Dr. Mlungisi Duma | Artificial Intelligence | Best Researcher Award

Dr. Mlungisi Duma | Artificial Intelligence | Best Researcher Award

Dr. Mlungisi Duma, University of Johannesburg, South Africa

Dr. Mlungisi Duma is a Senior Researcher and Development Manager at the University of Johannesburg. With over 19 years of experience in the IT profession, he has a rich background in software development and management. Dr. Duma holds a PhD in Electronic and Electrical Engineering, specializing in Artificial Intelligence, from the University of Johannesburg. His research focuses on machine learning, evolutionary computation, and optimization algorithms. He has successfully led numerous projects, published in top-tier journals, and is an active reviewer for multiple prestigious journals. His expertise extends to consultancy, where he manages application development for First National Bank, overseeing code maintenance for ATM systems. Dr. Duma’s contributions to academia and industry showcase his dedication to innovation and leadership in AI research.

Professional Profile

Scopus

Summary of Suitability for the Research for Best Researcher Award

Dr. Mlungisi Duma’s diverse contributions to the fields of artificial intelligence and evolutionary computation, along with his leadership in both academia and industry, make him highly suitable for the ‘Research for Best Researcher Award’. His ability to bridge the gap between theoretical research and real-world applications, coupled with his strong publication record and professional recognition, demonstrate his excellence as a researcher. He is well-positioned to receive this award, as his work contributes significantly to advancing the field of artificial intelligence.

šŸŽ“Ā Ā EducationĀ 

Dr. Mlungisi Duma holds a Masterā€™s degree in Computer Science and a PhD in Electronic and Electrical Engineering, both from the University of Johannesburg. His PhD specialization in Artificial Intelligence equipped him with expertise in machine learning, evolutionary computation, and optimization algorithms. Throughout his academic journey, Dr. Duma has actively engaged in cutting-edge research projects, collaborating with renowned academics and professionals. His commitment to education has been reflected in his role as a judge and reviewer at various academic institutions and scientific journals. As a Senior Member of IEEE and ACM, he continues to contribute to the advancement of AI technologies. Dr. Dumaā€™s academic background is a testament to his passion for innovation, making significant contributions to the field of Artificial Intelligence.

Ā šŸ’¼Ā Ā Experience

With 19 years of experience in the IT industry, Dr. Mlungisi Duma has held various technical and leadership roles. For nine years, he worked as a software developer, honing his skills in coding, system architecture, and application design. Following this, he spent a decade as a Development Manager and Software Architect at First National Bank, where he led teams responsible for developing and maintaining ATM systems. Currently, he is a Senior Researcher and Development Manager at the University of Johannesburg, where he leads AI research initiatives. His extensive experience spans both academic and industry settings, bridging the gap between theoretical research and practical applications. Dr. Dumaā€™s ability to manage multidisciplinary teams and deliver innovative solutions reflects his leadership and technical acumen.

šŸ…Awards and HonorsĀ 

Dr. Mlungisi Duma has received several prestigious awards and honors for his contributions to Artificial Intelligence and software development. He is a Senior Member of IEEE and ACM, recognizing his influence in the academic and professional communities. Dr. Dumaā€™s work has also been recognized through his membership in the Golden Key International Honour Society, a distinction for top-performing academics globally. He has served as a judge at the University of Johannesburgā€™s Academy of Computer Science project day and has been a frequent reviewer for renowned journals, including IEEE Access, Applied Soft Computing, and the Journal of Mathematical Problems in Engineering. These honors highlight Dr. Dumaā€™s commitment to academic excellence and his significant contributions to advancing AI technologies.

Ā šŸŒ Research Focus

Dr. Mlungisi Dumaā€™s research focuses on Artificial Intelligence, particularly in the fields of machine learning, evolutionary computation, and optimization algorithms. He has contributed extensively to developing novel AI models for predictive modeling, control parameter optimization, and feature selection. His recent work includes optimizing ant colony algorithms and using artificial immune systems for collaborative filtering in recommender systems. Dr. Dumaā€™s research is not only theoretical but also has practical applications, as demonstrated by his consultancy projects for First National Bank, where AI-driven solutions are implemented in ATM systems. He has published widely in top-tier journals, reflecting his thought leadership in AI and its applications in diverse sectors. His current projects aim to enhance AI’s role in automation and decision-making across industries.

