Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

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Dr. Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

Dr. Chan-Uk Yeom is a Research Professor at the Research Institute of IT, Chosun University, Korea. He specializes in time series data analysis using deep learning, granular computing, adaptive neuro-fuzzy inference systems, high-dimensional data clustering, and biosignal-based biometrics. Dr. Yeom has held several research positions, including at the Division of AI Convergence College at Chosun University and the Center of IT-BioConvergence System Agriculture at Chonnam National University. His work integrates artificial intelligence, fuzzy systems, and granular models for practical applications such as healthcare, biometrics, and energy efficiency. Dr. Yeom has published extensively in high-impact journals and conferences, holds multiple patents, and has received numerous awards for his innovative research contributions. He actively teaches courses related to AI healthcare applications and electronic engineering. His collaboration and problem-solving skills have been demonstrated through his involvement in competitive AI research challenges and global innovation camps.

Professional Profile

Education

Dr. Yeom completed his entire higher education at Chosun University, Korea. He earned his Ph.D. in Engineering (2022) from the Department of Control and Instrumentation Engineering, with a dissertation on fuzzy-based granular model design using hierarchical structures under the supervision of Prof. Keun-Chang Kwak. Prior to this, he obtained his M.S. in Engineering (2017), focusing on ELM predictors using TSK fuzzy rules and random clustering, and his B.S. in Engineering (2016) in Control and Instrumentation Robotics. His academic work laid a strong foundation in machine learning, granular computing, and fuzzy inference systems, which became the core of his future research trajectory. Throughout his education, Dr. Yeom demonstrated academic excellence, leading to multiple thesis awards, and developed expertise in AI-driven applications for healthcare, energy optimization, and biometrics.

Experience

Currently, Dr. Yeom serves as a Research Professor at the Research Institute of IT, Chosun University (since January 2025). Previously, he was a Research Professor at Chosun University’s Division of AI Convergence College (2023–2024) and a Postdoctoral Researcher at the Center of IT-BioConvergence System Agriculture, Chonnam National University (2022–2023). His extensive research spans user authentication technologies using multi-biosignals, brain-body interface development using AI multi-sensing, and optimization of solar-based thermal storage systems. In addition to research, Dr. Yeom has contributed to teaching undergraduate courses, including AI healthcare applications, electronic experiments, capstone design, and open-source software. He is also experienced in mentorship, student internships, and providing special employment lectures. His active participation in national and international research projects and conferences reflects his global engagement and multidisciplinary expertise in artificial intelligence, healthcare, biometrics, and advanced fuzzy models.

Research Interests

Dr. Yeom’s research integrates deep learning, granular computing, and adaptive neuro-fuzzy systems to solve complex problems in healthcare, biometrics, energy efficiency, and time series data analysis. His innovative work focuses on designing hierarchical fuzzy granular models, developing incremental granular models with particle swarm optimization, and applying AI-driven methods to biosignal-based biometric authentication. Dr. Yeom has developed cutting-edge models for predicting energy efficiency, vehicle fuel consumption, water purification processes, and disease classification from ECG signals. His contributions also extend to explainable AI, emotion recognition, and non-contact biosignal acquisition using 3D-CNN. In addition to academic publications, he has secured multiple patents related to ECG-based personal identification methods, intelligent prediction systems, and granular neural networks. His interdisciplinary approach combines theoretical modeling, real-world applications, and collaborative AI system design, advancing the fields of biomedical informatics, neuro-fuzzy computing, and healthcare convergence technologies.

Awards

Dr. Yeom has received numerous awards recognizing his academic excellence. He earned multiple Excellent Thesis Awards from prestigious conferences, including the International Conference on Next Generation Computing (ICNGC 2024), the Korea Institute of Information Technology (KIIT Autumn Conference 2024), and the Annual Conference of Korea Information Processing Society (ACK 2024). His doctoral work was recognized at Chosun University’s 2021 Graduate School Doctoral Degree Award Ceremony. He also received the Outstanding Presentation Paper Award at the 2020 Korean Smart Media Society Spring Conference and the Excellent Thesis Award at the Korea Information Processing Society 2018 Spring Conference. Earlier, his problem-solving capabilities were showcased as a finalist and top 9 team at the 2018 AI R&D Challenge and during participation in the 2016 Global Entrepreneurship Korea Camp. These honors highlight his sustained contributions to AI research, innovation, and applied technological development.

