Mrs. Golshid Ranjbaran | Artificial Intelligence | Best Researcher Award

Posted on by sciencefatherLeave a Comment on Mrs. Golshid Ranjbaran | Artificial Intelligence | Best Researcher Award

Mrs. Golshid Ranjbaran | Artificial Intelligence | Best Researcher Award

University of Saskatchewan, Canada.

Golshid Ranjbaran is a PhD Candidate in Computer Science at the University of Saskatchewan (USASK), specializing in Artificial Intelligence, Machine Learning, and Interpretability. With a Bachelor's degree in Software Engineering and a Master's in Artificial Intelligence from the Science and Research Branch in Iran, he has accumulated several awards, including the Best Paper Award at the IKT Conference in 2021 and Best Researcher at ITRC in 2022. Golshid's research is aimed at advancing AI methodologies and improving machine learning models for real-world applications. He was also a research associate at the Data Science & Big Data Lab in Seville, Spain, in 2023. 🌐

Profile

Google Scholar

Education 🎓

Golshid holds a Bachelor's degree in Software Engineering and a Master's degree in Artificial Intelligence from the Science and Research Branch in Iran. He is currently pursuing a Ph.D. in Computer Science at the University of Saskatchewan (USASK), Canada, where he focuses on AI, machine learning, and interpretability, aiming to bridge the gap between theoretical advancements and practical applications.

Experience 🏢

Golshid has been awarded several prestigious positions and accolades, including a research position at the Data Science & Big Data Lab in Seville, Spain (2023), and was recognized as the Best Researcher at ITRC (2022). He has also contributed to various consultancy projects and industry collaborations, such as working on AI systems at ITRC, smart meters algorithms, and data governance in Iran.

Research Interests 🔍

Enhancing model interpretability through methods like SHAP.

Exploring sentiment analysis for stock market prediction.

Developing augmented techniques for unbalanced tasks in the financial domain.

Improving network security through Moving Target Defense technology.

Investigating Federated Learning for wearable health devices and ontology-based text summarization for efficient information processing.

Awards 🏆

Best Paper Award at the IKT Conference (2021)

Best Researcher Award at the Iran Telecommunication Research Center (ITRC) (2022)

Research Position at the Data Science & Big Data Lab in Seville, Spain (2023)

Nomination for the Gala GSA Award at the University of Saskatchewan (2025).

Selected Publications 📚

C-SHAP: A Hybrid Method for Fast and Efficient Interpretability – Applied Sciences (Q2 Journal), Published 2025.

Leveraging Augmentation Techniques for Tasks with Unbalancedness within the Financial Domain – EPJ Data Science (Q1 Journal), Published 2023.

Investigating Sentiment Analysis of News in Stock Market Prediction – International Journal of Information and Communication Technology Research, Published 2024.

Unsupervised Learning Ontology-Based Text Summarization Approach with Cellular Learning Automata – Journal of Theoretical and Applied Information Technology, Published 2023.

Analyzing the Effect of News Polarity on Stock Market Prediction – Proceedings of the 12th International Conference on Information and Knowledge Technology (IKT), Published 2021.

 

 

Taye Mengistu | Machine learning | Best Researcher Award

Posted on by sciencefatherLeave a Comment on Taye Mengistu | Machine learning | Best Researcher Award

Mr. Taye Mengistu | Machine learning | Best Researcher Award

IT engineer, Jigjiga University, Ethiopia

Mr. Taye Mengistu is an innovative researcher and lecturer based at Jigjiga University, Ethiopia. With a strong foundation in computer science and information technology, he is making notable strides in applying machine learning to real-world challenges. His pioneering work includes utilizing ensemble convolutional neural networks (CNNs) for the classification of mango diseases, a significant advancement in agricultural technology aimed at enhancing crop health and productivity.

Profile

Google Scholar

Education 🎓

Mr. Taye Mengistu holds a Master’s degree in Computer Science from Jimma University, Ethiopia, where he graduated in January 2022 with a GPA of 3.58. Prior to this, he completed his Bachelor’s degree in Information Technology at Jigjiga University, Ethiopia, graduating in July 2017 with a GPA of 3.84. His strong academic background underscores his deep understanding and expertise in his field.

Experience 🏢

Mr. Taye Mengistu has been serving as a Lecturer at Jigjiga University, Ethiopia, since October 2017. With over five years of experience, he has played a crucial role in both teaching and research. His responsibilities include delivering lectures, supervising student projects, and conducting research in various domains. His tenure at the university reflects his dedication to academic excellence and his ongoing commitment to advancing knowledge in his field.

Research Interests 🔬

Classification of Mango Disease Using Ensemble Convolutional Neural Network

The classification of mango diseases using ensemble convolutional neural networks (CNNs) represents a cutting-edge research area at the intersection of agriculture and artificial intelligence. This research focuses on leveraging advanced machine learning techniques to accurately identify and categorize diseases affecting mango crops, which is crucial for improving agricultural productivity and sustainability.

Key Aspects of Research Interests:

Ensemble Convolutional Neural Networks (CNNs): Utilizing ensemble methods to combine multiple CNN models to enhance classification accuracy. This approach improves the robustness and reliability of disease detection systems by aggregating predictions from different models.

Disease Classification: Developing algorithms to classify various mango diseases based on visual symptoms captured in images. Accurate classification helps in timely diagnosis and effective management strategies, minimizing crop loss and ensuring better yield.

Image Processing and Analysis: Applying image processing techniques to preprocess and analyze mango leaf and fruit images. This includes noise reduction, feature extraction, and image augmentation to improve model performance.

