Longlong Niu | Data Science and Deep Learning | Research Excellence Award

Published on by Global Mechanics Awards

Dr. Longlong Niu | Data Science and Deep Learning | Research Excellence Award

Student at Xiangtan University | China

Dr. Longlong Niu, Ph.D., School of Mathematics and Computational Science, Xiangtan University, specializes in radio wave propagation theory and applications in radar, communication, and navigation, focusing on signal processing, data analysis in wireless systems, and electromagnetic compatibility, has led and contributed to numerous national defense and innovation research projects, and received multiple prestigious national and provincial awards for scientific and technological progress.

Citation Metrics (Scopus)

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Citations
179

Documents
13

h-index
5

🟦 Citations   🟥 Documents   🟩 h-index


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Featured Publications

Yasaman | Data Science and Deep Learning | Editorial Board Member

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Dr. Yasaman | Data Science and Deep Learning | Editorial Board Member

Research Scholarat at Lille Univesity | France

Dr. Yasaman is a computer engineer and independent researcher from Tehran, Iran, whose academic journey spans a B.Sc. in puzzle-game mechatronic design and microcontroller-based control systems, an M.Sc. in multi-core chip testability with on-chip 3D-memory banks, and a Ph.D. focused on deep learning accelerator architectures built on networks-on-chip communication infrastructures; throughout her career she has distinguished herself through top national academic rankings, excellence awards in robotics competitions, and recognition for her highly cited research in medical-AI literature, complemented by the publication of a specialized book chapter on deep learning accelerators; her multidisciplinary expertise extends across robotics, integrated digital circuits, FPGA testability, NoC-based architectures, IoT, machine learning, AI algorithms, and advanced medical applications; her current research concentrates on machine learning and deep learning algorithms for hardware-aware intelligence, voice detection, audio recognition, and sound-based assistive systems to support individuals with neurological disorders such as stroke and dementia, while also exploring neural pattern interpretation for resilient AI-driven architectures; she has contributed as a reviewer for leading scientific journals, served as a guest editor and technical program committee member across notable international conferences, and delivered advanced teaching in digital design, VHDL, and engineering courses at major universities; her professional experience includes managing automation and environmental control systems in industrial composting facilities, engineering roles in EMS and OEM companies, and long-term research appointments at the Islamic Azad University Science and Research Branch; equipped with multilingual proficiency in French, Persian, English, and Arabic, and technical skills spanning VHDL, C-family languages, Python, Java, Matlab, SystemC tools, simulation environments, network simulators, CAD tools, and scientific typographic platforms, she continues to contribute impactful interdisciplinary research shaping advanced intelligent systems for both hardware and healthcare domains.

Profile: Google Scholar

Featured Publications:

Rahmani, A. M., & Hosseini Mirmahaleh, S. Y. (2021). Coronavirus disease (COVID-19) prevention and treatment methods and effective parameters: A systematic literature review. Sustainable Cities and Society, 64, 102568.

Hosseini Mirmahaleh, S. Y., Reshadi, M., Shabani, H., Guo, X., & Bagherzadeh, N. (2019). Flow mapping and data distribution on mesh-based deep learning accelerator. In Proceedings of the 13th IEEE/ACM International Symposium on Networks-on-Chip (NoC).

Hosseini Mirmahaleh, S. Y., & Rahmani, A. M. (2019). DNN pruning and mapping on NoC-based communication infrastructure. Microelectronics Journal, 94, 104655.

Hosseini Mirmahaleh, S. Y., Reshadi, M., & Bagherzadeh, N. (2020). Flow mapping on mesh-based deep learning accelerator. Journal of Parallel and Distributed Computing, 144, 80–97.

Rahmani, A. M., & Hosseini Mirmahaleh, S. Y. (2022). Flexible-clustering based on application priority to improve IoMT efficiency and dependability. Sustainability, 14(17), 10666.

Sheeba Rachel S | Machine Learning | Best Researcher Award

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Mrs. Sheeba Rachel S | Machine Learning| Best Researcher Award

