Mr. Muhammad Tauqeer Iqbal | Machine Learning | Best Researcher Award

Posted on by Computer Scientists Awards

Mr. Muhammad Tauqeer Iqbal | Machine Learning | Best Researcher Award

Mr. Muhammad Tauqeer Iqbal , Yangzhou University, China

Iqbal Muhammad Tauqeer is a passionate researcher and master’s student at Yangzhou University, China , specializing in the domain of Machine Learning 🤖. With a solid foundation in both industry and academia, he has combined practical management experience with cutting-edge AI research. His dedication to data science applications and computer vision has led to a notable publication recognized as a best paper, showcasing his potential in the rapidly evolving tech landscape 🌟.

Professional Profile

ORCID

🎓 Education Background

Iqbal is currently pursuing his Master’s degree at Yangzhou University, China 📚, where his academic focus is on machine learning and its applications in computer vision. His academic pursuits have been driven by a commitment to advancing AI-driven solutions in environmental monitoring and digital recognition systems.

💼 Professional Experience

Before his transition into research, Iqbal gained valuable industry experience as an Assistant Production Manager at OPPO Mobile Company Pakistan 📱 for over two years. This role provided him with deep insights into production workflows and industry standards, bridging the gap between theoretical learning and practical application.

🏆 Awards and Honors

Iqbal’s research has already earned accolades, with his paper titled “A Transfer Learning-Based VGG-16 Model for COD Detection in UV–Vis Spectroscopy” being recognized as a Best Paper 🥇. This early recognition is a testament to the impact and novelty of his contributions to AI-powered environmental diagnostics.

🔬 Research Focus

His research interests lie primarily in Machine Learning, Deep Learning, Transfer Learning, and Computer Vision 🧠📊. He is particularly focused on applying these techniques to UV–Vis Spectroscopy and digital display recognition. He is currently working on a second research project that extends his work in pattern recognition and visual AI.

🔚 Conclusion

With a unique blend of industrial management experience and academic rigor, Iqbal Muhammad Tauqeer is emerging as a promising contributor to the field of Artificial Intelligence. His work in machine learning models for environmental monitoring reflects not only his technical skills but also his commitment to impactful innovation 🌍🔍.

📚 Publication Top Note

  1. Title: A Transfer Learning-Based VGG-16 Model for COD Detection in UV–Vis Spectroscopy
    Journal: Journal of Imaging
    Publisher: MDPI
    Published Year: 2025

 

Prof. Dr. Metin Zontul | Machine Learning | Best Researcher Award

Posted on by Computer Scientists Awards

Prof. Dr. Metin Zontul | Machine Learning | Best Researcher Award

Dean, Sivas University of Science and Technology, Turkey

Prof. Dr. Metin Zontul is a seasoned academic and researcher in the fields of machine learning, data mining, and intelligent systems, currently serving as Professor and Dean at the Faculty of Engineering and Natural Sciences, Sivas University of Science and Technology, Turkey. With over 30 years of academic experience, he has held various esteemed positions at several universities in Turkey and contributed significantly to national-level research projects, innovation in artificial intelligence, and academic leadership.

Publication Profile

Google Scholar

ORCID

🎓 Education Background

He earned his Ph.D. in Quantitative Methods in Business Administration (2004) from the Institute of Social Sciences, focusing his dissertation on clustering countries trading with Turkey using SOM-type artificial neural networks. He holds an M.Sc. in Computer-Aided Design, Manufacturing, and Programming (1996), where he analyzed local area network access protocols, and a B.Sc. in Computer Engineering (1993) from Middle East Technical University.

💼 Professional Experience

Prof. Zontul has held multiple academic ranks, starting as a Lecturer at Cumhuriyet University (1994–2005) and advancing to Assistant, Associate, and then Professor at institutions such as Istanbul Aydın University, Arel University, Ayvansaray University, and Topkapi University. He has been a key academic leader, serving as Dean and Department Chair across several faculties. Since 2023, he has led the Faculty of Engineering and Natural Sciences at Sivas UST. He also supervises graduate theses and collaborates on research with TUBITAK and other industry-linked projects.

