Ying Tan | Computer Science | Distinguished Scientist Award

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Distinguished Scientist Award

Ying Tan
Peking University, China

                      Ying Tan
Affiliation Peking University
Country China
Scopus ID 56098810800
Documents 359
Citations 8,578
h-index 46
Subject Area Computer Science
Event Global Innovation Technologist Awards
ORCID 0000-0001-8243-4731

The Distinguished Scientist Award profile recognizes Ying Tan’s reported scholarly record in computer science and its relevance to computational intelligence, optimization, and machine learning. Associated publication information includes research on fireworks algorithms, neural network optimization, and time-series prediction. The profile presents bibliometric indicators alongside selected publications to support an informed assessment of research activity. Award recognition remains subject to the event’s applicable evaluation and verification procedures.

Abstract

This article summarizes the supplied research profile of Ying Tan, affiliated with Peking University, China. The reported Scopus metrics comprise 359 documents, 8,578 citations, and an h-index of 46. Selected publications address optimization algorithms, neural networks, and sequence-based prediction. These topics connect computational methods with challenges in efficient model training and data-driven forecasting. The profile provides a structured basis for reviewing publication activity and research visibility, while acknowledging that bibliometric indicators alone cannot establish the originality or practical value of individual contributions. [1]

Keywords

Computer science; computational intelligence; swarm intelligence; fireworks algorithm; neural network optimization; contrastive learning; time-series prediction; bibliometrics; research evaluation.

Introduction

Computer science research increasingly combines optimization strategies, machine learning architectures, and intelligent computational systems. Research evaluation in this environment benefits from considering both measurable scholarly activity and the substance of published work. Ying Tan’s supplied profile and publication records provide examples of research addressing algorithmic search and predictive modeling. The Distinguished Scientist Award context offers an opportunity to assess this work through transparent criteria rather than relying exclusively on citation totals. [2]

Research Profile

The supplied profile identifies Peking University as the institutional affiliation and computer science as the subject area. Reported bibliometric indicators are 359 documents, 8,578 citations, and an h-index of 46. These figures describe the provided record and should be checked against the live Scopus profile before formal publication, since indexing and citation counts can change over time. [1]

Research Contributions

The supplied publication titles indicate several related research directions:

  • Developing fireworks-based optimization methods and investigating their enhancement through multimodal large language models.
  • Exploring contrastive learning to improve selectivity in Mamba-based time-series prediction.
  • Implementing multi-guiding spark fireworks algorithms on GPUs for black-box neural network optimization.

Together, these topics concern search efficiency, predictive modeling, and computational implementation. Their specific contributions should be assessed from the full papers and experimental results. [3]

Publications

  1. “Visual-Enhanced Fireworks Algorithm via multimodal large language models.” Swarm and Evolutionary Computation, listed publication date November 2026. [3]
  2. “Repetitive contrastive learning enhances Mamba’s selectivity in time series prediction.” Neural Networks, April 2026. [4]
  3. “A GPU Implementation of Multi-guiding Spark Fireworks Algorithm for Efficient Black-Box Neural Network Optimization.” Book chapter, 2026.. [5]

The first item’s supplied publication date is November 2026, which is later than the current date of 27 October 2026; it should therefore be treated as forthcoming or scheduled unless the publisher confirms otherwise.

Research Impact

The reported citation count and h-index suggest an established indexed publication record. However, citation indicators vary by discipline, publication age, co-authorship, and database coverage. A balanced assessment should also examine methodological soundness, reproducibility, independent uptake, and the significance of individual findings. DOI records provide stable routes for verifying publication metadata and locating the associated works. [6]

Award Suitability

The profile presents evidence relevant to consideration for a Distinguished Scientist Award, including sustained publication activity, citation visibility, and research spanning optimization and machine learning. A complete award assessment should independently verify affiliation, authorship, publication status, and bibliometric data, and evaluate originality and research quality against the award’s published criteria. This profile does not itself establish an award decision.

Conclusion

Ying Tan’s supplied research profile combines substantial reported bibliometric indicators with publications addressing computational optimization and predictive learning. These details provide a useful starting point for scholarly recognition, provided that publication records and metrics are verified and qualitative research assessment remains central.

References

  1. Elsevier. (n.d.). Scopus author details: Ying Tan, Author ID 56098810800. Scopus. Profile metrics are reported from the supplied input and require current verification.
    https://www.scopus.com/authid/detail.uri?authorId=56098810800
  2. ORCID. (n.d.). Ying Tan researcher identifier. ORCID record.
    https://orcid.org/0000-0001-8243-4731
  3. Elsevier. (2026). Visual-Enhanced Fireworks Algorithm via multimodal large language models. Swarm and Evolutionary Computation. DOI: 10.1016/j.swevo.2026.102577.
    https://doi.org/10.1016/j.swevo.2026.102577
  4. Elsevier. (2026). Repetitive contrastive learning enhances Mamba’s selectivity in time series prediction. Neural Networks. DOI: 10.1016/j.neunet.2025.108290.
    https://doi.org/10.1016/j.neunet.2025.108290
  5. Springer Nature. (2026). A GPU Implementation of Multi-guiding Spark Fireworks Algorithm for Efficient Black-Box Neural Network Optimization. Book chapter. DOI: 10.1007/978-981-95-0982-9_5.
    https://doi.org/10.1007/978-981-95-0982-9_5
  6. Global Innovation Technologist Awards. (n.d.). Official awards website. Award information and applicable recognition criteria should be consulted directly.
    innovationtechnologist.com

