Nael Radwan | Computer Science | Research Excellence Award

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Dr. Nael Radwan | Computer Science | Research Excellence Award

Dr. Nael Radwan is a computer science researcher specializing in Internet of Things, network security, and computer networks, with strong expertise in protocol optimization and distributed systems. His research focuses on securing IoT environments through adaptive flow control, authentication mechanisms, and performance evaluation under high-load conditions. He has contributed multiple peer-reviewed publications addressing MQTT protocol security and system resilience. His academic experience includes teaching, curriculum design, and student mentoring across diverse computing disciplines. He integrates research with teaching, emphasizing outcomes-based education, instructional technology, and ethical computing, while contributing to academic assessment, program development, and innovation in technology-enhanced learning environments.

Citation Metrics (Google Scholar)

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Citations
1111

h-index
22

🟦 Citations    🟥 i10-index    🟩 h-index


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Featured Publications


A Study: The Future of the Internet of Things and Its Home Applications

– International Journal of Computer Science and Information Security


Big Data Ethics

– International Journal of Computer Science and Information Security


MQTT in Focus: Understanding the Protocol and Its Recent Advancements

– International Journal of Computer Science and Security


Underwater Communication through Medium Access Control

– International Journal of Computer Science

 

Ouiem Bchir | Computer Science | Research Excellence Award

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Prof. Ouiem Bchir | Computer Science | Research Excellence Award

Professor | Computer Science Department, King Saud University | Saudi Arabia

Prof. Ouiem Bchir is a distinguished researcher in computer science with expertise in machine learning, deep learning, computer vision, and pattern recognition. Her research focuses on clustering techniques, semi-supervised and unsupervised learning, hyperspectral image analysis, and intelligent systems for healthcare, security, and multimedia applications. She has contributed extensively to advanced methodologies such as autoencoders, convolutional neural networks, and fuzzy clustering models. With a strong publication record, she has achieved an h-index of 12, with 527 citations across 65 documents. Her research approach integrates theoretical innovation with practical applications, significantly advancing intelligent data analysis and decision-making systems.

Citation Metrics (Scopus)

600

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300

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0

Citations
527

Documents
65

h-index
12

🟦 Citations    🟥 Documents    🟩 h-index


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    View Google Scholar Profile

Featured Publications

Ayogeboh Epizitone | Information | Research Excellence Award

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Dr. Ayogeboh Epizitone | Information | Research Excellence Award

Durban University of Technology | South Africa

Dr. Ayogeboh Epizitone is an interdisciplinary researcher specializing in information systems, business information management, and data-driven technologies. His research focuses on enterprise resource planning, health information systems, and advanced data analytics, integrating artificial intelligence, machine learning, and big data to enhance decision-making and organizational efficiency. He has contributed extensively through scholarly publications addressing healthcare analytics, ERP implementation, and digital transformation in education and business sectors. His professional experience includes academic teaching, research supervision, and project management, alongside consultancy in data systems and business intelligence. His publications record includes 15 documents with 119 citations and an h-index of 6, reflecting impactful contributions and a strong commitment to innovative, scalable, and sustainable research solutions.

Citation Metrics (Scopus)

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75

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Citations
119

Documents
15

h-index
6

🟦 Citations 🟥 Documents 🟩 h-index


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    View Orcid Profile
     View Google Scholar Profile

Featured Publications

Jun Tang | Computer Science | Best Researcher Award

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Mr. Jun Tang | Computer Science | Best Researcher Award

AI Algorithm Researcher | Chengdu Zhihui Heneng City Technology | China

Mr. Jun Tang is a researcher specializing in intelligent transportation and autonomous driving, with a strong focus on the integration of computer vision and artificial intelligence to enhance vehicular perception and decision making systems. His research primarily explores large vision foundation models and their applications in object detection, scene understanding, and adaptive driving environments. He has contributed to developing advanced detection frameworks that leverage reinforcement learning to improve recognition accuracy, robustness, and real time responsiveness in dynamic traffic conditions. Mr. Tang’s recent interests include prompt-guided object detection methods that utilize natural language and contextual cues to refine visual understanding within autonomous systems. Through his work at Chengdu Zhihui Heneng City Technology, he plays a key role in bridging the gap between theoretical AI models and practical intelligent mobility applications, fostering innovations that advance the safety, efficiency, and scalability of next generation transportation systems. His interdisciplinary approach combines deep learning, machine perception, and cognitive automation, contributing to the development of more adaptive and human like decision making in autonomous vehicles.

