Alina Diana Zamfir | Biomedical and Healthcare Applications | Best Researcher Award

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Prof. Alina Diana Zamfir | Biomedical and Healthcare Applications | Best Researcher Award

Professor |  National Institute for R&D in Electrochemistry | Romania

Prof. Dr. Alina D. Zamfir is a leading Romanian scientist recognized internationally for her pioneering research in mass spectrometry, glycomics, proteomics, and structural biology. She currently holds dual appointments as Senior Scientific Researcher at the National Institute for Research and Development in Electrochemistry and Condensed Matter Research, Timisoara, and as Professor at Aurel Vlaicu University of Arad, Romania. she has also served as a PhD Supervisor at the Faculty of Physics, West University of Timisoara, mentoring numerous young scientists in advanced analytical methodologies.Prof. Zamfir’s research has made seminal contributions to the development of advanced mass spectrometry platforms, particularly in the integration of microfluidics, ion mobility, and nanoelectrospray systems for glycoproteomics and glycolipidomics. She has been the Principal Investigator of 17 national and international projects, funded by the Romanian UEFISCDI and the European Union, focusing on the structural and functional elucidation of complex biological molecules, including gangliosides and proteoglycans. Her collaborations extend across prestigious institutions such as the University of Münster, University of Konstanz (Germany), and Clarkson University (USA), contributing significantly to global biomedical research.Author of 198 peer-reviewed publications with over 3,018 citations and an h-index of 37, Prof. Zamfir’s work has appeared in high-impact journals including Analytical Chemistry, Electrophoresis, Glycobiology, and the Journal of the American Society for Mass Spectrometry. She has also served as Guest Editor for Molecules and Frontiers in Molecular Biosciences and as President of the Romanian Society for Mass Spectrometry since 2009, promoting the advancement of analytical sciences in Romania and beyond.Through her academic leadership, editorial contributions, and innovative research, Prof. Zamfir has profoundly influenced modern bioanalytical chemistry, driving forward applications in biomarker discovery, neurodegenerative disease research, and precision medicine. Her work bridges fundamental science and societal impact, advancing both Romania’s scientific excellence and the global progress of molecular biosciences.

Profiles: Google Scholar | ResearchGate

Featured Publications

1, Zamfir, A. D. (2007). Recent advances in sheathless interfacing of capillary electrophoresis and electrospray ionization mass spectrometry. Journal of Chromatography A, 1159(1–2), 2–13. Cited By : 117

2. Zamfir, A., Vakhrushev, S., Sterling, A., Niebel, H. J., Allen, M., & Peter-Katalinić, J. (2004). Fully automated chip-based mass spectrometry for complex carbohydrate system analysis. Analytical Chemistry, 76(7), 2046–2054.
Cited By : 94

3. Sarbu, M., Robu, A. C., Ghiulai, R. M., Vukelić, Z., Clemmer, D. E., & Zamfir, A. D. (2016). Electrospray ionization ion mobility mass spectrometry of human brain gangliosides. Analytical Chemistry, 88(10), 5166–5178. Cited By : 81

4. Sisu, E., Flangea, C., Serb, A., & Zamfir, A. D. (2011). Modern developments in mass spectrometry of chondroitin and dermatan sulfate glycosaminoglycans. Amino Acids, 41(2), 235–256. Cited By : 68

5. Zamfir, A. D., Bindila, L., Lion, N., Allen, M., Girault, H. H., & Peter-Katalinić, J. (2005). Chip electrospray mass spectrometry for carbohydrate analysis. Electrophoresis, 26(19), 3650–3673.* Cited By : 66

Shakil Hossain | Multi-Modal and Cross-Modal Vision | Young Scientist Award

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Mr. Md. Shakil Hossain | Multi-Modal and Cross-Modal Vision | Young Scientist Award

Research Assistant | Bangladesh University of Business and Technology | Bangladesh 

