Rania Hamdani | Computer science | Best Researcher Award

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Mrs. Rania Hamdani | Computer science | Best Researcher Award

Mrs. Rania Hamdani, University of Luxembourg, Luxembourg

Rania Hamdani is a research scientist specializing in operational research, data management, and cloud architecture for Industry 5.0. Based in Luxembourg, she is currently affiliated with the University of Luxembourg, where she explores advanced methodologies for integrating and managing heterogeneous data sources. She holds an engineering degree in Software Engineering and has extensive experience in software development, AI, and DevOps. Rania has worked on multiple industry and academic projects, publishing three research papers in Ontology-Driven Knowledge Management and Cloud-Edge AI. With a strong background in programming, cloud computing, and AI-driven solutions, she has contributed to platforms ranging from job recommendation systems to adaptive human-computer interaction systems. Her expertise includes Python, SpringBoot, Kubernetes, and Azure DevOps. She is also an active member of IEEE and other technical organizations, promoting innovation and knowledge-sharing in AI and cloud technologies. 🌍💻🔬

Publication Profile

Orcid

🎓 Education

Rania Hamdani holds an Engineering Degree in Software Engineering from the National Higher School of Engineers of Tunis (2021–2024), where she specialized in advanced design, service-oriented architecture, object-oriented programming, database management, and operational research. Prior to this, she completed a two-year preparatory cycle at the Preparatory Institute for Engineering Studies of Tunis (2019–2021), undertaking intensive coursework in mathematics, physics, and technology to prepare for engineering studies. She also earned a Mathematics-specialized Baccalaureate from Pioneer High School Bourguiba Tunis (2015–2019), graduating with honors. Throughout her academic journey, she gained expertise in artificial intelligence, machine learning, cloud computing, and DevOps methodologies. Her education provided a solid foundation in programming languages, data processing techniques, and full-stack development. Additionally, she holds multiple Microsoft certifications in Azure fundamentals, AI, data security, and compliance, reinforcing her expertise in cloud-based solutions and AI-driven applications. 📚🎓💡

💼 Experience

Rania Hamdani is a research scientist at the University of Luxembourg, where she focuses on integrating and managing heterogeneous data sources for cloud-based decision-making. Previously, she was a research intern at the same institution, contributing to Ontology-Driven Knowledge Management and Cloud-Edge AI, with three published papers. She also worked as a part-time software engineer at CareerBoosts in Quebec (2021–2025), specializing in Python, Azure DevOps, Docker, and test automation. She gained industry experience through internships at Qodexia (Paris), Sagemcom (Tunisia), and Tunisie Telecom, working on smart recruitment platforms, employee management systems, and server monitoring solutions using SpringBoot, Angular, and PostgreSQL. Her technical expertise spans full-stack development, DevOps, AI-driven applications, and cloud computing. She has contributed to major projects, including an adaptive human-computer interaction system, a job recommendation system, and a problem-solving platform, demonstrating her versatility in research and software engineering. 🚀🖥️🔍

🏆 Awards & Honors

Rania Hamdani has been recognized for her outstanding contributions to AI-driven cloud computing and operational research. She received excellence awards during her engineering studies at the National Higher School of Engineers of Tunis and was among the top-performing students in her Mathematics-specialized Baccalaureate. Her research papers in Ontology-Driven Knowledge Management and Cloud-Edge AI have been acknowledged in academic circles, contributing to the advancement of Industry 5.0 technologies. She has also earned multiple Microsoft certifications in cloud and AI fundamentals, reinforcing her technical expertise. As an active member of IEEE and the Youth and Science Association, she has been involved in technology outreach and innovation-driven initiatives. Her leadership in ENSIT Junior Enterprise as a project manager further showcases her ability to lead and contribute to tech communities. These recognitions highlight her dedication to research, software development, and cloud-based AI applications. 🏅📜🌟

🔬 Research Focus

Rania Hamdani’s research focuses on operational research, data management, cloud-edge AI, and Industry 5.0 applications. She specializes in ontology-driven knowledge management, exploring methodologies for integrating heterogeneous data sources to optimize cloud-based decision-making processes. Her work includes artificial intelligence, machine learning, reinforcement learning, and human-computer interaction systems. She has contributed to projects involving job recommendation systems, adaptive human-computer interaction platforms, and cloud-based problem-solving platforms. Rania is particularly interested in scalable cloud architectures, leveraging technologies like FastAPI, Kubernetes, Docker, and Azure DevOps to build efficient AI-powered solutions. Her research also integrates graph databases, Apache Airflow, and big data analytics for enhanced data processing. By combining AI and cloud computing, she aims to develop innovative, data-driven solutions for automation, decision support, and optimization in various industrial applications. Her expertise bridges the gap between theoretical research and real-world software engineering. ☁️🤖📊

 

Publication Top Notes

Adaptive human-computer interaction for industry 5.0: A novel concept, with comprehensive review and empirical validation

 

Abba Bashir | Machine Learning | Best Researcher Award

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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

🎓 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.

