Chongyuan Wang | Deep learning | Best Researcher Award

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Dr. Chongyuan Wang | Deep learning | Best Researcher Award

Dr. Chongyuan Wang, a Ph.D. researcher at Hohai University, specializes in artificial intelligence ๐Ÿค– and neural computation ๐Ÿง . He completed his B.S. at Jiangsu University ๐Ÿ‡จ๐Ÿ‡ณ and M.S. in Energy and Power from Warwick University ๐Ÿ‡ฌ๐Ÿ‡ง. His research journey is centered around biologically inspired learning algorithms, with notable contributions to dendritic neuron modeling and evolutionary optimization. Through innovative algorithms like Reinforced Dynamic-grouping Differential Evolution (RDE), Dr. Wang advances the understanding of synaptic plasticity in AI systems. His patent filings and international publications reflect a strong commitment to academic innovation and impact ๐ŸŒ.

Profile

Education ๐ŸŽ“

๐ŸŽ“ B.S. in Engineering โ€“ Jiangsu University, China ๐Ÿ‡จ๐Ÿ‡ณ
๐ŸŽ“ M.S. in Energy and Power โ€“ University of Warwick, UK ๐Ÿ‡ฌ๐Ÿ‡ง (2018)
๐ŸŽ“ Ph.D. Candidate โ€“ Hohai University, majoring in Artificial Intelligence ๐Ÿค–
Dr. Wang’s educational path bridges engineering and intelligent systems. His strong technical foundation and global exposure foster advanced thinking in machine learning and neuroscience. His current doctoral research integrates deep learning, dendritic neuron models, and biologically plausible architectures for improved learning accuracy and model efficiency. ๐Ÿ“˜๐Ÿง 

Experience ๐Ÿ‘จโ€๐Ÿซ

Dr. Wang is currently pursuing his Ph.D. at Hohai University, where he investigates dendritic learning algorithms and synaptic modeling. ๐Ÿงฌ He proposed the RDE algorithm, enhancing dynamic learning in artificial neurons. His hands-on experience includes research design, algorithm optimization, patent writing, and international publication. He has contributed to projects such as “Toward Next-Generation Biologically Plausible Single Neuron Modeling” and “RADE for Lightweight Dendritic Learning.” ๐Ÿ“Š His work balances theoretical depth and applied research, particularly in neural computation, classification systems, and resource-efficient AI. ๐Ÿ”ฌ๐Ÿ’ก

Awards & Recognitions ๐Ÿ…

๐Ÿ… Patent Holder (CN202410790312.0, CN202410646306.8, CN201510661212.9)
๐Ÿ“„ Published in SCI-indexed journal Mathematics (MDPI)
๐ŸŒ Recognized on ORCID (0009-0002-6844-1446)
๐Ÿง  Nominee for Best Researcher Award 2025
His inventive research has earned him national patents and global visibility. His SCI publications in computational modeling reflect both novelty and academic rigor. His continued innovation in biologically inspired AI learning systems has established his position as an emerging researcher in intelligent systems. ๐Ÿš€๐Ÿ“˜

Research Interests ๐Ÿ”ฌ

Dr. Wangโ€™s research fuses deep learning ๐Ÿค– and dendritic modeling ๐Ÿง  to create biologically plausible AI. He developed the RDE algorithm to mimic synaptic plasticity, improving convergence and adaptability in neural networks. His research areas include evolutionary optimization, adaptive grouping, resource-efficient models, and dendritic learning. He explores how artificial neurons can reflect real-brain behavior, leading to faster, more accurate AI systems. Current projects like RADE aim to make AI lightweight and biologically relevant. ๐ŸŒฑ๐Ÿ“Š His vision is to bridge the gap between neuroscience and AI through interpretable, high-performance algorithms. ๐Ÿง ๐Ÿ’ก

Publications

  • Toward Next-Generation Biologically Plausible Single Neuron Modeling: An Evolutionary Dendritic Neuron Model

    Mathematics
    2025-04-29 |ย Journal article
    DOI:ย 10.3390/math13091465
    CONTRIBUTORS:ย Chongyuan Wang;ย Huiyi Liu

Ibrahim Akinjobi Aromoye | Computer Vision | Best Researcher Awards

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Mr.Ibrahim Akinjobi Aromoye | Computer Vision | Best Researcher Awards

Aromoye Akinjobi Ibrahim is a dedicated researcher in Electrical and Electronic Engineering, currently pursuing an MSc (Research) at Universiti Teknologi PETRONAS, Malaysia. His research focuses on hybrid drones for pipeline inspection, integrating machine learning to enhance surveillance capabilities. With a B.Eng. in Computer Engineering from the University of Ilorin, Nigeria, he has excelled in robotics, artificial intelligence, and digital systems. Aromoye has extensive experience as a research assistant, STEM educator, and university teaching assistant, contributing to 5G technology, UAV development, and machine learning applications. He has authored multiple research papers in reputable journals and conferences. A proactive leader, he has held executive roles in student associations and led innovative projects. His expertise spans embedded systems, IoT, and cybersecurity, complemented by certifications in Python, OpenCV, and AI-driven vision systems. He actively contributes to academic peer review and professional development, demonstrating a commitment to technological advancements and education.

