Md. Emran Biswas | Data science | Best Researcher Award

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Mr. Md. Emran Biswas | Data science | Best Researcher Award

Research Assistant, Hajee Mohammad Danesh Science and Technology University, Bangladesh

🌟 Md. Emran Biswas, hailing from Dinajpur, Bangladesh, is a passionate researcher and technologist specializing in machine learning, optimization algorithms, and their societal applications. He has actively contributed to predictive analysis, bioinformatics-based drug discovery, and developing AI solutions for global good. As a skilled programmer and researcher, Emran’s work has earned recognition through multiple publications, accolades, and groundbreaking projects in his field.

Publication Profile

Scopus

Education

🎓 Md. Emran Biswas completed his B.Sc. in Electronics and Communication Engineering at Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur, Bangladesh, from March 2019 to November 2024, with an impressive CGPA of 3.412/4.00. His academic journey is marked by a focus on deep learning, predictive modeling, and optimization algorithms.

Experience

💼 Emran served as a Research Assistant at Petarhub and DIOT Lab, HSTU, contributing to machine learning, predictive modeling, and optimization projects. His notable achievements include developing the ApexBoost Regression model, managing large datasets, and publishing impactful research in reputed journals like IEEE and Electronics.

Research Interests

🔍 Emran’s research focuses on machine learning, optimization algorithms, and their transformative applications in areas like bioinformatics-based drug discovery, predictive analysis, and societal challenges. His work aligns with the vision of ‘AI for Good,’ driving impactful innovation.

Awards

🏆 Emran has earned recognition for his innovative projects, including First Runner-Up at the Project Exhibition 2022 for his “Face Detection-Based Attendance System” and Second Runner-Up in 2023 for his “AI-Based Health Checking System.” These awards reflect his technical expertise and creative problem-solving skills.

Publications

Machine Learning Approach to Estimate Requirements for Target Productivity of Garments Employees. IEEE ICEEICT 2024 (Cited by: 5)

An Effective Data-Driven Approach to Predict Bike Rental Demand. Google Scholar (Cited by: 12)

Spatio-Temporal Feature Engineering and Selection-Based Flight Arrival Delay Prediction Using Deep Feedforward Regression Network. Electronics, 13(24), p.4910 (Cited by: 9)

 

Sara Tehsin | Deep learning | Best Researcher Award

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Ms. Sara Tehsin | Deep learning | Best Researcher Award

PhD Student, National University of Sciences and Technology, Islamabad, Pakistan

Sara Tehsin is a motivated and results-driven professional with over ten years of experience in Image Processing and Machine Learning. As an Engineering Lecturer at HITEC University in Taxila, Pakistan, she excels in delivering high-quality educational experiences and has a proven track record of producing outstanding results through her strong work ethic, adaptability, and effective communication skills. She is passionate about academic development and seeks opportunities to contribute her expertise while furthering her professional growth. 📚💻

Publication Profile

Google Scholar

Education

Sara Tehsin is currently pursuing a PhD in Computer Engineering at the National University of Sciences and Technology (NUST), Islamabad, where she has achieved a remarkable GPA of 3.83/4.00. Her research focuses on Digital Forensics, Deep Learning, and Digital Image Processing. She holds a Master’s degree in Computer Engineering from NUST, where she graduated with a GPA of 3.7/4.0, and a Bachelor’s degree from The Islamia University of Bahawalpur, with a GPA of 3.36/4.00. 🎓🌟

Experience

Sara has extensive teaching experience, currently serving as an Engineering Lecturer at HITEC University since September 2019, where she develops engaging curriculum and delivers lectures aligned with international standards. Previously, she was a Computer Science Lecturer at Sharif College of Engineering and Technology, and she also served as a Teaching Assistant at NUST and a Lab Engineer at Foundation University. Her roles have encompassed curriculum development, practical instruction, and student support in various computer science subjects. 👩‍🏫🔧

Research Interests

Sara’s research interests encompass Digital Forensics, Deep Learning, Digital Image Processing, and Machine Learning. She focuses on developing innovative solutions for image recognition and forgery detection, contributing significantly to the fields of computer vision and machine learning. Her work aims to enhance the accuracy and efficiency of image processing systems. 🧠🔍

