Lei Yao | Artificial Intelligence | Research Excellence Award

Published on by Scientific World Research Awards

Mr. Lei Yao | Artificial Intelligence | Research Excellence Award

Ph.D. Candidate | Jilin University | China

Mr. Lei Yao is an advancing researcher whose work lies at the intersection of artificial intelligence, biomedical engineering, and intelligent monitoring systems. His research focuses on designing innovative AI-driven solutions for healthcare diagnostics, cognitive evaluation, physiological signal analysis, and smart livestock management. Through the integration of deep learning, multi-task learning, and generative models, he aims to improve the accuracy, efficiency, and scalability of real-world intelligent sensing applications.A core area of Mr. Yao’s work is biomedical signal processing, especially electrocardiogram (ECG) analysis. His contribution to MMS-Net, a multi-task learning framework, provides a transformative method for reconstructing full 12-lead ECG signals using only 3-lead inputs while simultaneously performing disease classification. This technology enhances diagnostic capabilities in low-resource settings and supports more accessible cardiology screening.Mr. Yao also investigates synthetic data generation using modern generative adversarial networks. His work on SGECG, a StarGAN-based system for ECG generation and augmentation, aids in overcoming data scarcity, a major limitation in machine-learning-based healthcare research.Beyond biomedical applications, Mr. Yao significantly contributes to smart agriculture and automaed animal health monitoring. His publication SideCow-VSS introduces a comprehensive video semantic segmentation dataset designed for intelligent dairy cow health assessment in smart ranch environments—an important advancement for precision livestock farming.His interdisciplinary research further includes cognitive assessment, demonstrated by MLCDT, a fine-grained multi-task learning model that enhances automated analysis of the clock drawing test, an essential tool in early detection of cognitive impairment.Overall, Mr. Lei Yao’s research integrates AI, signal processing, and intelligent sensing to create impactful solutions for healthcare, cognitive diagnostics, and smart agricultural systems.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

  1. Yao, L., Garmash, O., Bianchi, F., Zheng, J., Yan, C., Kontkanen, J., Junninen, H., … (2018). Atmospheric new particle formation from sulfuric acid and amines in a Chinese megacity. Science, 361(6399), 278–281.
    Citations: 611

  2. Xiao, S., Wang, M. Y., Yao, L., Kulmala, M., Zhou, B., Yang, X., Chen, J. M., Wang, D. F., … (2015). Strong atmospheric new particle formation in winter in urban Shanghai, China. Atmospheric Chemistry and Physics, 15(4), 1769–1781.
    Citations: 176

  3. Zheng, J., Ma, Y., Chen, M., Zhang, Q., Wang, L., Khalizov, A. F., Yao, L., Wang, Z., … (2015). Measurement of atmospheric amines and ammonia using the high-resolution time-of-flight chemical ionization mass spectrometry. Atmospheric Environment, 102, 249–259.
    Citations: 165

  4. Yan, C., Nie, W., Äijälä, M., Rissanen, M. P., Canagaratna, M. R., Massoli, P., … Yao, L., … (2016). Source characterization of highly oxidized multifunctional compounds in a boreal forest environment using positive matrix factorization. Atmospheric Chemistry and Physics, 16(19), 12,715–12,731.
    Citations: 164

  5. Wang, X., Hayeck, N., Brüggemann, M., Yao, L., Chen, H., Zhang, C., Emmelin, C., … (2017). Chemical characteristics of organic aerosols in Shanghai: A study by ultrahigh-performance liquid chromatography coupled with Orbitrap mass spectrometry. Journal of Geophysical Research: Atmospheres, 122(21), 11,703–11,722.
    Citations: 157

Mr. Lei Yao’s research significantly advances atmospheric chemistry by uncovering the mechanisms of new particle formation, characterizing organic aerosols, and improving high-resolution chemical detection technologies. His contributions enhance scientific understanding of air pollution sources, support policymakers in designing effective climate and air-quality interventions, and strengthen industrial environmental monitoring frameworks. Through high-impact studies published in globally respected journals, his work drives innovation in atmospheric measurement, fosters healthier urban environments, and informs global strategies for mitigating particulate pollution and its effects on human and environmental health.

 

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.