Dr. Mohsin Masood – Data Scientist – Best Researcher Award 

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Dr. Mohsin Masood - Data Scientist - Best Researcher Award 

Imperial College London - United Kingdom

Author Profile 

GOOGLE SCHOLAR 

🎓 Early academic pursuits

Mohsin Masood began his academic journey with a strong foundation in computer science, securing a merit-based msc funding grant from the university of east london (2011–2012). his early academic achievements laid the groundwork for advanced training in health data science, culminating in a phd in statistical modelling from the university of strathclyde, funded by a competitive grant (2014–2017). his scholarly curiosity extended internationally through the erasmus exchange research grant at vsb-technical university of ostrava (2015–2018), where he deepened his expertise in predictive modelling and bioinformatics. during this phase, his interest in power electronics and algorithmic optimization in medical diagnostics emerged as an interdisciplinary focus.

💼 Professional endeavors

With over a decade of experience, mohsin has held impactful roles across academia and research institutions. as an assistant professor in computer science at abasyn university (2018–2022), he designed and taught advanced courses in ai, ml, and data science, incorporating real-world applications in epidemiology and digital health. later, as a senior research fellow at the university of leeds (2022–2023), he led data engineering for a large-scale 2.9 million patient cohort, employing advanced machine learning and nlp tools to analyze multimorbidity and Data Scientist cardiovascular risks. currently, he serves as a research associate at imperial college london (2023–present), where he leads predictive modelling projects on population health, integrating datasets like uk biobank, sail databank, and triphic. he also fosters collaborations with institutions such as sheffield university and a* university of singapore.

🧠 Contributions and research focus

Mohsin’s core research focus lies in multimorbidity, disease progression modelling, and epidemiological risk assessment, particularly within cardiovascular and pulmonary health Data Scientist domains. he applies deep learning, survival analysis, and multistate modelling to interpret complex patient trajectories and disease clustering. his work with electronic health records (ehrs) like hospital episode statistics (hes) reflects a commitment to improving precision medicine. integrating technologies from power electronics into patient monitoring systems has been a unique dimension of his research, promoting real-time diagnostics and health alerts. he has also contributed to educational leadership by mentoring bsc and msc students in ai-based health science projects.

🌍 Impact and influence

Mohsin’s influence extends beyond academia through his involvement in cross-institutional research, digital outreach, and public health strategy. at imperial’s national heart and lung institute (nhli), he manages social media communications to boost public engagement with medical data science. his high-impact publications have contributed to the growing intersection of Data Scientist computational modelling and health outcomes, often cited in cardiovascular and cancer research communities. he is recognized for bridging the gap between statistical modelling and power electronics, showing how smart analytics can optimize hardware for healthcare delivery.

📚 Academic cites

His academic influence is reflected in high citation counts across journals focusing on biostatistics, machine learning in medicine, and epidemiology. notable contributions include projects funded by imperial nhli’s pilot project award (2023–2024) and royan pharmaceutical’s breast cancer research grant (2020–2021). his statistical toolkits and predictive algorithms are widely referenced in studies involving survival analysis, deep learning applications in ehrs, and risk prediction modelling.

🧬 Legacy and future contributions

Mohsin’s legacy lies in fostering a data-driven healthcare ecosystem where interdisciplinary tools empower clinicians and researchers alike. he aims to develop scalable, explainable ai systems for public health, enabling real-time decision-making in hospitals and remote settings. leveraging power electronics, he envisions the integration of wearable technologies with ai for early disease detection and patient monitoring. his commitment to mentoring the next generation of health data scientists ensures a lasting academic and societal impact.

Notable Publications 

  • Title: Emotion classification and crowd source sensing; a lexicon based approach
    Authors: R. Kamal, M.A. Shah, C. Maple, M. Masood, A. Wahid, A. Mehmood
    Journal: IEEE Access

  • Title: An improved particle swarm algorithm for multi-objectives based optimization in MPLS/GMPLS networks
    Authors: M. Masood, M.M. Fouad, R. Kamal, I. Glesk, I.U. Khan
    Journal: IEEE Access

  • Title: Energy efficient software defined networking algorithm for wireless sensor networks
    Authors: M. Masood, M.M. Fouad, S. Seyedzadeh, I. Glesk
    Journal: Transportation Research Procedia

  • Title: Proposing bat inspired heuristic algorithm for the optimization of GMPLS networks
    Authors: M. Masood, M.M. Fouad, I. Glesk
    Journal: 2017 25th Telecommunication Forum (TELFOR)

  • Title: Analysis of artificial intelligence-based metaheuristic algorithm for MPLS network optimization
    Authors: M. Masood, M.M. Fouad, I. Glesk
    Journal: 2018 20th International Conference on Transparent Optical Networks (ICTON)

Mr. Hassan Gharoun – AI – Best Researcher Award

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Mr. Hassan Gharoun - AI - Best Researcher Award

University of Technology Sydney - Australia

Author Profile 

SCOPUS 

🎓 Early academic pursuits

Hassan Gharoun was born on 23rd july 1992 in iran. his journey into academia began with a bachelor's degree in industrial engineering with a specialization in industrial production from kharazmi university of tehran (2010–2014). following this, he pursued his master's degree in industrial engineering at the university of tehran (2014–2017), where he deepened his expertise in data-driven systems and optimization. his early academic performance and passion for analytical research laid a strong foundation for his future endeavors. although his background initially focused on industrial systems, his early interest in machine learning and applications in power electronics gradually became prominent.

