Pardeep Kumar | Deep Learning | Innovative Research Award

Published on by Technology Scientists

Innovative Research Award

                   Pardeep Kumar
Affiliation Jaypee University of Information Technology
Country India
Scopus ID 55098732300
Documents 121
Citations 3,262
h-index 29
Subject Area Deep Learning
Event Technology Scientists Awards
ORCID 0000-0001-5303-7219

Pardeep Kumar

Pardeep Kumar is a researcher affiliated with Jaypee University of Information Technology, India, whose scholarly work emphasizes deep learning, artificial intelligence, cybersecurity, cloud computing, and intelligent healthcare applications. His research portfolio demonstrates sustained academic productivity through peer-reviewed publications, interdisciplinary collaborations, and measurable scholarly impact. His contributions to emerging computational technologies have supported advancements in intelligent decision-making systems and practical engineering applications while maintaining relevance to contemporary technological challenges.[1]

Abstract

Pardeep Kumar has established a distinguished academic profile through significant contributions to deep learning, cloud computing, cybersecurity, intelligent healthcare, and energy-efficient computing systems. His research integrates advanced artificial intelligence techniques with practical engineering applications to address real-world technological challenges. With more than one hundred twenty scholarly publications, over three thousand citations, and a strong h-index, his work demonstrates sustained scientific influence across interdisciplinary domains. His research outputs have appeared in reputable international journals and continue to support innovation in intelligent systems, medical image analysis, secure communication protocols, and cloud infrastructure optimization, reflecting both academic excellence and practical technological relevance.[1][2]

Keywords

Deep Learning, Artificial Intelligence, Medical Image Analysis, Breast Cancer Detection, Cybersecurity, Session Initiation Protocol, Cloud Computing, Energy Efficiency, Machine Learning, Healthcare Analytics, Intelligent Systems, Data Science, Technology Innovation, Pattern Recognition, Scientific Research.

Introduction

Pardeep Kumar has developed an extensive research portfolio focused on deep learning, artificial intelligence, cybersecurity, and cloud computing. His investigations emphasize practical technological solutions supported by rigorous scientific methodologies, resulting in internationally recognized publications that contribute to advancing intelligent computational systems across healthcare, communication networks, and distributed computing environments.[2]

Research Profile

Affiliated with Jaypee University of Information Technology, Pardeep Kumar has authored more than one hundred twenty scholarly publications while accumulating over three thousand citations and an h-index of twenty-nine. His research demonstrates consistent interdisciplinary engagement, collaborative scholarship, and sustained contributions across artificial intelligence, cloud technologies, cybersecurity, and healthcare informatics.[1]

Research Contributions

His scientific contributions include developing advanced deep learning frameworks for medical diagnosis, strengthening authentication mechanisms for secure communication protocols, and improving energy-efficient cloud resource management. These interdisciplinary studies combine theoretical innovation with practical implementation, supporting reliable, scalable, and intelligent technological systems across multiple application domains.[2][3]

Publications

His recent publications address breast cancer detection through stacked ensemble learning, improved authentication techniques for Session Initiation Protocol security, and optimized host selection frameworks for cloud data centres. These studies collectively demonstrate expertise in artificial intelligence, cybersecurity, and sustainable computing while addressing contemporary technological challenges.[2][3][4]

Research Impact

The measurable scholarly influence of his research is reflected through extensive citation performance, sustained publication productivity, and broad interdisciplinary applicability. His findings contribute to scientific progress in intelligent healthcare, secure digital communication, and efficient cloud infrastructure, providing valuable references for researchers, engineers, and technology practitioners worldwide.[1]

Award Suitability

Based on documented scholarly achievements, publication quality, citation metrics, and sustained technological innovation, Pardeep Kumar demonstrates strong alignment with the objectives of the Innovative Research Award. His interdisciplinary research promotes meaningful scientific advancement while delivering practical solutions addressing current challenges in modern computing and engineering disciplines.[1]

