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

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)

200

160

120

80

40

0

Citations
179

Documents
13

h-index
5

🟦 Citations   🟥 Documents   🟩 h-index


View Scopus Profile

Featured Publications

Yasaman | Data Science and Deep Learning | Editorial Board Member

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.

VANI VATHSALA ATLURI | Machine Learning | Best Researcher Award

Dr. VANI VATHSALA ATLURI | Machine Learning | Best Researcher Award

Professor and HOD | CVR College of Engineering | India

Dr. A. Vani Vathsala is a distinguished academician and researcher in the field of Computer Science and Engineering, currently serving as a Professor and Head of the Department of Computer Science and Engineering at CVR College of Engineering, Hyderabad. With a rich background spanning both academia and industry, including a foundational professional stint at Tata Consultancy Services, Dr. Vathsala has built a career marked by innovation, leadership, and impactful research. She holds a Ph.D. in Computer Science from the University of Hyderabad, preceded by a Master of Technology in Computer Science from the same university and a Bachelor’s degree in Computer Science and Engineering from Nagarjuna University. Her research portfolio reflects expertise in artificial intelligence, cloud computing, machine learning, data security, and sustainable computing systems. She has published extensively in Scopus and SCI-indexed journals, contributing to emerging technologies that bridge healthcare, security, and IoT-driven solutions. Her notable publications include Early Diagnosis for the Bacterial Infections for the Patients under the Medical Intervention of Automated Peritoneal Dialysis Using Machine Learning Techniques, A Cloud Integrity Verification and Validation Model Using Double Token Key Distribution Model, Energy-Efficient and Sustainable Cluster-Based Routing in IoT-Based WSNs Using Metaheuristic Optimization, and Enhanced Real-Time Surveillance and Suspect Identification Using CNN-LSTM Based Body Language Analysis.

Profile: Google Scholar 

Featured Publications: