zeenat khadim | Remote Sensing | Best Research Article Award

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Dr. zeenat khadim | Remote Sensing | Best Research Article Award

Zeenat Khadim Hussain is a passionate PhD Researcher 🧠 at Wuhan University, China 🇨🇳 specializing in Photogrammetry and Remote Sensing 🌍. With strong analytical skills and innovative thinking 💡, she explores the fusion of Deep Learning 🤖 with geospatial technologies for solving real-world problems 🌱. Her research has made contributions to urban sustainability 🌇, building detection 🏢, and solar energy potential mapping ☀️. Zeenat’s scholarly journey is marked by impactful publications 📚 in reputed journals, advancing cutting-edge solutions for environmental monitoring 🌐. She actively engages in international research collaborations 🤝 and industry-driven projects like Pakistan’s Flood Emergency Reconstruction 🌊. Zeenat remains committed to promoting sustainable development goals (SDGs) through scientific research 📖 and practical applications. Her work reflects dedication to empowering urban resilience 🏙️ and advancing geospatial science 📡 for a smarter and greener future 🌿.

Profile

Education 🎓

Zeenat Khadim Hussain holds an exceptional academic background 🏅. Currently pursuing a PhD in Photogrammetry and Remote Sensing 🛰️ at Wuhan University, China 🇨🇳, her education combines theoretical excellence 📚 and real-world applications 🌍. Prior to this, she achieved commendable academic success in her undergraduate and postgraduate studies 🎓, where she cultivated her passion for geospatial technologies 🔭 and machine learning 💻. Through rigorous training at Wuhan University’s prestigious School of Remote Sensing 🏫, she honed her research methodology, scientific writing ✍️, and computational modeling skills 🧠. Her strong educational foundation underpins her ability to bridge the gap between advanced technology 💡 and environmental sustainability 🌱, positioning her as a forward-thinking researcher 🔬 with global perspectives 🌐. Zeenat’s academic record reflects her commitment to lifelong learning 📖 and excellence in scientific inquiry 🏆.

Experience 👨‍🏫

Zeenat Khadim Hussain has rich research and project experience 🏗️, including 3+ major research projects on urban analysis 🏙️, solar irradiance assessment ☀️, and building footprint extraction 🧱 using deep learning algorithms 🤖 and Sentinel-2 satellite imagery 🛰️. She has also contributed to Pakistan’s Flood Emergency Reconstruction and Resilience Project 🌊, applying her expertise to real-world disaster management 🌐. Zeenat has authored 6 publications in high-impact journals (SCI, Scopus) 📰 and is continuously expanding her research collaborations 🤝. Her commitment to developing AI-powered geospatial solutions 💡 for urban sustainability and disaster resilience 🚧 has made her a rising star 🌟 in remote sensing. From conceptualizing research frameworks 📋 to deploying machine learning pipelines ⚙️, Zeenat’s experience spans both academic and applied research environments 🧪, establishing her as a dynamic and results-oriented researcher 🔬.

Awards & Recognitions 🏅

Zeenat Khadim Hussain’s academic journey is distinguished by multiple honors 🎖️ and global recognition 🌐. Her groundbreaking work on building detection and rooftop solar assessment 🏢☀️ has earned commendation in academic conferences and journal publications 📚. She has been nominated for competitive research awards 🥇 such as the Best Research Scholar Award 🧠 and Excellence in Research 🌟, highlighting her commitment to scientific innovation 🔬. Her practical impact through Pakistan’s Flood Reconstruction Project 🌊 and interdisciplinary collaborations 🤝 showcases her leadership potential and societal contribution 💪. Zeenat’s consistent publication record in reputed platforms 📰 and successful completion of funded research projects 💸 emphasize her growing academic influence 🚀. Her career reflects resilience, intellectual curiosity 🧠, and a passion for using technology to solve environmental and urban challenges 🌍.

Research Interests 🔬

Zeenat Khadim Hussain’s research focuses on the fusion of Deep Learning 🤖, Photogrammetry 📏, and Remote Sensing 🛰️ to tackle urban, environmental, and energy-related challenges 🌍. She specializes in building footprint detection 🏢, rooftop solar potential mapping ☀️, and disaster risk management 🌊 using geospatial data and AI algorithms ⚙️. Her work bridges the gap between theoretical modeling 📐 and real-world applications in sustainable urban planning 🌱 and renewable energy assessment ⚡. She is especially passionate about translating satellite data into actionable insights 🔎 for climate adaptation, resilience, and urban growth 📊. Zeenat’s latest research introduces deep learning architectures 🧠 that enhance rooftop extraction accuracy, as published in Remote Sensing Applications: Society and Environment 🌐. Her aim is to push the frontiers of automated geospatial analysis 🚀, enabling smarter and greener cities 🏙️ for future generations 🌿.

