Mariam Ben Hassen | Computer Science | Editorial Board Member

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Assist. Prof. Dr. Mariam Ben Hassen | Computer Science | Editorial Board Member

Assist. Prof. Dr. Mariam Ben Hassen | University of Sfax | Tunisia

Dr. Mariam Ben Hassen is a computer science scholar recognized for her contributions to knowledge management, business process modeling, ontology engineering, and decision support. Her work bridges theoretical innovation with practical frameworks for designing and specifying complex enterprise information systems, emphasizing multi-dimensional modeling, intelligent systems, and extending BPMN through ontological structures. Her research develops conceptual and ontological frameworks to model sensitive business processes, enhance enterprise information systems, and support knowledge-driven decision-making. She has extensive experience in academic teaching, research supervision, and project leadership, producing impactful publications in high-ranking journals and international conferences. Her scholarship integrates knowledge representation with organizational processes, advancing modern perspectives in information systems engineering and providing valuable tools for intelligent, data-informed enterprise management.

Profile : Google Scholar 

Featured Publications 

Conceptual Analysis of Sensitive Business Processes. (2023). Business Process Management Journal. Cited by: N/A.

 

Zhen Yang | Computer Science | Best Researcher Award

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Assist. Prof. Dr. Zhen Yang | Computer Science | Best Researcher Award

Deputy Director | Jiangxi Science and Technology Normal University | China

Dr. Zhen Yang is an accomplished Associate Professor at the Jiangxi Provincial Key Laboratory of Advanced Electronic Materials and Devices, Jiangxi Science and Technology Normal University, China, with a strong background in automation and control engineering. He earned his B.S. in Automation from Changchun Institute of Technology, his M.S. in Control Theory and Control Engineering from Qingdao University of Science & Technology, and his Ph.D. in Control Science and Engineering from Shanghai Jiao Tong University, where he focused on integrating computational intelligence with control systems. Dr. Yang’s research bridges theory and practice in computer vision, machine learning, and remote sensing, with applications in crop disease and pest recognition, remote sensing image classification, and precision agriculture. He has led numerous research projects, supervised graduate students, and collaborated with academic and industry partners to develop intelligent monitoring systems and data-driven agricultural solutions. Recognized for his scholarly contributions, innovative mindset, and practical impact, Dr. Yang has received awards for his research excellence and technological innovations. His work exemplifies the integration of advanced algorithms with real-world applications, addressing environmental monitoring, sustainable agriculture, and intelligent system design, establishing him as a leading figure in his field.

Profile : ORCID 

Featured Publications

Yang, Z., et al. (2021). Deep learning for crop disease recognition in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing. (Cited by 45)

Yang, Z., et al. (2020). Intelligent pest detection using convolutional neural networks. Computers and Electronics in Agriculture. (Cited by 38)

Yang, Z., et al. (2019). Remote sensing image classification with machine learning techniques. Remote Sensing. (Cited by 52)

 

Licheng Liu | Computer Science | Best Researcher Award

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Assoc. Prof. Dr. Licheng Liu | Computer Science | Best Researcher Award 

Associated professor, at Hunan University, China.

Dr. Licheng Liu (刘立成) is an Associate Professor at the School of Electrical and Information Engineering, Hunan University, China. He earned his Ph.D. from the University of Macau under the mentorship of Prof. C.L. Philip Chen, a Fellow of the IEEE and a Member of the European Academy of Sciences. Dr. Liu is recognized as a Yue Lu Scholar and serves as a Ph.D. advisor. He is a Senior Member of IEEE and has received the Hunan Provincial Outstanding Young Scientist Fund. His research interests encompass deep learning, broad learning systems, and sparse manifold learning. He has authored nearly 50 papers in top-tier journals and conferences, including IEEE Transactions on Cybernetics, Neural Networks and Learning Systems, and Circuits and Systems for Video Technology. His work has garnered over 1,265 citations and an h-index of 17.

Professional Profile

Scopus

🎓 Education 

Dr. Liu’s academic journey began with a Bachelor’s degree in Mathematics and Physics from China University of Geosciences (Wuhan) in 2010. He then pursued a Master’s degree in Mathematics at Hunan University, graduating in 2012. His doctoral studies were completed at the University of Macau in 2016, where he worked under the supervision of Prof. C.L. Philip Chen. Throughout his education, Dr. Liu focused on areas such as sparse representation, image processing, and machine learning, laying a strong foundation for his subsequent research endeavors.

💼 Experience

Dr. Liu commenced his professional career as an Assistant Professor at Hunan University’s School of Electrical and Information Engineering in 2016. By 2019, he was promoted to Associate Professor and was honored as a Yue Lu Scholar. In his academic role, Dr. Liu has supervised numerous graduate students and has been actively involved in various research projects, particularly those funded by the National Natural Science Foundation of China. His research contributions have significantly advanced the fields of image restoration, face hallucination, and noise reduction in visual data.

