Yusuf Shehu | Physics | Excellence in Research

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Dr. Yusuf Shehu | Physics | Excellence in Research

Dr at Aliko Dangote University of Science and Technology, Wudil, Nigeria

Dr. Yusuf Shehu is an accomplished physicist and researcher specializing in semiconductor devices, nanotechnology, and optoelectronics. With over a decade of teaching and research experience, he has demonstrated deep expertise in UV photodetectors, thin film fabrication, and spintronic materials. His research integrates experimental techniques such as laser-assisted chemical bath deposition and theoretical simulations like SCAPS-1D and density functional theory. Dr. Shehu has authored over 20 peer-reviewed publications in Scopus-indexed journals and actively contributes to international conferences and academic societies. His academic journey reflects global engagement, having pursued advanced degrees in Malaysia and Turkey. Beyond academia, he is actively involved in mentoring students, community outreach, and scientific awareness initiatives. As a member of professional organizations including the Nigerian Institute of Physics and RAESON, he continuously expands his network and influence. Dr. Shehu’s combined strengths in research, teaching, and leadership position him as a promising academic and innovator in applied physics.

Professional Profile 

Education🎓

Dr. Yusuf Shehu holds a robust academic background in physics, beginning with a Bachelor of Science degree from Kano University of Science and Technology, Wudil, in 2010, where he conducted research on groundwater radioactivity. He earned a Master of Science in Physics from Fatih University, Istanbul, Turkey, in 2014, completing a thesis on spin-dependent properties in two-dimensional systems. Currently, he is pursuing a Ph.D. in Physics at Universiti Sains Malaysia (USM), expected to complete in 2025. His doctoral research focuses on the fabrication and characterization of antimony-doped TiO₂ nanoparticles for UV photodetectors using laser-assisted chemical bath deposition. In addition to his formal education, he has also earned a diploma in Data Processing and Information Technology, strengthening his proficiency in computational tools. His academic trajectory reflects both breadth and depth, with a strong emphasis on nanomaterials, simulation methods, and device physics, all of which are foundational to his ongoing research and teaching activities.

Professional Experience📝

Dr. Yusuf Shehu has accumulated over a decade of academic experience at Aliko Dangote University of Science and Technology, Wudil, serving in various capacities from Graduate Assistant to his current role as Lecturer I. His teaching portfolio includes undergraduate courses in mechanics, electromagnetism, modern physics, and experimental physics. He has supervised numerous student projects on topics ranging from fluid surface tension to nanodevice construction. Internationally, he has served as a Research Assistant at Universiti Sains Malaysia during his doctoral studies, contributing to funded research projects on photodetectors and nanomaterials. In addition to his academic roles, Dr. Shehu has held administrative positions such as departmental examination officer, level adviser, and committee member for admissions and student awards. He has also contributed to pre-degree and remedial education programs, demonstrating his commitment to academic development across multiple levels. His professional journey reflects a balance of teaching, mentorship, research, and institutional service.

Research Interest🔎

Dr. Yusuf Shehu’s research interests are grounded in the fields of nanomaterials, semiconductor devices, photodetectors, and spintronics. He is particularly focused on the synthesis and characterization of materials for optoelectronic applications, including UV photodetection, molecular electronics, and LEDs. His work explores TiO₂ nanostructures, doped materials, and 2D systems for enhanced electronic and optical performance. He is proficient in SCAPS-1D simulations and density functional theory (DFT), employing these tools to model nanostructures and optimize device performance. His research also includes spin-polarized transport, electronic and magnetic properties of materials, and high-k dielectric heterostructures. Dr. Shehu is interested in bridging theory and practice, using both computational and experimental methods to address real-world technological challenges. He is particularly motivated by the potential of nanotechnology to revolutionize energy harvesting, sensing, and electronic systems. His future goals include expanding into quantum device modeling and sustainable energy applications using novel materials and hybrid nanostructures.

Award and Honor🏆

Dr. Yusuf Shehu has been recognized for both academic excellence and research contributions. He received the Best Paper Presenter Award at the 6th International Conference on Clean Energy and Technology held at University Malaya, Malaysia in 2023, affirming the quality and impact of his scientific work. His academic journey has been supported by prestigious scholarships, including the TETFund Overseas Ph.D. Scholarship awarded by the Nigerian government in 2020 and a Kano State Government scholarship for his Master’s program in Turkey in 2012. Additionally, he has received certificates of appreciation and participation from numerous scientific conferences, workshops, and training sessions. These include the OSH course at USM, international symposia on electric mobility, and several national conferences organized by the Nigerian Institute of Physics and NAMP. His contributions have also earned him honorary recognitions from academic societies and student associations, reflecting his active involvement in academic and community-based initiatives.

Research Skill🔬

Dr. Yusuf Shehu possesses a strong skillset in both experimental and computational research methods, making him versatile in advanced physics and materials science investigations. Experimentally, he is proficient in laser-assisted chemical bath deposition, chemical bath techniques, and ac/dc sputtering, which he uses for fabricating high-performance nanostructures and semiconductor devices. His computational expertise includes SCAPS-1D for device simulation, and density functional theory (DFT), particularly with Quantum ATK software, to model the electronic, magnetic, and optical properties of materials. He also employs the Green’s function technique for analyzing electron transport in nanostructures. His data analysis skills are further supported by his IT background, with competency in Python, LaTeX, and common data processing tools. Dr. Shehu integrates theory and practice in his research workflow, allowing for a more comprehensive understanding of materials behavior. His multidisciplinary approach supports innovation in optoelectronics, spintronics, and nanotechnology applications.

Conclusion💡

Dr. Yusuf Shehu is a deserving candidate for the Best Researcher Award due to his significant and sustained contributions to the fields of nanomaterials, semiconductor devices, and photodetection technologies. His research has advanced the development of antimony-doped TiO₂-based UV photodetectors and contributed to the broader understanding of nanoscale electronic and optical properties. Through impactful publications, active participation in international research projects, and dedication to mentoring students, he has made meaningful contributions to both scientific knowledge and societal advancement. With his strong academic foundation, international exposure, and drive for innovation, Dr. Shehu is well-positioned to emerge as a future leader in applied physics and nano-optoelectronics, driving impactful research and fostering global collaborations in the years to come.

Publications Top Noted✍

  • Title: Direct and Residual Contributions of Symbiotic Nitrogen Fixation by Legumes to the Yield and Nitrogen Uptake of Maize (Zea mays L.) in the Nigerian Savannah
    Authors: UR Pal, Y Shehu
    Year: 2001
    Cited by: 58

  • Title: Yield and chemical composition responses of Lablab purpureus to nitrogen, phosphorus and potassium fertilisers
    Authors: Y Shehu, WS Alhassan, UR Pal, CJC Phillips
    Year: 2001
    Cited by: 41

  • Title: The Effect of Intercropping Lablab purpureus L. with Sorghum on Yield and Chemical Composition of Fodder
    Authors: Y Shehu, WS Alhassan, UR Pal, CJC Phillips
    Year: 1999
    Cited by: 22

  • Title: The productivity of pure and mixed grass-legume pastures in the northern Guinea Savanna zone of Nigeria
    Authors: Y Shehu, JO Akinola
    Year: 1995
    Cited by: 21

  • Title: Gamma Ray and FTIR studies in zinc doped lead borate glasses for radiation shielding application
    Authors: ZI Takai, RS Kaundal, MK Mustafa, S Asman, A Idris, Y Shehu, …
    Year: 2018
    Cited by: 17

  • Title: The Effects of Sowing Date on the Growth and Nutritive Value of Lablab purpureus
    Authors: Y Shehu, WS Alhassan, UR Pal, CJC Phillips
    Year: 2001
    Cited by: 16

  • Title: The effects of green manuring and chemical fertiliser application on maize yield, quality and soil composition
    Authors: Y Shehu, WS Alhassan, GWK Mensah, A Aliyu, CJC Phillips
    Year: 1998
    Cited by: 16

  • Title: The effect of intercropping maize with Stylosanthes hamata at different row spacings on grain and fodder yields and chemical composition
    Authors: Y Shehu, WS Alhassan, CJC Phillips
    Year: 1997
    Cited by: 13

  • Title: Effect of Gross Alpha and Beta in Groundwater intake and Estimation of Groundwater Table in Kano University of Science and Technology, Wudil
    Authors: S Yusuf, G Iliyasu, UA Danbatta, AY Hotoro
    Year: 2015
    Cited by: 12

  • Title: The Effects of Timing of an Interim Harvest on the Yield and Composition of Lablab purpureus
    Authors: Y Shehu, WS Alhassan, UR Pal, CJC Phillips
    Year: 2001
    Cited by: 7

  • Title: Fabrication of TiO2 nanoparticles/porous-silicon heterostructure photodetector for UV detection
    Authors: Y Shehu, SAM Samsuri, NM Ahmed, S Aslam
    Year: 2024
    Cited by: 6

  • Title: Energy efficiency improvement in fish production systems in Oyo State, Nigeria: a path towards sustainable protein supply
    Authors: YU Oladimeji, SA Adepoju, HO Yusuf, S Yusuf
    Year: 2018
    Cited by: 6

  • Title: The Effects of Plant Population Density on the Growth and Chemical Composition of Lablab purpureus Grown for Fodder Production in a Semi‐Arid Region
    Authors: Y Shehu, WS Alhassan, UR Pal, CJC Phillips
    Year: 2001
    Cited by: 6

Muhammad Muzzammil Sajjad | Applied AI in Agriculture | Best Researcher Award

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Mr. Muhammad Muzzammil Sajjad | Applied AI in Agriculture | Best Researcher Award

R&D Executive at Khadija Foods, Pakistan

Muhammad Muzzammil Sajjad is a dynamic researcher and engineer with a strong background in food engineering, artificial intelligence, and computer vision. He combines academic rigor with practical experience, focusing on AI-driven solutions for agriculture and food processing. His work involves developing machine learning models for disease detection, fruit maturity prediction, and quality assessment using deep learning and image processing techniques. With a deep understanding of both engineering principles and software technologies, Mr. Sajjad has demonstrated the ability to deliver impactful solutions that address real-world challenges in food security and smart farming. He has contributed to peer-reviewed publications, developed mobile applications for disease detection, and participated in cross-functional roles in the food industry. Alongside his research, he is also a skilled graphic designer and has been active in academic societies and student leadership. His unique blend of technical expertise, creativity, and passion for innovation makes him a promising contributor to science and technology.

Professional Profile 

Education🎓

Muhammad Muzzammil Sajjad completed his higher education at the University of Agriculture Faisalabad, one of Pakistan’s leading institutions for agricultural and engineering sciences. He earned a Master of Science (Hons) in Food Engineering, where his thesis focused on developing an AI-based image processing method for detecting citrus canker diseases. This research emphasized the integration of deep learning and mobile application development for smart agricultural solutions. Prior to this, he earned his Bachelor’s degree in Food Engineering from the same university, during which he designed and fabricated a citrus juice extractor as part of his final year project. His educational journey is characterized by a strong emphasis on innovation, practical problem-solving, and multidisciplinary integration. In addition to formal academic programs, Mr. Sajjad has completed numerous online courses and certifications in deep learning, MATLAB programming, neural networks, mobile app development, and AutoCAD, reflecting his commitment to continuous learning and professional development.

