Vinod Kumar | Big Data Analytics | Best Researcher Award

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Assoc Prof Dr. Vinod Kumar | Big Data Analytics | Best Researcher Award

FLAME University, India

Author Profile

Google Scholar

🌱 Early Academic Pursuits

Assoc Prof Dr. Vinod Kumar began his academic journey with a strong foundation in Mechanical Engineering, earning a Bachelor’s degree from Haryana College of Technology & Management, Kaithal. He then transitioned into the realm of business, acquiring a Master of Business Administration (MBA) in Marketing from Chandigarh Business School. His intellectual pursuit culminated in a Ph.D. in Marketing from the prestigious Indian Institute of Technology (IIT) Roorkee. His doctoral thesis, titled “Assessing the Influence of Stakeholders on Sustainability Marketing Strategy,” focused on how various stakeholders shape the strategies for sustainable marketing.

💼 Professional Endeavors

Dr. Kumar’s professional trajectory spans over a decade in academia, with positions at renowned institutions. His current role as an Associate Professor at FLAME University in Pune, Maharashtra, marks the latest phase in a distinguished career. Prior to this, he held faculty positions at MICA, Ahmedabad, Symbiosis Institute of Business Management, and the Indian Institute of Information Technology, Lucknow, among others. Additionally, Dr. Kumar’s experience in industry as a Sales Executive and Water Treatment Specialist adds practical insight to his academic teaching. This mix of academia and industry experience enriches his approach to teaching and research.

📚 Contributions and Research Focus

Dr. Kumar’s research primarily focuses on Brand Management, Digital Marketing, Green Marketing, Digital Business Strategy, and Social Media Marketing. His interdisciplinary approach integrates traditional marketing principles with the evolving digital landscape. Notable research projects include studying the relationship between stakeholders and sustainability strategies, and he has contributed significantly to the literature on stakeholder identification in marketing. Dr. Kumar has published papers in high-impact journals such as Management Research Review and has been honored with the Emerald Literati Network Award for Excellence in 2017 for his outstanding research.

🌍 Impact and Influence

Through his academic and professional roles, Dr. Kumar has made a substantial impact in shaping the marketing landscape. His research has not only contributed to the academic body of knowledge but has also influenced industry practices. The recognition of his work through awards like the Best Paper Award at AMRIT-2020 and Emerald Literati Network Awards for Excellence reflects his significant influence in the marketing and digital business strategy domains. Additionally, his consulting project on Digital Marketing Strategies and Tools for God’s Grace Distributions showcases his practical expertise and ability to drive business transformation.

📑 Academic Cites and Recognition

Dr. Kumar’s research has garnered widespread recognition and citation in leading academic forums. His work on Sustainability Marketing and Stakeholder Analysis is widely referenced in marketing and sustainability studies. His recognition by various academic bodies, including the FIIB Business Review as a reviewer and the AIM-AMA Sheth Foundation Travel Grant, further solidifies his standing as an authority in his field.

💻 Technical Skills

Dr. Kumar possesses advanced proficiency in digital marketing tools, UI/UX Design, Social Media Marketing, and Data Analytics. His expertise in Digital Business Strategy enables him to leverage digital platforms effectively for both academic purposes and real-world business solutions. This technical expertise is integrated into his teaching, providing students with a modern, practical learning experience that blends theory with the latest industry practices.

🎓 Teaching Experience

Dr. Kumar has held teaching positions across multiple prestigious institutions, where he has designed and taught courses on Marketing Management, Digital Marketing, Brand Management, and Sales and Distribution Management. His role as the Founding Head of the Department at IIIT Lucknow enabled him to create a dynamic curriculum and research environment for future leaders in business management. His approach to teaching combines innovative strategies, a deep understanding of digital platforms, and a focus on practical application, which has been appreciated by students and colleagues alike.

🌟 Legacy and Future Contributions

As a respected academic, Dr. Kumar has not only imparted knowledge but has also shaped the future of marketing education. His research continues to evolve, with ongoing supervision of Ph.D. students, such as Ms. Isha Sharma and Mr. Vibhav Singh. Dr. Kumar’s legacy is built upon his commitment to enhancing the relevance of marketing education in the digital age and his contribution to sustainability marketing. His future contributions are likely to have a significant impact on both academic literature and industry practices in digital business strategy, making him a thought leader to watch in the coming years.

📖 Top Noted Publications 

Evolution of Sustainability as Marketing Strategy: Beginning of New Era
    • Authors: V Kumar, Z Rahman, AA Kazmi, P Goyal
    • Journal: Procedia-Social and Behavioral Sciences
    • Year: 2012
Sustainability Marketing Strategy: An Analysis of Recent Literature
    • Authors: V Kumar, Z Rahman, AA Kazmi
    • Journal: Global Business Review
    • Year: 2013
Examining Consumer-Brand Relationships on Social Media Platforms
    • Authors: NK Jain, S Kamboj, V Kumar, Z Rahman
    • Journal: Marketing Intelligence and Planning
    • Year: 2018
Stakeholder Identification and Classification: A Sustainability Marketing Perspective
    • Authors: V Kumar, Z Rahman, AA Kazmi
    • Journal: Management Research Review
    • Year: 2016
Why Export Competitiveness Differs Within Indian Textile Industry? Determinants and Empirical Evidence
    • Authors: R Dhiman, V Kumar, S Rana
    • Journal: Review of International Business and Strategy
    • Year: 2020
Investigating the Impact of Online Service Convenience on Customer Engagement, Attitude and Intention to Use Food Delivery Apps
    • Authors: V Vandana, S Kumar, V Kumar, P Goyal
    • Journal: International Journal on Food System Dynamics
    • Year: 2023

Vahid Naghashi | Temporal Data Patterns | Best Researcher Award

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Mr. Vahid Naghashi | Temporal Data Patterns | Best Researcher Award

Innovation Chair in Animal Welfare and Artificial Intelligence (WELL-E), Canada

Author Profile

Google Scholar

Early Academic Pursuits 📚

Mr. Vahid Naghashi is currently a Ph.D. candidate, focusing on forecasting dairy income during future lactations using deep learning models. His academic journey reflects a strong foundation in time series forecasting, machine learning, and deep learning. Throughout his academic career, he has excelled in advancing knowledge in computational models and applied artificial intelligence.

