Andy Anderson Bery – Machine Learning in Geophysics – Best Researcher Award

Assoc. Prof. Dr. Andy Anderson Bery - Machine Learning in Geophysics - Best Researcher Award 

Universiti Sains Malaysia - Malaysia 

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🎓 Early Academic Pursuits

Assoc. Prof. Dr. Andy Anderson Bery embarked on his academic journey with a strong foundation in geophysics, gaining his undergraduate and graduate education from renowned institutions. His early academic pursuits were characterized by a deep interest in the intersection of geophysics, geology, and advanced technologies. His dedication to research and innovation led him to further specialize in applying geophysical methods to environmental and geological challenges, particularly focusing on subsurface imaging and modeling. These early academic pursuits laid the groundwork for his later contributions to machine learning in geophysics.

💼 Professional Endeavors

Assoc. Prof. Dr. Bery has had a distinguished career, serving as an academic leader and researcher. His role as a faculty member and researcher has been marked by a commitment to advancing geophysical methodologies, especially through the integration of machine learning in geophysics. Throughout his professional endeavors, Dr. Bery has served as a primary and co-supervisor for numerous Ph.D. and Master’s students, guiding them through complex research projects. His expertise spans various geophysical methods, such as electrical resistivity tomography, seismic refraction, and the application of machine learning in geophysics to enhance subsurface imaging techniques.

🔬 Contributions and Research Focus

Dr. Bery's research focus includes the application of advanced geophysical techniques, with a strong emphasis on integrating machine learning in geophysics. His contributions to improving seismic signal detectability, soil shear strength modeling, and the development of geophysical-geotechnical relationships have been pivotal. He has also contributed to significant advancements in subsurface imaging techniques, including the use of electrical resistivity tomography in identifying geological structures and landslide susceptibility mapping. His ongoing research continues to explore the potential of machine learning in geophysics to revolutionize the interpretation and analysis of geophysical data, making these techniques more efficient and accurate.

🌍 Impact and Influence

Assoc. Prof. Dr. Bery’s impact extends beyond his direct academic contributions; he has significantly influenced the development of new geophysical methodologies that incorporate machine learning in geophysics. His research has helped shape the direction of modern geophysics, particularly in the context of subsurface imaging and environmental assessment. Dr. Bery’s work has been widely recognized within the academic community, and he has presented his findings at numerous conferences, collaborating with international experts in the field. His influence continues to resonate in the field as emerging technologies such as machine learning in geophysics become integral to geophysical practices.

🏆Academic Cites

Assoc. Prof. Dr. Andy Anderson Bery’s academic contributions have been widely cited in top-tier geophysical and geological journals. His work, especially related to machine learning in geophysics, is frequently referenced by other researchers in the field, illustrating its importance and relevance. The impact of his research can be seen in the increasing number of citations and the use of his methodologies by both academic and industry professionals. His papers have laid a foundation for future research in geophysical applications and the use of machine learning to analyze geophysical data.

🌟 Legacy and Future Contributions

As a seasoned academic and researcher, Dr. Bery’s legacy is already well established, particularly in the integration of machine learning in geophysics. His future contributions promise to further push the boundaries of geophysical methods, especially in the areas of subsurface imaging and environmental monitoring. As he continues to mentor the next generation of researchers, his influence will persist in the development of cutting-edge technologies that merge geophysics with machine learning. His work is expected to play a central role in revolutionizing the ways geophysical data is interpreted and utilized.

📝Machine Learning in Geophysics

Assoc. Prof. Dr. Bery’s research in machine learning in geophysics has been a cornerstone of his academic career. His innovative approaches to integrating machine learning in geophysics have not only enhanced the accuracy and efficiency of geophysical methods but have also led to the development of novel geophysical models. The ongoing use of machine learning in geophysics in Dr. Bery's future work will continue to redefine the field, offering more advanced, scalable, and accurate solutions to geophysical challenges.

Notable Publication


📝Application of Electrical Resistivity Tomography and Induced Polarization for Pre-Construction Site Assessment in Ipoh, Perak, Malaysia

Authors: Musty, S.B., Bery, A.A.

Journal: BIO Web of Conferences

Year: 2024

Citations: 0


📝Integrated Geophysical Investigation using Aero-radiometric and Electrical Methods for Potential Gold mineralization within Yauri/Zuru Schist Belts, Kebbi State NW Nigeria | Investigación geofísica integrada de prospección aerorradiométrica y métodos eléctricos para definir el potencial de mineralización aurífera en el cinturón de esquistos de Yauri/Zuru, en el estado de Kebbi, en el noroeste de Nigeria

Authors: Augie, A.I., Salako, K.A., Bery, A.A., Rafiu, A.A., Jimoh, M.O.

Journal: Earth Sciences Research Journal

Year: 2024

Citations: 0


📝Surface–subsurface characterization via interfaced geophysical–geotechnical and optimized regression modeling

Authors: Akingboye, A.S., Bery, A.A., Aminu, M.B., Bala, G.A., Ale, T.O.

Journal: Modeling Earth Systems and Environment

Year: 2024

Citations: 1


📝A novel machine learning approach for interpolating seismic velocity and electrical resistivity models for early-stage soil-rock assessment

Authors: Dick, M.D., Bery, A.A., Okonna, N.N., Bashir, Y., Akingboye, A.S.

Journal: Earth Science Informatics

Year: 2024

Citations: 4


📝Subsurface Lithological Characterization Via Machine Learning-assisted Electrical Resistivity and SPT-N Modeling: A Case Study from Sabah, Malaysia

Authors: Dick, M.D., Bery, A.A., Akingboye, A.S., Moses, E., Purohit, S.

