Xin SU | Multi-temporal remote sensing information extraction | Best Researcher Award

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Prof Dr. Xin SU | Multi-temporal remote sensing information extraction | Best Researcher Award

Xin Su, PhD, is an Associate Professor at the School of Artificial Intelligence, Wuhan University. He supervises both masterโ€™s and PhD students. He earned his doctorate in Signal and Image Processing from Telecom ParisTech in 2015. He then worked as a postdoctoral researcher at INRIA, France, from 2015 to 2018. His research focuses on intelligent analysis of time-series images, spatiotemporal target recognition, and large-scale remote sensing models. He has led and participated in multiple national research projects, publishing extensively in top-tier journals such as IEEE TIP, IEEE TGRS, ISPRS, and JAG. ๐Ÿ“š๐ŸŽ“

Profile

Education ๐ŸŽ“

  • PhD (2015): Telecom ParisTech, France โ€“ Signal and Image Processing ๐ŸŽ“
  • Masterโ€™s Studies (2008-2011, uncompleted): Wuhan University โ€“ Signal and Information Processing ๐Ÿซ
  • Bachelorโ€™s (2004-2008): Wuhan University โ€“ Electronic Science and Technology (Engineering) ๐ŸŽ“

Experience ๐Ÿ‘จโ€๐Ÿซ

  • 2015-2018: Postdoctoral Researcher, INRIA, France ๐Ÿ‡ซ๐Ÿ‡ท
  • 2015: Postdoctoral Researcher, Telecom ParisTech, France ๐ŸŽ“

Research Interests ๐Ÿ”ฌ

Xin Su specializes in intelligent analysis of time-series remote sensing images, spatiotemporal object recognition, and large-scale AI models for remote sensing. His work spans geospatial applications, UAV-based surveillance, and hyperspectral data processing. He actively contributes to developing advanced AI techniques for satellite video analysis and infrastructure monitoring. ๐Ÿš€๐ŸŒ

Awards & Recognitions ๐Ÿ…

Xin Su has been recognized for his contributions to remote sensing and AI, receiving multiple national research grants and awards for excellence in scientific research and innovation. He has secured funding from National Natural Science Foundation projects and defense-related initiatives. His research has been featured in top IEEE and ISPRS journals, reinforcing his position as a leading researcher in the field. ๐ŸŒŸ๐Ÿ…

Publications ๐Ÿ“š

Grazia Ragone | Human-Computer Interaction | Best Researcher Award

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Dr. Grazia Ragone | Human-Computer Interaction | Best Researcher Award

๐Ÿ”ฌ Grazia Ragone is a researcher in Human-Computer Interaction (HCI) with a focus on autism and interactive systems. ๐Ÿซ She earned her PhD from the University of Sussex, UK, where she investigated social motor synchrony in autistic children through motion capture and sonification. ๐ŸŽผ With a background in psychology, developmental science, and music therapy, she integrates interdisciplinary methods into assistive technology. ๐Ÿ’ป She has extensive teaching experience in research methods, cognitive science, and HCI at the University of Sussex. ๐Ÿ† Her research has been recognized with multiple international awards, including Microsoft Researchโ€™s Best Student Research Competition. ๐ŸŒ She actively contributes as a reviewer and associate chair for HCI conferences and journals. ๐Ÿ“– Her work bridges psychology, technology, and education, aiming to enhance accessibility and interaction for neurodiverse individuals.

Profile

Education ๐ŸŽ“

She completed her PhD in 2023 at the University of Sussex, UK, where her research focused on autism, motion capture, and social motor synchrony. Prior to this, she earned an MSc in Psychological Methods from the University of Sussex in 2018, with a focus on autism and interactional features. She also holds an MPhil in Developmental Psychology from London Metropolitan University (2015), specializing in child development and interaction. In 2014, she completed her BSc in Developmental Psychology at London Metropolitan University, studying early cognitive and social development. She further enriched her expertise with a Masterโ€™s in Music & Art Therapy from Tor Vergata University in Rome (2006), where she focused on therapeutic interventions for individuals with special needs. Her academic journey began with a BA in Humanities from the University of Pavia, Italy (2004), where she studied philosophy, linguistics, and cultural studies.

