Zubair Akhtar Mohd | Artificial Intelligence | Best Researcher Award

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Mr. Zubair Akhtar Mohd | Artificial Intelligence | Best Researcher Award

 Mr. Zubair Akhtar Mohd, Technische Hochschule Ingolstadt, Germany

Mr. Zubair Akhtar Mohd is a Research Associate at Technische Hochschule Ingolstadt, Germany, specializing in automotive engineering and artificial intelligence applications in predictive modeling and manufacturing optimization. He holds a Master’s degree in Automotive Engineering from THI and a Bachelor’s in Mechanical Engineering from Aligarh Muslim University, India. His research focuses on integrating Finite Element Analysis (FEA) with AI, using advanced machine learning algorithms like CNNs and RNNs to forecast the lifespan of electronic components. Mr. Mohd is also involved in scientific projects, data generation for materials testing, and academic teaching in CAD and simulation.

 

Professional Profiles:

 

🎓 Education :

Holds a Master’s degree in Automotive Engineering from Technische Hochschule Ingolstadt, Germany, with a GPA of 1.9, focusing on production optimization and AI in automotive systems. Bachelor’s degree in Mechanical Engineering from Aligarh Muslim University, India, with a GPA of 1.6, specializing in vehicle technology and CAD/CAE programming.

 

🏢 Experience:

Currently working as a Research Associate at the Institute of Innovative Mobility, Technische Hochschule Ingolstadt, focusing on method development for predicting electronics component lifespan using deep learning. Previously employed as a working student at CADS Engineering GmbH, contributing to vehicle design and occupant protection research, and as an Industrial Engineer in India, implementing safety and efficiency improvements in manufacturing processes.

🛠️Skills:

Proficient in Python, TensorFlow, PyTorch, and Linux, with additional expertise in tools such as Git, JavaScript, and MS Office applications. Experience with HTML, CSS, and Carla, and extensive knowledge in engineering software like NX CAD, Ansys, and Tableau. Fluent in English and German at B2 level, alongside native proficiency in Hindi.

 

Research Focus :

Specialized in the integration of Finite Element Analysis (FEA) simulation data with deep learning for predictive modeling. Research includes advanced deep learning models, such as CNNs and RNNs, and generative forecasting with VQ-VAE. Emphasis on machine learning algorithms for materials inspection and automated data collection.

 

🔬Awards:

Awarded various certifications, including specialization in self-driving car technologies from the University of Toronto and advanced machine learning courses from DeepLearning.AI. Actively involved in technical leadership roles, such as Technical Coordinator for AMU’s national college fest and team leader for the American Society of Mechanical Engineers (ASME). Published work in Springer Publications on ergonomics for productivity improvement.

Conclusion:

Mr. Zubair Akhtar Mohd’s interdisciplinary skills, innovative research focus, and dedication to academia make him a deserving candidate for the Best Researcher Award. With potential to expand his publication record and increase his collaborative efforts, Mr. Mohd’s career trajectory reflects both current excellence and promise for further significant contributions to engineering and AI research.

 Publications:

Deepshikha Bhati |Artificial Intelligence | Best Researcher Award

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Ms.Deepshikha Bhati |Artificial Intelligence | 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 Artificial Intelligence (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