Publications

Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition
S. Tehsin, S. Rehman, M.O.B. Saeed, F. Riaz, A. Hassan, M. Abbas, R. Young, …
IEEE Access, 5, 24495-24502 (2017)
Cited by: 21

Improved maximum average correlation height filter with adaptive log base selection for object recognition
S. Tehsin, S. Rehman, A.B. Awan, Q. Chaudry, M. Abbas, R. Young, A. Asif
Optical Pattern Recognition XXVII, 9845, 29-41 (2016)
Cited by: 18

Fully invariant wavelet enhanced minimum average correlation energy filter for object recognition in cluttered and occluded environments
S. Tehsin, S. Rehman, F. Riaz, O. Saeed, A. Hassan, M. Khan, M.S. Alam
Pattern Recognition and Tracking XXVIII, 10203, 28-39 (2017)
Cited by: 12

Comparative analysis of zero aliasing logarithmic mapped optimal trade-off correlation filter
S. Tehsin, S. Rehman, A. Bilal, Q. Chaudry, O. Saeed, M. Abbas, R. Young
Pattern Recognition and Tracking XXVIII, 10203, 22-37 (2017)
Cited by: N/A

PETROS PATIAS | Data science | Best Researcher Award

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Prof. PETROS PATIAS | Data science | Best Researcher Award

CEO, KIKLO – GEOSPATIAL INFORMATION TECHNOLOGIES P.C., Greece

Prof. Petros Patias is a prominent expert in photogrammetry and remote sensing, serving as Professor and Director at the Laboratory of Photogrammetry & Remote Sensing at Aristotle University of Thessaloniki (AUTH), Greece. A leader in his field, he has held esteemed roles, including Vice Rector at the University of Western Macedonia and former President of the Hellenic Society for Photogrammetry & Remote Sensing. Prof. Patias has made groundbreaking contributions internationally through the ISPRS and CIPA, cementing his legacy as an Honorary President and Fellow of these global scientific communities. His impact continues through extensive research, teaching, and scientific guidance worldwide.

Publication Profile

ORCID

Education 🎓📚

Prof. Patias holds a MEng from Aristotle University (1981), an MSc (1985), and a PhD (1987) in Geodetic Science and Surveying from The Ohio State University, USA. His extensive education laid the foundation for his international recognition and contributions in geospatial sciences.

Experience 🏛️🌍

Prof. Patias has held numerous prestigious academic and leadership roles, such as ex-Chairman of the School of Rural and Surveying Engineering at AUTH, board member of the Department of Urban Planning, and Vice Rector at the University of Western Macedonia. He served as President of the Hellenic Society for Photogrammetry & Remote Sensing and led ISPRS Working Groups and Commissions. His experience extends globally as a Visiting Professor at renowned institutions like TU Delft, ETH Zurich, and Universidad del País Vasco.

Research Focus 🔍🌐

Prof. Patias’s research focuses on photogrammetry, remote sensing, and geospatial sciences, with applications in architectural photogrammetry and urban planning. He collaborates internationally, advising institutions such as ETH Zurich, University of Maine, Politecnico di Milano, and IIT Roorkee, and leads impactful projects through European and National organizations.

Awards and Honors 🏆🌟

Prof. Patias has received numerous honors, including an ISPRS Fellowship (2016) and lifetime honorary presidencies with both CIPA and ISPRS. His leadership contributions have earned him esteemed positions, reflecting his commitment to advancing photogrammetry and remote sensing worldwide.

Publications Top Notes 📝📅

“Aerial Photogrammetry for Urban Planning” (2020) published in Remote Sensing; cited by 48 articles.

“Geospatial Data Applications in Urban Development” (2018) published in Geodetic Science Journal; cited by 32 articles.

“Remote Sensing in Archaeological Mapping” (2017) published in International Journal of Archaeology; cited by 45 articles.

“Photogrammetric Techniques for Heritage Conservation” (2016) published in Heritage Science Review; cited by 60 articles.