💼 Professional endeavors

After completing his master’s degree, hassan contributed to the academic community through teaching, notably at the university of tehran. he served as a tutor in advanced econometrics in fall 2017, where he handled responsibilities such as mentoring projects, tutoring in time series algorithms, and grading. he also provided practical insights using sequential deep neural networks and python programming. alongside teaching, he expanded his knowledge base by engaging in international conferences and collaborations, where he actively contributed to data science and optimization research, including subjects related to power electronics and predictive modeling.

🔬 Contributions and research focus

Hassan Gharoun is currently pursuing a ph.d. in analytics at the university of technology sydney (2022–2026). his research spans several methodological domains, including data-driven AI optimization, probabilistic machine learning, meta-learning, and deep learning. these methodologies are employed in solving complex real-world problems where predictive decision-making is crucial. his application-focused research extends to fields like predictive modeling and power electronics, where data-centric models drive intelligent systems. his interdisciplinary approach enhances the integration of machine learning into classical engineering problems, bringing innovative contributions to the analytics landscape.

🌍 Impact and influence

Hassan’s scholarly impact has been marked by both national and international recognition. he received the best paper award at the international conference on industrial engineering and AI operations management held in paris in july 2018, demonstrating his capability to produce high-quality and impactful research. his teaching and mentorship in econometrics and deep learning have also positively influenced students at the university of tehran. by blending industrial engineering insights with cutting-edge analytics and applications such as power electronics, he continues to be a bridge between traditional engineering and emerging ai-driven solutions.

📚 Academic cites

Although still in the early stages of his doctoral journey, hassan gharoun has already begun establishing a scholarly footprint through academic conferences and teaching contributions. his AI recognition at an international level hints at the growing citation potential of his works. his research is expected to be cited across multiple domains, particularly in data analytics, predictive systems, and interdisciplinary applications in sectors like power electronics. as his phd progresses, the impact and citation metrics of his work are projected to increase significantly.

🚀 Legacy and future contributions

Looking ahead, hassan gharoun is poised to make significant contributions to academia and industry through the integration of analytical methods with practical applications. his ambition is to lead innovations at the intersection of deep learning and optimization, enhancing intelligent systems in fields like healthcare, energy, and power electronics. with a strong academic trajectory and international exposure, he is building a legacy rooted in methodological rigor and impactful applications. his future goals include expanding collaborative research, publishing in top-tier journals, and contributing to sustainable technological solutions globally.

Notable Publications 

Title: A comprehensive bibliometric analysis on social network anonymization: current approaches and future directions
Author(s): Navid Yazdanjue, Hossein Yazdanjouei, Hassan Gharoun, Babak Amiri, Amir H. Gandomi
Journal: [No journal information available] — the source or journal name was not provided in the data you shared. If you have a DOI or additional details, I can help track it down further.

Prof. Xinxiao Li –  Big Data Technologies – Best Researcher Award 

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Prof. Xinxiao Li -  Big Data Technologies - Best Researcher Award 

Shonan Institute of Technology - Japan

Author Profile 

SCOPUS 

ORCID 

🎓 Early academic pursuits

Prof. Xinxiao Li’s academic journey began with a strong foundation in biomedical engineering and medical devices at chongqing university, where he earned his b.eng. his passion for technology and innovation led him to pursue an m.eng. in electronic physics at xi'an jiaotong university, further refining his expertise in control systems and computational methodologies. he continued his academic pursuit with a ph.d. in control science and engineering at xi'an jiaotong university, focusing on advanced control theories and digital systems. his rigorous education laid the groundwork for his remarkable contributions to software engineering and digital innovation.

🚀 Professional endeavors

with a distinguished career spanning both academia and industry, prof. li has held pivotal roles at prestigious institutions and global corporations. he began his professional journey as an assistant professor at xi'an jiaotong university, where he quickly advanced to associate professor, contributing to cutting-edge research in control systems. transitioning to japan, he served as a visiting researcher at keio university, where he expanded his expertise in software and information systems. his industrial career flourished at sony emcs corporation, followed by a long tenure at toshiba corporation, where he held key positions such as principal researcher, senior specialist, chief researcher, and expert at the digital innovation center. his work at toshiba played a crucial role in advancing digital transformation, software engineering, and human-in-the-loop systems.

