Raziyeh Erfanifar | Mathematics | Research Excellence Award

Published on by Book of Award

Dr. Raziyeh Erfanifar | Mathematics | Research Excellence Award

Shahid Beheshti University | Iran

Dr. Raziyeh Erfanifar is a researcher in applied mathematics and computational engineering whose work centers on high-order iterative methods, nonlinear equations, matrix and tensor computations, and their applications in control theory, signal processing, image processing, data mining, and fractional calculus. The research contributions emphasize the development of efficient, inversion-free, and high-convergence iterative algorithms for solving nonlinear systems, algebraic Riccati equations, nonlinear matrix and tensor equations, and computing Moore–Penrose and Drazin inverses. A significant portion of the work advances fixed-point theory, weight-splitting strategies, Newton-type schemes, and parametric multi-step methods, with rigorous convergence analysis and efficiency evaluation. These methods have been successfully applied to problems in electrical engineering, vibration and control systems, differential equations, tensor equations with Einstein products, data whitening, and numerical simulation. The scholarly output includes 36 peer-reviewed journal articles published in leading journals such as Journal of the Franklin Institute, Journal of Complexity, Computational and Applied Mathematics, Circuits, Systems, and Signal Processing, Applied Numerical Mathematics, and Engineering Computations. According to Google Scholar metrics, this body of work has received 274 citations, with an h-index of 11 and an i10-index of 11, reflecting sustained impact and strong visibility in numerical analysis, computational mathematics, and engineering applications.

 

Citation Metrics (Google Scholar)

300

240

180

120

60

0

Citations
274

h-index
11

i10-index
11

Citations

h-index

i10-index


View Google Scholar Profile

Featured Publications

Several efficient iterative algorithms for solving nonlinear tensor equation
X + AT*N X−1*N A = I with Einstein product
– Computational and Applied Mathematics, 2024

Splitting iteration methods to solve non-symmetric algebraic Riccati matrix equation
YAY − YB − CY + D = 0
– Numerical Algorithms, 2023

Swathi Priyadarshini Tigulla | Computer Science | Best Researcher Award

Published on by Book of Award

Dr. Swathi Priyadarshini Tigulla | Computer Science | Best Researcher Award

Osmania University | India

Author Profile

Scopus

Early Academic Pursuits

Dr. Swathi Priyadarshini Tigulla laid the foundation of her academic journey with a degree in Information Technology, followed by a master’s program in Information Technology with a specialization in network security. Her pursuit of advanced knowledge culminated in a doctoral degree in Computer Science and Engineering from Osmania University. From the beginning, she demonstrated a strong inclination toward solving computational problems and a keen interest in the emerging domains of artificial intelligence, machine learning, and network security.

Professional Endeavors

Her professional career reflects an extensive teaching and mentoring journey across reputed institutions. She began her career as an Assistant Professor in engineering colleges where she taught computer science, network security, and software engineering, and guided student projects. Over the years, she progressed to significant academic roles, including serving as Head of the Department, coordinating extracurricular activities, and contributing to student training and placement. Presently, she continues her academic engagement as an Assistant Professor specializing in artificial intelligence and machine learning, while also actively mentoring projects and participating in innovative academic initiatives such as GEN-AI teams and project schools.

Contributions and Research Focus

Dr. Tigulla’s research is strongly anchored in artificial intelligence, machine learning, and soft computing, with a particular focus on healthcare applications such as heart stroke prediction models. Her publications have proposed innovative approaches that integrate clustering, classification, and deep learning techniques to enhance medical predictions, combining accuracy with practical applicability. Beyond healthcare, her work also explores security strategies in cloud computing and data-driven approaches to protect systems from vulnerabilities. This blend of healthcare informatics and cyber security positions her research at the intersection of technology and community impact.

Accolades and Recognition

Her expertise has been recognized through publications in reputed international journals such as Measurement: Sensors and Journal of Positive School Psychology, along with contributions to international conferences under IEEE. She has served as a reviewer for scholarly journals and academic book chapters, demonstrating her standing as a trusted evaluator in her field. Her involvement as an organizer of technical workshops, hackathons, and project expos reflects her commitment to academic innovation and student skill development, further reinforcing her recognition as a versatile academic leader.

Impact and Influence

The impact of Dr. Tigulla’s work is evident in both her research outcomes and her teaching contributions. Her models for heart stroke prediction contribute significantly to community health by combining artificial intelligence with real-world medical applications. As an educator, she has influenced generations of students by equipping them with knowledge in machine learning, artificial intelligence, and advanced computational concepts. Her leadership in academic events has fostered a culture of innovation, creativity, and hands-on learning among students, thereby extending her influence beyond traditional teaching.

Legacy and Future Contributions

Dr. Tigulla’s legacy is one of blending research excellence with community benefit. By focusing on both healthcare prediction models and system security, she has addressed two domains of immense social importance—public health and digital trust. Looking forward, her future contributions are expected to further deepen the integration of artificial intelligence into real-world applications, enhance her role as a reviewer and academic guide, and continue her efforts to shape students into innovative researchers and industry-ready professionals.