Experience ๐Ÿ‘จโ€๐Ÿซ

From 2019 to 2023, she worked as a Teaching Assistant at the University of Sussex, UK, where she taught Human-Computer Interaction (HCI), research methods, and professional skills. Prior to this, she served as a Research Assistant at the University of Sussex (2016-2018), focusing on technology designed for neurodiverse children. From 2014 to 2016, she conducted research on autism and interactive environments at London Metropolitan University. Earlier in her career, she was a Research Assistant at CNR-ISTI Pisa, Italy (2008-2014), where she contributed to the development of assistive software for autistic children. Her experience also includes working as a Music Therapist for the Rome City Council (2005-2010), providing therapeutic interventions for autistic children. Additionally, from 2010 to 2019, she worked as a Trainer and Consultant, conducting workshops and training programs for professionals in the field of autism.

Research Interests ๐Ÿ”ฌ

Her research focuses on Human-Computer Interaction (HCI) and autism, developing interactive systems to support neurodiverse individuals. She explores the role of music and sonification in enhancing motor and social skills through auditory feedback. Her work also includes investigating social motor synchrony using motion capture technology. She designs AI-powered assistive technology to support autistic children and applies user-centered design principles to create accessible interfaces for individuals with special needs.

Awards & Recognitions ๐Ÿ…

She has received several prestigious awards and honors for her contributions to autism research and assistive technology. In 2021, she was awarded the Best Student Research Award by Microsoft Research at the ASSETS Conference. Her work was also recognized with the Best Work in Progress Award at the IDC Conference on autism research in 2020. In 2013, she received the Horizon Research Award from London Metropolitan University for outstanding research. Her contributions to autism research earned her a Massachusetts Senate Citation in 2012, and in 2011, she was honored with the Rotary Club Research Award from CNR Pisa for excellence in autism studies.

Publications ๐Ÿ“š

  • ย Supporting and understanding autistic childrenโ€™s non-verbal interactions through OSMoSIS, a motion-based sonic system
    International Journal of Child-Computer Interaction
    2025-02 |ย Journal article
    DOI:ย 10.1016/j.ijcci.2025.100726
    CONTRIBUTORS:ย Grazia Ragone;ย Judith Good;ย Kate Howland

  • Child-Centered AI for Empowering Creative and Inclusive Learning Experiences

    Proceedings of ACM Interaction Design and Children Conference: Inclusive Happiness, IDC 2024
    2024 |ย Conference paper
    DOI:ย 10.1145/3628516.3661157

    EID:

    2-s2.0-85197894406

    Part ofISBN:ย 9798400704420
    CONTRIBUTORS:ย Ragone, G.;ย Ali, S.A.;ย Esposito, A.;ย Good, J.;ย Howland, K.;ย Presicce, C.

  • Designing Safe and Engaging AI Experiences for Children: Towards the Definition of Best Practices in UI/UX Design

    arXiv
    2024 |ย Other
    DOI:ย 10.48550/arXiv.2404.14218

    EID:

    2-s2.0-85192517180

    Part ofย ISSN:ย 23318422
    CONTRIBUTORS: Ragone, G.; Buono, P.; Lanzilotti,

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

 

 

