Muhammad Ishaq | Data Science | Best Paper Award

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Assist Prof Dr. Muhammad Ishaq | Data Science | Best Paper Award

Assist Prof Dr. Muhammad Ishaq, The University of Agriculture Peshawar, Pakistan

Dr. Muhammad Ishaq earned his PhD in Computer Science with Distinction from Harbin Engineering University as an HEC Scholar in 2012. With 12 years of post-PhD teaching experience, he has significantly contributed to academia by organizing conferences and launching programs like BS (Bioinformatics), BS (Artificial Intelligence), MS (Data Science), and PhD (Computer Science). Dr. Ishaq has played a pivotal role in enhancing curricula and spearheading university computerization projects. He manages the HEC’s Digital Learning and Skills Enrichment Initiative (DLSEI) and has published numerous high-quality research papers. His dedication to supervising research theses and submitting projects to funding agencies showcases his commitment to excellence. 📚✨

Publication Profile

Scopus

🖥️ Academic Background 🎓

Dr. Muhammad Ishaq earned a PhD in Computer Science with Distinction from Harbin Engineering University as an HEC Scholar in 2012.

Research Focus

Dr. Muhammad Ishaq’s research focuses on machine learning, neural networks, and optimization algorithms. He has made significant contributions to data imputation in categorical datasets, robust crowd counting, and medical data classification. His work also includes optimizing neural network weights using accelerated particle swarm optimization and improving task scheduling for computational alignment of biological sequences. Dr. Ishaq’s research in agri-informatics and wireless body area networks further highlights his diverse expertise. His publications in esteemed journals and conference papers reflect his dedication to advancing computational methods and artificial intelligence. 📊🤖💡

 

Publication Top Notes

  • Machine Learning Based Missing Data Imputation in Categorical Datasets (Ishaq, M., et al., IEEE Access, 2024) – 📄🕵️‍♂️
  • Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet (Zahir, S., et al., Computer Systems Science and Engineering, 2023) – 📄🕵️‍♂️2 citations
  • NUMERICAL SOLUTION of WAVELET NEURAL NETWORK LEARNING WEIGHTS USING ACCELERATED PARTICLE SWARM OPTIMIZATION ALGORITHM (Zeb, A., et al., Fractals, 2023) – 📄🕵️‍♂️1 citation
  • Optimizing connection weights of functional link neural network using APSO algorithm for medical data classification (Khan, A., et al., Journal of King Saud University – Computer and Information Sciences, 2022) – 📄🕵️‍♂️11 citations
  • A dynamic swift association scheme for wireless body area networks (Sheraz, A., et al., Transactions on Emerging Telecommunications Technologies, 2022) – 📄🕵️‍♂️
  • Comprehensive selective improvements in agri-informatics semantics (Ishaq, M., et al., Journal of Information Science, 2022) – 📄🕵️‍♂️1 citation
  • Smart Control System for User Confirmation Based on IoT (Khan, A., et al., Lecture Notes in Networks and Systems, 2022) – 📄🕵️‍♂️
  • An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences (Ishaq, M., et al., Computational and Mathematical Methods in Medicine, 2022) – 📄🕵️‍♂️1 citation
  • Current Trends and Ongoing Progress in the Computational Alignment of Biological Sequences (Ishaq, M., et al., IEEE Access, 2019) – 📄🕵️‍♂️3 citations
  • Cognition in a cognitive routing system for mobile ad-hoc network through leaning automata and neural network (Afridi, M.I., et al., Applied Mechanics and Materials, 2013) – 📄🕵️‍♂️

Milad Jamali-dolatabad | Data Science Award | Best Researcher Award

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Mr. Milad Jamali-dolatabad | Data Science Award | Best Researcher Award

Mr. Milad Jamali-dolatabad, Tabriz University of Medical Sciences, Iran

Milad Jamali-Dolatabad is a biostatistics expert and researcher specializing in traffic injury prevention and data analysis. He earned his Master’s degree in Biostatistics from Tabriz University of Medical Sciences, Iran, focusing on applying advanced statistical methods to traffic data (🎓📊). His research, which includes publications on pedestrian accident outcomes and road traffic mortality, has been featured in renowned journals like BMC Public Health and Traffic Injury Prevention (📚🚶‍♂️). Proficient in R, Stata, and SPSS, Milad is involved in several projects that analyze traffic accident data and model road traffic mortality trends (💻🔍). He is fluent in Turkish and Persian, with intermediate English proficiency (🗣️🌐).

Publication profile

Google Scholar

Academic Background 📚

Milad Jamali-Dolatabad holds a Master’s degree in Biostatistics from Tabriz University of Medical Sciences, Iran, earned between 2016 and 2020. His thesis, supervised by Dr. Parvin Sarbakhsh, focused on the application of Partial Least Squares (PLS) methods to analyze traffic data, comparing their efficacy with traditional approaches. He achieved an impressive dissertation grade of 18.89 out of 20, reflecting his academic excellence (🎓).

Professional Experience 💼

Milad has been instrumental in various research projects. Notably, he contributed to the development of frameworks for synchronized data mining using driving simulators and EEG data in traffic laboratories. His expertise extends to modeling road traffic mortality dynamics and identifying hidden patterns in pedestrian characteristics using statistical methodologies.

Research Focus

Milad Jamali-Dolatabad’s research primarily focuses on traffic injury prevention and epidemiology, leveraging advanced statistical methodologies. His studies often involve analyzing predictors of fatal outcomes in pedestrian accidents and road traffic injuries in Iranian populations. Using techniques like Partial Least Squares (PLS) and categorical principal component analysis (CATPCA), he explores hidden patterns and trends in mortality data, particularly related to pedestrian safety and traffic accident dynamics (🚶‍♂️📊). His work contributes significantly to understanding factors influencing road traffic mortality and developing strategies for safer transportation systems, reflecting a commitment to improving public health through rigorous statistical analysis and evidence-based research.

 

Publication Top Notes