Publications

Article: Developing Heart Stroke Prediction Model using Deep Learning with Combination of Fixed Row Initial Centroid Method with Naïve Bayes, Decision Tree, and Artificial Neural Network
Authors: T. Swathi Priyadarshini, Vuppala Sukanya, Mohd Abdul Hameed
Journal: Measurement: Sensors
Year: 2024

Article: Collaboration of Clustering and Classification Techniques for Better Prediction of Severity of Heart Stroke using Deep Learning
Authors: T. Swathi Priyadarshini, Vuppala Sukanya, Mohd Abdul Hameed
Journal: Measurement: Sensors
Year: 2025

Article: Deep Learning Prediction Model for Predicting Heart Stroke using the Combination Sequential Row Method Integrated with Artificial Neural Network
Authors: T. Swathi Priyadarshini, Mohd Abdul Hameed, Balagadde Ssali Robert
Journal: Journal of Positive School Psychology
Year: 2022

Article: Methods of Hidden Pattern Usage in Cloud Computing Security Strategies with K-means Clustering
Authors: T. Swathi Priyadarshini, Dr. S. Ramachandram
Journal: AIJREAS
Year: 2021

Article: A Review on Security Issue Solving Methods in Public and Private Cloud Computing
Authors: T. Swathi Priyadarshini, S. Ramachandram
Journal: IJMTST
Year: 2020

Conclusion

Dr. Swathi Priyadarshini Tigulla embodies the qualities of an academician and researcher who successfully bridges the gap between theoretical advancements and community impact. Her journey, marked by academic rigor, extensive teaching experience, and impactful research, showcases her dedication to advancing artificial intelligence and machine learning for practical applications. Recognized as both a researcher and a mentor, she continues to inspire through her contributions in education, healthcare, and cyber security. In conclusion, her career highlights a sustained commitment to knowledge, innovation, and community-oriented research, establishing her as a distinguished academic voice in the field of computer science and engineering.

 

Benjamin Kwakye Danso | Computer Science | Best Paper Award

Published on by Book of Award

Mr. Benjamin Kwakye Danso | Computer Science | Best Paper Award

University of Science and Technology of China | China

Author Profile

Scopus

Orcid

Google Scholar

🌱 Early Academic Pursuits

Mr. Benjamin Kwakye Danso began his academic journey in Ghana, earning a DBE in Science and Mathematics from the University of Cape Coast in 2011. He continued to build a strong foundation in mathematics with a B.Ed. in Mathematics from Valley View University. His academic excellence led to recognition on the Vice Chancellor’s List of Excellent Students. Driven by a passion for analytical sciences, he pursued a Master’s degree in Mathematics at Hohai University, China, and is currently a Ph.D. candidate in Statistics at the University of Science and Technology of China, focusing on cutting-edge statistical methods and optimization techniques.

💼 Professional Endeavors

Mr. Kwakye Danso has a rich background in teaching and project leadership. He served as a mathematics and integrated science teacher in Ghana’s education system, holding leadership roles such as Secretary of the PTA and Head of the Examination Committee. His international exposure expanded in China, where he leads the Project Management Team at Grace Outreach Global Foundation. He has also supported various academic and cultural initiatives, including student registration and the Sino-Cultural Festival organization.

🔬 Contributions and Research Focus

Mr. Kwakye Danso's research is deeply rooted in optimization, metaheuristic algorithms, feature selection, and mathematical statistics. His 2024 publication in Expert Systems with Applications introduces a particle-guided metaheuristic algorithm for complex global optimization problems. His interdisciplinary work spans radar systems, agricultural technologies, and data science, contributing to IEEE conferences and journals like Signal Processing, Science Journal of Chemistry, and Pharmacognosy Journal. His academic rigor is complemented by practical insights in data analysis and high-dimensional feature modeling.

🏆 Accolades and Recognition

His academic achievements have been recognized with Chinese University Scholarships from both Hohai University and the University of Science and Technology of China. Additionally, his placement on the Vice Chancellor’s List reflects his sustained excellence. He has also been selected for prestigious international conferences, such as ASPAI 2022 and IEEE ICSIP 2020.

🌍 Impact and Influence

Benjamin's work bridges applied mathematics and real-world challenges, particularly in optimization and data modeling for signal processing and agricultural innovation. His research has contributed to solving high-dimensional problems in radar communication and food science, while also influencing educational standards in Ghana. His leadership in academic communities and volunteering initiatives exemplifies a commitment to societal advancement through science.

🔮 Legacy and Future Contributions

Mr. Kwakye Danso’s scholarly path showcases an inspiring trajectory of growth from local educator to international researcher. With ongoing Ph.D. research and active membership in global academic networks such as the Institute of Mathematical Statistics and OAAD, he is poised to make substantial contributions to AI-driven optimization, statistical modeling, and STEM education in Africa. His future work promises to enrich both academic theory and practical applications.