Professional Endeavors 💼

Mr. Naghashi’s career has also been shaped by professional industry experience, including an internship at a multimedia company. During his internship, he worked on projects involving Large Language Models (LLMs) and audio processing with foundation models. His exposure to real-world applications of deep learning models has broadened his expertise in both academic and industry settings.

Contributions and Research Focus 🔬

Mr. Naghashi’s research primarily focuses on time series forecasting using deep learning techniques. His major research project involves the prediction of milk profits in future lactations, an area of great relevance to the dairy industry. One of his notable contributions is the development of a multiscale model for multivariate time series forecasting, which has applications across various fields, published in Nature Scientific Reports.

Impact and Influence 🌍

The impact of Mr. Naghashi’s work extends beyond academia. His contributions to the field of deep learning for time series forecasting can significantly transform industries like dairy farming. His work in forecasting and data analytics has proven to be useful in real-world scenarios, with a growing citation index, indicating the broader influence of his research.

Academic Cites 📑

His work has been recognized in several prestigious journals, including his recent article published in Nature Scientific Reports, which has been widely cited. Additionally, his research article “A multiscale model for multivariate time series forecasting” is a key example of his contributions to the academic community, helping to shape future research directions in time series analysis and forecasting.

Technical Skills 🧑‍💻

Mr. Naghashi possesses extensive technical expertise in deep learning, machine learning, and time series forecasting. He has proficiency in advanced data science tools and techniques, including large-scale machine learning models, deep neural networks, and predictive analytics, which are crucial in his research and consultancy projects.

Teaching Experience 👨‍🏫

While there is no direct mention of formal teaching roles, Mr. Naghashi’s research and consultancy work undoubtedly involve mentoring and knowledge sharing within his academic collaborations and industry projects. His involvement with the Innovation Chair in Animal Welfare and Artificial Intelligence (WELL-E) demonstrates his commitment to educating others in his field.

Legacy and Future Contributions 🔮

Looking forward, Mr. Naghashi aims to further advance deep learning methodologies, particularly in forecasting applications across industries such as agriculture, healthcare, and finance. His future research goals include enhancing predictive models and working on larger-scale applications that have a lasting impact on both academic and professional communities.

Top Noted Publications 📖

Co-occurrence of adjacent sparse local ternary patterns: A feature descriptor for texture and face image retrieval
    • Authors: V. Naghashi
    • Journal: Optik
    • Year: 2018
Evaluation of dominant and non-dominant hand movements for volleyball action modelling
    • Authors: F. Haider, F. Salim, V. Naghashi, SBY Tasdemir, I. Tengiz, K. Cengiz, …
    • Journal: Adjunct of the 2019 International Conference on Multimodal Interaction
    • Year: 2019
Volleyball action modelling for behavior analysis and interactive multi-modal feedback
    • Authors: FA Salim, F. Haider, SBY Tasdemir, V. Naghashi, I. Tengiz, K. Cengiz, …
    • Journal: eNTERFACE’19: 15th International Summer Workshop on Multimodal Interfaces
    • Year: 2020
A searching and automatic video tagging tool for events of interest during volleyball training sessions
    • Authors: F. Salim, F. Haider, SBY Tasdemir, V. Naghashi, I. Tengiz, K. Cengiz, …
    • Journal: 2019 International Conference on Multimodal Interaction
    • Year: 2019
A multiscale model for multivariate time series forecasting
    • Authors: V. Naghashi, M. Boukadoum, AB Diallo
    • Journal: Scientific Reports
    • Year: 2025

Naiwei Lu | Data Analysis | Best Researcher Award

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Assoc Prof Dr. Naiwei Lu | Data Analysis | Best Researcher Award

Changsha University of Science of Technology, China

Author Profiles

Orcid

Scopus

Research Gate

👨‍🏫 Associate Prof. Dr. Naiwei Lu

Assoc. Prof. Dr. Naiwei Lu is an Associate Professor in Civil Engineering at Changsha University of Science and Technology (CSUST), China. His research primarily focuses on reliability and safety in bridge engineering. He has a strong background in system reliability evaluation, fatigue analysis, and structural health monitoring, which he applies to the engineering and maintenance of long-span bridges. Dr. Lu is an active member of various international research communities and has been involved in multiple national and international projects.

🎓 Education Background

Dr. Naiwei Lu earned his Ph.D. in Civil Engineering from Changsha University of Science and Technology (CSUST) in 2015, under the supervision of Prof. Yang Liu. Prior to that, he completed his Master’s degree in Civil Engineering in 2011 and his Bachelor’s degree in Bridge Engineering in 2008, all at CSUST, Changsha, China. His academic journey laid a strong foundation for his expertise in bridge engineering and reliability analysis.

