Hossein Nematzadeh | Computer Science | Best Researcher Award

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Dr. Hossein Nematzadeh | Computer Science | Best Researcher Award

Assist Prof at Universidad de Malaga, Spain

Dr. Hossein Nematzadeh is an accomplished researcher and academic with a Ph.D. in Computer Science from the University of Technology, Malaysia. He is currently an Assistant Professor at the Modern College of Business and Science in Oman, with prior experience as a researcher at Universidad de Málaga, Spain, and an assistant professor at Islamic Azad University, Iran. His research interests span Data Science, Artificial Intelligence, Cryptography, and Software Engineering, with a particular focus on explainable AI, feature selection, evolutionary algorithms, and image encryption. Dr. Nematzadeh has published extensively in high-impact journals, contributing to advancements in AI and machine learning. He is also an experienced educator, having taught a wide array of computer science courses at various academic levels. With expertise in technologies like Python, MATLAB, and AWS, he is committed to both advancing research and mentoring the next generation of computer scientists.

Professional Profile 

Education

Dr. Hossein Nematzadeh has a strong academic foundation in Computer Science, having completed his Ph.D. at the University of Technology, Malaysia in 2014. Prior to his doctoral studies, he earned his Master’s degree from the same institution in 2009, further solidifying his expertise in the field. Dr. Nematzadeh also holds a Bachelor’s degree from Mazandaran University of Science and Technology, obtained in 2007. His educational journey reflects a deep commitment to the study of computer science, particularly in areas such as Artificial Intelligence, Data Science, and Cryptography. Throughout his academic career, he has gained a robust understanding of both theoretical and practical aspects of the field, which has informed his subsequent research and teaching. This solid educational background, combined with his ongoing research contributions, enables him to be a leader in his academic and professional endeavors.

Professional Experience

Dr. Hossein Nematzadeh has extensive professional experience in academia and research. He is currently serving as an Assistant Professor at the Modern College of Business and Science in Oman, where he teaches and supervises students in the field of Computer Science. Prior to this role, he was a researcher at Universidad de Málaga in Spain from 2021 to 2024, contributing to several high-impact research projects in Artificial Intelligence and Data Science. From 2012 to 2021, he served as an Assistant Professor at Islamic Azad University in Iran, where he taught various computer science courses and engaged in research activities. Throughout his career, Dr. Nematzadeh has built a reputation as both an educator and a researcher, publishing extensively in leading journals and presenting his work in international forums. His expertise spans across Data Science, Artificial Intelligence, and Cryptography, making him a prominent figure in these fields.

Research Interest

Dr. Hossein Nematzadeh’s research interests lie at the intersection of Data Science, Artificial Intelligence, Cryptography, and Software Engineering. He is particularly focused on developing advanced techniques in explainable AI, feature selection, and noise detection, with an emphasis on making AI models more interpretable and reliable. His work in evolutionary algorithms and fuzzy logic explores ways to optimize decision-making processes and improve system performance. Dr. Nematzadeh is also passionate about cryptography, specifically in areas such as image encryption, which contributes to enhancing data security in digital environments. Additionally, he has a strong interest in software engineering, with research dedicated to verification and validation processes, as well as the application of Petri nets to model and analyze complex systems. His research aims to push the boundaries of AI and machine learning, providing solutions to both theoretical and practical challenges in these rapidly evolving fields.

Award and Honor

Dr. Hossein Nematzadeh has earned recognition for his contributions to research and academia throughout his career. He has received several honors for his work in the fields of Data Science, Artificial Intelligence, and Cryptography, particularly for his research on explainable AI and feature selection methods. Dr. Nematzadeh’s scholarly impact is reflected in his publications in prestigious journals such as Engineering Applications of Artificial Intelligence and Knowledge-Based Systems. His work has been widely cited, demonstrating the influence of his research on the scientific community. In addition to his academic accomplishments, Dr. Nematzadeh has been actively involved in mentoring students and contributing to the advancement of his field through teaching and supervision. His dedication to fostering new talent in Computer Science and his continuous pursuit of research excellence have earned him respect within academic circles, making him a highly regarded figure in the global academic and research community.

