Cheng-Mao Zhou | Artificial intelligence | Best Researcher Award

Dr. Cheng-Mao Zhou | Artificial intelligence | Best Researcher Award 

Dr. Cheng-Mao Zhou, Central People’s Hospital of Zhanjiang, China

Dr. Cheng-Mao Zhou is a distinguished medical professional affiliated with the Central People’s Hospital of Zhanjiang, China. With extensive expertise in clinical practice and research, Dr. Zhou specializes in advancing patient care through innovative medical treatments and health management strategies. His contributions to the medical field reflect a commitment to excellence and the well-being of his community.

Profile

Orcid

Scopus

🎓 Educational Qualification

Dr. Cheng-Mao Zhou’s academic journey is deeply rooted in a passion for innovation and excellence in medical science. From his formative years, he demonstrated an extraordinary aptitude for problem-solving and an eagerness to explore the intersection of technology and healthcare. His early academic focus on applied research set the foundation for his current expertise in artificial intelligence (AI) and perioperative medicine. By integrating traditional medical practices with emerging AI technologies, Dr. Zhou carved out a unique niche, positioning himself as a leader in predictive healthcare solutions.

🩺 Professional Endeavors

Currently serving at the Central People’s Hospital of Zhanjiang, Dr. Zhou has applied his expertise to revolutionize perioperative care. His professional trajectory reflects a seamless blend of clinical practice and research, where he employs machine learning and deep learning algorithms to address critical challenges in postoperative complication prediction and prevention. Over the years, he has developed and implemented cutting-edge AI models that significantly enhance diagnostic accuracy and patient outcomes, making complex healthcare processes more efficient and reliable.

🧠 Contributions and Research Focus

Dr. Zhou’s research centers on leveraging artificial intelligence to solve pressing issues in perioperative medicine. His focus areas include:

  1. Postoperative Complication Prediction
  2. Integration of AI in Medicine
  3. Educational Contributions

🏆 Accolades and Recognition

Dr. Zhou’s dedication and contributions have not gone unnoticed. Among his many affiliations, his membership with esteemed organizations like the American Society for Honorary Scientific Research (Sigma Xi) and the Big Data Group of Anesthesiology Branch of the Chinese Medical Association underscore his credibility and influence. Furthermore, his position as a young member of the Comfort Medical Branch of the China Cardiovascular Anesthesia Society reflects his commitment to advancing comfort-focused medical practices.

His academic contributions have been recognized in prestigious journals, with 40 publications indexed in SCI and Scopus. His citation index of 13 highlights the scholarly impact of his work on the global research community.

🌟 Impact and Influence

Dr. Zhou’s influence transcends the realm of academia. His innovative methodologies have significantly enhanced clinical practices, offering a roadmap for integrating AI into everyday medical care. By addressing critical challenges such as delayed diagnoses and suboptimal postoperative management, his work has led to improved patient safety and healthcare efficiency.

Through his articles and clinical implementations, Dr. Zhou has effectively raised awareness about the practical potential of AI in medicine, inspiring fellow researchers and healthcare practitioners to adopt data-driven solutions. His contributions serve as a blueprint for hospitals and medical institutions aiming to optimize their operational processes through technology.

🌍 Legacy and Future Contributions

Dr. Zhou envisions a future where AI seamlessly integrates with medical systems to revolutionize patient care worldwide. His ongoing research projects aim to further refine AI algorithms for predicting and preventing a broader range of complications. Beyond innovation, he is committed to mentoring young researchers, fostering interdisciplinary collaborations, and advocating for ethical AI practices in medicine.

📝Publication Top Notes

Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology.

Contributors: Zhou CM; Wang Y; Yang JJ; Zhu Y

Journal: BMC medical informatics and decision making
Year: 2023

Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms.

ContributorsChengmao Zhou; Wang Y; Xue Q; Zhu Y
Journal: Cancer control
Year: 2023

Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer

Contributors: Zhou, C.-M.; Wang, Y.; Ye, H.-T.; Yan, S.; Ji, M.; Liu, P.; Yang, J.-J.
Journal: Scientific Reports
Year: 2021

Mohamed Reda Shoeib | Artificial Intelligence | Best Researcher Award

Dr. Mohamed Reda Shoeib | Artificial Intelligence | Best Researcher Award

Nanyang Technological University at School of Computer Science and Engineering, Nanyang Technological University, Singapore.

Mohamed R. Shoaib is a dedicated Machine Learning and Data Scientist Engineer with extensive expertise in AI and its applications. He holds a Master’s degree in Engineering Science from Menoufia University and is currently pursuing a PhD at Nanyang Technological University. Mohamed has received certifications from Udacity, DataCamp, IBM, and Udemy, demonstrating proficiency in machine learning, deep learning, NLP, and AI. He has a rich professional background, including his role at Shgardi Company where he works on recommendation systems, fraud detection, and user interaction analysis. His research interests span the utilization of AI in biomedical applications, smart agriculture, and global food security. Mohamed’s skills encompass machine learning, deep learning, computer vision, NLP, and embedded systems, making him a valuable asset in the AI and data science community.

Professional Profiles:

Education 🎓

Mohamed R. Shoaib is currently pursuing his Doctor of Philosophy at Nanyang Technological University (NTU), Singapore, within the School of Computer Science and Engineering (SCSE). He began this journey in January 2023, focusing his research on Machine Learning, Data Science, and Artificial Intelligence, particularly their applications in Biomedical, Agriculture, and communication sectors. Prior to this, Mohamed completed his Master’s Degree in Engineering Science from the Faculty of Engineering, Menoufia University, from October 2019 to March 2022. His thesis centered on the Utilization of Artificial Intelligence Techniques in Healthcare Applications, earning a Pre-Master GPA of 3.46/4. Additionally, Mohamed holds a Diploma in Artificial Intelligence from the Information Technology Institute (ITI), which he completed in collaboration with EPITA School of Engineering and Computer Science, between April 2021 and January 2022.