 

Publications

📝 Particle guided metaheuristic algorithm for global optimization and feature selection problems

Authors: B.D. Kwakye (Benjamin Danso Kwakye), Y. Li (Yongjun Li), H.H. Mohamed (Halima Habuba Mohamed), E. Baidoo (Evans Baidoo), T.Q. Asenso (Theophilus Quachie Asenso)

Journal: Expert Systems with Applications

Year: 2024

 

Bing Cai | Computer Science | Best Researcher Award

Published on by Book of Award

Mr. Bing Cai | Computer Science | Best Researcher Award

Anhui Institute of Information Technology | China

Author Profile

Scopus

Orcid

Google Scholar

Early Academic Pursuits 🎓

Mr. Bing Cai embarked on his academic journey with a strong foundation in engineering. He earned his Bachelor of Engineering in Electronics and Information Engineering from Anhui University in 2014, where he developed a keen interest in computing and information systems. His thirst for advanced knowledge led him to pursue a Master of Engineering in Computer Technology at Anhui Polytechnic University, completing his degree in 2024 with a commendable GPA of 3.36. His rigorous academic training laid the groundwork for his expertise in software development and multi-view clustering techniques.

Professional Endeavors 🌟

Mr. Cai has accumulated extensive professional experience in both academia and industry. From 2014 to 2017, he worked as a Software Engineer at iFLYTEK Co., Ltd., where he contributed to the development of Android and iOS applications. His responsibilities included designing app frameworks, optimizing performance, and conducting comprehensive testing for speech synthesis systems. His tenure at iFLYTEK honed his skills in software architecture, application development, and embedded systems testing. Transitioning to academia in 2017, Mr. Cai served as a Corporate Teacher at Anhui Institute of Information Technology. Here, he played a pivotal role in teaching Web Front-End Development, guiding students in research and graduation projects, and mentoring them for competitions. His ability to bridge theoretical knowledge with practical applications made him a valuable asset in the field of computer and software engineering education.

Contributions and Research Focus 📚

Mr. Cai's research primarily focuses on multi-view clustering, tensor subspace clustering, and machine learning methodologies. His scholarly contributions include several high-impact publications in prestigious journals such as IEEE Transactions on Multimedia, Pattern Recognition, and Signal Processing. His research introduces innovative clustering techniques using tensorized and low-rank representations, significantly advancing the field of multi-view learning. Notably, his studies on high-order manifold regularization and tensorized bipartite graph clustering have provided new insights into handling large-scale and incomplete multi-view data. His work is instrumental in improving data representation and clustering efficiency in artificial intelligence applications.

Accolades and Recognition 🏆

Mr. Cai's dedication to excellence has been recognized with several prestigious awards. In 2023, he won the Bronze Prize in the Anhui Province "Internet+" College Student Innovation and Entrepreneurship Competition, highlighting his innovative approach to problem-solving. He also received the Outstanding Paper Award from the Anhui Association for Artificial Intelligence in 2022, further cementing his reputation as a leading researcher in his field. His academic excellence was also acknowledged through the National Scholarship for Postgraduate Students in 2022, a testament to his scholarly contributions.

Impact and Influence 🌍

Mr. Cai's work has had a profound impact on both academia and industry. His contributions to multi-view clustering have influenced the development of more robust and efficient data analysis techniques in AI and machine learning. His research findings are widely cited, reflecting their significance in advancing computational intelligence. Furthermore, his role as an educator has shaped the next generation of computer scientists, inspiring students to engage in research and innovation.

Legacy and Future Contributions 🚀

With a strong foundation in research and industry, Mr. Cai is poised to make even greater contributions to the field of computer technology. His ongoing work in multi-view clustering and tensor-based machine learning will likely lead to more breakthroughs in AI-driven data processing. As he continues to explore innovative clustering methodologies, his research is expected to influence a wide range of applications, from big data analytics to artificial intelligence-driven decision-making systems. His commitment to excellence ensures that he will remain at the forefront of technological advancements in the years to come.

 

Publications

  • 📄 Multi-view subspace clustering with a consensus tensorized scaled simplex representation
    Author(s): Hao He, Bing Cai, Xinyu Wang
    Journal: Information Sciences
    Year: 2025-03

  • 📄 Tensorized Scaled Simplex Representation for Multi-View Clustering
    Author(s): Bing Cai, Gui-Fu Lu, Hua Li, Weihong Song
    Journal: IEEE Transactions on Multimedia
    Year: 2024

  • 📄 Aligned multi-view clustering for unmapped data via weighted tensor nuclear norm and adaptive graph learning
    Author(s): Bing Cai, Gui-Fu Lu, Liang Yao, Jiashan Wan
    Journal: Neurocomputing
    Year: 2024

  • 📄 Complete multi-view subspace clustering via auto-weighted combination of visible and latent views
    Author(s): Bing Cai, Gui-Fu Lu, Guangyan Ji, Weihong Song
    Journal: Information Sciences
    Year: 2024