💼 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.

🏆 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.

🔬 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 | 📅 2022

2️⃣ An overview of streamflow prediction using random forest algorithm | Cited by: 19 | 📅 2022 🌊🤖

3️⃣ Analysis of Bamboo fibre reinforced beam | Cited by: 17 | 📅 2018 🎍🏗️

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 | 📅 2019 🏥🧪

5️⃣ Assessment of Water Quality Changes at Two Locations of Yamuna River Using the National Sanitation Foundation of Water Quality (NSFWQI) | Cited by: 15 | 📅 2015 🚰📊

6️⃣ High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm | Cited by: 14 | 📅 2023 🏗️🤖

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 | 📅 2022 🌊🧠

8️⃣ Comparison of Properties of Coarse Aggregate Obtained from Recycled Concrete with that of Conventional Coarse Aggregates | Cited by: 5 | 📅 2018 ♻️🏗️

9️⃣ Machine Learning: A Way to Smart Environment | Cited by: 1 | 📅 2021 🤖🌱

🔟 A new strategy using intelligent hybrid learning for prediction of water binder ratio of concrete with rice husk ash as a supplementary cementitious material | 📅 2025 🏗️📊

 

 

 

 

 

Zuheng Ming | Artificial intelligence | Best Researcher Award

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Dr. Zuheng Ming | Artificial intelligence | Best Researcher Award

Associate professor at Sorbonne Paris North University, France

🧑‍🏫 Dr. Zuheng Ming is an Assistant Professor at L2TI, Sorbonne Paris North University, France. He earned his PhD in 2013 from Grenoble Alpes University 🇫🇷, specializing in speech parameter mapping. His expertise spans multimodal learning, computer vision, and deep learning 🤖. Dr. Ming has 30+ publications 📝 in top-tier journals (JCR Q1/Q2) and conferences (ICIP, ICPR, ICDAR). He has supervised doctoral and master’s theses and collaborated internationally with CVC, RIKEN AIP, and Oulu University 🌍. He has led funded research projects on face anti-spoofing and document analysis 📄. Additionally, he serves as a guest editor and reviewer for prestigious journals. ✨

Publication Profile

Google Scholar

🏅 Professional Experience

Dr. Zuheng Ming is an accomplished researcher and educator in computer vision and deep learning 🤖. Since September 2022, he has been serving as an Assistant Professor at L2TI, Sorbonne Paris North University, France 🇫🇷. Prior to this, he was a Lecture-Researcher at L3i, La Rochelle University (2021-2022) 📚. From 2016 to 2021, he worked as a Postdoctoral Fellow and Assistant Lecturer at L3i, La Rochelle University. Earlier, from 2014 to 2015, he pursued a postdoctoral fellowship at Bordeaux University 🏛️, contributing significantly to cutting-edge research in multimodal learning and artificial intelligence. ✨

🎓 Educational Background

Dr. Zuheng Ming holds a PhD in Computer Science from Grenoble Alpes University, France (2013) 🇫🇷, where he specialized in spectral parameters mapping for cued speech using multi-linear and GMM approaches 🔬. He earned his Master’s degree in Pattern Recognition and Artificial Intelligence from Beijing Institute of Technology (2008) 🎭🤖. His academic journey began with a Bachelor’s degree in Electronic and Automatic Systems Engineering from Hunan University, China (2003) ⚡. His strong educational foundation has driven his research contributions in computer vision, deep learning, and multimodal learning 📚✨.

🔬 Research Activities

Dr. Zuheng Ming has been actively involved in research supervision, mentoring 1 PhD thesis, 2 Master’s theses, and 6 internships 🎓📖. He has established six international collaborations with prestigious institutions, including CVC (Spain) 🇪🇸, RIKEN AIP (Japan) 🇯🇵, Oulu University (Finland) 🇫🇮, Northwestern Polytechnical University (China) 🇨🇳, and Xidian University (China) 🇨🇳. His global academic engagement also includes an academic visit to Kyoto University, Japan, in 2015 🌍🏫. Through his extensive research network, Dr. Ming continues to make significant contributions to computer vision, deep learning, and multimodal learning 📊🤖.