🏆 Awards and Honors

Prof. Zontul has received Publication Incentive Awards from Istanbul Aydın University in 2014 and 2016 for his scholarly contributions. He is a former member of IEEE and holds a 2024 patent for a Personnel Assignment and Routing System related to unit failure and maintenance operations.

🔬 Research Focus

His research interests span machine learning, deep learning, data mining, signal processing, natural language processing, and intelligent systems. He has contributed extensively to the scientific community through 25+ peer-reviewed journal articles, 20+ conference papers, and collaborative projects involving academia and industry. His supervision of numerous theses and his involvement in over 30 national research projects reflect his commitment to practical and academic advancements in AI.

🔚 Conclusion

Prof. Dr. Metin Zontul stands as a multifaceted academician blending research, leadership, and innovation. His significant contributions to AI, education, and national research initiatives have cemented his reputation as a leading scholar in his field.

📚 Top Publications 

  1. Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes (2021)
    Journal: Waste Management & Research
    Cited by: 92
    Co-authors: G. Coskuner, M.S. Jassim, S. Karateke

  2. Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation (2022)
    Journal: Waste Management & Research
    Cited by: 49
    Co-authors: M.S. Jassim, G. Coskuner

  3. Urban bus arrival time prediction: A review of computational models (2013)
    Journal: International Journal of Recent Technology and Engineering (IJRTE)
    Cited by: 123
    Co-author: M. Altinkaya

  4. Measuring the efficiency of telecommunication sectors of OECD countries using data envelopment analysis (2005)
    Journal: CU Journal of Economics and Administrative Sciences
    Cited by: 41
    Co-authors: O. Kaynar, H. Bircan

  5. Wind speed forecasting using reptree and bagging methods in Kirklareli-Turkey (2013)
    Journal: Journal of Theoretical and Applied Information Technology
    Cited by: 35
    Co-authors: F. Aydin, G. Dogan, S. Sener, O. Kaynar

  6. The prediction of the ZnNi thickness and Ni% of ZnNi alloy electroplating using a machine learning method (2021)
    Journal: Transactions of the IMF
    Cited by: 34
    Co-authors: R. Katirci, H. Aktas

  7. A smart and mechanized agricultural application: From cultivation to harvest (2022)
    Journal: Applied Sciences
    Cited by: 31
    Co-authors: F. Kiani, G. Randazzo, I. Yelmen, A. Seyyedabbasi, S. Nematzadeh, F.A. Anka, et al.

 

 

Dr. Ashkan Tashk | Applied AI | Excellence Award (Any Scientific field)

Posted on by Computer Scientists Awards

Dr. Ashkan Tashk | Applied AI | Excellence Award (Any Scientific field)

postdoc, Technical University of Denmark.

Dr. Ashkan Tashk is a highly accomplished electrical engineer and postdoctoral researcher with deep expertise in telecommunications, machine learning, and biomedical imaging. With a strong academic and teaching background, he has worked across multiple prestigious institutions in Denmark, Germany, and Iran. His career blends theoretical knowledge with applied innovations, particularly in AI-driven healthcare technologies, contributing significantly to interdisciplinary research and development. He is known for his dedication to science communication, teaching, and AI-based applications in medicine.

Publication Profile

Google scholar

🎓 Education Background:

Ashkan Tashk received his Ph.D. in Electrical Engineering with a focus on Telecommunications in 2015, following his M.Sc. (2010) and B.Sc. (2006) in the same field. His undergraduate project involved designing and constructing a prototype sunlight tracking platform—an early indication of his strong interest in applied engineering and innovation. His academic journey provided a solid foundation in electronics, signal processing, and machine learning, which continues to influence his research today.

💼 Professional Experience:

Dr. Tashk currently serves as a Postdoctoral Researcher at Denmark’s leading universities (2019–present). Prior to that, he worked as a telecommunications expert at FREC and completed a research internship at Karlsruhe Institute of Technology (KIT), Germany. His career includes teaching roles at the University of Southern Denmark, University of Copenhagen, and various Iranian academic institutions. He has taught courses in electrical circuits, microprocessors, statistics, numerical analysis, and MATLAB programming, while also publishing Persian-language technical tutorials and conducting workshops in Europe and Iran.

