Mingshou An | Artificial Intelligence | Excellence in Research Award

Published on by Scientific World Research Awards

Mr. Mingshou An | Artificial Intelligence | Excellence in Research Award

Lecturer | Xi’an Technological University | China

Mr. Mingshou An is an emerging researcher affiliated with Dong-A University, Busan, South Korea, recognized for his contributions to computer science and intelligent image processing. His research primarily focuses on deep learning, image denoising, medical and natural image enhancement, convolutional neural networks, U-Net architectures, and multi-scale attention mechanisms, with a strong emphasis on improving image quality and model efficiency. He possesses solid research skills in machine learning, deep neural network design, algorithm optimization, data preprocessing, model evaluation, and scientific computing, supported by hands-on experience in developing advanced attention-based architectures. His notable work includes an open-access publication on Multi-scale Attention Dense U-Net for image denoising, reflecting innovation in AI-driven image restoration. According to Scopus, Mr. An has 11 research documents, 15 citations , and an h-index of 3, demonstrating growing academic impact. While formal awards and honors are not yet listed, his citation growth indicates rising recognition. In conclusion, Mr. Mingshou An represents a promising researcher whose work contributes meaningfully to the advancement of intelligent imaging and deep learning applications.

Citation Metrics (Scopus)

15
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5
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Citations

15

Documents

11

h-index

3

Citations

Documents

h-index

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Featured Publications


A Method of Visual Positioning of Tank Truck Openings via Two-Stage Fine-Tuning


– Book Chapter, 2025 | DOI: 10.1007/978-3-031-94962-3_14


An Enhanced LSTM with Hippocampal-Inspired Episodic Memory for Urban Crowd Behavior Analysis


– Electronics (Journal), 2025 | DOI: 10.3390/electronics15010101


A Method of Image Denoising via Dense Attention DnCNN


– Book Chapter, 2024 | DOI: 10.1007/978-981-97-4182-3_43


Fusion Self-Attention Feature Clustering Mechanism Network for Person ReID


– Book Chapter, 2024 | DOI: 10.1007/978-981-99-9416-8_55


A Study on Deep Learning Algorithm for Fire Detection based on Attention BiFPN


– Journal of Korean Institute of Information Technology, 2024 | DOI: 10.14801/jkiit.2024.22.9.1

 

Amna Ikram | Artificial Intelligence | Best Researcher Award

Published on by Scientific World Research Awards

Dr. Amna Ikram | Artificial Intelligence | Best Researcher Award

Senior Lecturer | Government Sadiq College Women University | Pakistan

Dr. Amna Ikram is an accomplished researcher recognized for her pioneering contributions in machine learning, image processing, Internet of Things (IoT), obstacle detection, and smart agriculture. Her work emphasizes the integration of artificial intelligence and data-driven technologies to develop intelligent, efficient, and socially impactful systems. With a citation count exceeding 170, an h-index of 7, and an i10-index of 5, Dr. Ikram’s scholarly record highlights her commitment to addressing modern challenges in automation, healthcare, and sustainable agriculture.Her research focuses on creating AI-enabled frameworks and hybrid computational models that enhance decision-making and predictive accuracy in real-world applications. In agriculture, her widely cited paper, “Crop Yield Maximization Using an IoT-Based Smart Decision System” (Journal of Sensors, 2022), presents a robust model for optimizing crop productivity using sensor data, environmental parameters, and predictive algorithms. This work has significantly influenced the development of precision agriculture and IoT-driven farming systems.Expanding her expertise into healthcare and assistive technologies, Dr. Ikram has contributed to several innovative studies such as “Forensic Radiology: A Robust Approach to Biological Profile Estimation from Bone Image Analysis Using Deep Learning” and “Transformer-Based ECG Classification for Early Detection of Cardiac Arrhythmias.” These contributions showcase her ability to blend AI with biomedical imaging for diagnostic advancements and patient support.Her recent publications, including “A DETR-Based Approach for Enhancing Object Detection in Assistive Technology for the Visually Impaired” and “AI-Enabled Vision Transformer for Automated Weed Detection,” highlight her continuous drive to apply machine learning and computer vision to enhance accessibility and agricultural efficiency. Furthermore, her exploration of fuzzy-optimized hybrid neural networks and IoT sensor integration has resulted in innovative frameworks for yield prediction, crop disease detection, and obstacle recognition.Dr. Ikram’s interdisciplinary approach bridges technology and sustainability. By merging deep learning, IoT infrastructure, and intelligent vision systems, her work supports the creation of smarter, adaptive environments that empower both humans and industries. Her research continues to advance the frontiers of AI-driven automation, smart agriculture, and assistive IoT technologies, contributing profoundly to sustainable innovation and societal betterment.

Profiles: ORCID | Google Scholar

Featured Publications

  1. Ikram, A., Aslam, W., Aziz, R. H. H., Noor, F., Mallah, G. A., Ikram, S., & Ahmad, M. S. (2022). Crop yield maximization using an IoT-based smart decision system. Journal of Sensors, 2022(1), 2022923.
    Citations: 71

  2. Batool, S. N., Yang, J., Gilanie, G., Latif, A., & Ikram, A. (2025). Forensic radiology: A robust approach to biological profile estimation from bone image analysis using deep learning. Biomedical Signal Processing and Control, 105.
    Citations: 19

  3. Malik, M., Ikram, A., Batool, S. N., & Aslam, W. (2018). A performance assessment of rose plant classification using machine learning. In Proceedings of the International Conference on Intelligent Technologies and Applications (pp. 745–756).
    Citations: 15

  4. Hassan, J. U., Missen, M. M. S., Firdous, A., Maham, A., & Ikram, A. (2023). An adaptive M-learning usability model for facilitating M-learning for slow learners. International Journal of Interactive Mobile Technologies, 17(19).
    Citations: 14

  5. Naveed, S., Husnain, M., Alsubaie, N., Samad, A., Ikram, A., Afreen, H., & Gilanie, G. (2024). Drug efficacy recommendation system of glioblastoma (GBM) using deep learning. IEEE Access.
    Citations: 13

Dr. Amna Ikram’s research bridges artificial intelligence, IoT, and data-driven innovation to create intelligent solutions that enhance agriculture, healthcare, and assistive technologies. Her pioneering work advances sustainable development, automation, and societal well-being through smart, human-centered innovations that connect science with real-world impact.