Abba Bashir | Machine Learning | Best Researcher Award

Mr. Abba Bashir | Machine Learning | Best Researcher Award

Mr. Abba Bashir, Federal University Dutsin-ma, Nigeria

Abba Bashir is a civil engineer and academic dedicated to sustainable infrastructure and structural optimization. He is a lecturer at the Federal University Dutsin-ma (FUDMA), Katsina, Nigeria, specializing in structural engineering and artificial intelligence applications in construction. With over 100 citations and an h-index of 6, his research focuses on recyclability, fiber-reinforced concrete, and computational mechanics. He has authored a book on bamboo fiber-reinforced concrete and actively contributes to accreditation and curriculum development. As the AI Research Leader at FUDMA’s Faculty of Engineering, he integrates machine learning into structural design for sustainable and resilient infrastructures.

Publication Profile

Scopus

Orcid

Google Scholar

πŸŽ“ Education

Abba Bashir is currently pursuing a Master of Technology in Structural Engineering at Mewar University, India (2023–2025). He holds a Bachelor of Technology in Civil Engineering from Sharda University, India, graduating in 2017 with an 8.3/10 CGPA. His early education includes a Senior Secondary School Certificate from Nasara Academy, Kano, Nigeria (2007) and a Primary School Leaving Certificate from Maitasa Special Primary School, Kano, Nigeria (2001). His academic journey has equipped him with expertise in structural analysis, computational mechanics, and sustainable construction materials. His continuous pursuit of knowledge fuels his research in optimizing civil engineering designs through artificial intelligence and machine learning.

πŸ’Ό Experience

Abba Bashir has been a lecturer at Federal University Dutsin-ma (FUDMA) since 2020, teaching courses such as Structural Analysis, Concrete Design, and Construction Materials. He has supervised undergraduate research projects and actively contributes to curriculum development and accreditation at the university. As a practicing civil engineer since 2017, he has designed and constructed residential, commercial, and institutional structures, integrating AI-driven optimization techniques. He is a member of FUDMA’s Concrete and Steel Research Group and serves as the AI Research Leader. His expertise spans finite element modeling, numerical analysis, and sustainable building materials. He is proficient in ABAQUS, ANSYS, AutoCAD, MATLAB, and Python for structural simulations.

πŸ† Awards & Honors

Abba Bashir has been recognized for his contributions to structural engineering and AI-driven construction methodologies. He has received accolades for his research on bamboo fiber-reinforced concrete and his role in advancing sustainable materials. His academic leadership in AI applications within civil engineering has earned him university recognition. His book on bamboo fiber-reinforced concrete is a significant contribution to sustainable construction literature. As a mentor and research leader, he plays a crucial role in developing new undergraduate programs and fostering innovation in civil engineering education. His expertise in computational mechanics and recyclability research continues to influence the field.

πŸ”¬ Research Focus

Abba Bashir’s research integrates artificial intelligence, machine learning, and optimization algorithms into structural engineering. His work focuses on fiber-reinforced concrete, recyclability, and sustainability in construction materials. He has extensive experience in finite element modeling using ABAQUS and ANSYS, with a strong emphasis on computational mechanics. His studies explore mechanical properties and durability of cementitious materials with micro/nano reinforcements. He also investigates the optimization of structural designs to reduce environmental impact and enhance resilience. His multidisciplinary research combines AI, numerical modeling, and advanced construction materials to create sustainable and cost-effective infrastructure solutions.

 

Publication Top Notes

1️⃣ Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste | Cited by: 50 | πŸ“… 2022

2️⃣ An overview of streamflow prediction using random forest algorithm | Cited by: 19 | πŸ“… 2022 πŸŒŠπŸ€–

3️⃣ Analysis of Bamboo fibre reinforced beam | Cited by: 17 | πŸ“… 2018 πŸŽπŸ—οΈ

4️⃣ Antioxidant, hypolipidemic and angiotensin converting enzyme inhibitory effects of flavonoid-rich fraction of Hyphaene thebaica (Doum Palm) fruits on fat-fed obese Wistar rats | Cited by: 16 | πŸ“… 2019 πŸ₯πŸ§ͺ

5️⃣ Assessment of Water Quality Changes at Two Locations of Yamuna River Using the National Sanitation Foundation of Water Quality (NSFWQI) | Cited by: 15 | πŸ“… 2015 πŸš°πŸ“Š

6️⃣ High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm | Cited by: 14 | πŸ“… 2023 πŸ—οΈπŸ€–

7️⃣ Performance analysis and control of wastewater treatment plant using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Linear Regression (MLR) techniques | Cited by: 8 | πŸ“… 2022 🌊🧠

