Invited Speakers of ICIIT 2025
2026 11th International Conference on Intelligent Information Technology (ICIIT 2026) 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 2026 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.
Assoc. Prof. 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.
Prof. Mohd Nazri Bin Ismail
National Defence University of Malaysia, Malaysia
Prof. Dr. Mohd Nazri became Lecturer at National Defence University of Malaysia. Prof. Dr. Mohd Nazri Ismail had a deep involvement in computer network research and was awarded the prestigious “Educator Award 2009 – R&D/Education category” by MARA (Malaysia Agency). He has supervised Ph.D. and Master Students and teaching at undergraduate and post graduate level. Assoc. Prof. Dr. Mohd Nazri Ismail has published more than 100 papers in national and international journals (indexed ISI, SCOPUS, IET) and IEEE conferences. He has attended many international conferences throughout the world and has chaired many technical sessions. He has appointed as Technical Program Committee and organized more than 60 national and international conferences. He has appointed as Editorial Board member more than 90 international journals and 40 international reviewer panels (journal/proceeding). Awards and laurels won by Assoc. Prof. Dr. Mohd Nazri Ismail run into volumes and he has received 28 awards in R&D/Education. Assoc. Prof. Dr. Mohd Nazri Ismail is an International Association of Engineers (IAENG), IEEE Cloud Computing Community, Society of Digital Information and Wireless Communications (SDIWC), International Association of Engineers and Scientists (IAEST), Universal Association of Computer & Electronics Engineers (UACEE).
Speech Title:“Analysis of 5G in Indoor and Outdoor Environment Via Wireless Simulation Tool”
Abstract: A comprehensive understanding of 5G New Radio (NR) technology's performance in various environments is essential for its successful knowledge on how the technologies work in real life. Using the NetSim network simulator by UPNM to mimic the real world, this researcher explores how different areas affect 5G NR performance both indoors and outdoors. The project simulates 5G NR scenarios using the seven-phase waterfall model. To measure the impact of these obstacles, key performance indicators (KPIs) like data throughput, signal strength, and path loss are examined. The expected result is a thorough performance analysis of 5G NR that considers the impact of different environments in both indoor and outdoor settings. The KPIs will be used to present this analysis, giving Malaysian network service providers (NSPs) insightful information. The successful demonstration of 5G NR performance in current networks while different areas are present is the desired outcome, which will boost user experience and network performance.
Assoc. Prof. Thabet Kacem
University of the District of Columbia, USA
Dr. Kacem’s research portfolio, demonstrated through 29 peer-reviewed publications in journals and conferences, spans cybersecurity, intelligent transportation systems, smart cities, cyber-physical systems, blockchains, and climate change. As the principal inventor of U.S. Patent 11022696, entitled “ADS-BSec: A Holistic Framework to Secure ADS-B,” he has made innovative contributions to aviation cybersecurity. Dr. Kacem has served as principal and co-principal investigator on several projects funded by the National Science Foundation and National Security Agency, underscoring his leadership in high-impact research. His recent work includes leveraging transformer models and multi-task learning frameworks to advance Android threat detection and improve mobile security. Additionally, he has pioneered generative AI applications for environmental modeling, explored zero trust architectures, and addressed ADS-B vulnerabilities. As General Chair of ISNCC24, Dr. Kacem displayed exceptional leadership, orchestrating conference activities that led to 130 published papers. A recognized expert and sought-after speaker, he will deliver an invited talk at ICIIT 2025, further cementing his global standing in the field.
Speech Title:“Advancing Android Threat Detection & Classification with Transformers and Multi-Task Learning”
Abstract: The exponential growth of Android devices has made them prime targets for sophisticated and evolving cyber threats. This talk explores advanced methodologies to address these challenges by leveraging transformer architectures and multi-task learning frameworks for Android threat detection and classification. First, it presents Trandroid, a state-of-the-art transformer-based approach that significantly outperforms traditional classifiers, achieving a record-high accuracy of 99.25% on the TUANDROMD dataset. By employing robust deep learning algorithms, Trandroid demonstrates exceptional capability in detecting advanced cyberattacks targeting the Android ecosystem. Building upon this foundation, the talk also introduces a multi-task learning (MTL) framework designed to enhance Android security through simultaneous malware detection and family classification. This framework optimizes shared task representations, leading to improved efficiency and accuracy, as validated on the CCCS-CIC-AndMal-2020 dataset. Techniques such as feature dimensionality reduction and hyper-parameter tuning further reinforce its real-world applicability. The talk underscores the synergy between transformer models and MTL in advancing mobile security solutions, emphasizing the importance of integrating dynamic analysis and real-time deployment to combat emerging threats. These advancements pave the way for scalable and intelligent Android security systems.