Nawel Benchaabane | Medical Image Analysis | Research Excellence Award

Dr. Nawel Benchaabane | Medical Image Analysis | Research Excellence Award

Dr Chef De Projects | Audensiel Technologies | France 

Dr. Nawel Benchaabane is a researcher at Audensiel Technologies, Paris, France, specializing in artificial intelligence for healthcare and medical decision support. Her research focuses on AI-driven gait analysis, medical image understanding, and visual question answering for clinical diagnosis. She has authored 2 Scopus-indexed publications, with 17 citations and an h-index of 1, reflecting early but growing scientific impact. Her work has been published in high-impact venues such as Scientific Reports, IEEE EMBS Conference, and Intelligent Systems with Applications. Through interdisciplinary collaborations between AI and medical domains, her research contributes to improved diagnosis, patient monitoring, and data-driven healthcare innovation with tangible societal benefits.

Citation Metrics (Scopus)

40

30

20

10

0

Citations
17

Documents
2

h-index
1

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile

Featured Publications

Qi Lai | Medical Image Analysis | Women Researcher Award

Dr. Qi Lai | Medical Image Analysis | Women Researcher Award

Assistant Professor | Shenzhen Institutes of Advanced Technology | China

Dr. Qi Lai is a researcher at the Shenzhen Institutes of Advanced Technology, China, specializing in weakly supervised learning, medical image analysis, and multi-instance learning. He has authored 11 peer-reviewed publications, receiving over 52 citations with an h-index of 5. His work spans deep learning for pathology, object detection, semantic segmentation, and medical image restoration, often in collaboration with international teams across leading institutions. Notable contributions include interactive and hybrid MIL frameworks and contrastive learning methods that enhance diagnostic precision. His research advances reliable AI-driven clinical decision support, contributing to improved healthcare technologies and societal well-being.

 

Citation Metrics (Scopus)

200

150

100

50

0

Citations
52

Documents
11

h-index
5

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
             View Google Scholar Profile
             View ORCID Profile

Featured Publications


Fast broad multiview multi-instance multilabel learning (FBM3L) with viewwise intercorrelation.

– IEEE Transactions on Neural Networks and Learning Systems (2024). Cited By: 5 

Interactive multiple instance learning network for whole slide image analysis.

– Expert Systems with Applications. (2026). Cited By:  1

Joint discriminative latent subspace learning for image classification

– IEEE Transactions on Circuits and Systems for Video Technology. (2022). Cited By:: 15

Mueen Uddin | Medical Image Analysis | Research Excellence Award

Prof. Dr. Mueen Uddin | Medical Image Analysis | Research Excellence Award

Professor | University Of Doha For Science and Technology | Qatar

Dr. Mueen Uddin is an Associate Professor of Cybersecurity and Data Sciences at the University of Doha for Science & Technology (UDST), Qatar. He is an internationally recognized researcher whose work bridges cybersecurity, blockchain technologies data science artificial intelligence, and healthcare security. His scholarly contributions reflect a strong commitment to advancing secure, intelligent, and sustainable digital systems across multidisciplinary domains.Dr. Uddin has authored over 192 peer-reviewed research publications in leading international journals and conferences, including IEEE Access, IEEE Network Renewable  Sustainable Energy Reviews, Sustainability and Health Informatics Journal. His research impact is evidenced by more than 7,404 citations an h-index of 43, and an i10-index of 99, underscoring the consistency quality and global relevance of his work. Several of his publications are widely cited benchmarks particularly in handwritten OCR systems medical image segmentation, blockchain for healthcare and digital twins energy-efficient data centers and IoT-enabled cybersecurity infrastructures.His research expertise spans Blockchain and Web 3.0, IoT and Cybersecurity Healthcare Security Metaverse technologies Deep Learning and Green IT systems. Dr. Uddin has played a pivotal role in advancing blockchain-based drug traceability solutions secure electronic health records intrusion detection systems and AI-driven healthcare analytics contributing directly to combating counterfeit drugs enhancing patient data security and improving diagnostic intelligence.Dr. Uddin actively collaborates with researchers across Asia Europe the Middle East and Africa fostering interdisciplinary and cross-border research initiatives. These collaborations have resulted in impactful studies addressing real-world challenges in smart cities sustainable development healthcare digitalization and intelligent network security.Beyond academia his work demonstrates strong societal and industrial relevance offering scalable secure solutions aligned with global priorities such as digital trust, sustainable computing, and resilient healthcare systems. Through high-impact research, academic leadership, and global collaboration Dr. Mueen Uddin continues to shape the future of cybersecurity and data-driven innovation worldwide.