Md. Shakil Hossain is an emerging researcher and academic specializing in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), and Multimodal Learning. He currently serves as a Research Assistant at the Advanced Machine Intelligence Research (AMIR) Lab, where his work focuses on hybrid deep learning architectures, large language models (LLMs), and multimodal fusion systems for real-world AI applications. His research aims to bridge the gap between intelligent computation and societal needs, with contributions spanning sentiment analysis, mental health assessment, and cross-lingual text processing.Before joining AMIR Lab, he worked as a Market Research Analyst at Gram Ltd., where he conducted in-depth market and competitive analyses to support the launch of Dhopa Elo, an innovative startup product revolutionizing laundry services. He also utilized data analytics, customer segmentation, and ROI optimization to strengthen marketing strategies and business performance.Md. Hossain received a research grant from the Bangladesh University of Business and Technology (BUBT) for his project, Smart Agro-Monitor: IoT-Based Precision Farming for Enhanced Crop Management.” This initiative leveraged IoT and AI to improve water management, pest control, and crop health monitoring, empowering farmers with data-driven insights for sustainable agriculture.He has authored and co-authored 16 research papers in leading journals and conferences such as Scientific Reports, IEEE Access, Knowledge-Based Systems, and Neural Networks. His publications have collectively received 31 citations, with an h-index of 3 and an i10-index of 1, reflecting his growing academic impact. Collaborating with renowned scholars including Prof. Dr. A. B. M. Shawkat Ali, Md. Hossain continues to pursue interdisciplinary AI research that promotes innovation, ethics, and societal advancement through intelligent technologies.

Profiles: Google Scholar | ORCID  | Scopus

Featured Publications

1.Hossain, M. M., Hossain, M. S., Mridha, M. F., Safran, M., & Alfarhood, S. (2025). Multi-task opinion enhanced hybrid BERT model for mental health analysis.  Cited By: 13

2.Hossain, M. M., Hossain, M. S., Hossain, M. S., Mridha, M. F., & Safran, M. (2024). TransNet: Deep attentional hybrid transformer for Arabic posts classification. IEEE Access. Cited By: 7

3.Hossain, M. M., Hossain, M. S., Safran, M., Alfarhood, S., & Alfarhood, M. (2024). A hybrid attention-based transformer model for Arabic news classification using text embedding and deep learning. IEEE Access. Cited By: 6

4.Hossain, M. M., Hossain, M. S., Chaki, S., Hossain, M. R., Rahman, M. S., & Ali, A. B. M. (2025). CrosGrpsABS: Cross-attention over syntactic and semantic graphs for aspect-based sentiment analysis in a low-resource language.  Cited By: 2

5.Hossain, M. S., Hossain, M. M., Hossain, M. S., Mridha, M. F., & Safran, M. (2025). EmoNet: Deep attentional recurrent CNN for X (formerly Twitter) emotion classification. IEEE Access. Cited By: 2

Md. Shakil Hossain’s research advances the integration of AI, NLP, and IoT to solve real-world problems in healthcare, agriculture, and digital communication. His work promotes human-centered, sustainable, and data-driven innovation, empowering industries and societies to harness intelligent technologies for global progress.

Nagaraj | Deep Learning for Computer Vision | Excellence in Research

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Dr. P. Nagaraj | Deep Learning for Computer Vision | Excellence in Research

Associate Professor | SRM Institute of Science and Technology  | India 

Dr. P. Nagaraj is an esteemed Associate Professor at the SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India. With research expertise spanning Artificial Intelligence, Data Science, Data Analytics, Machine Learning, and Recommender Systems, he has made substantial contributions to intelligent computing and healthcare analytics. His innovative work focuses on applying deep learning, fuzzy inference, and explainable AI (XAI) techniques to real-world challenges in medical diagnosis, cybersecurity, and sustainable automation.Dr. Nagaraj has an impressive research portfolio, with over 208 indexed publications, 2,736 citations, and an h-index of 32, reflecting the global relevance and scholarly influence of his work. His notable publications include advancements in diabetes prediction, brain tumor classification, Alzheimer’s disease analysis, and cyberattack detection using AI-driven frameworks. His studies on distributed denial-of-service (DDoS) detection, IoT-based healthcare systems, and intelligent recommendation models have been widely cited and applied across multiple interdisciplinary domains.In recognition of his outstanding research, Dr. Nagaraj has been consecutively listed among the World’s Top 2% Scientists (2023–2025), highlighting his sustained impact in computer science and data-driven innovation. He is also a two-time recipient of the prestigious India AI Fellowship (Ministry of Electronics and Information Technology, MeitY), each worth ₹1 Lakh, for his pioneering projects titled AgriTech of Next-Gen Automation for Sustainable Crop Production and A Deep Learning Approach to Improve Pulmonary Cancer Diagnosis Using CNN.Through collaborations with national and international scholars, Dr. Nagaraj continues to advance the frontier of intelligent data analytics for societal benefit. His research contributes significantly to sustainable digital transformation, healthcare improvement, and agricultural innovation, positioning him as a leading figure in India’s AI research landscape and a global advocate for technology-driven social progress.