  • 📄 Auto-weighted multi-view clustering with the use of an augmented view
    Author(s): Bing Cai, Gui-Fu Lu, Jiashan Wan, Yangfan Du
    Journal: Signal Processing
    Year: 2024

 

Hongzhen Cui | Computer Science | Best Researcher Award

Published on by Book of Award

Dr. Hongzhen Cui | Computer Science | Best Researcher Award

University of Science and Technology Beijing | China

Author Profile

Orcid

🚀 Early Academic Pursuits

Dr. Hongzhen Cui embarked on his academic journey in computer science with a Bachelor's degree from Zaozhuang University, where he built a solid foundation in computational principles. His passion for technology and problem-solving led him to pursue a Master's degree at Harbin Engineering University, refining his expertise in advanced computing methodologies. Currently, he is a Ph.D. candidate at the University of Science and Technology Beijing, where he specializes in cutting-edge fields such as Natural Language Processing (NLP), Knowledge Graphs, and Deep Learning, with a strong focus on cardiovascular disease research.

💼 Professional Endeavors

Dr. Cui's career has been marked by a blend of research and practical experience. As a System R&D Engineer at Meituan, he contributed to large-scale distributed systems, optimizing performance and collaborating with cross-functional teams to drive technological advancements. His passion for academia led him to a teaching position at Zaozhuang University, where he inspired students in subjects such as Data Structures, Algorithm Design, and Software Engineering. Through these roles, he has seamlessly combined industry expertise with academic mentorship.

🔬 Contributions and Research Focus

Dr. Cui’s research delves deep into the intersection of artificial intelligence and healthcare. His work in Natural Language Processing and Knowledge Graphs plays a pivotal role in extracting meaningful insights from medical data. With a keen interest in cardiovascular disease feature mining, he develops AI-driven models for disease prediction and analysis, aiding in early diagnosis and medical decision-making. His interdisciplinary approach bridges the gap between engineering and medicine, contributing to the evolution of intelligent healthcare solutions.

🏆 Accolades and Recognition

Dr. Cui’s dedication to research and academia has earned him recognition in both scientific and professional communities. His contributions to NLP and deep learning applications in healthcare have been acknowledged through publications, conference presentations, and collaborative projects. His role as a mentor and lecturer has also been praised for shaping future generations of computer scientists.

🌍 Impact and Influence

Through his research, Dr. Cui has made significant strides in the application of AI to medical diagnostics. His work on disease information extraction and prediction not only enhances medical research but also paves the way for AI-assisted healthcare innovations. As an educator, he has influenced countless students, guiding them towards research excellence and industry preparedness.

🔮 Legacy and Future Contributions

Dr. Cui's future aspirations involve furthering AI’s role in medical advancements, refining predictive models for cardiovascular diseases, and expanding the capabilities of knowledge graphs in healthcare applications. His interdisciplinary research continues to break barriers, promising a future where AI-driven solutions revolutionize disease prevention and treatment.

 

Publications

📄ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
Author(s): Hongzhen Cui, Shenhui Ning, Shichao Wang, Wei Zhang, Yunfeng Peng
Journal: Algorithms
Year: 2025

 

Caiming Zhang | Decision Sciences | Best Researcher Award

Published on by Book of Award

Prof. Caiming Zhang | Decision Sciences | Best Researcher Award

China University of Labor Relations | China

Author profile

Scopus

Orcid

🌱 Early Academic Pursuits

Prof. Caiming Zhang's educational journey showcases his steadfast dedication to Industrial Economics and Management. He began with a Bachelor of Engineering from Nanchang University in 1998, followed by a Master of Management from Beijing University of Technology in 2001. His academic ambition culminated in a Ph.D. in Industrial Economics from Beijing Jiaotong University in 2008. These formative years laid the foundation for his future contributions to academia and industry.

🏛️ Professional Endeavors

Prof. Zhang's career trajectory reflects a seamless integration of academic leadership and industry innovation. As the Dean of the School of Labor Relations and Human Resources at China University of Labor Relations, he continues to guide the next generation of thinkers. His prior roles as Vice Dean and Director within the same institution underscore his impactful leadership. Beyond academia, Prof. Zhang is the Founder and President of Beijing Dimensional Insight Inc., a hub for cutting-edge research in big data and business intelligence, showcasing his entrepreneurial spirit and technical expertise.

🔬 Contributions and Research Focus

Prof. Zhang’s research encompasses pivotal areas such as big data, artificial intelligence, and Industry 4.0. His work has been recognized globally through publications in esteemed journals like Journal of Industrial Information Integration and Information Systems Frontiers. His patents, including innovative methods for big data analysis, highlight his contributions to technological advancement. Furthermore, his hosting of national research projects and development of big data systems for institutions like Beijing Metro Commission and Hebei Hospitals reflect his commitment to practical applications of research.

