic Cryptography, Applied Cryptography, image encryption, Cloud Computing, Machine Learning, and Deep Learning. Dr. Inam has contributed to over 20 publications, accumulating 247 citations, and her work has garnered more than 3,367 reads. Notably, she co-authored the article “An efficient image encryption algorithm using 3D-cyclic Chebyshev map and elliptic curve,” published in November 2024.

Publication Profile

Google Scholar

Orcid

Education :

Dr. Saba Inam holds a PhD in Mathematics from Capital University of Science and Technology (CUST), Islamabad (2019). She completed her MS in Mathematics from COMSATS Institute of Information Technology, Islamabad, in 2007 with a CGPA of 3.6/4, achieving 1st Division. She earned her M.Sc. in Mathematics from Quaid-i-Azam University, Islamabad (2005), and her B.Sc. in Mathematics (Maths A, Maths B, Stats) from the University of the Punjab (2003), both with 1st Division.

Experience :

Dr. Inam has extensive academic and research experience. Since September 2007, she has been serving as a Lecturer in Mathematics at Fatima Jinnah Women University, Rawalpindi. She also held the position of Incharge, Department of Mathematical Sciences from August 2016 to January 2018. Before that, she worked as a Research Associate at COMSATS Institute of Information Technology, Islamabad, from March to August 2007.

Research Focus :

Dr. Inam’s research interests span across multiple domains, including:

Cryptography & Security: Algebraic Cryptography, Cryptology, CryptanalysisAI & Data Security: Image Encryption, Blockchain, IoT, Deep Learning, Machine LearningMathematical Sciences: Fluid Mechanics, Geometric Function Theory.

 

Awards:

Dr. Inam’s academic excellence has been recognized through various awards and honors:

Scholarship – Capital University of Science and Technology (CUST), Islamabad (2013-2018)Dean’s Roll of Honor – Received twice during PhD courseworkDiploma in Academic Excellence in Discrete Mathematics – Abdul Salam School of Mathematical Sciences, GC University, Lahore (2012)Scholarship – COMSATS Institute of Information Technology, Islamabad (2005-2007)

Skills:

Dr. Inam possesses expertise in:Programming & Computational Tools: Matlab, Python, Mathematica, APCoCoA, Scientific Workplace, LaTeXOffice & Documentation: Proficient in Microsoft Office Suite,Dr. Saba Inam continues to contribute significantly to the fields of cryptography, image encryption, and mathematical security frameworks, with a strong focus on deep learning and blockchain applications.

Publication :

 

Three dimensional boundary layer flow of a viscoelastic nanofluid with Soret and Dufour effects

Numerical simulation of Darcy–Forchheimer 3D unsteady nanofluid flow comprising carbon nanotubes with Cattaneo–Christov heat flux and velocity and thermal slip conditions

An effective color image encryption based on Henon map, tent chaotic map, and orthogonal matrices

A new ElGamal-like cryptosystem based on matrices over groupring

Analytic study of a novel color image encryption method based on the chaos system and color codes

Blockchain based medical image encryption using Arnold’s cat map in a cloud environment

A numerical study of rotating Bödewadt flow of micropolar fluid over porous disk

A Novel Public Key Cryptosystem and Digital Signatures

A new method of image encryption using advanced encryption Standard (AES) for network security

Research on color image encryption approach based on chaotic Duffing map

Conclusion

Dr. Inam’s strengths, including her academic excellence, research impact, and leadership in academia, position her as a strong candidate for the “Research for Women Researcher Award.” While there are areas to enhance collaboration and public outreach, her work is already shaping critical developments in data security and encryption. Recognizing her would not only celebrate her achievements but also inspire more women to pursue careers in research and technology, contributing significantly to both academic and societal progress.

Deepshikha Bhati |Computer Aided Design In Mechanical Engineering | Best Researcher Award

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Ms.Deepshikha Bhati |Computer Aided Design In Mechanical Engineering | Best Researcher Award

Ms, Deepshikha Bhati；Kent State University, United States.

Deepshikha Bhati is a dedicated graduate student at Kent State University, where she is pursuing her academic interests with a focus on [specific field or research area, if known]. With a strong foundation in [relevant skills or previous education], she is passionate about advancing her knowledge and contributing to innovative solutions in her field. Deepshikha is known for her collaborative spirit and commitment to academic excellence, actively engaging in research projects and campus initiatives that promote learning and community involvement.

Summary:

The candidate’s extensive academic and teaching experience, coupled with a strong foundation in relevant research areas, positions them as a worthy contender for the Research for Best Researcher Award. Their commitment to applying advanced technologies to solve real-world problems, alongside a proactive approach to seeking collaboration and funding, underscores their potential to drive innovation in their field.

Professional Profiles:

Google Scholar

🎓 Education :

I am currently pursuing a Ph.D. in Computer Science at Kent State University, focusing on my dissertation titled “Semantic Features Based Explanation of Image Classification and Tools for Geographical Multimedia Data.” I hold a Master of Technology in Computer Science from Dr. A.P.J. Abdul Kalam Technical University, India, which I completed in 2017. I also earned my Bachelor of Technology in Computer Science from the same university in 2014.

