26 - 27 October at Le Méridien Chiang Mai, Chiang Mai, Thailand

The 15th International Conference
on Information Technology and Electrical Engineering

ICITEE 2023 Proceeding
Schedule might be changed regarding to COVID-19 virus situation in Thailand.

About Event

The Annual Conference for New Research, Innovative Ideas, and Technologies

From 26-27 October 2023, the 15th International Conference on Information Technology and Electrical Engineering (ICITEE 2023) is organized by IEEE CIS Thailand Chapter and co-organized by King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand and Universitas Gadjah Mada (UGM), Yogyakarta, Indonesia.

ICITEE has been supported by a professional organization for over 5 years and is indexed in the Scopus.

Topics of Interest

The accepted and presented papers will be submitted to IEEE Xplore® and indexed by SCOPUS, EI Compendex, ISI Conference Proceedings Citation Index, and major databases. The topics of interest include but are not limited to the following areas

Special Session
Business Management and Informatics
Entrepreneurships, Futuristic finance, Digital marketing, Sustainability, Logistics and Supply chain management, Customer relationship management, Electronic business, Technology and innovation management, Digital economy, Start-up and social enterprise, Business Strategies
14th Joint Symposium on Computational Intelligence (JSCI14)
Explainable AI (XAI): Model-specific XAI Techniques, Model-agnostic XAI Techniques, Evaluation Metrics for XAI, Visualizing AI Model Decisions, Rule-based Explanations, Natural Language Explanations, Ethical Considerations in XAI, XAI in Healthcare, XAI in Finance, XAI in Autonomous Systems, Human-AI Interaction with XAI, Trust and Adoption of XAI, XAI for Deep Learning

Information Technology

Cloud-Based Application, Distributed Systems, Mobile Computing and Application, Distance Learning and E-learning, Internet of Things, Software Engineering, Information Systems, Decision Support System, Knowledge Discovery, Human Computer Interaction, Visualization and Computer Graphics, Augmented and Virtual Reality.

Artificial Intelligence & Machine Learning

Artificial Intelligence, Machine Learning, Big Data and Data Mining, Natural Language Processing, Web and Text Mining, Information Retrieval and Recommender Systems, Source Coding and Algorithmic Information Theory, Signal, Image, and Video Analysis, Computer Vision, Image Understanding, Multimedia Modeling, Bioinformatics

Communication & Network Technologies

Networking and Telecommunication Systems, Wireless Ad-hoc and Sensor Networks, Cognitive Radio, Cooperative Communications, Radio Resource Management and Optimization, Vehicular Communications, Channel Coding and Information Theory, Software Defined Networking

Electronics, Circuits, and Systems

Green Design for VLSI and Micro Electronic Circuits, Embedded Systems and SoC design, RF Devices and Circuits, Computer-Aided Electronics Design and Technology, Device Material and Manufacturing Technology, Photonic and Optoelectronic Circuits

Power System

Power Generation, Protection, and Conversion, Power Engineering and Systems, High-Voltage Engineering, Power Transmission and Distributions, Electric Motors, Power Electronics, Smart Grid, Renewable Energy, Microgrid and Distributed Generations

Control System

Control Theory and Applications, Robotics and Autonomous Systems, Adaptive and Intelligent Control, Robust and Nonlinear Control, Industrial Automation and Control Systems Technology, Control of Infinite Dimensional Systems

Paper Submission

The ICITEE 2023 will feature regular paper presentations, invited sessions, and keynote addresses.

