Dr. Inam Illahi | Software Engineering | Best Researcher Award

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Dr. Inam Illahi | Software Engineering | Best Researcher Award

Dr. Inam Illahi, Emerson University Mutlan, Pakistan

Inam Illahi is an accomplished Assistant Professor at Emerson University Multan, Pakistan. With a rich academic background and over a decade of teaching experience, he has made significant contributions to the field of computer science. Inam holds a PhD in Computer Science and Technology from the Beijing Institute of Technology, where he focused on Assistant Technologies for Crowdsourcing Software Development. His research encompasses machine learning, deep learning, and software development, yielding several publications in prestigious journals. In addition to his academic pursuits, Inam has worked in various educational institutions, enhancing the quality of education and fostering student engagement. His dedication to research and teaching reflects a passion for advancing knowledge and technology, making him a respected figure in his field. Inam’s commitment to improving educational practices and research outcomes highlights his role as a leader in academia.

Professioanl Profile

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Summary of Suitability for the Research for Best Researcher Award

Inam Illahi is a highly qualified candidate for the Research for Best Researcher Award, showcasing a solid academic and professional background in computer science, particularly in the field of software development and machine learning. His extensive teaching experience at various reputable universities, including his current role as an Assistant Professor at Emerson University Multan, highlights his commitment to academia and his ability to contribute significantly to the educational sector.

🎓  Education

Inam Illahi’s educational journey is marked by notable achievements and a commitment to excellence. He earned his PhD in Computer Science and Technology from the Beijing Institute of Technology, China, between 2016 and 2022. His research during this time focused on Assistant Technologies for Crowdsourcing Software Development, resulting in impactful publications. Prior to his PhD, Inam completed his Master’s in Software Engineering and Management from Chalmers University of Technology, Sweden, in 2010, where he gained insights into software development practices. He also holds a Master of Computer Science from the University of Sargodha, Pakistan, which he completed in 2007. His educational foundation is complemented by a Bachelor of Arts in Computer Science and Economics from the same institution. Inam’s diverse academic experiences, along with his international exposure in Sweden and Denmark, have equipped him with a global perspective and a strong skill set in technology and education.

💼   Experience 

Inam Illahi possesses extensive experience in academia, contributing to various educational institutions over the past decade. Since March 2024, he has been serving as an Assistant Professor at Emerson University Multan, where he is involved in teaching and research activities. Before that, he held a Tenure Track Assistant Professor position at the University of Education, Lahore, Multan Campus, from August 2023 to March 2024. His earlier roles include Assistant Professor at the Institute of Southern Punjab and Lecturer positions at National Textile University, Faisalabad, and Riphah International University. Inam has also served as an Academic Coordinator at COMSATS Institute of Technology, where he played a crucial role in teaching and administration. His experience as a Deputy Director at the Quality Enhancement Cell at The University of Faisalabad further underscores his leadership abilities. Inam’s diverse roles highlight his commitment to enhancing the educational landscape through effective teaching and administrative practices.

🏅  Awards and Honors 

Inam Illahi’s commitment to excellence in research and education has earned him several accolades throughout his career. Notably, his innovative work in crowdsourcing software development and machine learning has resulted in multiple publications in reputable journals, receiving recognition from his peers. His research on the “Dr. Wheat” web-based expert system for diagnosing diseases in Pakistani wheat was presented at the International Conference of Information Security and Internet Engineering in London in 2008, showcasing his contributions to agricultural technology. In addition to research-related recognition, Inam has been actively involved in various academic committees and organizations, where his leadership skills have been acknowledged. His role as Deputy Director at The Quality Enhancement Cell highlighted his commitment to improving educational quality, further solidifying his reputation in academia. Inam’s dedication to research and education continues to inspire students and colleagues alike, contributing to his growing list of honors and achievements.

🌍  Research Focus

Inam Illahi’s research focuses primarily on the intersection of software development and artificial intelligence, with a particular emphasis on crowdsourcing and machine learning. His PhD thesis explored Assistant Technologies for Crowdsourcing Software Development, where he analyzed motivating and inhibiting factors for developers and success prediction in competitive crowdsourcing projects. His innovative contributions include the application of machine learning techniques for resolution prediction, enhancing the success rates of software development initiatives. Inam has published several influential papers in leading journals, examining various aspects of software project management and quality assurance. Notable works include studies on bug report prioritization using convolutional neural networks and severity prediction models. Through his research, Inam aims to bridge the gap between theory and practice in software development, providing valuable insights and tools for industry practitioners. His commitment to advancing knowledge in this rapidly evolving field makes him a key player in the research community.