🤖 Contributions and research focus

prof. Li’s research is at the forefront of software engineering, information visualization, and human-computer interaction. he has significantly contributed to the development of user-friendly Big Data Technologies interfaces, augmented reality applications, and intelligent image processing techniques. his work integrates human-in-the-loop systems, ensuring seamless interaction between technology and users. his innovative approach has driven advancements in digital solutions for industrial applications, medical systems, and consumer technology. his interdisciplinary expertise bridges the gap between engineering, artificial intelligence, and user experience, making his research highly impactful across multiple domains.

🏆 Accolades and recognition

throughout his career, prof. li has been recognized for his pioneering work in digital innovation. his research contributions have been published in prestigious journals and presented at Big Data Technologies international conferences, solidifying his reputation as a thought leader in software engineering. his tenure at toshiba corporation saw him lead multiple groundbreaking projects, earning him industry-wide recognition for his expertise in digital transformation and user interface design. his academic contributions at xi'an jiaotong university and keio university have also garnered respect, influencing new generations of researchers and engineers.

👨‍💻 Impact and influence

Prof. Li’s influence extends beyond academia and corporate research labs; his work has had a lasting impact on the technological landscape. his advancements in information visualization and image processing have been integrated into various industrial applications, improving operational efficiency and user experience. his leadership in software development and augmented Big Data Technologies reality has paved the way for innovative applications in both enterprise and consumer markets. through his collaborations with leading institutions and corporations, he has played a key role in shaping the future of digital systems and interactive technologies.

🌍 Legacy and future contributions

As a visionary in software engineering and digital systems, prof. li’s legacy is defined by his commitment to technological advancement and knowledge dissemination. his work has set new standards in user interface design, human-in-the-loop systems, and software engineering methodologies. looking ahead, he continues to explore emerging technologies, driving innovations in artificial intelligence, augmented reality, and interactive digital environments. his mentorship of students and researchers ensures that his impact will continue to shape the future of software engineering, fostering a new era of intelligent and user-centric digital solutions.

Notable Publication 

  • Title: Integrated visualization with controllable deep linking for distributed datasets
    Author(s): Li, X.; Kuroda, A.
    Journal: ACM International Conference Proceeding Series

  • Title: International Conference on Visual Computing (ICVC 99)
    Author(s): Xinxiao Li
    Journal: International Conference on Visual Computing

  • Title: Polyspector™: An interactive visualization platform optimized for visual analysis of big data
    Author(s): Li, X.; Kuroda, A.; Matsuzaki, H.
    Journal: UIST 2016 Adjunct - Proceedings of the 29th Annual Symposium on User Interface Software and Technology

  • Title: Advanced aggregate computation for large data visualization
    Author(s): Li, X.; Kuroda, A.; Matsuzaki, H.; Nakajima, N.
    Journal: IEEE Symposium on Large Data Analysis and Visualization 2015, LDAV 2015 - Proceedings

  • Title: Distributed aggregate computation between server and client for interactive visualization
    Author(s): Li, X.; Kuroda, A.; Matsuzaki, H.; Nakajima, N.
    Journal: IEEE Symposium on Large Data Analysis and Visualization 2015, LDAV 2015 - Proceedings

Dr. Divya A – Applied Mathematics – Best Researcher Award 

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Dr. Divya A - Applied Mathematics - Best Researcher Award 

Madanapalle Institute of Technology & Science - India 

Author Profile 

ORCID 

🔢 Early academic pursuits

Divya a’s journey in mathematics began with a strong academic foundation in arcot, ranipet, where she excelled in her secondary and higher secondary education. she secured an impressive 93.17% in her pre-degree and 86.6% in sslc, showcasing her dedication and aptitude for the subject. her passion for mathematics led her to pursue an integrated m.sc. in mathematics at the central university of tamil nadu, where she graduated in 2019 with a commendable 78.4% average. her academic aspirations further propelled her toward advanced research, leading to a ph.d. in mathematics from the prestigious vellore institute of technology, vellore, in 2023.

🎓 Professional endeavors

Divya’s professional career commenced as a teaching cum research assistant at the school of advanced sciences, vellore institute of technology. from june 2019 to june 2022, she was actively involved in both teaching and research, sharpening her expertise in mathematical concepts and pedagogy. in august 2023, she took on the role of assistant professor in the department of mathematics at madanapalle institute of technology & science, andhra pradesh. her position allows her to inspire young minds and contribute significantly to mathematical education and research.

🧮 Contributions and research focus

Her research primarily revolves around chemical graph theory, a specialized field of mathematics that plays a crucial role in the study of chemical structures and their properties. through her Applied Mathematics work, she has explored mathematical models that contribute to a deeper understanding of molecular interactions and graph-theoretical applications in chemistry. her commitment to research continues to push the boundaries of mathematical science, offering innovative solutions and new perspectives in her domain.

🏆 Accolades and recognition

Divya’s academic excellence has been recognized through various achievements, including her success in the graduate aptitude test in engineering (gate) from march 2019 to march 2022. Applied Mathematics qualifying gate is a prestigious accomplishment that highlights her proficiency and deep understanding of mathematical concepts. her research contributions and dedication to academics have positioned her as a promising scholar in the field of mathematical sciences.

