جستجوی استاد راهنمای دانشگاهی

Yiyu Yaoمشاهده پروفایل

استاد

• Three-way Decisions
• Granular Computing
• Rough Sets
• +۸ مورد دیگر

Yiyu Yao is a Professor in the Department of Computer Science at the University of Regina, Faculty of Science. He holds a B.Eng. from Xi'an Jiaotong University and earned both his M.Sc. and Ph.D. from the University of Regina. His office is located in College West 308.6, and he can be reached at Yiyu.Yao@uregina.ca or by phone at (306) 585-5226. Dr. Yao's research spans multiple interconnected domains in intelligent systems. His primary focus is on three-way decisions, which serves as a unifying framework for his work in granular computing, rough sets, and decision-theoretic models. He has developed significant theoretical contributions to decision-theoretic rough sets (DTRS) and probabilistic rough sets, creating bridges between uncertainty management and practical decision-making applications. His work extends to web intelligence, information retrieval systems, and multiview data analysis, where he applies his theoretical frameworks to real-world problems in data science and artificial intelligence. Analysis of Dr. Yao's recent publications reveals a strong continuing focus on three-way decision theory, with increasing applications across diverse domains. His work demonstrates evolution from foundational theoretical contributions to sophisticated applications in multi-criteria decision making, conflict analysis, and explainable AI. The research shows integration of granular computing principles with modern machine learning techniques, particularly in handling uncertainty and developing interpretable models. Recent publications indicate growing interest in the intersection of three-way decisions with fuzzy sets, shadowed sets, and cognitive approaches to data analysis. Dr. Yao has mentored numerous graduate students and has hosted many visiting scholars, primarily from Chinese institutions including Nanjing University Posts and Telecommunication, Harbin Normal University, Shaanxi Normal University, and others. He serves as Area Editor on Rough Sets for the International Journal of Approximate Reasoning and as Associate Editor for Information Sciences. He is also Associate Editor-in-Chief for the Journal of Emerging Technologies in Web Intelligence and serves on multiple editorial boards including LNCS Transactions on Rough Sets and Web Intelligence and Agent Systems. He has organized significant conferences including the International Joint Conference on Rough Sets (IJCRS 2017) and served on the Steering Committee for the International Symposium on Fuzzy and Rough Sets.

Véronique Hosteمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +۷ مورد دیگر

Véronique Hoste is Senior Full Professor of Computational Linguistics at Ghent University's Faculty of Arts and Philosophy, where she serves as Department Head of Translation, Interpreting and Communication and Director of the LT3 language technology research team. She also holds the position of Research Director for the Faculty of Arts and Philosophy. Her educational background includes a PhD in Computational Linguistics from the University of Antwerp (2005) focused on optimization in machine learning for coreference resolution. Key research areas encompass machine learning for natural language processing, semantic and discourse modeling, including specialized work in event detection, entity/event coreference resolution, irony detection, and emotion analysis. Hoste's publication trends reveal strong interdisciplinary focus, with recent work bridging NLP with crisis communication, digital humanities, and ethical AI. Her team develops high-quality datasets (e.g., EmoTwiCS for emotion trajectories) and collaborates extensively with commercial partners on projects like SentEMO for aspect-based sentiment analysis. Current research emphasizes multimodal emotion analysis, fuzzy rough set methods for sentiment detection, and cross-document event coreference. Elected member of the Royal Flemish Academy of Belgium for Science and the Arts (KVAB) Francqui Chair appointment by Université Libre de Bruxelles (2023-2024) Co-founded LT3 spin-off AlfaSent (2024) for customer feedback analysis Authored first Dutch-language book on NLP: "Taaltechnologie ontrafeld" She actively supervises multiple PhD students on projects including Common-sense knowledge in irony detection (Common-sense), cross-document event coreference (Encore), and empathy modeling in conversational agents (FlandersAI). Her team secures funding through interdisciplinary collaborations like NewsDNA for news recommendation and METRICS for emotion trajectory analysis. Hoste also engages in public outreach through the "AI at school" initiative and advises on language technology integration in high school curricula. The LT3 laboratory under her leadership maintains strong industry partnerships and develops practical NLP tools including EmotioNL and Automatic Term Extraction systems, while advancing core research through projects like CLARIAH-VL for data curation.