Profiles: Scopus | ORCID | Googlescholar 

Featured Publications

1.Memon, J., Sami, M., Ahmed, R., & Uddin, M. (2020). Handwritten optical character recognition (OCR): A comprehensive systematic literature review (SLR). IEEE Access, 8, 142642–142668. https://doi.org/10.1109/ACCESS.2020.3012542. Cited By : 730

2.Norouzi, A., Rahim, M. S. M., Altameem, A., Saba, T., Rad, A. E., Rehman, A., & Uddin, M. (2014). Medical image segmentation methods, algorithms, and applications. IETE Technical Review, 31(3), 199–213. https://doi.org/10.1080/02564602.2014.906861. Cited By : 440

3.Uddin, M., & Rahman, A. A. (2012). Energy efficiency and low carbon enabler green IT framework for data centers considering green metrics. Renewable and Sustainable Energy Reviews, 16(6), 4078–4094. https://doi.org/10.1016/j.rser.2012.03.002. Cited By : 318

4.Yaqoob, M. I. I., Salah, K., Uddin, M., Jayaraman, R., & Omar, M. (2020). Blockchain for digital twins: Recent advances and future research challenges. IEEE Network, 34(5), 290–298. https://doi.org/10.1109/MNET.2020.9225779. Cited By :  291

5.Uddin, M. (2021). Blockchain MedLedger: Hyperledger Fabric–enabled drug traceability system for counterfeit drugs in pharmaceutical industry. International Journal of Pharmaceutics, 597, 120235. https://doi.org/10.1016/j.ijpharm.2021.120235. Cited By : 273

Dr. Mueen Uddin’s research advances global innovation by integrating cybersecurity, blockchain, and AI to build secure, trustworthy, and sustainable digital ecosystems. His work delivers high-impact solutions for healthcare security, smart infrastructure, and data-intensive systems, translating scientific excellence into real-world societal and industrial benefits worldwide.

Mr. Spencer Upton | Medical Image Analysis | Best Researcher Award

Mr. Spencer Upton | Medical Image Analysis | Best Researcher Award

Spencer Upton at University of Missuour, United States

Profiles

Scopus

Orcid

Google Scholar

Research Gate

Linked In

 Academic Background:

Mr. Spencer Upton is a dedicated PhD student in Cognition and Neuroscience at the University of Missouri-Columbia (MU), with extensive academic and professional experience in the fields of psychology and neuroscience. His career spans a range of roles from research assistantships to project coordination, and he has actively engaged in teaching and mentoring throughout his academic journey. Spencer's commitment to the field is evident in his contributions to various scientific communities and his recognition through multiple scholarships and awards.

Education:

Mr. Spencer began his academic career at Butler County Community College (BC3) in 2014 before earning a BS in Psychology with a focus on neuroscience and philosophy from Slippery Rock University (SRU) in 2019. He then pursued an MS in Integrative Neuroscience at Georgetown University (GU) from 2019 to 2020. Spencer is currently working towards a PhD in Cognition and Neuroscience at MU, where he also completed his MA. His doctoral research is supervised by Dr. Brett Froeliger.

Professional Experience:

Mr. Spencer’s professional journey includes roles such as a Research Specialist (Project Coordinator) at the Health Neuroscience Center, MU, where he managed research projects from 2020 to 2022. He has also worked in various capacities outside the academic realm, including as a landscaper and a front desk attendant. His early professional experiences include positions as a dishwasher, cashier, and warehouse attendant, reflecting a diverse work background.

 Research Interests:

Mr. Spencer’s research interests focus on understanding the cognitive and neural mechanisms underlying addiction and motivation. His work includes exploring the effects of nicotine and other substances on cognitive processes and neural functioning. This interest is reflected in his involvement with organizations such as the Society for Neuroscience (SFN) and the Society for Research on Nicotine and Tobacco (SRNT), as well as his contributions as an assistant reviewer for journals like Addictive Behaviors and Neuropsychopharmacology.

💰 Honors and Scholarships:

He has received various honors and scholarships, such as the Biomedical Graduate Education Scholarship from GU (2019), and multiple scholarships from SRU, including the Rose and Dale Kaufman Scholarship (2018) and the Meiping Cheng Memorial Scholarship (2017). Notably, he received the Undergraduate Mentoring Award from MU in 2023.

👨‍🏫 Teaching Experience:

Mr. Spencer has been involved in teaching as an assistant for courses such as Psych3351: Positive Motivation and Psych 3160: Perception and Thought in Fall 2022. He also contributed as a lecturer for an MRI Workshop Series at the Cognitive Neuroscience Systems Core Facility.

 Publications:

Mesocorticolimbic system reactivity to alcohol use-related visual cues as a function of alcohol sensitivity phenotype: A pilot fMRI study
  • Authors: Roberto U Cofresí, Spencer Upton, Alexander A Brown, Thomas M Piasecki, Bruce D Bartholow, Brett Froeliger
  • Journal: Addiction Neuroscience
  • Year: 2024
Spencer Upton, Alexander A. Brown, Mojgan Golzy, Eric L. Garland and Brett Froeliger
  • Authors: S Upton
  • Journal: Addiction and the Brain: Current Knowledge, Methods, and Perspectives
  • Year: 2024
Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step
  • Authors: Andrew Smith, Musa Azeem, Chrisogonas O Odhiambo, Pamela J Wright, Hanim E Diktas, Spencer Upton, Corby K Martin, Brett Froeliger, Cynthia F Corbett, Homayoun Valafar
  • Journal: Sensors
  • Year: 2024
Associations between right inferior frontal gyrus morphometry and inhibitory control in individuals with nicotine dependence
  • Authors: Alexander A Brown, Spencer Upton, Stephen Craig, Brett Froeliger
  • Journal: Drug and alcohol dependence
  • Year: 2023
Effects of hyperdirect pathway theta burst transcranial magnetic stimulation on inhibitory control, craving, and smoking in adults with nicotine dependence: A double-blind …
  • Authors: Spencer Upton, Alexander A Brown, Muaid Ithman, Roger Newman-Norlund, Greg Sahlem, Jim J Prisciandaro, Erin A McClure, Brett Froeliger
  • Journal: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
  • Year: 2023