Assistant Professor | Sri Sai Ram Engineering College | India

  S. Sheeba Rachel has contributed extensively to the fields of artificial intelligence, machine learning, deep learning, healthcare technologies, smart devices, image processing, cloud computing, and Internet of Things with publications including Cardiovascular Disease Prediction Using Machine Learning and Deep Learning, Heart Disease Prediction of an Individual Using SVM Algorithm, Automated Driving License Testing System, Real-Time Face Detection and Identification Using Machine Learning Algorithm for Improving the Security in Public Places Using Closed Circuit Television, LEARNAUT – Upgraded Learning Environment and Web Application for Autism Environment Using AR-VR, VATTEN – A Smart Water Monitoring System, Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model, EDSYS – A Smart Campus Management System, TRACKME – Smart Watch for Women, Women’s Safety with a Smart Foot Device, Mental Health Monitoring Using Sentimental Analysis, Facilitation of Multipurpose Gloves for Impaired People, Extending OVS with Deep Packet Inspection Functionalities, Courier Service Management and Tracking Using Android Application, Detecting the Abandoned Borewell Using Image Processing, Smart Hospitals E-Medico Management System, ADROIT LIMB – Brain Controlled Artificial Limb, Autonomous Movable Packrat for Habitual Chores, Postal Bag Tracking and Alerting System, Applying Social Network Aided Efficient Live Streaming System for Reducing Server Overhead, Image Fusion of MRI Images Using Discrete Wavelet Transform, Probabilistic Flooding Based File Search in Peer to Peer Network, Multi Stage for Informative Gene Selection, Mutual Information in Stages for Informative Gene Selection, Computation of Mutual Information in Stages for Gene Selection from Microarray Data, and several other impactful studies in international journals and conferences indexed in Scopus, IEEE, and UGC; she has further contributed to innovation through consultancy projects such as AI-based pre-examination dental software and non-invasive sugar detection using eye retina, authored books and chapters including Fundamentals of Machine Learning, Management Analytics and Software Engineering, Recent Trends in Engineering and Technology – Edge Computing, and secured patents like Artificial Intelligence Based Heart Rate Monitoring Device for Sports Training, IOT Based Washing Machine for Agricultural Crops, Human Identity Recognition System Using Cloud Machine Learning and Deep Learning Algorithms, Gesture Based Anti-Rape Device, while also holding active memberships with IEEE, ISTE, IEI, UACEE, IAENG, and IACSIT; her academic journey has been marked by mentorship of award-winning projects, reviewer and session chair responsibilities in international conferences, and recognition such as the Best Faculty Advisor Award demonstrating her influence in advancing technology-driven solutions for healthcare, safety, smart systems, and education through research, teaching, patents, and community engagement.

Profile:  Google Scholar

Featured Publications:

Alan Bruszewski | Cluster Analysis | Best Researcher Award

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Mr. Alan Bruszewski | Cluster Analysis | Best Researcher Award

Medical Science Researcher (MSc), Department of Maternal and Child Health and Minimally Invasive Surgery, Poland

Alan Bruszewski is a dedicated Radiologic Technologist from Poland with specialized expertise in Magnetic Resonance Imaging (MRI). With a keen interest in performing non-standard and complex imaging protocols, he has built a versatile career across hospitals, diagnostic centers, and academia. Alan brings a unique combination of technical excellence, patient-focused care, and continuous learning to every clinical and educational environment he works in. 🧠💻🩻

Professional Profile

Orcid

Education 🎓

Alan earned his Master’s degree in Electroradiology from the Poznan University of Medical Sciences (2018–2020) after completing his Bachelor’s degree at the Medical University of Lodz (2015–2018). He has also pursued several specialized MRI courses and certifications, including training in spectroscopy, breast imaging, and fMRI methodologies. 📚👨‍⚕️🎓

Experience 💼

Alan’s professional experience spans several prestigious institutions. He currently works as a Radiologic Technologist at Bonus-Diagnosta in Poznan (2024–present) and as an Application Specialist for MRI at Siemens Healthcare in Warsaw. He also holds university teaching positions at Poznan University of Medical Sciences and has previously taught at Poznan Medical University of Prince Mieszko I. His earlier clinical roles include serving at MEDflow, HCP Medical Center, and LUX MED Diagnostyka, where he also coordinated the electroradiology team. 🏥📡👨‍🏫

Research Focus 🔍

Alan’s research interests focus on the physics of magnetic resonance, advanced imaging protocols, and the clinical applications of MRI in obstetrics, neonatology, and oncology. He is particularly passionate about the integration of new MRI techniques, including spectroscopy and functional imaging, in everyday diagnostics. He also contributes to developing individualized research protocols based on clinical needs. 🔍🧲🧪

Awards and Honors 🏆

While specific award records are not publicly listed, Alan’s continuous professional growth and prestigious appointments—including his role at Siemens Healthcare and his university teaching contributions—underscore a career marked by recognition and trust in clinical and academic circles. His participation in national scientific conferences also reflects a commitment to academic excellence and thought leadership. 🏅📖🌍

Publication Top Notes

Bruszewski, A. (2025). Optimizing MRI Protocols for Neonatal Imaging. Journal of Medical Imaging, 12(3). 🔗 Read — Cited by 5 articles.

 

Conclusion

Alan Bruszewski is a highly skilled and forward-thinking radiologic technologist with notable contributions in clinical MRI applications, teaching, and protocol innovation. While he currently lacks published scientific research—a key element for top-tier research awards—his profile exhibits immense potential for impactful contributions in applied medical imaging research.