Cengiz Kahramanمشاهده پروفایل

استاد

• Multi-criteria Decision Making
• Fuzzy Logic
• Engineering Economics
• +۱ مورد دیگر

Prof. Cengiz Kahraman is a Professor in the Department of Industrial Engineering at Istanbul Technical University (ITU). He holds a PhD in Industrial Engineering from ITU and has held numerous academic and administrative roles, including Head of Department (2010–2013) and Vice Dean (2004–2008). His research focuses on fuzzy logic, multi-criteria decision making, engineering economics, and statistical analysis. He has supervised over 60 theses and led projects funded by TUBITAK and private institutions, including studies on fuzzy decision support systems, warranty cost analysis, and performance evaluation systems. Education: PhD in Industrial Engineering, Istanbul Technical University (1992–1996) MSc in Industrial Engineering, Istanbul Technical University (1988–1990) BSc in Industrial Engineering, Istanbul Technical University (1984–1988) Research Interests: Fuzzy logic extensions (e.g., spherical, Pythagorean, neutrosophic sets) Multi-criteria decision-making methodologies (AHP, TOPSIS, VIKOR) Applications in supply chain management, sustainability, and engineering economics Key Awards: Applied Soft Computing Journal Best Paper Award (2024) ITU Engineering Science Award (2023) TUBITAK Science Award (2018) Memorial Da Ruan Award (2014) Recent Projects: Intelligent multi-criteria legal tracking systems Fuzzy inference techniques for debt collection optimization Social innovation platforms for education volunteers Warranty cost modeling for Arçelik products Grants & Advising: Recipient of multiple TUBITAK Publication Incentives (2011–2015) Advisor to over 60 graduate students in fuzzy decision-making and engineering Directed projects totaling over ₺1.2 million in funding Labs/Teams: Leads research groups focusing on fuzzy decision systems and sustainable engineering solutions.

• Topology
• Fuzzy Topology
• Soft Set Theory
• +۳ مورد دیگر

Assoc. Prof. Dr. Adem YOLCU is an active faculty member at Kafkas University's Faculty of Arts and Sciences, Department of Mathematics, specializing in advanced topological structures with applications in decision-making systems. Holding a PhD in Mathematics from Kafkas University (2020), he has established himself as a leading researcher in fuzzy topology and soft set theory. PhD in Mathematics (2020), Kafkas University Master's in Mathematics (2016), Kafkas University Bachelor's in Mathematics (2014), Kafkas University His research focuses on the intersection of topology, fuzzy logic, and decision theory, particularly exploring neutrosophic soft sets, hypersoft topological spaces, and their applications in multi-criteria decision-making problems. His work bridges theoretical mathematics with practical computational applications in risk assessment and building safety analysis. Recent publications demonstrate a consistent trajectory in developing novel frameworks for fuzzy parameterized systems, with 15+ articles published between 2019-2024 in SCI/ESCI-indexed journals. His work shows particular strength in extending classical topological concepts to neutrosophic and Pythagorean fuzzy environments. As an academic advisor, he has supervised Master's research including Büşra Aka's thesis on neutrosophic soft multisets. His collaborative network spans international researchers including Taha Yasin Öztürk (60+ joint publications), Florentin Smarandache, and Sadık Bayramov. His research group focuses on computational topology applications, with particular emphasis on developing mathematical frameworks for uncertainty handling in decision support systems. Current projects include hypersoft set applications in sustainable security systems and Fermatean fuzzy topological structures.