🌍 Impact and influence

As an educator and researcher, divya has significantly influenced her students and colleagues by fostering a culture of curiosity and analytical thinking. her dual role in teaching and research allows her to bridge the gap between theoretical knowledge and practical application. by guiding students in mathematical problem-solving and research methodologies, she continues to Applied Mathematics inspire the next generation of mathematicians and researchers.

📚 Legacy and future contributions

Divya envisions a future where mathematical research in chemical graph theory advances interdisciplinary collaborations, benefiting fields such as chemistry, material science, and computational biology. she aims to expand her research contributions, publish impactful studies, and mentor young researchers in exploring the vast potential of applied mathematics. her dedication to teaching and research ensures that her legacy in mathematics will continue to grow, shaping future scholars and innovations.

Notable Publications 

  • Title: Extremal trees for the geometric-arithmetic index with the maximum degree
    Author(s): A. Divya, A. Manimaran
    Journal: Discrete Mathematics Letters, 2022

  • Title: Computation of certain topological indices for 2D nanotubes
    Author(s): A. Divya, A. Manimaran
    Journal: Ricerche di Matematica, 2021

  • Title: Topological indices for the iterations of Sierpiński rhombus and Koch snowflake
    Author(s): A. Divya, A. Manimaran
    Journal: European Physical Journal: Special Topics, 2021

Dr. Bruce a. wade – Computational Mathematics – Best Researcher Award

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Dr. Bruce a. wade - Computational Mathematics - Best Researcher Award

University of Louisiana at Lafayette - United States

Author Profile 

SCOPUS 

Early academic pursuits 🎓

Bruce a. wade's journey in mathematics began with an unwavering passion for numerical analysis and computational mathematics. he pursued his bachelor’s degree in mathematics from the university of wisconsin-madison in 1982, followed by a master’s degree in mathematics in 1984. eager to delve deeper into the complexities of mathematical modeling and optimization, he completed his ph.d. in mathematics in 1987 under the guidance of professor j.c. strikwerda. during his doctoral research, he explored numerical methods for solving partial differential equations, setting the foundation for his future contributions to computational mathematics.

Professional endeavors 💼

Bruce wade has had a distinguished academic career, holding key positions at several esteemed institutions. he began his professional journey as a post-doctoral fellow at cornell university’s mathematical sciences institute, where he worked under the mentorship of l.b. wahlbin. he later joined the university of wisconsin–milwaukee, progressing from assistant professor to full professor, and ultimately serving as chair of the department of mathematical sciences. in 2018, he transitioned to the university of louisiana at lafayette, where he currently serves as professor and head of the department of mathematics. his leadership roles have significantly shaped academic programs, research initiatives, and technological advancements within these institutions.

Contributions and research focus 🔬

Bruce wade's research is deeply rooted in numerical analysis and computational mathematics, particularly in the areas of partial differential equations, optimization, data science, and machine learning. he has developed advanced numerical techniques for solving complex mathematical models, contributing to various fields such as industrial mathematics and stochastic processes. Computational Mathematics his interdisciplinary approach has led to significant advancements in modeling real-world phenomena, including pigment and filler settling in coatings and nonlinear control systems for the paper industry. his research also extends to numerical solutions of reaction-diffusion equations and adaptive techniques for satellite orbit calculations.

Accolades and recognition 🏆

Bruce wade’s contributions to mathematics have been widely recognized, earning him prestigious positions and honors. he was appointed as the c.b.i.t. tc/leqsf regents professor at the Computational Mathematics university of louisiana at lafayette in 2019. in addition, he has served as a professor emeritus at the university of wisconsin-milwaukee since 2018. his extensive list of grants from esteemed institutions, including the national science foundation (nsf) and the national security agency (nsa), showcases the significance of his research in applied mathematics and computational techniques.

Impact and influence 🌎

The impact of bruce wade's work extends beyond academia, influencing industries and governmental research initiatives. as the founder and director of the center for industrial mathematics at the university of wisconsin-milwaukee, he played a pivotal role in bridging the gap between theoretical mathematics and practical industrial applications. his collaborative projects with Computational Mathematics companies like rust-oleum corporation and rockwell automation have led to innovative solutions in material science and control systems. his research has not only enhanced scientific understanding but has also contributed to technological advancements with real-world implications.

Legacy and future contributions 🎨

Bruce wade’s legacy is defined by his unwavering commitment to advancing computational mathematics and fostering future generations of mathematicians. his mentorship and leadership have inspired countless students and researchers to pursue careers in numerical analysis and data science. as he continues his academic and research journey, his future contributions promise to further expand the applications of numerical modeling in various scientific domains. his dedication to exploring new mathematical frontiers ensures that his influence in the field of computational mathematics will endure for years to come.