Profile: Orcid

Featured Publications

Tang, J., Li, D., Yang, J., Chen, J., & Yuan, R. (2025). Leveraging large visual models for enhanced object detection: An improved SAM-YOLOv5 model. Knowledge-Based Systems, 114757.

Tang, J. (2025, August 29). RT-DETR-based intelligent transportation object detection optimization method and system with prompt mechanism fusion.

Tang, J. (2025, May 27). Object detection method and system based on prompt engineering and regional text description.

Tang, J. (2025, April 11). Quantitative evaluation method and system for multimodal large models.

Tang, J. (2025, January 17). Evaluation method and system for urban governance multimodal large models based on text labeling.

Nooshin Nemati | Computer Science | Best Researcher Award

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Ms. Nooshin Nemati | Computer Science | Best Researcher Award

Ankara University, Turkey

Dr. Nooshin Nemati is a dedicated researcher in the fields of Artificial Intelligence, Deep Learning, and Medical Image Processing, currently pursuing her PhD in Computer Engineering at Ankara University, where she also contributes to multiple AI-based cancer detection projects. She holds a Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University and a Bachelor’s from Qazvin Azad University.

Profile:

Educational Background:

Nooshin Nemati is currently a PhD candidate in Computer Engineering at Ankara University. She earned her Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University with a completed her undergraduate studies at Qazvin Azad University in Iran.

Research Areas:

Her main research interests lie in Artificial Intelligence, Deep Learning, Medical Image Processing, and Computer Vision, particularly applied to cancer detection in histopathology images. She focuses on segmentation, classification, and detection tasks using advanced deep learning frameworks.

Projects and Contributions:

She has actively contributed to significant research initiatives such as the TUBITAK 1001 Project, focused on deep learning methodologies for breast cancer detection, and the BAP Project, which deals with cancer region detection in histopathology images. She has also been involved in the development of important datasets such as NuSeC and MiDeSeC, aimed at supporting machine learning in medical imaging. In addition, she has applied her technical skills in software development projects including system analysis and automation tools for banks.

Technical Skills:

Nooshin is proficient in AI, Machine Learning, Deep Learning, and programming frameworks such as ASP.NET and WordPress. She also holds certifications like Network+ and CCNA, showcasing her broad technical competence.

Citation Metrics:

  • Total Citations: 75

  • Citations Since 2020: 71

  • h-index: 6

  • h-index Since 2020: 5

  • i10-index: 3

  • i10-index Since 2020: 3

Publication Top Notes:

  • An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images
    2023
    Citations: 22

  • Detection of colorectal cancer with vision transformers
    2022
    Citations: 11

  • Effect of color normalization on nuclei segmentation problem in H&E stained histopathology images
    2022
    Citations: 10

  • A hybridized deep learning methodology for mitosis detection and classification from histopathology images
    2023
    Citations: 8

  • CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images
    2024
    Citations: 7

 

Ali Raza | Computer Science and Artificial Intelligence | Young Scientist Award

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Mr. Ali Raza | Computer Science and Artificial Intelligence | Young Scientist Award

lecturer The University Of Lahore Pakistan

Ali Raza is a passionate researcher, educator, and developer specializing in computer science. With a strong academic background and extensive experience in machine learning, deep learning, and computer vision, he has contributed significantly to cutting-edge research. Currently serving as a Lecturer at the University of Lahore, Ali has also worked as a Visiting Lecturer at KFUEIT and a Full Stack Python Developer in the software industry. His expertise lies in AI-driven solutions, research writing, and technological advancements in artificial intelligence.

Profile

Google Scholar

Education 🎓

  • MS Computer Science (2021-2023) | Khwaja Fareed University of Engineering and Information Technology (KFUEIT), CGPA: 3.93
  • BS Computer Science (2017-2021) | KFUEIT, CGPA: 3.47

Professional Experience 💼

  • Lecturer | University of Lahore (2024 – Present)
  • Visiting Lecturer | KFUEIT (2022 – 2023)
  • Full Stack Python Developer | BuiltinSoft Software Industry (2020 – 2021)

Research Interests 📈

Ali Raza’s research focuses on artificial intelligence, machine learning, deep learning, and computer vision. He is particularly interested in developing AI-driven solutions for medical imaging, agricultural applications, and energy consumption prediction. His contributions span multiple domains, showcasing his ability to integrate AI with real-world challenges.