Professional Experience 💼

Mohamed R. Shoaib is currently working as a full-time Machine Learning Engineer and Data Scientist at Shgardi Company, a position he has held since February 2022. In this role, he leverages Amazon Personalized tools and time-series data to enhance recommendation systems. He is also involved in developing solutions to detect fraud and fake user interactions. Alongside his professional role, Mohamed is a full-time researcher at Nanyang Technological University (NTU), Singapore, where he focuses on applying AI in Biomedical, Agriculture, and communication applications. His academic and professional endeavors demonstrate a robust integration of advanced AI techniques to address practical challenges in various fields.

Research Interest 🔍

Mohamed R. Shoaib’s research interests lie at the intersection of machine learning, data science, and artificial intelligence, with a specific focus on their applications in healthcare and environmental fields. He is particularly interested in utilizing artificial intelligence techniques for critical environmental applications, including smart agriculture and satellite imagery analysis. His work also extends to developing deep learning models for biomedical applications, such as brain tumor diagnosis, and enhancing recommendation systems using advanced AI tools. Mohamed is dedicated to advancing the practical applications of AI to solve real-world problems, improve healthcare outcomes, and contribute to sustainable environmental practices.

Awards and Honors 🏆

Mohamed R. Shoaib has received several prestigious awards and honors recognizing his contributions to the fields of machine learning, data science, and artificial intelligence. His outstanding academic and research performance has earned him the Global Health Impact Award 2024 for his innovative work in applying AI to healthcare. He was also recognized by Udacity with a Nanodegree certification in AWS Machine Learning Foundations in 2021, demonstrating his expertise in leveraging cloud-based tools for AI applications. Additionally, Mohamed has been honored by DataCamp and IBM for his proficiency in data science, machine learning, and natural language processing, having completed extensive certification programs in these areas. These accolades reflect his commitment to advancing the field of AI and his exceptional skills in utilizing AI techniques for impactful and transformative solutions.

Research Skills 🧠

Mohamed R. Shoaib is highly skilled in machine learning, deep learning, and AI, specializing in areas such as transfer learning, object detection, and time series forecasting. He excels in computer vision, image processing, NLP, and recommender systems, with additional strengths in reinforcement learning and embedded systems. Proficient in C, Python, and MATLAB, Mohamed is also adept at using AI frameworks like TensorFlow, Keras, and Scikit-learn, as well as Big Data tools like Spark. His problem-solving abilities and expertise in data visualization and dashboard creation with Plotly enhance his capability to develop innovative solutions. Mohamed’s broad skill set equips him to address complex AI challenges and contribute significantly to the field.

Publications

  1. Efficient Framework for Brain Tumor Detection Using Different Deep Learning Techniques
    • Authors: F Taher, M Shoaib, HM Emara, KM Abdelwahab, A El-Samie, E Fathi, …
    • Journal: Frontiers in Public Health
    • Year: 2022
    • Citations: 22
  2. Deep convolutional neural networks for COVID‐19 automatic diagnosis
    • Authors: Emara HM, Shoaib MR, Elwekeil M, El‐Shafai W, Taha TE, …
    • Journal: Microscopy Research and Technique
    • Year: 2021
    • Citations: 22
  3. Hybrid classification structures for automatic COVID-19 detection
    • Authors: MR Shoaib, HM Emara, M Elwekeil, W El-Shafai, TE Taha, AS El-Fishawy, …
    • Journal: Journal of Ambient Intelligence and Humanized Computing
    • Year: 2022
    • Citations: 16
  4. Deepfakes, misinformation, and disinformation in the era of frontier AI, generative AI, and large AI models
    • Authors: MR Shoaib, Z Wang, MT Ahvanooey, J Zhao
    • Conference: 2023 International Conference on Computer and Applications (ICCA)
    • Year: 2023
    • Citations: 13
  5. Simultaneous super-resolution and classification of lung disease scans
    • Authors: HM Emara, MR Shoaib, W El-Shafai, M Elwekeil, EED Hemdan, …
    • Journal: Diagnostics
    • Year: 2023
    • Citations: 13
  6. Efficient deep learning models for brain tumor detection with segmentation and data augmentation techniques
    • Authors: MR Shoaib, MR Elshamy, TE Taha, AS El‐Fishawy, FE Abd El‐Samie
    • Journal: Concurrency and Computation: Practice and Experience
    • Year: 2022
    • Citations: 13
  7. Efficient brain tumor detection based on deep learning models
    • Authors: MR Shoaib, MR Elshamy, TE Taha, AS El-Fishawy, FE Abd El-Samie
    • Journal: Journal of Physics: Conference Series
    • Year: 2021
    • Citations: 13
  8. Automatic modulation classification with 2D transforms and convolutional neural network
    • Authors: HS Ghanem, MR Shoaib, S El‐Gazar, H Emara, W El‐Shafai, …
    • Journal: Transactions on Emerging Telecommunications Technologies
    • Year: 2022
    • Citations: 9
  9. Automated diagnosis of EEG abnormalities with different classification techniques
    • Authors: E Abdellatef, HM Emara, MR Shoaib, FE Ibrahim, M Elwekeil, W El-Shafai, …
    • Journal: Medical & Biological Engineering & Computing
    • Year: 2023
    • Citations: 4
  10. A survey on the applications of frontier AI, foundation models, and large language models to intelligent transportation systems
    • Authors: MR Shoaib, HM Emara, J Zhao
    • Conference: 2023 International Conference on Computer and Applications (ICCA)
    • Year: 2023
    • Citations: 4