Ā šŸ“– Publication Top Notes

  • Sparseness reduction in collaborative filtering using a nearest neighbour artificial immune system with genetic algorithms
    • Citations: 22
  • Optimising latent features using artificial immune system in collaborative filtering for recommender systems
    • Citations: 20
  • Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm
    • Citations: 30
  • Classification with missing data using multi-layered artificial immune systems
    • Citations: 4
  • Partial imputation to improve predictive modelling in insurance risk classification using a hybrid positive selection algorithm and correlation-based feature selection
    • Citations: 2

Mr.Danish Javed | Data Science | Best Researcher Award

Mr.Danish Javed | Data Science | Best Researcher Award

Mr.Danish Javed, Taylor’s University Lakeside Campus, Malaysia

Danish Khan is a Ph.D. scholar specializing in Data Science at Taylor’s University, Malaysia, where he is advancing research in natural language processing (NLP). With a strong academic background in Software Engineering from Bahria University, Islamabad, Danish has built expertise in Python, machine learning, and deep learning. He has held teaching roles as a Senior Lecturer at the University of Central Punjab, Lahore, and is currently a tutor at Taylorā€™s University, Malaysia. His career reflects a commitment to advancing computer science education, mentoring students, and leading post-graduate councils. Danish is also a prolific researcher, contributing to various data science and sentiment analysis projects, particularly in the analysis of social media content and NLP.

Professional Profile

google scholar

Summary of Suitability for the Research for Best Researcher Award

Danish Javed presents a strong candidacy for the Research for Best Researcher Award based on his significant academic and research contributions, particularly in the fields of data science, machine learning, and natural language processing (NLP). His ongoing PhD in Data Science from Taylor’s University demonstrates his deep commitment to advancing research, especially in topics like sentiment analysis, deep learning, and Twitter bot detection. Danish has published research in Scopus-indexed conferences and Q1/Q4 journals, which highlights the academic impact of his work.

šŸŽ“ EducationĀ 

Danish Khan has consistently pursued excellence in academia, currently working toward his Ph.D. in Data Science at Taylor’s University, Lakeside Campus, Malaysia. His doctoral research focuses on natural language processing (NLP), specifically exploring frameworks for sentiment analysis, bot detection, and text analytics. Prior to his Ph.D., Danish earned his M.S. in Software Engineering from Bahria University, Islamabad, Pakistan, where he delved into the intricacies of machine learning, image processing, and artificial intelligence. His academic foundation also includes a B.S. in Software Engineering from Bahria University, during which he developed strong programming skills in Python, Java, and C++, equipping him with the tools to tackle complex computational problems. His academic journey reflects his deep interest in understanding data structures and algorithms, making him proficient in implementing advanced analytics and programming solutions.

šŸ’¼ ExperienceĀ 

Danish Khan has a broad range of teaching and leadership experience, with over five years in academia. He is currently a Tutor at Taylor’s University, Malaysia, where he conducts tutorials in data science, supervises student projects, and plays a key role in shaping the post-graduate student experience. He previously served as the President of the Post-Graduate Student Council, organizing events and representing student perspectives in university meetings. Prior to his current role, Danish was a Senior Lecturer in the Faculty of Information Technology at the University of Central Punjab, Lahore, where he taught computer science and software engineering courses, supervised final-year projects, and contributed to extracurricular activities such as organizing sporting events. Additionally, Danish has experience as a QA Analyst at Orbit Institute of Technology in Lahore, where he maintained the quality standards in the Software Engineering department.

šŸ…Awards and Honors

Danish Khan has received notable recognition throughout his academic and professional career. His published research in data science and natural language processing has been featured in prominent Scopus-indexed conferences and reputed journals. As a Ph.D. scholar, he has been honored with several merit-based scholarships for academic excellence. Danish was also recognized for his leadership efforts while serving as President of the Post-Graduate Student Council at Taylorā€™s University, where he played a pivotal role in advocating for post-graduate student welfare. During his tenure at the University of Central Punjab, Lahore, he earned commendations for his outstanding contributions to teaching and student mentorship. Additionally, his development of an Android application, “Forex Profit Gain,” has garnered attention, earning placement in the Google Play Store. These accolades reflect his deep commitment to both academic rigor and innovative problem-solving in the field of data science.

šŸŒ Research FocusĀ 

Danish Khanā€™s research is centered on data science, particularly natural language processing (NLP), machine learning, and sentiment analysis. His Ph.D. work at Taylorā€™s University, Malaysia, focuses on advanced techniques in deep learning to analyze and classify text-based data. His key areas of research include social media analytics, Twitter bot detection, and sentiment analysis of public opinion during crises such as the COVID-19 pandemic. Danish has contributed to frameworks that improve sentiment analysis by leveraging oversampling techniques and random minority oversampling, which enhance the accuracy of sentiment classification in user-generated content. His research also extends to explainable artificial intelligence (AI), where he has designed models for transparent and interpretable detection of bots on social media platforms. Danish’s academic pursuit aims to contribute practical, data-driven insights to solve real-world problems using cutting-edge AI and NLP technologies.