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 (2018โ€“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 2016โ€“2017. Her academic background also includes a Masterโ€™s degree in Mathematical Engineering (Applied Statistics) from Paris-Sud University (2004โ€“2007) and a Masterโ€™s degree in Mathematics from the University of Marmara in Istanbul (1995โ€“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 โ€œData Analysis for Business Managementโ€ (BBA3), โ€œData Processingโ€ (MSc, e-learning), โ€œDigital and Salesโ€ (GEP 5th year), and โ€œIntroduction to Statisticsโ€ (BBA1). Between September 2020 and October 2021, she was an adjunct faculty member at EMLV (Paris), where she taught โ€œQuantitative Data Analytics โ€“ SPSSโ€ (GEP 4th year, hybrid learning) and supervised masterโ€™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 โ€œCaisse de Prรฉvoyance Socialeโ€ (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 CPSโ€™s perspectives, offering actionable insights and data-driven forecasts to aid long-term financial planning. Prior to that, in 2019โ€“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 ADDACTISยฎ Pricing software, she worked on database processing for BNP Paribas Cardif, facilitating APLe software operations for quarterly account closings.

Memberships and Projects:

โ€ข Membership of the American Risk and Insurance Association (ARIA)
โ€ข Membership of the academic association AFSE.
โ€ข Member of the RED Flag Project of the University of Orlรฉans in cooperation with CRJPothier.
โ€ข Participation at 3 Erasmus+ Projects: Artificial Intelligence to support Education (EducAItion).
โ€ข Virtual Incubator Tailored to All Entrepreneurs (VITAE).
โ€ข 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)
    Banulescuโ€Radu, D., & Yankolโ€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

Jianbang Liu | AI-driven emotion | Best Researcher Award

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Dr. Jianbang Liu | AI-driven emotion | Best Researcher Award

JianBang Liu is a faculty member at the Xinyu University, China, where he actively contributes to both research and education. His research interests lie at the intersection of Artificial Intelligence (AI), Human-Computer Interaction (HCI), and Artificial Sentiment Analysis, with a specific focus on developing AI-driven emotion and cognition analysis. He has published extensively in international journals, significantly advancing the fields of HCI and AI. He continues to explore innovative applications of these technologies, aiming to bridge theoretical research with practical implementations.

Profile

Education

JianBang Liu obtained his Master’s degree from Qilu University of Technology (Shandong Academy of Sciences), China, in 2018. He then completed his Ph.D. at the Institute of Visual Informatics, UniversitiKebangsaan Malaysia (National University of Malaysia), specializing in Human-Computer Interaction (HCI) and Artificial Intelligence (AI).

Research Interests

Artificial Intelligence (AI), Human-Computer Interaction (HCI), AI-driven emotion and cognition analysisRe

Research Innovation

Completed/Ongoing Research Projects: State the number of research projects you have completed or are currently working on.

Citation Index: Provide information about your citation index in relevant databases such as SCI, Scopus, etc.

Consultancy/Industry Projects: Indicate the number of consultancy or industry-sponsored projects you have been involved in.

Books Published (ISBN): Specify the number of books you have published with ISBN numbers.

Patents Published/Under Process: Mention the number of patents you have published or are currently in the process of publishing.

JournalsPublished: State the number of articles you have published in indexed journals.

Editorial Appointments: If applicable, list any editorial positions you hold in journals or conferences.

Collaborations: Describe any significant collaborations you have been part of in your research career.

Professional Memberships: List memberships in professional organizations or societies relevant to your field.

Areas of Research: Specify the main areas or topics you focus on in your research work.

Books /Chapters in Books๏ผš

Local optimal Issue in Bees Algorithm: Markov Chain Analysis and Integration with Dynamic Particle Swarm Optimization Algorithm (Intelligent Engineering Optimisation with the Bees Algorithm (978-3-031-64935-6/ 978-3-031-64936-3 (eBook)))

Publication

  • Emotion assessment and application in human-computer interaction interface based on backpropagation neural network and artificial bee colony algorithm (SCI Q1)
  • Emotion assessment and application in human-computer interaction interface based on backpropagation neural network and artificial bee colony algorithm (SCI Q1)
  • Personalized Emotion Analysis Based on Fuzzy Multi-Modal Transformer Model (SCI Q2)
  • Immersive VR Learning experiences from the perspective of telepresence, emotion, and cognition(SSCI Q1)