Guideline
App screenshot

Important Dates

DateEvent
1 September 2023Submission Deadline (Batch #3)
15 September 2023Notification of Acceptance (Batch #2,#3)
25 September 2023Camera Ready Deadline
25 September 2023Early Registration Deadline
5 October 2023Regular Registration Deadline

"Time zone for submission is UTC/GMT -12 hours"

Schedule

TimeGrand BallroomPassage RoomExpedition Room
08.30 - 09.00Registration
09.00 - 09.30Opening Ceremony
09.30 - 10.15Plenary Session I
10.15 - 10.30Coffee break
10.30 - 11.15Plenary Session II
11.15 - 12.00Plenary Session III
12.00 - 13.00Lunch Break
13.00 - 15.00Oral Session I (6)Oral Session I (6)Oral Session I (6)
15.00 - 15.15Coffee break
15.15 - 17.15Oral Session II (6)Oral Session II (6)Oral Session II (6)
18.30 - 20.30Conference Banquet

Keynote Speakers

avatar

Prof. Dr. Ujjwal Maulik

Department of Computer Science and Engineering Jadavpur University, India

BIODr. Ujjwal Maulik is a Professor in the Department of Computer Science and Engineering, Jadavpur University since 2004. He was also the former Head of the same Department. He also held the position of the Principal in charge and the Head of the Dept. of Computer Science and Engineering, Kalyani Government Engineering College. Dr. Maulik has worked in many universities and research laboratories around the world as visiting Professor/ Scientist including Los Alamos National Lab., USA in 1997, Univ. of New South Wales, Australia in 1999, Univ. of Texas at Arlington, USA in 2001, Univ. of Maryland at Baltimore County, USA in 2004, Fraunhofer Institute for Autonome Intelligent Systems, St. Augustin, Germany in 2005, Tsinghua Univ., China in 2007, Sapienza Univ., Rome, Italy in 2008, Univ. of Heidelberg, Germany in 2009, German Cancer Research Center (DKFZ), Germany in 2010, 2011 and 2012, Grenoble INP, France in 2010, 2013 and 2016, University of Warsaw in 2013 and 2019, University of Padova, Italy in 2014 and 2016, Corvinus University, Budapest, Hungary in 2015 and 2016, University of Ljubljana, Slovenia in 2015 and 2017, International Center for Theoretical Physics (ICTP), Trieste, Italy in 2014, 2017 and 2018. He was the recipient of the Alexander von Humboldt Fellowship during 2010, 2011 and 2012 and Senior Associate of ICTP, Italy during 2012-2018. He is the Fellow of Indian National Academy of Engineers (INAE), India, National Academy of Science India (NASI), India, International Association for Pattern Recognition (IAPR), USA, The Institute of Electrical and Electronics Engineers (IEEE), USA and Asia-Pacific Artificial Intelligence Association (AAIA), Singapore. He is also the Distinguished Member of the ACM. He is a Distinguished Speaker of IEEE as well as ACM. His research interests include Machine Learning, Pattern Analysis, Data Science, Bioinformatics, Multi-objective Optimization, Social Networking, IoT and Autonomous Car. In these areas he has published ten books, more than four hundred papers, mentoring several start-ups, filed several patents and already guided twenty five doctoral students. His other interests include outdoor Sports and Classical Music.
TopicCurrent Trend and Future Challenges of Artificial Intelligence and Data Sciences
AbstractIn this lecture first we will describe current trends in Artificial Intelligence (AI) and Data Science (DS). Supervised and unsupervised pattern classification are important Machine Learning (ML) techniques which are integral part of AI. Supervised pattern classification methods use training samples for classify the unlabelled data along with cross validation. On the other hand, unsupervised classification partitions the data points into homogeneous groups based on some similarity/dissimilarity metric. Deep Learning (DL) is a popular ML technique that help feature engineering as well as classification. DL have wide range of applications. We will demonstrate how Deep Learning Techniques can be used efficiently for Intelligence Autonomous Car as well as for healthcare applications. In addition to DL, we will demonstrate the importance of using Graph neural network (GNN). While DL has been used very successfully for image analysis, GNN are being used extensively for unstructured datasets including biological datasets available in the form of graphs containing the interaction between genes, drugs, diseases etc. The importance of explainable AI will be demonstrated. The second part of the lecture will be focused on the issues and challenges in data science. How CPU speed, complexity of algorithms, architecture all are important for the analysis of complex as well as big data will be described. Finally the benefits and risk of applying sophisticated AI techniques will be presented. The current challenges and future of AI research will also be discussed.
avatar