🎓 Teaching Experience

Dr. Zuheng Ming has extensive teaching experience in cutting-edge technologies related to artificial intelligence and computer vision 🧠📸. He has taught courses on Deep Learning, Advanced Image Processing, and Intelligent Systems in Computer Vision 🤖🖼️, equipping students with the latest advancements in AI. Additionally, he has imparted knowledge in Database Management and Object-Oriented Programming 💾💻, fostering strong software development skills. His expertise in both theoretical foundations and practical applications makes him a valuable mentor in the field of AI and computer vision, guiding students toward innovative research and industry-ready solutions 🚀📚.

🔍 Research Focus

Dr. Zuheng Ming’s research primarily focuses on computer vision, deep learning, and document security 🧠📸🔏. His contributions span facial recognition, anti-spoofing techniques, and face liveness detection 🤖😃, enhancing biometric security. He has also worked extensively on document image classification and authentication 📄🔍, improving identity verification systems. His expertise in multi-modal learning, pattern recognition, and deep feature fusion enables advancements in AI-driven document forensics and secure authentication 🚀🔐. Collaborating internationally, he applies machine learning and self-attention networks to solve real-world challenges in face recognition, fraud detection, and intelligent systems 🌍🔬.

Publication Top Notes

📸 A survey on anti-spoofing methods for facial recognition with RGB cameras of generic consumer devices – Z Ming, M Visani, MM Luqman, JC Burie | Journal of Imaging | 88 citations | 2020

📄 Visual and textual deep feature fusion for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | IEEE/CVF Conference on Computer Vision | 63 citations | 2020

🔍 Simple triplet loss based on intra/inter-class metric learning for face verification – Z Ming, J Chazalon, MM Luqman, M Visani, JC Burie | IEEE/CVF International Conference on Computer Vision | 57 citations | 2017

😊 Facial action units intensity estimation by fusion of features with multi-kernel SVM – Z Ming, A Bugeau, JL Rouas, T Shochi | IEEE International Conference on Automatic Face and Gesture Recognition | 54 citations | 2015

🆔 MIDV-2020: A comprehensive benchmark dataset for identity document analysis – BK Bulatovich, EE Vladimirovna, TD Vyacheslavovich, SN Sergeevna, … | Computer Optics | 51 citations | 2022

🙂 Dynamic Multi-Task Learning for Face Recognition with Facial Expression – Z Ming, J Xia, MM Luqman, JC Burie, K Zhao | IEEE/CVF International Conference on Computer Vision Workshop | 40 citations | 2019

📜 VLCDoC: Vision-language contrastive pre-training model for cross-modal document classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol, OR Terrades | Pattern Recognition | 33 citations | 2023

🔐 FaceLiveNet: End-to-end networks combining face verification with interactive facial expression-based liveness detection – Z Ming, J Chazalon, MM Luqman, M Visani, JC Burie | International Conference on Pattern Recognition | 30 citations | 2018

📑 Cross-modal deep networks for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | IEEE International Conference on Image Processing | 23 citations | 2020

📃 Document liveness challenge dataset (DLC-2021) – DV Polevoy, IV Sigareva, DM Ershova, VV Arlazarov, DP Nikolaev, Z Ming, … | Journal of Imaging | 21 citations | 2022

📹 ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detection – Z Ming, Z Yu, M Al-Ghadi, M Visani, M Muzzamil Luqman, JC Burie | IEEE International Conference on Image Processing | 21 citations | 2022

🌲 Multiple sources data fusion via deep forest – J Xia, Z Ming, A Iwasaki | IGARSS IEEE International Geoscience and Remote Sensing Symposium | 15 citations | 2018

🆔 Face detection in camera captured images of identity documents under challenging conditions – S Bakkali, MM Luqman, Z Ming, JC Burie | International Conference on Document Analysis and Recognition Workshops | 11 citations | 2019

📑 EAML: Ensemble self-attention-based mutual learning network for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | International Journal on Document Analysis and Recognition | 10 citations | 2021

🧠 Synthetic evidential study as augmented collective thought process – Preliminary report – T Nishida, M Abe, T Ookaki, D Lala, S Thovuttikul, H Song, Y Mohammad, … | ACIIDS Asian Conference | 10 citations | 2015