💼 Employment and Research Experience

Dr. Lu’s academic career began in 2015 when he became a Postdoctoral Researcher at Southeast University in Nanjing, China, where he worked under Prof. Mohammad Noori. He also held a Postdoctoral Researcher position at Leibniz University Hannover, Germany, working with Prof. Michael Beer. Since 2020, Dr. Lu has been serving as an Associate Professor at CSUST, where he also previously worked as a Lecturer from 2017 to 2019. His diverse academic and international experiences contribute to his deep expertise in bridge engineering.

💰 Research Funding

Dr. Lu has been the Principal Investigator (PI) of several funded research projects, including the National Science Funding in China for research on structural health monitoring of suspension bridges and the fatigue reliability evaluation of steel bridge decks. He has received significant funding support for his work in system reliability and dynamic analysis, with total funding exceeding ¥1 million for his various research initiatives. These projects aim to develop advanced methods for assessing the reliability and safety of long-span bridges.

🔍 Research Interests

Dr. Lu’s research spans several cutting-edge topics in structural engineering. His main research interests include system reliability evaluation of long-span bridges using intelligent algorithms 🤖, fatigue reliability of stay cables and orthotropic steel bridge decks 🌉, and probabilistic modeling using data from structural health monitoring 📊. He is also focused on intelligent maintenance and management of in-service bridges ⚙️, as well as uncertainty quantification in structural engineering 🛠️.

📚 Teaching Experience

As an educator, Dr. Lu teaches undergraduate courses such as Principle of Structural Design (in both Chinese and English) and Building Information Modeling (BIM) Technology (in Chinese). He has supervised 24 undergraduate students and 4 postgraduate students between 2017 and 2020, guiding them in various aspects of civil engineering, particularly related to bridge design, maintenance, and reliability.

🏗️ Engineering Activities

In addition to his academic role, Dr. Lu is a Registered Construction Engineer and a Registered Inspection Engineer in Municipal Engineering and Bridge & Tunnel Engineering, respectively. His engineering activities include overseeing the structural safety of long-span bridges during construction 🏗️, conducting structural health monitoring of suspension bridges 🌉, and contributing to the detection and reinforcement of aging bridges 🛠️. He has also been involved in the advance geological forecasting and monitoring of tunnels during construction, working on over five extra-long tunnels.

Dr. Naiwei Lu’s work integrates advanced intelligent algorithms, data-driven methods, and structural health monitoring to enhance the safety, reliability, and longevity of bridges and infrastructure systems. His research and engineering expertise continue to make significant contributions to the field of civil engineering.

📖Top Noted Publications 

Structural damage diagnosis of a cable-stayed bridge based on VGG-19 networks and Markov transition field: numerical and experimental study

Authors: Naiwei Lu, Zengyifan Liu, Jian Cui, Lian Hu, Xiangyuan Xiao, Yiru Liu

Journal: Smart Materials and Structures

Year: 2025

Coupling effect of cracks and pore defects on fatigue performance of U-rib welds

Authors: Yuan Luo, Xiaofan Liu, Fanghuai Chen, Haiping Zhang, Xinhui Xiao, Naiwei Lu

Journal: Structures

Year: 2025

A Time–Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen

Authors: Naiwei Lu, Yiru Liu, Jian Cui, Xiangyuan Xiao, Yuan Luo, Mohammad Noori

Journal: Sensors

Year: 2024

A Novel Method of Bridge Deflection Prediction Using Probabilistic Deep Learning and Measured Data

Authors: Xinhui Xiao, Zepeng Wang, Haiping Zhang, Yuan Luo, Fanghuai Chen, Yang Deng, Naiwei Lu, Ying Chen

Journal: Sensors

Year: 2024

Experimental and numerical investigation on penetrating cracks growth of rib-to-deck welded connections in orthotropic steel bridge decks

Authors: Zitong Wang, Jun He, Naiwei Lu, Yang Liu, Xinfeng Yin, Haohui Xin

Journal: Structures

Year: 2024

Fatigue Reliability Assessment for Orthotropic Steel Decks: Considering Multicrack Coupling Effects

Authors: Jing Liu, Yang Liu, Guodong Wang, Naiwei Lu, Jian Cui, Honghao Wang

Journal: Metals

Year: 2024

Xibing Xu | Data Science | Best Researcher Award

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Dr. Xibing Xu | Data Science | Best Researcher Award

Shenzhen Medical Academy of Research and Translation, China

Author Profiles

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Early Academic Pursuits 🎓

Dr. Xibing Xu’s academic journey began with a solid foundation in biological sciences, leading to his Master’s degree from Shandong University in 2012. His Master’s thesis focused on the two-component signal transduction systems of Streptomyces hygroscopicus. This foundational research sparked his interest in bacterial biology and set the stage for his doctoral studies at Sichuan University. Dr. Xu’s Ph.D. thesis, completed in 2015, centered on “Protein homeostasis regulation and ubiquitin-like systems in bacteria,” deepening his expertise in bacterial systems and protein regulation.

Professional Endeavors 💼

his Ph.D., Dr. Xu expanded his research horizons by embarking on various international and prestigious roles. In 2017-2018, he was awarded a visiting scholarship by the China Scholarship Council, enabling him to work as a visiting scholar at the Laboratoire de Microbiologie et Génétique Moléculaire (LMGM) in Toulouse, France. This experience enhanced his research capabilities and fostered a deeper understanding of bacterial molecular mechanisms. From 2020 to the present, Dr. Xu has served as a Postdoctoral Fellow at LMGM, where he leads the ANR project M-Tox, which investigates translational control by HigB1 and MenT toxins in Mycobacterium tuberculosis.