🏢 Experience:

Since August 2022, I have been serving as a Full-Time Non-Tenure Track Lecturer in the Department of Computer Science at Kent State University, Stark Campus. I have taught a range of courses, including CS III Programming Patterns, Intro to Database System Design, and Algorithm and Programming. My prior roles include part-time instructor and graduate assistantship positions within the same department, where I contributed to courses on Computer Science Principles and various advanced topics. Additionally, I gained practical experience as a Summer Software Developer, working on the “Geo Visuals Mobile Application (GVM app).”

🛠️Skills:

I possess strong programming skills in languages such as C, C++, Python, Java, Embedded C, PHP, Swift, and Kotlin. My web design skills include HTML, CSS, and JavaScript, while I am proficient in tools like MATLAB, SciLab, Visual Studio, and Android Studio. My expertise extends to database management systems such as SQLite, Realm, and MongoDB, along with data analysis and visualization tools including Pandas, NumPy, and Matplotlib. Additionally, I am well-versed in machine learning frameworks such as OpenCV, Scikit-learn, and TensorFlow, and have experience with deep learning architectures including CNNs, RNNs, and Transformers.

Research Focus :

My research interests encompass Information Visualization, Image Processing, Deep Learning, Machine Learning, and Mobile Computing. I actively participate in the Graphics and Visualization Research Group at Kent State University under the guidance of Prof. Ye Zhao.

🔬Awards:

Throughout my academic career, I have received numerous accolades, including the Fall 2024 PAAC Travel Award for participation in the IEEE 7th International Conference on Knowledge Innovation and Invention in Japan, and the Best Conference Paper Awards for two separate papers presented at the IEEE International Conference on Knowledge Innovation and Invention in 2024. I have also been nominated for the Distinguished Teaching Award at Kent State University and have received several travel awards for participation in international conferences.

Conclusion:

Overall, this profile indicates a dedicated and capable researcher whose work aligns well with the goals of the Research for Best Researcher Award. Addressing the highlighted areas for improvement, particularly in research publication and collaboration, would further bolster their candidacy. With continued effort in these domains, the candidate has the potential to make significant contributions to both academic research and practical applications in computer science.

Publications :

  • Title: Survey—A comparative analysis of face recognition technique
    Authors: D Bhati, V Gupta
    Year: 2015
    Cited by: 16

 

  • Title: A Multimodal Conversational Interface to Support the creation of customized Social Stories for People with ASD
    Authors: DB Rita Francese, Angela Guercio, Veronica Rossano
    Year: 2022
    Cited by: 7*

 

  • Title: VisualCommunity: a platform for archiving and studying communities
    Authors: S Jamonnak, D Bhati, M Amiruzzaman, Y Zhao, X Ye, A Curtis
    Year: 2022
    Cited by: 5

 

  • Title: Current Advances in Locality-Based and Feature-Based Transformers: A Review
    Authors: A Srivastava, M Chandra, A Saha, S Saluja, D Bhati
    Year: 2024
    Cited by: 3

 

  • Title: DNA Sequence in Cryptography: A Study
    Authors: R Joshi, MC Trivedi, V Goyal, D Bhati
    Year: 2022
    Cited by: 3

 

  • Title: Interactive Visualization and Capture of Geo-Coded Multimedia Data on Mobile Devices
    Authors: D Bhati, M Amiruzzaman, S Jamonnak, Y Zhao
    Year: 2022
    Cited by: 3

 

  • Title: Face Recognition Stationed on DT-CWT and Improved 2DPCA employing SVM Classifier
    Authors: D Bhati
    Year: 2017
    Cited by: 3

 

  • Title: BookMate: Leveraging Deep Learning to Empower Caregivers of People with ASD in Generation of Social Stories
    Authors: D Bhati, A Guercio, V Rossano, R Francese
    Year: 2023
    Cited by: 2

 

  • Title: Recent Trends for Practicing Steganography Using Audio as Carrier: A Study
    Authors: R Joshi, MC Trivedi, V Goyal, D Bhati
    Year: 2022
    Cited by: 2

 

  • Title: Exploring Fine-Grained Feature Analysis for Bird Species Classification using Layer-wise Relevance Propagation
    Authors: K Arquilla, ID Gajera, M Darling, D Bhati, A Singh, A Guercio
    Year: 2024
    Cited by: 1

 

  • Title: Visualizing Routes with AI-Discovered Street-View Patterns
    Authors: TH Wu, M Amiruzzaman, Y Zhao, D Bhati, J Yang
    Year: 2024
    Cited by: 1

 

  • Title: A Survey on Explainable Computer Aided Design In Mechanical Engineering (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging
    Authors: D Bhati, FNU Neha, M Amiruzzaman
    Year: 2024
    Cited by: 0

 

  • Title: Large Language Model-Driven Immersive Agent
    Authors: A Singh, S Kumar, A Ehtesham, TT Khoei, D Bhati
    Year: 2024
    Cited by: 0

 

  • Title: Predictive Analytics in Law Enforcement: Unveiling Patterns in NYPD Crime through Machine Learning and Data Mining
    Authors: JS Kumar, M Amiruzzaman, AA Bhuiyan, D Bhati
    Year: 2024
    Cited by: 0

 

  • Title: Solving Classification Problem using Reduced Dimension and Eigen Structure in RSVM
    Authors: M Pal, D Bhati, B Kaushik, H Banka
    Year: 2017
    Cited by: 0

 

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