Guoyun Lu | Engineering | Best Researcher Award

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Best Researcher Award

Guoyun Lu
Taiyuan University of Technology, China

                   Guoyun Lu
Researcher Guoyun Lu
Affiliation Taiyuan University of Technology
Country China
Scopus ID 7403460724
Documents 120
Citations 657
h-index 13
Subject area Engineering
Event Global Innovation Technologist Awards

Guoyun Lu is an engineering researcher affiliated with Taiyuan University of Technology, China. The available publication information highlights research involving structural engineering, steel construction, advanced concrete materials, impact-resistant structures and computational modelling. The Best Researcher Award profile recognises this research activity in the context of the Global Innovation Technologist Awards, using the supplied bibliometric indicators and selected recent publications as its principal points of reference. [1]

Abstract

This academic recognition profile summarises Guoyun Lu’s engineering research based on supplied Scopus metrics and three listed publications from 2026. The identified work addresses concrete-filled steel tube columns, stress-wave behaviour in welded hollow spherical joints and impact performance in hierarchical thin-walled structures. These topics connect experimental investigation with numerical simulation and, in one publication, machine-learning modelling. The profile provides a concise overview of research themes, publication activity and the potential relevance of this work to the Best Researcher Award. [1]

Keywords

Engineering research; structural engineering; steel structures; geopolymer concrete; steel-fibre reinforcement; stress-wave transmission; impact resistance; numerical simulation; machine learning; research impact.

Introduction

Engineering research supports the development and assessment of structural systems intended to withstand compression, impact and complex loading conditions. Investigations into composite columns, welded connections and lightweight protective structures contribute to understanding how materials and structural configurations respond under different mechanical demands. Lu’s supplied publication record provides examples of these research directions, linking material behaviour and structural performance with experimental and computational approaches. [2]

Research Profile

Lu’s documented research themes include steel–concrete composite members, geopolymer-based construction materials, welded structural components and thin-walled systems subjected to axial impact. The listed studies indicate engagement with experimental testing and numerical analysis, with machine-learning modelling also represented in research on square concrete-filled steel tube columns. Together, these topics illustrate an engineering-oriented approach to evaluating structural response and investigating alternative material and design strategies. Detailed methods, individual findings and author-contribution statements require verification against the full articles.

Research Contributions

The supplied publications represent three complementary areas of investigation:

  • Composite construction: examining the compression performance of square concrete-filled steel tube columns incorporating steel-fibre-reinforced geopolymer concrete.
  • Structural connections: investigating stress-wave transmission and attenuation in welded hollow spherical joint–rod components through experimental and numerical methods.
  • Impact engineering: evaluating aluminum foam-filled hierarchical thin-walled structures under axial impact.

These subjects address material efficiency, structural response and mechanical performance. The available titles establish the research scope but do not, by themselves, demonstrate specific performance improvements or establish the individual contribution of each co-author. [2]

Publications

Three 2026 publications supplied for this profile are:

  1. “Compression performance of square concrete-filled steel tube columns with steel-fibre-reinforced geopolymer concrete: Experiment, simulation and machine-learning modelling.” Journal of Constructional Steel Research, 2026. Listed co-authors include Wenjie Chang, Guoyun Lu, Dongtao Xia and Biao Li.
  2. “Experimental and numerical study of stress-wave transmission and attenuation in welded hollow spherical joint-rod components.” Journal of Constructional Steel Research, 2026. Listed co-authors include Baiqian Sun, Haixiang Zhao, Chaojie Zheng and Guoyun Lu.
  3. “Performance of Aluminum Foam-Filled Hierarchical Thin-Walled Structures Under Axial Impact.” Materials, 2026. The supplied record identifies this article as open access and lists Guoyun Lu among its co-authors.

Research Impact

The supplied bibliometric figures report 120 documents, 657 citations and an h-index of 13 for the researcher profile. These indicators offer complementary measures of publication volume and citation activity. They should be interpreted alongside disciplinary citation practices, publication dates and verified author-profile records. The three listed 2026 articles each show zero citations in the supplied snapshot; this may reflect the recency of publication and should not be interpreted as a definitive assessment of their eventual influence. [1]

Award Suitability

Lu’s reported publication and citation indicators, together with the breadth of the supplied engineering research topics, provide relevant material for consideration under the Best Researcher Award at the Global Innovation Technologist Awards. A comprehensive assessment should additionally examine article quality, research originality, documented individual contributions, practical applications and independent evidence of impact. This profile summarises the available information and does not constitute an independently verified award decision.

Conclusion

Guoyun Lu’s supplied research profile reflects activity in structural and materials engineering, particularly composite columns, welded connections and impact-resistant structures. The reported bibliometric indicators and selected 2026 publications provide a foundation for academic recognition, while full-text verification and further evidence would strengthen any assessment of research quality and broader impact.