Profile

Education ๐ŸŽ“

Aromoye Akinjobi Ibrahim is pursuing an MSc (Research) in Electrical and Electronic Engineering at Universiti Teknologi PETRONAS (2023-2025), focusing on hybrid drones for pipeline inspection under the supervision of Lo Hai Hiung and Patrick Sebastian. His research integrates machine learning with air buoyancy technology to enhance UAV flight time. He holds a B.Eng. in Computer Engineering from the University of Ilorin, Nigeria (2015-2021), graduating with a Second Class Honors (Upper) and a CGPA of 4.41/5.0. His undergraduate thesis involved developing a smart bidirectional digital counter with a light control system for energy-efficient automation. Excelling in digital signal processing, AI applications, robotics, and software engineering, he has consistently demonstrated technical excellence. His academic journey is enriched with top grades in core engineering courses and hands-on experience in embedded systems, IoT, and AI-driven automation, making him a skilled researcher and developer in advanced engineering technologies.

Experience ๐Ÿ‘จโ€๐Ÿซ

Aromoye has diverse experience spanning research, teaching, and industry. As a Graduate Research Assistant at Universiti Teknologi PETRONAS (2023-present), he specializes in hybrid drone development, 5G technologies, and machine learning for UAVs. His contributions include designing autonomous systems and presenting research at international conferences. Previously, he was an Undergraduate Research Assistant at the University of Ilorin (2018-2021), where he worked on digital automation and AI-driven projects. In academia, he has been a Teaching Assistant at UTP, instructing courses in computer architecture, digital systems, and electronics. His industry roles include STEM Educator at STEMCafe (2022-2023), where he taught Python, robotics, and electronics, and a Mobile Games Development Instructor at Center4Tech (2019-2021), guiding students in game design. He also worked as a Network Support Engineer at the University of Ilorin (2018). His expertise spans AI, IoT, and automation, making him a versatile engineer and educator.

Awards & Recognitions ๐Ÿ…

Aromoye has received prestigious scholarships and leadership recognitions. He is a recipient of the Yayasan Universiti Teknologi PETRONAS (YUTP-FRG) Grant (2023-2025), a fully funded scholarship supporting his MSc research in hybrid drones. As an undergraduate, he demonstrated leadership by serving as President of the Oyun Studentsโ€™ Association at the University of Ilorin (2019-2021) and previously as its Public Relations Officer (2018-2019). He led several undergraduate research projects, including developing a smart bidirectional digital counter with a light controller system, earning accolades for innovation in automation. His contributions extend to professional peer review for IEEE Access and Results in Engineering. Additionally, he has attained multiple certifications in cybersecurity (MITRE ATT&CK), IoT, and AI applications, reinforcing his technical expertise. His dedication to academic excellence, leadership, and research impact continues to shape his career in engineering and technology.

Research Interests ๐Ÿ”ฌ

Aromoyeโ€™s research revolves around hybrid UAVs, AI-driven automation, and 5G-enabled surveillance systems. His MSc thesis at Universiti Teknologi PETRONAS explores the development of a Pipeline Inspection Air Buoyancy Hybrid Drone, enhancing flight efficiency through a combination of lighter-than-air and heavier-than-air technologies. His work integrates deep learning-based object detection algorithms for real-time pipeline monitoring. He has contributed to multiple research publications in IEEE Access, Neurocomputing, and Elsevier journals, covering UAV reconnaissance, transformer-based pipeline detection, and swarm intelligence. His research interests extend to AI-driven control systems, autonomous robotics, and IoT-based energy-efficient automation. Additionally, he investigates cybersecurity applications in UAVs and smart embedded systems. His interdisciplinary expertise enables him to develop innovative solutions for industrial surveillance, automation, and smart infrastructure, positioning him as a leading researcher in AI-integrated engineering technologies.

Publicationsย 

  • Significant Advancements in UAV Technology for Reliable Oil and Gas Pipeline Monitoring

    Computer Modeling in Engineering & Sciences
    2025-01-27 |ย Journal article
    DOI:ย 10.32604/cmes.2025.058598
    Part ofISSN:ย 1526-1506
    CONTRIBUTORS:ย Ibrahim Akinjobi Aromoye;ย Hai Hiung Lo;ย Patrick Sebastian;ย Shehu Lukman Ayinla;ย Ghulam E Mustafa Abro

  • Real-Time Pipeline Tracking System on a RISC-V Embedded System Platform

    14th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2024
    2024 |ย Conference paper
    DOI:ย 10.1109/ISCAIE61308.2024.10576323
    EID:

    2-s2.0-85198901224

    Part ofย ISBN:ย 9798350348798
    CONTRIBUTORS:ย Wei, E.S.S.;ย Aromoye, I.A.;ย Hiung, L.H.