Conclusion

Dr. Chan-Uk Yeom is a dynamic researcher whose pioneering contributions to granular computing, neuro-fuzzy systems, and AI healthcare applications demonstrate his exceptional expertise, innovative thinking, and global scientific impact, making him a valuable contributor to the advancement of next-generation intelligent systems.

 Publications

  • A Design of CGK-Based Granular Model Using Hierarchical Structure

    Applied Sciences
    2022-03 | Journal article | Author
    DOI: 10.3390/app12063154
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

  • Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach

    Applied Sciences
    2020-11 | Journal article | Author
    DOI: 10.3390/app10238495
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

Hong Wang | Memristors | Best Researcher Award

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Prof. Dr. Hong Wang | Memristors | Best Researcher Award

Dr. Hong Wang is an accomplished Associate Professor at Hebei University, China, specializing in the field of neuromorphic electronics and low-dimensional ferroelectric materials. With a strong academic foundation in Physics, Integrated Circuits, and Optical Engineering, she has rapidly advanced in her field since earning her doctorate in 2021. Her research has led to 15 SCI-indexed publications as a first author, 8 patents, and over 1300 citations, underscoring her scientific impact. Dr. Wang actively collaborates with leading researchers from institutions such as the National University of Singapore, the Chinese Academy of Sciences, and Jilin University, achieving multiple experimental firsts in ferroelectricity and memristor behavior. Her innovative work bridges material science and cognitive computing, making significant contributions to optoelectronic sensing and neuromorphic systems. She is a member of several prestigious scientific societies, including the Chinese Optical Society. Dr. Wang’s dedication and research excellence make her a standout in cognitive science innovations.

Profile

🎓 Education

Dr. Hong Wang’s academic journey began with a Bachelor’s degree in Physics from Beihua University in 2016, which laid the foundation for her interdisciplinary approach to electronic materials. She then earned her Master’s degree in Integrated Circuits from Hebei University in 2018, further refining her expertise in semiconductor and electronic system design. Driven by a passion for optical and neuromorphic technologies, she pursued a PhD in Optical Engineering at Hebei University, completing it in 2021. Her doctoral research focused on the application of low-dimensional ferroelectric materials, contributing valuable insight into the behavior of memristive systems and their implications for artificial neural networks. This strong educational background has enabled her to explore innovative technologies in cognitive sensing and computing, bridging physics, materials science, and neural engineering. Her academic training not only exemplifies depth and rigor but also reflects a unique ability to translate theoretical research into applied cognitive systems.

🧪 Experience

Since 2021, Dr. Hong Wang has served as an Associate Professor at the School of Electronic Information and Engineering, Hebei University. In this role, she has taken on responsibilities spanning research leadership, mentoring graduate students, and leading interdisciplinary projects at the frontier of neuromorphic computing. She has directed five major research projects and collaborated internationally with scholars from Singapore, the Chinese Academy of Sciences, and Jilin University. Her work has provided novel insights into ferroelectricity in materials like SnSe and ReSe₂, and its application in memristive devices. In addition to her academic duties, Dr. Wang has contributed to two industry consultancy projects, aligning academic innovation with technological advancement. Her ability to bridge material innovation with neural system architecture distinguishes her as a versatile and future-oriented cognitive scientist. Her professional experience is marked by innovation, collaboration, and a commitment to enhancing cognitive systems through novel material applications.

🏅 Awards and Honors

While specific awards are not explicitly listed, Dr. Hong Wang’s impressive research metrics and collaborations signify her recognition within the global scientific community. With 15 SCI-indexed publications as first author and over 1365 citations, her work has garnered significant academic attention. Her successful collaborations with leading institutions like the National University of Singapore and the Chinese Academy of Sciences validate her contributions through groundbreaking experimental confirmations in ferroelectric behavior. Additionally, she holds 8 patents, reflecting the originality and applied potential of her research in neuromorphic computing. Her memberships in the Chinese Optical Society, the Chinese Institute of Electronics, and the Chinese Society for Optical Engineering indicate peer recognition and professional trust. These accomplishments, coupled with her high-impact research output, suggest that Dr. Wang is a strong contender for prestigious awards in cognitive science and materials research, and she is an exemplary nominee for the Best Researcher Award in Cognitive Science.