 

Robin Augustine | Artificial Intelligence | Excellence in Research

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Assoc. Prof. Dr. Robin Augustine | Artificial Intelligence | Excellence in Research

Associate Professor, Uppsala University, Sweden

🎓 Associate Professor Robin Augustine is a renowned expert in Medical Engineering and Microwave Technology, leading research at Uppsala University in Sweden. He heads the Microwaves in Medical Engineering Group at the Angstrom Laboratory, Department of Electrical Engineering, and serves as an Associate Editor for IET journals. His interdisciplinary work spans medical sensor development, bioelectromagnetic interactions, and innovative in-body communication technologies. Robin has collaborated globally as a visiting professor and researcher, focusing on advancements in medical engineering through impactful research projects.

Publication Profile

Scopus

Education

📚 Dr. Robin Augustine earned his Ph.D. in Electronics and Optronics Systems from Université de Paris Est Marne La Vallée, specializing in human tissue electromagnetic modeling and its implications for medical sensor design. He holds an MSc in Electronics Science with a focus on Robotics from Cochin University of Science and Technology, and a BSc in Electronics Science from Mahatma Gandhi University. His expertise is further strengthened by advanced training in Diagnostic and Therapeutic Applications of Electromagnetics from Politecnico di Torino, Italy.

Experience

💼 Robin’s career includes extensive experience as a senior lecturer and associate professor at Uppsala University, where he has been leading research in microwave applications for medical technology since 2011. He has held visiting professorships and research roles at institutions such as the Beijing Institute of Nanoenergy and Nanosystems and University Medical Center Maastricht, contributing to medical sensor innovation and orthopedic measurement systems. Robin has also worked internationally, including postdoctoral research in France, with expertise in antenna design, bioelectromagnetics, and microwave characterization.

Research Focus

🔬 Robin’s research focuses on medical engineering, bioelectromagnetics, and intra-body communication, including developing microwave-based sensors for diagnosing conditions like osteoporosis, skin cancer, and muscular atrophy. As a leader in the B-CRATOS and COMFORT projects, he explores body-centric technologies and in-body wireless communication to enhance medical diagnostics. His pioneering work addresses the integration of electromagnetic technology with healthcare, making strides in non-invasive monitoring systems.

Awards and Honours

🏆 Dr. Augustine’s impactful research has attracted numerous grants and awards, including significant EU funding for projects like PERSIMMON and DIAMPS. He has secured research funding from bodies such as the Swedish Research Council, Vinnova, and the Foundation for Strategic Research, supporting his innovative work on body communication systems and medical diagnostics. His research has earned recognition through the Swedish Excellence Grant for Young Researchers and multiple grants for advancing medical engineering solutions.

Publication Top Notes

Biphasic lithium iron oxide nanocomposites for enhancement in electromagnetic interference shielding properties

Rotation insensitive implantable wireless power transfer system for medical devices using metamaterial-polarization converter

Improving burn diagnosis in medical image retrieval from grafting burn samples using B-coefficients and the CLAHE algorithm

 

Syed Ijaz Ul Haq | Machine Learning | Best Researcher Award

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Dr. Syed Ijaz Ul Haq | Machine Learning | Best Researcher Award 

Research associate, Shandong University of Technology, China

Syed Ijaz Ul Haq is a dedicated Research Assistant in Agronomy at Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan, since September 2021. Currently pursuing a Ph.D. in Agriculture Engineering and Food Science at Shandong University of Technology, China, he is passionate about advancing research in remote sensing, artificial intelligence, and deep learning. With a commitment to excellence and professional development, Syed aims to explore innovative solutions in agriculture. 🌱📚

Publication Profile

ORCID

Strengths for the Award

  1. Specialized Research Interests: Syed has a clear focus on Remote Sensing, AI, and Deep Learning, which are critical areas in modern agricultural research. His work on machine learning techniques for pest detection and weed analysis demonstrates innovative applications of technology in agriculture.
  2. Academic Background: Currently pursuing a Ph.D. in Agricultural Engineering and Food Science at Shandong University of Technology, Syed is in an excellent position to contribute cutting-edge research to the field.
  3. Professional Experience: His role as a Research Assistant at Pir Mehr Ali Shah Arid Agriculture University allows him to gain practical experience and engage with ongoing research projects, enhancing his research skills.
  4. Publication Record: With multiple publications in reputable journals, including articles on trace elements’ effects on crop growth and the use of AI for weed detection, he demonstrates the ability to conduct and disseminate impactful research.
  5. Peer Review Engagement: His involvement as a reviewer for the American Society of Plant Biologists reflects recognition by peers and contributes to his professional development.

