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

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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

Umesh Kumar Lilhore | Deep Learning | Best Researcher Award

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Dr. Umesh Kumar Lilhore | Deep Learning | Best Researcher Award

Professor at Galgotias University, India

Dr. Umesh Kumar Lilhore is a seasoned Professor and Researcher in Computer Science and Engineering (CSE) at Galgotias University, Greater Noida, India. With over 18 years of experience in academia and research, he has established himself as an expert in Artificial Intelligence (AI), Deep Learning, and Environmental Studies. Dr. Lilhore has earned a Ph.D. and M.Tech in CSE, complemented by a Postdoctoral fellowship from the USA. He has published over 100 research articles in indexed journals, with more than 3,800 citations and an h-index of 29+, showcasing his impactful contributions to the academic community.

Publication ProfileĀ 

Scopus

Educational Background šŸŽ“

  • Ph.D.: Computer Science and Engineering (Institution not specified)
  • M.Tech: Computer Science and Engineering (Institution not specified)
  • Postdoctoral Fellowship: USA (Institution not specified)

Professional Experience šŸ’¼

  • Designation: Professor, Computer Science and Engineering
  • Institution: Galgotias University, Greater Noida, India
  • Years of Experience: Over 18 years in teaching and research
  • Editorial Appointment: Editorial Board Member, Springer Journal: BMC Medical Informatics and Decision Making
  • Collaborations: National and international collaborations with institutions such as:
    • National University of Science and Technology Politehnica Bucharest
    • Pitesti University Center, Romania
    • University of Louisiana, USA
    • Arab Minch University

Research Interests šŸ”¬

  • Artificial Intelligence (AI)
  • Deep Learning
  • Environmental Studies

Awards and HonorsšŸ†āœØ

  • Patents:
    • 35 Indian patents
    • 2 UK design patents
  • Books Published: 10+ Scopus-indexed books
  • Projects: Completed AICTE-funded Air Quality Analysis project
  • Professional Memberships: IEEE, ACM

Contributions and Achievements

  • Published 51 SCI-indexed and 102 Scopus-indexed research papers.
  • Google Scholar citation index: 28+ with 3,800+ citations and an h-index of 29+.
  • Collaborated on research projects with prestigious international institutions.
  • Actively engaged in advancing AI and sustainability research.

ConclusionšŸŒŸ

Dr. Umesh Kumar Lilhore exemplifies excellence in academia, research, and innovation. His prolific contributions to AI, Deep Learning, and Environmental Studies reflect his dedication to addressing critical global challenges. With a strong record of publications, patents, and collaborative projects, he has significantly advanced knowledge and applications in his field. Dr. Lilhore continues to inspire as a thought leader, mentor, and innovator in computer science and engineering.

Publications šŸ“š

šŸ“„ Systematic Review on Cardiovascular Disease Detection and Classification
Authors: Pandey, V., Lilhore, U.K., Walia, R.
Journal: Biomedical Signal Processing and Control, 2025, 102, 107329.
šŸ“Š Citations: 0

šŸ“š An Attention-Driven Hybrid Deep Neural Network for Enhanced Heart Disease Classification
Authors: Lilhore, U.K., Simaiya, S., Alhussein, M., Aurangzeb, K., Hussain, A.
Journal: Expert Systems, 2025, 42(2), e13791.
šŸ“Š Citations: 0

āš ļø Erratum: Hybrid CNN-LSTM Model with Efficient Hyperparameter Tuning for Prediction of Parkinsonā€™s Disease
Authors: Lilhore, U.K., Dalal, S., Faujdar, N., Thangaraju, P., Velmurugan, H.
Journal: Scientific Reports, 2024, 14(1), 27077.
šŸ“Š Citations: 0

āš™ļø Improving Efficiency and Sustainability via Supply Chain Optimization Through CNNs and BiLSTM
Authors: Dalal, S., Lilhore, U.K., Simaiya, S., Radulescu, M., Belascu, L.
Journal: Technological Forecasting and Social Change, 2024, 209, 123841.
šŸ“Š Citations: 0

ā¤ļø Enhancing Heart Disease Classification with M2MASC and CNN-BiLSTM Integration for Improved Accuracy
Authors: Pandey, V., Lilhore, U.K., Walia, R., Baqasah, A.M., Algarni, S.
Journal: Scientific Reports, 2024, 14(1), 24221.
šŸ“Š Citations: 0

šŸ§¬ Intelligence Model on Sequence-Based Prediction of PPI Using AISSO Deep Concept with Hyperparameter Tuning Process
Authors: Thareja, P., Chhillar, R.S., Dalal, S., Baqasah, A.M., Algarni, S.
Journal: Scientific Reports, 2024, 14(1), 21797.
šŸ“Š Citations: 0

šŸ”¬ Optimizing Protein Sequence Classification: Integrating Deep Learning Models with Bayesian Optimization for Enhanced Biological Analysis
Authors: Lilhore, U.K., Simiaya, S., Alhussein, M., Dalal, S., Aurangzeb, K.
Journal: BMC Medical Informatics and Decision Making, 2024, 24(1), 236.
šŸ“Š Citations: 0

