Mohd Usama | Machine Learning | Best Researcher Award

Posted on 17/05/202517/05/2025 by sciencefather

Assist. Prof. Dr. MohdUsama|MachineLearning
|Best Researcher Award

Postdoctoral Researcher at Umea University, Sweden Sweden.

Dr. Mohd Usama is a Postdoctoral Researcher at the Department of Diagnostics and Intervention, Umea University, Sweden. He holds a Ph.D. in Computer Science from Huazhong University of Science and Technology, China, focusing on deep learning for disease prediction and sentiment analysis. His research bridges artificial intelligence and medical imaging, particularly using GANs for domain adaptation and plaque detection in ultrasound imagery. With a solid teaching and research background across reputed institutions in India, he has significantly contributed to developing AI-based clinical decision support systems. His scholarly work, practical innovation, and interdisciplinary expertise make him highly suitable for the Best Researcher Award, exemplifying excellence in research, innovation, and educational service in the domains of biomedical engineering and artificial intelligence.

🌍 Professional Profile:

Scopus

Orcid

🏆 Suitability for the Best Researcher Award

Dr. Mohd Usama exemplifies the qualities of a top-tier researcher through his impactful contributions to AI-driven medical imaging and clinical decision support systems. Currently a Postdoctoral Researcher at Umea University, Sweden, his work on generative adversarial networks for ultrasound-based atherosclerosis risk assessment addresses critical challenges in healthcare diagnostics. His strong academic foundation, interdisciplinary approach, and global research collaborations demonstrate exceptional innovation and dedication. Dr. Usama’s ability to translate deep learning research into real-world clinical applications, alongside a consistent record of teaching, publishing, and mentoring, positions him as a leader in his field. His scientific rigor, creativity, and societal impact make him a highly deserving candidate for the Best Researcher Award.

🎓 Education

Dr. Usama earned his Ph.D. in Computer Science from Huazhong University of Science and Technology, China (2016–2020), with a dissertation on “Recurrent Deep Learning for Text Processing with Application to Disease Prediction and Sentiment Analysis,” supervised by Prof. Min Chen. He completed his Master’s in Computer Science and Applications (71.78%, First Division) from Aligarh Muslim University (2013–2016), focusing on cloud-based electric vehicle charging management. His undergraduate degree is a B.Sc. (Hons) in Statistics (71.07%, First Division), also from Aligarh Muslim University (2009–2012), with a thesis on students’ opinions on the Ombudsman Bill in India. His academic journey reflects a blend of statistical foundations, computing applications, and interdisciplinary insights, crucial for innovative AI research in biomedical domains.

🏢 Work Experience

Dr. Mohd Usama has served as a Postdoctoral Researcher at Umea University, Sweden (Dec 2022–Present), contributing to AI-powered clinical decision support systems and generative models for carotid ultrasound imaging. Previously, he worked as an Assistant Professor at the University of Petroleum and Energy Studies (2022), Kalasalingam Academy of Research and Education (2021–2022), and Madanapalle Institute of Technology and Science (2020–2021). He taught various courses including Deep Learning, Algorithms, Programming, and Information Security. His work spans both academia and research, with a deep engagement in curriculum development and applied machine learning. His experience in medical imaging research and teaching demonstrates a strong integration of theoretical and practical knowledge, making him a well-rounded and impactful scholar.

🏅 Awards and Honors

Dr. Mohd Usama has been recognized for his innovative interdisciplinary research contributions at the intersection of artificial intelligence and healthcare. He received prestigious academic scholarships for his doctoral studies in China and earned consistent recognition throughout his academic career. He has been invited to deliver expert lectures and guest talks on AI, deep learning, and statistical computing at various institutions. His role in international collaborative projects on ultrasound imaging and disease prediction further demonstrates his global impact. As a frequent reviewer for reputed journals and contributor to academic forums, he maintains high standards of scholarly excellence. These achievements, coupled with his dedication to knowledge dissemination and impactful research, position him as a strong candidate for the Best Researcher Award.