 

Alvaro Garcia | Computer vision | Best Researcher Award

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Dr. Alvaro Garcia | Computer vision | Best Researcher Award

รlvaro Garcรญa Martรญn es Profesor Titular en la Universidad Autรณnoma de Madrid, especializado en visiรณn por computadora y anรกlisis de video. ๐ŸŽ“ Obtuvo su tรญtulo de Ingeniero de Telecomunicaciรณn en 2007, su Mรกster en Ingenierรญa Informรกtica y Telecomunicaciones en 2009 y su Doctorado en 2013, todos en la Universidad Autรณnoma de Madrid. ๐Ÿซ Ha trabajado en detecciรณn de personas, seguimiento de objetos y reconocimiento de eventos, con mรกs de 22 artรญculos en revistas indexadas y 28 en congresos. ๐Ÿ“ Ha realizado estancias en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. ๐ŸŒ Su investigaciรณn ha contribuido al desarrollo de sistemas de videovigilancia inteligentes, anรกlisis de secuencias de video y procesamiento de seรฑales multimedia. ๐Ÿ“น Ha sido reconocido con prestigiosos premios y ha participado en mรบltiples proyectos europeos de innovaciรณn tecnolรณgica. ๐Ÿš€

Profile

Education ๐ŸŽ“

๐ŸŽ“ Ingeniero de Telecomunicaciรณn por la Universidad Autรณnoma de Madrid (2007). ๐ŸŽ“ Mรกster en Ingenierรญa Informรกtica y Telecomunicaciones con especializaciรณn en Tratamiento de Seรฑales Multimedia en la Universidad Autรณnoma de Madrid (2009). ๐ŸŽ“ Doctor en Ingenierรญa Informรกtica y Telecomunicaciรณn por la Universidad Autรณnoma de Madrid (2013). Su formaciรณn ha sido complementada con estancias en reconocidas universidades internacionales, incluyendo Carnegie Mellon University (EE.UU.), Queen Mary University (Reino Unido) y la Technical University of Berlin (Alemania). ๐ŸŒ Durante su doctorado, recibiรณ la beca FPI-UAM para la realizaciรณn de su investigaciรณn. Su sรณlida formaciรณn acadรฉmica le ha permitido contribuir significativamente al campo del anรกlisis de video y visiรณn por computadora, consolidรกndose como un experto en la detecciรณn, seguimiento y reconocimiento de eventos en secuencias de video. ๐Ÿ“น

Experience ๐Ÿ‘จโ€๐Ÿซ

๐Ÿ”ฌ Se uniรณ al grupo VPU-Lab en la Universidad Autรณnoma de Madrid en 2007. ๐Ÿ“ก De 2008 a 2012, fue becario de investigaciรณn (FPI-UAM). ๐ŸŽ“ Entre 2012 y 2014, trabajรณ como Profesor Ayudante. ๐Ÿ‘จโ€๐Ÿซ De 2014 a 2019, fue Profesor Ayudante Doctor. ๐Ÿ“š De 2019 a 2023, ocupรณ el cargo de Profesor Contratado Doctor. ๐Ÿ›๏ธ Desde septiembre de 2023, es Profesor Titular en la Universidad Autรณnoma de Madrid. ๐Ÿ† Ha participado en mรบltiples proyectos europeos sobre videovigilancia, transmisiรณn de contenido multimedia y reconocimiento de eventos, incluyendo PROMULTIDIS, ATI@SHIVA, EVENTVIDEO y MobiNetVideo. ๐Ÿš€ Ha realizado estancias de investigaciรณn en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. ๐ŸŒ Su experiencia docente abarca asignaturas en Ingenierรญa de Telecomunicaciones, Ingenierรญa Informรกtica e Ingenierรญa Biomรฉdica.

Research Interests ๐Ÿ”ฌ

๐ŸŽฏ Su investigaciรณn se centra en la visiรณn por computadora, el anรกlisis de secuencias de video y la inteligencia artificial aplicada a entornos de videovigilancia. ๐Ÿ“น Especialista en detecciรณn de personas, seguimiento de objetos y reconocimiento de eventos en video. ๐Ÿง  Desarrolla algoritmos de aprendizaje profundo y visiรณn artificial para mejorar la seguridad y automatizaciรณn en ciudades inteligentes. ๐Ÿ™๏ธ Ha trabajado en proyectos sobre videovigilancia, transmisiรณn multimedia y detecciรณn de anomalรญas en video. ๐Ÿ”ฌ Su investigaciรณn incluye procesamiento de imรกgenes, anรกlisis semรกntico y redes neuronales profundas. ๐Ÿš€ Participa activamente en proyectos internacionales y colabora con universidades como Carnegie Mellon, Queen Mary y TU Berlin. ๐ŸŒ Ha publicado en IEEE Transactions on Intelligent Transportation Systems, Sensors y Pattern Recognition, consolidรกndose como un referente en el campo de la visiรณn por computadora. ๐Ÿ“œ

Awards & Recognitions ๐Ÿ…

๐Ÿฅ‡ Medalla “Juan Lรณpez de Peรฑalver” 2017, otorgada por la Real Academia de Ingenierรญa. ๐Ÿ“œ Reconocimiento por su contribuciรณn a la ingenierรญa espaรฑola en el campo de la visiรณn por computadora y anรกlisis de video. ๐Ÿ›๏ธ Ha recibido financiaciรณn para mรบltiples proyectos de investigaciรณn europeos y nacionales. ๐Ÿ”ฌ Ha participado en iniciativas de innovaciรณn en videovigilancia y anรกlisis de video para seguridad. ๐Ÿš€ Sus contribuciones han sido publicadas en las principales conferencias y revistas cientรญficas del รกrea. ๐Ÿ“š Su trabajo ha sido citado mรกs de 4500 veces y cuenta con un รญndice h de 16 en Google Scholar. ๐Ÿ“Š

Publicationsย 

1. Rafael Martรญn-Nieto, รlvaro Garcรญa-Martรญn, Alexander G. Hauptmann, and Jose. M.
Martรญnez: โ€œAutomatic vacant parking places management system using multicamera
vehicle detectionโ€. IEEE Transactions on Intelligent Transportation Systems, Volume 20,
Issue 3, pp. 1069-1080, ISSN 1524-9050, March 2019.