Assoc. Prof. Dr. Sansanee Auephanwiriyakul

Computer Engineering Department, Faculty of Engineering, Biomedical Engineering Institute Chiang Mai University, Thailand

BIOSansanee Auephanwiriyakul (S’98–M’01) received the B.Eng. (Hons.) degree in electrical engineering from the Chiang Mai University, Thailand (1993), the M.S. degree in electrical and computer engineering and Ph.D. degree in computer engineering and computer science, both from the University of Missouri, Columbia, in 1996, and 2000, respectively. After receiving her Ph.D. degree, she worked as a post-doctoral fellow at the Computational Intelligence Laboratory, University of Missouri-Columbia. She is currently an Associate Professor in the Department of Computer Engineering and a deputy director of the Biomedical Engineering Institute, Chiang Mai University, Thailand. Dr. Auephanwiriyakul is a senior member of the Institute of Electrical and Electronics Engineers (IEEE). She is an Associate Editor of the IEEE Transactions on Fuzzy System, the IEEE Transactions on Neural Networks and Learning Systems, IEEE Computational Intelligence Magazine, IEEE Transactions on Artificial Intelligence, Engineering Applications of Artificial Intelligence, and ECTI Transactions on Computer and Information Technology. She was a general chair of the IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2016). She will be a general chair of the IEEE World Congress on Computational Intelligence (WCCI) 2024 (IEEE International Conference on Fuzzy Systems 2024). She was a Technical Program Chair, Organizing Committee in several major conferences including the IEEE International, Conference Fuzzy Systems. She is also a member of several important IEEE CIS technical committees.
TopicComputational Intelligence in Biomedical Engineering
AbstractComputational Intelligence (CI) relies on and combines several algorithms in fuzzy systems, neural networks, evolutionary computation, swarm intelligence, fractals, chaos theory, artificial immune systems, wavelets, etc., to produce an algorithm that is intelligent somehow. CI has been utilized in many applications for several years. One of the areas that CI has an impact on is the area of biomedical engineering, e.g., medical image processing, medical signal processing and biometrics. One of the CI tools used in those mentioned application is classification or sometimes called decision making. The major area in the classification is to develop a classifier, including, feature generation and selection. In this talk, feature generation methods and classifier methods based on the CI will be presented. We also show those methods on real application, including, medical image diagnosis, medical signal diagnosis, and data analysis in health care system.
avatar

Dr. Lukito Edi Nugroho

Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Indonesia

BIODr. Lukito Edi Nugroho is a Professor at the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada (UGM). He obtained his Engineer degree from UGM, M.Sc. from James Cook University, and Ph.D. from Monash University. His research interests include pervasive and mobile computing, and ICT in education and city/region development. In recent years, Professor Nugroho has done extensive work on e-governments and smart cities. In 2007, he designed a concentration in our Master of Information Technology program with the focus on e-government implementation in central and local government institutions. Since then, not only has he been carrying out research on benefitting city/region development by the use of technology, but also evaluating Indonesian cities' and regions' performance in their smart city initiatives and assisting them to develop their smart city development master plan. These works are done within the umbrella of Indonesia's National Smart City Movement initiated by the Ministry of Communication and Information Technology. Professor Nugroho has supervised more than 40 PhD and 150 master students, about half of his master students took a thesis topic on e-government or smart city fields.
TopicSmart cities in developing countries: how far can they be made smarter?
AbstractSmart cities are often associated with the application of ICT in solving city problems. Their "smartness" is used to improve the quality of life of the citizens. However, this is not the case in many cities/regions, especially in developing countries. They have been deploying numerous ICT systems, however, life quality indicators do not improve as expected. From our experiences in assisting cities/regions in Indonesia in transforming themselves to smart cities, we understand that ICT is not the only factor that determines the success. It appears that innovations in crafting solutions of city problems are more predominant than ICT itself. We also learn that a smart city initiative has to be carried out in a holistic perspective and cannot focus on ICT alone. After all, our experiences lead to an understanding that smart city implementation in developing countries takes a different direction compared to that of developed countries. This talk focuses on smart city development from the perspective of cities/regions in developing countries. First we discuss the adjustment of smart city concepts to suit the characteristics of cities/regions in developing countries. We also discuss how innovations, not ICT, becomes a better smart city accelerator and how they can be aligned with city's development plans. Finally we explain some research directions that can help cities/regions in developing countries solve their problems and improve citizen's quality of life using smart approaches.
avatar