Professional Experience📝

Muhammad Muzzammil Sajjad has gained significant professional experience in both industry and freelance roles, contributing to quality assurance, production management, and technical development. Currently serving as a Quality Control Executive at Khadija Foods in Faisalabad, he performs analytical testing, ensures compliance with food safety standards, and supports process optimization. Previously, he worked as a Production and QA Officer at Madina Sweets and Bakers, where he oversaw daily operations, enforced hygiene standards, and improved production efficiency. Beyond traditional roles, he has worked as a freelance graphic designer on Fiverr, delivering logo designs, animations, PDF forms, and vector illustrations for clients globally. His ability to manage diverse projects demonstrates strong organizational and communication skills. Earlier in his career, he completed an internship at Dawn Bread, where he gained hands-on experience in bakery product processing and operations. This diverse professional background strengthens his adaptability and enhances his ability to merge technical innovation with practical execution.

Research Interest🔎

Muhammad Muzzammil Sajjad’s research interests lie at the intersection of agricultural engineering, artificial intelligence, computer vision, and precision agriculture. He is particularly focused on the development of AI-based models for disease detection, quality control, and fruit maturity prediction in postharvest systems. His work integrates deep learning, convolutional neural networks (CNNs), and attention mechanisms to solve complex problems in image analysis and classification. He is also interested in engineering design and robotics applications in the food sector, aiming to develop autonomous systems that improve productivity and reduce waste. His goal is to leverage modern AI technologies to create practical solutions that support sustainable and intelligent agriculture. Mr. Sajjad’s work is application-driven, with a strong emphasis on usability in real-world farming and food processing environments. By combining computational intelligence with field-specific expertise, he contributes toward a future where agriculture is more data-driven, efficient, and environmentally responsible.

Award and Honor🏆

Muhammad Muzzammil Sajjad has received several awards and recognitions that reflect his academic excellence and commitment to leadership. He was awarded a Silver Medal for securing academic distinction in his undergraduate studies in Food Engineering at the University of Agriculture Faisalabad. His consistent academic performance also earned him the Rafhan Merit and Need-Based Scholarship, granted by Rafhan Maize Products Co. Ltd. for outstanding achievements and leadership potential. He was further recognized with the University Merit Scholarship, highlighting his strong dedication to scholarly pursuits. In addition to academic honors, Mr. Sajjad has actively participated in co-curricular activities, serving as a graphic designer for student societies and participating in sports competitions, including badminton and basketball. These accolades not only highlight his academic capabilities but also his well-rounded personality, involvement in student life, and proactive attitude. His achievements establish a strong foundation for continued growth and excellence in research and professional endeavors.

Research Skill🔬

Muhammad Muzzammil Sajjad possesses a robust set of research skills spanning deep learning, image processing, software development, and engineering design. He is proficient in Python and MATLAB for AI model development and data analysis, with expertise in convolutional neural networks (CNNs), attention modules, and transfer learning techniques. He has experience in mobile app development using Android Studio and React Native, allowing for real-time deployment of AI models in agricultural applications. His design and simulation skills include tools like AutoCAD, SolidWorks, and Ansys, essential for prototype development and mechanical design in food engineering. He is adept in MATLAB modeling, EES calculations, and using MS Excel for data evaluation. Additionally, he has developed technical reports, academic manuscripts, and graphical representations for scientific communication. These interdisciplinary skills enable him to manage all aspects of a research project — from design and experimentation to analysis, visualization, and practical implementation in industry settings.

Conclusion💡

Muhammad Muzzammil Sajjad is a deserving candidate for the Best Researcher Award due to his innovative work at the intersection of food engineering and artificial intelligence. His research contributions, particularly in AI-based image processing for disease detection and quality monitoring in agriculture, address critical challenges in food security and sustainable farming. With a strong foundation in both academic research and industry practice, he has demonstrated a unique ability to translate complex technologies into practical solutions that benefit society. Looking ahead, his potential to lead interdisciplinary research, foster international collaborations, and contribute to the advancement of intelligent agricultural systems positions him as a future leader in the field.

Publications Top Noted✍

  • Title: Advancement in Artificial Intelligence for On-Farm Fruit Sorting and Transportation
    Authors: Z. Zhou, U. Zahid, Y. Majeed, Nisha, S. Mustafa, M. M. Sajjad, H. D. Butt, L. Fu
    Year: 2023
    Citations: 40

  • Title: Conventional and Advanced Extraction Methods of Some Bioactive Compounds with Health Benefits of Food and Plant Waste: A Comprehensive Review
    Authors: M. Waseem, Y. Majeed, T. Nadeem, L. H. Naqvi, M. A. Khalid, M. M. Sajjad, …
    Year: 2023
    Citations: 36

  • Title: Climate Change, Flood Disaster, and Food Insecurity in Pakistan
    Authors: M. Waseem, Y. Majeed, T. Nadeem, L. H. Naqvi, M. A. Khalid, M. Shafiq, …
    Year: 2022
    Citations: 3

Kazeem Aderemi Bello | Additive Manufacturing | Innovation Excellence Award

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Dr. Kazeem Aderemi Bello | Additive Manufacturing | Innovation Excellence Award

Postdoctoral Fellow at Durban University of Technology, Steven Biko Campus, South Africa

Dr. Kazeem Aderemi Bello is a highly accomplished industrial and mechanical engineer with extensive expertise in smart manufacturing, lean production systems, and industrial process optimization. He currently serves in the Department of Mechanical Engineering at Durban University of Technology, South Africa, and holds a postdoctoral fellowship. With a strong academic foundation and over two decades of experience, Dr. Bello has contributed significantly to both academia and industry. His research output spans over 20 Scopus-indexed publications, addressing crucial areas such as sustainable production, machining processes, occupational safety, and engineering education. A registered COREN engineer and a member of several national and international professional organizations, he is widely respected for his leadership, innovation, and commitment to training future engineers. He has also earned recognition for his excellence in teaching and has participated in international workshops and certifications. Dr. Bello exemplifies dedication to research, education, and global collaboration in engineering sciences.

Professional Profile 

Education🎓

Dr. Kazeem Aderemi Bello’s academic journey reflects a progressive and multidisciplinary advancement in engineering. He earned his Ph.D. in Industrial Engineering from the University of Benin, Nigeria, in 2018, with a dissertation focused on manufacturing and safety in fiber cement sheet production. Prior to his doctoral degree, he obtained a Master’s in Engineering Management (2014) and a Postgraduate Diploma in Mechanical Engineering (2008), also from reputable Nigerian institutions. His foundational qualifications include a Higher National Diploma (HND) in Mechanical Engineering from Yaba College of Technology (1998) and a Senior Secondary Certificate from Idi-Araba High School (1993). Additionally, his credentials have been evaluated and recognized by the South African Qualifications Authority (SAQA) in 2022, further validating his academic standing internationally. His education has equipped him with both theoretical knowledge and applied skills, enabling him to conduct impactful research and contribute effectively to academic and professional engineering practices.

Professional Experience📝

Dr. Kazeem Aderemi Bello brings a rich blend of academic and industrial experience spanning over two decades. He is currently serving as a Postdoctoral Research Fellow in the Department of Mechanical Engineering at Durban University of Technology, South Africa, where he conducts advanced research in smart manufacturing and production optimization. Prior to this role, he held teaching and research positions at several Nigerian universities, including the University of Benin, where he contributed to curriculum development, student supervision, and departmental growth. Beyond academia, Dr. Bello has also engaged in technical consulting and project implementation related to health and safety systems, industrial audits, and mechanical system design. His professional journey is enriched by his participation in numerous international workshops and certifications, including NEBOSH training in the UK and the Royal Academy of Engineering programs in Kenya. His leadership and practical experience make him a versatile engineer and educator with both local and global impact.

Research Interest🔎

Dr. Bello’s research interests lie at the intersection of smart manufacturing technology, production process optimization, and lean manufacturing systems. His work focuses on improving efficiency, sustainability, and adaptability within industrial operations, especially through digital and data-driven approaches. He is particularly interested in the life cycle assessment of manufacturing systems, machining processes, and renewable energy integration. His published studies have addressed issues such as resource recycling for zero-waste production, nano-fluid applications in machining, safety analysis in construction, and e-learning adaptation in engineering education. His ongoing projects include the integration of intelligent systems into production environments and the modeling of complex industrial operations for optimal performance. Dr. Bello also has a keen interest in occupational health and safety, contributing to safer and more efficient industrial workflows. By aligning academic research with industrial needs, he aims to bridge the gap between theoretical innovation and practical application across engineering sectors.

Award and Honor🏆

Dr. Kazeem Aderemi Bello has received several prestigious awards and honors that recognize his excellence in research, teaching, and professional service. Most notably, he is a Postdoctoral Fellowship Awardee at Durban University of Technology, South Africa, a competitive research position awarded based on academic merit and research potential. In 2019, he was honored with the Best Lecturer Award by the Nigerian Universities Engineering Students Association (NUESA) at Afe Babalola University, reflecting his dedication to education and student mentorship. He has also been listed among the Top 500 Nigerian Authors by scholarly output for 2021–2025, affirming his research productivity and impact. Additionally, he holds certifications from NEBOSH UK in occupational safety and has participated in capacity-building workshops under the Royal Academy of Engineering. His fellowships and professional affiliations highlight his influence in both academic and industrial communities, making him a respected leader and contributor in the engineering domain.

Research Skill🔬

Dr. Bello possesses a wide array of research skills that span experimental design, computational modeling, statistical analysis, and process optimization in manufacturing and industrial systems. He is proficient in conducting factorial and parametric studies, developing machine prototypes, and implementing life cycle assessments for sustainability evaluation. His methodological approach often incorporates tools such as Taguchi analysis, response surface methodology, and finite element modeling, enabling accurate simulation and optimization of engineering systems. Dr. Bello also demonstrates expertise in health and safety audits, quality management, and environmental compliance through certifications in ISO standards and NEBOSH training. He has contributed to interdisciplinary projects involving mechanical design, green energy, machining, and educational innovation. His publications reflect strong skills in technical writing, data interpretation, and collaborative research, often co-authoring with international teams. Overall, Dr. Bello’s research skills are rooted in practical relevance and technical rigor, positioning him as an effective problem-solver in modern engineering challenges.

Conclusion💡

Dr. Kazeem Aderemi Bello is a deserving candidate for the Best Researcher Award due to his outstanding contributions to industrial and mechanical engineering, particularly in the areas of smart manufacturing, lean systems, and sustainable production. His prolific research output in Scopus and IEEE-indexed journals, along with his active involvement in professional bodies and international collaborations, underscores his commitment to advancing both academic knowledge and practical engineering solutions. Beyond research, his leadership in student mentorship, engineering education, and occupational safety demonstrates a strong societal impact. With his continued focus on innovation, global engagement, and academic excellence, Dr. Bello possesses significant potential to emerge as a leading voice in industrial systems engineering and global manufacturing transformation.