References

  1. Elsevier. (n.d.). Scopus author details: Guoyun Lu, Author ID 7403460724. Scopus. Bibliometric figures in this article are based on the information supplied for the profile and should be checked against the live author record.
    https://www.scopus.com/authid/detail.uri?authorId=7403460724
  2. Publisher-indexed publication records supplied for Guoyun Lu (2026). Article titles and journal details include records from Journal of Constructional Steel Research and Materials. Individual DOI identifiers and complete bibliographic metadata were not supplied; verify these details against the publisher pages before publication.
  3. Global Innovation Technologist Awards. Official award website. Information about the event and award programme should be confirmed against current official announcements.
    innovationtechnologist.com

Elaheh Bakhtiari | Computer Science and Artificial Intelligence | Innovative Research Award

Published on by Innovation Technologist

Innovative Research Award

Elaheh Bakhtiari
University of Messina, Italy

Elaheh Bakhtiari
Affiliation University of Messina
Country Italy
Scopus ID 60866885400
Documents 1
Citations 1
h-index 1
Subject Area Computer Science and Artificial Intelligence
Event Global Innovation Technologist Awards
ORCID 0009-0009-1571-355X

Elaheh Bakhtiari is a researcher affiliated with the University of Messina, Italy, whose documented research profile is situated within Computer Science and Artificial Intelligence. Available bibliographic information records one indexed document, one citation, and an h-index of 1. Her documented publication activity includes research examining an AI-assisted framework for anxiety support among university students, connecting computational methods with an applied behavioral-health context.

Abstract

Bakhtiari’s documented research includes the September 2026 journal article A Hybrid AI-Assisted Framework for Anxiety Support: System Description and Exploratory Findings Among University Students, published in Behavioral Sciences. The work describes an AI-assisted framework addressing anxiety-support needs among university students and represents an interdisciplinary intersection of artificial intelligence, computational systems, and behavioral science. The publication provides the principal documented basis for assessing the researcher’s current scholarly profile. [1]

Keywords

  • Artificial intelligence
  • AI-assisted systems
  • Anxiety support
  • University students
  • Behavioral science

Introduction

Artificial intelligence is increasingly being investigated as a component of digital systems designed to support complex human-centered needs. Within this context, Bakhtiari’s reported publication addresses an AI-assisted framework for anxiety support among university students. The study is notable for positioning computational assistance within a behavioral-science application rather than treating artificial intelligence solely as a technical research problem. [1]

Research Profile

The available Scopus profile records one document, one citation, and an h-index of 1. These indicators describe an early or limited indexed publication record and should be interpreted in relation to the documented size and maturity of the profile. The stated subject area, Computer Science and Artificial Intelligence, provides the primary disciplinary classification for the researcher’s current recognition profile. [2]

Research Contributions

The principal documented contribution is the exploration of a hybrid AI-assisted framework for anxiety support. By combining artificial intelligence with a student-centered behavioral application, the work contributes to discussion around how computational systems may be incorporated into supportive environments. Its exploratory character also provides a basis for further investigation, validation, and development of AI-enabled support approaches. [1]

Publications

A Hybrid AI-Assisted Framework for Anxiety Support: System Description and Exploratory Findings Among University Students. Published in Behavioral Sciences in September 2026. The article is available through the publisher’s website. [1]

Research Impact

The currently documented bibliometric impact is modest, with one citation and an h-index of 1. Nevertheless, the publication’s interdisciplinary focus places the work at the intersection of artificial intelligence and behavioral support, an area in which continued research could contribute to the development and evaluation of human-centered computational systems. Impact should be reassessed as additional publications and citations become available. [2]

Award Suitability

For the Global Innovation Technologist Awards, the documented profile demonstrates a relevant connection between artificial intelligence research and an applied human-centered problem. The publication provides evidence of current scholarly activity and an interdisciplinary research direction. Award consideration should be based on the documented publication, research relevance, originality, methodological contribution, and the broader evidence available during formal evaluation.

Conclusion

Elaheh Bakhtiari’s documented research profile reflects an emerging contribution in Computer Science and Artificial Intelligence, with a particular interdisciplinary application to AI-assisted anxiety support. The 2026 publication provides a clear basis for recognizing research activity while the limited bibliometric record indicates that longer-term impact remains to be established through subsequent scholarly output and citation development.

References

  1. Bakhtiari, E. (2026). A Hybrid AI-Assisted Framework for Anxiety Support: System Description and Exploratory Findings Among University Students. Behavioral Sciences, 16(10), 1781.
    https://doi.org/10.3390/bs16101781
  2. Elsevier. (n.d.). Scopus author details: Elaheh Bakhtiari, Author ID 60866885400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60866885400
  3. MDPI. (2026). Behavioral Sciences, Volume 16, Issue 10, Article 1781.
  4. ORCID. (n.d.). Elaheh Bakhtiari, ORCID record.
    https://orcid.org/0009-0009-1571-355X
  5. Global Innovation Technologist Awards. (n.d.). Official award website.
    innovationtechnologist.com

Li Sun | Computer Science | Young Scientist Award

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Assoc. Prof. Dr. Li Sun | Computer Science | Young Scientist Award

Beijing University of Posts and Telecommunications | China

Assoc. Prof. Dr. Li Sun specializes in data mining, deep learning, and graph-based foundation models, with a strong emphasis on Riemannian geometry in machine learning. His research advances graph neural networks, hyperbolic representation learning, and structural entropy–based data analysis for complex systems. According to Scopus, he has authored 65 publications, with 1,484 citations and an h-index of 17. His work is widely recognized in premier venues such as ICML, NeurIPS, ICLR, KDD, AAAI, and IEEE journals. Dr. Li Sun’s contributions focus on scalable graph learning, social network modeling, and privacy-preserving data mining, significantly impacting modern artificial intelligence and large-scale data analytics.

