Rania Hamdani | Computer science | Best Researcher Award

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Mrs. Rania Hamdani | Computer science | Best Researcher Award

Mrs. Rania Hamdani, University of Luxembourg, Luxembourg

Rania Hamdani is a research scientist specializing in operational research, data management, and cloud architecture for Industry 5.0. Based in Luxembourg, she is currently affiliated with the University of Luxembourg, where she explores advanced methodologies for integrating and managing heterogeneous data sources. She holds an engineering degree in Software Engineering and has extensive experience in software development, AI, and DevOps. Rania has worked on multiple industry and academic projects, publishing three research papers in Ontology-Driven Knowledge Management and Cloud-Edge AI. With a strong background in programming, cloud computing, and AI-driven solutions, she has contributed to platforms ranging from job recommendation systems to adaptive human-computer interaction systems. Her expertise includes Python, SpringBoot, Kubernetes, and Azure DevOps. She is also an active member of IEEE and other technical organizations, promoting innovation and knowledge-sharing in AI and cloud technologies. 🌍💻🔬

Publication Profile

Orcid

🎓 Education

Rania Hamdani holds an Engineering Degree in Software Engineering from the National Higher School of Engineers of Tunis (2021–2024), where she specialized in advanced design, service-oriented architecture, object-oriented programming, database management, and operational research. Prior to this, she completed a two-year preparatory cycle at the Preparatory Institute for Engineering Studies of Tunis (2019–2021), undertaking intensive coursework in mathematics, physics, and technology to prepare for engineering studies. She also earned a Mathematics-specialized Baccalaureate from Pioneer High School Bourguiba Tunis (2015–2019), graduating with honors. Throughout her academic journey, she gained expertise in artificial intelligence, machine learning, cloud computing, and DevOps methodologies. Her education provided a solid foundation in programming languages, data processing techniques, and full-stack development. Additionally, she holds multiple Microsoft certifications in Azure fundamentals, AI, data security, and compliance, reinforcing her expertise in cloud-based solutions and AI-driven applications. 📚🎓💡

💼 Experience

Rania Hamdani is a research scientist at the University of Luxembourg, where she focuses on integrating and managing heterogeneous data sources for cloud-based decision-making. Previously, she was a research intern at the same institution, contributing to Ontology-Driven Knowledge Management and Cloud-Edge AI, with three published papers. She also worked as a part-time software engineer at CareerBoosts in Quebec (2021–2025), specializing in Python, Azure DevOps, Docker, and test automation. She gained industry experience through internships at Qodexia (Paris), Sagemcom (Tunisia), and Tunisie Telecom, working on smart recruitment platforms, employee management systems, and server monitoring solutions using SpringBoot, Angular, and PostgreSQL. Her technical expertise spans full-stack development, DevOps, AI-driven applications, and cloud computing. She has contributed to major projects, including an adaptive human-computer interaction system, a job recommendation system, and a problem-solving platform, demonstrating her versatility in research and software engineering. 🚀🖥️🔍

🏆 Awards & Honors

Rania Hamdani has been recognized for her outstanding contributions to AI-driven cloud computing and operational research. She received excellence awards during her engineering studies at the National Higher School of Engineers of Tunis and was among the top-performing students in her Mathematics-specialized Baccalaureate. Her research papers in Ontology-Driven Knowledge Management and Cloud-Edge AI have been acknowledged in academic circles, contributing to the advancement of Industry 5.0 technologies. She has also earned multiple Microsoft certifications in cloud and AI fundamentals, reinforcing her technical expertise. As an active member of IEEE and the Youth and Science Association, she has been involved in technology outreach and innovation-driven initiatives. Her leadership in ENSIT Junior Enterprise as a project manager further showcases her ability to lead and contribute to tech communities. These recognitions highlight her dedication to research, software development, and cloud-based AI applications. 🏅📜🌟

🔬 Research Focus

Rania Hamdani’s research focuses on operational research, data management, cloud-edge AI, and Industry 5.0 applications. She specializes in ontology-driven knowledge management, exploring methodologies for integrating heterogeneous data sources to optimize cloud-based decision-making processes. Her work includes artificial intelligence, machine learning, reinforcement learning, and human-computer interaction systems. She has contributed to projects involving job recommendation systems, adaptive human-computer interaction platforms, and cloud-based problem-solving platforms. Rania is particularly interested in scalable cloud architectures, leveraging technologies like FastAPI, Kubernetes, Docker, and Azure DevOps to build efficient AI-powered solutions. Her research also integrates graph databases, Apache Airflow, and big data analytics for enhanced data processing. By combining AI and cloud computing, she aims to develop innovative, data-driven solutions for automation, decision support, and optimization in various industrial applications. Her expertise bridges the gap between theoretical research and real-world software engineering. ☁️🤖📊