Publications Top Noted✍

  • Title: Resource recycling with the aim of achieving zero-waste manufacturing
    Authors: O. Awogbemi, D.V.V. Kallon, K.A. Bello
    Year: 2022
    Citations: 70

  • Title: Modelling, simulation and experimental validation of the milling operation of titanium alloy (Ti6Al4V)
    Authors: I. Daniyan, F. Fameso, F. Ale, K. Bello, I. Tlhabadira
    Year: 2020
    Citations: 36

  • Title: Photocatalytic degradation of an anionic dye in aqueous solution by visible light responsive zinc oxide-termite hill composite
    Authors: A.S. Yusuff, K.A. Bello, T.M. Azeez
    Year: 2020
    Citations: 19

  • Title: Comparative economic analysis of organic and inorganic vegetable production in Ogun State, Nigeria
    Authors: A.O. Dipeolu, K.A. Bello, S.O. Akinbode
    Year: 2006
    Citations: 19

  • Title: Synthesis of fatty acid methyl ester via transesterification of waste frying oil by a zinc-modified pumice catalyst: Taguchi approach to parametric optimization
    Authors: A.S. Yusuff, K.A. Bello
    Year: 2019
    Citations: 17

  • Title: Resource recycling with the aim of achieving zero-waste manufacturing
    Authors: O. Awogbemi, D.V.V. Kallon, K.A. Bello
    Year: 2022
    Citations: 13 (duplicate listing)

  • Title: Life cycle assessment for the milling operation of titanium alloy (Ti6Al4V)
    Authors: I. Daniyan, K. Mpofu, K. Bello, R. Muvunzi
    Year: 2022
    Citations: 10

  • Title: Biomedical engineering in Nigeria: The genesis, present and the future
    Authors: A.A. Bamigboye, K.A. Bello
    Year: 2021
    Citations: 10

  • Title: Assessment of Alternative Fuels for Sustainable Road Transportation
    Authors: K.A. Bello, O. Awogbemi, M.G. Kanakana-Katumba
    Year: 2023
    Citations: 8

  • Title: Development and simulation of isotropic hardening for AISI 1035 weld stress prediction during design and welding assembly of lower brackets of rail cars
    Authors: I.A. Daniyan, K. Mpofu, F.O. Fameso, A.O. Adeodu, K.A. Bello
    Year: 2019
    Citations: 8

  • Title: A review of additive manufacturing post-treatment techniques for surface quality enhancement
    Authors: K.A. Bello, M.G. Kanakana-Katumba, R.W. Maladzhi
    Year: 2023
    Citations: 6

  • Title: The use of adaptive fuzzy-PID for vibration control in the suspension system of a railcar
    Authors: D. Ilesanmi, M. Khumbulani, A. Adefemi, B. Kazeem
    Year: 2020
    Citations: 6

  • Title: Comparative Economic Analysis of Organic and Inorganic Vegetable Production in Ogun State
    Authors: A.O. Dipeolu, K.A. Bello, A. S.O.
    Year: 2006
    Citations: 6

  • Title: Recent advances in smart manufacturing: A case study of small, medium, and micro enterprises (SMME)
    Authors: K.A. Bello, M.G. Kanakana-Katumba, R.W. Maladzhi, C.O. Omoyi
    Year: 2024
    Citations: 5

  • Title: Investigations into the tensile properties and microstructural features of Coconut fibre (Coir) reinforced Polylactic acid (PLA) biodegradable composites
    Authors: C.I. Madueke, O.J. Agunsoye, R. Umunakwe, B. Bolasodun, F. Kolawole, …
    Year: 2023
    Citations: 5

John Wilson | Urban hotspot event detection | Best Researcher Award

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Prof. Dr. John Wilson | Urban hotspot event detection | Best Researcher Award

Professor and Founding Director, USC Spatial Sciences Institute at University of Southern California, United States

Dr. John P. Wilson is a globally recognized expert in Geographic Information Science (GIS), spatial data science, and environmental modeling. As Professor and Founding Director of the Spatial Sciences Institute at the University of Southern California (USC), he has dedicated his career to advancing spatial thinking in public health, sustainability, and human-environment systems. His work integrates spatial analysis, GIS, and computational tools to address pressing urban and environmental challenges. With a career spanning several decades, Dr. Wilson has published extensively in high-impact journals and authored foundational books in his field. His leadership extends beyond academia through mentorship, editorial service, and global collaborations. He has also contributed to curriculum development and capacity building in spatial sciences internationally. Dr. Wilson’s visionary approach and interdisciplinary expertise continue to influence research, policy, and education worldwide, making him a key figure in advancing geospatial technologies for societal benefit.

Professional Profile 

Education🎓

Dr. John P. Wilson’s academic journey reflects a unique blend of geography, law, and scientific inquiry. He earned his Ph.D. in Geography from the University of Toronto in 1986, with a dissertation on the statistical modeling of environmental change in Canadian watersheds. He previously completed a Master of Science with Distinction and a First-Class Honors Bachelor’s degree in Geography from the University of Canterbury, New Zealand. In addition to his geographic training, he also holds an LL.B. in Law, which enriches his understanding of regulatory and policy frameworks tied to spatial planning and environmental management. This broad interdisciplinary foundation has enabled Dr. Wilson to address spatial problems from multiple perspectives, bridging gaps between data, policy, and real-world applications. His academic credentials have laid a strong groundwork for his impactful career in GIS, environmental modeling, and public health analysis through spatial science.

Professional Experience📝

Dr. John P. Wilson has built a rich and influential professional career in academia and research leadership. He currently serves as a Professor of Spatial Sciences and Founding Director of the Spatial Sciences Institute at the University of Southern California (USC), where he also established the Wilson Map Lab. Over the years, he has held faculty and administrative roles at USC, the University of California, and visiting appointments at institutions such as the Australian National University, Utrecht University, and the Chinese Academy of Sciences. Dr. Wilson has also directed key research centers, including the USC GIS Research Laboratory and has served as Principal Investigator on projects funded by NSF, NIH, and NASA. He is a leader in developing spatial science education and interdisciplinary research programs, mentoring scholars across geography, urban planning, and public health. His work bridges research, education, and societal engagement at both national and international levels.

Research Interest🔎

Dr. Wilson’s research interests span a wide spectrum of spatial sciences, with a strong focus on environmental modeling, digital terrain analysis, spatial epidemiology, and urban sustainability. He is deeply engaged in developing geospatial tools to understand coupled human-environment systems, using spatial analysis, GIS, remote sensing, and computational modeling. His work is particularly focused on GeoAI, public health disparities, and landscape dynamics, where spatial data science is applied to study the spread of diseases, assess environmental justice, and optimize urban planning. He also investigates how physical environments affect behavioral outcomes, such as physical activity and health risks, in diverse communities. His research integrates real-world geospatial data with advanced modeling techniques to produce actionable insights for policy and planning. By combining technical innovation with social relevance, Dr. Wilson’s research not only advances theory but also offers practical applications for public agencies, non-profits, and global sustainability efforts.

Award and Honor🏆

Dr. John P. Wilson has received numerous awards and honors in recognition of his exceptional contributions to geographic information science and interdisciplinary research. He is a Fellow of the American Association of Geographers (AAG) and the University Consortium for Geographic Information Science (UCGIS)—prestigious accolades awarded to scholars who have made sustained and distinguished contributions to their fields. He received the USC Raubenheimer Outstanding Senior Faculty Award and the Mellon Award for Excellence in Mentoring, both acknowledging his commitment to teaching and mentorship. His leadership in academic publishing is reflected in his role as Founding Editor-in-Chief of Transactions in GIS, an influential international journal. He has also been recognized by global programs such as China’s Thousand Talents of Foreign Experts. These honors underscore his scholarly excellence, educational leadership, and global impact in spatial science, reaffirming his role as a key figure in advancing the discipline worldwide.

Research Skill🔬

Dr. Wilson possesses a versatile and high-level skillset across core areas of geographic information science and spatial analysis. He is proficient in GIS software (ArcGIS, QGIS), spatial statistics, digital terrain modeling, remote sensing, and computational modeling. His work often involves advanced spatial data integration, Python and R programming, and machine learning techniques tailored for environmental and urban data. Dr. Wilson is also skilled in data visualization, map design, and building spatially enabled decision-support systems. His expertise in spatial epidemiology and urban informatics allows him to model public health phenomena with precision. Beyond technical skills, he demonstrates strength in interdisciplinary project management, international collaboration, and academic publishing. He actively contributes to editorial boards, leads curriculum development in geospatial education, and mentors researchers on advanced analytical workflows. These multifaceted research skills empower him to solve complex societal problems using cutting-edge spatial science approaches.

Conclusion💡

Dr. John P. Wilson is a highly deserving candidate for the Best Researcher Award, given his outstanding contributions to the fields of GIS, spatial data science, and environmental modeling over a distinguished academic career. His pioneering research has significantly advanced our understanding of human-environment interactions and public health through geospatial technologies, while his leadership in establishing research institutes and academic programs has shaped the next generation of spatial scientists. With a global presence, extensive publication record, and strong community engagement, Dr. Wilson continues to influence both academia and applied science. His ongoing work in GeoAI, urban sustainability, and spatial health analytics positions him to remain at the forefront of interdisciplinary innovation and policy-relevant research in the years ahead.