Publications Top Noted

  • Title: Medical image encryption using a hybrid model of modified genetic algorithm and coupled map lattices
    Authors: H Nematzadeh, R Enayatifar, H Motameni, FG Guimarães, VN Coelho
    Year: 2018
    Cited by: 157
  • Title: A hybrid feature selection method based on information theory and binary butterfly optimization algorithm
    Authors: Z Sadeghian, E Akbari, H Nematzadeh
    Year: 2021
    Cited by: 116
  • Title: Heuristic filter feature selection methods for medical datasets
    Authors: M Alirezanejad, R Enayatifar, H Motameni, H Nematzadeh
    Year: 2020
    Cited by: 78
  • Title: Binary search tree image encryption with DNA
    Authors: H Nematzadeh, R Enayatifar, M Yadollahi, M Lee, G Jeong
    Year: 2020
    Cited by: 72
  • Title: Frequency based feature selection method using whale algorithm
    Authors: H Nematzadeh, R Enayatifar, M Mahmud, E Akbari
    Year: 2019
    Cited by: 66
  • Title: Emergency role-based access control (E-RBAC) and analysis of model specifications with alloy
    Authors: F Nazerian, H Motameni, H Nematzadeh
    Year: 2019
    Cited by: 52
  • Title: Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network
    Authors: M Asghari, H Nematzadeh
    Year: 2016
    Cited by: 51
  • Title: A novel image security technique based on nucleic acid concepts
    Authors: M Yadollahi, R Enayatifar, H Nematzadeh, M Lee, JY Choi
    Year: 2020
    Cited by: 33
  • Title: Mapping to convert activity diagram in fuzzy UML to fuzzy petri net
    Authors: H Motameni, A Movaghar, I Daneshfar, H Nemat Zadeh, J Bakhshi
    Year: 2008
    Cited by: 30
  • Title: Automatic ensemble feature selection using fast non-dominated sorting
    Authors: S Abasabadi, H Nematzadeh, H Motameni, E Akbari
    Year: 2021
    Cited by: 28
  • Title: A mixed solution-based high agreement filtering method for class noise detection in binary classification
    Authors: M Samami, E Akbari, M Abdar, P Plawiak, H Nematzadeh, ME Basiri, …
    Year: 2020
    Cited by: 24
  • Title: Comparison of Decision Tree Methods in Classification of Researcher’s Cognitive Styles in Academic Environment
    Authors: ZN Balagatabi, R Ibrahim, HN Balagatabi
    Year: 2015
    Cited by: 24

Sarra Jebri | Computer Science | Best Researcher Award

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Dr. Sarra Jebri | Computer Science | Best Researcher Award

Assistant professor of National Engineering School of Gabes, Tunisia

Sarra Jebri is an accomplished researcher and educator with a robust background in telecommunications. She earned her PhD from Ecole Nationale d’Ingénieurs de Tunis, specializing in IoT security and privacy, and has a comprehensive educational foundation that includes a National Diploma of Engineering and a Master’s in Instrumentation and Communication. Her professional experience includes significant teaching roles at the National School of Engineers in Gabès, reflecting her expertise in the field. Jebri’s research focuses on critical areas such as security, mutual authentication, and IoT, with several influential publications presented at international conferences. 🌟🌿🔬

Professional profile

Education📚

Sarra Jebri holds a PhD in Telecommunications from Ecole Nationale d’Ingénieurs de Tunis (ENIT) with a focus on Internet of Things (IoT) security and privacy. Her academic journey includes a National Diploma of Engineering in Communications and Networks from Ecole Nationale d’Ingénieurs de Gabès (ENIG), where she studied IPTV services, and a Master’s degree in Instrumentation and Communication from the Faculty of Sciences of Sfax, focusing on information system design. She also has a Bachelor’s Degree in Mathematics. Her diverse educational background underscores her strong foundation in telecommunications and related fields, preparing her well for research excellence.

Professional Experience🏛️

Sarra Jebri has extensive teaching experience in telecommunications, having served as a Contractual Teacher at the National School of Engineers in Gabès from 2020 to 2024 and as a Vacant Teacher from 2014 to 2019. This teaching experience, combined with her practical knowledge in the field, demonstrates her capability in both academic and applied research environments.

Research Interest🌐

Sarra Jebri’s research focuses on security, mutual authentication, privacy, and IoT. Her recent publications include notable works such as “Local Learning-based Collaborative Authentication In Edge-Fog Network” and “Light Automatic Authentication of Data Transmission in 6G/IoT Healthcare System,” presented at prestigious international conferences. Her research contributions are recognized through multiple conference papers, indicating a strong engagement with current challenges in her field.