Contributions and Research Focus 🔬

Dr. Xu’s research is at the cutting edge of microbiology, focusing on bacterial growth inhibition mechanisms mediated by toxins in toxin-antitoxin systems. Specifically, his work on deleterious tRNA nucleotidyltransferase toxins, like MenT in Mycobacterium tuberculosis, has unveiled novel bacterial mechanisms that regulate translation and protein homeostasis. His research involves exploring the role of tRNA biology in bacterial growth control and the interaction between tRNA and toxin proteins, a focus that has the potential to inform future therapeutic strategies for bacterial infections. His expertise extends to RNA-seq, enzymology, and protein-RNA/DNA interactions, all of which contribute to a deeper understanding of bacterial survival and virulence.

Impact and Influence 🌍

Dr. Xu’s work has far-reaching implications in microbial Microbiologie and therapeutic development. By identifying key mechanisms that control bacterial growth through toxin-antitoxin systems, his research addresses critical challenges in combating drug-resistant bacterial infections. His investigations into tRNA biology have provided insights into how bacteria regulate protein synthesis, which could lead to the development of novel antimicrobial strategies. His innovative research has garnered attention within the scientific community, reflected in invitations to present at major international conferences such as the 29th International tRNA Conference in 2024.

Academic Citations 📚

Dr. Xu’s contributions to the field have not only enhanced scientific knowledge but have also been widely recognized and cited in academic publications. His work on toxin-antitoxin systems and tRNA nucleotidyltransferase toxins has attracted significant attention in the microbiology and biochemistry fields. As his research continues to influence studies on bacterial translation control, his citation index is expected to grow, cementing his reputation as an expert in bacterial growth regulation and antibiotic resistance mechanisms.

Technical Skills 🛠️

Dr. Xu possesses a broad array of technical skills in biochemistry and molecular biology, enabling him to conduct complex experimental research. He is proficient in RNA-seq library design and construction, protein-DNA/RNA interaction analysis, and toxin protein purification and activity assays. Additionally, he has extensive experience in cell-free transcription and translation assays, essential for studying the mechanistic details of translation regulation in bacteria. His technical prowess underpins the success of his innovative research in bacterial biology and protein homeostasis.

Teaching Experience 👨‍🏫

Dr. Xu has a strong track record in teaching and academic supervision. From 2015 to 2020, he taught various courses such as Medical Biology, Medical Genetics, and Medical Cell Biology at Henan University of Science and Technology. His teaching not only involved extensive classroom hours but also practical experimental courses, allowing students to gain hands-on experience in molecular biology techniques. Since 2024, Dr. Xu has been supervising Master’s students at the University Paul Sabatier in Toulouse, mentoring students on projects related to phage peptides and protein homeostasis in bacteria, ensuring the next generation of scientists is equipped with the necessary research skills.

Legacy and Future Contributions 🌟

Dr. Xu’s current and future research promises to continue his legacy of groundbreaking work in microbiology, particularly in the areas of bacterial toxin-antitoxin systems and translation control. As a postdoctoral fellow and researcher at LMGM, he is leading projects that could revolutionize our understanding of bacterial growth regulation, with direct applications in combating drug-resistant Mycobacterium tuberculosis. His ongoing research, mentorship, and commitment to expanding scientific knowledge will ensure that his contributions to the field of microbiology will continue to grow in impact and influence.

 Top Noted Publications 📖

Title: Inducible auto-phosphorylation regulates a widespread family of nucleotidyltransferase toxins

Authors: Tom J. Arrowsmith, Xibing Xu, Shangze, Ben Usher, Peter Stokes, Megan Guest, Agnieszka K. Bronowska, Pierre Genevaux, Tim R. Blower
Journal: Nature Communications
Year: 2024

Title: MenT nucleotidyltransferase toxins extend tRNA acceptor stems and can be inhibited by asymmetrical antitoxin binding

Authors: Xibing Xu, Ben Usher, C. Gutierrez, R. Barriot, T.J. Arrowsmith, X. Han, P. Redder, O. Neyrolles, T.R. Blower, P. Genevaux
Journal: Nature Communications
Year: 2023

Title: Substrate recognition and cryo-EM structure of the ribosome-bound TAC toxin of Mycobacterium tuberculosis

Authors: Moïse, Emmanuel Giudice, Xibing Xu, Hatice Akarsu, Patricia Bordes, Valérie Guillet, Donna-Joe Bigot, Nawel Slama, Gaetano D’Urso, Sophie Chat et al.
Journal: Nature Communications
Year: 2022

Title: ClpXP-mediated Degradation of the TAC Antitoxin is Neutralized by the SecB-like Chaperone in Mycobacterium tuberculosis

Authors: Pauline Texier, Patricia Bordes, Jyotsna Nagpal, Ambre Julie Sala, Moise Mansour, Anne-Marie Cirinesi, Xibing Xu, David Andrew Dougan, Pierre Genevaux
Journal: Journal of Molecular Biology
Year: 2021

Title: The ubiquitin-like modification by ThiS and ThiF in Escherichia coli

Authors: Xibing Xu, Tao Wang, Yulong Niu, Ke Liang, Yi Yang
Journal: International Journal of Biological Macromolecules
Year: 2019

Title: BAH1 an E3 Ligase from Arabidopsis thaliana Stabilizes Heat Shock Factor σ32 of Escherichia coli by Interacting with DnaK/DnaJ Chaperone Team

Authors: Xibing Xu, Ke Liang, Yulong Niu, Yan Shen, Xuedong Wan, Haiyan Li, Yi Yang
Journal: Current Microbiology
Year: 2018

Jing An | Artificial Intelligence | Best Researcher Award

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Dr. Jing An | Artificial Intelligence | Best Researcher Award

Yancheng Institute of Technology, China

👨‍🎓Professional Profile

Scopus Profile

👨‍🏫 Summary

Dr. Jing An is a distinguished professor and master tutor at Yancheng Institute of Technology, China. He specializes in intelligent manufacturing engineering, industrial big data fault diagnosis, and residual life prediction. With numerous patents and software copyrights, Dr. An has published over 20 SCI/EI indexed papers and contributed to key academic texts. His pioneering research in artificial intelligence-based fault diagnosis has earned him prestigious awards, including first-place recognition in the China Commerce Federation Science and Technology Award .

🎓 Education

Dr. An holds a Ph.D. in Computer Science from Hohai University (2021) and a Master’s degree in Computer Science from Harbin University of Science and Technology (2006). His academic foundation has strongly influenced his research in AI and fault diagnosis.

💼 Professional Experience

Having joined Yancheng Institute of Technology in 2006, Dr. An is also the Vice President of Science and Technology for Jiangsu Province’s “Double Innovation Plan.” He has led numerous provincial-level projects, and his expertise has extended to more than 10 industry partnerships 🚀.

📚 Academic Citations

Dr. An has authored over 20 peer-reviewed papers in prestigious journals such as IEEE Access and Mathematical Problems in Engineering. His impactful research on AI-based fault diagnosis methods is frequently cited within the academic community .

🔧 Technical Skills

Dr. An is skilled in AI-based fault diagnosis, deep learning, machine learning, and industrial big data analytics. He has expertise in Convolutional Neural Networks (CNNs) and intelligent manufacturing systems, focusing on improving machinery reliability and efficiency .

🧑‍🏫 Teaching Experience

Dr. An has been recognized as an outstanding teacher twice at Yancheng Institute of Technology. He teaches courses in intelligent manufacturing engineering and industrial big data fault diagnosis, preparing students to advance in the field of AI and data science .

🔍 Research Interests

Dr. An’s research is focused on developing intelligent systems for fault diagnosis in rotating machinery, predictive maintenance, and the application of deep learning techniques in industrial big data. His work aims to enhance manufacturing processes and equipment reliability .

📖Top Noted Publications

Hybrid Mechanism and Data-Driven Approach for Predicting Fatigue Life of MEMS Devices by Physics-Informed Neural Networks

Authors: Cheng, J., Lu, J., Liu, B., An, J., Shen, A.

Journal: Fatigue and Fracture of Engineering Materials and Structures

Year: 2024

Bearing Intelligent Fault Diagnosis Based on Convolutional Neural Networks

Authors: An, J., An, P.

Journal: International Journal of Circuits, Systems and Signal Processing

Year: 2022

Deep Clustering Bearing Fault Diagnosis Method Based on Local Manifold Learning of an Autoencoded Embedding

Authors: An, J., Ai, P., Liu, C., Xu, S., Liu, D.

Journal: IEEE Access

Year: 2021

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Riemann Metric Correlation Alignment

Authors: An, J., Ai, P.

Journal: Mathematical Problems in Engineering

Year: 2020

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

Authors: An, J., Ai, P., Liu, D.

Journal: Shock and Vibration

Year: 2020

Haixia Mao | Data Science | Best Researcher Award

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Dr. Haixia Mao | Data Science | Best Researcher Award

Shenzhen Polytechnic University, China

👨‍🎓Professional Profile

Scopus Profile

👩‍🏫 Summary

Dr. Haixia Mao is an Associate Professor at the School of Automobile and Transportation at Shenzhen Polytechnic University, China. She holds a Ph.D. in Geographic Information System (GIS) and Remote Sensing from the Hong Kong Polytechnic University. Her research focuses on GIS-T, big data analytics, and analyzing urban mobility patterns, using spatial-temporal data from transportation systems to improve urban planning and human behavior modeling.

🎓 Education

Dr. Mao earned her Ph.D. in GIS and Remote Sensing from the Hong Kong Polytechnic University in 2010. She also completed her M.S. in GIS at Wuhan University in 2004 and her B.S. in Photogrammetry and Remote Sensing from Wuhan University.

💼 Professional Experience

Dr. Mao currently serves as an Associate Professor at Shenzhen Polytechnic University (since 2012), where she has contributed to the development of transportation-related GIS courses. Previously, she worked as a Postdoctoral Researcher at the Hong Kong Polytechnic University from 2014 to 2016, furthering her expertise in urban computing and GIS.

📚 Research Interests

Her research encompasses GIS-T (Geographic Information Systems for Transportation), big data analytics, remote sensing, deep learning, and urban planning. She investigates the mobility patterns of citizens through traffic data (including taxis, buses, and metro) and leverages spatial-temporal data to enhance urban infrastructure and predict human behavior.

🏅 Academic Contributions

Dr. Mao has authored over 20 peer-reviewed journal articles and conference papers. She has also been awarded a patent for a caching system with a new cache placement method. She has received research grants for spatial-temporal data analytics in transportation and has actively contributed to major projects in urban computing.

💻 Technical Skills

Dr. Mao’s technical expertise spans GIS, remote sensing, big data analytics, deep learning, and spatial-temporal data analysis. She integrates these advanced techniques to solve complex transportation and urban planning challenges.

📚 Teaching Experience

Dr. Mao has taught several courses since 2012 at Shenzhen Polytechnic University, including GIS for Transportation, Traffic Engineering Cartography, and Traffic Information Processing. She has a long-standing dedication to educating future professionals in the field of urban planning and transportation technology.