Journal: Earth Systems and Environment

Year: 2024

Citations: 0


📝Geometry Analysis of Penang Island Faults Based on Satellite Gravity Data

Authors: Pambayun, T., Hilyah, A., Lestari, W., Bery, A.A.

Journal: IOP Conference Series: Earth and Environmental Science

Year: 2024

Citations: 0

Zubair Saeed – AI Based Automatic Brain Tumors Detection – Best Researcher Award

Mr. Zubair Saeed - AI Based Automatic Brain Tumors Detection - Best Researcher Award  

Texas A&M University - United States 

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🎓 Early Academic Pursuits

Mr. Zubair Saeed’s academic journey is marked by a consistent dedication to innovation and excellence in the field of computer engineering. Beginning with a Bachelor’s degree in Computer Engineering from HITEC University, Pakistan, he graduated with a stellar CGPA, securing a Silver Medal and receiving a fully funded final-year project grant from IGNITE Pakistan. His undergraduate design project on IoT-based plant disease detection showcased his early interest in using machine learning for practical, real-world applications. Pursuing his Master's in Computer Engineering at UET Taxila, he graduated with a Gold Medal, focusing on real-time small object detection for UAVs. This rigorous academic foundation paved the way for his Ph.D. at Texas A&M University, where he continues to advance his expertise in AI and medical imaging.

💼 Professional Endeavors

Mr. Saeed’s professional experience is a testament to his versatility and impact in artificial intelligence and computer vision. At Texas A&M University, he serves as a Graduate Teaching Assistant, teaching lab courses in machine learning and digital system design. His research assistant role at Texas A&M University at Qatar involved critical data collection and analysis at Hammad Medical Corporation, where he worked on AI solutions for segmenting organs-at-risk and tumors. His prior roles at UET’s Swarm Robotics Lab and as a researcher for CRD highlight his expertise in computer vision and small object detection, where he conducted impactful projects related to COVID-19 detection, plant disease identification, and real-time pothole detection.

🔬 Contributions and Research Focus

Mr. Zubair Saeed’s contributions center around AI-driven medical imaging, specifically focusing on AI-Based Automatic Brain Tumor Detection and lung cancer detection. His Ph.D. research is concentrated on predicting radiotherapy treatment responses for lung and head-neck cancers through advanced AI. His work integrates AI with medical imaging to develop segmentation solutions that accurately detect tumors and organs-at-risk. Additionally, he has published significant research on disease detection, small object detection, and cancer classification in prestigious, peer-reviewed journals. His work on AI-Based Automatic Brain Tumor Detection represents a groundbreaking approach that aims to enhance accuracy and efficiency in diagnostics, ultimately benefiting healthcare outcomes.

🌍 Impact and Influence

Mr. Saeed’s influence extends beyond his publications. As a reviewer for various IEEE journals, he actively contributes to the academic discourse in artificial intelligence and machine learning. His work has earned him numerous awards, including a Deep Learning Training Certificate from LUMS, the Best Article Presenter award at ICRAI’21, and several recognitions for his contributions in artificial intelligence. His efforts in AI-Based Automatic Brain Tumor Detection underscore his impact on the field, with his methodologies providing new perspectives for future AI applications in medical diagnostics.

🏆Academic Cites

Mr. Saeed’s research has garnered significant academic citations, reflecting the scientific community’s acknowledgment of his contributions. His work on AI and medical imaging is frequently referenced in research related to cancer detection and AI in healthcare. His expertise in fields such as small object detection and AI-driven diagnostic tools is highly regarded, with his work laying the groundwork for further advancements in AI-Based Automatic Brain Tumor Detection and other medical imaging applications.

🌟 Legacy and Future Contributions

As he continues his Ph.D. studies, Mr. Saeed’s future contributions are set to influence the intersection of AI and healthcare profoundly. His commitment to developing AI-based diagnostic tools that enhance the accuracy of treatments for life-threatening conditions, such as brain and lung cancers, positions him as a pioneer in the field. His legacy will be characterized by his significant contributions to AI-Based Automatic Brain Tumor Detection, AI-driven diagnostics, and his broader influence on machine learning in medical imaging. His career promises to leave a lasting impact, setting new standards for precision in healthcare technology.

📝Notable Publication


📝An Efficient Ensemble Approach for Brain Tumors Classification Using Magnetic Resonance Imaging

Authors: Saeed, Z., Torfeh, T., Aouadi, S., Ji, X., Bouhali, O.

Journal: Information (Switzerland)

Year: 2024

Citations: 0


📝Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

Authors: Saeed, Z., Bouhali, O., Ji, J.X., Aouadi, S., Torfeh, T.

Journal: Bioengineering

Year: 2024

Citations: 2


📝Demand Forecasting in Supply Chain Management for Rossmann Stores Using Weather Enhanced Deep Learning Model

Authors: Ul Haq Qureshi, N., Javed, S., Javed, K., Raza, A., Saeed, Z.

Journal: IEEE Access

Year: 2024

Citations: 0


📝Advances, Application and Challenges of Lithography Techniques

Authors: Raza, A., Saeed, Z., Aslam, A., Habib, K., Malik, A.N.

Conference: 2024 5th International Conference on Advancements in Computational Sciences (ICACS 2024)

Citations: 1


📝On-Board Small-Scale Object Detection for Unmanned Aerial Vehicles (UAVs)

Authors: Saeed, Z., Yousaf, M.H., Ahmed, R., Velastin, S.A., Viriri, S.

Journal: Drones

Year: 2023

Citations: 12


📝A Review of Swarm Robotics in a NutShell

Authors: Shahzad, M.M., Saeed, Z., Akhtar, A., Baloach, N.K., Hussain, F.

Journal: Drones

Year: 2023

Citations: 12