Profiles: Google Scholar | ORCID  | Scopus

Featured Publications

1.Sudar, K. M., Beulah, M., Deepalakshmi, P., Nagaraj, P., & Chinnasamy, P. (2021). Detection of distributed denial of service attacks in SDN using machine learning techniques. In Proceedings of the 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1–6). IEEE. Cited By : 158

2.Nagaraj, P., & Deepalakshmi, P. (2022). An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis. International Journal of Imaging Systems and Technology, 32(4), 1373–1396. Cited By : 100

3.Nagaraj, P., Muneeswaran, V., Reddy, L. V., Upendra, P., & Reddy, M. V. V. (2020). Programmed multi-classification of brain tumor images using deep neural network. In Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1–6). IEEE. Cited By : 85

4.Nagaraj, P., Deepalakshmi, P., & Romany, F. M. (2021). Artificial flora algorithm-based feature selection with gradient boosted tree model for diabetes classification. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 14, 2789–2802. Cited By : 79

.5.Nagaraj, P., & Deepalakshmi, P. (2020). A framework for e-healthcare management service using recommender system. Electronic Government, an International Journal, 16(1–2), 84–100. Cited By : 70

Dr. P. Nagaraj’s research advances global innovation by integrating artificial intelligence and data analytics to address critical challenges in healthcare, agriculture, and cybersecurity. His vision is to harness intelligent automation and explainable AI to create sustainable, data-driven solutions that enhance human well-being, industrial efficiency, and societal resilience.

Catalin Dumitrescu | Biometrics and Security | Best Researcher Award

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Prof. Catalin Dumitrescu | Biometrics and Security | Best Researcher Award

Prof. Habil. Artificial Intelligence | University Politehnica of Bucharest | Romania

Assoc. Prof. Dr. Catalin Dumitrescu is a distinguished researcher and academic specializing in Artificial Intelligence (AI), Digital Signal Processing (DSP), and Machine Learning (ML) with a strong interdisciplinary focus on computer vision, cognitive radio, cyber defence, and multimedia security. His research integrates advanced AI algorithms into industrial electronics, telecommunications, and defence technologies, with a particular emphasis on IMINT/SIGINT systems and cyber defence infrastructures.With an impressive research portfolio comprising over 50 scientific publications, his work has garnered 536 citations, an h-index of 10, and i10-index of 15, reflecting his growing influence in the fields of intelligent systems and adaptive signal processing. Dr. Dumitrescu’s publications in leading journals such as Sensors, Electronics, Applied Sciences, and Fractal and Fractional (MDPI) highlight his expertise in deep learning, visual classification, object detection, and decision-making algorithms. His recent studies focus on AI-driven noise reduction, fractal-based steganography for data security, and UAV detection systems using sensor data fusion and fuzzy logic.His research interests span a wide spectrum, including neural networks for image and audio processing, machine learning-based EEG signal classification, brain-computer interfaces, digital watermarking and cryptography, and real-time signal and image analysis. Through collaborations with academia and industry, he has contributed to the development of automated, intelligent systems for security, communication, and transportation applications, bridging theoretical innovation with practical deployment.Dr. Dumitrescu’s commitment to advancing AI and DSP research extends to mentoring and consultancy, where he collaborates with organizations across industrial electronics, telecommunication, and defence sectors. His work has had a significant societal impact in enhancing the reliability, efficiency, and security of next-generation digital systems. His contributions continue to shape the global discourse on intelligent signal processing, autonomous systems, and secure information technologies.

