Ferdib Al Islam | Machine Learning | Excellence in Research

Published on by Young Scientists Awards

Mr. Ferdib Al Islam | Machine Learning | Excellence in Research

Assistant Professor, Northern University of Business and Technology Khulna Bangladesh

Ferdib-Al-Islam is an Assistant Professor at Northern University of Business and Technology Khulna, Bangladesh. He holds a Master’s and Bachelor’s degree in Computer Science and Engineering from Khulna University of Engineering & Technology (KUET) and Bangabandhu Sheikh Mujibur Rahman Science and Technology University (BSMRSTU), respectively. His research expertise encompasses Machine Learning, Deep Learning, IoT, Data Science, and Computer Vision. Ferdib’s career includes experience as a software engineer in IoT R&D and lecturer roles, contributing significantly to academic and research pursuits.

Publication Profile

Google Scholar

🎓 Education

M.Sc. Eng. in Computer Science and Engineering from KUET (2023) – GPA: 3.50. B.Sc. Eng. in Computer Science and Engineering from BSMRSTU (2018) – GPA: 3.55. HSC in Science from Govt. PC College, Bagerhat (2012) – 5.00. SSC in Science from Bagerhat Govt. Secondary School (2010) – 5.00

💼 Experience

Ferdib has progressed from an intern to a Senior Lecturer, and now an Assistant Professor at Northern University of Business and Technology Khulna. He served as a Lecturer in Computer Science and Engineering from March 2020 to January 2024. His career also includes a Jr. Software Engineer role at W3 Engineers Ltd. in the IoT R&D sector. Ferdib brings practical industry experience into his academic roles, fostering innovation and research.

🏆 Awards & Honors

Ferdib has earned notable accolades, including the Gold Award at Semarak International Research Article Competition III 2024 for his work on Autism Spectrum Disorder detection. He also received the Best Paper Award at ICETIS 2021 for his research on Diabetes Mellitus prediction and the Honorable Mention Award at BDML 2020 for his IoT-based health monitoring tool.

🔬 Research Focus

Ferdib’s primary research interests are in Machine Learning, Deep Learning, IoT, Large Language Models, and Computer Vision. His work focuses on the application of AI techniques to healthcare, predictive modeling, and intelligent systems. His aim is to leverage machine learning for real-world applications like healthcare diagnostics, smart monitoring systems, and data-driven insights in various fields.

Conclusion

Ferdib Al-Islam is an exceptional researcher with notable accomplishments in machine learning, deep learning, and IoT. His commitment to advancing knowledge in these areas, demonstrated by his numerous awards and research contributions, marks him as a leading figure in his field. However, fostering greater interdisciplinary collaborations and increasing his global academic presence will be beneficial for his continued growth as a researcher. He is undoubtedly a deserving candidate for the Excellence in Research award, given his dedication, achievements, and potential for further contributions to the scientific community.

Publication Top Notes

  • Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques
    • Year: 2021
    • Authors: L Akter, Ferdib-Al-Islam, MM Islam, MS Al-Rakhami, MR Haque
    • Citation: 73
  • An IoT Enabled Health Monitoring Kit Using Non-Invasive Health Parameters
    • Year: 2021
    • Authors: A Das, SD Katha, MS Sadi, Ferdib-Al-Islam
    • Citation: 31
  • Hepatocellular Carcinoma Patient’s Survival Prediction Using Oversampling and Machine Learning Techniques
    • Year: 2021
    • Authors: Ferdib-Al-Islam, L Akter, MM Islam
    • Citation: 21
  • An Enhanced Stroke Prediction Scheme Using SMOTE and Machine Learning Techniques
    • Year: 2021
    • Authors: Ferdib-Al-Islam, M Ghosh
    • Citation: 20
  • Early Identification of Parkinson’s Disease from Hand-drawn Images using Histogram of Oriented Gradients and Machine Learning Techniques
    • Year: 2020
    • Authors: Ferdib-Al-Islam, L Akter
    • Citation: 19
  • Dementia Identification for Diagnosing Alzheimer’s Disease using XGBoost Algorithm
    • Year: 2021
    • Authors: L Akter, Ferdib-Al-Islam
    • Citation: 17
  • COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning
    • Year: 2021
    • Authors: P Saha, MS Sadi, OFMRR Aranya, S Jahan, FA Islam
    • Citation: 9
  • Detection of Hepatitis C Virus Progressed Patient’s Liver Condition Using Machine Learning
    • Year: 2022
    • Authors: Ferdib-Al-Islam, L Akter
    • Citation: 6*
  • Diabetes Mellitus Prediction and Feature Importance Score Finding Using Extreme Gradient Boosting
    • Year: 2021
    • Authors: L Akter, Ferdib-Al-Islam
    • Citation: 4
  • COV-Doctor: A Machine Learning Based Scheme for Early Identification of COVID-19 in Patients
    • Year: 2022
    • Authors: Ferdib-Al-Islam, M Ghosh
    • Citation: 3*
  • Breast Cancer Risk Prediction Using Different Clustering Techniques
    • Year: 2022
    • Authors: L Akter, M Raihan, M Raihan, M Sarker, M Ghosh, N Alvi, Ferdib-Al-Islam
    • Citation: 3
  • Crop-RecFIS: Machine Learning Classifiers for Crop Recommendation and Feature Importance Scores Calculation
    • Year: 2023
    • Authors: MS Sanim, KM Hasan, MM Alam, MAA Walid, MR Islam
    • Citation: 2
  • Prediction of Dementia Using SMOTE Based Oversampling and Stacking Classifier
    • Year: 2023
    • Authors: Ferdib-Al-Islam, MS Sanim, MR Islam, S Rahman, R Afzal, KM Hasan
    • Citation: 2*
  • An Ensemble Learning Model to Detect COVID-19 Pneumonia from Chest CT Scan
    • Year: 2022
    • Authors: PC Shill
    • Citation: 2