🆔 Identity documents authentication based on forgery detection of guilloche pattern – M Al-Ghadi, Z Ming, P Gomez-Krämer, JC Burie | arXiv preprint | 8 citations | 2022

 

Md Erfan | Machine Learning | Best Researcher Award

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Mr. Md Erfan | Machine Learning | Best Researcher Award

Mr. Md Erfan, University of Barishal, Bangladesh

Assistant Professor, Department of Computer Science and Engineering, University of Barishal, Bangladesh. His research focuses on flaky test detection, compilation error resolution, and AI applications in automation, decision-making, and problem-solving. He holds an MSSE and BSSE from the University of Dhaka. Erfan has published in Elsevier, Springer, and IEEE, exploring NLP, machine learning, and software engineering. He serves as Project Coordinator for Bangladesh’s EDGE Project and has mentored in NASA Space Apps Challenge. An athlete, he won medals in national athletic competitions. 

Publication Profile

Google Scholar

Education 🎓📚

Md Erfan holds a Master of Science in Software Engineering (MSSE) 🖥️ from the Institute of Information Technology, University of Dhaka (2016), with an impressive CGPA of 3.81/4.0 (WES Equivalent: 3.97/4.00). His thesis, supervised by Dr. Md Shariful Islam, focused on an Efficient Runtime Code Offloading Mechanism for Mobile Cloud Computing ☁️💻. He also earned a Bachelor of Science in Software Engineering (BSSE) 🏆 from the same institute in 2014, achieving a CGPA of 3.80/4.0 (WES Equivalent: 3.88/4.00). His undergraduate thesis, guided by Dr. Kazi Muhaimin-us-Sakib, explored approximating social ties based on call logs 📞📊.

Research Experience 🔬📊

In Summer 2024, Md Erfan worked as a Research Student in the UIUC+/ASSIP Summer Research Program 🎓. Collaborating with Dr. Wing Lam (George Mason University) 🏛️ and Dr. August Shi (University of Texas at Austin) 🤖, he focused on automating the end-to-end reproduction of flaky test methods 🛠️. His work involved leveraging issue data, compiling code, running tests, analyzing results, and logging dependencies. Additionally, he created Dockerized environments 🐳 to ensure reproducibility, enhancing software testing efficiency and reliability. His contributions aimed at improving software quality assurance and automation in test debugging 🔍✅.

Professional Experience 💼📚

Md Erfan is an Assistant Professor (2020–Present) at the Department of Computer Science and Engineering, University of Barishal 🏛️, where he teaches Software Engineering, Software Quality Assurance, Data Structures, Algorithms, and Mathematical Analysis 📖💻. Since January 2024, he has also served as a Project Coordinator for the EDGE Project 🌐, managing a 5 crore BDT ($384,615 USD) fund 💰 to enhance digital governance and the economy in Bangladesh. Previously, he worked as a Lecturer (2016–2020) 🎓, a Trainer (2015–2016) 🖥️, and a Software Engineer Intern (2014) 🔍, focusing on testing tools and Microsoft SharePoint development.

Awards and Achievements 🏆🎖️

Md Erfan has been a Regional Mentor (2021–2023) 🌍🚀 for the NASA Space Apps Challenge, guiding innovative projects. He received the Pre-graduation Merit Award (2015) 🎓 from the University of Dhaka for outstanding academic performance. Beyond academics, he has excelled in athletics, securing 3rd place 🥉 in the 5000m and 10000m races 🏃‍♂️ at the Bangladesh Inter-University Athletic Competition (2015) and 2nd place 🥈 in multiple track events (2014–2015). Since 2016, he has been the Coach and Manager ⚽🏅 of the University of Barishal Football and Athletics teams, fostering sports excellence.

 

Research Interests 🔍💻

Md Erfan’s research primarily focuses on Software Engineering, specializing in flaky test detection and mitigation as well as compilation error resolution to enhance software reliability and development efficiency. Additionally, he explores the applications of Artificial Intelligence (AI), leveraging Machine Learning (ML) 🤖, Natural Language Processing (NLP) 🗣️, and Computer Vision 👀 to tackle real-world challenges. His work aims to improve automation, decision-making, and problem-solving across various domains, ensuring smarter and more efficient technological advancements. Through his research, Erfan contributes to optimizing software development and AI-driven innovations for practical applications. 🚀

Research Focus Areas 🧑‍💻📡

Md Erfan’s research spans multiple domains in Software Engineering and Artificial Intelligence. His work focuses on Mobile Cloud Computing ☁️📱, including task allocation and code offloading for performance optimization. He explores Machine Learning 🤖 applications, such as flaky test detection, compilation error resolution, and autism spectrum disorder detection 🧠. His contributions in Natural Language Processing (NLP) 🗣️ involve cyberbullying classification and user similarity computation. Additionally, he applies Computer Vision 👁️ techniques for mosquito species identification and assistive robotics. His interdisciplinary approach integrates automation, decision-making, and problem-solving in real-world applications.