Machine Learning in Agriculture: Exploring the application of machine learning models to agricultural problems, particularly in disease detection and management. This research aims to bridge the gap between AI technology and practical agricultural needs.

Sustainable Agriculture: Enhancing disease management practices to promote sustainable agriculture. By accurately classifying and managing diseases, farmers can reduce the reliance on chemical treatments, leading to more eco-friendly farming practices.

Awards 🏆

Certification in HDP, Jigjiga University (May 2022) – Recognized for his advanced skills and knowledge in his field.

Publications 📚

Classification of mango disease using ensemble convolutional neural network– JSmart Agricultural Technology 2024. link

 

 

 

 

 

Aleka Melese | AI | Best Researcher Award

Posted on by sciencefather

🌟Mr. Aleka Melese, AI, Best Researcher Award🏆

Aleka Melese at Ayalew University of Gondar, Ethiopia

Aleka Melese Ayalew is an Ethiopian male with a Master’s degree in Information Technology from the University of Gondar. He is currently employed as an Information Technology Lecturer and Researcher at the same university. With a strong academic background and expertise in artificial intelligence, machine learning, and deep learning, Aleka has made significant contributions to the field through his research and publications. He is proficient in both English and his mother tongue, Amharic.

Author Metrics:

ORCID Profile

Google Scholar Profile

Aleka Melese Ayalew has a prolific publication record, with over 12 articles published in reputable journals. His research spans various domains such as COVID-19 detection, disease classification, sentiment analysis, and more. He has also served as a peer reviewer for multiple journals, contributing to the academic community by critically evaluating research and providing constructive feedback.

Citations: Aleka’s publications have been cited a total of 119 times since 2019, indicating the influence and relevance of his research within the academic community.

h-index: With an h-index of 6 since 2019, Aleka has published at least 6 papers that have each been cited at least 6 times, demonstrating a consistent level of impact in his field.

i10-index: Aleka’s i10-index, which measures the number of publications with at least 10 citations, is 3 since 2019, indicating the presence of 3 papers with 10 or more citations during this period. This metric further underscores the significance of his research output.

Education:

Aleka Melese Ayalew holds a Bachelor of Science degree in Information Technology from Adigrat University, earned between 2014 and 2017. He further pursued his academic journey by obtaining a Master’s degree in Information Technology from the University of Gondar, completing it in 2021.

Research Focus:

Aleka’s research focuses primarily on artificial intelligence, machine learning, and deep learning, with applications in various domains such as healthcare (COVID-19 detection, disease classification), sentiment analysis, and IoT.

Professional Journey:

Aleka Melese Ayalew’s professional journey began in 2018 when he joined the University of Gondar as an Information Technology Lecturer and Researcher. Since then, he has been actively involved in course design, classroom instruction, research, article publication, mentoring, advising, assessment, and evaluation.

Honors & Awards:

Throughout his academic and professional career, Aleka has been recognized for his outstanding contributions. He has received awards for outstanding contributions during the Trachoma Control Program Impact Assessment and for good academic performance during his time at Adigrat University.

Publications Noted & Contributions:

Aleka has contributed significantly to the academic community through his publications. Notable contributions include research on COVID-19 detection, disease classification, sentiment analysis, and IoT applications. He has published over 12 articles in reputable journals and has also served as a peer reviewer for multiple journals.

Atelectasis detection in chest X-ray images using convolutional neural networks and transfer learning with anisotropic diffusion filter

Journal: Informatics in Medicine Unlocked

Year: 2024

DOI: 10.1016/J.IMU.2024.101448

Contributors: Aleka Melese Ayalew, Yohannes Agegnehu Bezabih, Biniyam Mulugeta Abuhayi, Asemrie Yemata Ayalew

Classification of pumpkin disease by using a hybrid approach

Journal: Smart Agricultural Technology

Year: 2024

DOI: 10.1016/J.ATECH.2024.100398

Contributors: Yohannes Agegnehu Bezabh, Biniyam Mulugeta Abuhayi, Aleka Melese Ayalew, Asegie, Habtamu Ayenew

Lumbar Disease Classification Using an Involutional Neural Based VGG Nets (INVGG)

Journal: IEEE Access

Year: 2024

DOI: 10.1109/ACCESS.2024.3367774

Contributors: Biniyam Mulugeta Abuhayi, Yohannes Agegnehu Bezabh, Aleka Melese Ayalew

X-ray image-based pneumonia detection and classification using deep learning

Journal: Multimedia Tools and Applications

Year: 2024

DOI: 10.1007/S11042-023-17965-4

Contributors: Asnake, Nigus Wereta; Salau, Ayodeji Olalekan; Aleka Melese Ayalew

Classification of Mango Disease Using Ensemble Convolutional Neural Network

Journal: Smart Agricultural Technology

Year: 2024 (May)

DOI: 10.1016/j.atech.2024.100476

Contributors: Yohannes Agegnehu Bezabh, Aleka Melese Ayalew, Biniyam Mulugeta Abuhayi, Tensay Nigussie Demlie, Eshete Ayenew Awoke, Taye Endeshaw Mengistu

Research Timeline:

Aleka Melese Ayalew’s research timeline reflects a continuous dedication to academic and professional growth. Starting from his undergraduate years in 2014, he progressed to obtain his Master’s degree in 2021. Throughout this journey, he has actively engaged in research, publication, and peer review activities.

Collaborations and Projects:

Aleka has collaborated with colleagues and researchers in various projects and conferences. Notable collaborations include working with Dr. Belay Enyew and Dr. Yelkal Mulualem at the University of Gondar. He has also participated in conferences such as the International Conference on Decision Aid Sciences and Applications and the Deep Learning Indaba Conference.