Areas for Improvement

  1. Broader Research Impact: While Syed has several publications, expanding his research to include interdisciplinary collaborations or more diverse agricultural challenges could enhance his visibility and impact in the field.
  2. Networking and Collaboration: Actively seeking collaborations with other researchers or institutions could provide Syed with additional insights and resources, fostering a more extensive research network.
  3. Professional Development: Attending more international conferences and workshops could enhance his skills and provide opportunities for exposure to global trends in agricultural research and technology.
  4. Outreach and Application of Research: Engaging with local communities or agricultural practitioners to apply his findings could bridge the gap between research and real-world application, leading to significant societal impacts.

Education

Syed is currently enrolled in a Ph.D. program in Agriculture Engineering and Food Science at Shandong University of Technology, Zibo, Shandong, China, where he has been studying since July 2022. His academic focus revolves around integrating advanced technologies to enhance agricultural practices. 🎓🌾

Experience

Since September 2021, Syed has served as a Research Assistant in Agronomy at Pir Mehr Ali Shah Arid Agriculture University, where he contributes to various agricultural research projects, gaining valuable experience and insights into the field. His role involves collaborating with researchers to explore sustainable agricultural practices and technologies. 🧑‍🔬🌍

Research Focus

Syed’s research primarily focuses on the application of remote sensing, AI, and deep learning techniques in agriculture. His work aims to improve crop yield, pest detection, and weed management, making significant contributions to sustainable farming practices. 🤖🌿

Awards and Honours

Syed has been recognized for his contributions to agricultural research, including serving as a Reviewer for the American Society of Plant Biologists since 2021. His academic excellence is reflected in his ongoing Ph.D. studies, showcasing his dedication to advancing the field. 🏆📜

Publications

Influence of Trace Elements (Co, Ni, Se) on Growth, Nodulation and Yield of Lentil
Published in Polish Journal of Environmental Studies, 2024
Cited by: Crossref

Identification of Pest Attack on Corn Crops Using Machine Learning Techniques
Published in 2023
Cited by: Crossref

Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
Published in Applied Sciences, 2023
Cited by: Crossref

Conclusion

Syed Ijaz Ul Haq shows strong potential as a candidate for the Research for Best Researcher Award due to his focused research interests, current academic pursuits, publication record, and peer engagement. To further enhance his candidacy, he should consider broadening his research scope, expanding his professional network, and increasing the real-world applicability of his research findings. If he continues on this trajectory, he has the potential to make substantial contributions to agricultural research, making him a deserving recipient of this award.

 

Tesfay Gidey | Artificial Intelligence | Best Researcher Award

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Dr. Tesfay Gidey | Artificial Intelligence | Best Researcher Award

Lecturer, Addis Ababa Science and Technology University, Ethiopia

Tesfay Gidey Hailu is a highly skilled Information and Communication Engineer and data scientist with a passion for leveraging data to drive innovation and business insights. With expertise in computer science, software engineering, machine learning, and data analytics, he excels in problem-solving, leadership, and technology project management. Tesfay’s work focuses on indoor localization, signal processing, and health data applications, making him a forward-thinking leader in his field. His dedication to continuous learning and delivering actionable results underscores his impressive career in academia and industry. 💼🔧📊

Publication Profile

ORCID

Strengths for the Award:

  1. Diverse Expertise: Tesfay’s expertise spans across critical areas such as signal processing, indoor localization, machine learning, data fusion, and health informatics, aligning well with cutting-edge research areas.
  2. Impressive Academic Qualifications: Holding a Ph.D. in Information and Communication Engineering, along with two MSc degrees, he possesses deep knowledge in interdisciplinary fields.
  3. Research Contributions: He has authored numerous peer-reviewed publications in high-impact journals such as Sensors, Intelligent Information Management, and Journal of Biostatistics. His work in Wi-Fi indoor positioning, predictive modeling, and health informatics shows a broad application of research across industries.
  4. Leadership in Academia: His roles as Associate Dean and Head of Department demonstrate his leadership in driving research, improving curriculum quality, and promoting technology transfer.
  5. Innovative Research Focus: His Ph.D. dissertation on transfer learning for fingerprint-based indoor positioning and various data fusion methods reflect his innovative contributions to solving real-world problems with advanced technologies.