 

Publication Top Notes

Adaptive human-computer interaction for industry 5.0: A novel concept, with comprehensive review and empirical validation

 

K ASHWINI | Computer Science | Best Researcher Award

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K ASHWINI | Computer Science | Best Researcher Award

K ASHWINI, National Institute of Technology Rourkela, India

K. Ashwini is a dedicated Ph.D. candidate in Computer Science and Engineering at NIT Rourkela, specializing in deep learning applications for grading diabetic retinopathy. She holds an M.Tech. from VSSUT Burla and a B.Tech. from Synergy Institute of Engineering & Technology, Dhenkanal. Her research includes notable publications, such as her work on CNN-based diabetic retinopathy grading in Biomedical Signal Processing and Control. Skilled in Python, MATLAB, and LaTeX, she has actively participated in workshops on machine learning and signal processing. Ashwini is fluent in Hindi, Telugu, and English.

Publication profile

google scholar

Academic Background

Ms. K. Ashwini is a Research Scholar in Computer Science and Engineering (CSE) at NIT Rourkela, currently pursuing her Ph.D., with her research focused on diabetic retinopathy grading using deep learning techniques. Her advanced studies in deep learning, combined with an M.Tech. in CSE from VSSUT Burla, highlight her dedication to exploring complex topics within biomedical and computational research. She has maintained a strong academic record throughout her studies, underscoring her commitment and expertise in her field.

Research Focus and Publications

Ashwini’s primary research area is in biomedical signal processing, specifically targeting diabetic retinopathy grading using CNNs and soft attention mechanisms. She has contributed a journal article to Biomedical Signal Processing and Control and presented multiple conference papers at reputable IEEE and Springer conferences, indicating her active participation in disseminating her research findings. Notably, her publications demonstrate her capacity to employ and innovate with advanced computational methods for impactful health-related applications, a relevant focus for this award.

Technical Skills and Training

Her technical skill set, including Python, MATLAB, and LaTeX, complements her research competencies. Ashwini’s training in SQL and experience with clustering and fraud detection in mobile networks contribute to a robust and versatile research portfolio. Her academic research skills and fluency in programming languages further solidify her qualifications as a proficient researcher in her domain.

Workshops and Professional Development

Ms. Ashwini has participated in several workshops and short-term training programs across India, including those focused on biomedical signal processing, machine learning, and image processing applications. Her engagement in diverse professional development initiatives, such as faculty development programs and national seminars, showcases her continuous effort to enhance her knowledge base and technical skills.

Publication top notes

Grading diabetic retinopathy using multiresolution based CNN

Soft attention with convolutional neural network for grading diabetic retinopathy

Application of Generalized Possibilistic Fuzzy C-Means Clustering for User Profiling in Mobile Networks

Improving Diabetic Retinopathy grading using Feature Fusion for limited data samples

An intelligent ransomware attack detection and classification using dual vision transformer with Mantis Search Split Attention Network

Check for updates Modified Inception V3 Using Soft Attention for the Grading of Diabetic Retinopathy

Modified InceptionV3 Using Soft Attention for the Grading of Diabetic Retinopathy

Grading of Diabetic Retinopathy using iterative Attentional Feature Fusion (iAFF)

Conclusion

Ms. K. Ashwini exemplifies a suitable candidate for the Research for Best Researcher Award. Her specialized research in diabetic retinopathy grading, supported by a solid academic and technical background, positions her as a promising researcher. Her publications and active participation in workshops further validate her dedication and contributions to biomedical signal processing and computer vision applications, aligning well with the award’s criteria for excellence in research and innovation.