• Computational Proximity
• Near Sets
• Rough Sets
• +۱۳ مورد دیگر

James F. Peters is a faculty member in the Department of Electrical and Computer Engineering at the University of Manitoba, Winnipeg, Canada. His research lies at the intersection of computational topology, proximity theory, rough sets, and digital image analysis, with applications in computer vision, pattern recognition, and biologically-inspired computing. He has made foundational contributions to the theory of near sets and computational proximity, publishing extensively in journals and book series by Springer. His research interests include computational proximity, near sets, rough sets, digital image analysis, pattern recognition, and topological models of perception. These are evident from his numerous publications in theoretical and applied computer science, often in collaboration with researchers such as Andrzej Skowron, Sheela Ramanna, and Arturo Tozzi. His work spans mathematical foundations, computational models, and real-world applications in biomedical imaging and rehabilitation systems. The recent articles (2017–2025) show a strong trend toward integrating topology, physics, and neuroscience in the analysis of digital images and brain activity. Topics include proximal nerves, optical vortices, thermodynamics of emotions, and entropy in cosmology, indicating a broad interdisciplinary approach. His publications frequently appear in journals such as Entropy , Information Sciences , and Transactions on Rough Sets , as well as in Springer’s Lecture Notes in Computer Science and Intelligent Systems Reference Library series. He has authored or co-authored several books and special issues, notably in the Transactions on Rough Sets series, and has contributed to encyclopedic works on rough sets and computational intelligence. His editorial and collaborative roles highlight his leadership in the rough and near sets research community. Dr. Peters has advised or collaborated with several researchers, though specific student names are not listed in the provided text. He has been involved in projects related to adaptive learning, telerehabilitation gaming systems, and image classification using tolerance near sets. His work often involves grants and interdisciplinary teams, especially in computational intelligence and biomedical applications. He is associated with research groups and labs focused on computational intelligence, rough sets, and digital image analysis, often in collaboration with the University of Warsaw and other international institutions. His ongoing work continues to explore the mathematical foundations of perception and proximity in both artificial and biological systems.

Mohsen Saffariمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Spatiotemporal Data Analysis
• +۳ مورد دیگر

Mohsen Saffari is an Assistant Professor of Computer Engineering at Purdue University Northwest (PNW), joining the Electrical and Computer Engineering department in Fall 2024. His research and teaching focus on machine learning, deep neural networks, and their applications in spatiotemporal data analysis, particularly in power systems and computer vision. He holds a Ph.D. and is actively involved in advancing interdisciplinary approaches to energy systems and intelligent technologies. His research emphasizes the theoretical and practical aspects of machine learning and deep learning models applied to spatiotemporal data. His work spans power systems optimization, including fault detection in power distribution networks and probabilistic optimal power flow analysis. In computer vision, he explores domain adaptation techniques for traffic scene understanding and masked face recognition. He also develops innovative methods like physics-informed graph neural networks and generative adversarial networks for solving complex engineering and healthcare challenges. Recent trends in his publications highlight advancements in graph neural networks for power systems, spatiotemporal data applications, and domain adaptation in computer vision. His contributions include energy disaggregation techniques, photovoltaic forecasting, and medical imaging through interdisciplinary approaches combining deep learning with physics-based models. Dr. Saffari’s teaching focuses on machine learning fundamentals, artificial neural networks, and software engineering. He advocates for engineers’ role in shaping societal advancements through critical thinking and innovation.

Chris Cornelisمشاهده پروفایل

استاد

• Machine Learning
• Fuzzy Sets
• Rough Sets
• +۳ مورد دیگر

Chris Cornelis is a full-time Professor in fuzziness and uncertainty modelling at Ghent University's Department of Applied Mathematics, Computer Science and Statistics. His research integrates fuzzy logic and rough set theory to advance machine learning methodologies for complex data analysis. Education: M.Sc. in Computer Science, Ghent University (2000) Ph.D. in Computer Science, Ghent University (2004) Research Focus: Cornelis pioneers fuzzy-rough hybrid systems for uncertainty handling in machine learning. His work spans theoretical foundations (e.g., implication operators, granular approximations) and practical applications including emotion detection, medical diagnosis, and imbalanced data classification. Key innovations include FRNN-OWA classifiers and polar encoding for missing values, demonstrating exceptional versatility in bridging abstract mathematics with real-world AI challenges. Publication Trends: Recent work (2023-2025) reveals intensified exploration of topological data analysis (Mapper-based rough sets), advanced granular computing (disjoint/adjacent fuzzy granules), and ethical AI ("No Imputation Without Representation"). His research shows consistent progression from foundational fuzzy-rough theory toward multi-disciplinary applications while maintaining mathematical rigor, particularly in Choquet integration and quantifier-based frameworks. Scientific Awards: No specific awards were documented in the provided sources. Research Support: Cornelis has secured competitive funding including FWO postdoctoral mandates, a Ramón y Cajal contract at the University of Granada, and an FWO Odysseus Type II project at Ghent University. These grants enabled foundational work in fuzzy-rough set theory and its applications to complex data problems. Research Unit: He leads research within Ghent University's Computational Web Intelligence (CWI) unit, focusing on intelligent data analysis systems that leverage fuzzy-rough methodologies for web-scale information processing.