ā˜ļø Optimizing Energy Efficiency in MEC Networks: A Deep Learning Approach with Cybertwin-Driven Resource Allocation
Authors: Lilhore, U.K., Simaiya, S., Dalal, S., Baqasah, A.M., Algarni, S.
Journal: Journal of Cloud Computing, 2024, 13(1), 126.
šŸ“Š Citations: 0

šŸŒ¾ Maize Leaf Disease Recognition Using PRF-SVM Integration: A Breakthrough Technique
Authors: Bachhal, P., Kukreja, V., Ahuja, S., Alroobaea, R., Algarni, S.
Journal: Scientific Reports, 2024, 14(1), 10219.
šŸ“Š Citations: 1

āœ… Correction: Hybrid CNN-LSTM Model with Efficient Hyperparameter Tuning for Prediction of Parkinsonā€™s Disease
Authors: Lilhore, U.K., Dalal, S., Faujdar, N., Thangaraju, P., Velmurugan, H.
Journal: Scientific Reports, 2024, 14(1), 9335.
šŸ“Š Citations: 0

 

 

 

Dehong Gao | Artificial Intelligence | Outstanding Scientist Award

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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. šŸš€šŸ“ššŸ’”

Publication Profile :Ā 

Scopus

Educational Background šŸŽ“

  • 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 šŸ’¼

Dehong Gaoā€™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. šŸ’»šŸ“ˆ

Research Interests šŸ”¬

Gao’s research interests are focused on:

  • Large Language Models (LLMs) and Multimodal Learning šŸŒšŸ¤–
  • Natural Language Processing: Information retrieval, recommendation systems, sentiment analysis, and automated summarization šŸ“‘šŸ”
  • E-commerce AI: Developing search algorithms and multilingual representation learning for cross-border e-commerce applications šŸŒšŸ›’
  • Federated Learning and AI-driven personalization in business settings šŸ”’šŸ¤–

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. šŸ†šŸ“š

Publications šŸ“š

  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).

 

 

 

Kanaga Suba Raja S | Deep Learning | Best Researcher Award

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Prof. Dr. Kanaga Suba Raja S | Deep Learning | Best Researcher Award

Professor at Srm Institute Of Science And Technology Tiruchirappalli, India

Dr. S. Kanaga Suba Raja is a dedicated computer science educator and researcher with a passion for innovation and technology. With a rich history of academic leadership and groundbreaking research, he continues to inspire the next generation of engineers. šŸŒšŸ’”

Publication Profile :Ā 

Scopus

Orcid

Google Scholar

 

šŸŽ“ Educational Background :

Dr. Kanaga Suba Raja completed his Ph.D. in Computer Science and Engineering from Manonmaniam Sundaranar University in 2013. He holds a Masterā€™s degree in Computer Science and Engineering from Noorul Islam College of Engineering (2006) and a Bachelorā€™s degree from The Rajaas Engineering College (2003).

šŸ’¼ Professional Experience :

With over 19 years of experience in academia, Dr. Kanaga Suba Raja has held several prominent positions, including Professor and Head of the Department of Computer Science and Engineering at SRM Institute of Science and Technology, and Associate Dean at the School of Computing. His career spans roles as Associate Professor and Lecturer at various institutions under Anna University, where he contributed significantly to curriculum development and academic administration.

šŸ“š Research Interests :Ā 

Dr. Kanaga Suba Raja specializes in artificial intelligence, cloud computing, and biomedical engineering. He has published over 100 research papers, received numerous citations, and holds patents related to cloud computing and medical technologies.

šŸ“ Publication Top Notes :