Notable Publications 

  • Title: A fourth-order exponential time differencing scheme with dimensional splitting for non-linear reaction–diffusion systems
    Author(s): Emmanuel O. Asante-Asamani, Andreas Kleefeld, Bruce Alan Wade
    Journal: Journal of Computational and Applied Mathematics, 2025

  • Title: Global-Padé Approximation of the Three-Parameter Mittag-Leffler Function: Generalized Derivation and Numerical Implementation Issues
    Author(s): Yusuf O. Afolabi, Toheeb A. Biala, Ibrahim O. Sarumi, Bruce Alan Wade
    Journal: Communications on Applied Mathematics and Computation, 2025

  • Title: Two new generators of Archimedean copulas with their properties
    Author(s): Agnideep Aich, Ashit Baran Aich, Bruce Alan Wade
    Journal: Communications in Statistics - Theory and Methods, 2025

  • Title: Exploring a Mathematical Model with Saturated Treatment for the Co-Dynamics of Tuberculosis and Diabetes
    Author(s): Saburi Rasheed, Olaniyi S. Iyiola, S. I. Oke, Bruce Alan Wade
    Journal: Mathematics, 2024

Prof. Liuyang song – Condition Based Maintenance – Best Researcher Award

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Prof. Liuyang song - Condition Based Maintenance - Best Researcher Award

Beijing University of Chemical Technology - China 

Author Profile 

SCOPUS 

ORCID 

Early academic pursuits 📖

Liuyang song embarked on his academic journey with a strong foundation in engineering at beijing university of chemical technology. he earned his bachelor’s degree in engineering in 2010, followed by a master’s degree in 2013, both from the same institution. his passion for mechanical and electrical engineering led him to pursue a doctorate at mie university, japan, where he completed his doctoral studies in 2017. during his phd, he delved deep into advanced mechanical systems, honing his expertise in fault diagnosis and intelligent machine learning applications.

Professional endeavors 🏛️

Liuyang song has built a distinguished career in academia, holding key positions at beijing university of chemical technology. he started as a postdoctoral researcher in 2018, gradually rising through the ranks to become a lecturer, associate professor, and ultimately, a full professor in 2024. he also gained international experience as a postdoctoral researcher and temporary researcher at mie university, japan. his extensive experience in mechanical and electrical engineering has contributed to the development of cutting-edge diagnostic techniques and performance evaluation methodologies.

Contributions and research focus 🔬

Liuyang song's research primarily focuses on intelligent fault diagnosis, machine learning applications in engineering, and vibro-acoustic signal processing. he has been at the forefront of developing innovative algorithms for aeroengine spindle bearing diagnostics using multi-task neural networks and graph-structured data. his work also explores adaptive sparse Condition Based Maintenance representation methods for vibro-acoustic multi-source signal separation, lightweight convolutional neural networks, and small-sample machine learning for rail transit train equipment diagnostics. his research has significantly improved the accuracy and efficiency of fault detection in complex mechanical systems.

Accolades and recognition 🏅

Throughout his career, liuyang song has been recognized for his outstanding contributions to mechanical and electrical engineering. his research has been supported by prestigious funding Condition Based Maintenance bodies such as the national natural science foundation of china and the beijing natural science foundation. he has successfully led multiple research projects that have advanced the field of intelligent diagnostics. his contributions to academia have solidified his reputation as a leading researcher in his domain.

Impact and influence 🌍

Liuyang song's research has had a significant impact on industrial applications, particularly in aerospace and rail transit systems. his advanced diagnostic methodologies have enhanced the Condition Based Maintenance reliability and performance of critical mechanical components, reducing operational failures and maintenance costs. his work in machine learning-driven fault diagnosis has influenced both academic and industrial research, paving the way for more intelligent and efficient monitoring systems in engineering applications.

Legacy and future contributions 🔮

With a commitment to advancing mechanical and electrical engineering, liuyang song continues to push the boundaries of research in fault diagnostics and intelligent systems. his future work aims to integrate cutting-edge artificial intelligence techniques with mechanical diagnostics to further improve the precision and adaptability of predictive maintenance strategies. as a professor, he is dedicated to mentoring the next generation of engineers and researchers, ensuring that his legacy in intelligent fault diagnosis and machine learning applications continues to thrive.

Notable Publications 

  • Task-adaptive unbiased regularization meta-learning for few-shot cross-domain fault diagnosis
    Authors: Huaqing Wang, Dongrui Lv, Tianjiao Lin, Changkun Han, Liuyang Song
    Journal: Engineering Applications of Artificial Intelligence
  • Representations aligned counterfactual domain learning for open-set fault diagnosis under speed transient conditions
    Authors: Shen Liu, Jinglong Chen, Zhen Shi, Liuyang Song, Shuilong He
    Journal: Knowledge-Based Systems
  • A bearing fault diagnosis method with an improved residual Unet diffusion model under extreme data imbalance
    Authors: Wang H., Zhang W., Han C., Fu Z., Song L.
    Journal: Measurement Science and Technology
  • A Convolutional Neural Network with Hybrid Loss Function for Bearing Fault Diagnosis
    Authors: Lv D., Fu Z., Su Z., Ni H., Wang H., Song L.
    Journal: Mechanisms and Machine Science
  • A lightweight improved residual neural network for bearing fault diagnosis
    Authors: Wang H., Fu Z., Lin T., Han C., Zhang W., Song L.
    Journal: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