Areas for Improvement:

  1. Broader Industry Impact: While his research is highly academic, incorporating more industry-driven collaborations or commercial applications could strengthen the practical impact of his work.
  2. Public Engagement: Increasing public outreach and collaboration with non-academic sectors or public talks could elevate his visibility and expand the impact of his research findings.
  3. Global Collaboration: Expanding his research collaborations beyond local and regional levels, particularly with international industries, could further showcase the global relevance of his work.

Education 🎓

Tesfay holds a Ph.D. in Information and Communication Engineering from the University of Electronic Science and Technology of China (2023), where his research centered on signal and information processing applied to indoor positioning using machine learning algorithms. He also earned an MSc in Software Engineering from HILCOE School of Computer Science and Information Technology (2018) and an MSc in Health Informatics and Biostatistics from Mekelle University (2013). Additionally, he completed his BSc in Statistics with a minor in Computer Science at Addis Ababa University (2006). 📚💻📈

Experience 💼

Tesfay has held several leadership positions, including Associate Dean at Addis Ababa Science and Technology University (AASTU), where he led research, technology transfer, student recruitment, and faculty training initiatives. He was also the Head of Department and Coordinator at Jimma University, contributing to curriculum enhancement and student retention programs. His experience spans research in manufacturing industries, project management, and academic administration. 🏫📊👨‍🏫

Research Focus 🔬

Tesfay’s research focuses on signal processing, indoor localization, machine learning, data mining, and information fusion. He specializes in developing advanced models for indoor positioning systems, predictive modeling, and statistical quality control, aiming to solve complex problems in health informatics, manufacturing industries, and public health. His work integrates cutting-edge technologies to advance both theoretical and applied fields. 📡📉🤖

Awards and Honors 🏆

Tesfay has been recognized for his contributions to the fields of information and communication engineering and data science. He has received multiple awards and honors for his research and leadership roles in academia, particularly in driving innovative projects that bridge the gap between technology and industry. 🌍🎖️

Publications Highlights 📚

Tesfay has published extensively in top-tier journals, with a focus on indoor positioning systems, data fusion, and health informatics. His research includes the development of novel machine learning models and statistical analysis tools. His works have been widely cited, showcasing his impact in the academic community. 📊✍️

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection (2023). Intelligent Information Management, 15, 391-425. Cited by 2 articles. Link

Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting (2022). Sensors, 22, 8720. Cited by 15 articles. Link

Heterogeneous Transfer Learning for Wi-Fi Indoor Positioning Based Hybrid Feature Selection (2022). Sensors, 22, 5840. Cited by 10 articles. Link

OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning (2022). Sensors, 22, 9044. Cited by 5 articles. Link

A Multilevel Modeling Analysis of the Determinants and Cross-Regional Variations of HIV Testing in Ethiopia (2016). J Biom Biostat, 7, 277. Cited by 8 articles. Link

Conclusion:

Tesfay Gidey Hailu’s robust academic background, extensive research portfolio, and leadership roles make him a strong candidate for the Best Research Award. His work in signal processing, machine learning, and data-driven innovation in health informatics and communication systems demonstrates a clear commitment to advancing technology and solving societal problems. While his impact could be enhanced by deeper industry collaborations and global outreach, his current achievements already reflect substantial contributions to the field, making him deserving of recognition.

 

Christopher Ekeocha | Machine learning | Best Researcher Award

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Mr. Christopher Ekeocha | Machine learning | Best Researcher Award

Graduate Research Assistant, Africa Centre of Excellence in Future Energies and Electrochemical Systems (ACE-FUELS), Nigeria

Christopher Ikechukwu Ekeocha is a dedicated Assistant Research Fellow at the National Mathematical Centre in Abuja, Nigeria, with a keen interest in corrosion mitigation and environmental pollution. His extensive research focuses on developing innovative eco-friendly materials and computational simulation techniques to address corrosion and pollution challenges. He has represented Nigeria internationally at the International Chemistry Olympiad, guiding students to success in countries like Vietnam, Azerbaijan, Georgia, France, and China. 🌍🔬

Publication Profile

ORCID

Strengths for the Award:

  1. Academic Excellence: Christopher Ikechukwu Ekeocha has consistently performed at a high academic level throughout his education. His Ph.D. in Corrosion Technology (CGPA: 4.60/5.0) and Master’s in Environmental Chemistry (CGPA: 3.92/5.0) demonstrate his dedication to research and academic rigor.
  2. Innovative Research: His focus on developing eco-friendly, biomass-based anti-corrosion materials and using machine learning models for corrosion prediction is cutting-edge. His work combines experimental and computational techniques, pushing the boundaries of corrosion technology.
  3. Strong Publication Record: Ekeocha has published extensively in reputable, high-impact journals, with topics ranging from corrosion inhibitors to environmental chemistry. This demonstrates the relevance and quality of his work. Key publications include machine learning models and computational simulations for anti-corrosion research, which have been well-received in the scientific community.
  4. Interdisciplinary Collaboration: He has collaborated on multidisciplinary projects promoting circular economy and eco-friendly techniques for corrosion mitigation. His ability to work across various fields shows adaptability and leadership in research.
  5. Community Contribution: In addition to his academic work, Ekeocha has made significant contributions to the Chemistry Olympiad, leading Nigerian teams and authoring textbooks. His role in this capacity speaks to his leadership and commitment to education and knowledge dissemination.

Areas for Improvement:

  1. Research Diversification: While Ekeocha has made strong contributions in corrosion technology, expanding his research to other areas of environmental chemistry or further enhancing the practical applications of his work could strengthen his overall profile. Engaging in more diverse projects could showcase his versatility.
  2. Industry Engagement: Although his research is well-grounded in academia, there could be a stronger connection with industry to ensure his innovations, especially in corrosion mitigation, are applied in real-world settings. Collaborations with companies focusing on corrosion prevention or environmental impact assessments could enhance the practical impact of his research.
  3. International Recognition: While his publications are gaining recognition, presenting his research at more international conferences or collaborating with foreign institutions could boost his global visibility and increase the influence of his work.

Education

Christopher Ekeocha is affiliated with the Africa Centre of Excellence in Future Energies and Electrochemical Systems (ACE-FUELS) at the Federal University of Technology, Owerri (FUTO). His research emphasizes the permeation of ions across semi-permeable membranes, focusing on membrane thickness, permeation time, and electrolyte concentration. 🎓⚛️

Experience

With over a decade of experience, Christopher Ekeocha has served as an Assistant Research Fellow at the National Mathematical Centre, Abuja, since 2011. He leads Nigeria’s participation in the International Chemistry Olympiad, having represented the country in multiple international events. His expertise lies in corrosion studies, computational modeling, and eco-friendly corrosion inhibitors. 🌱🔧

Research Focus

Christopher’s research centers on the development of mathematical and predictive models for novel corrosion inhibitors. He specializes in using computational simulations and eco-friendly materials to mitigate metallic corrosion and conducting ecological risk assessments of environmental pollution. His work also covers adsorption kinetics, water and solvent treatment using nanoparticles, and pollutant removal with agricultural waste. 📊🔍

Awards and Honours

Ekeocha has gained recognition for his contributions to corrosion research and environmental protection. His participation in the International Chemistry Olympiad as a Nigerian team leader is notable, alongside his extensive academic publications and active role in global scientific conferences. 🏆🌟

Publication Top Notes

Christopher Ikechukwu Ekeocha has authored several influential articles in prestigious journals, including Materials Today Communications, Structural Chemistry, and African Scientific Reports. His works primarily focus on corrosion inhibition, eco-friendly materials, and environmental pollution. 📚✨

Ekeocha, C. I., et al. (2024). Data-Driven Machine Learning Models and Computational Simulation Techniques for Prediction of Anti-Corrosion Properties of Novel Benzimidazole Derivatives. Materials Today Communications https://doi.org/10.1016/j.mtcomm.2024.110156

Ekeocha, C. I., et al. (2024). Theoretical Study of Novel Antipyrine Derivatives as Promising Corrosion Inhibitors for Mild Steel in an Acidic Environment. Structural Chemistry https://doi.org/10.1007/s11224-024-02368-4

Ekeocha, C. I., et al. (2023). Review of Forms of Corrosion and Mitigation Techniques: A Visual Guide. African Scientific Reports, 2(3): 117. https://doi.org/10.46481/asr.2023.2.3.117

Conclusion:

Christopher Ikechukwu Ekeocha is an excellent candidate for the Research for Best Research Award. His innovative contributions in the field of corrosion technology, combined with his interdisciplinary approach and strong academic background, position him well for recognition. His research aligns with global trends toward eco-friendly solutions and computational advancements, making him a strong contender. However, increased industry engagement and further research diversification would further elevate his impact in both academic and practical domains.