🏅 Accolades and Recognition

Prof. Zhang’s contributions have earned numerous accolades. Among them are awards for groundbreaking research in artificial intelligence and educational excellence. His paper on AI prospects won the Third Award at the 16th Scientific Research Achievements in China University of Labor Relations. He has also been recognized for innovative curriculum design, securing prestigious teaching awards. As a Senior Member of IEEE and the Chinese Society of Technology Economics, his influence extends across academic and professional spheres.

🌍 Impact and Influence

As a visiting scholar at Old Dominion University and a sought-after speaker at international conferences, Prof. Zhang has brought Chinese academic insights to the global stage. His research on topics like the economic impact of AI and blockchain technology not only advances knowledge but also addresses contemporary industry challenges. Through his mentorship of graduate students and leadership in research, he has shaped the academic and professional paths of countless individuals.

🌟 Legacy and Future Contributions

Prof. Zhang's enduring legacy lies in his ability to bridge the gap between theory and practice. His work in advancing big data applications, combined with his passion for education and innovation, promises a future where technology continues to drive societal progress. As he leads projects on intelligent education and big data decision-making, Prof. Zhang remains a beacon of inspiration, paving the way for breakthroughs in both academia and industry.

 

Publications

📝 The Impact of Generative AI on Management Innovation

  • Author: Zhang, C., Zhang, H.
  • Journal: Journal of Industrial Information Integration
  • Year: 2025

📝 A Dynamic Attributes-driven Graph Attention Network Modeling on Behavioral Finance for Stock Prediction

  • Author: Zhang, Q., Zhang, Y., Yao, X., Zhang, C., Liu, P.
  • Journal: ACM Transactions on Knowledge Discovery from Data
  • Year: 2023

📝 Acquisition and Cognition Information of Human Body Swing

  • Author: Fan, J.-F., Sigov, A., Ratkin, L., Chen, S.-W., Zhang, C.-M.
  • Journal: Journal of Industrial Information Integration
  • Year: 2022

📝 A Literature Review of Social Commerce Research from a Systems Thinking Perspective

  • Author: Wang, X., Wang, H., Zhang, C.
  • Journal: Systems
  • Year: 2022

📝 Study on the Interaction Between Big Data and Artificial Intelligence

  • Author: Li, J., Ye, Z., Zhang, C.
  • Journal: Systems Research and Behavioral Science
  • Year: 2022

 

Luis Cavique | Computer Science | Best Research Award

Published on by Book of Award

Prof. Luis Cavique | Computer Science | Best Research Award

Universidade Aberta | Portugal

Author Profile

Scopus

Orcid

Early Academic Pursuits 🎓

Prof. Luís Cavique began his academic journey with a strong foundation in Computer Science, earning a degree in Computer Science Engineering in 1988 from the Faculty of Science and Technology at the New University of Lisbon. His pursuit of advanced knowledge continued with a Master’s in Operational Research and Systems Engineering in 1994 from the Instituto Superior Técnico, Technical University of Lisbon, where he focused on complex problems like crew scheduling. His academic commitment culminated in 2002 with a PhD in Engineering Systems, where he explored meta-heuristics for the Maximum Clique Problem, emphasizing applications in market basket analysis. This robust educational background set the stage for a career marked by analytical depth and academic rigor.

Professional Endeavors 👨‍🏫

Prof. Cavique’s teaching career spans several decades, beginning in 1991 in the Polytechnic Education System in Portugal, where he held adjunct positions at Setúbal and Lisbon Polytechnic Institutes until 2008. Since then, he has served as an Assistant Professor with tenure at the Universidade Aberta, focusing on computer science within the Department of Sciences and Technology. His dedication to fostering knowledge extends to graduate and doctoral levels, where he teaches courses such as Data Mining, Social Network Analysis, and Optimization. Beyond academia, Prof. Cavique also gained hands-on experience in the banking sector as a Systems Engineer at Banco Pinto & Sotto Mayor and through internships at prominent institutions like Banco Espírito Santo and the National Laboratory of Civil Engineering.

Contributions and Research Focus 🔍

With a strong interdisciplinary approach, Prof. Cavique’s research bridges Computer Science and Engineering Systems, focusing primarily on heuristic optimization and data mining. His work has addressed three core data mining challenges: classification, association, and segmentation. Notable publications include groundbreaking algorithms and tools, such as the LAID algorithm for classification, Ramex for association in financial product analysis, and ComDetection for community detection in social networks. These contributions have positioned Prof. Cavique at the forefront of data-driven research, and his methods are applied widely in sectors requiring complex data analysis.

Accolades and Recognition 🏆

Prof. Cavique's scholarly work has been recognized internationally, with several of his papers published in prestigious journals. His 1999 paper on crew scheduling received the IFORS-Lisbon Prize in 2000 from the Association of Operational Research in Portugal (APDIO). Many of his publications are highly cited, with articles featured in Q1-ranked journals, illustrating the high impact and quality of his research. His dedication to advancing data mining and optimization has earned him both peer recognition and a strong citation record, showcasing his influence in these fields.