🏆 Awards and Honors:

Dr. Ashkan Tashk became an IEEE Senior Member in 2022, recognizing his professional maturity and significant contributions to electrical engineering. He has served as a session chair at multiple international conferences such as ACSIT2020 in Copenhagen and ICCAIRO2019 in Athens. He has also completed prestigious programs like the “Science Communication” course by the Royal Danish Academy of Sciences and Letters and the RCR workshop at the University of Copenhagen, demonstrating his commitment to ethical and effective scientific practice.

🔬 Research Focus:

Ashkan’s research centers on the application of artificial intelligence and machine learning in biomedical engineering, particularly in image processing, ultrasound tomography, and cancer diagnostics. Notable projects include developing LSTM-RF models for metastatic prostate cancer prediction, CNN-based biomedical segmentation tools, and advanced metabolomics data imputation methods. His work also spans sonar signal processing, image-based fingerprint recognition, and microprocessor-controlled automation systems. These interdisciplinary projects reflect his strong problem-solving abilities and technological foresight.

🧩 Conclusion:

Dr. Ashkan Tashk is a dynamic academic, educator, and innovator whose work bridges electrical engineering and biomedical science using modern AI tools. His technical skill set, coupled with his teaching excellence and global collaborations, position him as a thought leader in the integration of engineering and healthcare. Fluent in Persian, English, and Danish, and proficient in tools like Python, MATLAB, and various PLC programming languages, he continues to impact both academia and industry with his visionary contributions.

📚 Top Publications & Citations:

Semantic Segmentation of Biomedical Images Using Deep Convolutional Neural Networks
Journal: Journal of Medical Imaging and Health Informatics
Cited by: 24 articles

Predicting Metastatic Prostate Cancer via Biochemical Parameters Using LSTM and RF
Journal: Computers in Biology and Medicine
Cited by: 18 articles

Machine Learning Imputation for Large-scale Metabolomics Data
 Journal: Metabolomics
Cited by: 10 articles

Eye-Tracking Data Analysis Using AI for Cognitive Study
 Journal: IEEE Transactions on Affective Computing
Cited by: 7 articles

Rab Nawaz Bashir | Machine Learning | Best Researcher Award

Posted on by Computer Scientists Awards

Dr. Rab Nawaz Bashir | Machine Learning | Best Researcher Award

Assistant Professor, COMSATS University, Pakistan

Dr. Rab Nawaz Bashir 🎓 is a distinguished computer scientist and Assistant Professor at COMSATS University Islamabad, Vehari Campus. He holds a Ph.D. in Computer Science from Islamia University Bahawalpur and has been instrumental in shaping research and education in artificial intelligence, machine learning, and the Internet of Things (IoT). With a passion for academic excellence, he has mentored numerous undergraduate, graduate, and Ph.D. students, guiding them toward impactful research and industry readiness. His dedication to scholarly contributions and innovative teaching has made him a respected figure in the field of computer science.

Publication Profile

Education 🎓

Dr. Bashir has a strong academic foundation, earning his Ph.D. in Computer Science (2021) from Islamia University Bahawalpur. Prior to that, he completed an MS in Computer Science (2015) from the same institution. His educational journey began with a Master in Computer Science (MCS) from Pir Mehr Ali Shah University of Arid Agriculture (2008). To further enhance his expertise, he is currently pursuing a Fellowship in Computer Science at Prince Sultan University, Saudi Arabia.

Experience 👨‍🏫

With over 15 years of experience, Dr. Bashir has significantly contributed to academia and research. Since 2022, he has served as an Assistant Professor at COMSATS University Islamabad, Vehari Campus, where he leads curriculum development, organizes seminars, and supervises undergraduate and Ph.D. research. Previously, he was a Lecturer at COMSATS University (2015–2022), University of Agriculture Faisalabad (2014–2015), and Institute of Southern Punjab, Multan (2010–2014). Before transitioning fully into academia, he worked in software development at the University of Agriculture Faisalabad (2008–2010), specializing in secure and scalable web applications using ASP.NET and SQL Server.