 

Avirup Roy | Machine Learning |Machine Learning Research Award

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Mr. Avirup Roy | Machine Learning |Machine Learning Research Award

PhD Student, Michigan State University, United States

Dr. Avirup Roy is a dedicated researcher and engineer specializing in networked embedded and wireless systems. Currently pursuing his PhD at Michigan State University, his work focuses on developing self-learning mechanisms for embedded hardware systems with limited computational resources. With a solid foundation in electronics and communication engineering, Avirup has gained extensive experience in both academia and industry, contributing to projects ranging from smart malaria detection to automated power management systems. His technical skills span machine learning, embedded systems, cloud computing, and web development. Beyond his professional life, Avirup is passionate about Indian classical music, photography, and swimming. 🌟📚🎵📷🏊‍♂️

Profile

ORCID

 

Education🎓

Michigan State University, East Lansing, MI, US PhD in Electrical and Computer Engineering (2020-Present). Dissertation: Self-learning mechanisms for Embedded hardware systems with limited computational resources. GPA: 3.75/4Maulana Abul Kalam Azad University of Technology, Kolkata, WB, India Bachelor of Technology (BTech) in Electronics and Communication Engineering (2013-2017)

Experience💼

Graduate Research Assistant, Michigan State University (Sep 2020 – Jul 2023),Developed an android and website application for smart malaria detection involving cloud database integration. Graduate Teaching Assistant, Michigan State University (Aug 2023 – Present), Instructed and graded labs for Embedded Cyber-physical Systems, VLSI Systems, and Digital Control courses. ICER Cloud Computing Fellow, Michigan State University (Sep 2023 – Present), Implemented Azure cloud resources in semi-supervised federated learning for embedded devices. Programmer Analyst, Cognizant Technology Solutions (Dec 2017 – Jul 2020), Developer and support analyst for ASP.NET based applications of MetLife Inc. Intern, Calcutta Electric Supply Corporation (CESC) Limited (Jul 2016 – Aug 2016), Worked on automated power management systems using SCADA communication. Intern, Bharat Sanchar Nigam Limited (BSNL) (Jun 2015 – Aug 2015), Explored general trends in wireless communication. Undergraduate Researcher, Maulana Abul Kalam Azad University of Technology (2015-2016), Presented research at various international conferences and served as the vice-president of SPIE Student Chapter.

Research Interests🔍

Embedded Machine Learning: Focused on developing efficient learning algorithms for resource-constrained devices.
Networked Embedded Systems: Exploring self-learning mechanisms and their applications in real-world scenarios.
Cloud Computing: Leveraging cloud resources for semi-supervised federated learning.
VLSI Systems: In-depth study and teaching of Very-Large-Scale Integration systems.
Cyber-Physical Systems: Research on embedded systems interacting with physical processes.

Awards🏆

National Social Entrepreneurship Programme (2014): Secured 2nd position for the ‘Hand-Made Paper Industry’ project.
SPIE Smart Structures and Non-destructive Evaluation Conference (2016): Presented research in Las Vegas, Nevada.
EAPE Conference (2015): Presented research on emerging areas of photonics and electronics.
Graduate Fellowships: Awarded multiple fellowships during PhD for research and teaching excellence.

Publications

Semi-Supervised Learning Using Sparsely Labelled Sip Events for Online Hydration Tracking Systems
A. Roy, H. Dutta, A. K. Bhuyan, and S. K. Biswas, 2023, International Conference on Machine Learning and Applications (ICMLA).
Cited by: 3 articles.

An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation
Roy, A., Dutta, H., Griffith, H., & Biswas, S., 2022, Sensors.
Cited by: 5 articles.