Publications Top Noted✍

  • Title: Terrain Analysis: Principles and Applications
    Authors: JP Wilson, JC Gallant
    Year: 2000
    Cited by: 2483

  • Title: Length-slope factors for the Revised Universal Soil Loss Equation: Simplified method of estimation
    Authors: ID Moore, JP Wilson
    Year: 1992
    Cited by: 1046

  • Title: Parks and park funding in Los Angeles: An equity-mapping analysis
    Authors: J Wolch, JP Wilson, J Fehrenbach
    Year: 2005
    Cited by: 960

  • Title: Digital Terrain Analysis
    Authors: JP Wilson, JC Gallant
    Year: 2000
    Cited by: 765

  • Title: Got green? Addressing environmental justice in park provision
    Authors: C Sister, J Wolch, J Wilson
    Year: 2010
    Cited by: 549

  • Title: GIS and land-surface–subsurface process modeling
    Authors: ID Moore, AK Turner, JP Wilson, SK Jenson, LE Band
    Year: 1993
    Cited by: 445

  • Title: Digital Terrain Modeling
    Authors: JP Wilson
    Year: 2012
    Cited by: 425

  • Title: On Human Nature 2
    Authors: J Wilson, California Energy Commission
    Year: 1978
    Cited by: 347

  • Title: From text to geographic coordinates: the current state of geocoding
    Authors: DW Goldberg, JP Wilson, CA Knoblock
    Year: 2007
    Cited by: 335

  • Title: Secondary Topographic Attributes
    Authors: JP Wilson, JC Gallant
    Year: 2000
    Cited by: 276

  • Title: TAPES-G: A grid-based terrain analysis program for the environmental sciences
    Authors: JC Gallant, JP Wilson
    Year: 1996
    Cited by: 256

  • Title: Erosional impact of hikers, horses, motorcycles, and off-road bicycles on mountain trails in Montana
    Authors: JP Wilson, JP Seney
    Year: 1994
    Cited by: 234

  • Title: Fuzzy k-means classification of topo-climatic data as an aid to forest mapping in the Greater Yellowstone Area, USA
    Authors: PA Burrough, JP Wilson, PFM Van Gaans, AJ Hansen
    Year: 2001
    Cited by: 224

  • Title: DEM resolution dependencies of terrain attributes across a landscape
    Authors: Y Deng, JP Wilson, BO Bauer
    Year: 2007
    Cited by: 214

  • Title: Ambient air pollutants have adverse effects on insulin and glucose homeostasis in Mexican Americans
    Authors: Z Chen, MT Salam, C Toledo-Corral, RM Watanabe, AH Xiang, …
    Year: 2016
    Cited by: 192

  • Title: Watershed urbanization and changing flood behavior across the Los Angeles metropolitan region
    Authors: J Sheng, JP Wilson
    Year: 2009
    Cited by: 190

  • Title: China’s improving inland surface water quality since 2003
    Authors: T Ma, N Zhao, Y Ni, J Yi, JP Wilson, L He, Y Du, …
    Year: 2020
    Cited by: 185

  • Title: The Handbook of Geographic Information Science
    Authors: JP Wilson, AS Fotheringham
    Year: 2008
    Cited by: 164

  • Title: Use of terrain variables for mapping gully erosion susceptibility in Lebanon
    Authors: RB Kheir, J Wilson, Y Deng
    Year: 2007
    Cited by: 149

  • Title: An effective and efficient approach for manually improving geocoded data
    Authors: DW Goldberg, JP Wilson, CA Knoblock, B Ritz, MG Cockburn
    Year: 2008
    Cited by: 143

Wenfeng Du | Structural Generation Design | Best Researcher Award

Published on by Computer Vision Awards

Prof. Dr. Wenfeng Du | Structural Generation Design | Best Researcher Award

Director at Henan University, China

Dr. Wenfeng Du is a distinguished professor and a leading researcher in structural engineering, with expertise in intelligent design, large-span spatial structures, and 3D printing manufacturing. He is currently a faculty member at Henan University, where he also leads several research institutes and technology innovation platforms. With over 180 publications, 80 patents, and multiple academic books, his work bridges the gap between theoretical innovation and practical application. Dr. Du integrates artificial intelligence, topology optimization, and prefabricated construction methods to create smarter, more sustainable engineering solutions. His commitment to interdisciplinary collaboration and academic leadership has positioned him at the forefront of structural design research in China and beyond. In addition to his scientific achievements, he is also recognized for his excellence in teaching and mentorship, having received multiple awards for his educational contributions. His work continues to influence both academic research and the construction industry at large.

Professional Profile 

Education🎓

Dr. Wenfeng Du holds a Ph.D. in Structural Engineering from Zhejiang University, which he earned in 2007. Prior to that, he completed both his Master’s and Bachelor’s degrees in Civil Engineering, also at Zhejiang University, one of China’s most prestigious institutions. His doctoral research focused on the mechanical performance and design optimization of large-span steel structures, laying a solid foundation for his future academic pursuits. To further broaden his academic exposure and international perspective, he served as a visiting scholar at the University of Alabama in the United States in 2014. This experience enriched his research methodologies and fostered interdisciplinary collaboration. Throughout his academic journey, Dr. Du has demonstrated a strong commitment to combining rigorous theoretical knowledge with practical engineering solutions, which continues to define his work. His educational background has equipped him with the technical acumen and innovative mindset required to tackle complex challenges in modern structural engineering.

Professional Experience📝

Dr. Wenfeng Du has cultivated a distinguished career as a professor, researcher, and institutional leader at Henan University. He currently serves as the Director of multiple research institutes, including the Steel and Spatial Structures Research Institute, the Intelligent Structure Team, and the Prefabricated Construction Engineering Technology Research Center. His professional roles involve managing large-scale research projects, supervising doctoral and postgraduate students, and promoting interdisciplinary innovation in structural design and construction. He has also played a pivotal role in the development and implementation of intelligent joint systems and 3D-printed shell structures. As a registered National First-Class Structural Engineer, Dr. Du contributes extensively to engineering standards and policy-making through active participation in national committees. His professional journey is marked by a balance of research excellence, academic leadership, and practical engineering application, making him a key figure in bridging the gap between academia and industry within the field of civil and structural engineering.

Research Interest🔎

Dr. Wenfeng Du’s research interests span a broad and interdisciplinary spectrum within structural engineering. His primary focus areas include large-span spatial structures, intelligent joint design, steel structures, and 3D printing-based construction. He is particularly interested in the integration of artificial intelligence with engineering design, especially through generative design and deep learning techniques. Dr. Du explores topology optimization to develop structurally efficient and lightweight components, which are essential for sustainable and cost-effective construction. His work also delves into prefabricated building systems and modular construction technologies, aiming to revolutionize how infrastructure is designed and assembled. Additionally, he is involved in smart materials, structural vibration energy harvesting, and computational modeling of shell and lattice structures. His research consistently aims to enhance structural performance, reduce resource consumption, and incorporate automation into construction processes. Through these efforts, Dr. Du seeks to redefine modern construction practices by combining advanced computation with real-world engineering applications.

Award and Honor🏆

Dr. Wenfeng Du has received numerous awards and honors recognizing his contributions to structural engineering, education, and scientific innovation. Among his most prestigious accolades is the Henan Youth Science and Technology Award, which acknowledges his pioneering work in intelligent structural systems. He has also been honored as a Smart Teaching Star and recognized as one of the Most Beautiful Teachers, reflecting his commitment to excellence in education and student mentorship. Dr. Du’s leadership and innovation have earned him significant roles in national research initiatives, and he has been selected for several provincial and municipal talent programs in Henan. His research outputs, including patents and high-impact publications, have been widely recognized by both academic and professional communities. Furthermore, he has served as a judge and reviewer for various scientific committees and journals, underlining the respect he commands among peers. These honors reflect his holistic impact on research, education, and engineering practice.

Research Skill🔬

Dr. Wenfeng Du possesses a comprehensive set of research skills that support his advanced work in structural engineering and intelligent design. He is highly proficient in topology optimization, finite element analysis (FEA), and computational modeling, which form the foundation of his work on structural performance and smart joint systems. Dr. Du is skilled in AI-based generative design, using neural networks and deep learning algorithms to automate and enhance structural form-finding and simulation. He is also adept at applying 3D printing technologies for experimental fabrication of steel joints and prefabricated components. His hands-on expertise extends to vibration analysis, energy harvesting mechanisms, and the integration of sensor-based monitoring in structural systems. Additionally, Dr. Du excels in academic writing, patent development, and collaborative research management. His ability to bridge theoretical research with practical engineering innovations underscores his role as a leading figure in the application of advanced technologies to modern civil engineering challenges.

Conclusion💡

Dr. Wenfeng Du is a highly deserving candidate for this award due to his outstanding contributions to structural engineering, particularly in the areas of intelligent design, prefabricated structures, and 3D printing manufacturing. His prolific research output, including over 180 publications, numerous patents, and leadership of cutting-edge projects, reflects his deep commitment to innovation and scientific advancement. Beyond academia, his work has significantly impacted engineering practices and sustainable construction technologies, contributing to societal progress. With a proven record of interdisciplinary research, strong mentorship, and institutional leadership, Dr. Du is well-positioned to drive future advancements at the intersection of AI and structural engineering, and to continue shaping the next generation of global engineering excellence.

Publications Top Noted✍

  • Title: Generation of innovative structural connection components using generative adversarial networks
    Year: 2025

  • Title: Robust form finding of tree-like structure by improved slime mould algorithm
    Year: 2025

  • Title: Bio-inspired clover-shaped piezoelectric energy harvester with enhanced performance for low-speed wind energy harvesting
    Year: 2025

  • Title: The Structural Configuration and Mechanical Performance of a New Cable-Supported Reciprocal Structure
    Year: 2025

  • Title: The influence of screen mesh interception device on hydrogen-oxygen explosion in closed detonation tube
    Year: 2025

  • Title: Collaborative form-finding of multiple treelike column structures based on improved numerical inverse hanging method
    Year: 2025
    Citations: 1

  • Title: Research on topology optimization of steel joints based on improved bi-directional progressive structural optimization method
    Year: 2024

  • Title: New shape optimization method for tree structures based on BP neural network
    Year: 2024
    Citations: 3

Minh-Son Dao | Deep Learning | Best Researcher Award

Published on by Computer Vision Awards

Dr. Minh-Son Dao | Deep Learning | Best Researcher Award

Researcher at The National Institute of Information and Communications Technology (NICT), Japan.

Dr. Minh-Son DAO is a distinguished Senior Researcher and Research Manager at the Big Data Integration Research Center, National Institute of Information and Communications Technology (NICT), Japan. With over two decades of research and leadership experience across academia and government, he leads cutting-edge initiatives in artificial intelligence, big data analytics, and smart IoT systems. He has played a pivotal role in Japan’s Society 5.0 vision through projects like MMCRAI and collaborative smart-city platforms. Dr. DAO is also a committed educator, serving as a thesis supervisor and adjunct lecturer across multiple international universities. His work has earned him numerous accolades, including multiple Best Challenge Awards, national recognitions, and research excellence honors. With over 100 peer-reviewed publications and international partnerships spanning Europe and Asia, he continues to bridge academic rigor with real-world impact. His current focus lies in multimodal AI frameworks and data-driven societal innovation.

Professional Profile

Suitability For Best Researcher Award – Dr. Minh-Son Dao

Dr. Minh-Son DAO exemplifies the qualities of an outstanding researcher through his sustained, interdisciplinary contributions to artificial intelligence, big data analytics, and smart IoT systems. With over 20 years of research leadership, a strong publication record (100+ peer-reviewed papers), and international collaboration across Europe and Asia, he has significantly influenced both theoretical advancements and real-world applications. His active role in Japan’s Society 5.0 vision and the development of the MMCRAI framework further underscore his commitment to data-driven societal innovation. Dr. DAO also demonstrates excellence in mentoring, editorial roles, and academic service, enriching the broader research ecosystem.

Education

Dr. Minh-Son DAO holds a Ph.D. in Information and Communications Technology from Trento University, Italy, where his research focused on similarity measures and shape matching using genetic algorithms. His doctoral dissertation introduced the Edge Potential Function (EPF), a novel contribution to shape-based image retrieval. Prior to that, he earned a Master’s degree in Computer Science from Vietnam National University, specializing in handwritten character recognition using Convolutional Neural Networks—an early demonstration of his interest in deep learning. His Bachelor’s degree, also in Computer Science from the University of HCM City, Vietnam, emphasized image processing and hypertext applications. These academic milestones laid a strong foundation in AI, machine learning, and multimedia processing, enabling him to merge theoretical knowledge with practical innovation throughout his career. His educational journey reflects a continuous pursuit of excellence across diverse computational and applied domains.