Awards and Honors 🏆

Dr. Bashir has been recognized multiple times for his outstanding contributions to research and academic excellence. He has received several Research Awards (2021, 2022, 2023, 2024) and Annual Performance Awards (2022, 2023, 2024) from his institution. Additionally, his leadership in securing National Standard for Education Accreditation Council (NSEAC) accreditation for the university in 2021 and 2024 highlights his commitment to academic quality and institutional development.

Research Focus 🔬

Dr. Bashir’s research spans various cutting-edge domains, including machine learning, IoT, and computer programming. His work emphasizes real-world applications, such as IoT-enabled smart agriculture, machine learning-based fraud detection, and deep learning for natural image processing. His interdisciplinary collaborations have resulted in high-impact research, contributing to advancements in federated learning, fog computing, and agricultural technology.

Conclusion 🌟

Dr. Rab Nawaz Bashir is a leading academic and researcher in computer science, with a strong focus on machine learning, IoT, and programming. His dedication to mentoring students, publishing impactful research, and advancing academic excellence has earned him numerous awards and recognitions. With a vision for future innovations, he continues to push the boundaries of computer science, contributing to both academia and industry through groundbreaking research and leadership.

Publications 📚

A Novel 1-Dimensional Cosine Chaotic Equation and Digital Image Encryption Technique (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3447889

Federated Learning (FL) Model of Wind Power Prediction (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3415781

IoT-Enabled Firmness Grades of Tomato in Cold Supply Chain Using Fusion of Whale Optimization Algorithm and Extreme Learning Machine (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3379327

Mushroom Species Classification in Natural Habitats Using Convolutional Neural Networks (CNN) (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3502543

Machine Learning and Fog Computing-Enabled Sensor Drift Management in Precision Agriculture (IEEE Sensors Journal, 2024) – DOI: 10.1109/JSEN.2024.3451662

Principal Component Analysis (PCA) and Feature Importance-Based Dimension Reduction for Reference Evapotranspiration (ET0) Predictions of Taif, Saudi Arabia (Computers and Electronics in Agriculture, 2024) – DOI: 10.1016/j.compag.2024.109036

Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency Transactions (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3310576

Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network  (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3301504

Stacked Ensemble Model for Tropical Cyclone Path Prediction (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3292907

Smart Reference Evapotranspiration Using Internet of Things and Hybrid Ensemble Machine Learning Approach (Internet of Things, 2023) – DOI: 10.1016/j.iot.2023.100962

Abdelhak Bouayad | machine Learning | Young Scientist Award

Posted on by Computer Scientists Awards

Dr. Abdelhak Bouayad | machine Learning | Young Scientist Award

PhD, UM6P, Morocco

📚 Abdelhak Bouayad is a dedicated researcher in artificial intelligence and privacy from the College of Computing at Mohammed VI Polytechnic University in Ben-Guérir, Morocco. His work explores innovative methods to protect sensitive data in machine learning models, ensuring both privacy and AI effectiveness. With a robust background in machine learning, data security, and federated learning, Abdelhak aims to drive advancements in privacy-preserving AI applications.

Publication Profile

Google Scholar

Education

🎓 Abdelhak Bouayad is currently pursuing a Ph.D. in Computer Science at Mohammed VI Polytechnic University under the guidance of Dr. Ismail Berrada. He holds an M.Sc. in Big Data Analytics and Smart Systems from Sidi Mohamed Ben Abdellah University, where he developed a thesis on lip reading for speech recognition, and a B.A. in Mathematics and Computer Science from the same institution in Fès, Morocco.

Experience

👨‍💻 Abdelhak has served as a Research Assistant at the College of Computing at Mohammed VI Polytechnic University since 2019. His research delves into the intersection of machine learning, privacy, and federated learning, with a focus on protocols to secure data exchanges and safeguard privacy within machine learning systems.