Assoc. Prof. Dr. Siridech Boonsang

School of Information Technology King Mongkut's Institute of Technology Ladkrabang, Thailand

BIODr. Siridech Boonsang is the current Dean of the School of Information Technology at King Mongkut's Institute of Technology Ladkrabang (KMITL). He was born in Thailand and has an impressive educational background, having earned his Bachelor's degree in Electrical Engineering with Second Class Honours from KMITL in 1994. Dr. Boonsang then went on to pursue his Master's degree in Electrical and Electronic Engineering with a specialization in Electronic Instrumentation System from the University of Manchester Institute of Science and Technology (UMIST) in 2001. He completed his Ph.D. in Instrumentation from the same institution in 2004. Dr. Boonsang is an expert in AI for Industrial Automation, Sensors and Actuators, and Optical and Electronic Materials. He has published numerous papers, including "A deep learning system for recognizing and recovering contaminated slider serial numbers in hard disk manufacturing processes," "Optical and Structural Properties of Insoluble and Flexible Biodegradable Regenerated Silk Films for Optically Transparent Hydrophilic Coating of Medical Devices," and "Evaluation of Micro- and Nano-Bismuth(III) Oxide Coated Fabric for Environmentally Friendly X-Ray Shielding Materials." In his current role as the Dean of the School of Information Technology at KMITL, Dr. Boonsang is responsible for overseeing the academic programs and research activities of the school. He is known for his dedication to promoting excellence in education and research and for his commitment to fostering innovation and creativity among his students and school members.
TopicGenerative AI for industrial manufacturing applications
AbstractThis work explores the application of generative models in industrial manufacturing, specifically in the context of creating synthetic images. The use of generative models to generate synthetic images has gained traction in various industries, including the automobile and manufacturing sectors. In the automobile industry, the Latent Diffusion Model (LDM) in combination with fine tuning techniques has been proposed as an approach to generate automobile images based on text input. However, limited datasets can lead to overfitting, and fine tuning is used to pre-train the model to handle smaller scale datasets. The synthetic images are generated based on conditional input, such as the brand, color, location, and automobile position. In industrial manufacturing, the development of automated surface inspection systems requires a large amount of representative product image data. However, obtaining such data, especially with defects that reflect real-world scenarios, can be challenging, resulting in difficulties in developing robust detection algorithms. Generative models can be utilized to create synthetic datasets that contain product images augmented with defects. These datasets provide images with a variety of defective shapes and positions over the surface, reflecting what would occur over longer production periods. In conclusion, the use of generative models has become essential in creating synthetic images in several industries, including industrial manufacturing. The LDM and fine-tuning techniques can be used to generate automobile images based on text input, while synthetically generated datasets with defects can help in the development of automated surface inspection systems.
avatar