Experience

Dr. Minh-Son DAO brings over 20 years of extensive research and leadership experience across Asia and Europe. Currently, he serves as Research Manager and Senior Researcher at NICT Japan, spearheading national AI and Smart IoT initiatives. His prior roles include Deputy Director and Senior Assistant Professor at Universiti Teknologi Brunei, where he also founded the ELEDIA@UTB lab focused on smart farming and wireless technologies. He has held prestigious research roles at Trento University, Osaka University (as a JSPS Fellow), and GraphiTech Italy. He has supervised more than 40 postgraduate students, co-authored over 100 publications, and led multi-institutional projects in smart cities, multimedia analytics, and health informatics. His teaching portfolio spans creative multimedia, data science, and database systems. Known for building strong global research networks, Dr. DAO has established successful collaborations with institutions in Norway, Ireland, Vietnam, and Switzerland, playing a vital role in cross-disciplinary and cross-cultural scientific advancements.

Professional Development

Dr. Minh-Son DAO has consistently invested in professional development to enhance his academic and leadership capabilities. He completed the UTB Faculty Development Program and the Foundations of University Learning and Teaching at Universiti Teknologi Brunei, gaining proficiency in teaching pedagogy, assessment strategies, and flipped classroom techniques. He also holds Oracle certifications in SQL, PL/SQL, and web application development. His involvement as a guest editor for high-impact journals such as IEEE ACCESS, ACM TOMM, and Frontiers in Big Data, along with his participation as program committee member for numerous international conferences, highlights his role as a thought leader in multimedia, AI, and big data. Dr. DAO frequently chairs and organizes conferences and workshops, including ICMLSC, ICCRD, and MediaEval. His holistic development in research, teaching, industry consulting, and international collaboration exemplifies a well-rounded professional commitment to lifelong learning and knowledge dissemination in cutting-edge computing technologies.

Research Focus

Dr. Minh-Son DAO’s research primarily focuses on multidisciplinary applications of Artificial Intelligence, Big Data Analytics, and Smart IoT systems, aligning closely with the vision of a data-driven, intelligent society (Society 5.0). His most notable initiative, the Multimodal and Cross-modal AI Framework (MMCRAI), demonstrates his commitment to converting raw multimodal data into actionable insights across domains like environmental monitoring, health informatics, multimedia forensics, and smart cities. He has applied his research to real-world challenges such as air pollution prediction, disaster management, and cheapfake detection. His work spans from foundational AI techniques to practical societal applications, including the integration of sensor networks, robotics, and citizen-driven data platforms. Through collaborative international projects, he explores the intersections between cyber-physical-social systems, smart urban planning, and sustainable development. This focus enables him to address complex problems with scalable, intelligent solutions that impact public health, education, urban resilience, and digital media integrity.

Research Skills

Dr. Minh-Son DAO possesses a comprehensive suite of research skills that bridge theoretical and applied domains. He is proficient in machine learning, deep learning, multimedia retrieval, and big data analytics, often applying these in cross-modal and multimodal AI frameworks. His technical abilities include programming in C++, R, SQL, HTML/JavaScript, and Python, and working with AI tools such as TensorFlow and Keras. Dr. DAO’s expertise spans data fusion, smart sensor integration, pattern recognition, event detection, and AI-based forecasting models, enabling him to tackle large-scale and heterogeneous data sources. Additionally, he has extensive experience in research project management, proposal writing, international collaboration, and supervising graduate students. His editorial and peer-review roles in IEEE, Springer, and Elsevier journals further reflect his analytical and evaluative skill set. These capabilities have allowed him to lead multi-disciplinary teams and create impactful AI-driven solutions for urban management, environmental monitoring, and personalized health analytics.

Awards and Honors

Dr. Minh-Son DAO has received numerous national and international awards recognizing his research excellence and innovation. Notably, he won the Best Challenge Awards at ICMR 2023 and ACM MM 2022 for his groundbreaking work in cheap fake detection. He was honored with the Excellent Performance Award by Japan’s NICT in 2022, reflecting his leadership in national projects. Earlier, he earned first-place awards at prestigious competitions such as image CLEF 2018 and Media Eval 2017 for his contributions to multimedia understanding and disaster response. He received the Research Excellence Mid-Career Academic Award from University Technology Brunei in 2017. His early career was marked by competitive international fellowships, including the JSPS International Fellowship (Japan) and ERCIM Fellowship (Europe), and he was awarded Vietnam’s highest youth scientific honor, the Creative Youth Medal. These accolades affirm his sustained contributions to AI, data science, and societal innovation across multiple countries and disciplines.

Conclusion

Dr. Minh-Son DAO’s profile aligns exceptionally well with the criteria for a Best Researcher Award. His work bridges high-impact research, global collaboration, and societal benefit. His innovations in AI and multimodal systems, combined with his leadership in international research initiatives and dedication to mentorship, make him a deserving candidate. His recognition through prestigious awards and fellowships across continents further validates his global research excellence.

Publication Top Notes

1. Deep learning for mobile multimedia: A survey

  • Authors: K Ota, MS Dao, V Mezaris, FGBD Natale

  • Journal: ACM Transactions on Multimedia Computing, Communications, and Applications

  • Cited by: 188

  • Year: 2017

Summary:
This comprehensive survey explores how deep learning techniques have been adapted and optimized for mobile multimedia applications. It covers both theoretical advancements and practical implementation challenges. The paper also discusses energy efficiency and processing limitations of mobile devices. It has become a foundational reference in mobile multimedia research.

2. Exploring convolutional neural network architectures for EEG feature extraction

  • Authors: I Rakhmatulin, MS Dao, A Nassibi, D Mandic

  • Journal: Sensors, Vol. 24(3), Article 877

  • Cited by: 62

  • Year: 2024

Summary:
This paper investigates CNN-based methods for extracting features from EEG signals, a key step in brain-computer interface development. Multiple CNN architectures are compared for performance and accuracy. The study demonstrates significant improvement in signal interpretation. It contributes to the emerging field of AI-powered neuro technology.

3. Daily human activities recognition using heterogeneous sensors from smartphones

  • Authors: MS Dao, TA Nguyen-Gia, VC Mai

  • Journal: Procedia Computer Science, Vol. 111, pp. 323–328

  • Cited by: 34

  • Year: 2017

Summary:
The paper presents a method for recognizing daily human activities using various smartphone sensors. It highlights sensor fusion techniques to improve detection accuracy. The approach is lightweight and suitable for real-time implementation. It holds potential for fitness, health, and smart environment applications.

4. A real-time complex event discovery platform for cyber-physical-social systems

  • Authors: MS Dao, S Pongpaichet, L Jalali, K Kim, R Jain, K Zettsu

  • Conference: International Conference on Multimedia Retrieval

  • Cited by: 34

  • Year: 2014

Summary:
This work proposes a real-time platform for discovering complex events from integrated cyber, physical, and social sources. It focuses on fusing multi-modal data streams for event detection. The platform is designed for smart city and situational awareness applications. It bridges the gap between social sensing and real-time analytics.

5. Edge potential functions (EPF) and genetic algorithms (GA) for edge-based matching of visual objects

  • Authors: MS Dao, FGB De Natale, A Massa

  • Journal: IEEE Transactions on Multimedia, Vol. 9(1), pp. 120–135

  • Cited by: 33

  • Year: 2006

Summary:
This paper introduces edge potential functions (EPF) combined with genetic algorithms for visual object matching. It enhances robustness in noisy or occluded conditions. The method shows improvements in object recognition performance. It contributes foundational techniques for multimedia and computer vision systems.

Shima Shafiee | Bioinformatics | Best Researcher Award

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Dr. Shima Shafiee | Bioinformatics | Best Researcher Award

Lecturer at Razi University, Iran

Dr. Shima Shafiee is an Iranian researcher specializing in artificial intelligence, bioinformatics, and computational biology. She has demonstrated remarkable expertise in applying machine learning models to complex biomedical problems, particularly in protein-peptide interaction prediction. With a solid foundation in computer engineering and years of academic and research experience, she has developed predictive models and hybrid algorithms that bridge the gap between computer science and life sciences. Dr. Shafiee’s extensive publication record in internationally recognized journals, her involvement in teaching and interdisciplinary research, and her dedication to lifelong learning and professional development highlight her as an influential and forward-looking scholar. She is currently engaged in postdoctoral research and academic roles, contributing to AI-driven solutions in healthcare and medical data analysis. Her collaborative efforts, teaching experience, and scientific contributions position her as a promising leader in both national and international research communities.

Professional Profile 

Education🎓

Dr. Shima Shafiee holds a Ph.D. in Computer Engineering with a specialization in Computer Systems Architecture from Razi University, where her dissertation focused on learning-based models for predicting protein-peptide binding interactions. Prior to this, she completed her M.Sc. in Computer Science from Tabari University, concentrating on optimization algorithms for the two-dimensional bin packing problem. She earned her B.Sc. in Computer Engineering from Kerman University, with a focus on the role of information technology in cybercrime and money laundering. Her academic path has been marked by a strong interdisciplinary orientation, merging principles of algorithm design, artificial intelligence, and systems engineering. Dr. Shafiee is currently preparing to begin her postdoctoral research at Shahid Bahonar University of Kerman, focusing on advanced machine learning applications in medical image analysis and bioinformatics. Her academic training reflects a rigorous and innovative approach to solving computational challenges in the biological and medical sciences.

Professional Experience📝

Dr. Shima Shafiee has accumulated a broad range of academic and research experience over the years. She has served as a lecturer at Razi University and Shahid Bahonar University of Kerman, teaching subjects such as artificial intelligence, computer programming, fundamentals of computing, and algorithm design. Her professional journey includes roles as a research assistant and educational collaborator on interdisciplinary projects in bioinformatics and medical imaging. She has participated in collaborative initiatives with departments in law and medical sciences, highlighting her cross-disciplinary competence. Dr. Shafiee’s earlier professional activities include internships, secondary school teaching, and work as an educational researcher at Tarbiat Modares University. Beyond academia, she actively delivers presentations and training sessions on AI tools in education, medical science, and government applications. Her career reflects a continuous evolution from foundational computing education to advanced AI-driven research and applications in healthcare and biotechnology.

Research Interest🔎

Dr. Shima Shafiee’s research interests center on artificial intelligence applications in bioinformatics, particularly the prediction and analysis of protein-peptide and protein-protein interactions. Her work leverages machine learning, deep learning, ensemble models, and evolutionary computation to develop predictive tools with real-world implications in biomedical science. She is passionate about creating models that improve the accuracy and efficiency of residue-level interaction predictions, utilizing sequence- and structure-based features. Recently, her research has expanded into medical image processing, AI-based diagnostic support systems, and applications of large language models in biological data analysis. She is also actively exploring segmentation-based algorithms and attention mechanisms for bioinformatic tasks. Her interdisciplinary approach connects computer science, structural biology, and clinical applications, aiming to provide computational insights that aid in drug discovery, diagnostics, and personalized medicine. Dr. Shafiee’s ongoing work represents the forefront of AI-driven solutions in life sciences.