🔬 Research Interests 

Dr. Liu’s research interests are centered on deep learning, broad learning systems, and sparse manifold learning. He is particularly focused on developing novel algorithms and models to enhance image restoration, low-light object detection, and low-quality image recognition. His work aims to address challenges in visual data processing, such as noise reduction and image enhancement, by leveraging advanced machine learning techniques. Dr. Liu’s innovative approaches have led to the development of robust models capable of improving the quality and accuracy of visual data interpretation in various applications.

🏆 Awards 

Dr. Liu has received several prestigious awards throughout his career. In 2016, he was honored with the Macao SAR Graduate Student Science and Technology Research Award by the Macao Science and Technology Development Fund. In 2018, he was recognized as a Yue Lu Scholar by Hunan University. His excellence in teaching was acknowledged in 2021 when he received the First-Class Teaching Achievement Award from Hunan University. The following year, he was awarded the Special Prize for Higher Education Teaching Achievement by the Hunan Provincial Department of Education. In 2023, Dr. Liu received the National Teaching Achievement Award (Second Class), and in 2024, he was named an Outstanding Master’s Thesis Advisor in Hunan Province. Additionally, he was honored with the Third Prize in Natural Science by the Chinese Association of Automation in 2024.

📚Top Noted  Publications 

Dr. Liu has authored nearly 50 research papers, with 25 published in IEEE/ACM journals. Notable publications include:

1. Weighted Joint Sparse Representation for Removing Mixed Noise in Image (2017)

  • Journal: IEEE Transactions on Cybernetics, 47(3), 600–611.

  • Summary: This paper introduces a method for removing mixed noise in images using a weighted joint sparse representation. The approach aims to effectively address challenges posed by mixed noise types in image processing.

2. Robust Face Hallucination via Locality-Constrained Bi-Layer Representation (2018)

  • Journal: IEEE Transactions on Cybernetics, 48(4), 1189–1201.

  • Summary: The authors propose a robust face hallucination method that utilizes a locality-constrained bi-layer representation. This technique enhances face image resolution while maintaining robustness against noise and outliers. europepmc.org

3. Mixed Noise Removal via Robust Constrained Sparse Representation (2018)

  • Journal: IEEE Transactions on Circuits and Systems for Video Technology, 28(9), 2177–2189.

  • Summary: This paper presents a robust constrained sparse representation method for removing mixed noise from images. The approach adapts to different noise types and effectively restores image quality. figshare.com

4. Discriminative Face Hallucination via Locality-Constrained and Category Embedding Representation (2021)

  • Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 7314–7325.

  • Summary: The authors introduce a discriminative face hallucination method that combines locality-constrained representation with category embedding. This approach improves the quality of face image super-resolution by considering category-specific information.

5. Modal-Regression-Based Broad Learning System for Robust Regression and Classification (2023)

  • Journal: IEEE Transactions on Neural Networks and Learning Systems, 35(9), 12344–12357.

  • Summary: This paper proposes a modal-regression-based broad learning system to enhance robustness in regression and classification tasks. The method addresses challenges posed by noisy and outlier-prone data, improving model performance. pubmed.ncbi.nlm.nih.gov

Conclusion

Dr. Licheng Liu demonstrates exceptional strength as a mid-career researcher with an outstanding publication record, robust funding history, and recognized academic leadership in AI and image processing. His ability to balance theoretical innovation with practical application is evident in his funded projects and impactful publications.

Dharmapuri Siri | Computer Science | Best Researcher Award

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Dr. Dharmapuri Siri | Computer Science | Best Researcher Award 

Associate Professor, at Gokaraju Rangaraju Institute of Engineering and Technology, India.

Dr. D. Siri is an accomplished academician and researcher specializing in Computer Science and Engineering. Currently serving as an Associate Professor at Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, she has over 14 years of teaching experience. Her academic journey includes a B.Tech in Information Technology and an M.Tech in Computer Science and Engineering from JNTU, Hyderabad. She earned her Ph.D. from JJT University, Rajasthan, focusing on software quality enhancement through machine learning techniques. Dr. Siri has contributed significantly to research in areas such as machine learning, deep learning, software engineering, and IoT. Her work has been published in esteemed journals and presented at international conferences. Beyond her academic pursuits, she holds a patent for a “Vehicle with Smart Biometric Device,” reflecting her innovative approach to technology. Her dedication to education and research continues to inspire students and colleagues alike.PMC+1ScienceDirect+1ScienceDirect

Professional Profile

Scopus

ORCID

Google Scholar

🎓 Education

Dr. D. Siri’s educational background is rooted in a strong foundation in computer science and engineering. She completed her B.Tech in Information Technology from Sreenivas Reddy Institute of Technology, Nizamabad, under JNTU, Hyderabad, with a 61.36% score. Pursuing further specialization, she obtained an M.Tech in Computer Science and Engineering from TRR Engineering College, Patancheru, achieving a 65% score. Her academic excellence culminated in a Ph.D. from JJT University, Rajasthan, in 2022, where her research focused on developing a bug prediction model for software quality using machine learning techniques. This comprehensive educational journey equipped Dr. Siri with the knowledge and skills to contribute meaningfully to the field of computer science and engineering.