Publications 

  • Spatiotemporal optimization for communication-navigation-sensing collaborated emergency monitoring

    International Journal of Digital Earth
    2025-12-31 | Journal article
    DOI: 10.1080/17538947.2025.2475517
    Part ofISSN: 1753-8947
    Part ofISSN: 1753-8955
    CONTRIBUTORS: xicheng tan; Bocai Liu; Chaopeng Li; Zeenat Khadim Hussain; Kaiqi Wang; Kai Wang; Mengyan Ye; Danyang Yang; Zhiyuan Mei

  • A Novel Architecture for Building Rooftop Extraction Using Remote Sensing and Deep Learning

    Remote Sensing Applications: Society and Environment
    2025-04 | Journal article | Author
    DOI: 10.1016/j.rsase.2025.101551
    Part ofISSN: 2352-9385
    CONTRIBUTORS: zeenat khadim; Jiang Congshi; Muhammad Adrees; Hamza Chaudhary; Rafia Shafqa

  • 3D spatial evolutionary particle swarm algorithm based emergency communication spatial deployment optimization

    Geo-spatial Information Science
    2025-03-11 | Journal article
    DOI: 10.1080/10095020.2025.2472006
    Part of ISSN: 1009-5020
    Part of ISSN: 1993-5153
    CONTRIBUTORS: Yumin Chen; xicheng tan; Jinguang Jiang; Xiaoliang Meng; Zeenat Khadim Hussain; Jianguang Tu; Huaming Wang; You Wan; Zongyao Sha

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 🎓

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 👨‍🏫

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 🏅

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 Students’ 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 🔬

Aromoye’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.

Publications 

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

    Computer Modeling in Engineering & Sciences
    2025-01-27 | Journal article
    DOI: 10.32604/cmes.2025.058598
    Part ofISSN: 1526-1506
    CONTRIBUTORS: Ibrahim Akinjobi Aromoye; Hai Hiung Lo; Patrick Sebastian; Shehu Lukman Ayinla; 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 | Conference paper
    DOI: 10.1109/ISCAIE61308.2024.10576323
    EID:

    2-s2.0-85198901224

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

 

Alvaro Garcia | Computer vision | Best Researcher Award

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Dr. Alvaro Garcia | Computer vision | Best Researcher Award

Álvaro García Martín es Profesor Titular en la Universidad Autónoma de Madrid, especializado en visión por computadora y análisis de video. 🎓 Obtuvo su título de Ingeniero de Telecomunicación en 2007, su Máster en Ingeniería Informática y Telecomunicaciones en 2009 y su Doctorado en 2013, todos en la Universidad Autónoma de Madrid. 🏫 Ha trabajado en detección de personas, seguimiento de objetos y reconocimiento de eventos, con más de 22 artículos en revistas indexadas y 28 en congresos. 📝 Ha realizado estancias en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. 🌍 Su investigación ha contribuido al desarrollo de sistemas de videovigilancia inteligentes, análisis de secuencias de video y procesamiento de señales multimedia. 📹 Ha sido reconocido con prestigiosos premios y ha participado en múltiples proyectos europeos de innovación tecnológica. 🚀

Profile

Education 🎓

🎓 Ingeniero de Telecomunicación por la Universidad Autónoma de Madrid (2007). 🎓 Máster en Ingeniería Informática y Telecomunicaciones con especialización en Tratamiento de Señales Multimedia en la Universidad Autónoma de Madrid (2009). 🎓 Doctor en Ingeniería Informática y Telecomunicación por la Universidad Autónoma de Madrid (2013). Su formación 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). 🌍 Durante su doctorado, recibió la beca FPI-UAM para la realización de su investigación. Su sólida formación académica le ha permitido contribuir significativamente al campo del análisis de video y visión por computadora, consolidándose como un experto en la detección, seguimiento y reconocimiento de eventos en secuencias de video. 📹

Experience 👨‍🏫

🔬 Se unió al grupo VPU-Lab en la Universidad Autónoma de Madrid en 2007. 📡 De 2008 a 2012, fue becario de investigación (FPI-UAM). 🎓 Entre 2012 y 2014, trabajó como Profesor Ayudante. 👨‍🏫 De 2014 a 2019, fue Profesor Ayudante Doctor. 📚 De 2019 a 2023, ocupó el cargo de Profesor Contratado Doctor. 🏛️ Desde septiembre de 2023, es Profesor Titular en la Universidad Autónoma de Madrid. 🏆 Ha participado en múltiples proyectos europeos sobre videovigilancia, transmisión de contenido multimedia y reconocimiento de eventos, incluyendo PROMULTIDIS, ATI@SHIVA, EVENTVIDEO y MobiNetVideo. 🚀 Ha realizado estancias de investigación en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. 🌍 Su experiencia docente abarca asignaturas en Ingeniería de Telecomunicaciones, Ingeniería Informática e Ingeniería Biomédica.