Publication Top Notes

  • Mobility aware task allocation for mobile cloud computing
    Cited by: 8
    Year: 2016 📱☁️
  • Task allocation for mobile cloud computing: State-of-the-art and open challenges
    Cited by: 4
    Year: 2016 📊
  • Identification of Vector and Non-vector Mosquito Species Using Deep Convolutional Neural Networks with Ensemble Model
    Cited by: 2
    Year: 2022 🦟🤖
  • Recurrent neural network based multiclass cyber bullying classification
    Cited by: 1
    Year: 2024 💻🗣️
  • User Similarity Computation Strategy for Collaborative Filtering Using Word Sense Disambiguation Technique
    Cited by: 1
    Year: 2023 🔍📚
  • Approximating Social Ties Based on Call Logs: Whom Should We Prioritize?
    Cited by: 1
    Year: 2015 📱📞
  • An exploration of machine learning approaches for early Autism Spectrum Disorder detection
    Year: 2025 🧠🤖
  • Experimental Study of Four Selective Code Smells Declining in Real Life Projects
    Year: 2024 🧑‍💻🔧
  • Autism Spectrum Disorder Detecting Mechanism on Social Communication Skills Using Machine Learning Approaches
    Year: 2023 🧠💡
  • Dynamic Method Level Code Offloading for Performance Improvement and Energy Saving
    Year: 2017 ⚡💻
  • A comparative study of early autism spectrum disorder detection using deep learning based models
    Year: 2017 🧠🔍
  • An Optimal Task Scheduling Mechanism for Mobile Cloud Computing
    Year: 2016 ☁️📊
  • WVGM: Water View Google Map, Introducing Water Paths on Rivers to Reach One’s Destination using Various Types of Vehicles
    Year: 2016 🌍🚗
  • A comprehensive survey of code offloading mechanisms for mobile cloud computing
    Year: 2016 ☁️🔄
  • MICROCONTROLLER BASED ROBOTICS SUPPORT FOR BLIND PEOPLE
    Year: 2016 🤖👨‍🦯

Conclusion 🌟

Mr. Md Erfan is a highly suitable candidate for the Research for Best Researcher Award due to his strong academic background, impactful research in software engineering and AI, extensive publications, leadership in digital governance projects, and active contributions to global research collaborations. His work demonstrates innovation, technical expertise, and a commitment to advancing knowledge in his field.

 

 

Deepali Bhamare | Deep Learning | Best Researcher Award

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Ms. Deepali Bhamare | Deep Learning | Best Researcher Award

Ms. Deepali Bhamare, S.S.V.P.S.B.D’s COE Dhule, India

Deepali Bhamare is an accomplished educator and engineer with over two decades of experience in electronics and telecommunication. She holds a B.E. in Electronics and Telecommunication from NMU Jalgaon (2002), an M.E. in Digital Communication from R.G.P.V. Bhopal (2012), and is pursuing a PhD in Electronics Engineering. Deepali has worked in industry as a QC and Testing Engineer before transitioning into academia, where she currently serves as Assistant Professor at S.S.V.P.S. College of Engineering Dhule. She has actively contributed to various institutional committees and has attended numerous FDPs and workshops related to AI, machine learning, and research methodologies. 🎓📊📡

 

Publication Profile

Scopus

Educational Qualification

Ms. Deepali Bhamare has a robust educational background in Electronics and Telecommunications. She completed her H.S.C. in Science with 74.08%, followed by a B.E. in Electronics and Telecommunication Engineering from N.M.U. Jalgaon (64%) in 2002. She also holds a master’s degree in Digital Communication from R.G.P.V. Bhopal (75.03%), and is currently pursuing her PhD in Electronics Engineering from N.M.U., which showcases her commitment to advancing her expertise in the field.

Professional Experience

Her extensive work experience spans across both industry and academia. She worked as a Quality Control Engineer at renowned firms like Satronix India Pvt Ltd and Penguin Audio Products Ltd. Since 2008, she has transitioned into academia, holding roles such as Lecturer and Assistant Professor at S.S.V.P.S. College of Engineering, Dhule. Her academic career, coupled with her technical experience, demonstrates her comprehensive understanding of engineering principles and practical applications.