8️⃣ Comparison of Properties of Coarse Aggregate Obtained from Recycled Concrete with that of Conventional Coarse Aggregates | Cited by: 5 | πŸ“… 2018 β™»οΈπŸ—οΈ

9️⃣ Machine Learning: A Way to Smart Environment | Cited by: 1 | πŸ“… 2021 πŸ€–πŸŒ±

πŸ”Ÿ A new strategy using intelligent hybrid learning for prediction of water binder ratio of concrete with rice husk ash as a supplementary cementitious material | πŸ“… 2025 πŸ—οΈπŸ“Š

 

 

 

 

 

John Mutinda | Deep learning | Best Researcher Award

Mr. John Mutinda | Deep learning | Best Researcher Award

Mr. John Mutinda, USTC china, China

John Kamwele Mutinda is a passionate researcher currently pursuing an MSc in Machine Intelligence at the African Institute for Mathematical Sciences in Senegal. He holds a previous MSc in Mathematical Sciences from AIMS Rwanda and a BSc in Statistics from South Eastern Kenya University, where he graduated with First Class Honours. His research interests include statistical modeling, data science, and machine learning. John has significant teaching experience, having mentored high school students in mathematics and science. He has received several scholarships and awards, including the African Master’s in Machine Intelligence Scholarship. πŸŒπŸ“ŠπŸ’»

Publication profile

Google Scholar


Education Background

Mr. John Kamwele Mutinda is currently pursuing his MSc in Machine Intelligence at the African Institute for Mathematical Sciences in Senegal (2022-2023). He previously earned an MSc in Mathematical Sciences from AIMS Rwanda, achieving an impressive cumulative GPA of 84.5/100 (Very Good Pass). John completed his BSc in Statistics at South Eastern Kenya University, graduating with First Class Honours and a GPA of 75.78/100. He also excelled in his Kenya Certificate of Secondary Education (KCSE) at Katwanyaa High School, obtaining a GPA of 67/84 (B+). πŸŽ“πŸ“šπŸŒ

 

Research Experience

Mr. John Kamwele Mutinda has actively contributed to significant research projects. In 2022, he modeled the impact of meteorological and air pollution parameters on COVID-19 transmission in the Western Cape Province of South Africa. He also applied Principal Component Analysis (PCA) within the health sector that same year. In 2020, John focused on modeling the human population growth rate in Kitui County, Kenya. His earlier work in 2019 involved time series modeling of infant child mortality rates in Kitui County. These experiences highlight his strong analytical skills and commitment to impactful research. πŸ“ŠπŸŒπŸ“ˆ

 

Teaching and Mentoring Experience

John Kamwele Mutinda has an extensive background in teaching and mentoring. In 2021, he provided tutorial services in Mathematics, Physics, and Chemistry at Katwanyaa High School, helping high school students excel academically. The previous year, he supported students in Mathematics, Agriculture, and Chemistry. His mentoring journey began in 2019, guiding students in Mathematics and Chemistry. In 2018, he taught Mathematics at Katwanyaa High School, and in 2017, he mentored students in Mathematics, Physics, and Agriculture. His commitment to education started as early as 2016 when he tutored Mathematics and Physics at Itheuni Secondary School. πŸ“šπŸ‘¨β€πŸ«βœ¨

 

Work Experience

John Kamwele Mutinda has diverse work experience in education and electoral roles. In 2021, he served as an Assistant Teacher and Departmental Assistant at Katwanyaa High School, where he was responsible for teaching, setting, supervising, and marking exams. He also acted as the Deputy Presiding Officer for the Independent Electoral and Boundaries Commission during the Machakos County senatorial elections. In 2019, he worked as an Enumeration Officer for the Kenya National Bureau of Statistics, conducting household and establishment surveys. Previously, in 2017, he was a Polling Clerk, responsible for verifying voters and counting votes during the general elections. In 2016, he was a Board of Management Teacher at Itheuni Secondary School, performing similar teaching duties. πŸ“šπŸ—³οΈπŸ‘¨β€πŸ«

 

Awards, Honours & Certificates

John Kamwele Mutinda has received numerous accolades for his academic and professional achievements. In 2023, he was awarded the prestigious African Master’s in Machine Intelligence Scholarship, funded by Facebook and Google, at the African Institute for Mathematical Sciences in Senegal. He also received the Next Einstein Initiative Master’s Scholarship Award in 2021. His educational accomplishments include a Certificate of Completion in Business Management from ESMT Germany and multiple Certificates of Merit in R, STATA, and SPSS from KESAP Research Centre. He has participated in various Mathematics Olympiads, earning certificates for his outstanding performance. πŸŽ“πŸ†πŸ“œ

