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.

 

Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

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Dr. Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

Yuanming Zhang is an Associate Professor at the College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. He earned his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His research focuses on data processing, graph neural networks, knowledge graphs, prognostics, health management, and condition monitoring. With expertise in deep learning and artificial intelligence, he has contributed significantly to neural network advancements. His work integrates cutting-edge technologies for intelligent data analysis and predictive maintenance. 📊🧠🔍

Profile

Education 🎓

Yuanming Zhang obtained his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His academic journey emphasized computational intelligence, machine learning, and advanced data analytics. He developed expertise in deep learning models, including convolutional and graph neural networks. His education laid a strong foundation for interdisciplinary research, integrating artificial intelligence with real-world applications. 📚🧑‍🎓📈

Experience 👨‍🏫

Yuanming Zhang has been an Associate Professor at Zhejiang University of Technology since completing his Ph.D. in 2010. His professional journey spans over a decade in academia, focusing on AI, neural networks, and knowledge graphs. He has supervised research projects, collaborated on industry applications, and contributed to advancements in predictive analytics and condition monitoring. His expertise extends to teaching, mentoring, and interdisciplinary AI applications. 🏫🤖📡

Research Interests 🔬

Yuanming Zhang specializes in deep learning, attention mechanisms, graph neural networks, and AI-driven predictive analytics. His research explores neural architectures for data processing, knowledge representation, and condition monitoring. His expertise spans convolutional networks, LSTMs, GRUs, and deep belief networks. His work contributes to advancements in AI-driven diagnostics, intelligent systems, and real-time health monitoring applications. 🧠📊🖥️

Awards & Recognitions 🏅

Yuanming Zhang has received recognition for his contributions to AI, machine learning, and data analytics. His work in deep learning and knowledge graphs has earned him accolades from research institutions and conferences. His papers in neural networks and predictive maintenance have been highly cited, solidifying his impact in the field. His research excellence has been acknowledged through grants and academic distinctions. 🎖️📜🔬

Publications 

 

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.

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 Elsevier’s World’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. 🌍📖

Profile

Education 🎓

  • Ph.D. in Control Science & Engineering (2014-2017) – University of Science & Technology of China (USTC), China 🇨🇳
    • Specialization: Pattern Recognition & Intelligent Systems
  • MS (Computer Science) (2009-2010) – Iqra University, Islamabad, Pakistan 🇵🇰
    • CGPA: 3.64 | Specialization: Image Processing
  • MCS (Master of Computer Science) (2004-2006) – COMSATS Institute of Information Technology, Pakistan
    • CGPA: 3.24 | 80% Marks
  • BCS (Bachelor in Computer Science) (1999-2003) – Punjab University, Lahore, Pakistan
    • CGPA: 3.28 | 64.25% Marks
  • Higher Secondary (Pre-Engineering) – Islamabad College for Boys
  • Matriculation (Science) – Islamabad College for Boys
    Dr. Raza’s academic journey is marked by top-tier universities and a strong focus on AI, pattern recognition, and cybersecurity. 🎓📚

Experience 👨‍🏫

  • Professor (2024-Present) – Namal University, Mianwali
    • Teaching AI, Cybersecurity, and Research Supervision
  • Associate Professor/Head AI & Cybersecurity Program (2023-2024) – HITEC University, Taxila
    • Led AI & Cybersecurity programs, supervised PhDs, and organized industry-academic collaborations
  • Associate Professor (2023) – COMSATS University, Islamabad
  • Assistant Professor (2012-2023) – COMSATS University, Islamabad
  • Lecturer (2008-2012) – COMSATS University, Islamabad
  • Research Associate (2006-2008) – 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. 🏫📊

Research Interests 🔬

Prof. Dr. Mudassar Raza’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 AI’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. 🧠📊

  • National Youth Award 2008 by the Prime Minister of Pakistan for contributions to Computer Science 🎖️
  • Listed in World’s Top 2% Scientists (2023) by Elsevier 🌍
  • Ranked #11 in Computer Science in Pakistan by AD Scientific Index 📊
  • Senior IEEE Member (ID: 91289691) 🔬
  • HEC Approved PhD Supervisor 🎓
  • Best Research Productivity Awardee at COMSATS University multiple times 🏆
  • Recognized by ResearchGate with a Research Interest Score higher than 97% of members 📈
  • Reviewer & Editor for prestigious journals including PLOS ONE 📝
    Dr. Raza has received numerous accolades for his contributions to AI, research excellence, and academia. 🌟

Publications 📚

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 🎓

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 👨‍🏫

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 🔬

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. 🚀

Awards & Recognitions 🏅

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 📚

Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

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Assist. Prof. Dr. Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

 