Dr. Agnieszka Niemczynowicz – Machine Learning – Best Researcher Award 

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Dr. Agnieszka Niemczynowicz - Machine Learning - Best Researcher Award 

Cracow University of technology - Poland

AUTHOR PROFILE 

ORCID 

EARLY ACADEMIC PURSUITS 🎓

Agnieszka Niemczynowicz began her academic journey in the field of solid-state physics, earning her Ph.D. from the Faculty of Physics and Applied Informatics at the University of Łódź, Poland, in 2014. Her early research laid a strong foundation in the fundamental aspects of physics, equipping her with a deep understanding of physical systems and analytical techniques.

PROFESSIONAL ENDEAVORS 🏢

Upon completing her doctorate, Agnieszka transitioned into academia, taking up the role of Associate Professor at the Cracow University of Technology. She has since been instrumental in bridging the gap between physics and computational sciences, expanding her research horizons to include computational and mathematical methods for analyzing complex data sets across various disciplines.

CONTRIBUTIONS AND RESEARCH FOCUS 🔍

Agnieszka’s research is at the forefront of computational analysis, focusing on multivariate statistics, chemometrics, and deep learning. She has developed advanced statistical and machine Machine Learning learning models that have found applications in diverse fields such as engineering, biology, medicine, and management. Her work is characterized by its interdisciplinary approach, integrating complex data analysis methods into practical applications.

ACCREDITATIONS AND RECOGNITION 🏅

A prolific researcher, Agnieszka has authored around 50 publications in international journals, contributing significantly to her field. Her excellence in research was recognized with the Machine Learning prestigious Doak Award in 2022, highlighting her impactful contributions to the scientific community and her role as a thought leader in computational analysis.

IMPACT AND INFLUENCE 🌍

Agnieszka’s work has had a significant impact on how complex analytical data is interpreted and utilized across various sectors. Her models have improved the accuracy of data-driven Machine Learning decisions in numerous applications, thereby enhancing the efficiency and effectiveness of processes in engineering, biology, medicine, and more.

LEGACY AND FUTURE CONTRIBUTIONS 🔮

Currently leading international research grants, Agnieszka investigates the mathematical foundations of hypercomplex neural networks and their applications. Her ongoing work promises to further unravel the complexities of data analysis, pushing the boundaries of what machine learning and computational methods can achieve. Her legacy lies in her pioneering efforts to integrate advanced mathematical models into practical solutions, ensuring that her influence will be felt across multiple disciplines for years to come.

NOTABLE PUBLICATIONS 

  • Title: A critical analysis of the theoretical framework of the Extreme Learning Machine
    Authors: Irina Perfilieva, Nicolás Madrid, Manuel Ojeda-Aciego, Piotr Artiemjew, Agnieszka Niemczynowicz
    Journal: Neurocomputing
  • Title: Use of physicochemical, FTIR and chemometric analysis for quality assessment of selected monofloral honeys
    Authors: Monika Kędzierska-Matysek, Anna Teter, Mariusz Florek, Arkadiusz Matwijczuk, Agnieszka Niemczynowicz, Alicja Matwijczuk, Grzegorz Czernel, Piotr Skałecki, Bożena Gładyszewska
    Journal: Journal of Apicultural Research
  • Title: Conclusions
    Authors: Joanna Nieżurawska, Radosław Antoni Kycia, Agnieszka Niemczynowicz
    Journal: (Book chapter, not a journal)
  • Title: Current research methods in mathematical and computer modelling of motivation management
    Authors: Agnieszka Niemczynowicz, Radosław Antoni Kycia
    Journal: (Book chapter, not a journal)
  • Title: Introduction
    Authors: Joanna Nieżurawska, Radosław Antoni Kycia, Agnieszka Niemczynowicz
    Journal: (Book chapter, not a journal)

Dr. Lamine Mbarki – Stochastic and probability – Best Researcher Award

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Dr. Lamine Mbarki - Stochastic and probability - Best Researcher Award

University of Tunis El Manar - Tunisia

Author Profile 

SCOPUS 

Early academic pursuits 🎓

Lamine Mbarki's academic journey began with a strong foundation in mathematics, earning his baccalaureate diploma in 2006. his passion for the subject led him to pursue a diplomate in mathematics and computer sciences from the faculty of sciences of monastir, tunisia, in 2008. he furthered his expertise with a master's degree in mathematics in 2010, followed by a master's of research in harmonic analysis in 2012. his research, supervised by mohamed sifi and dominique bakry, explored markovian operators and hypergroup properties, setting the stage for his ph.d. work. he completed his ph.d. at the faculty of sciences of tunis, focusing on the p(x)-laplacian operator and its associated operators, a significant contribution to the field of differential equations.