Award and Honor🏆

Throughout her academic career, Dr. Shima Shafiee has received several accolades in recognition of her scholarly excellence and scientific contributions. She was recognized as the top-performing student during her Ph.D. studies and earned third place in her M.Sc. program. One of her early works was selected among the best papers at the 2nd International Congress of Electrical Engineering, Computer Science, and Information Technology. Her research papers have been accepted at notable national and international conferences, and her interdisciplinary contributions have been acknowledged by academic bodies. She has also served as a reviewer for various scientific journals and conferences, including IEEE-related events and bioinformatics journals. Dr. Shafiee is an active member of multiple academic and scientific societies, which reflects her standing within the research community. Her dedication to advancing computational applications in biomedicine has earned her a respected position in her field.

Research Skill🔬

Dr. Shima Shafiee possesses a diverse and well-rounded research skill set, spanning programming, algorithm development, data modeling, and AI tool integration. She is proficient in Python, R, WEKA, SPSS, MATLAB, and BioPython, among other platforms, and applies these tools in both supervised and unsupervised machine learning environments. Her work includes the development of hybrid models combining genetic programming, deep learning, support vector machines, and ensemble techniques for high-precision biomedical predictions. Dr. Shafiee is also skilled in optimization algorithms, having previously worked on solving the two-dimensional bin packing problem using particle swarm optimization. She integrates her technical expertise with domain knowledge in protein chemistry, structural biology, and medical imaging to produce interdisciplinary solutions. In addition to computational methods, she is experienced in academic writing, peer reviewing, and scientific communication. Her technical and analytical capabilities are well-aligned with emerging challenges in bioinformatics and artificial intelligence.

Conclusion💡

Dr. Shima Shafiee is a highly deserving candidate for the Best Researcher Award due to her exceptional contributions to the fields of computational biology, artificial intelligence, and bioinformatics. Her innovative research on protein-peptide interaction prediction, coupled with a strong publication record in prestigious journals and conferences, reflects both scientific rigor and societal relevance, particularly in advancing biomedical research. With her interdisciplinary focus, commitment to academic excellence, and proactive engagement in teaching, reviewing, and professional development, Dr. Shafiee exemplifies the qualities of a forward-thinking researcher. As she continues to expand her work into medical image processing and international collaborations, she holds immense potential for future leadership and transformative impact in science and technology.

Publications Top Noted✍

  • Title: SPPPred: Sequence-based protein-peptide binding residue prediction using genetic programming and ensemble learning
    Authors: S. Shafiee, A. Fathi, G. Taherzadeh
    Year: 2022
    Citations: 12

  • Title: Prediction of protein–peptide-binding amino acid residues regions using machine learning algorithms
    Authors: S. Shafiee, A. Fathi
    Year: 2021
    Citations: 6

  • Title: Combination of genetic programming and support vector machine-based prediction of protein-peptide binding sites with sequence and structure-based features
    Authors: S. Shafiee, A. Fathi
    Year: 2021
    Citations: 6

  • Title: Prediction of protein–peptide binding residues using classification algorithms
    Authors: S. Shafiee, A. Fathi, F. Abdali-Mohammadi
    Year: 2020
    Citations: 6

  • Title: A Review of the Uses of Artificial Intelligence in Protein Research
    Authors: S. Shafiee, A. Fathi, F. Abdali-Mohammadi
    Year: 2019
    Citations: 5

  • Title: DP-site: A dual deep learning-based method for protein-peptide interaction site prediction
    Authors: S. Shafiee, A. Fathi, G. Taherzadeh
    Year: 2024
    Citations: 2

  • Title: Protein-peptide interaction region residues prediction using a generative sampling technique and ensemble deep learning-based models
    Authors: S. Shafiee, A. Fathi, G. Taherzadeh
    Year: 2025

  • Title: Integrating Structural Information: Comparing Classification-Based and Segmentation-Based Predictors in Bioinformatics (Case Study: Protein-Peptide Region Residue-Level Interaction)
    Authors: S. Shafiee, A. Fathi, F. Safari
    Year: 2025

  • Title: Leveraging a Structure-Based and Learning-Based Predictor Using Various Feature Groups in Bioinformatics (Case Study: Protein-Peptide Region Residue-Level Interaction)
    Authors: S. Shafiee, A. Fathi
    Year: 2024

  • Title: Application of Learning-Based Models in Predicting of Protein-Peptide Binding Interactions
    Authors: S. Shafiee
    Year: 2024

  • Title: Application of Combined Decision Trees Function for Optimization Issues
    Authors: A. Fathi, S. Shafiee
    Year: 2017

Shiming Ge | AI Safety | Best Researcher Award

Published on by Computer Vision Awards

Prof. Shiming Ge | AI Safety | Best Researcher Award

Professor at Chinese Academy of Sciences, China

Dr. Shiming Ge is a prominent researcher and academic leader in the field of artificial intelligence, with specializations in AI safety, multimedia security, visual understanding, and privacy-preserving machine learning. He serves as a Professor and the Head of the Intelligent Multimedia Security Lab at the Institute of Information Engineering, Chinese Academy of Sciences (CAS). Dr. Ge has made significant contributions to areas such as deepfake detection, robust learning from noisy data, and adversarial machine learning. His work bridges the gap between theoretical AI foundations and real-world applications, addressing modern technological challenges. With over 100 research papers in top-tier journals and conferences, he is recognized both nationally and internationally. He has also successfully led numerous national and industry-funded research projects. Dr. Ge is a Senior Member of IEEE and plays an active role in mentoring young researchers, shaping the future of AI research through leadership, innovation, and global academic engagement.

Professional Profile 

Education🎓

Dr. Shiming Ge pursued his higher education through an accelerated track, showcasing exceptional academic performance from an early stage. He earned his Ph.D. in Electronic Engineering from the University of Science and Technology of China (USTC) in 2008. Before his doctoral studies, he completed his bachelor’s degree in Electronic Engineering from USTC as part of an elite early-excellence program that allowed him to graduate one year ahead of schedule. His Ph.D. research focused on digital image recovery, laying a solid technical foundation that would later underpin his work in computer vision, machine learning, and multimedia analysis. His advanced education provided him with strong theoretical and practical expertise, which he has successfully applied in tackling pressing issues such as visual security, data privacy, and adversarial robustness. Dr. Ge’s academic journey reflects not only his intellectual capacity but also his long-term commitment to innovation and academic excellence in artificial intelligence.

Professional Experience📝

Dr. Shiming Ge currently holds the position of Professor and leads the Intelligent Multimedia Security Lab at the Institute of Information Engineering, Chinese Academy of Sciences (CAS). Over the years, he has taken on several prominent roles within the academic and industrial research community, contributing to both theoretical advancements and real-world applications. He has served as the principal investigator for more than 20 national and industrial research projects, including those funded by China’s National Key R&D Program and companies such as Siemens and Alibaba. His leadership in research and project management has yielded numerous successful outcomes in AI and multimedia security. Beyond project execution, he also teaches graduate-level courses, such as “AI Safety” and “Introduction to Deep Learning,” and actively mentors young researchers. His students have gone on to win prestigious awards such as the CAS President’s Award. His professional journey highlights a balanced dedication to academic innovation, industry collaboration, and talent development.

Research Interest🔎

Dr. Shiming Ge’s research interests span several high-impact domains within artificial intelligence, with a central focus on AI safety, multimedia security, visual understanding, adversarial machine learning, and privacy-preserving techniques. He is particularly known for his work on deepfake detection, robust learning under noisy conditions, and trustworthy machine learning—topics that are increasingly critical in today’s digital age. He has also contributed to the development of federated learning frameworks and few-shot learning models, enabling decentralized and efficient AI applications. His research addresses both theoretical challenges and practical implementations, emphasizing the societal importance of trustworthy and secure AI systems. Dr. Ge is also exploring the interpretability of neural networks to improve transparency and accountability in automated decision-making. His work has not only advanced the academic field but also had tangible impacts on real-world technologies, such as surveillance, identity verification, and online content moderation, making his research highly relevant and applicable.

Award and Honor🏆

Dr. Shiming Ge has received multiple prestigious awards in recognition of his groundbreaking research in artificial intelligence and multimedia security. He was honored with the IEEE TMM Best Journal Paper Award in 2025, underscoring the quality and impact of his published work. In addition, he is a recipient of the Wu Wenjun Artificial Intelligence Science and Technology Award, one of China’s most distinguished recognitions in the field of AI. His research team has earned top rankings in international competitions, including the CVPR’20 Anti-UAV Challenge and the ICCV’19 VisDrone-SOT Challenge, reflecting his capacity to translate research into high-performing systems. Dr. Ge’s students have also gained accolades under his mentorship, such as the CAS President’s Award and recognition as Outstanding Graduates of Beijing, further highlighting his excellence in academic supervision. These awards collectively affirm his leadership, innovation, and sustained contributions to advancing AI technologies globally.

Research Skill🔬

Dr. Shiming Ge possesses a comprehensive and multidisciplinary research skillset that enables him to lead innovative projects across multiple AI domains. He is proficient in deep learning frameworks such as TensorFlow and PyTorch, and has extensive experience in designing robust neural network architectures for tasks like object detection, face recognition, and anomaly detection. His technical expertise extends to adversarial training, federated learning, and privacy-preserving machine learning. Dr. Ge also excels in multimedia data analysis, image forensics, and deepfake identification techniques. Beyond technical implementation, he is highly skilled in hypothesis-driven research design, scientific writing, and cross-functional project coordination. His strong grasp of statistical modeling, algorithm optimization, and system-level integration has enabled the successful deployment of AI models in both academic and industry contexts. Furthermore, he has demonstrated a capacity for mentoring, peer reviewing, and leading multidisciplinary teams, making him a valuable contributor to collaborative and large-scale research environments.