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View Scopus Profile  View Google Scholar Profile

Featured Publications

Deborah Olaniyan | Artificial Intelligence | Innovative Research Award

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Dr. Deborah Olaniyan | Artificial Intelligence | Innovative Research Award

Postdoc | University of the Free State | South Africa

Dr. Deborah Olaniyan is an artificial intelligence researcher with expertise in AI-driven learning technologies, multimodal emotion recognition, computer vision, natural language processing, and intelligent assessment systems. Her work focuses on applying deep learning, machine learning, and learning analytics to develop adaptive, emotion-aware, and data-informed educational environments. She has contributed to research on hybrid AI frameworks integrating vision and language models for e-learning and conversational systems. Her scholarly output includes 8 research documents with 5 citations across 5 citing publications, reflecting an h-index of 1. Her research appears in reputable international venues and demonstrates growing impact in AI-enabled education and intelligent systems research.

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                🟦 Citations        🟥 Documents        🟩 h-index

View Scopus Profile View Orcid Profile

Featured Publication

Yousri Kessentini | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Yousri Kessentini | Artificial Intelligence | Best Researcher Award

Senior Researcher at Digital research center of Sfax, Tunisia

Assoc. Prof. Dr. Yousri Kessentini is a computer science researcher and Associate Professor at the Digital Research Center of Sfax, Tunisia, where he leads the DeepVision research team. He holds a Ph.D. from the University of Rouen, France, and specializes in deep learning, computer vision, and document image analysis. Dr. Kessentini has coordinated numerous national and international research projects and has received several awards, including honors from NVIDIA and the National Academy of Engineering. He is a certified Deep Learning instructor and an active contributor to the scientific community through publications, supervision, and editorial roles.

Publication Profile 

Scopus

Orcid

Educational Background 

Dr. Kessentini earned his Habilitation in Computer Science from the University of Sfax in 2021. He holds a Ph.D. in Computer Science (2006–2009) and a DEA (postgraduate diploma) in Computer Science (2004) from the University of Rouen, France. He also obtained an engineering diploma in computer science from ENIS in 2003 and completed his secondary education with a Scientific Baccalaureate in Mathematics in 1998.

Professional Experience

Dr. Kessentini has accumulated rich academic and industrial experience over two decades. Since 2022, he has served as Associate Professor and Head of the DeepVision research team at CRNS. From 2017 to 2021, he was a senior researcher at the same center. Between 2013 and 2017, he was an assistant professor at ISIMA University of Monastir. He also held postdoctoral and graduate assistant roles in France, including at ITESOFT/LITIS and the University of Rouen. Since 2018, he has been a certified instructor and ambassador of the NVIDIA Deep Learning Institute, reflecting his leadership in AI education and training.

Research Interests

His research spans a variety of deep learning applications, including document image recognition, handwritten text analysis, multi-script OCR, generative models, and satellite image fusion. Dr. Kessentini also explores the intersection of AI with healthcare, smart cities, and industrial automation. His recent projects involve federated learning for medical imaging, vehicle identity recognition, Arabic script analysis, and human action recognition through remote sensing and video surveillance.

Awards and Honors

Dr. Kessentini has received numerous accolades for his contributions to AI research and innovation. In 2025, he was selected for the prestigious U.S.-Africa Frontiers of Science, Engineering, and Medicine Symposium by the U.S. National Academy of Engineering. He ranked first in Tunisia’s national recruitment competition for associate professors in 2022. He received best student paper awards at ICPR 2020 and MedPRAI 2020 and earned a Jury Recognition Award in Tunisia’s national innovation competition in 2019. His research excellence was also recognized by NVIDIA with a GPU Grant in 2018, the same year he was certified as an official instructor and ambassador.

Publications 

Title: Information extraction from multi-layout invoice images using FATURA dataset

Year: 2025

Title: STF-Trans: A Two-stream SpatioTemporal Fusion Transformer for Very High Resolution Satellites Images

Year: 2024

Title: MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition

Year: 2023

Title: Spectral-Temporal Fusion of Satellite Images Via an End-to-End Two-Stream Attention With an Effective Reconstruction Network

Year: 2023

Title: Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwriting Recognition

Year: 2022

Conclusions

Assoc. Prof. Dr. Yousri Kessentini stands out as a leading figure in the fields of artificial intelligence and computer vision. His consistent contributions to scientific innovation, mentorship, and international collaboration have had a significant impact across academia and industry. His work demonstrates not only technical excellence but also a strong commitment to applying AI for societal and industrial benefit. With an impressive track record of publications, project leadership, and academic service, he is highly deserving of recognition in competitive research and innovation awards.