Profiles: Google Scholar | ORCID 

Featured Publications

Xiangfu Kong | BigData and LargescaleVision | Best Researcher Award

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Dr. Xiangfu Kong | BigData and LargescaleVision | Best Researcher Award

Assistant Researcher | Zhejiang Lab | China

Dr. Xiangfu Kong is a distinguished researcher at Zhejiang Lab, specializing in intelligent transportation systems (ITS), spatiotemporal data analytics, and urban mobility optimization. His work bridges computer science, artificial intelligence, and transportation engineering to develop data-driven models that enhance mobility efficiency safety, and sustainability in smart cities.With an Publications 6  h-index of 3, and 67 citations across recognized publications, Dr. Kong has made notable scholarly contributions to the field. He has published six peer-reviewed research articles, including influential works such as “Measuring Traffic Congestion with Taxi GPS Data and Travel Time Index and  A Scenario-Based Map-Matching Algorithm for Complex Urban Road Networks. His recent studies explore flood risk mapping, travel time reliability, and natural language processing for urban data interpretation, showcasing his interdisciplinary expertise.Dr. Kong’s research projects often involve large-scale real-world data, particularly GPS-based urban mobility and hydrological data, integrating AI algorithms and Bayesian frameworks to model and predict transportation dynamics under diverse conditions. His studies have direct implications for urban policy-making, disaster management, and infrastructure resilience.He has actively collaborated with industry and academic partners to design computational models that assist in traffic monitoring, path planning, and flood management, contributing to sustainable urban development initiatives. Dr. Kong’s innovative use of AI for understanding urban systems highlights his dedication to applying research outcomes to societal benefit.In addition to his publications, Dr. Kong contributes to the broader scientific community through editorial and peer-review roles in transportation and data science journals. His ongoing work in data-driven transportation intelligence and urban informatics positions him as a promising researcher contributing to the next generation of smart mobility systems.Through his research excellence and cross-disciplinary collaborations, Dr. Xiangfu Kong continues to push the boundaries of how AI and data analytics can transform urban transportation, improve public safety, and drive global sustainability efforts.

Profiles: Google Scholar | ORCID | Scopus 

Featured Publications

1. Kong, X., Yang, J., & Yang, Z. (2015). Measuring traffic congestion with taxi GPS data and travel time index. Proceedings of the CICTP 2015, 3751–3762. Cited By : 35

2. Kong, X., & Yang, J. (2019). A scenario-based map-matching algorithm for complex urban road network. Journal of Intelligent Transportation Systems, 23(6), 617–631.
Cited By : 19

3. Kong, X., Yang, J., Qiu, J., Zhang, Q., Chen, X., Wang, M., & Jiang, S. (2022). Post‐event flood mapping for road networks using taxi GPS data. Journal of Flood Risk Management,  Cited By : 8

4. Xiangfu, K., Bo, D., Xu, K., & Yongliang, T. (2023). Text classification model for livelihood issues based on BERT: A study based on hotline compliant data of Zhejiang province. Acta Scientiarum Naturalium Universitatis Pekinensis, 59(3), 456–466. Cited By : 3

5. Kong, X., & Yang, J. (2016). Path planning with information on travel time reliability. Proceedings of the CICTP 2016, 99–107. Cited By :  2

6. Kong, X., Yang, J., Xu, K., Dong, B., & Jiang, S. (2023). A Bayesian updating framework for calibrating hydrological parameters of road network using taxi GPS data. Hydrology and Earth System Sciences Discussions, 1–25.

Dr. Xiangfu Kong nresearch advances data-driven intelligent transportation and urban informatics, fostering safer, more efficient, and sustainable mobility systems. His innovative integration of AI, GPS analytics, and hydrological modeling contributes to scientific progress, climate-resilient infrastructure, and smart city innovation with lasting global impact.