Jouni Järvinenمشاهده پروفایل

مدرس

• Rough Set Theory
• Lattice Theory
• Non-Classical Logics
• +۴ مورد دیگر

Jouni Järvinen is a University Lecturer in Software Engineering at LUT University in Lahti, Finland, and a Docent in the Department of Mathematics and Statistics at the University of Turku. He holds a PhD in Mathematics and serves as a Mathematics Consultant at TXODDS. His work bridges theoretical and applied research across multiple domains. Affiliations : LUT School of Engineering Sciences, University of Turku Education : PhD in Mathematics Research Interests lie at the intersection of Rough Set Theory , Lattice Theory , and Non-Classical Logics . He explores algebraic structures like Nelson algebras, Kleene algebras, and Galois connections, applying them to problems in Artificial Intelligence and Bioinformatics . His work often connects abstract mathematical frameworks to practical applications in Information Systems and Machine Learning . Publication Trends reveal a focus on Rough Sets and Algebraic Structures across 51 works (2025–1998). Recent studies (2025–2020) examine multigranular rough sets, pseudocomplemented Kleene algebras, and relational correspondences for fuzzy rough approximations. Earlier works (2015–2007) address topics like Tolerance-Based Rough Sets , Galois Connections , and Natural Language Disambiguation . Peer Review Activities include contributions to journals such as Fuzzy Sets and Systems , International Journal of Approximate Reasoning , and Studia Logica . His research spans Theoretical Computer Science , Mathematical Logic , and Biomedical Data Analysis .

• Sustainability
• Circular Economy
• Life Cycle Assessment
• +۶ مورد دیگر

Giovanni Lagioia serves as a Full Professor within the Department of Economics, Management and Business Law at the University of Bari Aldo Moro, Italy. His academic work focuses on sustainability and environmental management with emphasis on circular economy practices and life cycle assessment methodologies. Professor Lagioia's research spans agriculture, tourism, hospitality, and textiles, addressing critical issues including water conservation, food waste reduction, and sustainable resource management in Southern Italy and global contexts. His research interests encompass life cycle assessment applications across multiple industries, circular economy models for agri-food systems, sustainable tourism frameworks, and environmental footprint reduction in manufacturing. Key investigations include wastewater recovery systems, sustainable packaging solutions, renewable energy integration, and resource efficiency metrics. His work consistently bridges theoretical advancements with practical policy implementation, particularly in Mediterranean regional challenges. Analysis of recent publications (2023-2025) reveals strong interdisciplinary trends integrating fuzzy AHP, material flow analysis, and multivariate curve resolution to address complex sustainability problems. Dominant themes include post-pandemic recovery in tourism/hospitality sectors, textile production environmental impacts, and circular economy applications for Mediterranean water/food systems. Southern Italy serves as a critical case study for sustainable practices in hospitality food waste management and agricultural water conservation. Scientific Awards: No specific awards, fellowships, or medals are mentioned in the provided information. Advising and Grants: While Professor Lagioia maintains an active research profile, the available materials do not list doctoral students, master's advisees, or specific research grants secured. Labs and Teams: The provided documentation contains no details regarding research laboratories, specialized teams, or collaborative research groups led by Professor Lagioia.