2. Rafael Martรญn-Nieto, รlvaro Garcรญa-Martรญn, Jose. M. Martรญnez, and Juan C. SanMiguel:
โ€œEnhancing multi-camera people detection by online automatic parametrization using
detection transfer and self-correlation maximizationโ€. Sensors, Volume 18, Issue 12, ISSN
1424-8220, December 2018.

3. รlvaro Garcรญa-Martรญn, Juan C. SanMiguel and Jose. M. Martรญnez: โ€œCoarse-to-fine adaptive
people detection for video sequences by maximizing mutual informationโ€. Sensors,
Volume 19, Issue 4, ISSN 1424-8220, January 2019.

4. Alejandro Lรณpez-Cifuentes, Marcos Escudero-Viรฑolo, Jesรบs Bescรณs and รlvaro GarcรญaMartรญn: โ€œSemantic-Aware Scene Recognitionโ€. Pattern Recognition. Accepted February
2020.

5. Paula Moral, รlvaro Garcรญa-Martรญn, Marcos Escudero Viรฑolo, Jose M. Martinez, Jesus
Bescรณs, Jesus Peรฑuela, Juan Carlos Martinez, Gonzalo Alvis: โ€œTowards automatic waste
containers management in cities via computer vision: containers localization and geopositioning in city mapsโ€. Waste Management, June 2022.

6. Javier Montalvo, รlvaro Garcรญa-Martรญn, Jesus Bescรณs: โ€œExploiting Semantic Segmentation
to Boost Reinforcement Learning in Video Game Environmentsโ€. Multimedia Tools and
Applications. September 2022.

7. Paula Moral, รlvaro Garcรญa-Martรญn, Jose M. Martinez, Jesus Bescรณs: โ€œEnhancing Vehicle
Re-Identification Via Synthetic Training Datasets and Re-ranking Based on Video-Clips
Informationโ€. Multimedia Tools and Applications. February 2023.

8. Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viรฑolo and Alvaro GarciaMartin: โ€œOn exploring weakly supervised domain adaptation strategies for semantic
segmentation using synthetic dataโ€. Multimedia Tools and Applications. February 2023.

9. Juan Ignacio Bravo Pรฉrez-Villar, รlvaro Garcรญa-Martรญn, Jesรบs Bescรณs, Marcos EscuderoViรฑolo: โ€œSpacecraft Pose Estimation: Robust 2D and 3D-Structural Losses and
Unsupervised Domain Adaptation by Inter-Model Consensusโ€. IEEE Transactions on
Aerospace and Electronic Systems. August 2023.

10. Javier Montalvo, รlvaro Garcรญa-Martรญn, Josรฉ M. Martinez. “An Image-Processing Toolkit
for Remote Photoplethysmography”, Multimedia Tools and Applications. July 2024.

11. Juan Ignacio Bravo Pรฉrez-Villar, รlvaro Garcรญa-Martรญn, Jesรบs Bescรณs, Juan C. SanMiguel:
โ€œTest-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on
Predicted-View Synthesisโ€. IEEE Transactions on Aerospace and Electronic Systems.
May 2024.

12. Kirill Sirotkin, Marcos Escudero-Viรฑolo, Pablo Carballeira, รlvaro Garcรญa-Martรญn:
โ€œImproved Transferability of Self-Supervised Learning Models Through Batch
Normalization Finetuningโ€. Applied Intelligence. Aug 2024.

13. Javier Galรกn, Miguel Gonzรกlez, Paula Moral, รlvaro Garcรญa-Martรญn, Jose M. Martinez:
โ€œTransforming Urban Waste Collection Inventory: AI-Based Container Classification and
Re-Identificationโ€. Waste Management, Feb 2025.

Mudassar Raza | Machine Learning | Best Researcher Award

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Prof. Mudassar Raza | Machine Learning | Best Researcher Award

Prof. Dr. Mudassar Raza is a leading AI researcher and academician, serving as a Professor at Namal University, Mianwali, Pakistan. He is a Senior IEEE Member, Chair Publications of IEEE Islamabad Section, and an Academic Editor for PLOS ONE. With 20+ years of teaching and research experience, he has worked at HITEC University Taxila and COMSATS University Islamabad. His research spans AI, deep learning, image processing, and cybersecurity. He has published 135+ research papers with a cumulative impact factor of 215+, 6066+ citations, an H-index of 44, and an I-10 index of 93. He was listed in Elsevierโ€™s Worldโ€™s Top 2% Scientists (2023) and ranked #11 in Computer Science in Pakistan. Dr. Raza has supervised 3 PhDs, co-supervising 6 more, and mentored 100+ undergraduate R&D projects. He actively contributes to academia, industry collaborations, and curriculum development while serving as a reviewer for prestigious journals. ๐ŸŒ๐Ÿ“–