🔬 Research Focus

Dr. Hong Wang’s research centers on the design and application of neuromorphic memristors using low-dimensional ferroelectric materials. She explores how novel quantum dots and two-dimensional semiconductors, such as SnSe and ReSe₂, can mimic synaptic behavior for brain-like computing. A notable achievement includes her demonstration of robust dual-mode optical sensing using ferroelectric quantum dots, enabling both short-range and remote synapse-like responses, leading to high-accuracy image recognition systems. Her experimental work debunks traditional notions in electronics, such as the inertness of Pd electrodes, and provides novel insights into conductive filament formation. Her research has practical implications in artificial vision systems, optoelectronic sensing, and cognitive learning circuits. She is pioneering the application of ferroelectric polarization for neuromorphic behavior, with implications for smart sensing and adaptive cognitive devices. Through multidisciplinary collaborations and material innovations, Dr. Wang is shaping the future of neuromorphic computing, advancing cognitive technologies toward higher efficiency and closer brain mimicry.

✅ Conclusion

Dr. Hong Wang is an emerging leader in neuromorphic computing, merging ferroelectric material innovation with cognitive system design, making her a strong candidate for the Best Researcher Award.

Publications

Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

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Dr. Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

Yuanming Zhang is an Associate Professor at the College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. He earned his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His research focuses on data processing, graph neural networks, knowledge graphs, prognostics, health management, and condition monitoring. With expertise in deep learning and artificial intelligence, he has contributed significantly to neural network advancements. His work integrates cutting-edge technologies for intelligent data analysis and predictive maintenance. 📊🧠🔍

Profile

Education 🎓

Yuanming Zhang obtained his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His academic journey emphasized computational intelligence, machine learning, and advanced data analytics. He developed expertise in deep learning models, including convolutional and graph neural networks. His education laid a strong foundation for interdisciplinary research, integrating artificial intelligence with real-world applications. 📚🧑‍🎓📈

Experience 👨‍🏫

Yuanming Zhang has been an Associate Professor at Zhejiang University of Technology since completing his Ph.D. in 2010. His professional journey spans over a decade in academia, focusing on AI, neural networks, and knowledge graphs. He has supervised research projects, collaborated on industry applications, and contributed to advancements in predictive analytics and condition monitoring. His expertise extends to teaching, mentoring, and interdisciplinary AI applications. 🏫🤖📡

Research Interests 🔬

Yuanming Zhang specializes in deep learning, attention mechanisms, graph neural networks, and AI-driven predictive analytics. His research explores neural architectures for data processing, knowledge representation, and condition monitoring. His expertise spans convolutional networks, LSTMs, GRUs, and deep belief networks. His work contributes to advancements in AI-driven diagnostics, intelligent systems, and real-time health monitoring applications. 🧠📊🖥️

Awards & Recognitions 🏅

Yuanming Zhang has received recognition for his contributions to AI, machine learning, and data analytics. His work in deep learning and knowledge graphs has earned him accolades from research institutions and conferences. His papers in neural networks and predictive maintenance have been highly cited, solidifying his impact in the field. His research excellence has been acknowledged through grants and academic distinctions. 🎖️📜🔬

Publications 

 

Chunyu Liu | Cognitive Computing | Best Researcher Award

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Dr. Chunyu Liu | Cognitive Computing | Best Researcher Award

Chunyu Liu is a Lecturer at North China Electric Power University, specializing in machine learning, neural decoding, and visual attention. 📚 She earned her B.S. in Mathematics and Applied Mathematics from Henan Normal University, an M.S. in Applied Mathematics from Northwest A&F University, and a Ph.D. in Computer Application Technology from Beijing Normal University. 🎓 She completed postdoctoral training at Peking University. 🔬 Her research integrates AI methodologies with cognitive neuroscience, focusing on neural encoding, decoding, and attention mechanisms. 🧠 She has published over 10 research papers, including six SCI-indexed publications as the first author. 📝 Her work aims to bridge artificial intelligence with human cognitive function understanding, contributing significantly to computational neuroscience. 🌍 Liu has also been involved in several major research projects, furthering advancements in neural signal analysis and cognitive computing. 🚀