Aguinaldo Robinson de Souza | Simulação Computacional | Excellence in Research

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Prof. Dr. Aguinaldo Robinson de Souza | Simulação Computacional | Excellence in Research

Professor Sênior of São Paulo State University, Brazil

Dr. Aguinaldo Robinson de Souza 🎓🔬, born on March 28, 1960, in Rincão, São Paulo, Brazil, is a renowned chemist and educator. With a Bachelor’s degree from UNESP (1984), a Master’s (1987) and Ph.D. (1993) in Chemistry from USP, and a postdoctoral fellowship at UCSD (1995-1996), he has made significant contributions to Theoretical Chemistry and Chemical Education. His career at UNESP, spanning over three decades, includes roles as an Adjunct Professor, researcher, and leader in various academic and administrative capacities. Dr. de Souza’s research focuses on computational simulation, molecular models, and educational software, aiming to innovate and improve chemical education. Even after retirement, he remains actively involved as a volunteer professor and researcher, showcasing his dedication to advancing science and education. 👨‍🏫🧪🌍

Publication profile :

Orcid

Education : 🌟📚

  • Ph.D. in Chemistry (Physical Chemistry) from the University of São Paulo, with a focus on Monte Carlo simulations of polyelectrolytes and polyampholytes.
  • Master’s in Chemistry (Physical Chemistry) from the University of São Paulo, with research on the photoreduction of safranine.
  • Bachelor’s in Chemistry from the Universidade Estadual Paulista.

Professional Experience :🌱🔧

  • Retired Adjunct Professor at Universidade Estadual Paulista (UNESP).
  • Extensive teaching experience in chemistry, with a focus on computational simulation, educational software, molecular models, and quantum chemistry.
  • Held various administrative and committee roles, demonstrating leadership and contributions to the academic community.

Research Interests :

Dr. de Souza’s research interests lie primarily in Theoretical Chemistry and Chemical Education. He focuses on computational simulation, educational software development, molecular models, density functional theory, and GRID computing. His work aims to enhance the understanding and teaching of chemistry through innovative computational tools and methodologies.

Publication Top Notes :

  • Farias, Ximenes Valdecir; Ikeda, Yoguim Maurício; Souza, Aguinaldo Robinson de; Henrique, Morgon Nelson. “Circular dichroism spectrum of ( )-(+)-3,3–dibromo-1,1–bi-2-naphthol in albumin: Alterations caused by complexation-Experimental and in silico investigation.” Chirality, v.36, p.e23675, 2024.
  • Souza, Aguinaldo Robinson de; Figueiredo, Márcia Camilo. “Como discentes compreendem a imprevisibilidade de trajetórias de partículas em contextos de jogo digital?” Revista Eletrônica de Educação (São Carlos), v.18, p.1 – 21, 2024.
  • Rosa; Souza, Aguinaldo Robinson de. “Dulong-Petit’s law and Boltzmann’s theoretical proof from the Kinetic Theory of Gases.” Journal of Physics: Conference Series (Print), v.2727, p.012009 – 1, 2024.
  • Giulia Saneti, Grandini; Valdecir Farias, Ximenes; Nelson Henrique, Morgon; Souza, Aguinaldo Robinson de. “Induced Chirality in Sulfasalazine by Complexation With Albumins: Theoretical and Experimental Study.” Chirality, v.36, p.1 – 12, 2024.
  • Dos, Santos Anderson José; Cunha, Serafim Ana Carolina; Cardoso, Siqueira Adriana de Paula; Augusto, Teixeira José; Souza, Aguinaldo Robinson de; H., Morgon Nelson; B., Siqueira Adriano. “Theoretical and Experimental Characterization of Trivalent Lanthanide (La-Eu) Aquacomplexes with Losartan.” Journal of Molecular Structure, v.1316, p.138944, 2024.
  • Trindade, José Odair da; Ferraz, Isabela Pereira; Cabral, Patrícia Fernanda de Oliveira; Souza, Aguinaldo Robinson de. “Uma Proposta de Mapa Conceitual Digital para Compreensão dos Conceitos de Multiletramentos e Multimodalidade.” Revista da Sociedade Brasileira de Ensino de Química, v.5, p.e052401, 2024.
  • S., Rosa Pedro; Souza, Aguinaldo Robinson de. “A prova teórica de Boltzmann da lei de Dulong-Petit: Uma perspectiva histórica.” Revista de Enseñanza de la Física (Online), v.35, p.259 – 274, 2023.
  • Silva, Moura Fagner da; S., Sobrinho Ygor; Carolina, Stellet; P., Serna Jilder D.; P., Ligiero Carolina B.; I., Yoguim Maurício; S., Cukierman Daphne; Renata, Diniz; C., Alves Odivaldo; H., Morgon Nelson; Souza, Aguinaldo Robinson de; A., Rey Nicolás. “Copper(II) complexes of a furan-containing aroylhydrazonic ligand: syntheses, structural studies, solution chemistry and interaction with HSA.” Dalton Transactions, v.52, p.1, 2023.
  • Andressa, Algayer da Silva Moretti; Daniela, Melaré Vieira Barros; Souza, Aguinaldo Robinson de. “Práticas colaborativas na Wikipédia: a internacionalização e a interculturalidade no ensino de química.” Eccos Revista Científica (Online), v.1, p.e24565, 2023.
  • Arnab, Roy; Subrata, Biswas; Surajit, Duari; Srabani, Maity; Kumar, Mishra Abhishek; Souza, Aguinaldo Robinson de; M., Elsharif Asma; H., Morgon Nelson; Srijit, Biswas. “Regioselective Transition Metal-Free Catalytic Ring Opening of 2-Azirines by Phenols and Naphthols; One-Pot Access to Benzo- and Naphthofurans.” Journal of Organic Chemistry (Online), v.7, p.10, 2023.