Dingming Wu | Computer Science | Best Researcher Award

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Dr. Dingming Wu | Computer Science | Best Researcher Award

 

Profile

  • scopus

Education

He holds a Ph.D. in Computer Science and Technology from Harbin Institute of Technology, where he studied under the supervision of Professor Xiaolong Wang from March 2018 to December 2022. Prior to that, he earned a Masterโ€™s degree in Probability Theory and Mathematical Statistics from Shandong University of Science and Technology in collaboration with the University of Chinese Academy of Sciences, completing his studies under the guidance of Professor Tiande Guo between September 2014 and July 2017. His academic journey began with a Bachelorโ€™s degree in Information and Computational Science from Shandong University of Science and Technology, which he completed between September 2006 and July 2010.

Work experience

He is currently a Postdoctoral Fellow at the University of Electronic Science and Technology of China, Chengdu, a position he has held since December 2022 and will continue until December 2024. His research focuses on EEG signal processing and algorithm feature extraction, specifically addressing the challenges posed by the complexity and individual variations of EEG signals. Given the limitations of traditional classification methods, his work aims to enhance recognition accuracy through advanced deep learning models, improving the decoding of intricate EEG signals and optimizing control accuracy. Additionally, he integrates artificial intelligence technologies to predict user intentions and provide proactive responses, ultimately enhancing the interactive experience. His system is designed for long-term stability and adaptability, leveraging self-learning mechanisms based on user feedback.

Previously, he worked as a Data Analyst at Qingdao Sanlujiu International Trade Co., Ltd., Shanghai, from September 2010 to July 2014. In this role, he was responsible for conducting statistical analysis of trade flow data.

Publication

  • [1] Dingming Wu, Xiaolong Wangโˆ—, and Shaocong Wu. Jointly modeling transfer learning of
    industrial chain information and deep learning for stock prediction[J]. Expert Systems with
    Applications, 2022, 191(7):116257.
    [2] Dingming Wu, Xiaolong Wangโˆ—, and Shaocong Wu.A hybrid framework based on extreme
    learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock
    prediction[J]. Expert Systems with Applications, 2022, 207(24):118006.
    [3] Dingming Wu, Xiaolong Wangโˆ—, and Shaocong Wu. Construction of stock portfolio based on
    k-means clustering of continuous trend features[J]. Knowledge-Based Systems, 2022,
    252(18):109358.
    [4] Dingming Wu, Xiaolong Wangโˆ—, Jingyong Su, Buzhou Tang, and Shaocong Wu. A labeling
    method for financial time series prediction based on trends[J]. Entropy, 2020, 22(10):1162.
    [5] Dingming Wu, Xiaolong Wangโˆ—, and Shaocong Wu. A hybrid method based on extreme
    learning machine and wavelet transform denoising for stock prediction[J]. Entropy, 2021,
    23(4):440.
    Papers to be published:
    [6] Wavelet transform in conjunction with temporal convolutional networks for time series
    prediction. Journal: PATTERN RECOGNITION; Status: under review; Position: Sole
    Author.
    [7] A Multidimensional Adaptive Transformer Network for Fatigue Detection. Journal: Cognitive
    Neurodynamics; Status: accept; Position: First Author.
    [8] A Multi-branch Feature Fusion Deep Learning Model for EEG-Based Cross-Subject Motor
    Imagery Classification. Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL
    INTELLIGENCE; Status: under review; Position: First Author.
    [9] A Coupling of Common-Private Topological Patterns Learning Approach for Mitigating Interindividual Variability in EEG-based Emotion Recognition. Journal: Biomedical Signal
    Processing and Control; Status: Revise; Position: First Corresponding Author.
    [10] A Function-Structure Adaptive Decoupled Learning Framework for Multi-Cognitive Tasks
    EEG Decoding. Journal: IEEE Transactions on Neural Networks and Learning Systems;
    Status: under review; Position: Co-First Author.
    [11] Decoding Topology-Implicit EEG Representations Under Manifold-Euclidean Hybrid Space.
    Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI);
    Status: under review; Position: Second Corresponding Author.
    [12] Style Transfer Mapping for EEG-Based Neuropsychiatric Diseases Recognition. Journal:
    EXPERT SYSTEMS WITH APPLICATIONS; Status: under review; Position: Second
    Corresponding Author.
    [13] An Adaptive Ascending Learning Strategy Based on Graph Optional Interaction for EEG
    Decoding. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: Second Corresponding Author.
    [14] A Transfer Optimization Methodology of Graph Representation Incorporating CommonPrivate Feature Decomposition for EEG Emotion Recognition. Computer conference:
    International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review;
    Position: Second Corresponding Author.
    [15] An Interpretable Neural Network Incorporating Rule-Based Constraints for EEG Emotion
    Recognition. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: First Author.