🔬 Research Focus

Dr. Mohd Usama’s research lies at the convergence of artificial intelligence, deep learning, and medical imaging. His work primarily involves the use of generative adversarial networks (GANs) to address domain adaptation, noise reduction, and feature interpolation in carotid ultrasound images. He develops AI-powered clinical decision support systems to enhance subclinical atherosclerosis risk prediction and ultrasound diagnostics. His doctoral research explored recurrent deep learning for text analysis in healthcare applications. He is also keenly interested in disease modeling, natural language processing, and sentiment analysis within clinical contexts. Dr. Usama’s work emphasizes real-world application of machine learning in healthcare, contributing to early diagnosis and precision medicine through robust, data-driven solutions, reinforcing his value as a research innovator.

📊 Publication Top Notes:

Usama, M., Nyman, E., Näslund, U., & Grönlund, C. (2025).
A domain adaptation model for carotid ultrasound: Image harmonization, noise reduction, and impact on cardiovascular risk markers.
Computers in Biology and Medicine.
https://doi.org/10.1016/j.compbiomed.2025.110030
Usama, M., & Grönlund, C. (2023).
Carotid Ultrasound Image Denoising Using Low-to-High Image Quality Domain Adaptation.
The Medical Technology Days (MTD), 2023, Stockholm.
Singh, A. P., Kumar, S., Kumar, A., & Usama, M. (2022).
Machine Learning based Intrusion Detection System for Minority Attacks Classification.
2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES).
https://doi.org/10.1109/cises54857.2022.9844381
Ahmad, B., Usama, M., Ahmad, T., Khatoon, S., & Alam, C. M. (2022).
An ensemble model of convolution and recurrent neural network for skin disease classification.
International Journal of Imaging Systems and Technology, 32(1), 15–24.
https://doi.org/10.1002/ima.22661
Ahmad, B., Usama, M., Huang, C. M., Hwang, K., Hossain, M. S., & Muhammad, G. (2020).
Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network.
IEEE Access, 8, 39098–39110.
https://doi.org/10.1109/ACCESS.2020.2975198
Qamar, S., Jin, H., Zheng, R., Ahmad, P., & Usama, M. (2020).
A variant form of 3D-UNet for infant brain segmentation.
Future Generation Computer Systems, 108, 618–628.
https://doi.org/10.1016/j.future.2019.11.021
Usama, M., Ahmad, B., Song, E., Hossain, M. S., Alrashoud, M., & Muhammad, G. (2020).
Attention-based sentiment analysis using convolutional and recurrent neural network.
Future Generation Computer Systems, 106, 336–347.
https://doi.org/10.1016/j.future.2020.07.022
Usama, M., Ahmad, B., Xiao, W., Hossain, M. S., & Muhammad, G. (2020).
Self-attention based recurrent convolutional neural network for disease prediction using healthcare data.
Computer Methods and Programs in Biomedicine, 187, 105191.
https://doi.org/10.1016/j.cmpb.2019.105191
Ahmad, P., Jin, H., Qamar, S., Zheng, R., Jiang, W., Ahmad, B., & Usama, M. (2019).
3D Dense Dilated Hierarchical Architecture for Brain Tumor Segmentation.
Proceedings of the 2019 4th International Conference on Big Data and Computing (ICBDC).
https://doi.org/10.1145/3335484.3335516
Ahmad, B., Usama, M., Lu, J., Xiao, W., Wan, J., & Yang, J. (2019).
Deep Convolutional Neural Network Using Triplet Loss to Distinguish the Identical Twins.
2019 IEEE Globecom Workshops (GC Wkshps).
https://doi.org/10.1109/GCWkshps45667.2019.9024704
Usama, M., Xiao, W., Ahmad, B., Wan, J., Hassan, M. M., & Alelaiwi, A. (2019).
Deep Learning Based Weighted Feature Fusion Approach for Sentiment Analysis.
IEEE Access, 7, 140361–140373.
https://doi.org/10.1109/ACCESS.2019.2940051
Usama, M., Ahmad, B., Yang, J., Qamar, S., Ahmad, P., Zhang, Y., Lv, J., & Guna, J. (2019).
Equipping recurrent neural network with CNN-style attention mechanisms for sentiment analysis of network reviews.
Computer Communications, 149, 111–121.
https://doi.org/10.1016/j.comcom.2019.08.002
Hao, Y., Usama, M., Yang, J., Hossain, M. S., & Ghoneim, A. (2019).
Recurrent convolutional neural network based multimodal disease risk prediction.
Future Generation Computer Systems, 98, 296–304.
https://doi.org/10.1016/j.future.2018.09.031

Shahrzad Falahat | Deep learning | Best Researcher Award

Posted on 05/05/202505/05/2025 by sciencefather

Dr. Shahrzad Falahat| Deep learning |Best Researcher Award

Lecturer at Shahid Bahonar University of Kerman,Iran.