Conclusion

Pardeep Kumar’s academic accomplishments reflect sustained excellence in deep learning and related technological disciplines. His influential publications, collaborative research initiatives, and measurable scholarly impact illustrate meaningful contributions to scientific knowledge. These achievements support recognition through the Innovative Research Award and demonstrate continued commitment to advancing global technology research.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Pardeep Kumar, Author ID 55098732300. Scopus.
    https://www.scopus.com/pages/authors/55098732300
  2. Kumar, P., et al. (2026). Robust multi-phase framework for breast cancer detection and classification using mammogram images with stacked ensemble learning. Biomedical Signal Processing and Control.
    https://www.sciencedirect.com/science/article/abs/pii/S1746809426004659
  3. Kumar, P., et al. (2026). Authentication improvements for the session initiation protocol. Peer-to-Peer Networking and Applications.
    https://link.springer.com/article/10.1007/s12083-026-02215-9
  4. Kumar, P., et al. (2026). Improved PROMETHEE-based energy efficient host selection framework for cloud data centres. International Journal of Grid and Utility Computing.
    https://www.inderscienceonline.com/doi/10.1504/IJGUC.2026.150667

Jingjing Wang | Neural Network | Editorial Board Member

Published on by Technology Scientists

Editorial Board Member

Jingjing Wang
Shandong Normal University
Jingjing Wang
Researcher Jingjing Wang
Affiliation Shandong Normal University
Country China
Scopus ID 57214140268
Documents 79
Citations 726
h-index 15
Subject Area Neural Network
Event Technology Scientists Awards
ORCID 0000-0003-1597-1793

Jingjing Wang is affiliated with Shandong Normal University and has contributed extensively to the field of neural network research, computational imaging, inverse scattering systems, and advanced signal processing methodologies. Her academic profile demonstrates active participation in multidisciplinary research involving microwave imaging, image fusion, radar systems, and machine learning-assisted imaging technologies.[1] Her publication portfolio indexed in Scopus reflects sustained scholarly productivity, citation impact, and international visibility within engineering and intelligent imaging research domains.[2]

Abstract

This article presents an academic overview of Jingjing Wang, focusing on her scholarly contributions to neural network applications, microwave imaging, inverse scattering systems, MIMO-SAR imaging, and image fusion methodologies. Her research demonstrates interdisciplinary integration between computational intelligence and advanced imaging technologies for engineering applications.[2] The analysis highlights her publication impact, research collaborations, technical innovations, and suitability for recognition within the Technology Scientists Awards framework.[3]

Keywords

Neural Network, Microwave Imaging, Inverse Scattering, MIMO-SAR Imaging, Image Fusion, Computational Intelligence, Signal Processing, Deep Learning, Radar Imaging, Artificial Intelligence.[1]

Introduction

The rapid advancement of neural network methodologies has significantly influenced imaging science, signal reconstruction, and computational sensing technologies. Researchers working at the intersection of artificial intelligence and engineering systems have contributed to improving imaging precision, computational efficiency, and multi-source data interpretation.[2] Jingjing Wang’s research profile reflects active engagement in these evolving domains, particularly in inverse scattering imaging, radar imaging optimization, and intelligent image fusion approaches.[3]

Her work combines deep learning principles with advanced engineering models to address practical limitations in high-contrast imaging, nonlinear reconstruction, and multichannel signal integration. Such interdisciplinary contributions align with the broader objectives of modern intelligent sensing and computational imaging research.[1]

Research Profile

Jingjing Wang has established a consistent academic record supported by Scopus-indexed publications, citation impact, and collaborative international research activities.[1] Her research specialization primarily focuses on neural network systems, computational imaging, inverse scattering, radar imaging technologies, and image fusion techniques utilizing machine learning frameworks.[2]

  • Advanced inverse scattering imaging systems
  • Neural network-assisted image enhancement
  • MIMO-SAR computational imaging methodologies
  • Signal processing and nonlinear reconstruction
  • Deep learning-based image fusion frameworks

Her scholarly output demonstrates integration of computational intelligence with practical imaging applications, supporting advancements in engineering visualization and sensing technologies.[3]

Research Contributions

One of Jingjing Wang’s notable research contributions involves the development of an enhanced contrast born iterative cascaded network for high-contrast inverse scattering imaging. This work explores advanced reconstruction strategies capable of improving imaging quality and computational efficiency in inverse scattering environments.[1]