Conclusion💡

Publications Top Noted✍

  • Title: Detecting masked faces in the wild with LLE-CNNs
    Authors: S. Ge, J. Li, Q. Ye, Z. Luo
    Year: 2017
    Citations: 576

  • Title: Selective-supervised contrastive learning with noisy labels
    Authors: S. Li, X. Xia, S. Ge, T. Liu
    Year: 2022
    Citations: 300

  • Title: Low-resolution face recognition in the wild via selective knowledge distillation
    Authors: S. Ge, S. Zhao, C. Li, J. Li
    Year: 2018
    Citations: 236

  • Title: Occluded face recognition in the wild by identity-diversity inpainting
    Authors: S. Ge, C. Li, S. Zhao, D. Zeng
    Year: 2020
    Citations: 130

  • Title: Student network learning via evolutionary knowledge distillation
    Authors: K. Zhang, C. Zhang, S. Li, D. Zeng, S. Ge
    Year: 2021
    Citations: 105

  • Title: Detecting Deepfake Videos with Temporal Dropout 3DCNN
    Authors: D. Zhang, C. Li, F. Lin, D. Zeng, S. Ge
    Year: 2021
    Citations: 99

  • Title: Estimating noise transition matrix with label correlations for noisy multi-label learning
    Authors: S. Li, X. Xia, H. Zhang, Y. Zhan, S. Ge, T. Liu
    Year: 2022
    Citations: 98

  • Title: Accurate temporal action proposal generation with relation-aware pyramid network
    Authors: J. Gao, Z. Shi, G. Wang, J. Li, Y. Yuan, S. Ge, X. Zhou
    Year: 2020
    Citations: 97

  • Title: Domain adaptive attention learning for unsupervised person re-identification
    Authors: Y. Huang, P. Peng, Y. Jin, Y. Li, J. Xing
    Year: 2020
    Citations: 89

  • Title: Look through masks: Towards masked face recognition with de-occlusion distillation
    Authors: C. Li, S. Ge, D. Zhang, J. Li
    Year: 2020
    Citations: 87

  • Title: Efficient low-resolution face recognition via bridge distillation
    Authors: S. Ge, S. Zhao, C. Li, Y. Zhang, J. Li
    Year: 2020
    Citations: 82

  • Title: Predicting aesthetic score distribution through cumulative Jensen-Shannon divergence
    Authors: X. Jin, L. Wu, X. Li, S. Chen, S. Peng, J. Chi, S. Ge, C. Song, G. Zhao
    Year: 2018
    Citations: 82

  • Title: Bootstrapping multi-view representations for fake news detection
    Authors: Q. Ying, X. Hu, Y. Zhou, Z. Qian, D. Zeng, S. Ge
    Year: 2023
    Citations: 80

  • Title: VisDrone-SOT2019: The vision meets drone single object tracking challenge results
    Authors: D. Du, P. Zhu, L. Wen, X. Bian, H. Ling, Q. Hu, J. Zheng, T. Peng, X. Wang, …
    Year: 2019
    Citations: 75

  • Title: ILGNet: Inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation
    Authors: X. Jin, L. Wu, X. Li, X. Zhang, J. Chi, S. Peng, S. Ge, G. Zhao, S. Li
    Year: 2019
    Citations: 73

  • Title: Attentive Deep Stitching and Quality Assessment for 360 Omnidirectional Images
    Authors: J. Li, Y. Zhao, W. Ye, K. Yu, S. Ge
    Year: 2019
    Citations: 63

  • Title: Aesthetic attributes assessment of images
    Authors: X. Jin, L. Wu, G. Zhao, X. Li, X. Zhang, S. Ge, D. Zou, B. Zhou, X. Zhou
    Year: 2019
    Citations: 59

  • Title: The 3rd anti-UAV workshop & challenge: Methods and results
    Authors: J. Zhao, J. Li, L. Jin, J. Chu, Z. Zhang, J. Wang, J. Xia, K. Wang, Y. Liu, …
    Year: 2023
    Citations: 53

  • Title: Deepfake video detection with spatiotemporal dropout transformer
    Authors: D. Zhang, F. Lin, Y. Hua, P. Wang, D. Zeng, S. Ge
    Year: 2022
    Citations: 51

  • Title: Deepfake video detection via predictive representation learning
    Authors: S. Ge, F. Lin, C. Li, D. Zhang, W. Wang, D. Zeng
    Year: 2022
    Citations: 50

Duy-Dong Le | Federated Learning | Best Researcher Award

Published on by Computer Vision Awards

Mr. Duy-Dong Le | Federated Learning | Best Researcher Award

Lecturer at UEH Campus Vinh Long, Vietnam

Mr. Duy-Dong Le is a dedicated academic and researcher in the field of Computer Science, currently serving as a faculty member at UEH University, Vietnam. He is affiliated with the Vinh Long Campus, where he teaches subjects such as Data Structures and Algorithms, Web Programming, Data Science, and IT for Business. His work bridges teaching, research, and community engagement. Over the years, he has led multiple research projects in deep learning, artificial intelligence, and federated learning with applications in public health, agriculture, and environmental monitoring. Beyond academic duties, Mr. Le plays a significant leadership role as head of the Association for Information Processing and as the leader of an AI research team. He is also an active organizer of educational and community development initiatives in the Mekong Delta. His scholarly contributions and civic involvement highlight his commitment to advancing both research and societal well-being through technology and education.

Professional Profile 

Education🎓

Mr. Duy-Dong Le began his academic journey at Vinh Long Technical and Teacher Training College, where he completed his undergraduate studies. He went on to earn a master’s degree in Computer Science from the University of Information Technology under Vietnam National University, Ho Chi Minh City. During his postgraduate studies, he developed expertise in deep learning, particularly focusing on the application of the Standard Adaptive Model (SAM) for predicting test performance. His thesis work in this domain was later published in conference proceedings and academic journals. Currently, he is pursuing his doctoral studies at the Industry University of Ho Chi Minh City, with a focus on advanced applications of artificial intelligence in areas such as federated learning and data-driven decision-making. His academic path reflects a consistent dedication to learning and advancing knowledge in computer science, while also integrating his research work with practical, community-centered challenges in education, healthcare, and environmental sustainability.

Professional Experience📝

Mr. Le currently holds a teaching and research position at UEH University’s Vinh Long Campus, where he has been actively involved in academic and institutional development. In his professional capacity, he teaches various undergraduate courses in computer science and information technology. His experience spans not only classroom instruction but also academic mentorship, project supervision, and curriculum development. Mr. Le leads the campus AI research team and serves as the head of the Association for Information Processing. He also contributes significantly to organizing events, contests, and workshops aimed at digital transformation, sustainable development, and community learning. His efforts have led to the successful execution of initiatives like the UEH Global Project, which provides free educational infrastructure to teachers and students across the Mekong Delta. His professional journey is marked by a unique combination of educational excellence, technical leadership, and service to the community, reflecting his multifaceted role as a teacher, researcher, and innovator.

Research Interest🔎

Mr. Duy-Dong Le’s research interests lie primarily in the fields of artificial intelligence, deep learning, and federated learning, with a strong focus on solving practical problems in education, agriculture, public health, and environmental sustainability. He has conducted research projects applying AI to predict agricultural prices, forecast COVID-19 transmission rates, and analyze air quality using collaborative machine learning models. He is particularly interested in the intersection of AI and social impact, often designing systems tailored for regional needs in Vietnam, especially within the Mekong Delta. His current studies involve exploring privacy-preserving learning techniques, such as federated learning, to handle distributed data more securely. Mr. Le’s research portfolio is further enhanced by interdisciplinary collaborations and a commitment to community-focused technology deployment. His work reflects a vision of using data science not just for theoretical exploration but as a practical tool for societal transformation and decision-making in both urban and rural contexts.

Award and Honor🏆

Mr. Duy-Dong Le has earned recognition for both his academic contributions and community engagement. His research work has been presented at various national and international conferences, including the FAIR and FDSE conferences, and published in prestigious outlets such as Springer and the MDPI Algorithms Journal. He has played a key role in several provincial and institutional research projects that have received acknowledgment for their innovation and practical relevance. Additionally, his leadership in educational outreach—especially the UEH Global Project—has been commended for supporting thousands of students and educators during the COVID-19 pandemic through free access to online learning platforms and digital tools. As an organizer and presenter of workshops and STEM events, he has significantly contributed to raising awareness about digital transformation and sustainable development in the region. His honors reflect not only his scholarly impact but also his sustained efforts to bridge academia with real-world community needs.

Research Skill🔬

Mr. Le possesses a strong set of research skills that span programming, data modeling, and applied machine learning. He is proficient in programming languages such as Python, PHP, and Java, and commonly uses platforms like Google Colab for deploying deep learning models using TensorFlow. His technical expertise includes predictive modeling, algorithm development, and system integration for real-world applications. In his recent projects, he has implemented AI-based frameworks for public health forecasting and federated learning systems for environmental monitoring. He is also skilled in preparing research manuscripts, presenting at conferences, and collaborating with both local and international research partners. His ability to connect theoretical models with practical solutions showcases a balanced research profile. Moreover, his experience in organizing and translating for academic events has strengthened his communication and cross-functional skills. Collectively, Mr. Le’s research abilities position him as a capable and innovative scholar committed to advancing knowledge and addressing real-life challenges through technology.

Conclusion💡

Mr. Duy-Dong Le is highly deserving of the Best Researcher Award due to his consistent and impactful contributions to applied computer science and artificial intelligence, particularly in addressing region-specific challenges in agriculture, health, and education. His research has not only led to peer-reviewed publications in respected journals and conferences but also translated into tangible benefits for local communities, such as predictive systems for crop pricing and pandemic modeling. Beyond academia, his leadership in organizing educational outreach, promoting STEM initiatives, and supporting digital access in underserved areas highlights his deep commitment to societal advancement. With ongoing doctoral studies and an expanding research portfolio, Mr. Le demonstrates strong potential to become a leading academic and thought leader in his field, both nationally and internationally.

Publications Top Noted✍

  • Title: Further Evaluation of Prompting Tactics for Establishing Intraverbal Responding in Children with Autism
    Authors: ET Ingvarsson, DD Le
    Year: 2011
    Citations: 77

  • Title: Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting
    Authors: DD Le, AK Tran, MS Dao, KC Nguyen-Ly, HS Le, XD Nguyen-Thi, …
    Year: 2022
    Citations: 25

  • Title: Leverage Boosting and Transformer on Text-Image Matching for Cheap Fakes Detection
    Authors: TV La, MS Dao, DD Le, KP Thai, QH Nguyen, TK Phan-Thi
    Year: 2022
    Citations: 11

  • Title: Proposed Distance and Entropy Measures of Picture Fuzzy Sets in Decision Support Systems
    Authors: H Van Pham, KP Thai, QH Nguyen, DD Le, TT Le, TXD Nguyen, …
    Year: 2023
    Citations: 9

  • Title: Federated Learning in Smart Agriculture: An Overview
    Authors: DD Le, MS Dao, AK Tran, TB Nguyen, HG Le-Thi
    Year: 2023
    Citations: 8

  • Title: Federated Learning for Air Quality Index Prediction: An Overview
    Authors: DD Le, AK Tran, MS Dao, MSH Nazmudeen, VT Mai, NH Su
    Year: 2022
    Citations: 5

  • Title: Modeling Transmission Rate of COVID-19 in Regional Countries to Forecast Newly Infected Cases in a Nation by the Deep Learning Method
    Authors: LD Dong, VT Nguyen, DT Le, MV Tiep, VT Hien, PP Huy, PT Hieu
    Year: 2021
    Citations: 5

  • Title: Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
    Authors: PT Nguyen, MS Dao, MA Riegler, RU Kiran, TT Dang, DD Le, …
    Year: 2023
    Citations: 4

  • Title: CrossHeteroFL: Cross-Stratified Sampling Composition-Fitting to Federated Learning for Heterogeneous Clients
    Authors: VP Tinh, HH Son, DNM Dang, NH Nam, DD Le, TB Nguyen, TQ Pham, …
    Year: 2024
    Citations: 2