Simy Baby | Applications of Computer Vision | Best Researcher Award

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Mrs. Simy Baby | Applications of Computer Vision | Best Researcher Award

Researcher | National Institute of Technology | India

Mrs. Simy Baby is a pioneering researcher at the National Institute of Technology, Tiruchirappalli, with extensive expertise in machine learning, semantic communication, computer vision, and mmWave radar signal processing. Her research bridges the gap between radar sensing and intelligent communication frameworks, focusing on efficient feature extraction, complex-valued encoding, and task-oriented inference.Her seminal work, “Complex Chromatic Imaging for Enhanced Radar Face Recognition” (Computers and Electrical Engineering,  introduced a novel representation that preserves amplitude and phase information of mmWave radar signals, achieving an exceptional recognition accuracy. Another significant contribution, “Complex-Valued Linear Discriminant Analysis on mmWave Radar Face Signatures for Task-Oriented Semantic Communication” (IEEE Transactions on Cognitive Communications and Networking ), proposed a CLDA-based encoding framework enhancing feature interpretability and robustness under channel variations. Current investigations include Data Fusion Discriminant Analysis (DFDA) for multi-view activity recognition and Semantic Gaussian Process Regression (GPR) for vehicular pose estimation, highlighting her commitment to multitask semantic communication systems.Dr. Baby has 21 publications with 20 citations and an h-index of 3.  demonstrating a rapidly growing impact in her field. She is an active member of the Indian Society for Technical Education (ISTE) and contributes to the scientific community through innovative research that combines theory and practical applications. Her work on radar-based recognition, semantic feature transmission, and multi-task inference frameworks holds significant potential for intelligent transportation systems, human activity recognition, and bandwidth-efficient communication technologies.Through her research, Dr. Baby has established herself as a leading figure in advancing radar imaging and semantic communication, providing scalable solutions that merge high-performance computing with real-world societal applications. Her vision continues to shape the future of intelligent sensing and communication systems globally.

Profiles: Google Scholar | ORCID | Scopus 

Featured Publications

1. Ansal, K. A., Rajan, C. S., Ragamalika, C. S., & Baby, S. M. (2022). A CPW fed monopole antenna for UWB/Ku band applications. Materials Today: Proceedings, 51, 585–590. Cited By : 5

2. Ansal, K. A., Ragamalika, C. S., Rajan, C. S., & Baby, S. M. (2022). A novel ACS fed antenna with comb shaped radiating strip for triple band applications. Materials Today: Proceedings, 51, 332–338. Cited By : 4

3. Ansal, K. A., Kumar, A. S., & Baby, S. M. (2021). Comparative analysis of CPW fed antenna with different substrate material with varying thickness. Materials Today: Proceedings, 37, 257–264. Cited By : 4

4. Baby, S. M., & Gopi, E. S. (2025). Complex chromatic imaging for enhanced radar face recognition. Computers and Electrical Engineering, 123, 110198. Cited By : 3

5.Ansal, K. A., Shanmuganatham, T., Baby, S. M., & Joy, A. (2015). Slot coupled microstrip antenna for C and X band application. International Journal of Advanced Research Trends in Engineering and Technology.Cited By : 3

Dr. Simy M. Baby’s research advances the integration of semantic communication and computer vision, enabling high-accuracy radar-based recognition and task-oriented inference. Her work has significant implications for intelligent transportation, human activity monitoring, and bandwidth-efficient communication, driving innovation in both science and industry globally.

Venkataraman Thangadurai | 3D Computer Vision | Best Researcher Award

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Prof. Dr. Venkataraman Thangadurai | 3D Computer Vision | Best Researcher Award

Professor | University of St Andrews | United Kingdom

Prof. Dr. Venkataraman Thangadurai is a globally renowned expert in solid-state chemistry, electrochemical energy storage, and advanced battery technologies. With a research focus on fast ion conductors, solid electrolytes, lithium- and sodium-based batteries, and fuel cell materials, he has made pioneering contributions to both fundamental science and practical energy solutions. Prof. Thangadurai has authored over 278 peer-reviewed journal articles, 6 book chapters, and 21 conference proceedings, and has delivered 180 conference presentations, 83 posters, and 80 invited talks at top universities, institutes, and companies worldwide. His work has resulted in 13 patents/patent applications and has placed him among the top 1% of authors in Royal Society of Chemistry journals by citations in 2020. As of March 2025, his research has received 25,991 citations with an h-index of 69, reflecting the high impact of his work globally.He is the Founder and Advisor of Ions Storage Systems, Maryland, USA (2012–present) and Founder and Director of Superionics, Calgary, Canada (2021–present), translating cutting-edge research into commercial energy storage technologies. His research highlights include optimizing lithium nucleation overpotentials in garnet-based hybrid solid-state batteries, developing doped sodium gadolinium silicate ceramics for fast Na⁺ conduction, and enhancing electrocatalysts for lithium–sulfur batteries.Prof. Thangadurai collaborates extensively with leading international researchers and institutions, including the University of Calgary, University of Maryland, University of St Andrews, University of Kiel, Yale University, and ANSTO, advancing cross-disciplinary solutions in energy materials. Beyond his scientific contributions, he mentors emerging scientists and actively promotes innovation that addresses global energy challenges. His work has significant societal impact, enabling safer, high-performance, and sustainable energy storage solutions critical for electric mobility, grid storage, and renewable energy integration.