📖 Publications Top Notes

  • Title: Deep neural network-based severity prediction of bug reports
    Cited by: 94
  • Title: CNN-based automatic prioritization of bug reports
    Cited by: 85
  • Title: Dr. Wheat: a Web-based expert system for diagnosis of diseases and pests in Pakistani wheat
    Cited by: 79
  • Title: Serum tumor necrosis factor-alpha as a competent biomarker for evaluation of disease activity in early rheumatoid arthritis
    Cited by: 19
  • Title: An empirical study on competitive crowdsource software development: motivating and inhibiting factors
    Cited by: 13

Mr. Rafael Siqueira | Nutrition | Best Researcher Award

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Mr. Rafael Siqueira | Nutrition | Best Researcher Award

Mr. Rafael Siqueira, Federal university of Bahia, Brazil

Rafael Pena Siqueira is a distinguished nutritionist and academic with a comprehensive background in nutrition, biosciences, and public health. He completed his PhD in Nutrition at the Federal University of Bahia (UFBA), focusing on the impacts of the COVID-19 pandemic on the lifestyle of higher education professionals and students. His Master’s degree explored analytical methods for detecting uranium in breast milk, further demonstrating his expertise in biosciences. Throughout his career, he has been actively involved in teaching, research, and extension activities at UFBA, where he supervises nutrition internships, oversees university food services, and participates in various academic committees. Rafael has also contributed to community health education, delivering lectures and courses on nutrition and public health. His ongoing research focuses on mental health and chronic diseases during the pandemic, highlighting his commitment to addressing real-world health challenges through innovative research.

Professional Profile

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Summary of Suitability for the Award

Rafael Pena Siqueira is a well-rounded candidate for the “Research for Best Researcher Award,” showcasing a solid academic and research background in the field of Nutrition. With a Ph.D. focused on the impact of the COVID-19 pandemic on lifestyle habits within higher education, Siqueira has tackled timely and relevant issues, demonstrating his ability to conduct impactful research. His prior work includes a Master’s thesis on the development of analytical methods for uranium detection in breast milk, highlighting his technical expertise in biosciences and health research.

🎓 Education 

Rafael Pena Siqueira holds a PhD in Nutrition from the Federal University of Bahia (UFBA), where he conducted a cohort study on the lifestyle impacts of the COVID-19 pandemic on students and faculty members in Brazilian higher education. His academic journey began with a Bachelor’s degree in Nutrition from UFBA’s Anísio Teixeira Campus, followed by a Master’s degree in Biosciences, where he developed an analytical method for detecting uranium in breast milk. His studies reflect a strong foundation in health and biosciences, with a focus on clinical and public health nutrition. Additionally, Rafael has pursued specialized courses, including Clinical Nutrition and Public Health from UNIGRAD, and short-term certifications in maternal and infant nutrition, sexually transmitted infections, and nutrition biochemistry. His education showcases a commitment to both academic excellence and practical applications in the field of nutrition, equipping him with the skills to address critical health issues.

💼   Experience 

Rafael Pena Siqueira has amassed extensive experience in both academic and public health sectors. Since 2016, he has been serving as a Technical Officer in Nutrition and Dietetics at the Federal University of Bahia (UFBA), where he supervises collective feeding internships, supports research and extension activities, and oversees food services contracts for the university’s dining facilities. Rafael has also served as an invited professor, teaching courses on topics like database research and artificial intelligence tools for scientific research. His role extends beyond the classroom, as he participates in various academic committees, including biosafety and internship coordination. He also facilitated community health education, acting as a mediator in courses designed to enhance the care for chronic non-communicable diseases in Bahia. His diverse roles highlight his ability to integrate academic knowledge with practical applications in nutrition and public health.

🏅  Awards and Honors 

Rafael Pena Siqueira has earned several accolades throughout his career, recognizing his contributions to nutrition and public health. In 2021, he received an award for his research on incorporating sociobiodiversity foods into the school diets of children with special dietary needs. This project, developed as part of the Postgraduate Program in Food and Nutrition at UFPR, exemplifies his commitment to improving public health through sustainable and inclusive dietary practices. Additionally, Rafael has been a scholarship recipient of prestigious Brazilian research agencies such as FAPESB and CNPq, which supported his studies and research projects during his Master’s and PhD programs. These honors underscore his expertise in the field of nutrition and his ongoing dedication to advancing nutritional science for the benefit of both academic communities and broader society.

🌍  Research Focus 

Rafael Pena Siqueira’s research primarily revolves around public health, nutrition, and the effects of chronic diseases. His current focus is on the mental health and lifestyle changes of students and educators in Brazilian higher education during the COVID-19 pandemic. This research is part of his PhD thesis at UFBA, where he examines how the pandemic has impacted physical activity, dietary habits, and mental health within academic settings. Previously, his Master’s research involved developing analytical methods for detecting uranium in breast milk, showcasing his expertise in biosciences. Rafael also explores community health interventions, particularly the integration of sociobiodiversity foods in school meals, and has contributed to courses and public health projects addressing chronic non-communicable diseases in Bahia. His work aims to bridge the gap between scientific research and practical health solutions, contributing to the improvement of public health through evidence-based nutritional practices.