šŸ“– publications Top Notes

“Framework for Improved Sentiment Analysis via Random Minority Oversampling for User Tweet Review Classification.”
Citation count: 25
“Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis.”
Citation count: 6
“Football Analytics for Goal Prediction to Assess Player Performance.”
Citation count: 4
“Explainable Twitter Bot Detection Model for Limited Features.”
Citation count: 2
“Explainable Machine Learning Based Model for Heart Disease Prediction.”
“Analyzing the Efficacy of Bot Detection Methods on Twitter/X.”

kelig mahe | fisheries research | Research Excellence Award

Dr kelig mahe | fisheries research | Research Excellence Award

Dr.kelig mahe, Ifremer, France

Dr. KĆ©lig MAHE is a leading researcher at IFREMER, specializing in marine science, particularly in the fields of sclerochronology and fisheries. Since 2021, he has been the head of the Halieutiques Manche-Mer du Nord Unit, overseeing numerous national and European projects related to fish age estimation and stock management. Dr. MAHEā€™s expertise spans biological data coordination, fish age determination workshops, and the development of TNPC software. He has co-supervised 11 PhD students and 12 master’s students, contributing significantly to scientific innovation, as demonstrated by his 2010 award for scientific and technological innovation at IFREMER. His contributions extend internationally, with leadership in numerous European research collaborations.

Professinal Profile

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    EducationĀ 

    Dr. KĆ©lig MAHEā€™s academic journey is rooted in marine and environmental sciences. He earned his PhD in Marine Biology from UniversitĆ© du Littoral CĆ“te d’Opale (ULCO) in 2019, focusing on fisheries and environmental ecosystems. Prior to this, he completed his DiplĆ“me dā€™Etudes Approfondies (DEA) in Coastal and Littoral Ecosystem Dynamics from ULCO in 2002. Dr. MAHEā€™s foundational knowledge in marine resource management was shaped during his engineering studies at the prestigious Ecole Nationale SupĆ©rieure dā€™Agronomie de Rennes (ENSAR), where he graduated with a specialization in Halieutics in 2003. His multidisciplinary education also includes a MaĆ®trise des Sciences et Techniques in Environmental Sciences from the University of Rouen, completed in 2001, providing him with a well-rounded scientific background in both marine biology and environmental management.

    ExperienceĀ 

    Dr. MAHE KĆ©lig has an extensive research career at IFREMER, where he has been actively engaged since 2007. His role evolved from a research cadre to leading the Halieutiques Manche-Mer du Nord Unit in 2021. His vast experience includes coordinating national biological data, managing research programs on fish age estimation, and providing expert advice on stock management. His involvement in European research projects is extensive, including leadership positions in projects like Med-Units, Sumaris, and Microtolithe. Dr. MAHE has also represented IFREMER in international workshops and played a pivotal role in advancing sclerochronology, both through research and the development of TNPC software. His management experience includes overseeing teams and mentoring students, making significant contributions to both scientific research and talent development.

    Awards and HonorsĀ 

    Dr. MAHE KĆ©ligā€™s distinguished contributions to marine science have earned him notable recognition. In 2010, he was awarded the IFREMER TrophĆ©e de lā€™innovation for his pioneering work in scientific, technical, and technological fields. His leadership and contributions have made a global impact, notably in European research collaborations, where he co-chaired 13 international workshops and participated in 4 European working groups. His expertise in sclerochronology has positioned him as an expert for IFREMER in international fora. Dr. MAHEā€™s influence extends to organizing international symposia, such as the 6th International Symposium on Biological Shape Analysis in 2019. His collaborative spirit and innovative work have consistently advanced marine research, particularly in fisheries biology, earning him respect and recognition within the global scientific community.

    Research Focus

    Dr. MAHE KĆ©ligā€™s research centers on sclerochronology, the study of calcified structures in fish, to improve age determination methods and enhance fisheries management. His work plays a critical role in understanding fish population dynamics and the impacts of environmental changes, such as temperature fluctuations and CO2 levels, on marine species. Dr. MAHE has been at the forefront of integrating modern technologies in fisheries research, particularly through his contributions to the TNPC software for processing calcified structures. His research also extends to interdisciplinary collaborations, where he has led and participated in numerous European projects, focusing on sustainable fishery practices, species population structures, and biogeographical studies. His commitment to research excellence is evidenced by his active role in advancing knowledge in otolith morphogenesis, age determination, and stock management, all of which are pivotal for sustainable marine resource management.

    Ā Publications Top Notes

    1. šŸŸ Asymmetry of Sagittal Otolith Shape in Mediterranean Red Mullet: Comparative Analysis of 2D and 3D Otolith Shape Data (2023).
    2. šŸŒŠ Effect of Temperature and CO2 on Morphogenesis of Sagittal Otoliths in Atlantic Herring (2023).
    3. šŸ“Š Spatial Structuring of Demersal Fish Around RĆ©union Island Based on Otolith Shape (2023).
    4. šŸ§¬ New Scale Analyses Reveal Centenarian African Coelacanths (2021).
    5. šŸ  Complementarity and Discriminatory Power of Genotype and Otolith Shape in Eastern English Channel Sole (2020).