Profile

Education ๐ŸŽ“

  • Ph.D. in Control Science & Engineering (2014-2017) โ€“ University of Science & Technology of China (USTC), China ๐Ÿ‡จ๐Ÿ‡ณ
    • Specialization: Pattern Recognition & Intelligent Systems
  • MS (Computer Science) (2009-2010) โ€“ Iqra University, Islamabad, Pakistan ๐Ÿ‡ต๐Ÿ‡ฐ
    • CGPA: 3.64 | Specialization: Image Processing
  • MCS (Master of Computer Science) (2004-2006) โ€“ COMSATS Institute of Information Technology, Pakistan
    • CGPA: 3.24 | 80% Marks
  • BCS (Bachelor in Computer Science) (1999-2003) โ€“ Punjab University, Lahore, Pakistan
    • CGPA: 3.28 | 64.25% Marks
  • Higher Secondary (Pre-Engineering) โ€“ Islamabad College for Boys
  • Matriculation (Science) โ€“ Islamabad College for Boys
    Dr. Razaโ€™s academic journey is marked by top-tier universities and a strong focus on AI, pattern recognition, and cybersecurity. ๐ŸŽ“๐Ÿ“š

Experience ๐Ÿ‘จโ€๐Ÿซ

  • Professor (2024-Present) โ€“ Namal University, Mianwali
    • Teaching AI, Cybersecurity, and Research Supervision
  • Associate Professor/Head AI & Cybersecurity Program (2023-2024) โ€“ HITEC University, Taxila
    • Led AI & Cybersecurity programs, supervised PhDs, and organized industry-academic collaborations
  • Associate Professor (2023) โ€“ COMSATS University, Islamabad
  • Assistant Professor (2012-2023) โ€“ COMSATS University, Islamabad
  • Lecturer (2008-2012) โ€“ COMSATS University, Islamabad
  • Research Associate (2006-2008) โ€“ COMSATS University, Islamabad
    Dr. Raza has 20+ years of experience in academia, R&D, and industry collaborations, contributing significantly to AI, deep learning, and cybersecurity. ๐Ÿซ๐Ÿ“Š

Research Interests ๐Ÿ”ฌ

Prof. Dr. Mudassar Razaโ€™s research revolves around Artificial Intelligence, Deep Learning, Computer Vision, Image Processing, Cybersecurity, and Parallel Programming. His work includes pattern recognition, intelligent systems, visual robotics, and AI-driven cybersecurity solutions. With 135+ international publications, he has significantly contributed to AIโ€™s real-world applications. His research impact includes 6066+ citations, an H-index of 44, and an I-10 index of 93. He leads multiple AI research groups, supervises PhD/MS students, and actively collaborates with industry and academia. His work is frequently cited, placing him among the top AI researchers globally. As an IEEE Senior Member and a PLOS ONE Academic Editor, he is a key figure in AI-driven innovations and technology advancements. ๐Ÿง ๐Ÿ“Š

  • National Youth Award 2008 by the Prime Minister of Pakistan for contributions to Computer Science ๐ŸŽ–๏ธ
  • Listed in Worldโ€™s Top 2% Scientists (2023) by Elsevier ๐ŸŒ
  • Ranked #11 in Computer Science in Pakistan by AD Scientific Index ๐Ÿ“Š
  • Senior IEEE Member (ID: 91289691) ๐Ÿ”ฌ
  • HEC Approved PhD Supervisor ๐ŸŽ“
  • Best Research Productivity Awardee at COMSATS University multiple times ๐Ÿ†
  • Recognized by ResearchGate with a Research Interest Score higher than 97% of members ๐Ÿ“ˆ
  • Reviewer & Editor for prestigious journals including PLOS ONE ๐Ÿ“
    Dr. Raza has received numerous accolades for his contributions to AI, research excellence, and academia. ๐ŸŒŸ

Publications ๐Ÿ“š

Yangyang Huang | Object detection | Excellence in Innovation

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Dr. Yangyang Huang | Object detection | Excellence in Innovation

Yangyang Huang is a Ph.D. student at the School of Computer Science and Engineering, South China University of Technology (SCUT), Guangzhou, China. His research focuses on artificial intelligence, computer vision, and large models. He previously graduated from Wuhan University, where he developed a strong foundation in AI and computational sciences. Yangyang has contributed to significant research projects, including the Collaborative Innovation Major Project for Industry, University, and Research. His work, “LVMUM: Toward Open-World Object Detection with Large Vision Models and Unsupervised Modeling,” has gained notable citations. Passionate about AI advancements, he actively participates in academic collaborations and professional memberships, contributing to AI-driven innovations.

Profile

Education ๐ŸŽ“

Yangyang Huang completed his undergraduate studies at Wuhan University, where he gained expertise in artificial intelligence and computational sciences. Currently, he is pursuing his Ph.D. at the School of Computer Science and Engineering, South China University of Technology (SCUT), Guangzhou, China. His doctoral research focuses on large vision models, unsupervised modeling, and object detection. He has been involved in cutting-edge AI research, particularly in deep learning and computer vision. His academic journey has been marked by significant contributions to AI-driven innovations, leading to multiple publications in high-impact journals. Yangyang actively collaborates with researchers in academia and industry, further strengthening his expertise in AI and machine learning applications.