Profile

Education 🎓

Chunyu Liu holds a strong academic background in mathematics and computational sciences. She obtained her B.S. degree in Mathematics and Applied Mathematics from Henan Normal University. ➕ She pursued her M.S. in Applied Mathematics at Northwest A&F University, where she deepened her expertise in mathematical modeling. 🔢 Continuing her academic journey, she earned a Ph.D. in Computer Application Technology from Beijing Normal University. 🖥️ Her doctoral research explored advanced AI techniques applied to neural decoding and cognitive processing. 🧠 To further refine her skills, she completed postdoctoral training at Peking University, focusing on integrating artificial intelligence with neural mechanisms. 🔬 Her academic pathway reflects a multidisciplinary approach, merging mathematics, computer science, and cognitive neuroscience to address complex challenges in brain science and AI. 📊 Liu’s education laid the foundation for her contributions to machine learning, visual attention studies, and neural encoding research.

Experience 👨‍🏫

Dr. Chunyu Liu is currently a Lecturer at North China Electric Power University, where she teaches and conducts research in cognitive computing and machine learning. 🎓 She has led and collaborated on multiple projects related to neural encoding and decoding, investigating how the brain processes object recognition, emotions, and attention. 🧠 Prior to her current role, she completed postdoctoral research at Peking University, where she worked on advanced AI-driven models for neural signal analysis. 🔍 Over the years, Liu has gained extensive experience in analyzing multimodal neural signals, including magnetoencephalography (MEG) and functional MRI (fMRI). 📡 She has also served as a reviewer for esteemed scientific journals and collaborated with interdisciplinary research teams on AI and brain science projects. 🔬 Her expertise extends to both academia and industry, where she has contributed to the development of novel computational models for decoding brain activity. 🚀

Research Interests 🔬

Dr. Chunyu Liu’s research integrates artificial intelligence and brain science to understand cognitive functions through neural decoding. 🧠 She employs multi-modal neural signals such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to analyze brain activity. 📡 Her work explores neural encoding and decoding, focusing on object recognition, emotion processing, and multiple-object attention. 🎯 She develops AI-based models to extract human brain features and gain insights into cognitive mechanisms. 🤖 By integrating psychological experimental paradigms with AI, Liu aims to advance computational neuroscience. 🏆 Her research also inspires the development of new AI theories and algorithms based on principles of brain function. 📊 She has led major projects in cognitive computing, contributing significantly to both theoretical advancements and practical applications in neural signal processing. 🚀 Through her work, she bridges the gap between human cognition and artificial intelligence, driving innovations in brain-computer interface research. 🏅

 

Awards & Recognitions 🏅

Dr. Chunyu Liu has received recognition for her outstanding contributions to cognitive computing and AI-driven neuroscience research. 🏅 She has been nominated for the prestigious International Cognitive Scientist Award for her pioneering work in neural decoding and visual attention mechanisms. 🎖️ Liu’s research publications have been featured in high-impact journals, earning her accolades from the scientific community. 📜 Her first-author papers in IEEE Transactions on Neural Systems and Rehabilitation Engineering, Science China Life Sciences, and IEEE Journal of Biomedical and Health Informatics have been widely cited. 📝 She has also been honored with research grants and funding for AI-driven cognitive studies. 🔬 Her innovative work in decoding brain signals has been recognized in international AI and neuroscience conferences. 🌍 Liu’s academic excellence and contributions continue to shape the field of computational neuroscience and machine learning applications in cognitive science. 🚀

Publications 📚

Pritpal Singh | Ambiguous set theory | Best Researcher Award

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Dr. Pritpal Singh | Ambiguous set theory | Best Researcher Award

Pritpal Singh is an Assistant Professor at the Department of Data Science and Analytics, Central University of Rajasthan, India. He earned his Ph.D. in Computer Science and Engineering from Tezpur (Central) University in 2015 and has held various academic and research positions in India, Taiwan, and Poland. His expertise includes soft computing, optimization algorithms, time series forecasting, image analysis, and machine learning. He has published extensively in high-impact journals like IEEE Transactions, Elsevier, and Springer. His research focuses on advanced computational techniques, including quantum-based optimization and fMRI data analysis. Dr. Singh has received prestigious research fellowships, including a Postdoctoral Fellowship from Taiwan’s Ministry of Science and Technology and an International Visiting Research Fellowship from Poland’s Foundation for Polish Science. His work significantly contributes to artificial intelligence, data science, and computational modeling, making him a key figure in these fields. 🚀📊📚