Impact and Influence 🌍

Throughout his career, Prof. Cavique has made a lasting impact on the fields of data mining and heuristic optimization. His research has influenced approaches in financial analytics, community detection in social networks, and data reduction techniques, providing foundational tools and algorithms that are utilized in academia and industry alike. His methodologies have empowered researchers and practitioners in various domains to make informed, data-driven decisions, underscoring his role as a pioneer in computational research.

Legacy and Future Contributions 🌟

As a leading academic and researcher, Prof. Cavique’s legacy is defined by his contributions to both knowledge and education in computer science. His focus on heuristic optimization and data mining continues to inspire new research, particularly in emerging fields such as bioinformatics and social network analysis. His commitment to teaching and mentoring the next generation of scientists ensures that his impact will extend well into the future, enriching the scientific community and driving innovation in computational methods.

 

Publications

  • 📝 Mitigating false negatives in imbalanced datasets: An ensemble approach
    Authors: Marcelo Vasconcelos; Luís Cavique
    Journal: Expert Systems with Applications
    Year: 2024
  • 📝 Assessment in Collaborative Learning
    Authors: Luis Cavique; M. Rosário Ramos
    Journal: Revista de Educación a Distancia (RED)
    Year: 2024

 

Okechukwu Obulezi | Mathematics | Editorial Board Member

Published on by Book of Award

Dr. Okechukwu Obulezi | Mathematics | Editorial Board Member

Nnamdi Azikiwe University | Nigeria

Author Profile

Scopus

Orcid

🎓 Early Academic Pursuits

Dr. Okechukwu Obulezi began his academic journey with a focus on statistical sciences, obtaining an ND in Statistics from Abia State Polytechnic in 2006. Driven by his passion, he continued with an HND in Statistics from the same institution, culminating in a strong foundation in quantitative analysis and data interpretation. Pursuing higher learning, he completed a PGD in Statistics at Nnamdi Azikiwe University in 2012, with a thesis on the "Discriminant Analysis of the Nigerian Fixed Assets." His master's thesis focused on "BIC-Based Relative Influence Measure for Outlier Detection and Variable Selection in Regression Analysis," setting the stage for his current Ph.D. in Statistics, where he explores complex statistical methods for predictive and analytical modeling.

💼 Professional Endeavors

Dr. Obulezi’s professional career spans several academic roles, starting as an Instructor at Abia State Polytechnic in 2011, where he advanced to Senior Instructor by 2015. In 2019, he joined Nnamdi Azikiwe University as an Assistant Lecturer in the Statistics Department, quickly progressing to Lecturer II and then Lecturer I by 2023. His responsibilities include teaching specialized courses such as Biostatistics, Statistical Methods, Probability Theory, and Matrix Algebra, where he imparts knowledge to the next generation of statisticians. Beyond teaching, he plays critical roles in advising undergraduates, coordinating seminars and projects, and contributing to departmental committees, such as the Faculty ICT Committee and Faculty Repair and Maintenance Committee.

🔬 Contributions and Research Focus

Dr. Obulezi’s research interests lie at the intersection of probability distributions, machine learning, and statistical modeling, with applications in survival analysis, acceptance sampling, and censored data analysis. His contributions to these fields include publications in high-impact journals, focusing on new statistical models for various real-world applications. Some of his significant research includes new distribution models for engineering, biomedical, and environmental data, optimization of GARCH models for financial volatility, and innovative sampling plans for reliability testing. His publications reflect his commitment to advancing the fields of statistics and machine learning by creating robust, practical solutions for complex data challenges.

🏆 Accolades and Recognition

Dr. Obulezi has earned recognition for his academic and research excellence, serving as a reviewer for several prestigious journals, such as Cogent Engineering, Scientific African, and Alexandria Engineering Journal. His expertise has also led him to contribute as a reviewer for academic book chapters, particularly in mathematical and computer science research. This engagement underscores his prominence as a thought leader in the statistical community and highlights the respect he commands among peers for his critical insights and expertise.

🌍 Impact and Influence

Dr. Obulezi's work in developing statistical models and distributions has had a significant impact, particularly in the areas of biomedicine, engineering, and environmental science. His innovative methodologies have helped refine statistical tools for better data analysis, contributing to improved outcomes in public health research, environmental risk assessment, and financial risk management. His influence also extends to mentoring students and collaborating with other academics, which enriches the statistical field and inspires the next generation of statisticians.

🔮 Legacy and Future Contributions

Looking forward, Dr. Obulezi aims to further his research in advanced statistical modeling, focusing on machine learning applications and data-driven decision-making in complex fields. His commitment to exploring new statistical approaches for big data and predictive analysis places him at the forefront of his field, positioning him to make enduring contributions that will drive innovation in statistical methodologies and applications for years to come.