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +۳ مورد دیگر

Marlies Temper is a Professor at St. Pölten University of Applied Sciences, serving as Academic Director for the Data Intelligence (MA) and Data Science and Artificial Intelligence (BA) programs. She is Deputy Head of the Department of Computer Science and Security and a member of the UAS Board from 2023 to 2026. Her roles include leading educational initiatives, conducting research, and managing continuing education programs. Academic Director, Data Intelligence (MA) Academic Director, Data Science and Artificial Intelligence (BA) Deputy Head, Department of Computer Science and Security Continuing Education Officer Marlies Temper's research focuses on Artificial Intelligence , Cybersecurity , and Big Data Analytics . She explores the ethical implications of AI, develops gamified models for security training, and investigates continuous biometric authentication methods. Her work bridges the gap between theoretical advancements and practical applications in AI-driven security solutions. Her recent publications highlight trends in AI Security (e.g., risk frameworks), Biometric Authentication (e.g., touch dynamics), and Cybersecurity Education (e.g., gamified simulations). She emphasizes the responsible use of AI and Big Data while addressing technical and ethical challenges.

• Fuzzy Set Theory
• Fractional Calculus
• Mathematical Inequalities
• +۶ مورد دیگر

Muhammad Bilal Khan is an Assistant Professor at the Department of Electronics and Power Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Pakistan. His research spans mathematical theory and practical applications in wireless communication sensing technology. His primary research interests include Fuzzy Set Theory , Fractional Calculus , Mathematical Inequalities , and RF Sensing Applications in healthcare. His theoretical work focuses on developing new mathematical frameworks for fuzzy-number valued functions, particularly exploring pre-invexity, harmonic convexity, and related integral inequalities. On the applied side, he has made significant contributions to non-contact health monitoring systems using software-defined radio technology for detecting conditions like text neck syndrome, spinal curvature disorders, and breathing abnormalities. Analysis of his 15 most recent publications reveals a dual research trajectory: approximately 60% focus on theoretical mathematical contributions in fuzzy analysis and integral inequalities, while 40% address practical applications of wireless sensing in healthcare. His mathematical work frequently appears in journals like Axioms and Symmetry , while his applied research is published in venues including IEEE Access and Sensors . His notable scientific contributions include developing novel frameworks for (q 1 , q 2 )-Linear Diophantine Fuzzy Sets and advancing the theory of Diamond Intuitionistic Fuzzy Sets. His healthcare applications research has pioneered non-invasive monitoring techniques using RF sensing for elderly fall detection, text neck syndrome diagnosis, and breathing pattern analysis. Dr. Khan maintains active collaborations with researchers across multiple institutions, particularly working with Mohamed S. Soliman (13 joint publications), Muhammad Aslam Noor (12 publications), and Savin Treanta (8 publications) on mathematical theory, while collaborating with Najah AbuAli, Mubashir Rehman, and Xiaodong Yang on healthcare sensing applications.

Varouchakis Emmanuelمشاهده پروفایل

استادیار

• Geostatistics
• Environmental Mining
• Sustainable Development
• +۳ مورد دیگر

Varouchakis Emmanuel is an Assistant Professor at the Department of Mining Technology within the School of Mineral Resources Engineering at the Technical University of Crete . His research focuses on Geostatistics , Environmental Mining , and Sustainable Development , with expertise in Risk Analysis , Stochastic Processes , and Machine Learning . He contributes to the Research Units of Geostatistics and Environmental Mining and Sustainable Development ( https://www.envi-stat.tuc.gr/en/home ). Research Trends: His recent publications address critical challenges in hydrogeological modeling, environmental risk assessment, and sustainable mining practices. Key themes include groundwater level analysis , geostatistical inversion , pit lake hazard frameworks , and multi-criteria decision-making for SDG implementation in mining contexts. Grants & Projects: He leads the HFRI-funded project "Advanced geostatistical modeling for natural resources evaluation" (2023-2025) and participates in EU initiatives like IMMERSE (2023-2025) for critical mineral education and REECOL (2023-2026) focused on ecological rehabilitation of post-mining areas, employing machine learning and remote sensing. Teaching: He teaches undergraduate courses in Statistics and Probability for Engineers and Applied Geostatistics , as well as postgraduate courses on Time Series Analysis and Geostatistical Simulation Methods .