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

Muhammad Waheed Rasheed | Artificial Intelligence | Best Researcher Award

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Mr. Muhammad Waheed Rasheed | Artificial Intelligence | Best Researcher Award

Research Assistant at COMSATS University Islamabad, Vehari Campus, Pakistan

Muhammad Waheed Rasheed is a dedicated mathematician and researcher known for his contributions to cryptography, fuzzy graph theory, and QSPR analysis. His academic and professional pursuits focus on creating innovative solutions to global challenges, particularly in molecular descriptors, graph theory, and their applications in chemistry and physics. With a passion for research and education, Mr. Rasheed embodies excellence in both theoretical and applied mathematics. His publications in high-impact journals like Frontiers in Chemistry and Frontiers in Physics reflect his ability to bridge disciplines and address real-world problems. As a motivated and dependable team player, he thrives in collaborative environments while excelling independently. His research outputs, which span drug efficacy studies and complex mathematical modeling, contribute significantly to scientific advancements and underscore his role as a rising star in the global mathematical community.

Profile

Scopus

Education ๐ŸŽ“

Mr. Rasheed earned an MS in Mathematics (2021โ€“2023) and a BS (Hons) in Mathematics (2017โ€“2021) from the University of Education Lahore, Pakistan, achieving CGPAs of 3.64/4.00 and 3.61/4.00, respectively. His coursework encompassed advanced topics such as algebraic graph theory, numerical methods, Galois theory, real analysis, and differential geometry. This robust educational foundation equipped him with the analytical and problem-solving skills needed to excel in multidisciplinary research areas, including graph theory and mathematical modeling.

Work Experience ๐Ÿ’ผ

Muhammad Waheed Rasheed is an accomplished researcher with expertise in cryptography, fuzzy graph theory, and QSPR analysis. His work focuses on molecular descriptors, graph labeling, energy graphs, and metric dimensions, addressing challenges in networking and drug efficacy analysis. With five impactful publications in journals like Frontiers in Chemistry and Frontiers in Physics, he demonstrates excellence in both independent and collaborative research. His ability to tackle complex problems and deliver innovative solutions highlights his readiness for advanced research roles in academia and industry.

Research Interests

Mr. Rasheedโ€™s research interests include cryptography, group theory, fuzzy graph theory, and QSPR analysis. He focuses on molecular descriptors, graph labeling, energy graphs, and metric dimensions, aiming to address critical issues in mathematics and its applications in healthcare and networking.

Research Skills

Muhammad Waheed Rasheed’s research interests lie at the intersection of advanced mathematics and real-world applications. He specializes in cryptography, fuzzy graph theory, and group theory, with a strong emphasis on molecular descriptors, graph labeling, energy graphs, and metric dimensions. His work extends to QSPR (Quantitative Structure-Property Relationship) analysis, where he investigates the properties of chemical compounds, such as alkaloids and medications, to improve therapeutic efficacy and understand their thermodynamic behavior. He is particularly passionate about exploring the role of graph theory in networking and healthcare, focusing on innovative solutions to complex problems. Through his interdisciplinary research, Mr. Rasheed aims to contribute significantly to global challenges, combining theoretical insights with practical applications in chemistry, physics, and beyond.