Profile

Education

She holds a Ph.D. in Econometrics and Machine Learning from the University of Orleans (2018–2022), where she investigated new machine learning approaches for financial fraud detection and survival analysis in the insurance industry under the supervision of S. Tokpavi. In addition, she earned a Data Science Certificate (Executive) from the Institute of Risk Management (IRM) in 2016–2017. Her academic background also includes a Master’s degree in Mathematical Engineering (Applied Statistics) from Paris-Sud University (2004–2007) and a Master’s degree in Mathematics from the University of Marmara in Istanbul (1995–1999). Since September 2022, she has been an Assistant Professor of Data Science at IPAG Business School in Nice and Paris. With extensive experience in the insurance sector, she integrates her professional insights into the classroom, emphasizing practical AI applications. Her curriculum reflects the latest trends in data science, fostering a dynamic learning environment tailored to students’ needs. She adapts resources and pedagogical methods to specific course objectives, utilizing tools such as Tableau for data visualization and exploring real-world business applications, including Netflix, Uber, ChatGPT, Gemini, and facial recognition technologies.

 

Work experience

She has held various academic and professional roles, combining her expertise in data science, machine learning, and business analytics. From September 2022 to January 2023, she was an adjunct faculty member at the International University of Monaco, where she taught Mathematics for Business. Prior to that, from September 2021 to August 2022, she served as an adjunct faculty member at IPAG Business School (Nice), teaching courses such as “Data Analysis for Business Management” (BBA3), “Data Processing” (MSc, e-learning), “Digital and Sales” (GEP 5th year), and “Introduction to Statistics” (BBA1). Between September 2020 and October 2021, she was an adjunct faculty member at EMLV (Paris), where she taught “Quantitative Data Analytics – SPSS” (GEP 4th year, hybrid learning) and supervised master’s theses for GEP 5th-year students.

In addition to her academic roles, she has extensive experience in the consulting and insurance sectors. From March to November 2020, she worked as a Senior Consultant at Fraeris (Paris), supporting clients in project development and providing technical solutions. She collaborated with the “Caisse de Prévoyance Sociale” (CPS) of French Polynesia, modeling healthcare expenditures using machine learning techniques. She developed predictive models to analyze healthcare costs from both the insured’s and CPS’s perspectives, offering actionable insights and data-driven forecasts to aid long-term financial planning. Prior to that, in 2019–2020, she was a Senior Manager in Pricing & Data P&C at Addactis (Paris), where she supported clients in project development, innovation, and strategic planning. As an expert referent for ADDACTIS® Pricing software, she worked on database processing for BNP Paribas Cardif, facilitating APLe software operations for quarterly account closings.

Memberships and Projects:

• Membership of the American Risk and Insurance Association (ARIA)
• Membership of the academic association AFSE.
• Member of the RED Flag Project of the University of Orléans in cooperation with CRJPothier.
• Participation at 3 Erasmus+ Projects: Artificial Intelligence to support Education (EducAItion).
• Virtual Incubator Tailored to All Entrepreneurs (VITAE).
• Artificial Intelligence in high Education (PRAIME),

Research topics:

Studies focus on the application of data science techniques to business issues, particularly in the insurance
sector, and on climate change. Another topic of study is the relationship between AI and education.

Publication

  • Yankol-Schalck, M. (2023). Auto Insurance Fraud Detection: Leveraging Cost Sensitive and Insensitive
    Algorithms for comprehensive Analysis, Insurance: Mathematics and Economics.(
    (https://www.sciencedirect.com/science/article/abs/pii/S0167668725000216)
    Banulescu‐Radu, D., & Yankol‐Schalck, M. (2024). Practical guideline to efficiently detect insurance fraud
    in the era of machine learning: A household insurance case. Journal of Risk and Insurance, 91(4), 867-
    913.
    Yankol-Schalck, M. (2022). A Fraud Score for the Automobile Insurance Using Machine Learning and
    Cross-Data set Analysis, Research in International Business and Finance, Volume 63, 101769.
    Schalck, C., Yankol-Schalck, M. (2021). Failure Prediction for SME in France: New evidence from
    machine learning techniques, Applied Economics, 53(51), 5948-5963.
    On- going research:
    Yankol-Schalck (2025). Auto Insurance Fraud Detection: Machine Learning and Deep Learning
    Applications, submitted in Journal of Risk and Insurance.
    Schalck, C., Yankol-Schalck, M. (2024). Churn prediction in the French insurance sector using Grabit
    model, revision in Journal of Forecasting.
    Schalck, C., Seungho, L., Yankol-Schalck, M. (2024). Characteristics of firms and climate risk
    management: a machine learning approach. Work in progress for The Journal of Financial Economics.
    Yankol-Schalck M.and Chabert Delio C., (2024). The application of machine learning to analyse changes in
    consumer behaviour in a major crisis. Work in progress.
    Yankol-Schalck M. and Nasseri A. (2024).An investigation into the integration of artificial intelligence in
    education: Implications for teaching and learning methods. Work in progress.

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

Mahmoud Alimoradi | Machine Learning | Best Researcher Award

Published on by Cognitive Scientist Awards

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 Master’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 Bachelor’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