Profile

Education 🎓

Dr. Pritpal Singh obtained his Ph.D. in Computer Science and Engineering from Tezpur (Central) University, Assam, India, in 2015, specializing in soft computing applications for time series forecasting. He completed his Master in Computer Applications (MCA) from Dibrugarh University, Assam, in 2008, following a B.Sc. in Physics, Chemistry, and Mathematics from the same university in 2005. His academic journey began with Higher Secondary (2002) from the Assam Higher Secondary Education Council and HSLC (1999) from the Secondary Education Board of Assam. His doctoral dissertation focused on improving fuzzy time series forecasting models through hybridization with neural networks and optimization techniques like particle swarm optimization. His strong foundation in computational sciences, mathematics, and engineering has shaped his research in AI-driven predictive modeling, optimization, and data analytics. 🎓📚🔬

Experience 👨‍🏫

Dr. Singh has extensive academic and research experience. He is currently an Assistant Professor at the Central University of Rajasthan (since June 2022). Previously, he was an Assistant Professor at CHARUSAT University, Gujarat (2015-2019), and a Lecturer at Thapar University, Punjab (2013-2015). His research experience includes serving as an Adjunct Professor (Research) at Jagiellonian University, Poland (2020-2022) and a Postdoctoral Research Fellow at National Taipei University of Technology, Taiwan (2019-2020). Throughout his career, he has mentored students, led research projects, and contributed significantly to data science, artificial intelligence, and computational modeling. His global exposure has enriched his expertise in optimization, machine learning, and interdisciplinary AI applications. 🌍📊

Research Interests 🔬

Dr. Singh’s research revolves around ambiguous set theory, optimization algorithms, time series forecasting, image analysis, and machine learning. He specializes in hybrid computational techniques, particularly quantum-based optimization and soft computing applications. His work extends to fMRI data analysis, mathematical modeling, and simulation. His research has been published in leading journals such as IEEE Transactions on Systems, Elsevier’s Information Sciences, and Artificial Intelligence in Medicine. His focus on interdisciplinary AI applications, particularly in healthcare and data science, has positioned him as a key contributor to advancing machine learning methodologies. 🧠📊🤖Awards & Recognitions 🏅

Dr. Singh has received multiple prestigious fellowships and recognitions. In 2019, he was awarded a Postdoctoral Research Fellowship by the Ministry of Science and Technology, Taiwan. In 2020, he received the International Visiting Research Fellowship from the Foundation for Polish Science, Poland. His contributions to artificial intelligence, optimization, and data science have been recognized globally through research grants, invited talks, and publications in top-tier journals. His work in soft computing and AI-driven predictive modeling continues to impact both academic and industrial research. 🏅🎖️📜

Publications 📚

  • Scopus 1-2023: P. Singh, An investigation of ambiguous sets and their application to
    decision-making from partial order to lattice ambiguous sets. Decision Analytics
    Journal (Elsevier), 08, 100286, 2023.
  • Scopus 2-2023: P. Singh, A general model of ambiguous sets to a single-valued ambiguous numberswith aggregation operators. Decision Analytics Journal (Elsevier), 08,
    100260, 2023.
  • Scopus 3-2023: P. Singh, Ambiguous set theory: A new approach to deal with unconsciousness and ambiguousness of human perception. Journal of Neutrosophic and
    Fuzzy Systems (American Scientific Publishing Group), 05(01), 52–58, 2023.
  • Scopus 4-2022: P. Singh, Marcin W ˛atorek, Anna Ceglarek, Magdalena F ˛afrowicz, and
    Paweł O´swi˛ecimka, Analysis of fMRI Time Series: Neutrosophic-Entropy Based
    Clustering Algorithm. Journal of Advances in Information Technology, 13(3), 224–
    229, 2022.

Jianbang Liu | AI-driven emotion | Best Researcher Award

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Dr. Jianbang Liu | AI-driven emotion | Best Researcher Award

JianBang Liu is a faculty member at the Xinyu University, China, where he actively contributes to both research and education. His research interests lie at the intersection of Artificial Intelligence (AI), Human-Computer Interaction (HCI), and Artificial Sentiment Analysis, with a specific focus on developing AI-driven emotion and cognition analysis. He has published extensively in international journals, significantly advancing the fields of HCI and AI. He continues to explore innovative applications of these technologies, aiming to bridge theoretical research with practical implementations.