 

Publications

  • 📄 Parameter estimation for reduced Type-I Heavy-Tailed Weibull distribution under progressive Type-II censoring scheme
  • Authors: Prakash, A., Maurya, R.K., Alsadat, N., Obulezi, O.J.
  • Journal: Alexandria Engineering Journal
  • Year: 2024
  • 📄 Sine generalized family of distributions: Properties, estimation, simulations and applications
  • Authors: Oramulu, D.O., Alsadat, N., Kumar, A., Bahloul, M.M., Obulezi, O.J.
  • Journal: Alexandria Engineering Journal
  • Year: 2024
  • 📄 A more flexible Lomax distribution: Characterization, estimation, group acceptance sampling plan and applications
  • Authors: Ekemezie, D.-F.N., Alghamdi, F.M., Aljohani, H.M., El-Raouf, M.M.A., Obulezi, O.J.
  • Journal: Alexandria Engineering Journal
  • Year: 2024
  • 📄 A new extension of Burr-Hatke exponential distribution with engineering and biomedical applications
  • Authors: Anyiam, K.E., Alghamdi, F.M., Nwaigwe, C.C., Aljohani, H.M., Obulezi, O.J.
  • Journal: Heliyon
  • Year: 2024
  • 📄 Group acceptance sampling plan based on truncated life tests for Type-I heavy-tailed Rayleigh distribution
  • Authors: Nwankwo, M.P., Alsadat, N., Kumar, A., Bahloul, M.M., Obulezi, O.J.
  • Journal: Heliyon
  • Year: 2024

 

Xinhai Wang | Computer Science | Best Researcher Award

Published on by Book of Award

Mr. Xinhai Wang | Computer Science | Best Researcher Award

Northeastern University | China

Author Profile

Orcid

Early Academic Pursuits 🎓

Mr. Xinhai Wang's academic journey began with an undergraduate degree in Mathematics and Applied Mathematics from Northeastern University, where he achieved a GPA of 3.81/5. His academic excellence earned him several accolades, such as the "Outstanding Student Cadre" and "Three Good Students" awards, reflecting his dedication to both academics and extracurricular activities. Wang was actively involved in numerous projects during his undergraduate years, honing his skills in advanced algebra, data mining, and mathematical modeling, laying the groundwork for his future endeavors.

Professional Endeavors 🏆

In September 2022, Xinhai Wang assumed the role of monitor for Northeastern University's Master of Science Class 2201, demonstrating exemplary leadership and organizational skills. His work extended beyond the classroom, where he helped in the construction of class activities and assisted in Party branch operations. Wang was awarded the honorary title of Outstanding Graduate Student Cadre for his relentless efforts in promoting student engagement and fostering a collaborative environment. As a deputy director in the Project Development Department of the Social Practice Department, he organized impactful student initiatives such as charity sales, making significant contributions to the student community.

Contributions and Research Focus 🔬

Mr. Wang's contributions to academia and research are vast, with his work primarily centered on applying advanced algorithms in real-world scenarios. He has engaged in several high-level projects, including the application of genetic algorithms in mobile chess and using deep learning techniques like Deep Q Networks for stock market predictions. His research has tackled challenges in time series prediction, exploring fractional order random configuration networks (FSCN) to address the inherent non-stationarity in real-world data. These projects showcase his technical expertise in MATLAB and Python, alongside his growing knowledge of reinforcement learning and machine learning.

Accolades and Recognition 🏅

Xinhai Wang's academic brilliance has been recognized throughout his career, both during his undergraduate and graduate studies. His GPA of 3.40/4 ranked him 2nd in his class, further earning him prestigious honors such as the President Scholarship and First-Class Academic Scholarship. His leadership in class and organizational roles has led to multiple "Outstanding Class Cadre" awards. Wang's academic achievements extend beyond his GPA and awards, with his research work being submitted to conferences and awaiting SCI journal reviews, positioning him as a rising star in applied statistics and data science.

Impact and Influence 🌟

Through his roles in student governance and research, Wang has had a lasting impact on both his peers and the academic community. He has innovated branch activities, guided students in social practice initiatives, and created platforms for broader engagement in scientific and social matters. His research endeavors, such as the application of deep learning to stock prediction and time series analysis, contribute to the growing body of knowledge in the field of statistical modeling and artificial intelligence, influencing future technological advancements.

Legacy and Future Contributions 💡

Mr. Xinhai Wang's journey reflects a commitment to excellence in academic leadership, research, and innovation. As he continues to explore the boundaries of machine learning, algorithm design, and data modeling, his future contributions will likely have a profound effect on emerging fields like stock prediction and industrial data analysis. His ongoing projects in MATLAB and Python, combined with his growing expertise in reinforcement learning, position him for future success in both academic and professional arenas.