Profiles: Google Scholar | ORCID | Scopus

Featured Publications

1. Murugan, R., Thangadurai, V., & Weppner, W. (2007). Fast lithium ion conduction in garnet-type Li₇La₃Zr₂O₁₂. Angewandte Chemie International Edition, 46(41), 7778–7781.
Cited By : 3691

2.Han, X., Gong, Y., Fu, K., He, X., Hitz, G. T., Dai, J., Pearse, A., Liu, B., Wang, H., … Thangadurai, V. (2017). Negating interfacial impedance in garnet-based solid-state Li metal batteries. Nature Materials, 16(5), 572–579. Cited By : 2088

3.Thangadurai, V., Narayanan, S., & Pinzaru, D. (2014). Garnet-type solid-state fast Li ion conductors for Li batteries: Critical review. Chemical Society Reviews, 43(13), 4714–4727. Cited By : 1712

4.Pal, B., Yang, S., Ramesh, S., Thangadurai, V., & Jose, R. (2019). Electrolyte selection for supercapacitive devices: A critical review. Nanoscale Advances, 1(10), 3807–3835.
Cited By : 1229

5.Wang, C., Fu, K., Kammampata, S. P., McOwen, D. W., Samson, A. J., Zhang, L., … Thangadurai, V. (2020). Garnet-type solid-state electrolytes: Materials, interfaces, and batteries. Chemical Reviews, 120(10), 4257–4300. Cited By : 1130

Prof. Dr. Venkataraman Thangadurai  pioneering research in solid-state chemistry and advanced battery technologies drives global innovation in energy storage, enabling safer, high-performance, and sustainable batteries that power electric mobility, renewable energy integration, and next-generation clean technologies.

Rachid Aliradi | Computer Vision for Robotics and Autonomous Systems | Best Researcher Award

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Assoc. Prof. Dr. Rachid Aliradi | Computer Vision for Robotics and Autonomous Systems | Best Researcher Award

Associate Professor | University of Louisville | Algeria

Dr. Rachid Aliradi is an accomplished researcher and academic with extensive expertise in computer vision, pattern recognition, biometrics, and machine and deep learning. Over a career spanning more than 20 years, Dr. Aliradi has significantly contributed to both foundational research and applied projects in image and multimedia analysis, 2D and 3D face recognition, kinship verification, crowd analysis, gesture analysis, and the development of efficient tensor analysis methods.Dr. Aliradi has held key academic and research positions, including Associate Professor at CERIST, Algeria (2011–2025), and Visiting Researcher roles at the University of Montreal, Canada (2015), and the University of Louisville, USA (2017–2018). He also served as a PhD Assistant Professor and contributed to the instruction and development of advanced courses ranging from algorithmics and object-oriented programming to digital communication and image analysis across undergraduate and graduate levels.His research portfolio includes numerous high-impact projects such as facial recognition for early cancer detection, multimedia security, biometric systems, and crowd behavior analysis, as well as ongoing work in optimizing cardiac diagnostics. Dr. Aliradi has collaborated with international researchers, including Prof. Abdelmalik Ouamane, Prof. Adel Elmaghraby, and Dr. Youssef Amghar, among others, reflecting a strong record of global academic partnerships.Dr. Aliradi has authored over 50 publications, with 84 citations and an h-index of 5, in leading journals and conferences including Multimedia Tools and Applications, IEEE/WIC/ACM International Conferences, and Preprints.org. Notable contributions include the development of DIEDA for face and kinship verification, TXQEDA tensor-based methods for facial pain detection, and novel descriptors for image analysis and semantic multimedia indexing.Beyond research, Dr. Aliradi’s work has had tangible societal impact, particularly in healthcare and security, by advancing early disease detection, improving biometrics, and enhancing automated image-based analysis systems. His ongoing dedication to scientific innovation, education, and cross-border collaboration positions him as a leading expert in the fields of computer vision and intelligent systems.