 

📖Top Noted Publications

Multiple sclerosis lesions segmentation based on 3D voxel enhancement and 3D alpha matting

Authors: Sun, Y., Zhang, Y., Wang, X., Zeng, Z., Mao, H.
Journal: Chinese Journal of Medical Physics
Year: 2022

Customer attractiveness evaluation and classification of urban commercial centers by crowd intelligence

Authors: Mao, H., Fan, X., Guan, J., Wang, Y., Xu, C.
Journal: Computers in Human Behavior
Year: 2019

 When Taxi Meets Bus: Night bus stop planning over large-scale traffic data

Authors: Xiao, L., Fan, X., Mao, H., Lu, P., Luo, S.
Conference: Proceedings – 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016
Year: 2017

Characterizing On-Bus WiFi passenger behaviors by approximate search and cluster analysis

Authors: Jiang, M., Fan, X., Zhang, F., Mao, H., Liu, R.
Conference: Proceedings – 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016
Year: 2017

Efficient visibility analysis for massive observers

Authors: Wang, W., Tang, B., Fan, X., Yang, H., Zhu, M.
Conference: Procedia Computer Science
Year: 2017

Web access patterns enhancing data access performance of cooperative caching in IMANETs

Authors: Fan, X., Cao, J., Mao, H., Zhao, Y., Xu, C.
Conference: Proceedings – IEEE International Conference on Mobile Data Management
Year: 2016

Giuliana Ramella | Artificial Intelligence | Best Researcher Award

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Dr. Giuliana. Ramella | Artificial Intelligence | Best Researcher Award

National Research Council, Italy

Professional Profile 👨‍🎓

Scopus Profile

Orcid Profile

Research Gate Profile

Early Academic Pursuits 🎓

Dr. Giuliana Ramella embarked on her academic journey with a strong foundation in Physics, specializing in Cybernetics, from the University of Naples “Federico II”, Italy, where she obtained her Laurea degree in 1990. Her early career was distinguished by a fellowship granted by the Italian National Research Council (CNR) at the Institute of Cybernetics “E. Caianiello”, which marked the beginning of her deep engagement with interdisciplinary research. Her academic background was further enriched by participation in several international and national schools, covering topics from biophysics to machine vision, which laid the groundwork for her future research endeavors.

Professional Endeavors 🧑‍🔬

Dr. Ramella’s professional career at the CNR has spanned decades, beginning in 1991 with a fellowship at the Institute of Cybernetics, now known as the Institute of Applied Sciences and Intelligent Systems (CNR-ISASI). Over the years, she transitioned into permanent research roles, contributing significantly to numerous research projects. Her professional milestones include being a visiting researcher at LIAMA (Sino-French Laboratory) in Beijing, China, and overseeing major research initiatives such as those focused on image processing and the conservation of cultural heritage.

Contributions and Research Focus 🔬

Dr. Ramella’s research spans multiple fields, including image processing, artificial intelligence, neurosciences, and cultural heritage conservation. Notable contributions include leadership in projects such as CNR-IAC-CNR DIT.AD021.077 (focused on color image processing) and the Campania Imaging Infrastructure for Research in Oncology. Her work blends theoretical and practical applications, particularly in the intersection of machine learning and image analysis, demonstrating her commitment to advancing computational methods in complex scientific domains.

Impact and Influence 🌍

Dr. Ramella has made a significant impact both in Italy and internationally, particularly in the fields of biophysics, neurosciences, and cultural heritage preservation. She has led several high-profile projects, influencing the development of automated systems for monitoring and diagnosing cultural heritage, as well as data analysis systems for oncology research. Her leadership in educational coordination has also contributed to the professional development of individuals in specialized fields, including image and data management.

Academic Citations and Scholarly Recognition 📚

Throughout her career, Dr. Ramella has built a robust academic reputation, frequently cited for her work in machine learning frameworks like Pytorch, TensorFlow, and Keras, as well as her contributions to computer vision. Her involvement in international workshops and conferences further underscores her standing as a thought leader in the scientific community. Additionally, her research has had a profound effect on the fields of visual perception and neuroscience, solidifying her as a key figure in these interdisciplinary areas.

Technical Skills and Expertise 💻

Dr. Ramella is highly skilled in a range of programming languages, including Matlab, C/C++, and Python, which are essential tools for her work in data analysis and machine learning. She has a strong command over popular machine learning frameworks like Pytorch, TensorFlow, and Keras, which she applies to advanced research in image processing, signal analysis, and high-dimensional data modeling. Her technical expertise is fundamental to her contributions to automated systems in the analysis of cultural artifacts and medical imaging.

Teaching Experience 🍎

In addition to her research, Dr. Ramella has played an essential role in educational coordination. She co-led specialist courses for unemployed individuals and workers in mobility, aimed at developing technical skills for sectors like image management and building heritage monitoring. Her role in shaping the professional development of students in the fields of image analysis and information technology has left a lasting impact on both the academic and professional communities.

Legacy and Future Contributions 🔮

Looking ahead, Dr. Ramella’s legacy is poised to continue making waves in the fields of artificial intelligence and machine learning, especially in the areas of healthcare, cultural heritage conservation, and data-driven methodologies. Her ongoing involvement in projects like the Agritech research program, funded by the European Union through Next Generation EU, speaks to her future aspirations to contribute to cutting-edge research and technological advancements. Dr. Ramella’s future work promises to leave an indelible mark on interdisciplinary fields, continuing her legacy of innovation and impact across global scientific communities.