๐Ÿ“š Publications

Neighborhood Face Index: A New QSPR Approach for Predicting Physical Properties of Polycyclic Chemical Compounds

  • Authors: A. Raza, M.W. Rasheed, A. Mahboob, M. Ismaeel
  • Journal: International Journal of Quantum Chemistry
  • Year: 2024
  • Volume: 124(24), e27524
  • Citations: 0

Block Cipher Construction Using Minimum Spanning Tree from Graph Theory and Its Application with Image Encryption

  • Authors: M.W. Rasheed, A. Mahboob, M. Bilal, K. Shahzadi
  • Journal: Science Progress
  • Year: 2024
  • Volume: 107(4)
  • Citations: 0

Entropy Measures of Dendrimers Using Degree-Based Indices

  • Authors: A. Ovais, F. Yasmeen, M. Irfan, M.W. Rasheed, S. Kousar
  • Journal: South African Journal of Chemical Engineering
  • Year: 2024
  • Volume: 50, pp. 168โ€“181
  • Citations: 0

Computing Connection-Based Topological Indices of Carbon Nanotubes

  • Authors: E.U. Haq, A. Mahboob, M.W. Rasheed, S. Sattar, M. Waqas
  • Journal: South African Journal of Chemical Engineering
  • Year: 2024
  • Volume: 48, pp. 121โ€“129
  • Citations: 0

QSPR Analysis of Physicochemical Properties and Anti-Hepatitis Prescription Drugs Using a Linear Regression Model

  • Authors: A. Mahboob, M.W. Rasheed, A.M. Dhiaa, I. Hanif, L. Amin
  • Journal: Heliyon
  • Year: 2024
  • Volume: 10(4), e25908
  • Citations: 5

Approximating Properties of Chemical Solvents by Two-Dimensional Molecular Descriptors

  • Authors: A. Mahboob, M.W. Waheed Rasheed, I. Hanif, I. Siddique
  • Journal: International Journal of Quantum Chemistry
  • Year: 2024
  • Volume: 124(1), e27305
  • Citations: 3

Role of Molecular Descriptors in QSPR Analysis of Kidney Cancer Therapeutics

  • Authors: A. Mahboob, M.W. Rasheed, I. Hanif, L. Amin, A. Alameri
  • Journal: International Journal of Quantum Chemistry
  • Year: 2024
  • Volume: 124(1), e27241
  • Citations: 9

Face Irregular Evaluations of Family of Grids

  • Authors: J.H.H. Bayati, A. Ovais, A. Mahboob, M.W. Rasheed
  • Journal: AKCE International Journal of Graphs and Combinatorics
  • Year: 2024 (In Press)
  • Citations: 0

Enhancing Breast Cancer Treatment Selection Through 2TLIVq-ROFS-Based Multi-Attribute Group Decision Making

  • Authors: M.W. Rasheed, A. Mahboob, A.N. Mustafa, Z.A.A. Ali, Z.H. Feza
  • Journal: Frontiers in Artificial Intelligence
  • Year: 2024
  • Volume: 7, 1402719
  • Citations: 0

QSAR Modeling with Novel Degree-Based Indices and Thermodynamics Properties of Eye Infection Therapeutics

  • Authors: M.W. Rasheed, A. Mahboob, I. Hanif
  • Journal: Frontiers in Chemistry
  • Year: 2024
  • Volume: 12, 1383206
  • Citations: 0

Conclusionย 

Muhammad Waheed Rasheed is a talented researcher whose academic achievements and innovative research demonstrate a promising career in mathematics and its applications. His dedication, interdisciplinary focus, and impactful publications make him a strong candidate for prestigious accolades and research opportunities.