Jianglong Yang | Artificial Intelligence | Best Researcher Award

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Dr. Jianglong Yang | Artificial Intelligence | Best Researcher Award

Deputy Director of Collaborative Innovation Center at Beijing Wuzi University, China

Dr. Jianglong Yang is a prominent scholar in the field of logistics and supply chain management, currently serving at Beijing Wuzi University. With a Doctorate in Management, Dr. Yang has developed an outstanding portfolio of interdisciplinary research focused on intelligent logistics systems, optimization of e-commerce warehousing, and green supply chain innovations. His scholarly contributions include top-tier SCI and CSSCI publications, project leadership roles in national and municipal research programs, and a growing presence in China’s logistics innovation and policymaking circles.

Publication Profile 

Scopus

Educational Background 🎓

  • Degree: Doctor of Management

  • Major: Logistics and Supply Chain Management

  • Institution: Not explicitly stated, but affiliated with major universities like Beijing Wuzi University and Beijing University of Technology.

Professional Experience 💼

Dr. Jianglong Yang has held a series of prominent academic and leadership roles across China’s logistics and information management sectors. Since June 2024, he has been serving as the Party Secretary of the Information Management Teachers’ Party Branch at the School of Information, Beijing University of Technology, where he contributes to academic leadership and institutional governance. In August 2022, he was appointed Deputy Director of the Beijing Intelligent Logistics System Collaborative Innovation Center, a position that places him at the forefront of logistics innovation and policy research. He also currently serves as the Deputy Secretary-General of the Beijing System Engineering Society (since March 2024), reflecting his influence in advancing system-level thinking within logistics and engineering domains. In addition, Dr. Yang became an Assistant Researcher at the China Logistics Group Science and Technology Research Institute in June 2023, where he engages in cutting-edge research on national logistics strategies. Most recently, in May 2024, he was appointed Director of the Expert Committee of Beijing Huixu Technology Co., Ltd., further expanding his influence in industry-academic integration and application of intelligent logistics technologies.

Research Interests 🔬

  • Intelligent logistics systems

  • E-commerce packaging and warehousing optimization

  • Genetic algorithms and spatial modeling for logistics

  • Multimodal transportation under the Belt and Road Initiative

  • Green and circular economy in logistics

  • Smart supply chain coordination and service quality improvement

Awards and Honors🏆✨

  1. Outstanding Doctoral Dissertation Award, China Logistics Society Annual Conference (2024)

  2. First Prize, China Logistics Academic Annual Conference (2021) – Global Supply Chain Restructuring Research

  3. Research Awards, China Logistics Society & China Federation of Logistics and Purchasing (2021) – First and Third Prizes

  4. First-Class Doctoral Academic Scholarships, Two consecutive years (2019–2021)

Conclusion🌟

Dr. Jianglong Yang exemplifies the qualities of a leading researcher through his consistent output of high-impact publications, strategic roles in academic and industry organizations, and project leadership in pioneering logistics research. His work contributes significantly to the modernization of China’s intelligent logistics systems, with practical implications for sustainable e-commerce and supply chain management. His research excellence and active participation in the advancement of logistics innovation make him a strong candidate for competitive research awards and international collaborations.

Publications 📚

  • 📘 Monograph

    • Yang Jianglong, Liu Huwei, Zhou Li. Research on Intelligent Optimization of E-commerce Warehousing Packing Decision-making Based on Data Driven. Capital University of Economics and Business Press, Sept. 2023.
      (Academic monograph, 440,000 words – First Author)

  • 🧠 Journal Article

    • Yang Jianglong, Shan Man, Liang Kaibo, et al. “Research on intelligent decision-making of e-commerce three-dimensional packing based on spatial particle model.” Frontiers of Engineering Management Science and Technology, 2024, 43(06): 41–48.
      (First Author, CSSCI Core, A-level Journal)

  • 🧬 Algorithm & AI

    • Yang J, Liu H, Liang K, et al. “Variable neighborhood genetic algorithm for multi-order multi-bin open packing optimization.” Applied Soft Computing, 2024: 111890.
      (SCI Zone 1 TOP, First Author)

  • 🤖 AI in Logistics

    • Yang J, Liu H, Liang K, et al. “A Genetic Algorithm with Lower Neighborhood Search for the Three‐Dimensional Multiorder Open‐Size Rectangular Packing Problem.” International Journal of Intelligent Systems, 2024(1): 4456261.
      (SCI Zone II TOP, First Author)

  • 📦 E-commerce Optimization

    • Yang J, Liang K, Liu H, et al. “Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system.” PeerJ Computer Science, 2023, 9: e1613.
      (SCI Zone 4, First Author)

  • 🚨 Emergency Logistics

    • Liu Huwei, Zhou Li, Yang Jianglong*. “Research on hierarchical collaborative distribution of emergency materials under sudden public events.” Journal of Engineering Mathematics, 2024, 41(01): 53–66.
      (CSCD Core Journal, Corresponding Author)

  • 🚆 Multimodal Transport Policy

    • Yu Lin, Yang Jianglong. “Problems and policy recommendations for the development of multimodal transport under the ‘Belt and Road’ strategy.” SASAC Research Center, Oct. 2023.
      (Think Tank Report, Second Author)

  • 📡 Smart Picking Systems

    • Zhou Li, Yang Jianglong*. “Research on multi-channel intensive mobile shelf order picking based on genetic algorithm.” Operations Research and Management, 2021, 30(2):7.
      (CSCD Core Journal, Corresponding Author)