 

Top Noted Publications 📖

An Open Image Resizing Framework for Remote Sensing Applications and Beyond

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Remote Sensing
Year: 2023

Image Scaling by de la Vallée-Poussin Filtered Interpolation

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Journal of Mathematical Imaging and Vision
Year: 2023

Filtered Polynomial Interpolation for Scaling 3D Images

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Electronic Transactions on Numerical Analysis
Year: 2023

 Lagrange–Chebyshev Interpolation for Image Resizing

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Mathematics and Computers in Simulation
Year: 2022

Saliency-based Segmentation of Dermoscopic Images Using Colour Information

Author: Ramella, G.
Journal: Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Year: 2022

Shubing Dai | Algorithm Development | Best Researcher Award

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Prof. Shubing Dai | Algorithm Development | Best Researcher Award

Prof. Shubing Dai at Northwest A&F University, China

Professional Profile👨‍🎓

👨‍🏫 Summary

Prof. Shubing Dai is an Associate Professor at Northwest A&F University, specializing in Hydraulic Engineering. With a background in Hydraulics and River Dynamics, he has published over 20 research papers and actively participated in over 20 research projects. His work primarily focuses on urban flood management, hydraulic structures, and water flow dynamics. He is also an active member of key professional organizations, including the China Society for Hydroelectric Engineering and the International Association for Hydro-Environment Engineering and Research (IAHR).

🎓 Education

Prof. Dai began his academic career at Northwest A&F University, where he earned his Bachelor’s (2008–2012) and Master’s (2012–2015) degrees in Hydraulic and Hydroelectric Engineering. He then pursued a Ph.D. in Hydraulics and River Dynamics at Dalian University of Technology (2016–2021). Additionally, he worked as a visiting scholar at the University of La Coruña in Spain (2024).

💼 Professional Experience

Prof. Dai has extensive professional experience, both in academia and industry. He currently serves as an Associate Professor at Northwest A&F University and the Department Secretary of the Hydraulic Engineering Department. He also previously worked as an Assistant Engineer at the Changjiang Survey, Planning, and Design Institute (2015–2016), contributing to several major hydropower and hydraulic engineering projects.

📚 Academic Contributions

Prof. Dai is an active contributor to the academic community, serving as a reviewer for several leading journals such as Journal of Hydrology and Physics of Fluids. He also serves as an editorial board member for an international journal. His research focuses on water resources, hydraulic structures, and flow dynamics, with over 20 published papers and several high-impact research projects.

🔧 Technical Skills

Prof. Dai has developed a range of technical skills, including:

  • Hydraulic Engineering: Focus on energy dissipation and optimization of flood discharge systems.
  • Urban Flooding & Drainage: Expertise in urban drainage system design, stormwater management, and flood mitigation.
  • Flow Dynamics: Proficient in Computational Fluid Dynamics (CFD) and non-steady flow simulations for hydraulic applications.
  • Hydropower Engineering: In-depth research on dam-break flood modeling, hydraulic structures, and their interactions with water flow.

👩‍🏫 Teaching Experience

As a dedicated educator, Prof. Dai mentors master’s students and teaches courses on fluid dynamics, hydraulic modeling, and water resource management. He is also responsible for the administration of the Water Resources and Hydroelectric Engineering department at Northwest A&F University, playing a key role in curriculum development and academic planning.

🔬 Research Interests

Prof. Dai’s research interests are focused on:

  • Hydraulic Engineering: Research on energy dissipation in hydraulic structures and optimization of flood discharge processes.
  • Flood Disaster Management: Urban flood management, drainage systems, and dam-break flood evolution.
  • Non-steady Flow & Hydrodynamics: Investigating the dynamics of free-surface flows, wave behavior, and non-constant flow conditions in hydraulic systems.

🔍 Research Projects

Prof. Dai leads and participates in several important research projects, such as:

  • City Flood and Drainage Systems Simulation (2022–2025)
  • Optimization of Urban Drainage Under Extreme Rainfall Conditions (2023–2025)
  • Dam-Break Flood Evolution and Structural Interaction Studies (2024–2026)
    He is also involved in national and regional projects on hydraulic structure optimization, flood forecasting, and water resource management.

 

📖Top Noted Publications

Numerical study of roll wave development for non-uniform initial conditions using steep slope shallow water equations

Authors: Dai, S., Liu, X., Zhang, K., Liu, H., Jin, S.
Journal: Physics of Fluids
Year: 2024

Discharge coefficients formulae of grate inlets of complicated conditions for urban floods simulation and urban drainage systems design

Authors: Dai, S., Hou, J., Jin, S., Hou, J., Liu, G.
Journal: Journal of Hydrology
Year: 2023

Land Degradation Caused by Construction Activity: Investigation, Cause and Control Measures

Authors: Dai, S., Ma, Y., Zhang, K.
Journal: International Journal of Environmental Research and Public Health
Year: 2022

Numerical investigations of unsteady critical flow conditions over an obstacle using three models

Authors: Dai, S., Jin, S.
Journal: Physics of Fluids
Year: 2022

Interception efficiency of grate inlets for sustainable urban drainage systems design under different road slopes and approaching discharges

Authors: Dai, S., Jin, S., Qian, C., Ma, Y., Liang, C.
Journal: Urban Water Journal
Year: 2021

Ying-Chih Sun | Business Intelligence and Analytics | Best Researcher Award

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Assist Prof Dr. Ying-Chih Sun | Business Intelligence and Analytics | Best Researcher Award

Assist Prof Dr. Ying-Chih Sun at East Central University, United States

👨‍🎓Professional Profiles

📚 Summary

Dr. Ying-Chih Sun is an Assistant Professor of Business Administration – Information Technology Management at East Central University. With a Ph.D. in Information Systems Engineering and Management and a strong academic background in economics, data analytics, and IT management, his research focuses on AI applications in healthcare, telehealth, and supply chain management. He is passionate about integrating business analytics with IT solutions to improve operational efficiencies across industries.