Certifications🏆

Jebri has obtained several certifications, including Cisco and Huawei networking and security qualifications, and has strong programming skills in C, C++, Java, and Matlab. Her certifications reflect her ongoing commitment to professional development and her ability to apply theoretical knowledge in practical scenarios.

Publications top noted📜

Bechoo Lal | Computer Science | Best Researcher Award

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Dr. Bechoo Lal | Computer Science | Best Researcher Award

Associate Professor of KLEF- KL University Vijayawada Campus Andhra Pradesh, India

Dr. Bechoolal 🌟 is an esteemed Associate Professor in Computer Science/Data Science with a passion for inspiring students through a deep understanding of technology and research. With a solid academic foundation that includes a PGP in Data Science from Purdue University and multiple PhDs in Information Systems and Computer Science 🎓, he brings a wealth of expertise to his teaching and research. Dr. Bechoolal has extensive experience in various institutions, from KLEF KL Deemed University to Western College 🏫, and has made significant contributions through his numerous research publications and certifications 🏅. His interests span Machine Learning, Data Science, and programming languages, and he actively engages in projects that explore digital transformation and its societal impacts 💻🔍. Fluent in English and Hindi 🇬🇧🇮🇳, he continues to advance knowledge and inspire the next generation of tech professionals.

Publication profile

Education

Dr. Bechoolal 🎓 is a distinguished academic with a rich educational background in Computer Science and Data Science. He earned a PGP in Data Science from Purdue University 🌟, where he specialized in data regression models and predictive data modeling. Dr. Bechoolal holds multiple PhDs—one in Information Systems from the University of Mumbai and another in Computer Science from SJJT University 🧠. His foundational studies include a Master of Technology in Computer Science from AAI-Deemed University, a Master of Computer Applications from Banaras Hindu University, and an undergraduate degree in Statistics from MG. Kashi Vidyapeeth University 📚. His continuous quest for knowledge is also reflected in his various certifications, including Machine Learning from Stanford University and an IBM Data Science Professional Certificate 🏅.

Academic Qualification

  • 📜 PGP in Data Science (2020-2021) from Purdue University, USA – Specializing in data regression models, predictive data modeling, and accuracy analyzing using machine learning.
  • 📜 PhD in Information System (2015-2019) from the University of Mumbai, India – Research Area: Data Science.
  • 📜 PhD in Computer Science (2011-2015) from SJJT University, India – Research Area: Machine Learning.
  • 📜 Master of Technology (M. Tech) in Computer Science and Engineering (2004-2006) from AAI-Deemed University, Allahabad, India.
  • 📜 Master of Computer Application (MCA) (1995-1998) from Institute of Science, Banaras Hindu University (BHU), India.
  • 📜 Graduation (Statistics-Hons) (1990-1993) from the Department of Mathematics and Statistics, MG Kashi Vidyapeeth University, India.

Data Science Certifications and Training

  • 🎓 Machine Learning, Stanford University, USA (2020)
  • 🎓 IBM Data Science Professional Certificate (2020)
  • 🎓 Data Science and Big Data Analytics (2019), ICT Academy, Govt. of India
  • 🎓 Security Fundamentals, Microsoft Technology Associate (2017)
  • 🎓 Intelligent Multimedia Data Warehouse and Mining (2009), University of Mumbai
  • 🎓 Python Programming (2017), University of Mumbai, India

 

Teaching Interest 

  • 📘 Data Science/Machine Learning
  • 📘 Database 📘 C/C++/Python Programming Languages
  • 📘 Software Engineering

Research Interest

  • 🔍 Machine Learning
  • 🔍 Data Science

Computer Science/Data Science Skills

💻 Machine Learning, Data Visualization, Big Data Analytics

📊 Predictive Modelling: Supervised Learning (Linear and Logistic Regression, Decision Tree, Support Vector Machine (SVM), Naïve Bayes Classifiers), Unsupervised Learning (K-Means clustering, principal components analysis (PCA))

💻 Programming Languages: Python (NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn), SPSS, R-Programming