Prof. Dr. Franck Leprévost

University of Luxembourg, Luxembourg

BIOBorn in 1965, Franck Leprévost is a French mathematician and computer scientist. He is professor at the University of Luxembourg since 2003, and was its Vice-President from 2005 until 2015. Before joining the University of Luxembourg, he held academic positions in France (CNRS Paris, University Joseph Fourier Grenoble), and in Germany (Max Planck-Institut für Mathematik Bonn, Technische Universität Berlin). He holds a PhD and a Habilitation in Mathematics. He was researcher or visiting professor at many universities and research institutions (Max Planck-Institut für Mathematik Bonn, Technische Universität Berlin, Peter the Great Saint Petersburg Polytechnic University, South Ural State University, Warsaw University of Technology, Cardinal Wyszynski University, Kiev Polytechnic Institute, Shanghai Normal University, etc.). He was recipient of the Alexander von Humboldt Fellowship. He was member of the board of directors or member of the scientific council of private and public entities (ATTF, CEPS, FNR, Luxtrust S.A., Unica, etc.). His scientific interests include algorithmic number theory, mathematics of cryptology, convolutional neural networks, deep learning, artificial intelligence and evolutionary algorithms on the one hand, and the management of higher education and research organizations, international rankings and the civilizational role of universities on the other hand. Professor Leprévost has contributed to the worldwide standardization process of public-key algorithms (IEEE-P1363). He published eight scientific books, including three tutorials used by students in several universities in Europe and beyond, and a reference book about international university rankings. He is the author of about 70 scientific articles in international peer-reviewed journals, essentially in pure mathematics and in computer science. His reports for the European Parliament, in particular his contribution to the report on the Echelon network, have had a substantial technical and legal impact in most European countries. Professor Leprévost has also publicly expressed his concerns about the risks of decline that face Western universities, the dangers of wokism within these same universities, and the actions to be taken to fight this ideology. He currently leads the Laboratory of Algorithmics, Cryptology and Security. Professor Leprévost's expertise in the field of higher education and research as well as his managerial skills are frequently solicited by organizations, governments, and companies worldwide. In addition to his scientific and management activities, Franck Leprévost is a writer and an art collector. His publications are on www.franckleprevost.com
TopicConvolutional Neural Networks at Image Recognition Tasks: Assessing the Risks.
AbstractAn image is claimed to be worth 1000 words. Today’s digital society is surely contributing to this belief, as our environment seems more and more driven by images. Images are used in social media to witness the evolution of people’s life: Images show the new girlfriend or boyfriend (and sometimes the banned ex), the holidays spent here or there, with whom, doing what, eating what, enjoying what, seeing what. Images on Facebook, Instagram and others represent whose family member is suffering from which disease and since how long, etc. But images are also used for a large and increasing series of applications, that may either ease or complexify people’s life: self-driving cars, face recognition, security access, medical diagnosis, satellite vision, robotisation and automation of processes, automatic language translation, etc. Due to the profusion of images and of their usage, technologies have been developed to process them automatically, starting with the creation of processes able to sort images according to what they represent. At this task, Convolutional Neural Networks (CNNs) are among the most widely used and developed technologies. However, CNNs are not immune against mistakes. In fact, adversarial images may be purposely designed by so-called attacks to deceive CNNs at image recognition and classification. Attacks can contribute to protect privacy issues and to limit people’s tracking. Attacks can have catastrophic consequences, e.g. for self-driving cars or CNN’s based medical diagnosis. In this talk, we shall describe the typology of attacks, the scenarios they follow, and the challenges they face. We shall give an overview of some of the most recent attacks used to create adversarial images. These attacks provide convincing evidence supporting (for good or bad) the following outcome: Even denoting the very fact that an attack occurred at all may, in future, become increasingly highly problematic.

Chiang Mai, Thailand

Organized by

IEEE Computational Intelligence Society Thailand Chapter

Organizer

IEEE Computational Intelligence Society Thailand Chapter

School of Information Technology, King Mongkut's Institute of Technology Ladkrabang (KMITL), Thailand

Co-Organizer

School of Information Technology, King Mongkut's Institute of Technology Ladkrabang (KMITL), Thailand

Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada (UGM), Indonesia

Co-Organizer

Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada (UGM), Indonesia

ICITEE 2023 Committees

Organizing Committees
Advisory Boards
Chu Kiong Loo, UM, MalaysiaHanung Adi Nugroho, UGM, Indonesia
Kuntpong Woraratpanya, KMITL, ThailandLukito Edi Nugroho, UGM, Indonesia
Masanori Sugimoto, HU, JapanOyas Wahyunggoro, UGM, Indonesia
Risanuri Hidayat, UGM, IndonesiaSasongko Pramono Hadi, UGM, Indonesia
Siridech Boonsang, KMITL, ThailandUjjwal Maulik, JU, India
General Chair
Arit Thammano, KMITL, ThailandRoni Irnawan, UGM, Indonesia
Technical Program Chair
Kitsuchart Pasupa, KMITL, ThailandSyukron Abu Ishaq Alfarozi, UGM, Indonesia
Track Chairs
Dzuhri Radityo Utomo, UGM, IndonesiaNat Dilokthanakul, KMITL, CIS Thailand Chapter
Praphan Pavarangkoon, KMITL, ThailandRidwan Wicaksono, UGM, Indonesia
Somying Thainimit, KU, ThailandYohan Fajar Sidik, UGM, Indonesia
Jonglak Pahasa, UP, Thailand
Special Session Chairs
Kuntpong Woraratpanya, KMITL, ThailandSingha Chaveesuk, KMITL, Thailand
Organizing Community and Committees
Ahmad Ataka Awwalur Rizqi, UGM, IndonesiaAhmad Nasikun, UGM, Indonesia
Araya Ariya, LPRU, ThailandAzkario Rizky Pratama, UGM, Indonesia
Bundit Busaba, LPRU, ThailandChanboon Sathitwiriyawong, KMITL, CIS Thailand Chapter
Ferdin Jon, TNI, CIS Thailand ChapterJonathan Hoyin Chan, KMUTT, Thailand
Kamesh Namuduri, UNT, USAKamol Wasapinyokul, KMITL, Thailand
Kuroki Yoshimitsu, NIT-Kurume College, JapanMaleerat Maliyaem, KMUTNB, CIS Thailand Chapter
Nat Dilokthanakul, KMITL, CIS Thailand ChapterOlarn Wongwirat, KMITL, Thailand
Phayung Meesad, KMUTNB, CIS Thailand ChapterPichaphob Panphae, RMUTL, Thailand
Piraprob Junsantor, LPRU, ThailandPramuk Boonsieng, TNI, CIS Thailand Chapter
Praphan Pavarangkoon, KMITL, ThailandRoni Irnawan, UGM, Indonesia
Samart Moodleah, KMITL, ThailandSarayut Nonsiri, TNI, CIS Thailand Chapter
Shigeru Kuchii, NIT-Kitakyushu College, JapanSirasit Lochanachit, KMITL, Thailand
Somnuk Phon-Amnuaisuk, UTB, BruneiSyukron Abu Ishaq Alfarozi, UGM, Indonesia
Vithida Chongsuphajaisiddhi, KMUTT, ThailandWannaporn Teekeng, RMUTL, Thailand
Warune Buavirat, KMITL, ThailandWorapoj Kreesuradej, KMITL, Thailand

Event Direction & Info

Contact Us

If you have any question, please let us know by sending us a message.

King Mongkut's Institute of Technology Ladkrabang
1 Chalong Krung 1 Alley, Lat Krabang,
Bangkok, Thailand

(+66) 2-723-4900

icitee2023@it.kmitl.ac.th

Contact Form

All Proceedings

LocationWebsite
2022Yogyakarta, Indonesiahttps://icitee.ugm.ac.id/
2021Chiang Mai, Thailandhttps://icitee2021.it.kmitl.ac.th/
2020Yogyakarta, Indonesiahttps://icitee.ugm.ac.id/
2019Pattaya, Thailandhttp://icitee2019.it.kmitl.ac.th/
2018Bali, Indonesiahttps://icitee.ugm.ac.id/
2017Phuket, Thailandhttp://icitee2017.it.kmitl.ac.th/
2016Yogyakarta, Indonesiahttps://icitee.ugm.ac.id/
2015Chiang Mai, Thailandhttp://icitee2015.it.kmitl.ac.th/
2014Yogyakarta, Indonesiahttp://icitee2014.te.ugm.ac.id/
ICITEE 2023 ProceedingAll proceedings on IEEE Xplore