Her research also includes efficient range migration algorithms integrated with chunked nonlinear normalized weights and SNR-based multichannel fusion methods for MIMO-SAR imaging systems. These approaches contribute to improved imaging robustness, enhanced signal integration, and optimization of radar imaging performance under complex conditions.[2]

In the field of image fusion, Jingjing Wang contributed to KCUNET, a framework that combines KAN and convolutional layers for multi-focus image fusion. This contribution reflects the increasing role of hybrid neural architectures in computational imaging and intelligent feature integration.[3]

Publications

  • Enhanced Contrast Born Iterative Cascaded Network for High-Contrast Inverse Scattering Imaging.[1]
  • An Efficient RMA with Chunked Nonlinear Normalized Weights and SNR-Based Multichannel Fusion for MIMO-SAR Imaging.[2]
  • KCUNET: Multi-Focus Image Fusion via the Parallel Integration of KAN and Convolutional Layers.[3]

Research Impact

The research impact of Jingjing Wang is reflected through her Scopus-indexed publication profile, citation record, and ongoing contributions to computational imaging technologies.[1] Her interdisciplinary work supports broader developments in radar imaging, neural network optimization, image reconstruction, and intelligent sensing systems utilized across engineering and applied science disciplines.[2]

Her collaborations with multiple researchers in signal processing and imaging science further indicate active participation in contemporary scientific research networks. The combination of theoretical modeling and practical implementation in her publications contributes to both academic advancement and technological innovation.[3]

Award Suitability

Jingjing Wang demonstrates strong suitability for recognition within the Technology Scientists Awards due to her consistent scholarly productivity, research relevance, and contributions to neural network-enabled imaging technologies.[1] Her work addresses important technical challenges in inverse scattering systems, radar imaging optimization, and intelligent image fusion methodologies.[2]

The interdisciplinary nature of her research aligns with the objectives of technological innovation, computational intelligence advancement, and engineering-oriented scientific development. Her publication metrics and collaborative research activities further support her recognition as an active contributor within the scientific community.[3]

Conclusion

Jingjing Wang’s academic contributions illustrate the integration of neural networks, intelligent imaging systems, and computational sensing methodologies within modern engineering research.[1] Her work in inverse scattering imaging, MIMO-SAR systems, and image fusion demonstrates technical depth and interdisciplinary relevance.[2] Through scholarly publications, collaborative research, and impactful engineering studies, she continues to contribute to advancements in computational intelligence and imaging science.[3]

References

  1. Wang, J., Li, Z., Xu, H., & Hu, N. (2025). Enhanced Contrast Born Iterative Cascaded Network for High-Contrast Inverse Scattering Imaging. IEEE Antennas and Wireless Propagation Letters.
    DOI:https://doi.org/10.1109/LAWP.2025.3593269
  2. Wang, J., Chen, H., Duan, H., Sun, R., Yang, K., Fang, J., Xu, H., & Song, P. (2025). An Efficient RMA with Chunked Nonlinear Normalized Weights and SNR-Based Multichannel Fusion for MIMO-SAR Imaging. Remote Sensing, 17(18), 3232.
    DOI:https://doi.org/10.3390/rs17183232
  3. Fang, J., Wang, R., Ning, X., Wang, R., Teng, S., Liu, X., Zhang, Z., Lu, W., Hu, S., & Wang, J. (2025). KCUNET: Multi-Focus Image Fusion via the Parallel Integration of KAN and Convolutional Layers. Entropy, 27(8), 785.
    DOI:https://doi.org/10.3390/e27080785
  4. Elsevier. (n.d.). Scopus author details: Jingjing Wang, Author ID 57214140268. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57214140268

Karim El Moutaouakil | Neural network | Research Excellence Award

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Prof. Dr. Karim El Moutaouakil | Neural network | Research Excellence Award