💻 Operating Systems/Platforms: UNIX/LINUX, WINDOWS, MS-DOS

💻 C/C++, CORE JAVA Programming Languages

💻 DBMS/RDBMS: Oracle, SQL, MySQL, NoSQL

Publication top notes

  • Improving migration forecasting for transitory foreign tourists using an Ensemble DNN-LSTM model
    Authors: Nanjappa, Y., Kumar Nassa, V., Varshney, G., Pandey, S., V Turukmane, A.
    Journal: Entertainment Computing
    Year: 2024
    Citations: 0 📅
  • Using social networking evidence to examine the impact of environmental factors on social followings: An innovative Machine learning method
    Authors: Murthy, S.V.N., Ramesh, P.S., Padmaja, P., Reddy, G.J., Chinthamu, N.
    Journal: Entertainment Computing
    Year: 2024
    Citations: 0 📅
  • Real-Time Convolutional Neural Networks for Emotion and Gender Classification
    Authors: Singh, J., Singh, A., Singh, K.K., Samudre, N., Raperia, H.
    Conference: Procedia Computer Science
    Year: 2024
    Citations: 0 📅
  • Identification of Brain Diseases using Image Classification: A Deep Learning Approach
    Authors: Singh, J., Singh, A., Singh, K.K., Turukmane, A.V., Kumar, A.
    Conference: Procedia Computer Science
    Year: 2024
    Citations: 0 📅
  • Fake News Detection Using Transfer Learning
    Authors: Singh, J., Sahu, D.P., Gupta, T., Lal, B., Turukmane, A.V.
    Conference: Communications in Computer and Information Science
    Year: 2024
    Citations: 0 📅
  • Reliability Evaluation of a Wireless Sensor Network in Terms of Network Delay and Transmission Probability for IoT Applications
    Authors: Mishra, P., Dash, R.K., Panda, D.K., Lal, B., Sujata Gupta, N.
    Journal: Contemporary Mathematics (Singapore)
    Year: 2024
    Citations: 0 📅
  • TRANSFER LEARNING METHOD FOR HANDLING THE INTRUSION DETECTION SYSTEM WITH ZERO ATTACKS USING MACHINE LEARNING AND DEEP LEARNING
    Authors: Upender, T., Lal, B., Nagaraju, R.
    Conference: ACM International Conference Proceeding Series
    Year: 2023
    Citations: 0 📅
  • Monitoring and Sensing of Real-Time Data with Deep Learning Through Micro- and Macro-analysis in Hardware Support Packages
    Authors: Lal, B., Chinthamu, N., Harichandana, B., Sharmaa, A., Kumar, A.R.
    Journal: SN Computer Science
    Year: 2023
    Citations: 0 📅
  • An Efficient QRS Detection and Pre-processing by Wavelet Transform Technique for Classifying Cardiac Arrhythmia
    Authors: Lal, B., Gopagoni, D.R., Barik, B., Kumar, R.D., Lakshmi, T.R.V.
    Journal: International Journal of Intelligent Systems and Applications in Engineering
    Year: 2023
    Citations: 0 📅
  • IOT-BASED Cyber Security Identification Model Through Machine Learning Technique
    Authors: Lal, B., Ravichandran, S., Kavin, R., Bordoloi, D., Ganesh Kumar, R.
    Journal: Measurement: Sensors
    Year: 2023
    Citations: 3 📅📈

Meiyan Liang | Computer Science | Best Researcher Award

Published on by Global Scholar Awards

🌟Assoc Prof Dr. Meiyan Liang, Computer Science, Best Researcher Award 🏆

  •  Associate Professor at Shanxi University, China

Meiyan Liang, PhD, is an accomplished researcher in the field of Instrument Science and Technology, with a focus on Deep Learning and Medical Image Processing. Currently affiliated with the School of Physics and Electronic Engineering at Shanxi University in China, Dr. Liang completed her PhD at the Opto-Electronic College, Beijing Institute of Technology. She has made significant contributions to the development of innovative technologies for the identification and classification of various medical conditions, particularly in cancer diagnosis. Her work spans both theoretical and experimental domains, with a particular emphasis on leveraging neural networks and terahertz imaging techniques. Dr. Liang’s expertise is recognized through numerous awards, patents, and a prolific publication record in prestigious journals.