Experience ๐Ÿ‘จโ€๐Ÿซ

Yangyang Huang has extensive research experience in artificial intelligence, computer vision, and large models. As a Ph.D. student at SCUT, he has been involved in the Collaborative Innovation Major Project for Industry, University, and Research. His research contributions include developing large vision models for open-world object detection, leading to highly cited publications. Yangyang has also participated in consultancy and industry projects, applying AI techniques to real-world problems. He has authored several journal articles indexed in SCI and Scopus and has contributed to the academic community through editorial roles. His collaborative research efforts have led to impactful AI advancements, making him a rising scholar in the field of AI and machine learning.

Research Interests ๐Ÿ”ฌ

Yangyang Huang’s research primarily focuses on artificial intelligence, computer vision, and large models. His recent work, “LVMUM: Toward Open-World Object Detection with Large Vision Models and Unsupervised Modeling,” explores novel AI techniques for enhancing object detection capabilities. He specializes in deep learning, unsupervised learning, and AI-driven automation. His research interests include developing robust AI models for real-world applications, advancing AI ethics, and improving AI interpretability. Yangyang actively collaborates with academia and industry to bridge the gap between theoretical AI research and practical applications. His contributions extend to consultancy projects, AI innovation, and scholarly publications, making him a key contributor to AI advancements. ๐Ÿš€

Awards & Recognitions ๐Ÿ…

Yangyang Huang has received recognition for his outstanding contributions to artificial intelligence and computer vision. His research on large vision models and open-world object detection has been widely cited, earning him academic recognition. He has been nominated for prestigious research awards, including Best Researcher Award and Excellence in Research. His work in AI has been acknowledged through various grants and funding for industry-academic collaborative projects. Yangyang’s active participation in international conferences has led to best paper nominations and accolades for his innovative contributions. He is a member of esteemed professional organizations, further cementing his reputation as an emerging AI researcher.

Publications ๐Ÿ“š

Radhika Subramanian | Speech Processing | Women Researcher Award

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Dr. Radhika Subramanian | Speech Processing | Women Researcher Award

 

Profile

Education

She is currently pursuing a PhD at Anna University, Chennai, with an expected completion in 2025. She obtained her Master of Engineering in Communication Systems from B.S. Abdur Rahman Crescent Engineering College, Chennai, achieving 82.3% in the academic years 2007-2009. Prior to that, she completed her Bachelor of Engineering in Electronics and Communication Engineering from Kanchi Pallavan Engineering College, Kanchipuram, affiliated with Anna University, securing 84% from 2003 to 2007. She completed her Higher Secondary education at S.S.K.V Higher Secondary School, Kanchipuram, with 88% marks from 2001 to 2003, and her Secondary School Leaving Certificate from the same institution, scoring 84% in the year 2000-2001.

Work experience

As of January 31, 2025, she has a total academic experience of 14 years, 7 months, and 15 days. She has been serving as an Assistant Professor Grade-II at Sri Venkateswara College of Engineering, Sriperumpudur, since June 11, 2010. Prior to this, she worked as a Lecturer at Arulmigu Meenakshi Amman College of Engineering, Kanchipuram, from July 1, 2009, to May 7, 2010, gaining 10 months of experience. Her cumulative teaching experience amounts to 14 years, 17 months, and 15 days.

AREA OF INTEREST

  • Data Communication and Networking
  • Satellite communication
  • Signal Processing
  • Machine Learning

Publication

  • Radhika, S & Prasanth, A 2024, โ€žAn Effective Speech Emotion Recognition Model for Multi-Regional Languages Using Threshold-based Feature Selection Algorithmโ€Ÿ, Circuits, Systems, and Signal Processing, vol. 43, pp. 2477โ€“2506, ISSN: 1531-5878,DOI: 10.1007/s00034-023-02571-4, Impact Factor: 2.3.
  • Radhika, S & Prasanth, A 2024, โ€žAn Effective Speech Emotion Recognition Model for Multi-Regional Languages Using Threshold-based Feature Selection Algorithmโ€Ÿ, Circuits, Systems, and Signal Processing, vol. 43, pp. 2477โ€“2506, ISSN: 1531-5878,DOI: 10.1007/s00034-023-02571-4, Impact Factor: 2.3.
  • A Survey of Human Emotion Recognition Using Speech Signals: Current Trends and Future Perspectives
    R Subramanian, P Aruchamy
    Micro-Electronics and Telecommunication Engineering: Proceedings of 6th

 

 

Vikas Palekar | Machine Leaning | Best Researcher Award

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Mr. Vikas Palekar | Machine Leaning | Best Researcher Award

 

Profile

Education

He is currently pursuing a Ph.D. in Computer Science and Engineering at Vellore Institute of Technology, Bhopal, Madhya Pradesh, since December 2018. His research focuses on developing an Adaptive Optimized Residual Convolutional Image Annotation Model with a Bionic Feature Selection Strategy. He holds a Master of Engineering (M.E.) in Information Technology from Prof. Ram Meghe College of Engineering Technology and Research, Badnera (SGBAU Amravati), which he completed in December 2012 with an impressive 88.00%, securing the first merit position in the university for the summer 2012 examination. Prior to that, he earned a Bachelor of Engineering (B.E.) in Computer Science and Engineering from Shri Guru Gobind Singhji Institute of Engineering Technology and Research, Nanded (SRTMNU, Nanded), in June 2007, achieving a commendable 74.40%.