 

Publications

📄  Prediction of Ship-Unloading Time Using Neural Networks
Author: Zhen Gao, Danning Li, Danni Wang, Zengcai Yu, Witold Pedrycz, Xinhai Wang
Journal: Applied Sciences
Year: 2024-09

📄  Novel Admissibility Criteria and Multiple Simulations for Descriptor Fractional Order Systems with Minimal LMI Variables
Author: Xinhai Wang, Jin-Xi Zhang
Journal: Fractal and Fractional
Year: 2024-06

 

Dimitrios Karapiperis | Computer Science | Best Research Award

Published on by Book of Award

Dr. Dimitrios Karapiperis | Computer Science | Best Research Award

International Hellenic University | Greece

Author Profile

Scopus

Orcid

Google Scholar

🎓 Early Academic Pursuits

Dr. Dimitrios Karapiperis embarked on his academic journey with a BSc degree in Information Technology from the Technological Educational Institute of Thessaloniki, Greece, where he developed a strong foundation in applied technology. His passion for computer science led him to pursue an MSc degree in Software Engineering at the University of York, UK, funded by the State Scholarships Foundation of Greece (ΙΚΥ). During this time, he honed his skills in software engineering and expanded his knowledge in computer science.

Furthering his academic aspirations, Dr. Karapiperis earned a PhD in Computer Science from the Hellenic Open University, Greece. His research during this period focused on the field of Entity Resolution (Record Linkage), where he developed similarity algorithms, data structures, approximation schemes, and scalable distributed solutions. This phase of his education laid the groundwork for his future contributions to the field of computer science.

💼 Professional Endeavors

Dr. Karapiperis' professional career is marked by his dedication to both teaching and research. He has held various academic positions, including his current role as a lecturer at the Hellenic Open University, where he teaches courses on Data Mining and Machine Learning techniques. He also serves as an adjunct lecturer at the International Hellenic University, Greece, where he imparts knowledge on subjects such as Knowledge Management in the Web, Big Data and Cloud Computing, and Exploratory Data Analysis and Visualization. His previous roles include an adjunct lecturer position at the University of Western Macedonia, Greece, where he taught courses in Data Technologies and Database Management. Additionally, Dr. Karapiperis has experience as a research intern at the University of York, UK, and as a research assistant at the University of Macedonia, Greece, where he developed web and database applications.

🔬 Contributions and Research Focus

Dr. Karapiperis has made significant contributions to the field of computer science, particularly in the area of privacy-preserving record linkage. His research work includes the design of similarity algorithms, data structures, and approximation schemes that enable large-scale systems to perform record linkage while preserving privacy. His innovative use of randomization schemes, such as Locality-Sensitive Hashing (LSH) and count-min sketches, has advanced the field and provided practical solutions for handling voluminous data. In addition to his research, Dr. Karapiperis has supervised over 30 post-graduate theses at the International Hellenic University and Hellenic Open University, guiding students in topics related to Big Data management and the design of efficient algorithms.

🏆 Accolades and Recognition

Throughout his career, Dr. Karapiperis has earned recognition for his contributions to academia and research. His dedication to teaching, research, and the development of innovative algorithms has positioned him as a respected figure in the field of computer science. His expertise and commitment to advancing knowledge have garnered him the respect of his peers and students alike.

🌍 Impact and Influence

Dr. Karapiperis' work has had a profound impact on the field of computer science, particularly in the areas of data management and privacy-preserving technologies. His research on scalable and distributed solutions for Entity Resolution has influenced the development of more secure and efficient systems for handling large datasets. Moreover, his role as an educator has enabled him to shape the minds of future computer scientists, ensuring that his influence extends beyond his own research.

🚀 Legacy and Future Contributions

As Dr. Karapiperis continues his academic and research endeavors, his legacy is one of innovation, dedication, and impact. His ongoing work in developing cutting-edge algorithms and scalable solutions positions him as a leader in the field. With a strong foundation in both education and research, Dr. Karapiperis is poised to make even greater contributions to computer science in the years to come.

 

Publications

  • 📝Predicting Football Match Results Using a Poisson Regression Model
    Authors: Konstantinos Loukas, Dimitrios Karapiperis, Georgios Feretzakis, Vassilios S. Verykios
    Journal: Applied Sciences
    Year: 2024
  • 📝A Suite of Efficient Randomized Algorithms for Streaming Record Linkage
    Authors: Dimitrios Karapiperis, Christos Tjortjis, Vassilios S. Verykios
    Journal: IEEE Transactions on Knowledge and Data Engineering
    Year: 2024
  • 📝Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition
    Authors: Georgios Feretzakis, Aikaterini Sakagianni, Athanasios Anastasiou, Ioanna Kapogianni, Rozita Tsoni, Christina Koufopoulou, Dimitrios Karapiperis, Vasileios Kaldis, Dimitris Kalles, Vassilios S. Verykios
    Journal: Applied Sciences
    Year: 2024
  • 📝Tracing Student Activity Patterns in E-Learning Environments: Insights into Academic Performance
    Authors: Evgenia Paxinou, Georgios Feretzakis, Rozita Tsoni, Dimitrios Karapiperis, Dimitrios Kalles, Vassilios S. Verykios
    Journal: Future Internet
    Year: 2024