Awards & Certifications 🏆

  • Best Researcher Award | ScienceFather (26/06/2024)
  • Use of Generative AI in Higher Education | Punjab Higher Education Commission
  • Machine Learning with Python (ML0101EN) | IBM Developer Skills Network

Publications Top Notes: 📚

Ali Raza has authored 61 research publications in reputed journals with high impact factors. Below are some of his recent publications:

“Novel Transfer Learning Approach for Hand Drawn Mathematical Geometric Shapes Classification” (2025) PeerJ Computer Science (IF: 3.8)

“Citrus Diseases Detection Using Innovative Deep Learning Approach and Hybrid Meta-Heuristic” (2025) PLOS ONE (IF: 2.9)

“Novel Deep Neural Network Architecture Fusion for Energy Consumption Prediction” (2025) PLOS ONE (IF: 2.9)

“Novel Transfer Learning Based Bone Fracture Detection Using Radiographic Images” (2025) BMC Medical Imaging (IF: 2.9)

“Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crops” (2025) Food Science & Nutrition (IF: 3.5)

“BdSentiLLM: A Novel LLM Approach to Sentiment Analysis of Product Reviews” (2024) IEEE Access (IF: 3.4)

“An Innovative Artificial Neural Network Model for Smart Crop Prediction” (2024) PeerJ Computer Science (IF: 3.8)

“Enhanced Interpretable Thyroid Disease Diagnosis Using Synthetic Oversampling and Machine Learning” (2024) BMC Medical Informatics (IF: 3.3)

“Diagnosing Epileptic Seizures Using EEG Data and Independent Components” (2024) Digital Health (IF: 3.7)

“A Novel Meta Learning Based Approach for Thyroid Syndrome Diagnosis” (2024) PLOS ONE (IF: 2.9)

 

João Oliveira | Computer Science | Best Researcher Award

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Mr. João Oliveira | Computer Science | Best Researcher Award

Researcher Instituto de Telecomunicações Portugal

João Diogo Videira Oliveira is a dedicated researcher in vehicular communications and telematics engineering, currently contributing to advanced research at the Instituto de Telecomunicações in Aveiro, Portugal. With a strong background in computer and telematics engineering, João’s work focuses on enhancing communication protocols and intelligent transportation systems (ITS).

Profile

Orcid

Education 🎓

  • M.Sc. in Computer and Telematics Engineering (2021–2023)
    Institution: Universidade de Aveiro, Portugal | ua.pt
  • B.Sc. in Computer and Telematics Engineering Sciences (2018–2021)
    Institution: Universidade de Aveiro, Portugal | ua.pt

João has excelled academically, building a robust foundation in vehicular networks and communication protocols.

Experience 🛠️

  • Researcher | Instituto de Telecomunicações, Aveiro, Portugal
    Duration: March 2024 – Present
  • Research Scholarship | Instituto de Telecomunicações, Aveiro, Portugal
    Duration: February 2023 – February 2024

João’s experience involves extensive work on V2X systems, C-ITS protocols, and simulation frameworks like Vanetza and Artery V2X, contributing to innovations in vehicular communication and safety systems.

Research Interests 🔍

João’s primary research interests include:

  • Vehicular Communications (V2X)
  • Communication Protocols for ITS (C-ITS, ITS-G5)
  • Simulation frameworks such as OMNeT++, SUMO, and Artery V2X
  • Automated driving and fault simulation systems

His work addresses challenges in maneuver coordination, safety systems, and intelligent transportation technologies.

Awards & Certifications 🏆

  • Fault Simulation Training | Segula Testcenter, Rodgau (October 2024)
    • Training focused on safety driver responsibilities and practical driving maneuvers involving error scenarios.
    • Developed expertise in automated and partially automated vehicle systems.

Publications Top Notes: 📚

A Maneuver Coordination Analysis Using Artery V2X Simulation Framework (2024)

Reference: Oliveira, J., Vieira, E., Almeida, J., Ferreira, J., & Bartolomeu, P. C. (2024).
Electronics, 13(23), 4813.
Read here: https://doi.org/10.3390/electronics13234813

Cited by: Researchers working on V2X communication protocols and vehicular network safety systems.