Dr. Shahrzad Falahat is a visionary researcher specializing in computer vision and remote sensing with a proven track record in academic and industrial AI. Holding a Ph.D. in Computer Vision, she has led interdisciplinary AI projects for over five years, collaborating across sectors such as electrical, medical, and railway industries. Her innovations include software for fault detection in power lines, cutting electricity outages by 70%, and automatic cartography tools that significantly improve mapping efficiency. Dr. Falahat’s technical proficiency spans Python, PyTorch, TensorFlow, and embedded AI systems, making her a versatile leader in AI development. Her outstanding contributions, impactful publications, and real-world implementations make her an exceptional candidate for the Best Researcher Award.

🌍 Professional Profile:

Google scholar

Orcid

🏆 Suitability for the Best Researcher Award

Dr. Shahrzad Falahat exemplifies excellence in applied AI research, making her a highly suitable candidate for the Best Researcher Award. With a Ph.D. in Computer Vision and over five years of impactful industrial experience, she has led innovative projects that address critical real-world challenges. Her development of AI-powered fault detection software for power transmission lines reduced outages by 70%, while her automated cartography system cut map production time by 80%. She combines deep technical expertise in Python, PyTorch, TensorFlow, and embedded AI with strong project management and cross-sector collaboration. Her work integrates research and practice, resulting in scalable, intelligent solutions with tangible societal benefits, positioning her as a leader in the field of AI and computer vision.

🎓 Education

Dr. Shahrzad Falahat earned her Ph.D. in Computer Vision, focusing on advanced deep learning techniques and remote sensing applications. Her academic journey equipped her with a robust foundation in machine learning, optimization, and AI-driven image processing. Throughout her doctoral studies, she published influential research on automated systems in industrial and environmental monitoring. Her educational background is enriched by expertise in embedded systems, GPU computing, and multi-platform AI development. With a blend of theoretical insight and practical execution, Dr. Falahat continues to bridge academia and industry, pushing the frontiers of computer vision and applied AI technologies.

🏢 Work Experience

Dr. Shahrzad Falahat currently serves as a Researcher at Shahid Bahonar University of Kerman, where she leads AI projects across industries including energy, agriculture, and transportation. She has spearheaded projects that translated complex research into deployable AI solutions. Previously, she was a Medical Imaging Data Scientist at Azin Eye Surgery Center, developing real-time diagnostic systems for eye diseases. Her contributions extend to edge AI deployments using NVIDIA Jetson Nano and STM32 AI, and leading product management from conception to deployment. She excels in dataset design, stakeholder collaboration, and technical documentation. Dr. Falahat’s blend of academic depth and real-world implementation underscores her excellence in delivering innovative, scalable AI solutions.

🏅 Awards and Honors

Dr. Shahrzad Falahat’s contributions to AI-driven innovation have earned her recognition in both research and industrial domains. She has been honored for her work on reducing electricity outages through intelligent fault detection systems and for her impactful software tools that enhance mapping and diagnosis. Her projects have received institutional support, including collaborations with the Islamic Republic of Iran Railways and Azin Eye Surgery Center. She has been an invited presenter at several national workshops and conferences and is respected for her role in bridging AI research with industrial applications. Her consistent excellence in technical leadership, publication, and applied innovation positions her as a distinguished candidate for research excellence awards.