Profile: Google Scholar

Featured Publications

1.Aliradi, R., Belkhir, A., Ouamane, A., & Elmaghraby, A. S. (2018). DIEDA: Discriminative information based on exponential discriminant analysis combined with local features representation for face and kinship verification. Multimedia Tools and Applications, 1–18. Cited By : 23

2.Aliradi, R., Bouzera, N., Meziane, A., & Belkhir, A. (2013). Detection of facial components based on SVM classification and invariant feature. In 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (pp. xx–xx). IEEE.Cited By : 16

3.Aliradi, R., & Ouamane, A. (2024). A novel descriptor (LGBQ) based on Gabor filters. Multimedia Tools and Applications, 83(4), 11669–11686. Cited By : 12

4.Amrane, A., Mellah, H., Aliradi, R., & Amghar, Y. (2014). Semantic indexing of multimedia content using textual and visual information. International Journal of Advanced Media and Communication, 5(2–3), 182–194. Cited By : 10

5.Aliradi, R., Belkhir, A., Ouamane, A., Aliane, H., Sellam, A., & Amrane, A. (2018). Face and kinship image based on combination descriptors-DIEDA for large scale features. In 21st Saudi Computer Society National Computer Conference (NCC) (pp. 1–6).Cited By : 7

Dr. Aliradi’s research advances intelligent image and video analysis, enabling breakthroughs in biometrics, healthcare, and multimedia security. His innovative methods for facial recognition, kinship verification, and crowd analysis directly enhance societal safety, early disease detection, and global technological innovation

YuanYuan Ma | Image Steganography and Steganalysis | Best Researcher Award

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Assoc. Prof. Dr. YuanYuan Ma | Image Steganography and Steganalysis | Best Researcher Award

Teaching Secretary | Henan Normal University | China

Assoc. Prof. Dr. Ma Yuanyuan is an accomplished Associate Professor and Master’s Supervisor at the School of Computer and Information Engineering, Henan Normal University, Xinxiang, China. Holding a Ph.D. in Computer Science, she has established herself as a leading researcher in network and information security, granular computing, data mining, image steganalysis, and rough set theory. Her scholarly pursuits lie at the intersection of artificial intelligence and digital forensics, with a focus on developing intelligent and secure data analysis frameworks to safeguard multimedia and information systems.With a distinguished academic record, Dr. Ma has authored 50 research papers indexed in Scopus, which have garnered 535 citations from 474 scholarly documents, resulting in an h-index of 13. Her extensive research collaborations span 94 co-authors, reflecting her strong interdisciplinary engagement and contribution to collective scientific advancement. Her works have been featured in several high-impact journals, including IEEE Transactions on Information Forensics and Security, Scientific Reports, IET Image Processing, and Chinese Journal of Computers. Notable recent publications include “An Image Robust Batch Steganography Framework with Minimum Embedding Signs” (IEEE TIFS, 2025) and “LGS-Net: A Lightweight Convolutional Neural Network Based on Global Feature Capture for Spatial Image Steganalysis” (IET Image Processing, 2025).Dr. Ma has successfully led and contributed to national and provincial-level research projects, including those funded by the National Natural Science Foundation of China (NSFC) and the Henan Provincial Natural Science Foundation. Her work has been recognized with several prestigious awards, such as the First Prize of the Henan Provincial Natural Science Award and the ACM Zhengzhou Rising Star Award, underscoring her academic excellence and leadership in research innovation.Beyond academia, Dr. Ma’s research contributes significantly to societal information security, enhancing digital privacy protection, secure communication, and data integrity in an era of growing cyber threats. Through her pioneering contributions, she continues to advance the frontiers of intelligent computing and cybersecurity, inspiring innovation and collaboration across the global research community.

Profiles: Scopus

Featured Publications

1. Ma, Y., (2025). Digital image steganalysis network strengthening framework based on evolutionary algorithm. Scientific Reports.

