Md. Emran Biswas | Data science | Best Researcher Award

Mr. Md. Emran Biswas | Data science | Best Researcher Award

Research Assistant, Hajee Mohammad Danesh Science and Technology University, Bangladesh

🌟 Md. Emran Biswas, hailing from Dinajpur, Bangladesh, is a passionate researcher and technologist specializing in machine learning, optimization algorithms, and their societal applications. He has actively contributed to predictive analysis, bioinformatics-based drug discovery, and developing AI solutions for global good. As a skilled programmer and researcher, Emran’s work has earned recognition through multiple publications, accolades, and groundbreaking projects in his field.

Publication Profile

Scopus

Education

🎓 Md. Emran Biswas completed his B.Sc. in Electronics and Communication Engineering at Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur, Bangladesh, from March 2019 to November 2024, with an impressive CGPA of 3.412/4.00. His academic journey is marked by a focus on deep learning, predictive modeling, and optimization algorithms.

Experience

💼 Emran served as a Research Assistant at Petarhub and DIOT Lab, HSTU, contributing to machine learning, predictive modeling, and optimization projects. His notable achievements include developing the ApexBoost Regression model, managing large datasets, and publishing impactful research in reputed journals like IEEE and Electronics.

Research Interests

🔍 Emran’s research focuses on machine learning, optimization algorithms, and their transformative applications in areas like bioinformatics-based drug discovery, predictive analysis, and societal challenges. His work aligns with the vision of ‘AI for Good,’ driving impactful innovation.

Awards

🏆 Emran has earned recognition for his innovative projects, including First Runner-Up at the Project Exhibition 2022 for his “Face Detection-Based Attendance System” and Second Runner-Up in 2023 for his “AI-Based Health Checking System.” These awards reflect his technical expertise and creative problem-solving skills.

Publications

Machine Learning Approach to Estimate Requirements for Target Productivity of Garments Employees. IEEE ICEEICT 2024 (Cited by: 5)

An Effective Data-Driven Approach to Predict Bike Rental Demand. Google Scholar (Cited by: 12)

Spatio-Temporal Feature Engineering and Selection-Based Flight Arrival Delay Prediction Using Deep Feedforward Regression Network. Electronics, 13(24), p.4910 (Cited by: 9)

 

Assoc. Prof. Dr.Pabrício Lopes | Data Science | Best Researcher Award

Assoc. Prof. Dr. Pabrício Lopes | Data Science | Best Researcher Award

Professor, UFRPE, Brazil

🌟 Pabrício Marcos Oliveira Lopes is a dedicated scholar specializing in Remote Sensing, Agrometeorology, and Physical Geography. He is a Professor of Agronomy at the Federal Rural University of Pernambuco (UFRPE) in Recife, Brazil, contributing significantly to the fields of geospatial analysis and climate studies. With over 62 impactful publications, Dr. Lopes is a leader in exploring environmental phenomena, emphasizing sustainability and climate adaptation. 📚🌍

Publication Profile

ORCID

Education

🎓 Dr. Lopes earned his Ph.D. in Remote Sensing from the National Institute for Space Research (INPE) in 2006. He holds an M.Sc. in Agrometeorology from the Federal University of Campina Grande (UFCG, 1999) and dual undergraduate degrees in Meteorology (UFCG, 1997) and Physics (UEPB, 1999). His educational journey showcases a robust interdisciplinary expertise in physical and environmental sciences. 📊🌤️

Experience

🏫 Dr. Lopes serves as a Professor of Agronomy at UFRPE, where he integrates research and teaching to address agricultural and environmental challenges in Brazil’s semi-arid regions. His expertise includes geospatial technologies, climate modeling, and phenological monitoring, making him a valuable contributor to academia and applied science. 🌾🛰️

Research Interests

📖 Dr. Lopes’ research focuses on phenological monitoring, aridity conditions, climate extremes, and desertification, with a particular emphasis on the Brazilian semi-arid region. His work leverages satellite data, GIS modeling, and time-series analysis to develop innovative solutions for environmental monitoring and sustainable agriculture. 🌱📡

Awards

🏆 Dr. Lopes has received recognition for his academic contributions, though specific awards were not listed. His significant impact in climate studies and geospatial research is widely acknowledged in the scientific community. 🌟🎖️

Publications

Phenological Monitoring of Irrigated Sugarcane Using Google Earth Engine, Time Series, and TIMESAT in the Brazilian Semi-Arid
AgriEngineering, 2024-10-18 | DOI: 10.3390/agriengineering6040217
Cited by: Information not available.

Influência de eventos climáticos extremos na ocorrência de queimadas e no poder de regeneração vegetal
Revista Brasileira de Geografia Física, 2024-03-14 | DOI: 10.26848/rbgf.v17.2.p1098-1113
Cited by: Information not available.

Geospatial Insights into Aridity Conditions: MODIS Products and GIS Modeling in Northeast Brazil
Hydrology, 2024-02-26 | DOI: 10.3390/hydrology11030032
Cited by: Information not available.

Assessment of Desertification in the Brazilian Semiarid Region Using Time Series of Climatic and Biophysical Variables
Revista Brasileira de Geografia Física, 2023-12-29 | DOI: 10.26848/rbgf.v16.6.p3424-3444
Cited by: Information not available.