Professional endeavors 👨‍🏫

Currently an assistant professor in the mathematics department at the faculty of sciences of tunis, lamine mbarki has been dedicated to academia, imparting knowledge and advancing mathematical research. his teaching and mentorship have influenced many students, particularly in areas such as partial differential equations, fractional calculus, and stochastic analysis. his institutional affiliation with the university of tunis el manar reflects his commitment to academic excellence and research development.

Contributions and research focus 🔬

Dr. Mbarki's research focuses on the mathematical study of partial differential equations, fractional calculus, probability, and dynamical systems. his significant contributions include his work on the hypergroup property and the study of the p(x)-laplacian operator. this research has advanced the understanding of complex mathematical structures and their applications in various scientific fields. his interdisciplinary approach bridges theoretical mathematics with practical applications,  Stochastic and probability particularly in stochastic processes and dynamic systems.

Accolades and recognition 🏅

Throughout his academic career, dr. mbarki has received recognition for his pioneering research in mathematics. his collaborations with esteemed supervisors such as dominique bakry and mounir bezzarga have cemented his reputation as a notable researcher in harmonic analysis and operator theory. his work continues to be  Stochastic and probability cited and utilized by researchers across the globe, particularly in the areas of partial differential equations and probability theory.

Impact and influence 🌍

Dr. Mbarki’s research has had a wide-reaching impact, particularly in advancing the understanding of complex mathematical systems. his work on fractional calculus and stochastic analysis has applications in physics, engineering, and economics, influencing how mathematical models are employed in real-world scenarios. as a  Stochastic and probability teacher and researcher, his contributions have shaped the careers of many students and young researchers, fostering a new generation of mathematical minds.

Legacy and future contributions 🔮

As a dedicated mathematician, dr. mbarki's legacy will be defined by his innovative work in partial differential equations and stochastic processes. his research continues to evolve, and he is expected to contribute further to the fields of fractional calculus and dynamical systems. his future work will likely continue to impact both theoretical mathematics and its applied fields, ensuring that his contributions remain influential for years to come.

Notable Publications 

  1. Title: "A degenerate Kirchhoff-type problem involving variable s(·)-order fractional p(·)-Laplacian with weights"
    Authors: Allaoui, M., Hamdani, M.K., Mbarki, L.
    Journal: Periodica Mathematica Hungarica, 2024, 88(2), pp. 396–411
  2. Title: "Existence and Multiplicity of Solutions for a Class of Kirchhoff–Boussinesq-Type Problems with Logarithmic Growth"
    Authors: Carlos, R.D., Mbarki, L., Yang, S.
    Journal: Mediterranean Journal of Mathematics, 2024, 21(3), 108
  3. Title: "Existence of Multiple Solution for a Singular p(x)-Laplacian Problem"
    Authors: Ghanmi, A., Mbarki, L., Choudhuri, D.
    Journal: Complex Analysis and Operator Theory, 2024, 18(2), 26
  4. Title: "Unified approach to nonlinear Caputo fractional derivative boundary value problems: extending the upper and lower solutions method"
    Authors: Talib, I., Batool, A., Sousa, J.V.D.C., Lamine, M.
    Journal: Journal of Mathematics and Computer Science, 2024, 37(1), pp. 20–31
  5. Title: "Solutions for a Nonlocal Elliptic System with General Growth"
    Authors: Mbarki, L., Tavares, L.S., Sousa, J.V.C.
    Journal: Complex Analysis and Operator Theory, 2023, 17(8), 134

Mr. Clement Asare – Statistics – Young Scientist Award

Published on by Innovator Awards

Mr. Clement Asare - Statistics - Young Scientist Award

Kwame Nkrumah University of Science and Technology - Ghana

Author Profile 

GOOGLE  SCHOLAR

Early academic pursuits 🎓

Clement Asare’s academic journey began with a passion for statistical learning and its applications in solving real-world problems. he pursued a bachelor of science degree in actuarial science from the kwame nkrumah university of science and technology in kumasi, ghana, graduating with first-class honors. his solid foundation in mathematics and statistics sparked his interest in machine learning, leading him to explore the potential of combining statistical techniques with cutting-edge technology.

Professional endeavors 💼

Clement has worked across various sectors, applying his expertise in statistical and actuarial methods to tackle complex challenges. as an enthusiast of machine learning, he has developed solutions that integrate statistical principles with advanced machine learning algorithms. his proficiency in programming languages like python, r, and matlab has allowed him to deliver impactful projects, contributing to sectors such as finance, insurance, and beyond. his career is marked by a dedication to continuous learning and innovation.

Contributions and research focus 🔍

Clement’s research focuses on statistical machine learning, where he applies data-driven approaches to solve pressing issues. his work emphasizes the importance of leveraging data for prediction and decision-making, particularly in actuarial science and risk management. he is passionate about exploring how machine learning Statistics models can improve efficiency and accuracy in forecasting, risk analysis, and pattern recognition, aiming to bridge the gap between theoretical statistics and practical applications.

