Invited Speakers
2025 10th International Conference on Intelligent Information Technology (ICIIT 2025) aims to gather professors, researchers, scholars and industrial pioneers all over the world. ICIIT is the premier forum for the presentation and exchange of past experiences and new advances and research results in the field of theoretical and industrial experience. The conference welcomes contributions which promote the exchange of ideas and rational discourse between educators and researchers all over the world. We aim to building an idea-trading platform for the purpose of encouraging researcher participating in this event. ICIIT 2025 is welcome qualified persons to delivery a speech in the related fields. If you are interested, please send a brief CV with photo to the conference email box: iciit@cbees.net.
Prof. Dimiter Velev
University of National and World Economy (UNWE), Bulgaria
Prof. Dr. Dimiter Velev is with the Department of Informatics at the University of National and World Economy (UNWE), Bulgaria, and the Director of the Science Research Center for Disaster Risk Reduction at UNWE. He holds a M.Sc. degree in Electro-Engineering from the Sofia Technical University, Bulgaria and a Ph.D. degree in Computer Complexes, Systems and Networks from the Pukhov Institute for Modelling in Energy Engineering at the National Academy of Sciences of Ukraine. Prof. Velev is a member of the International Federation for Information Processing (IFIP), in which he is the Chair of Technical Committee #5 – Information Technology Applications, and the Chair of the IFIP Domain Committee on Quantum Computing. His main areas of academic interest are ICT, Information Systems for Disaster Management, AI, Cybersecurity, VR, Quantum Computing. Prof. Velev is a regular speaker at conferences in Asia and Europe and a reviewer of scientific publications. He has published more than 250 ICT-related papers.
Speech Title:“Sustainable Business Transformation through Advanced Information Governance”
Abstract: The sustainable business transformation is becoming a key element for the long-term success of organizations in modern digital age. Such a transformation through advanced information management addresses the questions of how effective data and information management can support the achievement of sustainability goals. By implementing advanced information management frameworks, businesses can optimize resource management, reduce waste and increase operational efficiency while complying with regulatory requirements and data protection standards. The integration of sustainability into information management enables organizations to make data-driven decisions that are aligned with environmental, social and corporate governance criteria. This improves transparency, accountability and encourages innovation across businesses. At the same time, advanced information governance helps reduce the risks associated with cyber-attacks and non-compliance with regulations that can compromise sustainability efforts. The speech explores how companies and organizations can use modern information management tools to achieve sustainable transformation and create more successful and responsible businesses.
Dr. Jiaxin Cai
Xiamen University of Technology, China
Jiaxin Cai received his Ph.D. degree in Information and Computation Science from Sun Yat-Sen University in 2014. He also received his M.S. degree and B.Sc. degree in Bio-medical Engineering from Southern Medical University in 2011 and 2008 respectively. Currently, he is an associate professor in the School of Mathematics and Statistics at Xiamen University of Technology. He has authored over 38 peer-reviewed papers at academic journals and conferences. His current research interests include machine learning, computer vision and bio-medical engineering.
Speech Title:“Later Temporal Attention in Computer Aided Medical Diagnosis”
Abstract: The clinical course of COVID-19, as well as the immunological reaction, is notable for its extreme variability. Identifying the main associated factors might help understand the disease progression and physiological status of COVID-19 patients. The dynamic changes of the antibody against Spike protein are crucial for understanding the immune response. This work explores a temporal attention (TA) mechanism of deep learning to predict COVID-19 disease severity, clinical outcomes, and Spike antibody levels by screening serological indicators over time. We use feature selection techniques to filter feature subsets that are highly correlated with the target. The specific deep Long Short-Term Memory (LSTM) models are employed to capture the dynamic changes of disease severity, clinical outcome, and Spike antibody level. We also propose deep LSTMs with a TA mechanism to emphasize the later blood test records because later records often attract more attention from doctors. Risk factors highly correlated with COVID-19 are revealed. LSTM achieve the highest classification accuracy for disease severity prediction. Temporal Attention Long Short-Term Memory (TA-LSTM) achieves the best performance for clinical outcome prediction. For Spike antibody level prediction, LSTM achieves the best permanence. The experimental results demonstrate the effectiveness of the proposed models. Simple factors like LDH, Mono%, ALB, LYMPH%, DM, and Sex are critical factors in disease severity. LDH, Neu#, hs-CRP, PLT, and Urea are critical factors in clinical outcomes. We further find that Age, RDW_CV, PLT, LDH, eGFR (CKD-EPI), LYMPH#, RDW_SD, PCT, and TCHO are the Top-9 significant predictors of the Spike antibody level. The proposed models can provide a computer-aided medical diagnostics system by simply using time series of serological indicators.