Profile

Education

JianBang Liu obtained his Master’s degree from Qilu University of Technology (Shandong Academy of Sciences), China, in 2018. He then completed his Ph.D. at the Institute of Visual Informatics, UniversitiKebangsaan Malaysia (National University of Malaysia), specializing in Human-Computer Interaction (HCI) and Artificial Intelligence (AI).

Research Interests

Artificial Intelligence (AI), Human-Computer Interaction (HCI), AI-driven emotion and cognition analysisRe

Research Innovation

Completed/Ongoing Research Projects: State the number of research projects you have completed or are currently working on.

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Patents Published/Under Process: Mention the number of patents you have published or are currently in the process of publishing.

JournalsPublished: State the number of articles you have published in indexed journals.

Editorial Appointments: If applicable, list any editorial positions you hold in journals or conferences.

Collaborations: Describe any significant collaborations you have been part of in your research career.

Professional Memberships: List memberships in professional organizations or societies relevant to your field.

Areas of Research: Specify the main areas or topics you focus on in your research work.

Books /Chapters in Books：

Local optimal Issue in Bees Algorithm: Markov Chain Analysis and Integration with Dynamic Particle Swarm Optimization Algorithm (Intelligent Engineering Optimisation with the Bees Algorithm (978-3-031-64935-6/ 978-3-031-64936-3 (eBook)))

Publication

  • Emotion assessment and application in human-computer interaction interface based on backpropagation neural network and artificial bee colony algorithm (SCI Q1)
  • Emotion assessment and application in human-computer interaction interface based on backpropagation neural network and artificial bee colony algorithm (SCI Q1)
  • Personalized Emotion Analysis Based on Fuzzy Multi-Modal Transformer Model (SCI Q2)
  • Immersive VR Learning experiences from the perspective of telepresence, emotion, and cognition(SSCI Q1)

Muhammad Waheed Rasheed | Artificial Intelligence | Best Researcher Award

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Mr. Muhammad Waheed Rasheed | Artificial Intelligence | Best Researcher Award

Research Assistant at COMSATS University Islamabad, Vehari Campus, Pakistan

Muhammad Waheed Rasheed is a dedicated mathematician and researcher known for his contributions to cryptography, fuzzy graph theory, and QSPR analysis. His academic and professional pursuits focus on creating innovative solutions to global challenges, particularly in molecular descriptors, graph theory, and their applications in chemistry and physics. With a passion for research and education, Mr. Rasheed embodies excellence in both theoretical and applied mathematics. His publications in high-impact journals like Frontiers in Chemistry and Frontiers in Physics reflect his ability to bridge disciplines and address real-world problems. As a motivated and dependable team player, he thrives in collaborative environments while excelling independently. His research outputs, which span drug efficacy studies and complex mathematical modeling, contribute significantly to scientific advancements and underscore his role as a rising star in the global mathematical community.

Profile

Scopus

Education 🎓

Mr. Rasheed earned an MS in Mathematics (2021–2023) and a BS (Hons) in Mathematics (2017–2021) from the University of Education Lahore, Pakistan, achieving CGPAs of 3.64/4.00 and 3.61/4.00, respectively. His coursework encompassed advanced topics such as algebraic graph theory, numerical methods, Galois theory, real analysis, and differential geometry. This robust educational foundation equipped him with the analytical and problem-solving skills needed to excel in multidisciplinary research areas, including graph theory and mathematical modeling.

Work Experience 💼

Muhammad Waheed Rasheed is an accomplished researcher with expertise in cryptography, fuzzy graph theory, and QSPR analysis. His work focuses on molecular descriptors, graph labeling, energy graphs, and metric dimensions, addressing challenges in networking and drug efficacy analysis. With five impactful publications in journals like Frontiers in Chemistry and Frontiers in Physics, he demonstrates excellence in both independent and collaborative research. His ability to tackle complex problems and deliver innovative solutions highlights his readiness for advanced research roles in academia and industry.

Research Interests

Mr. Rasheed’s research interests include cryptography, group theory, fuzzy graph theory, and QSPR analysis. He focuses on molecular descriptors, graph labeling, energy graphs, and metric dimensions, aiming to address critical issues in mathematics and its applications in healthcare and networking.