🔬 Research Focus

Dr. Falahat’s research centers on the intersection of computer vision, deep learning, and remote sensing. She develops intelligent systems for infrastructure monitoring, medical diagnostics, and geospatial mapping. Her key focus lies in real-time AI deployment, optimization of deep learning models, and embedded system integration. Notable projects include automatic fault detection in power lines and real-time eye disease detection using medical imaging, both demonstrating high accuracy and operational efficiency. She is also actively involved in railway infrastructure monitoring. Her work leverages edge computing, cloud AI platforms, and domain-specific datasets to deliver practical, scalable solutions. Dr. Falahat’s applied research addresses real-world challenges, making significant contributions to both technological advancement and societal needs.

📊 Publication Top Notes:

Maize tassel detection and counting using a Yolov5-based model
Cited by: 16
Author(s): S Falahat, A Karami
Year: 2023
Influence of thickness on the structural, optical and magnetic properties of bismuth ferrite thin films
Cited by: 15
Author(s): H Maleki, S Falahatnezhad, M Taraz
Year: 2018
Synthesis and study of structural, optical and magnetic properties of BiFeO3–ZnFe2O4 nanocomposites
Cited by: 9
Author(s): S Falahatnezhad, H Maleki
Year: 2018
Deep fusion of hyperspectral and LiDAR images using attention-based CNN
Cited by: 7
Author(s): S Falahatnejad, A Karami
Year: 2022
PTSRGAN: Power transmission lines single image super-resolution using a generative adversarial network
Cited by: 5
Author(s): S Falahatnejad, A Karami, H Nezamabadi-pour
Year: 2024
Influence of synthesis method on the structural, optical and magnetic properties of BiFeO3–ZnFe2O4 nanocomposites
Cited by: 5
Author(s): S Falahatnezhad, H Maleki, AM Badizi, M Noorzadeh
Year: 2019
A comparative study on predicting the characteristics of plasma activated water: artificial neural network (ANN) & support vector regression (SVR)
Cited by: 2
Author(s): S Karimian, S Falahat, ZE Bakhsh, MJG Rad, A Barkhordari
Year: 2024
A Spectral-Spatial Augmented Active Learning Method for Hyperspectral Image Classification
Cited by: 2
Author(s): S Falahatnejad, A Karami
Year: 2023
PTSRDet: End-to-End Super-Resolution and object-detection approach for small defect detection of power transmission lines
Cited by: 0
Author(s): S Falahatnejad, A Karami, H Nezamabadi-pour
Year: 2025
Building Footprint Segmentation Using the Modified YOLOv8 Model
Cited by: 0
Author(s): S Falahatnejad, A Karami, R Sharifirad, M Shirani, M Mehrabinejad, …
Year: 2024

Zhou Yang | Artificial Intelligence | Best Researcher Award

Posted on 21/04/202521/04/2025 by sciencefather

Mr. Zhou Yang| Artificial Intelligence
| Best Researcher Award

PhD Candidate at Fuzhou University , China .

Zhou Yang is a PhD candidate in Computer Science and Technology at Fuzhou University, specializing in artificial intelligence. With a robust academic foundation from Chongqing University of Technology and Chongqing University of Posts and Telecommunications, he ranks in the top 10% of his class throughout. Zhou has gained significant industry experience as an algorithm engineer and research intern at top tech companies like Sohu, Baidu, and Qihoo 360, where he focused on deep learning, recommendation systems, and natural language processing. His research contributions include publications in top-tier venues such as ACL, EMNLP, and IPM. Zhou’s work in empathetic dialogue systems and personalized recommendation demonstrates strong interdisciplinary innovation, making him a promising young talent in AI-driven intelligent systems.

🌍 Professional Profile:

Scopus

🏆 Suitability for the Best Researcher Award

Zhou Yang exemplifies the qualities of a top emerging researcher. His academic excellence, cutting-edge research in AI, and impactful industry experience align with the values of the Best Researcher Award. Zhou has published in top-tier conferences and journals like ACL, EMNLP, and IPM, showcasing his thought leadership in natural language processing and recommendation systems. His work on empathetic models, deep learning architectures, and real-world applications reflects technical depth and societal relevance. Beyond academia, his contributions at Sohu, Qihoo 360, and Baidu show a consistent record of applied innovation. With a blend of scholarly rigor, innovation, and collaborative spirit, Zhou is highly suited for this award as a next-generation research leader in artificial intelligence.