 

 

 

Jianping Zhang | Decision Sciences | Best Researcher Award

Published on by Young Scientists Awards

Prof. Dr. Jianping Zhang | Decision Sciences | Best Researcher Award

Director of Uncrewed Aircraft Intelligent Traffic Technology Center at Second Research Institute of Civil Aviation Administration of China China

Zhang Jianping, born in 1976, is a renowned researcher and academic in the field of unmanned aircraft systems and air mobility. With a PhD in Engineering from Nanjing University of Aeronautics and Astronautics, he currently serves as a Researcher at the Second Research Institute of Civil Aviation Administration of China and as a Distinguished Professor at Southwest Jiaotong University. Zhang is the Director of the Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province. He has made significant contributions to the development of unmanned aircraft traffic management (UTM) systems and international standards.

Profile

Scopus

🎓 Education 

Ph.D. in Engineering: Nanjing University of Aeronautics and Astronautics, specializing in air traffic management systems. Pioneered research in unmanned traffic management, urban air mobility, and advanced air mobility. Developed expertise in engineering solutions for intelligent air traffic systems during doctoral studies.

💼 Experience

Researcher: Second Research Institute of Civil Aviation Administration of China; led national projects in UTM development. Distinguished Professor: Southwest Jiaotong University, mentoring PhD candidates in air mobility and traffic systems. Director: Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province; guided innovations in air traffic systems.

🏆 Awards & Honors

Special Award for Scientific and Technological Progress: China Communications and Transportation Association. First Prize: Civil Aviation Administration of China Science and Technology Award. Recognized for impactful contributions to UTM systems and intelligent air traffic management products.

🔬 Research Focus

Development of unmanned aircraft traffic management (UTM) systems. Urban air mobility (UAM) and advanced air mobility (AAM). International standards for unmanned aircraft systems (ISO 23629-9). Intelligent air traffic management systems for civil aviation applications.

Conclusion

Zhang Jianping’s exemplary achievements in unmanned aircraft traffic management and intelligent air traffic systems position him as a leading contender for the Best Researcher Award. His visionary leadership, significant contributions to both national and international projects, and ability to bridge the gap between research and real-world applications make him a deserving candidate. By expanding his global collaborations and mentoring future researchers, Zhang Jianping has the potential to continue making transformative advancements in the field of air mobility.

📚Publications 

  1. Title: SPE-SHAP: Self-paced ensemble with Shapley additive explanation for the analysis of aviation turbulence triggered by wind shear events
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Chen, F.

 

  1. Title: A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Chen, F.; H. Almaliki, A.

 

  1. Title: Estimating Wind Shear Magnitude Near Runways at Hong Kong International Airport Using an Interpretable Local Cascade Ensemble Strategy
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Chen, F.; Almujibah, H.

 

  1. Title: Wind Shear and Aircraft Aborted Landings: A Deep Learning Perspective for Prediction and Analysis
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Hussain, A.; Almujibah, H.

 

  1. Title: AI-supported estimation of safety critical wind shear-induced aircraft go-around events utilizing pilot reports
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Chen, F.; Matara, C.M.

 

  1. Title: An optimisation model of hierarchical facility location problem for urban last-mile delivery with drones
    • Year: 2024
    • Authors: Zhang, G.; Zhang, J.; He, B.; Zhang, R.; Zou, X.

 

  1. Title: Estimating Turbulence Due to Low-Level Wind Shear in Airport Runway Zones Using TabNet-SHAP Framework
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Almaliki, A.H.; Mongina Matara, C.

 

  1. Title: Explainable Boosting Machine: A Contemporary Glass-Box Strategy for the Assessment of Wind Shear Severity in the Runway Vicinity Based on the Doppler Light Detection and Ranging Data
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Chen, F.; Almujibah, H.

 

  1. Title: Assessment of Wind Shear Severity in Airport Runway Vicinity using Interpretable TabNet approach and Doppler LiDAR Data
    • Year: 2024
    • Authors: Khattak, A.; Zhang, J.; Chan, P.-W.; Chen, F.

 

  1. Title: The Architecture of a Comprehensive System for Civil Unmanned Aerial Vehicle Traffic Management in Urban Low-Altitude Airspace
    • Year: 2023
    • Authors: Cao, L.; Xu, Q.; Chen, C.; Wu, Q.; Zhang, J.