  • 🧮 Batch Order Optimization

    • Yang J, Zhou L, Liu H. “Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse.” PLOS ONE, 2021, 16.
      (SCI Zone 2, First Author)

  • 🔐 Security in IoT

    • Yang J, Yang W, Liu H, et al. “Design and Simulation of Lightweight Identity Authentication Mechanism in Body Area Network.” Security and Communication Networks, 2021(3):1–18.
      (SCI Zone 4, First Author)

 

 

 

 

Mihir Parekh | Machine Learning | Best Researcher Award

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Mr. Mihir Parekh | Machine Learning | Best Researcher Award

Research Scholar at Nirma University, India

Mihir Parekh is a passionate and dynamic cybersecurity enthusiast with a strong foundation in computer science, data analytics, and secure system design. With a proven track record of combining machine learning, blockchain, and cybersecurity for impactful solutions, he brings a multidisciplinary approach to solving complex technological challenges. Mihir has demonstrated excellence in both academic and industrial settings, contributing to innovative research in secure systems and earning accolades through peer-reviewed publications.

Publication Profile 

Google Scholar

Educational Background 🎓

  • M.Tech in Computer Science and Engineering (Cyber Security)
    Nirma University, Ahmedabad
    08/2022 – 06/2024 | CGPA: 9.21
    Focus: Cybersecurity, Blockchain, Data Analysis, Machine Learning

  • Bachelor of Engineering in Information Technology
    G.H. Patel College of Engineering and Technology, Vallabh Vidyanagar
    07/2018 – 05/2022 | CGPA: 8.22

Professional Experience 💼

  • Data Analyst
    Contrado Imaging India Pvt. Ltd., Ahmedabad
    06/2023 – 10/2023

    • Performed data cleaning and preprocessing using Python.

    • Developed SQL queries to fetch and analyze data.

    • Used Kibana and Elasticsearch for data visualization.

  • Business Process Analyst
    Kevit Technologies, Rajkot
    12/2021 – 07/2022

    • Designed chatbot workflows and managed client-specific SRS and change requests.

    • Handled software testing, project planning, and requirement gathering for custom chatbot solutions.

Research Interests 🔬

  • Cybersecurity and Digital Forensics

  • Blockchain Applications and Cryptographic Protocols

  • Machine Learning and Deep Learning

  • Federated Learning & Secure Data Sharing

  • Anomaly Detection and Fraud Prevention

  • Secure Industrial IoT Systems

Awards and Honors🏆✨

  • 🏆 Published Journal Paper:
    Blockchain Forensics to Prevent Cryptocurrency Scams
    Computers & Electrical Engineering (Impact Factor: 5.5)

  • 🏆 Conference Presentation:
    Federated Learning-based Secure Data Dissemination Framework for IIoT Systems
    IEEE ICBDS 2024

  • 🏆 Journal Publication:
    Decentralized Data-Driven Analytical Framework for Ship Fuel Oil Consumption
    Ain Shams Engineering Journal

  • 🎖️ Infineon Hackathon Finalist – AES-128 Cryptanalysis Challenge

Conclusion🌟

Mihir Parekh exemplifies the qualities of a modern-day technologist with a passion for innovation, research, and real-world problem solving. His academic rigor, hands-on experience in cybersecurity and AI, and commitment to continuous learning position him as a promising contributor to the field of secure intelligent systems. Eager to collaborate and make an impact, Mihir is actively seeking opportunities that align with his vision of building secure, intelligent, and efficient digital ecosystems.

Publications 📚

📘 Parekh, M., Jadav, N. K., Tanwar, S., Pau, G., Alqahtani, F., & Tolba, A. (2025). ANN and blockchain-orchestrated decentralized data-driven analytical framework for ship fuel oil consumption. Applied Ocean Research, 158, 104553.
🔗 https://doi.org/10.xxxxx/aor.2025.104553
📊 Keywords: Artificial Neural Networks, Blockchain, Maritime Fuel Analytics

📕 Parekh, M., Jadav, N. K., Pathak, L., Tanwar, S., & Yamsani, N. (2024). Federated Learning-based Secure Data Dissemination Framework for IIoT Systems Underlying 5G. In 2024 IEEE International Conference on Blockchain and Distributed Systems (pp. xx–xx). IEEE.
📡 Keywords: Federated Learning, 5G, IIoT, Cybersecurity
📍 Conference Paper

📄 Parekh, M. (2024). Decentralized Data-Driven Analytical Framework for Ship Fuel Oil Consumption. Institute of Technology.
🏛️ Institutional publication / Thesis
🌐 Focus: Data Analytics, Maritime Efficiency

Yousef Daradkeh | Computer Science | Best Academic Researcher Award

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Prof. Dr. Yousef Daradkeh | Computer Science | Best Academic Researcher Award

Professor at Prince Sattam Bin Abdulaziz University, Jordan

Prof. Dr. Yousef Daradkeh is a distinguished professor of Computer Engineering and Information Technology at the Department of Computer Engineering and Information, College of Engineering, Prince Sattam bin Abdulaziz University, Saudi Arabia. He holds a Doctor of Engineering Sciences in Computer Engineering and Information Technology and has over 19 years of extensive academic and administrative experience. Prof. Daradkeh is internationally recognized for his research and teaching in software engineering, computer networks, artificial intelligence, and cybersecurity. As an academic leader, researcher, author, and workshop facilitator, he has significantly contributed to scientific communities through over 128 peer-reviewed publications, multiple books, and active participation in international conferences and editorial boards.