Accolades and recognition 🏆

throughout his academic and professional journey, clement has earned recognition for his exceptional skills and dedication. his first-class degree in actuarial science is a testament to his academic excellence, while his proficiency in programming languages like python, r, and matlab highlights his technical acumen. though early in Statistics his career, his contributions have already positioned him as a promising talent in the field of statistical machine learning.

Impact and influence 🌍

Clement’s impact extends beyond his immediate work. his application of machine learning techniques to real-world problems demonstrates the transformative potential of combining data science with industry-specific knowledge. he seeks to collaborate with global academic professionals, expanding his understanding and Statistics sharing his insights to contribute to the broader data science community. his approach to solving complex problems is both innovative and pragmatic, positioning him as an emerging leader in statistical learning.

Legacy and future contributions 🔮

looking ahead, clement aims to leave a lasting legacy in the field of statistical machine learning. his drive for continuous learning and collaboration signals his commitment to advancing the field and contributing to its growing influence on industries worldwide. he is poised to develop more sophisticated models and solutions that will not only push the boundaries of machine learning but also impact various sectors, from finance to environmental science.

Notable Publications 

 Exploring the optimal climate conditions for a maximum maize production in Ghana 

 A critical review of the impact of uncertainties on green bonds

Improving mortality forecasting using a hybrid of Lee–Carter and stacking ensemble model

Predictive analysis on the factors associated with birth Outcomes: A machine learning perspective

Asymmetric Impact of Heterogenous Uncertainties on the Green Bond Market

Dr. Ajay K. Palit – Computational Intelligence – Best Researcher Award

Published on by Innovator Awards

Dr. Ajay K. Palit - Computational Intelligence - Best Researcher Award

University of Bremen - Germany

Professional Profile 

SCOPUS

EARLY ACADEMIC PURSUITS 🎓

Ajay K. Palit and Dobrivoje Popovic, distinguished scholars in the field of computational intelligence, began their academic journeys with a robust foundation in engineering and mathematics. Their early work laid the groundwork for their future contributions to the field of time series forecasting and industrial process control. Both scholars pursued advanced studies and research in their respective areas, developing a deep understanding of computational techniques and their applications in real-world scenarios.

PROFESSIONAL ENDEAVORS 🏢

Dr. Ajay K. Palit and Dr. Dobrivoje Popovic are both affiliated with the University of Bremen, Germany, where they continue to drive innovation in computational intelligence. Their professional careers are marked by significant roles in academia and industry, focusing on enhancing the effectiveness of time series forecasting through advanced computational techniques. Their expertise extends to the practical applications of these techniques in industrial systems and process control.

CONTRIBUTIONS AND RESEARCH FOCUS 🔬

Palit and Popovic have made notable contributions to the field of computational intelligence, particularly in time series forecasting. Their research encompasses a range of intelligent technologies, including neural networks, fuzzy logic, and evolutionary computation. They have pioneered hybrid computational approaches, such as neuro-fuzzy and transparent fuzzy/neuro-fuzzy modeling, which offer improved quality, Computational Intelligence model building, and predictive control in industrial processes. Their work addresses the challenges of on-line application and computational efficiency in industrial settings.

ACCREDITATIONS AND RECOGNITION 🏅

Their pioneering work has been widely recognized and has earned them respect in the academic and industrial communities. The book "Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications" by Palit and Popovic is acclaimed for its Computational Intelligence comprehensive exploration of intelligent technologies and their practical applications. The authors' contributions are integral to the ongoing development and refinement of computational tools used in industrial process control and forecasting.

IMPACT AND INFLUENCE 🌍

The influence of Palit and Popovic’s work extends across various industries, including manufacturing, process control, and research. By advancing computational intelligence techniques, they have significantly improved the ability to forecast and control industrial processes. Their research has helped bridge the gap between theoretical knowledge and practical application, making a tangible impact on industrial efficiency Computational Intelligence and predictive accuracy.

LEGACY AND FUTURE CONTRIBUTIONS 🔮

Ajay K. Palit and Dobrivoje Popovic continue to inspire and lead in their field, shaping the future of computational intelligence in time series forecasting. Their legacy is reflected in their innovative approaches and the ongoing relevance of their research in addressing complex industrial challenges. As they advance their work, they remain committed to exploring new computational techniques and applications, ensuring their contributions will have a lasting impact on the field.

NOTABLE PUBLICATIONS

Fault diagnosis of crosstalk induced glitches and delay faults  2010(1)

Test pattern generation and compaction for crosstalk induced glitches and delay faults  2010(6)

Decoupled victim model for the analysis of crosstalk noise between on-chip coupled interconnects  2009(14)

Equivalent victim model of the coupled interconnects for simulating crosstalk induced glitches and delays  2009(7)

Distributed RLGC transient model of coupled interconnects in DSM chips for crosstalk noise simulation 2008(8)