Université Sidi Mohamed Ben Abdellah | Morocco

Prof. Dr. Karim El Moutaouakil is an active researcher in artificial intelligence, computational intelligence, and applied optimization, with a strong emphasis on machine learning for data-driven decision systems. His work spans deep learning architectures (LSTM, CNNs, HRNNs), metaheuristic and fractional optimization algorithms, fuzzy systems, and their applications in financial forecasting, big data analytics, healthcare prediction, energy systems, and smart decision support. He has authored 98 peer-reviewed publications, receiving 650 citations with an h-index of 15, reflecting consistent scholarly impact. His research demonstrates methodological innovation, notably the integration of Ito-based optimizers, genetic algorithms, fuzzy logic, and hybrid AI frameworks to enhance model accuracy and robustness. With extensive international collaboration involving 90+ co-authors, his work contributes to interdisciplinary knowledge exchange. The societal relevance of his research is evident in applications addressing economic forecasting, personalized tourism, medical risk prediction, and energy optimization, supporting data-informed policy and sustainable technological development at a global level.

Citation Metrics (Scopus)

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Top 5 Featured Publications

Amirhossein Ghasemi Abyaneh | Machine Learning | Best Researcher Award

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Mr. Amirhossein Ghasemi Abyaneh | Machine Learning | Best Researcher Award

Researcher | Kharazmi University | Iran

Mr. Amirhossein Ghasemi Abyaneh is an emerging scholar in the field of artificial intelligence applications in sustainable supply chains, affiliated with Kharazmi University, Tehran, Iran. His academic endeavors focus on integrating advanced data analytics, optimization techniques, and machine learning frameworks to enhance decision-making, efficiency, and sustainability across complex supply chain networks. With 3 published research papers and an h-index of 1, Mr. Abyaneh has begun establishing a scholarly footprint that bridges technology-driven innovation with environmental and operational resilience. His work, including the open-access article “An Analytical Review of Artificial Intelligence Applications in Sustainable Supply Chains” (2025, Supply Chain Analytics), provides critical insights into the evolving intersection of AI and sustainability, emphasizing how digital intelligence can optimize resource utilization, reduce carbon footprints, and strengthen circular economy practices. Having received citations from international scholars, he actively contributes to the global academic dialogue on sustainable logistics, smart manufacturing, and responsible innovation. Mr. Abyaneh’s collaborative research network includes seven co-authors from diverse academic and institutional backgrounds, reflecting a strong interdisciplinary approach that combines engineering, data science, and environmental management. His studies aim to foster both theoretical advancement and practical applicability, offering valuable implications for policymakers, corporations, and researchers seeking to transition toward greener, data-driven supply chains. Beyond academic impact, his contributions align with global sustainability goals, promoting knowledge transfer, digital equity, and responsible AI adoption for societal benefit.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Sharbati, A., Movahed, A. B., Abyaneh, A. G., & Rahmanian, F. (2025). Risk assessment of healthcare systems using the FMEA method: Medication management process. Journal of Future Digital Optimization, 1(1), 71–85.
Cited by: 4

2. Abyaneh, A. G., Movahed, A. B., Abyari, A., Nodehfarahani, A., & Khakbazan, M. (2025). Evaluating the RFID technology in Costco Company: A focus on logistics and supply chain management. Applied Innovations in Industrial Management, 5(2), 34–51.
Cited by: 2

3. Movahed, A. B., Abyaneh, A. G., Khakbazan, M., & Movahed, A. B. (2025). Smart economy cybersecurity: AI-driven risk management in digital markets. In Dynamic and Safe Economy in the Age of Smart Technologies (pp. 49–72).
Cited by: 2

4. Abyaneh, A. G., Ghanbari, H., Mohammadi, E., Amirsahami, A., & Khakbazan, M. (2025). An analytical review of artificial intelligence applications in sustainable supply chains. Supply Chain Analytics, 100173.
Cited by: 1

5. Abyaneh, A. G., Khakbazan, M., & Movahed, A. B. (2026). Artificial intelligence in digital marketing: Trends, challenges, and strategic opportunities. In Improving Consumer Engagement in Digital Marketing Through Cognitive AI (pp. 225–260)

Mr. Amirhossein Ghasemi Abyaneh envisions a future where artificial intelligence empowers sustainable industrial transformation, enabling supply chains to become more adaptive, transparent, and environmentally responsible. His research advances the integration of smart analytics and sustainability principles, fostering innovation that supports global climate resilience and ethical technological progress.