Publication Profile 

Google Scholar

Educational Background 🎓

  • Ph.D. in Computer Engineering and Information Technology
    Belarusian State University of Informatics and Radioelectronics, 2006

    • Dissertation: Interpretation of Polymorphous Networks Models in Real-Time Allocation Systems

    • Accredited by the Higher Education Accreditation Commission (HEAC)

  • Postdoctoral Research Fellowships

    • University of Calgary, Canada (2007–2009) – Systems, Networks, and Devices of Telecommunications

    • University of New South Wales, Australia (2010) – Computer Science and Engineering

    • Massey University, New Zealand (2011) – School of Engineering and Advanced Technology

    • Istanbul Technical University, Turkey (2023–2024) – Electronics and Communication Engineering

  • M.Sc. in Software Engineering
    Belarusian National Technical University, 2003

    • Thesis: Mathematical and Software Optimization of Models of Power Equipment Operations

  • B.Sc. in Computer Engineering
    Technical University of Moldova, 2000

    • Graduation Project: Device for Transcendent Functions Calculation

Professional Experience 💼

  • Current Position:

    • Full Professor & Assistant Dean for Administrative Affairs
      Prince Sattam bin Abdulaziz University, Saudi Arabia

  • Previous Academic Roles:

    • University of Calgary, Canada – Postdoctoral Researcher & Faculty Developer

    • The University of Jordan – Faculty Member, Systems and Information Technology

    • Al-Balqa Applied University, Jordan – Faculty Member, Department of IT

    • Yarmouk University – Instructor, Computer and Information Centre

    • AL-Hussein Bin Talal University – Faculty Member, Engineering & IT

    • Jadara University – Faculty, Department of Software Engineering

    • Kazakh University of Economics, Finance, and International Trade – Online Course Lecturer

  • Other Roles:

    • Designed accredited computer engineering and IT courses

    • Conducted workshops on scientific writing and research methodology

    • Editorial and scientific committee member for numerous international journals and conferences

Research Interests 🔬

  • Wireless Networking and Telecom Systems

  • Modeling of Discrete and Polymorphous Systems

  • Computer Systems and Networks

  • Artificial Intelligence & Data Science

  • Machine Learning & Deep Learning

  • Vision Computing and Digital Image Processing

  • Cybersecurity and Information/Economic Security

  • Agent-Based and Context-Aware Software Engineering

  • E-Government and E-Learning Applications

  • Location-Based Services (LBS) and Geo Services

  • Knowledge Representation and Reasoning

  • Optimization and Real-Time Systems

  • Web and Java Development, Databases

Awards and Honors🏆✨

  • Higher Distinction Scientific Awards – From International Universities

  • Certificate of Appreciation – Ministry of Youth and Sport, Jordan (1999)

  • Faculty Teaching Certificate (FTC) – University of Calgary, Canada (2008)

  • Care Services Certificates – University of Calgary (2008)

  • IT Management Professional Award – Microsoft, Excellent Train, Jordan (2011)

  • Staff Development Certificate – University of Jordan (2012)

  • Life Ambassador First Aid Training Certificate – Riyadh

  • Certificate of English Language (CEL) – AMIDEAST, Jordan

  • Certificate of Russian & Romanian Languages – TUM, BNTU, BSUIR

Conclusion🌟

Prof. Dr. Yousef Daradkeh is a visionary academic leader and prolific researcher who has made significant contributions to the fields of computer engineering, software systems, and emerging technologies. His work continues to bridge theoretical advancements with practical applications, particularly in improving security, efficiency, and intelligence in digital systems. Through his extensive teaching, research, international collaborations, and service to the academic community, Prof. Daradkeh remains a highly influential figure in engineering education and innovation.

Publications 📚

  • 📡 6G mobile communication technology: Requirements, targets, applications, challenges, advantages, and opportunities
    🧾 Alexandria Engineering Journal 64, 245-274
    👥 Cited by: 360 | 📅 Year: 2023

  • 🧠 A hybrid deep learning-based approach for brain tumor classification
    🧾 Electronics 11 (7), 1146
    👥 Cited by: 248 | 📅 Year: 2022

  • 🚗 Key challenges, drivers and solutions for mobility management in 5G networks: A survey
    🧾 IEEE Access 8, 172534-172552
    👥 Cited by: 180 | 📅 Year: 2020

  • 🔍 Tools for fast metric data search in structural methods for image classification
    🧾 IEEE Access 10, 124738-124746
    👥 Cited by: 104 | 📅 Year: 2022

  • 🎗️ Intelligent hybrid deep learning model for breast cancer detection
    🧾 Electronics 11 (17), 2767
    👥 Cited by: 101 | 📅 Year: 2022

  • 🤖 Development of effective methods for structural image recognition using fuzzy logic
    🧾 IEEE Access 9, 13417-13428
    👥 Cited by: 98 | 📅 Year: 2021

  • 📷 Methods of classification of images based on statistical distributions
    🧾 IEEE Access 9, 92964-92973
    👥 Cited by: 90 | 📅 Year: 2021