2.Ma, Y., (2025). Siamese network and cross-attention for spatial and JPEG image steganalysis. Jisuanji Xuebao (Chinese Journal of Computers).

3.Ma, Y.,  (2025). An active defense method for image covert communication based on S&P and Rec-Net. Jisuanji Xuebao (Chinese Journal of Computers).

4.Ma, Y.,  (2025). LGS-Net: A lightweight convolutional neural network based on global feature capture for spatial image steganalysis. IET Image Processing. Cited By : 1

5.Ma, Y.,  (2025). An image robust batch steganography framework with minimum embedding signs. IEEE Transactions on Information Forensics and Security.

Assoc. Prof. Dr. Ma Yuanyuan pioneering research in network security, data mining, and image steganalysis enhances global digital trust and information protection, empowering secure communication and intelligent data analysis. Her innovations bridge scientific discovery and real-world cybersecurity, fostering technological resilience and societal safety in the digital age.

Felix Lankester | Face Recognition and Analysis | Research Impact Award

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Prof. Dr. Felix Lankester | Face Recognition and Analysis | Research Impact Award

Professor | Washington State University | United Kingdom

Dr Felix Lankester is an accomplished veterinary scientist with extensive experience in global health, wildlife conservation, and zoonotic disease research. He earned his PhD from the University of Glasgow, where his research focused on the impact and control of malignant catarrhal fever in Tanzania. He also holds an MSc in Wild Animal Health from the University of London and a Bachelor of Veterinary Science from the University of Liverpool. Dr Lankester serves as a Clinical Associate Professor at the Paul G. Allen School for Global Health, Washington State University, and previously worked as Director of Tanzanian Programs at the Lincoln Park Zoological Society and Country Director for the Pandrillus Foundation in Cameroon. His professional journey also includes roles as Project Director and Head Veterinarian at the Limbe Wildlife Centre, wildlife consultant in Kenya, and veterinary surgeon in the UK and Borneo. His research interests focus on zoonotic disease transmission, particularly rabies and other infectious diseases affecting marginalized communities in East Africa, as well as emerging pathogens with pandemic potential through his leadership in the DEEP VZN project. Dr Lankester has received recognition for his contributions to One Health, disease control, and wildlife health education. His research skills encompass field epidemiology, infectious disease modeling, surveillance design, and interdisciplinary collaboration across human and animal health systems. He continues to mentor young researchers and contribute to the scientific community through publications and international teaching engagements. His work has achieved 2,497 citations by 72 documents and an h-index of 25.

Profiles: Scopus | ORCID

Featured Publications

1.Kibona, T., Buza, J., Shirima, G., Lankester, F., Ngongolo, K., Hughes, E., Cleaveland, S., & Allan, K. J. (2022). The prevalence and determinants of Taenia multiceps infection (cerebral coenurosis) in small ruminants in Africa: A systematic review. Parasitologia.

2.Lankester, F., Kibona, T. J., Allan, K. J., de Glanville, W., Buza, J. J., Katzer, F., Halliday, J. E., Mmbaga, B. T., Wheelhouse, N., Innes, E. A., et al. (2024). Livestock abortion surveillance in Tanzania reveals disease priorities and importance of timely collection of vaginal swab samples for attribution. eLife.

3.Lankester, F., Lugelo, A., Changalucha, J., Anderson, D., Duamor, C. T., Czupryna, A., Lushasi, K., Ferguson, E., Swai, E. S., Nonga, H., et al. (2024). A randomized controlled trial of the effectiveness of a community-based rabies vaccination strategy. Preprint.

4.Kibona, T., Buza, J., Shirima, G., Lankester, F., Nzalawahe, J., Lukambagire, A.-H., Kreppel, K., Hughes, E., Allan, K. J., & Cleaveland, S. (2022). Taenia multiceps in northern Tanzania: An important but preventable disease problem in pastoral and agropastoral farming systems. Parasitologia.

5.Lugelo, A., Hampson, K., Ferguson, E. A., Czupryna, A., Bigambo, M., Duamor, C. T., Kazwala, R., Johnson, P. C. D., & Lankester, F. (2022). Development of dog vaccination strategies to maintain herd immunity against rabies. Viruses.