 📖 Publication Top Notes

  1. Mediating effect of emotional distress on the relationship between noncommunicable diseases and lifestyle among Brazilian academics during the COVID-19 pandemic
  2. A dispersive liquid–liquid microextraction based on solidification of floating organic drop and spectrophotometric determination of uranium in breast milk after optimization using Box-Behnken design

Assoc. Prof. Dr. Hui Zhang | Artificial Intelligence | Industry Achievement Award

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Assoc. Prof. Dr. Hui Zhang | Artificial Intelligence | Industry Achievement Award

Assoc. Prof. Dr. Hui Zhang, Guizhou University of Finance and Economics, China

Hui Zhang is a distinguished senior engineer and associate professor at the School of Information, Guizhou University of Finance and Economics, China. With a Ph.D. in Computational Mathematics from Guizhou Normal University, Zhang has a diverse background spanning both academia and industry. His expertise ranges from computational mathematics to big data and cloud computing, having held prominent roles in R&D departments in China’s tech industry. Additionally, Zhang has served as a reviewer for prestigious journals, contributed to key projects, and holds multiple patents in data science and technology. His ongoing research and professional services make him a well-recognized expert in his field.

Professional Profile

scopus

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Summary of Suitability for the Research for Industry Achievement Award

Dr. Hui Zhang demonstrates a unique combination of academic and industrial achievements, making him a strong candidate for the Research for Industry Achievement Award. His background in computational mathematics and computer science, coupled with his experience in R&D and leadership roles in the big data industry, aligns well with the award’s focus on impactful industrial contributions.

🎓    Education

Hui Zhang completed his Bachelor’s in Information and Computational Science from Guizhou Normal University in 2010, alongside a Bachelor’s in English Education. He pursued a Master’s in Computational Mathematics at the same institution from 2010 to 2013. Hui continued his academic journey by earning a Ph.D. in Computational Mathematics, focusing on innovative computational methods. His educational experience highlights a strong foundation in both mathematics and interdisciplinary learning, setting the stage for his research and teaching career at Guizhou University of Finance and Economics.

💼   Experience

Hui Zhang’s career includes a range of positions in both academia and industry. After completing his master’s degree, he worked as a Business Manager at the Postal Savings Bank of China. He then transitioned to academic research while pursuing his Ph.D., contributing to key projects at the Guizhou Key Laboratory of Information and Computing Science. In industry, Zhang served as a Senior R&D Engineer and later General Manager of the R&D Department at Guizhou-Cloud Big Data Industry Development Co. Since 2022, he has been an associate professor at Guizhou University of Finance and Economics and a postdoctoral researcher, continuing to innovate in computer science and data science fields.

🏅  Awards and Honors

Hui Zhang’s numerous recognitions include being a Review Expert for multiple academic and governmental bodies, such as the Guizhou Provincial Department of Science and Technology. His technical contributions have earned him industry accolades, and he was invited to join the Big Data Expert Committee of the China Computer Federation. Zhang has also been honored as an Industrial Mentor in Guizhou Province and contributed as an expert reviewer for the prestigious Alexandria Engineering Journal. His expertise in computational mathematics and data science has made him a sought-after advisor and collaborator.

🌍  Research Focus

Hui Zhang’s research focuses on computational mathematics, big data, and information systems, particularly in developing algorithms and systems for data processing and analysis. He has worked extensively on Pulsar Data Processing, contributing to the design and implementation of comparative analysis and visualization systems. His research extends into numerical analysis, with a focus on finite element methods for solving complex mathematical problems. Zhang’s interdisciplinary approach combines theoretical mathematics with practical applications in data science, making significant advances in these fields.

📖 Publication Top Notes

  1. Generalized picture fuzzy Frank aggregation operators and their applications
  2. A second-order accurate and unconditionally energy stable numerical scheme for nonlinear sine-Gordon equation
  3. Asymmetrical interactions driven by strategic persistence effectively alleviate social dilemmas
  4. A Certificateless Verifiable Bilinear Pair-Free Conjunctive Keyword Search Encryption Scheme for IoMT
  5. Deformations and Extensions of Modified λ-Differential 3-Lie Algebras

Mr. Daniel Morariu | Resilience | Best Researcher Award

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Mr. Daniel Morariu | Resilience | Best Researcher Award

Mr. Daniel Morariu, Lucian Blaga University of Sibiu, Romania

Morariu Ionel Daniel is an esteemed associate professor at “Lucian Blaga” University of Sibiu, Romania, with expertise in computer science, automatic systems, data mining, and machine learning. Born on September 17, 1974, in Sighisoara, he has dedicated over two decades to education and research. He holds a Bachelor’s and Master’s degree in Computer Science from “Lucian Blaga” University and completed his PhD in Computer Science with a focus on “Automatic Knowledge Extraction from Unstructured Data” in 2007. Daniel has been a consistent contributor to advanced research, particularly in data mining, neural networks, and natural language processing. With a robust portfolio of software engineering and academic experience, his career includes impactful projects in automation systems, energy control solutions, and numerous published research papers. His dedication to knowledge dissemination and technological advancements has earned him respect in both academic and industrial circles.