GAN XU | Computer Science | Best Researcher Award

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Mr. GAN XU | Computer Science | Best Researcher Award

PhD. candidate, capital university of economics and business, China

Xu Gan is a Doctoral student at Capital University of Economics and Business, Beijing, specializing in Rural Finance, Financial Theory and Policy, and Supply Chain Finance. With a solid academic background and practical experience, Xu has contributed significantly to research in financial services and blockchain technology. 🏛️📚

Profile

Scopus

 

Education

Xu Gan completed a Master of Finance from Capital University of Economics and Business in Beijing (2018-2021) and a Bachelor of Biotechnology from Beijing Union University (2006-2010). Xu also has an extensive academic background in these fields, providing a strong foundation for research and analysis. 🎓📈

Experience

Xu has actively participated in several high-profile research projects, including the National Social Science Foundation of China’s research on rural financial services and the China Mobile Communication Federation’s blockchain technology applications in finance. Xu was responsible for designing research sub-topics and writing research reports. 📊🔬

Research Interests

Xu Gan’s research interests include Rural Finance, Financial Theory and Policy, and Supply Chain Finance. Xu is focused on improving rural financial services and exploring innovative financial technologies such as blockchain. 🌾💡

Awards

Xu has been recognized for academic excellence with several honors: Beijing Outstanding Graduates (2020), Outstanding Graduate of Beijing Union University (2020), and several awards for excellent papers at various financial and rural finance forums. 🏅📜

Publications

Yang, GZ., Xu, G., Zhang, Y., et al. Financial density of village banks and income growth of rural residents. Economic Issues, 2021, 504(08): 89-94.

Zhang, F., Xu, G., Zhang, XY, Cheng, X. Knowledge mapping analysis of seven decades of rural finance research in China. Rural Finance Research, 2020(01): 10-20.

Zhang, F., Xu, G., Cheng, X. A review of blockchain applications in the financial sector. Technology for Development, 2019, 15(08): 865-871.

Hafiza Sadia Nawaz | Computer Science | Women Researcher Award

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Dr. Hafiza Sadia Nawaz | Computer Science | Women Researcher Award

Shenzhen University, China

🎓 Dr. H. Sadia Nawaz is a distinguished researcher and academic in Computer Vision, with a Ph.D. from Ocean University of China. Her work focuses on Temporal Activity Detection using Natural Language. With a background in both teaching and research, she has contributed significantly to the field of computer vision and continues to pursue a career as a postdoctoral researcher. Her extensive knowledge and dedication to the subject make her a valuable asset to any research group.

Profile

Scopus

 

Education

📚 Dr. H. Sadia Nawaz holds a Ph.D. in Computer Science from Ocean University of China, specializing in Temporal Moment Localization using Natural Language. She also completed her Master’s in Information Science and Technology at the University of Sargodha and an MS in Computer Science and Technology with a focus on Cyber Security at the University of Lahore. Her academic background provides a strong foundation for her research and teaching roles.

Experience

💼 Dr. Nawaz has a robust teaching background, having served as a Programming Teaching Assistant at Heriot-Watt University and Ocean University of China, and as a Computer Science Lecturer at Superior University of Lahore and the University of Sargodha. Her roles in academia have honed her skills in teaching, mentoring, and research, contributing to her expertise in computer vision and machine learning.

Research Interests

🔍 Dr. Nawaz’s research interests include Activity Localization in Videos via Language, Temporal Moment Detection, and Natural Language Processing. She is particularly focused on moment detection in complex scenarios and the integration of machine learning methods with computer vision techniques to enhance video understanding and activity localization.

Awards

🏅 Dr. H. Sadia Nawaz has been recognized for her research contributions in the field of computer vision, particularly for her work on Temporal Moment Localization using Natural Language. Her research has been published in high-impact journals, reflecting her expertise and commitment to advancing the field.

Publications

Temporal Moment Localization via Natural Language by Utilizing Video Question Answers as a Special Variant and Bypassing NLP for Corpora

IVTEN – Integration of Visual-Textual Entities for Temporal Activity Localization via Language in Video

Revelation of Novel Paradigm in Temporal Moment Localization by Using Natural Language in Video

Multi-level Edges and Context Arranger Architecture Using Referencing Expressions for Temporal Moment Localization via Natural Language

A Frame-Based Feature Model for Violence Detection from Surveillance Cameras Using ConvLSTM Network