  • Title: Joint Federated Learning Using Deep Segmentation and the Gaussian Mixture Model for Breast Cancer Tumors
    Authors: PD Lam, VP Tinh, DD Le, NH Nam, TA Khoa
    Year: 2024
    Citations: 2

  • Title: Violence Detection in Videos Based on CNN Feature for ConvLSTM2D
    Authors: TD Trinh, TS Vu-Ngoc, LT Le-Nhi, DD Le, TB Nguyen, TB Pham
    Year: 2024
    Citations: 2

  • Title: A Survey of Model Compression and Its Feedback Mechanism in Federated Learning
    Authors: DD Le, AK Tran, TB Pham, TN Huynh
    Year: 2024
    Citations: 2

  • Title: Correlation-Based Weighted Federated Learning with Multimodal Sensing and Knowledge Distillation: An Application on a Real-World Benchmark Dataset
    Authors: DD Le, DT Huynh, PT Bao
    Year: 2025
    Citations: 1

  • Title: A Novel Solution for Energy-Saving and Lifetime-Maximizing of LoRa Wireless Mesh Networks
    Authors: HH Son, VP Tinh, DNM Dang, BT Duyen, DD Le, TT Dang, QH Nguyen, …
    Year: 2024
    Citations: 1

  • Title: Latency-Aware Split Learning Optimization via Genetic Algorithms
    Authors: LH Trung, TY Nguyen, DD Le, TT Dang, TA Khoa
    Year: 2025

  • Title: FedNolowe: A Normalized Loss-Based Weighted Aggregation Strategy for Robust Federated Learning in Heterogeneous Environments
    Authors: DD Le, N Huynh-Tuong, AK Tran, MS Dao, PT Bao
    Year: 2025

  • Title: SMixSL: The Smashed-Mixture Technique for Split Learning with Localizable Features
    Authors: VP Tinh, TA Khoa, PD Lam, NH Nam, DNM Dang, DD Le, TT Dang, …
    Year: 2025

  • Title: Factors Affecting the Adoption of Information Technology in Medium and Small Enterprises: A Case Study in Mekong Delta, Vietnam
    Authors: TL Nguyen-Thi, DD Le, KC Nguyen-Ly, TT Nguyen, MSH Nazmudeen
    Year: 2024

Jabar Habashi | Hyperspectral Data Processing | Best Researcher Award

Published on by Computer Vision Awards

Dr. Jabar Habashi | Hyperspectral Data Processing | Best Researcher Award

Student at University of Kentucky, Iran

Mr. Jabar Habashi is a dedicated researcher specializing in remote sensing, mineral exploration, and geospatial data science. With a strong academic background and technical expertise, he has contributed to the advancement of hyperspectral image analysis, AI-driven geological mapping, and environmental impact assessment. His work bridges the fields of Earth sciences and data science, focusing on developing predictive models for resource estimation and sustainable land use. Jabar has co-authored over 12 publications in reputed international journals such as ISPRS Journal of Photogrammetry and Remote Sensing and Remote Sensing (MDPI), and presented at leading conferences. He has also served as a reviewer for peer-reviewed journals, demonstrating engagement with the scientific community. Currently working as a Data Scientist at Scan Miner Solutions, Jabar combines practical industry insight with strong research acumen. His interdisciplinary approach and commitment to impactful science make him a promising figure in global geoscience and Earth observation research.

Professional Profile 

Education🎓

Jabar Habashi holds a Master of Science in Mining Exploration Engineering from Sahand University of Technology, Iran, completed in 2023. His MSc thesis focused on multispectral data classification using Hyperion satellite data, highlighting the role of remote sensing in mineral exploration. He previously earned a Bachelor of Science in Mineral Engineering from Malayer University in 2019, where his undergraduate research involved processing and analyzing magnetometric data from the Nadushan region in Yazd, Iran. Throughout his academic journey, Jabar was recognized for his academic excellence, earning full scholarships and achieving high ranks in national entrance examinations. His education provided a strong foundation in mining and geological sciences, with a focus on satellite data processing and geospatial analysis. He also gained valuable teaching experience as a teaching assistant in various laboratories, including mineralogy, petrology, and cartography, which reflects his active engagement with both theoretical and hands-on aspects of Earth sciences.

Professional Experience📝

Jabar Habashi is currently working as a Data Scientist at Scan Miner Solutions (since July 2024), where he applies advanced data analytics and remote sensing techniques to solve real-world challenges in the mining and exploration industry. His previous hands-on experience includes internships at the Department of Industry, Mining, and Trade of Sonqor County, and Gelali Iron Mine in Qorveh, Iran, where he gained insights into mining operations and data interpretation. During his academic years, he held several teaching assistant positions at Malayer University, supporting laboratory sessions in descriptive mineralogy, optical mineralogy, petrology, and cartography. These roles not only strengthened his technical and instructional skills but also laid a foundation for future academic contributions. His combination of fieldwork, laboratory training, and data-driven industry experience allows him to connect theoretical research with practical applications. This balanced background equips Jabar with the multidisciplinary insight essential for innovation in geoscience and mining technologies.

Research Interest🔎

Jabar Habashi’s research interests span a broad array of topics within remote sensing, mineral exploration, and environmental geoscience. He is particularly focused on hyperspectral image analysis, multisource data fusion, and AI-driven geological mapping. His work aims to automate mineral target recognition using deep learning, optimize alteration mineral detection, and advance predictive modeling for resource estimation. He is also passionate about hydrologic modeling, LiDAR data processing, climate change analysis, and geohazard assessment, reflecting a strong commitment to sustainable Earth system science. In recent studies, he has explored Antarctic terrains using PRISMA hyperspectral data and contributed to mapping projects in semi-arid and mountainous regions. His interest in mine closure and post-mining land use demonstrates a forward-thinking approach to environmental reclamation and sustainable mining practices. By integrating satellite data, field observations, and AI, Jabar aims to develop tools that enhance decision-making in exploration, environmental monitoring, and climate-sensitive resource management.

Award and Honor🏆

Jabar Habashi has received multiple awards and honors in recognition of his academic excellence and research capabilities. He earned a national rank that secured him admission to Malayer University with a full undergraduate scholarship in the mathematics branch. Later, he achieved a top national ranking in the MSc entrance exam for Mining Exploration Engineering, which granted him a full scholarship at Sahand University of Technology. These distinctions reflect his strong academic caliber and dedication to scholarly achievement. In addition to his academic honors, Jabar has contributed to the scientific community by reviewing manuscripts for respected journals such as Remote Sensing Applications: Society and Environment, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), and Earth Science Informatics. These contributions highlight his growing reputation in the fields of Earth observation and geospatial research. His ongoing commitment to excellence and collaboration makes him a distinguished researcher in his discipline.

Research Skill🔬

Jabar Habashi possesses a wide range of technical and analytical research skills essential for modern geoscientific inquiry. He is proficient in ENVI, ArcGIS Pro, Surfer, MATLAB, and Geosoft Oasis Montaj, with specialization in magnetometry and remote sensing software. He has advanced command of Python programming, particularly for image classification, data preprocessing, and deep learning applications. Jabar’s skill set extends to satellite image fusion, LiDAR and PALSAR data processing, and the integration of field and geospatial datasets for exploration and environmental modeling. He has experience in multispectral and hyperspectral classification, geohazard analysis, and mine site monitoring, showcasing his versatility in both theoretical and applied research. Additionally, he holds multiple certifications in GIS, remote sensing, and Python from Coursera. His analytical capabilities, combined with a deep understanding of geological systems and AI integration, enable him to address complex challenges in mineral exploration and Earth system science with precision and innovation.

Conclusion💡

Mr. Jabar Habashi is highly deserving of the Best Researcher Award for his outstanding contributions to remote sensing, mineral exploration, and geospatial data science. His innovative research—spanning hyperspectral image analysis, deep learning applications in mineral mapping, and environmental impact assessment—has significantly advanced the field while addressing pressing global challenges such as sustainable resource management and climate resilience. With a strong record of international publications, active peer-review service, and technical excellence, he exemplifies the qualities of a dedicated and impactful researcher. As he continues to expand his scholarly reach and takes on greater leadership roles, Mr. Habashi is well-positioned to become a prominent figure in Earth observation and environmental geoscience research on a global scale.

Publications Top Noted✍

  • Title: PRISMA hyperspectral remote sensing data for mapping alteration minerals in Sar-e-Châh-e-Shur region, Birjand, Iran
    Authors: J. Habashi, H. Jamshid Moghadam, M. Mohammady Oskouei, A.B. Pour, et al.
    Year: 2024
    Citations: 13

  • Title: Optimizing alteration mineral detection: A fusion of multispectral and hyperspectral remote sensing techniques in the Sar-e-Chah-e Shur, Iran
    Authors: J. Habashi, M.M. Oskouei, H.J. Moghadam, A.B. Pour
    Year: 2024
    Citations: 10

  • Title: Classification of ASTER Data by Neural Network to Mapping Alterations Related to Copper and Iron Mineralization in Birjand
    Authors: J. Habashi, M.M. Oskouei, H. Jamshid Moghadam
    Year: 2024
    Citations: 5

  • Title: Remote Sensing for Geophysicists
    Authors: M. Gupta
    Year: 2025
    Citations: 1

  • Title: 19 Mineral Remote Identification Sensing Data Using
    Authors: A.B. Pour, S. Niroomand, R. Lavaei, S. Mirzaee, J. Habashi, H.J. Moghadam
    Year: 2025
    Citations: 1

  • Title: Integration of ASTER imagery and field data for chromite exploration in the Eastern Khoy Ophiolite Complex, NW Iran
    Authors: G. Nabatian, A. Songjian, A.B. Pour, F. Abdollahi, J. Habashi
    Year: 2025
    Citations: 1

  • Title: Recurrent-spectral convolutional neural networks (RecSpecCNN) architecture for hyperspectral lithological classification optimization
    Authors: S. Hajaj, A.E. Harti, A.B. Pour, Y. Khandouch, N. Benaouiss, M. Hashim, J. Habashi, et al.
    Year: 2025
    Citations: 1

  • Title: Revealing critical mineralogical insights in extreme environments using deep learning technique on hyperspectral PRISMA satellite imagery: Dry Valleys, South Victoria Land, Antarctica
    Authors: J. Habashi, A.B. Pour, A.M. Muslim, A.M. Afrapoli, J.K. Hong, Y. Park, A. Almasi, et al.
    Year: 2025

  • Title: Mineral Identification Using Remote Sensing Data
    Authors: A.B. Pour, S. Niroomand, R. Lavaei, S. Mirzaee, J. Habashi, H.J. Moghadam
    Year: 2025

  • Title: Advancing Planetary Sustainability Through Lithological Mapping: ASTER Remote Sensing in Antarctica’s Dry Valleys
    Authors: K. Riaz, A.B. Pour, A.M. Muslim, S. Khurram, J. Habashi