🎓 Education

Zhou Yang is pursuing a PhD in Computer Science and Technology at Fuzhou University, a “Double First-Class” institution, expected to graduate in 2025. He holds a Master’s degree in Computer System Structure from Chongqing University of Technology, completed in collaboration with the Chinese Academy of Sciences, where he was ranked in the top 10% of his cohort. During his undergraduate studies at Chongqing University of Posts and Telecommunications, he majored in Software Engineering and again stood in the top 10% academically. He has been recognized with prestigious honors such as the National Inspirational Scholarship and multiple awards for innovation, leadership, and academic excellence, setting a strong foundation for his advanced research in AI and computing systems.

🏢 Work Experience

Zhou Yang brings a wealth of applied research and industry experience. At Sohu’s Smart Media R&D Center, he led deep learning initiatives for search and recommendation, implementing scalable big data architectures. While at the Chinese Academy of Sciences, he worked on the MatchZoo framework, enhancing state-of-the-art text matching models like DRMM and KNRM. At Qihoo 360, he applied deep learning models like BERT in recommendation systems, while at Baidu, he contributed to the Totem Project, working on image query and data analytics using distributed systems. His consistent focus on high-impact AI applications bridges academic research and practical deployment, marking him as an innovator in applied machine learning, recommendation systems, and intelligent information retrieval.

🏅 Awards and Honors

Zhou Yang has received numerous accolades throughout his academic journey. As an undergraduate, he was awarded the National Inspirational Scholarship twice and earned recognition as an Outstanding Student Cadre and Three Good Student at Chongqing University of Posts and Telecommunications. His graduate work earned him a Best Paper Candidate award at CCIR for his contribution on deep relevance matching models. He has also achieved notable placements in national innovation and entrepreneurship competitions. Zhou’s research excellence continues with his publications in top-tier journals and conferences like ACL, EMNLP, and IPM. These honors highlight both his scholarly impact and leadership potential in artificial intelligence research and innovation.

🔬 Research Focus

Zhou Yang’s research focuses on artificial intelligence, particularly natural language processing, recommendation systems, and deep learning architectures. His recent work includes empathetic dialogue generation, emotional semantic correlation modeling, and sequential recommendation enhanced with side information. He is also engaged in research on associative memory models for empathetic responses and preference-driven denoising methods. Zhou has worked extensively with text matching models (e.g., DRMM, KNRM) and frameworks like MatchZoo, contributing to substantial performance improvements on key datasets. His interdisciplinary approach integrates reinforcement learning, big data, and neural networks to solve real-world problems in smart search and personalized systems, paving the way for more human-centric AI applications.

📊 Publication Top Notes:

Citation:

Zhu, X., & Yang, Z. (2025). A Preference-driven Conjugate Denoising Method for Sequential Recommendation with Side Information. Information Processing & Management, 62(2), 103997. ACM Digital Library

Authors:

Xiaofei Zhu
Zhou YangGoogle Sites

Publication Year:

2025

Journal:

Information Processing & Management

Volume and Issue:

Volume 62, Issue 2

Article Number:

103997ACM Digital Library

Title: An Iterative Associative Memory Model for Empathetic Response Generation

Authors: Zhou Yang, Zhaochun Ren, Wang Yufeng, Haizhou Sun, Chao Chen, Xiaofei Zhu, Xiangwen Liao

Published in: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)

Year: 2024

URL: https://aclanthology.org

Title : The second publication seems incomplete. However, based on the author list you shared:

Authors (Partial): Zhou Yang, Zhaochun Ren, Wang Yufeng, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Wu Yunbing, Yisong Su, Sibo Ju, Xiangwen Liao
Please provide the title of the second paper to finalize its full citation.

Elahe Karampour | Artificial Intelligence | Best Researcher Award

Posted on 07/04/202507/04/2025 by sciencefather

Ms. Elahe Karampour | Artificial Intelligence| Best Researcher Award

Ms. Elahe Karampour K.N.Toosi University Of Technology, Iran

Elahe Karampour is a dedicated researcher specializing in Geodesy and Geomatics Engineering with a focus on spatial-temporal data analysis and network science. She is currently pursuing her Master of Science at K.N. Toosi University of Technology, Tehran, Iran, with a thesis on analyzing location-based social networks using geometric curves. She has served as a research assistant, contributing to advancements in community detection and link prediction in spatial networks. Elahe is also an experienced educator, having taught GIS, spatial databases, and social network analysis. She has received national recognition for her academic excellence and was awarded a fully funded research visit to ScaDS.AI in Germany. Her expertise spans programming, spatial modeling, and AI-driven geospatial analytics.

