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 Master’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 Bachelor’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 Wang∗, 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 Wang∗, 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 Wang∗, 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 Wang∗, 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 Wang∗, 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 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

Nuo Yu | Radiomics | Best Researcher Award

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Ms. Nuo Yu | Radiomics | Best Researcher Award

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

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

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

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

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

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

Profile

Scientific Publications

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 🎓

Mr. Rasheed earned an MS in Mathematics (2021–2023) and a BS (Hons) in Mathematics (2017–2021) 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 💼

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. Rasheed’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.

📚 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. 168–181
  • 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. 121–129
  • 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

Conclusion 

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.

 

Ling Mei | Cognitive Science | Best Researcher Award

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Dr. Ling Mei | Cognitive Science | Best Researcher Award

Doctorate at Wuhan University of Science and Technology, China

Dr. Ling Mei is an accomplished researcher in artificial intelligence and cognitive science, with a robust academic and professional background. He holds a Ph.D. in Engineering from Sun Yat-sen University, one of China’s top universities, and completed a prestigious visiting scholar program at the University of British Columbia (UBC). Currently serving as a tenured faculty and master’s supervisor, Dr. Mei has published 16 papers, including 7 in SCI-indexed journals, contributed to nine books, and has three national invention patents granted. Recognized as a Provincial Research Talent of China in 2024, he work integrates advanced computational models with societal needs, such as urban planning and public safety. Dr. Mei has collaborated internationally with top-tier institutions like UBC and Carnegie Mellon University, cementing he reputation as a leader in he field.

Profile

Google Scholar

Orcid

Education 🎓

Dr. Mei earned he Ph.D. in Engineering from Sun Yat-sen University in 2021, a prestigious institution ranked among China’s top 10 universities. He academic journey also includes a year-long visiting scholar program at the Department of Computer Science, UBC, as part of the National Outstanding Young Researchers Program. This international exposure provided he with cutting-edge knowledge and interdisciplinary skills, enabling he to excel in artificial intelligence and cognitive science.

Work Experience 💼

Currently, Dr. Mei is a tenured faculty member and master’s supervisor at a leading Chinese university. He experience includes overseeing multiple research projects, consulting on seven industry-sponsored projects, and serving as a reviewer for the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY. He has also been instrumental in fostering international collaborations with institutions like UBC and CMU, contributing to impactful research publications and patents.

Awards and Honors

In 2024, Dr. Mei was recognized as a Provincial Research Talent of China, highlighting he exceptional contributions to science and technology. He has also earned accolades through he impactful patents and high-quality publications.

Research Interests

Dr. Mei’s research focuses on artificial intelligence, pedestrian trajectory prediction, and public safety strategies. He innovations include the LSN-GTDA framework, which integrates behavioral and stochastic factors for better uncertainty management. He interdisciplinary approach bridges cognitive science, computational models, and societal applications, ensuring he work’s relevance and impact.

Research Skills

Dr. Mei possesses advanced skills in AI modeling, thermal diffusion processes, and signal and system theory. He expertise includes patent development, SCI journal publications, and interdisciplinary collaborations. He is adept at integrating computational techniques with practical applications, as seen in he trajectory prediction research.

📚 Publications

Crowd Density Estimation via Global Crowd Collectiveness Metric

  • Journal: Drones
  • Date: 2024-10-28
  • DOI: 10.3390/drones8110616
  • Contributors: Ling Mei, Mingyu Yu, Lvxiang Jia, Mingyu Fu

More Quickly-RRT: Improved Quick Rapidly-Exploring Random Tree Star Algorithm Based on Optimized Sampling Point with Better Initial Solution and Convergence Rate*

  • Journal: Engineering Applications of Artificial Intelligence
  • Date: 2024-07
  • DOI: 10.1016/j.engappai.2024.108246
  • Contributors: Xining Cui, Caiqi Wang, Yi Xiong, Ling Mei, Shiqian Wu

Learning Domain-Adaptive Landmark Detection-Based Self-Supervised Video Synchronization for Remote Sensing Panorama

  • Journal: Remote Sensing
  • Date: 2023-02-09
  • DOI: 10.3390/rs15040953
  • Contributors: Ling Mei, Yizhuo He, Farnoosh Fishani, Yaowen Yu, Lijun Zhang, Helge Rhodin

Illumination-Invariance Optical Flow Estimation Using Weighted Regularization Transform

  • Journal: IEEE Transactions on Circuits and Systems for Video Technology
  • Date: 2020-02
  • DOI: 10.1109/TCSVT.2019.2890861
  • Contributors: Ling Mei, Jianhuang Lai, Xiaohua Xie, Junyong Zhu, Jun Chen

Feature Visualization Based Stacked Convolutional Neural Network for Human Body Detection in a Depth Image

  • Type: Book Chapter
  • Year: 2018
  • DOI: 10.1007/978-3-030-03335-4_8
  • Contributors: Xiao Liu, Ling Mei, Dakun Yang, Jianhuang Lai, Xiaohua Xie

Conclusion 

Dr. Ling Mei is a strong contender for the Best Researcher Award due to he robust academic background, impactful research, and significant contributions to AI and cognitive science. To further enhance he candidacy, increasing citation influence and emphasizing community impact would solidify he position as an exemplary researcher deserving of recognition. 🌟