Training and Development

Ms. Bhamare has actively participated in various Faculty Development Programs (FDPs), Short-Term Training Programs (STTPs), and workshops. Notable topics include “Next Generation Artificial Intelligence,” “Python Programming with Django,” “Machine Learning and Deep Learning,” “Neural Networks and Fuzzy Logic,” and “Artificial Intelligence in Healthcare.” These programs indicate her continuous efforts to stay updated with emerging technologies, particularly in AI, machine learning, and data science.

Academic Involvement

In addition to teaching, she has held several key positions in her college, such as Member of the Anti-Ragging Committee, BC Cell, and Extra Curricular Cell, as well as Lab In-Charge. These roles highlight her dedication to both student welfare and the efficient management of college resources.

Conclusion

Ms. Deepali Bhamare’s well-rounded qualifications, research pursuits in electronics, and ongoing professional development through numerous FDPs and workshops position her as a strong candidate for the Best Researcher Award. Her blend of academic knowledge, research focus, and involvement in emerging technologies such as AI and machine learning, make her a notable contributor to the field of electronics engineering.

 

Publication Top Notes  

A Review on Person Identification Using Periocular Biometrics

Person Identification System Using Periocular Biometrics Based on Hybrid Optimal Dense Capsule Network

John Mutinda | Deep learning | Best Researcher Award

Published on by Global Academic Awards

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. 🌍📊💻

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+). 🎓📚🌍

 

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. 📊🌍📈

 

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. 📚👨‍🏫✨

 

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. 📚🗳️👨‍🏫

 

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. 🎓🏆📜

 

Publication Top Notes

  • Covid-19 impact analysis: assessing African sectors-commodity, service, manufacturing, and education using mixed model approach – Cited by 1, 2023 🦠📊
  • African Institute for Mathematical Sciences (AIMS), Rwanda – Cited by 1, 2023 🇷🇼
  • Stock price prediction using combined GARCH-AI models – Cited by 0, 2024 📈🤖
  • Enhancing Obesity Detection Through SMOTE-based Classification Models: A comparative Study – Cited by 0, 2024 🏋️‍♂️🔍
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Dharmapuri Siri | Deep Learning Award | Best Researcher Award

Published on by Global Academic Awards

Dr. Dharmapuri Siri | Deep Learning Award | Best Researcher Award

Dr. Dharmapuri Siri, Gokaraju Rangaraju Institute of Engineering and Technology, India

Based on Dr. Dharmapuri Siri’s resume, here is a conclusion on his suitability for the Research for Best Researcher Award:

Publication profile

Scopus

Career Experience

Dr. Dharmapuri Siri has extensive teaching experience spanning over 11 years across various institutions, including TRR Engineering College, TRR College of Engineering, and Malla Reddy Engineering College for Women. His role as an Assistant Professor in Computer Science and Engineering highlights a solid foundation in academic and practical knowledge.

Educational Background

Dr. Siri’s educational qualifications are robust, with a Ph.D. in Computer Science and Engineering from JJT University, an M.Tech from JNTU Hyderabad, and a B.Tech from JNTU Hyderabad. His academic background demonstrates a strong commitment to his field and a continual pursuit of advanced knowledge.

Research 

Dr. Siri has made significant contributions to research, particularly in the areas of software quality, machine learning, and image analysis. His journal publications and conference presentations reflect a broad range of research interests, from bug prediction models to sentiment analysis and cancer diagnosis. Notable papers include his work in IEEE Access and various webE3S conferences.

Workshops and Training

He has actively participated in multiple workshops and refresher courses, focusing on cloud computing, innovative teaching methods, and problem-solving techniques. This engagement in continuous professional development underscores his dedication to staying current in his field.

Patent and Projects

Dr. Siri holds a patent for a “Vehicle with Smart Biometric Device,” showcasing his ability to apply theoretical knowledge to practical solutions. His Ph.D. thesis on “Bug Prediction Model For Software Quality Using Machine Learning Techniques” further emphasizes his research focus and expertise.

Conclusion

Dr. Dharmapuri Siri is a strong candidate for the Research for Best Researcher Award due to his comprehensive academic background, extensive teaching experience, substantial research contributions, and practical innovations. His work in improving software quality through machine learning and his active involvement in professional development make him a suitable candidate for this accolade.

 

Publications Top Notes

Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis

Automated System for Bird Species Identification Using CNN