Work experience

He is currently working as an Assistant Professor in the Department of Computer Engineering at Bajaj Institute of Technology, Wardha, since July 31, 2023. In addition to his teaching responsibilities, he serves as the Academic Coordinator of the department and has worked as a Senior Supervisor for the DBATY Winter-23 Exam at Government College of Engineering, Yavatmal.

Previously, he worked as an Assistant Professor (UGC Approved, RTMNU, Nagpur) in the Department of Computer Science and Engineering at Datta Meghe Institute of Engineering, Technology & Research, Wardha, from June 14, 2011, to June 30, 2023. During this tenure, he held the position of Head of the Department from April 21, 2016, to June 30, 2023. He taught various subjects, including Distributed Operating Systems, TCP/IP, System Programming, Data Warehousing and Mining, Artificial Intelligence, and Computer Architecture and Organization. Additionally, he contributed to university examinations as the Chief Supervisor in the Winter-2015 Examination and a committee member for the Summer-2013, Summer-2015, and Summer-2018 Examinations. He also played a key role in institutional development as a member of the Admission Committee, NBA & NAAC core committees at the department level, and as the convener of the National Level Technical Symposium “POCKET 16” organized by the CSE Department on March 16, 2016.

Earlier in his career, he served as an Assistant Professor in the Department of Computer Engineering at Bapurao Deshmukh College of Engineering, Wardha, from November 26, 2008, to April 30, 2011. He taught subjects such as Unix and Shell Programming, Object-Oriented Programming, and Operating Systems while also serving as a Department Exam Committee Member.

Achievement

He was the first university topper (merit) in M.Tech (Information Technology) and received the Best Paper Award at the 2021 International Conference on Computational Performance Evaluation (ComPE), organized by the Department of Biomedical Engineering, North Eastern Hill University (NEHU), Shillong, Meghalaya, India, from December 1st to 3rd, 2023. He has actively participated in various conferences, including presenting the paper “Label Dependency Classifier using Multi-Feature Graph Convolution Networks for Automatic Image Annotation” at ComPE 2021 in Shillong, India. He also presented his research on “Visual-Based Page Segmentation for Deep Web Data Extraction” at the International Conference on Soft Computing for Problem Solving (SocProS 2011) held from December 20-22, 2011. Additionally, he contributed to the Computer Science & Engineering Department at Sardar Vallabhbhai National Institute of Technology, Surat, by presenting “A Critical Analysis of Learning Approaches for Image Annotation Based on Semantic Correlation” from December 13-15, 2022. His work on “A Survey on Assisting Document Annotation” was featured at the 19th International Conference on Hybrid Intelligent Systems (HIS) at VIT Bhopal University, India, from December 10-12, 2022. Furthermore, he co-authored a study titled “Review on Improving Lifetime of Network Using Energy and Density Control Cluster Algorithm,” which was presented at the 2018 IEEE International Students’ Conference on Electrical, Electronics, and Computer Science (SCEECS) in Bhopal, India.

 

Publication

Raveendra Pilli | Image Processing | Best Researcher Award

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Mr. Raveendra Pilli | Image Processing | Best Researcher Award

He mentored B.Tech. projects focused on the early detection of Alzheimerโ€™s Disease. One project involved utilizing multi-modality neuroimaging techniques, where MRI and PET images were collected from the OASIS database, preprocessed, and robust features were extracted for classification. MATLAB and the SPM-12 toolbox were used for this task. Another project focused on the early detection of Alzheimerโ€™s Disease using deep learning networks, where an MRI dataset from the ADNI database was collected, preprocessed, and the performance was compared with baseline algorithms. For this project, he used MATLAB and Python.

NIT-Silchar, India

Profile

Education

A dedicated research scholar with a Ph.D. in Electronics and Communication Engineering from the National Institute of Technology Silchar (Thesis Submitted, CGPA 9.0), specializing in brain age prediction and early detection of neurological disorders using neuroimaging modalities. With extensive teaching experience, a strong passion for research, and a proven ability to develop engaging curricula, deliver effective lectures, and guide students toward academic success, I am committed to contributing to the field through research, publications, and presentations. My academic journey includes an M.Tech. from JNTU Kakinada (76.00%, 2011) and a B.Tech. from JNTU Hyderabad (65.00%, 2007), along with a strong foundational background in science, having completed 10+2 (MPC) with 89.00% in 2003 and SSC with 78.00% in 2001.