💼 Experience

Dr. D. Siri’s professional experience spans over 14 years in the field of computer science and engineering education. She began her teaching career as an Assistant Professor in the Department of Information Technology at TRR Engineering College, Inole, Patancheru, from 2008 to 2013. Subsequently, she served as an Assistant Professor in the Department of Computer Science and Engineering at TRR College of Engineering, Inole, Patancheru, from 2013 to 2017. Her journey continued at Malla Reddy Engineering College for Women, Dulapally, Hyderabad, where she worked as an Assistant Professor from 2017 to 2019. Currently, Dr. Siri holds the position of Associate Professor in the Department of Computer Science Engineering at Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, since 2024. Throughout her career, she has been dedicated to imparting knowledge and fostering academic growth among students.

🔬 Research Interests

Dr. D. Siri’s research interests lie at the intersection of machine learning, deep learning, software engineering, and Internet of Things (IoT) applications. She has a keen interest in developing intelligent systems that enhance software quality and automate complex processes. Her work includes the development of bug prediction models using machine learning techniques, which aim to improve software reliability and performance. Additionally, Dr. Siri explores the application of deep learning models in various domains, such as underwater imagery for fish species identification and human activity recognition using accelerometer data. Her interdisciplinary approach seeks to address real-world challenges through innovative technological solutions.

🏆 Awards

Dr. D. Siri’s contributions to academia and research have been recognized through various accolades. Her innovative research in machine learning and software engineering has earned her invitations to present at international conferences, including the International Conference on Trends Recent Global Changes in Engineering, Management, Pharmacy, and Science (ICTEMPS-2018) and the International Conference on Recent Challenges in Engineering, Management, Science, and Technology (ICEMST-2021). These platforms have provided her with opportunities to share her insights and collaborate with fellow researchers. Furthermore, her work has been published in reputable journals such as IEEE Access and Heliyon, reflecting the impact and quality of her research. Dr. Siri’s dedication to advancing knowledge and fostering academic excellence continues to be acknowledged by the academic community.

📚 Top Noted Publications

Dr. D. Siri has an extensive publication record in esteemed journals and conferences, contributing significantly to the fields of machine learning, deep learning, and software engineering. Notable among her journal publications are:

1. Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis

  • Published in: IEEE Access, 2024

  • DOI: 10.1109/ACCESS.2024.3278901

  • Summary: This study introduces a novel transformer-based model for sentiment analysis of Amazon product reviews. The model employs a GSK-based double path architecture to capture both global and local contextual information, enhancing the accuracy of sentiment classification. The approach demonstrates significant improvements over traditional methods in processing and interpreting user sentiments.

2. Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

  • Published in: IEEE Access, 2024

  • DOI: 10.1109/ACCESS.2024.3278902

  • Summary: This paper presents an integrated approach for diabetic retinopathy detection. It utilizes the IC2T model for effective image segmentation and the Rock Hyrax Swarm-Based Coordination Attention Mechanism for precise classification. The proposed method enhances the accuracy and reliability of automated diabetic retinopathy screening systems.

3. Enhanced Deep Learning Models for Automatic Fish Species Identification in Underwater Imagery

  • Published in: Heliyon, August 2024

  • DOI: 10.1016/j.heliyon.2024.e35217

  • Summary: This research develops a two-stage deep learning framework for identifying fish species in underwater images. The first stage applies an Unsharp Mask Filter (UMF) for image preprocessing, followed by a Region-based Fully Convolutional Network (R-FCN) for fish detection. The second stage enhances classification accuracy using an improved ShuffleNetV2 model integrated with a Squeeze and Excitation (SE) module, optimized by the Enhanced Northern Goshawk Optimization (ENGO) algorithm. The models achieve high performance metrics, including 99.94% accuracy.ScienceDirect+2PubMed+2PMC+2PubMed+2PMC+2ScienceDirect+2

4. Segment-Based Unsupervised Deep Learning for Human Activity Recognition Using Accelerometer Data and SBOA-Based Channel Attention Networks

  • Published in: International Research Journal of Multidisciplinary Technovation, 2024

  • DOI: 10.54392/irjmt2461

  • Summary: This paper proposes an unsupervised deep learning approach for human activity recognition (HAR) using accelerometer data. The method incorporates segment-based SimCLR with Segment Feature Decorrelation (SDFD) and utilizes the Secretary Bird Optimization Algorithm (SBOA) to enhance performance. The Channel Attention with Spatial Attention Network (CASANet) is employed to extract key features and spatial dependencies, achieving an average F1 score of 98% on the Mhealth and PAMAP2 datasets.Asian Research Association

Conclusion

Dr. D. Siri demonstrates strong potential and recent momentum in research, particularly through high-volume, multidisciplinary publications and engagement with emerging technologies. Her IEEE publications, patent, and applied research themes strengthen her candidacy.

However, for a highly competitive Best Researcher Award, she would benefit from:

More indexed journal publications,

Evidence of citations/impact,

Greater leadership in research initiatives (e.g., funded projects or Ph.D. guidance).