  1. Priya, J., Kanaga Suba Raja, S., & Usha Kiruthika, S. (2024). State-of-art technologies, challenges, and emerging trends of computer vision in dental images. Computers in Biology and Medicine, 178. https://doi.org/10.1016/j.compbiomed.2024.108800
  2. Priya, J., Kanaga Suba Raja, S., & Sudha, S. (2024). An intellectual caries segmentation and classification using modified optimization-assisted transformer denseUnet++ and ViT-based multiscale residual denseNet with GRU. Signal, Image and Video Processing (SIViP). https://doi.org/10.1007/s11760-024-03227-9
  3. Chandra, & Kanaga Suba Raja, S. (2024). HHECC-AES: A novel hybrid cryptography scheme for developing the secured wireless body area network using heuristic-aided blockchain model. Ad Hoc & Sensor Wireless Networks, 59, 141ā€“179. https://doi.org/10.32908/ahswn.v59.10477
  4. Sandhiya, B., Kanaga Suba Raja, S., Shruthi, K., & Praveena Rachel Kamala, S. (2024). Brain tumour segmentation and classification with reconstructed MRI using DCGAN. Biomedical Signal Processing and Control, 92. https://doi.org/10.1016/j.bspc.2024.106005
  5. Sandhiya, B., & Kanaga Suba Raja, S. (2024). Deep learning and optimized learning machine for brain tumor classification. Biomedical Signal Processing and Control, 89(1). https://doi.org/10.1016/j.bspc.2023.105778
  6. Kausalya, K., & Kanaga Suba Raja, S. (2024). OTRN-DCN: An optimized transformer-based residual network with deep convolutional network for action recognition and multi-object tracking of adaptive segmentation using soccer sports video. International Journal of Wavelets, Multiresolution and Information Processing, 22(1). https://doi.org/10.1142/S0219691323500340
  7. Chandra, B., & Kanaga Suba Raja, S. (2023). Security in wireless body area network (WBAN) using blockchain. IETE Journal of Research. https://doi.org/10.1080/03772063.2023.2233472
  8. Hema, M., & Kanaga Suba Raja, S. (2023). A quantitative approach to minimize energy consumption in cloud data centres using VM consolidation algorithm. KSII Transactions on Internet and Information Systems, 17(2), 312-334. https://doi.org/10.3837/tiis.2023.02.002
  9. Pushpa, S. X., & Kanaga Suba Raja, S. (2022). Enhanced ECC based authentication protocol in wireless sensor network with DoS mitigation. Cybernetics and Systems, 53(2). https://doi.org/10.1080/01969722.2022.2055403
  10. Hema, M., & Kanaga Suba Raja, S. (2022). An efficient framework for utilizing underloaded servers in compute cloud. Computer Systems Science and Engineering, 43(5). https://doi.org/10.32604/csse.2023.024895
  11. Vivekanandan, M., & Kanaga Suba Raja, S. (2022). Virtex-II Pro FPGA-based smart agricultural system. Wireless Personal Communications, 125(1), 119ā€“141. https://doi.org/10.1007/s11277-022-09544-x
  12. Pushpa, S. X., & Kanaga Suba Raja, S. (2022). Elliptic curve cryptography-based authentication protocol enabled with optimized neural network-based DoS mitigation. Wireless Personal Communications, 124(27). https://doi.org/10.1007/s11277-021-08902-5
  13. Balaji, V., & Kanaga Suba Raja, S. (2021). Recommendation learning system model for children with autism. Intelligent Automation & Soft Computing, 31(2). https://doi.org/10.32604/iasc.2022.020287
  14. Valarmathi, K., & Kanaga Suba Raja, S. (2021). Resource utilization prediction technique in cloud using knowledge-based ensemble random forest with LSTM model. Concurrent Engineering: Research and Applications. https://doi.org/10.1177/1063293X211032622
  15. Kanaga Suba Raja, S., & Virgin Louis, B. A. (2021). A review of call admission control schemes in wireless cellular networks. Wireless Personal Communications, 120(4), 3369ā€“3388. https://doi.org/10.1007/s11277-021-08618-6

 

 

 

Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

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Mr. Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

PhD Student at Politecnico di Torino, Italy

Rafael Natalio Fontana Crespo is a dedicated and sociable Ph.D. student specializing in Computer and Control Engineering at Politecnico di Torino. With a strong academic background in mechatronics and practical experience in electrical energy analysis, he is passionate about tackling complex challenges through innovative solutions. šŸŒšŸ’”

Publication Profile :Ā 

Orcid

 

šŸŽ“ Educational Background :

Rafael is currently pursuing a Ph.D. in Computer and Control Engineering at Politecnico di Torino, Italy, since May 2022. He previously obtained a Masterā€™s Degree in Mechatronic Engineering from the same institution, graduating with 110/110 cum laude in July 2022. His master’s thesis focused on designing and developing a distributed software platform for additive manufacturing. Rafael studied Electromechanical Engineering at the Universidad Nacional de CĆ³rdoba, Argentina, where he also completed a double degree program.

šŸ’¼ Professional Experience :

Rafael gained practical experience during his internship at EPEC (Empresa Provincial de EnergĆ­a de CĆ³rdoba) in Argentina, where he worked in the Statistics and Technical Department from May 2020 to May 2021. He was involved in analyzing thermal images of electrical components to prevent failures, contributing to the overall safety and efficiency of electrical systems.

šŸ“š Research Interests :Ā 

Rafaelā€™s research interests lie at the intersection of computer engineering, control systems, and mechatronics, particularly focusing on additive manufacturing, machine learning applications in energy systems, and the optimization of neural networks.

šŸ“ Publication Top Notes :

      1. Fontana Crespo, R.N., E. Patti, S. Di Cataldo, D. Cannizzaro. (2022). Design and Development of a Distributed Software Platform for Additive Manufacturing. Master’s Thesis, Politecnico di Torino.
      2. Fontana Crespo, R.N. (2023). Machine Learning in Energy Applications. Course Exam Paper, Politecnico di Torino.
      3. Fontana Crespo, R.N. (2023). IoT Platforms for Spatial Analytics in Smart Energy Systems. Course Exam Paper, Politecnico di Torino.
      4. Fontana Crespo, R.N. (2023). Optimized Execution of Neural Networks at the Edge. Course Exam Paper, Politecnico di Torino.
      5. Fontana Crespo, R.N. (2023). Adversarial Training of Neural Networks. Course Exam Paper, Politecnico di Torino.