🎓 Education

Dr. Sun holds a Ph.D. in Information Systems Engineering and Management (ISEM) from Harrisburg University of Science and Technology (2023), where he graduated with a perfect 4.0 GPA. He also earned M.S. degrees in Economics from the University at Buffalo (2018) and Texas A&M University (2010), and an M.B.A. in Applied Economics (Summa Cum Laude) from National Taiwan Ocean University (2005). He completed his undergraduate studies in International Trade at Tamkang University (2003).

💼 Professional Experience

Dr. Sun is currently serving as an Assistant Professor at East Central University, teaching courses in Business Economics, Data Analytics, and Cloud Management. Before this, he was an Assistant Professor in Residence at Bradley University (2023-2024), where he contributed to research and teaching in business analytics. He also worked as a Research Assistant at Harrisburg University (2019-2023), conducting research on telehealth and healthcare delivery. Previously, he served as an IT Research Data Analyst at SUNY Buffalo and held roles at the Institute of Economics, Academia Sinica and Tri-Service General Hospital in Taiwan.

📝 Academic Publications & Conferences

Dr. Sun has presented at major conferences like the DSI Conference 2024, AMCIS 2024, and MWAIS 2024. His research papers focus on a range of topics, including telehealth efficiency, AI in healthcare, digital reputation in business schools, and supply chain optimization. His recent work on “Evaluating Telehealth Efficiency” and “Enhancing Judicial Decision-Making with AI” exemplifies his interdisciplinary approach to solving complex business and healthcare challenges.

💻 Technical Skills

Dr. Sun possesses advanced proficiency in data analytics tools like SQL, STATA, and Excel for statistical modeling and large data analysis. He is also familiar with Python and has hands-on experience in cloud management and telehealth technology. His technical skills enable him to approach research from both an analytical and technological perspective, focusing on improving efficiency in healthcare and business operations.

👨‍🏫 Teaching Experience

Dr. Sun has extensive teaching experience, having taught courses at East Central University (2024-present) in Business Economics, Data Analytics, and Cloud Management. He previously taught at Bradley University (2023-2024), covering Business Analytics and Business Analytics Software. Additionally, during his time at SUNY at Buffalo (2015-2018), he served as a Grader and Teaching Assistant for various econometrics courses and contributed to graduate-level teaching in Computational Econometrics.

🔬 Research Interests

Dr. Sun’s primary research interests lie at the intersection of AI, telehealth, and business analytics. He explores how data-driven decision-making can enhance healthcare delivery and operational efficiency, with a focus on telemedicine applications and the use of machine learning for healthcare and business management. His work on optimizing AI investments and supply chain management has important implications for improving cost-effectiveness and decision-making in businesses and healthcare systems.

 

📖 Top Noted Publications

A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide

Authors: Sun, Y.-C., Cosgun, O., Sharman, R., Mulgund, P., Delen, D.
Journal: Decision Analytics Journal
Year: 2024

The impact of policy and technology infrastructure on telehealth utilization

Authors: Sun, Y.-C., Cosgun, O., Sharman, R.
Journal: Health Services Management Research
Year: 2024

The Effect of Varying User Risk Levels on Perceived Social Presence in Mental Health Chatbots

Authors: Thimmanayakanapalya, S.S., Singh, R., Mulgund, P., Sun, Y.-C., Sharman, R.
Conference: 29th Annual Americas Conference on Information Systems (AMCIS 2023)
Year: 2023

Yun Xing | Computer Vision | Best Researcher Award

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Mr. Yun Xing | Computer Vision | Best Researcher Award

Mr. Yun Xing at The First Affiliated Hospital of Xi’an Jiaotong University, China

👨‍🎓Professional Profile

🎓 Education and Academic Background

I am currently a second-year Ph.D. student at VI-Lab, Nanyang Technological University (NTU), under the supervision of Prof. Shijian Lu. I received my B.S. degree in Biomedical Engineering and Instrument Science from Zhejiang University in 2021, where I worked under the guidance of Prof. Hong Zhou. Prior to my Ph.D., I completed my Master’s in Artificial Intelligence at NTU (2021–2022). My research interests primarily focus on vision-language pre-training and foundation model adaptation.

🔬 Research Interests

My research revolves around cutting-edge topics in vision-language pre-training and the adaptation of foundation models. I am particularly interested in methods that enable models to transfer knowledge effectively across different domains, such as few-shot learning, cross-domain adaptation, and improving model robustness in complex vision-language tasks.

🏆 Achievements and News

In recent years, I have been fortunate to have several papers accepted at top-tier conferences. These include NeurIPS 2024, ECCV 2024, CVPR 2024, and NeurIPS 2023, marking significant milestones in my academic journey. Notably, my work on object hallucination mitigation and segmentation adaptation has received considerable attention in the community.

🏅 Awards and Honors

Throughout my academic journey, I have received various recognitions for my contributions to research. These include the Outstanding Graduate Award from Zhejiang University in 2021, as well as the Academic Excellence Award (2018, 2019) and Academic Progress Award (2019) from the same institution.

💻 Service and Teaching

As an active member of the academic community, I contribute as a conference reviewer for prominent venues such as CVPR, ICML, ECCV, and NeurIPS 2024, where I was honored to be selected as one of the Top Reviewers at NeurIPS. Additionally, I am involved in teaching and mentoring at NTU. In Spring 2024, I will be assisting with the SC1015: Introduction to Data Science and Artificial Intelligence course.