  • ✈️ Handover management of drones in future mobile networks: 6G technologies
    🧾 IEEE Access 9, 12803-12823
    👥 Cited by: 89 | 📅 Year: 2021

  • 🧱 Classification of Images Based on a System of Hierarchical Features
    🧾 Computers, Materials & Continua 72 (1)
    👥 Cited by: 83 | 📅 Year: 2022

  • 🔧 Handover parameters optimisation techniques in 5G networks
    🧾 Sensors 21 (15), 5202
    👥 Cited by: 71 | 📅 Year: 2021

 

Dehong Gao | Artificial Intelligence | Outstanding Scientist Award

Published on by Innovation Technologist

Assoc. Prof. Dr. Dehong Gao | Artificial Intelligence | Outstanding Scientist Award

Associate Professor at Northwestern Polytechnical University, China

Dehong Gao is a distinguished expert in the fields of natural language processing, machine learning, and large language models. With over a decade of research and practical experience, he has made significant contributions to advertising technologies, multimodal AI models, and AI-driven e-commerce solutions. Gao currently leads groundbreaking work on multimodal pre-training models, including the award-winning 70B-FashionGPT model. As an associate professor at Northwestern Polytechnical University and a distinguished researcher at Zhejiang University of Technology, Gao continues to advance AI research, particularly in the areas of large language models, multimodal learning, and cross-lingual search. 🚀📚💡

Publication Profile : 

Scopus

Educational Background 🎓

  • Ph.D. in Computer Science from Hong Kong Polytechnic University (2014-2022), under the supervision of Li Wenjie.
  • Master’s in Automation from Northwestern Polytechnical University (2010).
  • Bachelor’s in Automation from Northwestern Polytechnical University (2007).

Professional Experience 💼

Dehong Gao’s career spans both academia and industry. He is currently an Associate Professor at the School of Cyberspace Security at Northwestern Polytechnical University. He also serves as a Distinguished Researcher at the Zhejiang University of Technology Artificial Intelligence Innovation Institute. Gao previously held the position of Senior Algorithm Expert (P8) at Alibaba Group, where he led a team of over 20 full-time algorithm engineers and contributed to the development of large-scale machine translation and AI-driven e-commerce solutions. As an expert in Alibaba AIR Project, Gao has been instrumental in technical breakthroughs related to large model technologies, fine-tuning multimodal models, and advancing AI-based search and advertising systems. 💻📈

Research Interests 🔬

Gao’s research interests are focused on:

  • Large Language Models (LLMs) and Multimodal Learning 🌐🤖
  • Natural Language Processing: Information retrieval, recommendation systems, sentiment analysis, and automated summarization 📑🔍
  • E-commerce AI: Developing search algorithms and multilingual representation learning for cross-border e-commerce applications 🌏🛒
  • Federated Learning and AI-driven personalization in business settings 🔒🤖

He has authored and co-authored several influential papers and has been a leading figure in the development of multimodal AI models for industries such as fashion, e-commerce, and healthcare. His work continues to push the boundaries of AI application in real-world environments. 🏆📚

Publications 📚

  1. Gao, D., Chen, K., Chen, B., et al. (2024). LLMs-based Machine Translation for E-commerce. Expert Systems with Applications, Volume 258 (SCI Zone 1, Top Journal).
  2. Chen, K., Chen, B., Gao, D., Dai, H., et al. (2024). General2Specialized LLMs Translation for E-commerce. The Web Conference (WWW), short paper (CCF-A).
  3. Shen, G., Sun, S., Gao, D., Yang, L., et al. (2023). EdgeNet: Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising. The 32nd ACM International Conference on Information & Knowledge Management (CIKM), (CCF-B).
  4. Gao, D., Ma, Y., Liu, S., Song, M., Jin, L., et al. (2024). FashionGPT: LLM Instruction Fine-tuning with Multiple LoRA-adapter Fusion. Knowledge-Based Systems, Volume 299 (SCI, Top Journal).
  5. Chen, B., Jin, L., Wang, X., Gao, D., et al. (2023). Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval. Industry Track of The Web Conference (WWW), (CCF-A).
  6. Mei, X., Yang, L., Jiang, Z., Cai, X., Gao, D., et al. (2024). An Inductive Reasoning Model Based on Interpretable Logical Rules Over Temporal Knowledge Graphs. Neural Networks, Volume 174, Pages (SCI Zone 1, Top Journal).
  7. Liang, Z., Chen, B., Ran, Z., Wang, Z., Gao, D., et al. (2024). Self-Renewal Prompt Optimizing with Implicit Reasoning. The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) findings (CCF-A).
  8. Yang, Z., Gao, H., Gao, D., Yang, L., et al. (2024). MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction. The 18th ACM Conference on Recommender Systems (RecSys), (CCF-B).
  9. Zhang, X., Wang, D., Gao, D., Jiang, W., et al. (2022). Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding via GAN. The 31st ACM International Conference on Information & Knowledge Management (CIKM), (CCF-B).
  10. Zhang, F., Zhang, Z., Gao, D., Zhuang, F., et al. (2022). Mind the Gap: Cross-lingual Information Retrieval with Hierarchical Knowledge Enhancement. The 36th AAAI Conference on Artificial Intelligence (AAAI), (CCF-A).