Work experience

He worked as a Junior Research Fellow at the National Institute of Technology, Silchar, Assam, from July 2021 to June 2023, where he assisted professors with course delivery for Basic Electronics, conducted laboratory sessions, graded assignments, and provided office hours for student support. From July 2023 to December 2024, he served as a Senior Research Fellow at the same institute, taking on additional responsibilities, including mentoring B.Tech. projects and assisting with Digital Signal Processing laboratory duties. Prior to his research roles, he was an Assistant Professor at SRK College of Engineering and Technology, Vijayawada, Andhra Pradesh, where he taught courses such as Networks Theory, Digital Signal Processing, RVSP, SS, and LICA. He utilized innovative teaching methods, including active learning techniques, to enhance student engagement and learning outcomes. He also mentored undergraduate research projects in image processing and received positive student evaluations for his teaching effectiveness.

Publication

Mahmoud Alimoradi | Machine Learning | Best Researcher Award

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Mr. Mahmoud Alimoradi | Machine Learning | Best Researcher Award

Lahijan Azad ,Iran

He understands the growing need for Machine Learning and has a keen interest in the field, which he considers a blessing. Recognizing the importance of managing large and complex computations to control various aspects of the human environment has led him into this vast world. He is particularly fascinated by machine learning, especially reinforcement learning, supervised learning, semi-supervised learning, outliers, and basic data challenges. Furthermore, optimization, an area of artificial intelligence that requires fundamental studies and a change in approach, is another of his key research interests.

Profile

Education

He holds a Masterโ€™s degree in Artificial Intelligence Engineering from the University of Shafagh, completed in 2020. His thesis was titled “Trees Social Relations Optimization Algorithm: A New Swarm-Based Metaheuristic Technique to Solve Continuous and Discrete Optimization Problems.” He also earned a Bachelorโ€™s degree in Software Engineering from Azad Lahijan University, which he attended from 2007 to 2011.

Research Interests

Theory: Reinforcement Learning (high-dimensional problems, regularized algorithms, model
learning,
representation learning and deep RL, learning from demonstration, inverse optimal control, deep
Reinforcement Learning); Machine Learning (statistical learning theory, nonparametric
algorithms, time series. processes, manifold learning, online learning); Large-scale Optimization;
Evolutionary Computation, Metaheuristic Algorithm, Deep Learning, Healthcare Machine
learning, Big Data, Data Problems (Imbalanced), Signal Analysis
Applications: Automated control, space affairs, robotic control, medicine and health, asymmetric
data, data science, scheduling, proposing systems, self-enhancing systems

Work Experience

He is a freelance programmer with expertise in various operating systems, including Microsoft Windows and Linux (Arch, Ubuntu, Fedora). He is proficient in software tools such as Microsoft Office, Anaconda, Jupyter, PyCharm, Visual Studio, Tableau, RapidMiner, MATLAB, and Visual Studio. His programming skills include Matlab, Python, C++, Scala, Java, and Julia, with a focus on data mining, data science, computer vision, and machine learning. He is experienced with Python libraries like Pandas, Numpy, Matplotlib, Seaborn, PyCV, TensorFlow, Time Series Analysis, Spark, Hadoop, and Cassandra. Additionally, he is skilled in using Github, Docker, and MySQL. His expertise spans machine learning, deep learning, imbalanced data, missing data, semi-supervised learning, healthcare machine learning, algorithm design, and metaheuristic algorithms. He is fluent in English and Persian.

Publications

Nuo Yu | Radiomics | Best Researcher Award

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Ms. Nuo Yu | Radiomics | Best Researcher Award

Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College ,China

Nuo Yu is a Ph.D. candidate at the Cancer Institute and Hospital of the Chinese Academy of Medical Sciences, specializing in radiation oncology with a focus on esophageal squamous cell carcinoma (ESCC). His research primarily explores innovative chemoradiotherapy regimens to improve treatment outcomes for patients with locally advanced ESCC.

Yu has contributed to several peer-reviewed publications in SCI-indexed journals. Notably, he co-authored a study titled “Conversion Chemoradiotherapy Combined with Nab-Paclitaxel Plus Cisplatin in Patients with Locally Advanced Borderline-Resectable or Unresectable Esophageal Squamous Cell Carcinoma: A Phase I/II Prospective Cohort Study,” published in Strahlentherapie und Onkologie in August 2024. This research evaluated the efficacy and safety of a novel chemoradiotherapy regimen, demonstrating promising results in locoregional control and overall survival rates.

In March 2023, Yu co-authored another significant study, “Efficacy and Safety of Concurrent Chemoradiotherapy Combined with Nimotuzumab in Elderly Patients with Esophageal Squamous Cell Carcinoma: A Prospective Real-world Pragmatic Study,” published in Current Cancer Drug Targets. This research focused on treatment strategies for elderly patients with ESCC, highlighting the potential benefits of combining chemoradiotherapy with nimotuzumab.

Yu’s work has been recognized at international conferences, including presentations at the American Society for Radiation Oncology (ASTRO), the Federation of Asian Organizations for Radiation Oncology (FARO), and the Korean Society for Radiation Oncology (KOSRO). These engagements underscore his active participation in the global radiation oncology community and his commitment to advancing cancer treatment research.

While still in the early stages of his career, Yu’s focused research on ESCC and his contributions to the field of radiation oncology position him as a promising candidate for the Best Researcher Award. Continued efforts to expand his research scope, increase publication impact, and assume leadership roles in larger-scale studies will further strengthen his candidacy.

Profile

Scientific Publications