Aguinaldo Robinson de Souza | Computational Science| Excellence in Research

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Prof Dr. Aguinaldo Robinson de Souza | Computational Science | Excellence in Research

Senior Professor, São Paulo State University, Brazil

Aguinaldo Robinson de Souza is a retired Adjunct Professor at Universidade Estadual Paulista (UNESP), Brazil. With a distinguished career in Chemistry, particularly Theoretical Chemistry and Chemical Education, he has made significant contributions through his research in computational simulations, educational software, and molecular models.

Profile

Scopus

Orcid

Google Scholar

Education 🎓

Aguinaldo Robinson de Souza earned a Doctorate in Chemistry from Universidade de São Paulo (USP), São Paulo, Brazil, in 1993. His doctoral thesis, “Propriedades Conformacionais de Polieletrólitos e Polianfóteros por Simulação Monte Carlo,” was supervised by Léo Degreve. He completed his Master’s in Chemistry at USP in 1987, with a thesis titled “Fotorredução da Safranina por N-Fenilglicinas e Respectivos Etil-Ésteres,” under the guidance of Miguel Guillermo Neumann. Aguinaldo holds a Bachelor’s degree in Chemistry from Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP), São Paulo, Brazil, awarded in 1984.

He pursued postdoctoral research at the University of California San Diego (UCSD), USA, from 1995 to 1996, supported by a scholarship from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP). Additionally, he obtained his Habilitation from Faculdade de Ciências – UNESP, Brazil, in 2005.

In terms of complementary education, Aguinaldo completed a Short Term Course in Química Teórica at Universitat Jaume I (UJI), Spain, in 2011. He has also participated in various short courses in computational chemistry, educational software, and materials science at institutions including Universidade de São Paulo, UNESP, and Instituto Sangari.

Professional Experience 💼

FINEP (2008 – Present): Engaged in various research financing roles.

Aguinaldo Robinson de Souza has been engaged with FINEP (2008 – Present), taking on various research financing roles. At Universidade Estadual Paulista – UNESP, he has served in several capacities:

Professor Voluntário in the Departamento de Química (2023 – Present).

RDIDP in the Departamento de Química (2011 – 2022).

Adjunto in the Departamento de Química (1989 – 2022).

He has also been involved in various committees, including the Comissão Permanente de Pesquisa and the Comitê Local de Internacionalização.

Research Interests 🔬

Aguinaldo Robinson de Souza focuses on several key research areas:

Computational Simulation and Molecular Modeling: Investigating various chemical systems using advanced computational techniques.

Density Functional Theory and GRID Computing: Applying density functional theory (DFT) and leveraging GRID computing for complex chemical calculations.

Educational Software and Chemical Education: Developing and using educational software to enhance chemical education and learning experiences.

Awards 🏆

Various: Recognized for contributions in theoretical chemistry and educational software.

Publications Top Notes 📚

“Novel aminoquinoline-based solvatochromic fluorescence probe: Interaction with albumin, lysozyme and characterization of amyloid fibrils” Dyes and Pigments, 2020. link

“TD-DFT Analysis of the Dissymmetry Factor in Camphor” Journal of the Brazilian Chemical Society, 2020. link

“Elucidação da quiralidade induzida na molécula dansilglicina na complexação com a proteína albumina do soro humano (HSA)” Revista Brasileira de Ciências Farmacêuticas, 2019. link

“Methyl divanillate: redox properties and binding affinity with albumin of an antioxidant and potential NADPH oxidase inhibitor” RSC Advances, 2019. link

“Solvent-induced Stokes’ shift in DCJTB: Experimental and theoretical results” Journal of Molecular Structure, 2019. link