Research Skills

Muhammad Waheed Rasheed’s research interests lie at the intersection of advanced mathematics and real-world applications. He specializes in cryptography, fuzzy graph theory, and group theory, with a strong emphasis on molecular descriptors, graph labeling, energy graphs, and metric dimensions. His work extends to QSPR (Quantitative Structure-Property Relationship) analysis, where he investigates the properties of chemical compounds, such as alkaloids and medications, to improve therapeutic efficacy and understand their thermodynamic behavior. He is particularly passionate about exploring the role of graph theory in networking and healthcare, focusing on innovative solutions to complex problems. Through his interdisciplinary research, Mr. Rasheed aims to contribute significantly to global challenges, combining theoretical insights with practical applications in chemistry, physics, and beyond.

📚 Publications

Neighborhood Face Index: A New QSPR Approach for Predicting Physical Properties of Polycyclic Chemical Compounds

  • Authors: A. Raza, M.W. Rasheed, A. Mahboob, M. Ismaeel
  • Journal: International Journal of Quantum Chemistry
  • Year: 2024
  • Volume: 124(24), e27524
  • Citations: 0

Block Cipher Construction Using Minimum Spanning Tree from Graph Theory and Its Application with Image Encryption

  • Authors: M.W. Rasheed, A. Mahboob, M. Bilal, K. Shahzadi
  • Journal: Science Progress
  • Year: 2024
  • Volume: 107(4)
  • Citations: 0

Entropy Measures of Dendrimers Using Degree-Based Indices

  • Authors: A. Ovais, F. Yasmeen, M. Irfan, M.W. Rasheed, S. Kousar
  • Journal: South African Journal of Chemical Engineering
  • Year: 2024
  • Volume: 50, pp. 168–181
  • Citations: 0

Computing Connection-Based Topological Indices of Carbon Nanotubes

  • Authors: E.U. Haq, A. Mahboob, M.W. Rasheed, S. Sattar, M. Waqas
  • Journal: South African Journal of Chemical Engineering
  • Year: 2024
  • Volume: 48, pp. 121–129
  • Citations: 0

QSPR Analysis of Physicochemical Properties and Anti-Hepatitis Prescription Drugs Using a Linear Regression Model

  • Authors: A. Mahboob, M.W. Rasheed, A.M. Dhiaa, I. Hanif, L. Amin
  • Journal: Heliyon
  • Year: 2024
  • Volume: 10(4), e25908
  • Citations: 5

Approximating Properties of Chemical Solvents by Two-Dimensional Molecular Descriptors

  • Authors: A. Mahboob, M.W. Waheed Rasheed, I. Hanif, I. Siddique
  • Journal: International Journal of Quantum Chemistry
  • Year: 2024
  • Volume: 124(1), e27305
  • Citations: 3

Role of Molecular Descriptors in QSPR Analysis of Kidney Cancer Therapeutics

  • Authors: A. Mahboob, M.W. Rasheed, I. Hanif, L. Amin, A. Alameri
  • Journal: International Journal of Quantum Chemistry
  • Year: 2024
  • Volume: 124(1), e27241
  • Citations: 9

Face Irregular Evaluations of Family of Grids

  • Authors: J.H.H. Bayati, A. Ovais, A. Mahboob, M.W. Rasheed
  • Journal: AKCE International Journal of Graphs and Combinatorics
  • Year: 2024 (In Press)
  • Citations: 0

Enhancing Breast Cancer Treatment Selection Through 2TLIVq-ROFS-Based Multi-Attribute Group Decision Making

  • Authors: M.W. Rasheed, A. Mahboob, A.N. Mustafa, Z.A.A. Ali, Z.H. Feza
  • Journal: Frontiers in Artificial Intelligence
  • Year: 2024
  • Volume: 7, 1402719
  • Citations: 0

QSAR Modeling with Novel Degree-Based Indices and Thermodynamics Properties of Eye Infection Therapeutics

  • Authors: M.W. Rasheed, A. Mahboob, I. Hanif
  • Journal: Frontiers in Chemistry
  • Year: 2024
  • Volume: 12, 1383206
  • Citations: 0

Conclusion 

Muhammad Waheed Rasheed is a talented researcher whose academic achievements and innovative research demonstrate a promising career in mathematics and its applications. His dedication, interdisciplinary focus, and impactful publications make him a strong candidate for prestigious accolades and research opportunities.