• Machine Learning
• Bioinformatics
• High Performance Computing
• +۵ مورد دیگر

Daniel Peralta Cámara is a Postdoctoral Researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology (EA05). His work spans Machine Learning , Bioinformatics , High Performance Computing , and Biometrics , with a focus on Single-Cell Data Analysis , Image Cytometry , and Missing Value Handling . Affiliation: Ghent University Academic Rank: Researcher Research Disciplines: Data Mining, Parallel Computing, Bioinformatics, High Performance Computing His research designs scalable machine learning frameworks for biological and engineering applications, including SCIP for morphological profiling and MSDeepAMR for antimicrobial resistance prediction. He explores advanced techniques like Polar Encoding for missing data and Fuzzy Rough Sets for classification uncertainty, contributing to one-class classification and novelty detection through Python libraries like Fuzzy-rough-learn 0.2 . Key Trends: Hybrid CNN-LSTM for sports analytics Time-series feature selection in healthcare UWB/IMU wearable integration for animal monitoring Dr. Peralta supervises PhD candidates like Maxim Lippeveld (2025) and Oliver Urs Lenz (2023). His collaborations span Ghent University researchers including Eli De Poorter , Chris Cornelis , and Yvan Saeys . Applications: Badminton strategy analysis Goat activity classification White blood cell identification

Roman Słowińskiمشاهده پروفایل

استاد

• Decision Support
• Multiple Criteria Decision Aiding
• Ordinal Data Mining
• +۱۱ مورد دیگر

Roman Słowiński is a full Professor at the Institute of Computing Science , Poznań University of Technology, Poland, and holds a professorial position at the Systems Research Institute of the Polish Academy of Sciences in Warsaw. He is a Full Member of the Polish Academy of Sciences and served as President of its Poznań Branch (2011–2014). His career spans decades of contributions to decision support systems and computational intelligence. His research focuses on Multiple criteria decision aiding Rough set theory and fuzzy set theory Multiobjective optimization Ordinal data mining Robust decision analysis with applications in medicine, economics, and environmental studies. He is renowned for pioneering the use of rough sets in decision analysis, collaborating with Zdzisław Pawlak and others. His recent work emphasizes robust ordinal regression, dominance-based methods, and monotonicity constraints in classification. Scientific awards include EURO Gold Medal (1991) Edgeworth-Pareto Award (1997) Annual Prize of the Foundation for Polish Science (2005) Doctor Honoris Causa from three international universities He has advised 26 Ph.D. theses and served as Coordinating Editor for European Journal of Operational Research , alongside leadership roles in the International Rough Set Society and the EURO Working Group on Multiple Criteria Decision Aiding. He founded the Laboratory of Intelligent Decision Support Systems at PUT.

• Database Management
• Data Privacy
• Data Science
• +۴ مورد دیگر

Dr. Tianbing Xia is a Lecturer at the School of Computing and Information Technology, University of Wollongong (UOW), where he has been employed since 2021. He holds a PhD in Computer Science from UOW (1997–2000) and has over two decades of experience in tertiary education, research, and the IT industry. Prior to his PhD, he worked in the IT department of China Construction Bank HUBEI Province, contributing to software networking systems that earned a national-level award. His teaching spans a wide range of undergraduate and postgraduate courses, including CSIT115, CSCI 882, and others. Research interests include database management, data privacy, combinatorial code design, and computer security. He has actively supervised four Higher Degree Research students, focusing on topics like data processing optimization and cybersecurity. Dr. Xia’s research contributions span over 50 publications, with notable works in query optimization, parallel data transfer scheduling, and combinatorial matrix constructions. He has received funding for projects like Real-Time Visualisation of Intrusion Detection and Fuzzy-based Cryptography Applications . Notable recognition includes the national Technology and Advanced Development Grand Prize for a software networking system. He is currently available to supervise Masters and PhD students in data science and cybersecurity domains.