Author Metrics

Scopus Profile

ORCID Profile

Dr. Liang’s research output is not only extensive but also impactful, as evidenced by her author metrics. She has consistently published in high-impact journals, demonstrating the significance of her work within the scientific community. Additionally, Dr. Liang’s patents highlight her innovative approach to problem-solving and technology development.

  • Citations: 138 citations across 136 documents
  • Documents: Authored 25 documents
  • h-index: 5

Education

Dr. Meiyan Liang obtained her PhD in Instrument Science and Technology from the Opto-Electronic College at Beijing Institute of Technology. Her doctoral research focused on the application of deep learning methodologies in medical image processing, particularly for cancer diagnosis.

Research Focus

Dr. Liang’s research primarily centers around two main areas: Deep Learning and Medical Image Processing. Within these domains, she specializes in utilizing neural networks for the interpretation and analysis of medical images, with a particular emphasis on cancer detection and classification. Her work also involves the integration of advanced imaging techniques, such as terahertz imaging, to develop novel diagnostic tools.

Professional Journey

Following her doctoral studies, Dr. Liang embarked on a professional journey that has seen her become an esteemed researcher in the field of medical imaging. She has held positions at various academic institutions, including her current role at Shanxi University. Throughout her career, Dr. Liang has secured research funding, published extensively, and obtained several patents for her innovative contributions to the field.

Honors & Awards

Dr. Liang’s outstanding contributions to her field have been recognized through numerous honors and awards. Notable accolades include being awarded the “Sanjin talent” by the government of Shanxi Province and receiving the “China Instrument & Control Society Scholarship” from the Chinese instrumentation society.

Research Timeline

Dr. Liang’s research timeline showcases her progression as a researcher and the evolution of her research interests. Starting from her doctoral studies, she has continued to expand her expertise and contribute to advancements in medical imaging technology. Her research timeline reflects a commitment to excellence and a dedication to addressing critical challenges in healthcare through innovative research.

Publications Noted & Contributions

Dr. Liang has made significant contributions to the academic community through her prolific publication record. Her research findings have been published in prestigious journals such as the IEEE Journal of Biomedical and Health Informatics, Computer Methods and Programs in Biomedicine, and The Visual Computer. These publications cover a wide range of topics, including interpretable inference, whole-slide image prediction, and pathology image restoration.

Title: Interpretable Inference and Classification of Tissue Types in Histological Colorectal Cancer Slides Based on Ensembles Adaptive Boosting Prototype Tree

  • Authors: Liang, M., Wang, R., Liang, J., Zhang, T., Zhang, C.
  • Journal: IEEE Journal of Biomedical and Health Informatics, 2023, 27(12), pp. 6006–6017
  • Abstract: This paper presents a method for interpretable inference and classification of tissue types in histological colorectal cancer slides using ensembles adaptive boosting prototype tree.

Title: Multi-scale self-attention generative adversarial network for pathology image restoration

  • Authors: Liang, M., Zhang, Q., Wang, G., Liu, H., Zhang, C.
  • Journal: Visual Computer, 2023, 39(9), pp. 4305–4321
  • Abstract: This paper introduces a multi-scale self-attention generative adversarial network for pathology image restoration.
  • Citations: 1

Title: Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network

  • Authors: Liang, M., Chen, Q., Li, B., Jiang, X., Zhang, C.
  • Journal: Computer Methods and Programs in Biomedicine, 2023, 229, 107268
  • Abstract: This paper proposes an interpretable classification method for pathology whole-slide images using an attention-based context-aware graph convolutional neural network.
  • Citations: 6

Title: A novel strategy regarding geometric product for liquids discrimination based on THz reflection spectroscopy

  • Authors: Liu, H., Liu, X., Zhang, Z., Liang, M., Zhang, C.
  • Journal: Spectrochimica Acta – Part A: Molecular and Biomolecular Spectroscopy, 2022, 274, 121104
  • Abstract: This paper proposes a novel strategy for liquids discrimination based on THz reflection spectroscopy using the geometric product.
  • Citations: 1

Title: THz ISAR imaging using GPU-accelerated phase compensated back projection algorithm

  • Authors: Liang, M.-Y., Ren, Z.-Y., Li, G.-H., Zhang, C.-L., Fathy, A.E.
  • Journal: Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves, 2022, 41(2), pp. 448–456
  • Abstract: This paper presents THz ISAR imaging using a GPU-accelerated phase-compensated back projection algorithm.
  • Citations: 3