Research Interests 🔬

🎯 Su investigación se centra en la visión por computadora, el análisis de secuencias de video y la inteligencia artificial aplicada a entornos de videovigilancia. 📹 Especialista en detección de personas, seguimiento de objetos y reconocimiento de eventos en video. 🧠 Desarrolla algoritmos de aprendizaje profundo y visión artificial para mejorar la seguridad y automatización en ciudades inteligentes. 🏙️ Ha trabajado en proyectos sobre videovigilancia, transmisión multimedia y detección de anomalías en video. 🔬 Su investigación incluye procesamiento de imágenes, análisis semántico y redes neuronales profundas. 🚀 Participa activamente en proyectos internacionales y colabora con universidades como Carnegie Mellon, Queen Mary y TU Berlin. 🌍 Ha publicado en IEEE Transactions on Intelligent Transportation Systems, Sensors y Pattern Recognition, consolidándose como un referente en el campo de la visión por computadora. 📜

Awards & Recognitions 🏅

🥇 Medalla “Juan López de Peñalver” 2017, otorgada por la Real Academia de Ingeniería. 📜 Reconocimiento por su contribución a la ingeniería española en el campo de la visión por computadora y análisis de video. 🏛️ Ha recibido financiación para múltiples proyectos de investigación europeos y nacionales. 🔬 Ha participado en iniciativas de innovación en videovigilancia y análisis de video para seguridad. 🚀 Sus contribuciones han sido publicadas en las principales conferencias y revistas científicas del área. 📚 Su trabajo ha sido citado más de 4500 veces y cuenta con un índice h de 16 en Google Scholar. 📊

Publications 

1. Rafael Martín-Nieto, Álvaro García-Martín, Alexander G. Hauptmann, and Jose. M.
Martínez: “Automatic vacant parking places management system using multicamera
vehicle detection”. IEEE Transactions on Intelligent Transportation Systems, Volume 20,
Issue 3, pp. 1069-1080, ISSN 1524-9050, March 2019.

2. Rafael Martín-Nieto, Álvaro García-Martín, Jose. M. Martínez, and Juan C. SanMiguel:
“Enhancing multi-camera people detection by online automatic parametrization using
detection transfer and self-correlation maximization”. Sensors, Volume 18, Issue 12, ISSN
1424-8220, December 2018.

3. Álvaro García-Martín, Juan C. SanMiguel and Jose. M. Martínez: “Coarse-to-fine adaptive
people detection for video sequences by maximizing mutual information”. Sensors,
Volume 19, Issue 4, ISSN 1424-8220, January 2019.

4. Alejandro López-Cifuentes, Marcos Escudero-Viñolo, Jesús Bescós and Álvaro GarcíaMartín: “Semantic-Aware Scene Recognition”. Pattern Recognition. Accepted February
2020.

5. Paula Moral, Álvaro García-Martín, Marcos Escudero Viñolo, Jose M. Martinez, Jesus
Bescós, Jesus Peñuela, Juan Carlos Martinez, Gonzalo Alvis: “Towards automatic waste
containers management in cities via computer vision: containers localization and geopositioning in city maps”. Waste Management, June 2022.

6. Javier Montalvo, Álvaro García-Martín, Jesus Bescós: “Exploiting Semantic Segmentation
to Boost Reinforcement Learning in Video Game Environments”. Multimedia Tools and
Applications. September 2022.

7. Paula Moral, Álvaro García-Martín, Jose M. Martinez, Jesus Bescós: “Enhancing Vehicle
Re-Identification Via Synthetic Training Datasets and Re-ranking Based on Video-Clips
Information”. Multimedia Tools and Applications. February 2023.

8. Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo and Alvaro GarciaMartin: “On exploring weakly supervised domain adaptation strategies for semantic
segmentation using synthetic data”. Multimedia Tools and Applications. February 2023.

9. Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós, Marcos EscuderoViñolo: “Spacecraft Pose Estimation: Robust 2D and 3D-Structural Losses and
Unsupervised Domain Adaptation by Inter-Model Consensus”. IEEE Transactions on
Aerospace and Electronic Systems. August 2023.

10. Javier Montalvo, Álvaro García-Martín, José M. Martinez. “An Image-Processing Toolkit
for Remote Photoplethysmography”, Multimedia Tools and Applications. July 2024.

11. Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós, Juan C. SanMiguel:
“Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on
Predicted-View Synthesis”. IEEE Transactions on Aerospace and Electronic Systems.
May 2024.

12. Kirill Sirotkin, Marcos Escudero-Viñolo, Pablo Carballeira, Álvaro García-Martín:
“Improved Transferability of Self-Supervised Learning Models Through Batch
Normalization Finetuning”. Applied Intelligence. Aug 2024.

13. Javier Galán, Miguel González, Paula Moral, Álvaro García-Martín, Jose M. Martinez:
“Transforming Urban Waste Collection Inventory: AI-Based Container Classification and
Re-Identification”. Waste Management, Feb 2025.

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 Alzheimer’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 Alzheimer’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