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.

 

Maliki Moustapha | Computer Science | Best Researcher Award

Published on by Scientific World Research Awards

Dr. Maliki Moustapha | Computer Science | Best Researcher Award

PhD | Erciyes University | Turkey

Dr. Maliki Moustapha, an accomplished researcher from Erciyes University, is recognized for his expertise in Artificial Intelligence (AI), Deep Transfer Learning, and Data Engineering, with a strong focus on the integration of intelligent algorithms and data-driven models to address real-world computational challenges. His academic background is rooted in computer science and engineering, where he developed advanced skills in machine learning, neural networks, data mining, and smart systems design. Professionally, Dr. Moustapha has been actively engaged in both research and academic mentorship, contributing to the development of innovative solutions in AI-powered automation, pattern recognition, and intelligent monitoring systems. His major research interests encompass computer vision, deep learning model optimization, spatiotemporal data analysis, and Internet of Things (IoT)-based smart healthcare systems. Among his most cited contributions is the publication titled “A Novel YOLOv5 Deep Learning Model for Handwriting Detection and Recognition” in the International Journal on Artificial Intelligence Tools (2023), which demonstrates superior accuracy and efficiency in image recognition. He has also published influential works on spatial and spatiotemporal clustering algorithms and IoT-based patient monitoring, bridging the gap between data intelligence and applied computing. His research skills span across Python programming, neural network modeling, big data analytics, data preprocessing, and model training for intelligent systems. Though early in his academic journey, Dr. Moustapha has earned recognition for his impactful work, showing promising potential in advancing AI technologies. According to Scopus and Google Scholar, he has achieved 9 citations, an h-index of 1, and several published documents reflecting growing international recognition. Dr. Moustapha’s research continues to contribute meaningfully to the fields of artificial intelligence and computational intelligence. In conclusion, his innovative approach, interdisciplinary mindset, and technological vision position him as a forward-thinking researcher committed to shaping the next generation of intelligent data systems and AI-driven innovations.

Profiles: ORCID | Google Scholar

Featured Publications

1. Moustapha, M., Taşyürek, M., & Öztürk, C. (2023). A novel YOLOv5 deep learning model for handwriting detection and recognition. International Journal on Artificial Intelligence Tools, 32(04), 2350016.

2. Moustapha, M. (2024). Spatial and spatiotemporal clustering algorithms in data mining. In Proceedings of the 3rd International Conference on Data and Electronics and Computing (ICDEC).

3. Moustapha, M. (2019). Alternative approach of patient monitoring system based on Internet of Things. In Proceedings of the II. International Science and Academic Congress (INSAC).