Professional Profile

google scholar

Summary of Suitability for the Award

Dr. Morariu Ionel Daniel stands out as a highly qualified candidate for the Research for Best Researcher Award, particularly due to his extensive academic background, research experience, and contributions in the field of Computer Science. His educational path, including a PhD focused on automatic knowledge extraction from unstructured data, demonstrates his depth in data mining and machine learning, areas that are essential in today’s technological landscape. Furthermore, his PhD was supported by SIEMENS Corporate Technology, highlighting the practical relevance of his work.

 🎓  Education 

Daniel Morariu completed his secondary education at “Mircea Eliade” Theoretic High School, Sighisoara, between 1989-1993. He pursued higher education at “Lucian Blaga” University of Sibiu’s Engineering Faculty, earning a Bachelor’s degree in Computer Science and Automatic Systems in 1998. His academic journey continued with a Master’s degree in Computer Science in 1999, specializing in “Parallel and Distribute Processing Systems” from the same university. His thirst for knowledge culminated in a PhD in Computer Science, awarded in April 2007. His PhD research focused on “Contributions to Automatic Knowledge Extraction from Unstructured Data,” under the supervision of Professor Lucian N. Vințan. Supported by SIEMENS Corporate Technology from Munich, his doctoral research provided significant insights into data mining and natural language processing. This strong educational foundation has positioned him as a distinguished academic in the field of computer science.

💼     Experience 

Daniel Morariu has held a variety of academic positions throughout his career. He began as a teaching assistant at “Lucian Blaga” University in 1998, contributing to courses such as Microprocessors and Object-Oriented Programming. From 2003 to 2007, he served as a lecturer, teaching advanced courses in Neural Networks and Data Mining. In 2007, he became an associate professor, focusing on courses like Data Mining, Machine Learning, and Interfaces and Communication Protocols. Outside academia, Morariu gained valuable industry experience. He worked with SC Consultens Informationstechnik SRL, a German software company, as a software engineer from 2001 to 2002. He also worked as an engineer at SC IRMES SA Sibiu from 1998 to 2000, developing software for monitoring generators and controlling gas supply in thermoelectric power stations. His career reflects a strong blend of academic expertise and practical industry experience, especially in computer science and automation systems.

🏅  Awards and Honors

Throughout his career, Daniel Morariu has been recognized for his contributions to computer science and engineering. His PhD research, supported by SIEMENS Corporate Technology from Munich, was a notable achievement, reflecting both scientific and financial backing from a prestigious institution. Over the years, his dedication to teaching and research has earned him accolades within the academic community at “Lucian Blaga” University, including recognition for his innovative approach to data mining and machine learning education. His work in automation systems, particularly in the energy sector, has also been praised for its practical applications, further solidifying his status as a leading figure in the intersection of academia and industry. Though specific awards are not listed, his consistent professional growth and contributions speak to a career filled with academic accomplishments and recognition.

 🌍  Research Focus

Daniel Morariu’s research primarily revolves around data mining, machine learning, and natural language processing. His academic focus is on extracting meaningful knowledge from unstructured data using advanced techniques such as Support Vector Machines (SVM) and neural networks. His PhD dissertation on “Contributions to Automatic Knowledge Extraction from Unstructured Data” set the foundation for his continuing research into text document processing and computational linguistics. Additionally, he explores the applications of these technologies in real-world problems, particularly in automation systems and energy sector monitoring. His work on computational linguistics helps bridge the gap between machine learning models and language understanding, while his research in data mining enhances predictive models across industries. Morariu’s blend of theoretical research and practical applications has made him a valuable contributor to advancements in these fields, influencing both academic research and industrial applications.

📖 Publication Top Notes

  • Feature selection methods for an improved SVM classifier
    • Cited by: 31
  • Meta-Classification using SVM Classifiers for Text Documents
    • Cited by: 27
  • The WEKA Multilayer Perceptron Classifier
    • Cited by: 22
  • Text Mining Methods Based on Support Vector Machine
    • Cited by: 22
  • Evolutionary Feature Selection for Text Documents Using the SVM
    • Cited by: 22