2024 9th International Conference on
Intelligent Information Technology (ICIIT 2024) 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 2024 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:
Invited Speaker I
Assist. Prof. Balachandran Manavalan
Sungkyunkwan University (SKKU),
Dr. Balachandran Manavalan is an
Assistant Professor at the Department of Integrative Biotechnology,
Sungkyunkwan University (SKKU), South Korea. He obtained his Ph.D.
in Computational Biology from Ajou University in 2011. He spent 10
years honing his research expertise, serving as a research fellow at
the KIAS, and a research assistant professor at Ajou University
School of Medicine, South Korea. He established his own research
group at SKKU’s Department of Integrative biotechnology in 2022. His
research interests include artificial intelligence, bioinformatics,
machine learning, big data, proteomics, and functional genomics. He
has been developing cutting-edge bioinformatics tools for
identifying peptide therapeutic functions, post-transcriptional
modifications, DNA epigenetic modification sites, and RNA
post-transcriptional modifications. His impressive publication
record includes over 100 papers, the majority of which are published
in top-tier journals ranked within the top 10% of the Journal
Citation Reports (JCR). His remarkable achievements have earned him
recognition as a top 2% highly cited researcher for the past four
consecutive years, according to Stanford University data.
Speech Title: "Artificial Intelligence to
Explore Multimodality Data and Accelerate Biomedical Knowledge Discovery"
Abstract: This talk introduces two of our recent
AI-driven methodologies: MeL-STPhos for identifying SARS-CoV2 phosphorylation
sites and H2Opred for identifying 2’-O-methylation (2OM) sites in human RNA.
Firstly, MeL-STPhos, a meta-learning model, for the accurate prediction of
SARS-Cov2 phosphorylation sites. Briefly, we conducted a comprehensive
exploration of 29 feature descriptors, assessed each descriptor’s ability using
14 distinct classifiers, and identified the best-performing model for each
descriptor. These individual models were then synergistically combined into a
robust meta-model. Notably, we developed both cell-specific and generic models
and demonstrated their practical application scenarios. Secondly, the novel
hybrid deep learning model, H2Opred, for the identification of 2OM sites in
human RNA. This is the first method that showed an advantage of having a generic
model compared to the nucleotide-specific models. H2Opred integrates stacked 1D
convolutional neural network and attention-based bidirectional gated recurrent
units blocks to learn and extract multi-modal features, both conventional
descriptors and NLP-based embeddings. This approach markedly improves predictive
accuracy over conventional ML-based models and state-of-the-art methods. These
AI-driven methodologies not only showcase the potential of AI in unraveling
intricate cellular processes but also accelerate biomedical knowledge discovery.
Invited Speaker II
Dr. Hung Dang
FPT Corporation, Vietnam
Dr. Hung Dang is currently the Head of
Research in the FPT CTO's office, and has been lecturing at the
Vietnam National University. Prior to joining FPT, he was a Research
Fellow at the Singapore National Research Foundation’s Strategic
Capability Research Centre in Privacy-preserving Technologies. His
research focuses on computer security and distributed systems, and
has received numerous scientific citations.
Speech Title: "What Else Does AI Stand For"
Abstract: Large Language Models and more recently
Large Vision Models have emerged spectacularly and left the world in awe. They
are commonly referred to as instances of Artificial Intelligence, or AI for
short. Much has been discussed about their applications, their potentials, and
even what existential threats they could be. Nonetheless, it remains unclear to
what extent those discussed points are realistically imminent, and which parts
of them are purely hype-driven. This talk will take a more sober approach to the
notion of AI, attempting to suggest different connotations of AI.
Invited Speaker III
Prof. Masaomi Kimura
Shibaura Institute Technology, Japan
Prof. Masaomi Kimura received a Ph.D.
degree from The University of Tokyo. After his career as a system
engineer in IBM, he started his career as a researcher at Shibaura
Institute of Technology (SIT) in 2004. Now, he is a director of the
international exchange center from 2023. He is also a professor in
the Department of Computer Science and Engineering in the College of
Engineering. His current research interests are in the areas of data
science and data engineering, with a particular focus on data
analysis as an application of artificial intelligence (machine
learning), especially using deep learning. His research ranges from
developing novel models for data analysis to their application in
solving real-world problems such as automatic generation of ASCII
art for transmission data compression and adversarial attack and
defense methods to improve security of deep learning models.
Speech Title: "Low Visibility Perturbation in
Adversarial Attacks on Image Recognition"
Abstract: Deep learning neural networks are a
promising solution for solving complex real-world problems. They are
particularly important in image recognition and are being applied to new
technologies such as automated driving. However, adversarial examples, which
apply perturbations to image pixels, pose a threat to the safety of deep
learning neural networks. This presentation will provide an overview of the
concept of adversarial examples and introduce a technique that makes it
difficult to visually distinguish when a perturbation has been added.