🌍 Professional Profile:

Orcid

🏆 Suitability for the Best Researcher Award

Elahe Karampour is an outstanding researcher with significant contributions to geospatial data analysis, particularly in network science and location-based social networks. Her pioneering research integrates Ricci curvature and hyperbolic geometry for community detection and link prediction, leading to novel advancements in spatial-temporal modeling. She has published influential research in high-impact journals, demonstrating her expertise and innovation. Elahe’s research excellence is further recognized through a prestigious fully funded research visit to ScaDS.AI in Germany. With strong technical proficiency in R, Python, PostgreSQL, and GIS software, she bridges theory and application, developing intelligent recommender systems for urban planning. Her exceptional academic record, teaching excellence, and innovative research make her a prime candidate for the Best Researcher Award.

🎓 Education

Elahe Karampour holds a Master of Science in Geodesy and Geomatics Engineering from K.N. Toosi University of Technology, Tehran, Iran (2021–2024). Her research focuses on spatial-temporal data analysis and social network modeling, with a thesis titled “Analysis of Location-Based Social Networks with Geometric Curves,” receiving a perfect grade of 20/20. She completed her Bachelor of Science in Geodesy and Geomatics Engineering at the University of Zanjan, Iran (2015–2020). Elahe ranked 23rd nationwide in the Iranian Master’s University Entrance Exam, showcasing her academic excellence. Her strong analytical and technical skills, coupled with expertise in GIS, AI, and network analysis, enable her to make significant contributions to the field of geospatial research and urban data science.

🏢 Work Experience

Elahe Karampour has extensive research and teaching experience in geospatial data analysis and network science. As a Research Assistant (2022–2024) at K.N. Toosi University of Technology, she developed advanced models for community detection and link prediction in location-based social networks using geometric methods. She also worked as a Lecturer (2024) at Babol Noshirvani University of Technology, teaching spatial analysis and visualization to undergraduate students. Additionally, she served as a Teaching Assistant (2023–2024), guiding master’s and PhD students in GIS, social network analysis, and spatial databases. Her technical expertise in R, Python, PostgreSQL, and QGIS, combined with her ability to integrate AI with geospatial analysis, has positioned her as a leader in her research domain.

🏅 Awards and Honors

Elahe Karampour has received multiple accolades for her academic and research excellence. She ranked 23rd nationwide in the Iranian Master’s University Entrance Exam (2021), demonstrating her strong academic foundation. She was awarded a fully funded research grant for a short-term visit to ScaDS.AI Center for Scalable Data Analytics and Artificial Intelligence in Germany (2023), recognizing her contributions to AI-driven geospatial analysis. Additionally, she was listed among the top-ranked teachers by her students for her exceptional teaching performance. Her work in network science and geospatial modeling has led to publications in high-impact journals, further cementing her status as a leading researcher. These achievements underscore her dedication and outstanding contributions to the field of geospatial and network science.

🔬 Research Focus

Elahe Karampour’s research centers on spatial-temporal data analysis, network science, and AI-driven geospatial modeling. She specializes in analyzing location-based social networks using advanced mathematical frameworks such as Ricci curvature and hyperbolic geometry for community detection and link prediction. Her work integrates graph-based modeling with GIS technologies to enhance urban planning, mobility analysis, and personalized recommender systems. Elahe has developed innovative approaches to analyzing complex data structures, utilizing machine learning and AI techniques for geospatial applications. She is particularly interested in the intersection of mathematics, computer science, and geospatial technologies, aiming to create data-driven solutions for urban analytics and smart city development. Her research has been recognized internationally, reinforcing her expertise in geospatial data science.

📊 Publication Top Notes:

Karampour, E., Malek, M.R., & Eidi, M. (2025). Discrete Ricci flow: A powerful method for community detection in location-based social networks. Computers and Electrical Engineering, 123, 110302.