Research Focus

🔍 Abdelhak’s research is centered on artificial intelligence, machine learning, and privacy-preserving mechanisms. His primary focus lies in creating algorithms and protocols that protect sensitive data in machine learning models from potential exploitation. He aims to strengthen federated learning systems to ensure robust data privacy without compromising AI performance.

Awards and Honors

🏆 Abdelhak was awarded the College of Computing Fellowship for a pre-doctoral fellowship at Mohammed VI Polytechnic University from October 2018 to October 2019. This fellowship recognizes his commitment to research excellence and contributions to privacy-preserving AI methods.

Publication Highlights

NF-NIDS: Normalizing Flows for Network Intrusion Detection Systems

On the atout ticket learning problem for neural networks and its application in securing federated learning exchanges

Investigating Domain Adaptation for Network Intrusion Detection

 

Hsiu Hsia Lin | Machine learning | Best Researcher Award

Posted on by Computer Scientists Awards

Prof. Hsiu Hsia Lin | Machine learning | Best Researcher Award

Research Fellow, Chang Gung Memorial Hospital, Taiwan

Dr. Hsiu-Hsia Lin is a dedicated Research Fellow at the Craniofacial Research Center, Chang Gung Memorial Hospital, Taiwan, and an Adjunct Assistant Professor at the Graduate Institute of Dental and Craniofacial Science, Chang Gung University. With a strong foundation in AI and 3D craniofacial image processing, her research contributes significantly to advancements in orthognathic surgery. Dr. Lin’s expertise in surgical navigation and CAD/CAM-assisted surgery is pivotal in improving craniofacial surgical outcomes. 🌟

Publication Profile

Education:

Dr. Lin earned her Ph.D. in Computer Science and Engineering from National Chung Hsing University, Taiwan, following a Master’s in Computer Science from Tunghai University. Her academic journey is deeply rooted in computer science, blending AI with craniofacial research. 🎓📚

Experience:

Dr. Lin has held key research positions, including Assistant Research Fellow and Postdoctoral Fellow at the Craniofacial Research Center, Chang Gung Memorial Hospital. Her postdoctoral work also extended to the Department of Computer Science and Engineering at National Chung Hsing University. Her extensive experience has helped bridge the gap between AI technology and clinical applications. 💼🔬

Research Focus:

Dr. Lin’s research revolves around Pattern Recognition, Artificial Intelligence, and 3D Craniofacial Image Processing. She specializes in computer-aided surgical simulation for orthognathic surgery, surgical navigation, and CAD/CAM-assisted procedures, aiming to optimize outcomes in facial surgery. 🧠💻

Awards and Honors:

Dr. Lin has received multiple recognitions for her contributions to craniofacial research and AI in surgery. Her work continues to shape modern surgical approaches, particularly in orthognathic surgery, enhancing patient outcomes. 🏆👏

Publication Top Notes:

Dr. Lin’s publications focus on integrating AI with medical applications, particularly in 3D craniofacial analysis and orthognathic surgery. Her studies offer novel methods for surgical planning, facial attractiveness assessment, and facial symmetry evaluation.

Quantification of facial symmetry in orthognathic surgery (Dec. 2024) in Comput Biol Med., cited by 5 articles. DOI

Average 3D virtual sk

eletofacial model for surgery planning (Feb. 2024) in Plast Reconstr Surg., cited by 3 articles. DOI

Facial attractiveness assessment using transfer learning (Jan. 2024) in Pattern Recognit., cited by 4 articles. DOI

Optimizing Orthognathic Surgery (Nov. 2023) in J. Clin. Med., cited by 6 articles. DOI

Single-Splint, 2-Jaw Orthognathic Surgery (Nov. 2023) in J Craniofac Surg., cited by 2 articles. DOI

Applications of 3D imaging in craniomaxillofacial surgery (Aug. 2023) in Biomed J., cited by 7 articles. DOI

Facial Beauty Assessment using Attention Mechanism (Mar. 2023) in Diagnostics, cited by 8 articles. DOI