 

Publication Top Notes

  • Covid-19 impact analysis: assessing African sectors-commodity, service, manufacturing, and education using mixed model approach – Cited by 1, 2023 πŸ¦ πŸ“Š
  • African Institute for Mathematical Sciences (AIMS), Rwanda – Cited by 1, 2023 πŸ‡·πŸ‡Ό
  • Stock price prediction using combined GARCH-AI models – Cited by 0, 2024 πŸ“ˆπŸ€–
  • Enhancing Obesity Detection Through SMOTE-based Classification Models: A comparative Study – Cited by 0, 2024 πŸ‹οΈβ€β™‚οΈπŸ”
  • Rainfall Pattern in Kenya: Bayesian Non-parametric Model Based on the Normalized Generalized Gamma Process – Cited by 0, 2024 πŸŒ§οΈπŸ“‰
  • Capital Asset Pricing Model: A Renewed Application on S&P 500 Index – Cited by 0, 2024 πŸ’ΉπŸ“ˆ
  • Spatial Regression Modeling of Child Survival on the Distribution of Births and Deaths in Kenya Based on the Kenya Demographic and Health Survey (KDHS) 2022 – Cited by 0, 2024 πŸ‘ΆπŸŒ
  • Exploring the Role of Dimensionality Reduction in Enhancing Machine Learning Algorithm Performance – Cited by 0, 2024 βš™οΈπŸ“‰
  • Modeling the Impact of Air Pollution and Meteorological Variables on COVID‐19 Transmission in Western Cape, South Africa – Cited by 0, 2024 🌫️🦠

 

Souhail Dhouib | Artificial Intelligence | Best Researcher Award

Prof. Souhail Dhouib | Artificial Intelligence | Best Researcher Award

Full Professor, Higher Institute of Industrial Management, University of Sfax, Tunisia

Prof. Souhail Dhouib 🌟 is a distinguished academic and industry expert specializing in Artificial Intelligence and Operations Research. He holds the position of Full Professor at the Higher Institute of Industrial Management, University of Sfax, Tunisia, where he has taught for over twenty years. Prof. Dhouib is renowned for his pioneering work in matrix optimization concepts and has made significant contributions to decision-making and planning through his innovative algorithms and methodologies.

ProfileArtificial IntelligenceΒ 

ORCID

 

Education

Prof. Dhouib completed hisΒ Ph.D. in Quantitative MethodsΒ from the Faculty of Management and Economics Sciences, Sfax University, Tunisia (2004-2009). He also holds aΒ Master’s degree in Operations Research and Production ManagementΒ (2001-2003) and aΒ Bachelor’s degree in Management Information SystemsΒ (1992-1996), all from the same institution.Β πŸ“šπŸŽ“

Experience

Prof. Dhouib has over twenty years of extensive experience in both academia and industry. He has served as a General Manager and an Analyst Programmer, where he was involved in software development and implementation. His academic roles include positions as a Full Professor, Associate Professor, and Assistant Professor, contributing significantly to teaching and research.Β πŸ’πŸ’»

Research Interests

Prof. Dhouib’s research focuses on Artificial Intelligence, Operations Research, and Optimization algorithms. His work spans various domains, including Logistics, Supply Chain Management, Business Intelligence Systems, and ERP. He is particularly known for his Dhouib-Matrix methods and their applications in cognitive robotics, multi-objective optimization, and path planning.Β πŸ”πŸ“ˆ

Awards

Prof. Dhouib’s contributions to the field have been recognized through numerous journal publications, although specific awards are not listed in the provided information. His innovative research and development efforts continue to impact the industry and academia.Β πŸ†πŸ‘

Publications

Intelligent Path Planning for Cognitive Mobile Robot Based on Dhouib-Matrix-SPP MethodΒ (2024) –Β Cognitive Robotics

Multi-Start Constructive Heuristic through Descriptive Statistical Metrics: The Dhouib-Matrix-4 MetaheuristicΒ (2024) –Β International Journal of Operational Research

Innovative Method to Solve the Minimum Spanning Tree Problem: The Dhouib-Matrix-MSTP (DM-MSTP)Β (2024) –Β Results in Control and Optimization

Enhancing the Dhouib-Matrix-4 Metaheuristic to Generate the Pareto Non-Dominated Set Solutions for Multi-objective Travelling Salesman Problem: The DM4-PMO MethodΒ (2024) –Β Results in Control and Optimization

Faster than Dijkstra and A* Methods for the Mobile Robot Path Planning Problem Using Four Movement Directions: The Dhouib-Matrix-SPP-4Β (2024) –Β Advances in Transdisciplinary Engineering, Mechatronics and Automation Technology