Session Tracks

AIoT Track 1: Artificial Intelligence (AI) Fundamentals

AIoT Track 1: Artificial Intelligence (AI) Fundamentals

  • Adaptive Control
  • Agent and Multi-Agent Systems
  • Artificial Neural Networks Spiking
  • Artificial Neural Networks
  • Bayesian Models
  • Biologically Inspired Neural Networks
  • Architectures Interacting with The Brain
  • Convolutional Neural Networks
  • Deep Learning
  • Big Data, Distributed AI Systems and Architectures
  • Evaluation of AI Systems
  • Evolving Systems – Optimization, Affective Computing
  • Grid-Based Computing
  • Knowledge Acquisition and Representation
  • Knowledge Engineering, Machine Learning
  • Multi-layer Perceptron, Multilayer Perceptron and Kernel Networks
  • Learning and Adaptive Systems
  • Mathematical Foundations of AI and Intelligent Computational methods
  • Media Machine Learning in Engineering
  • Natural Language Processing
  • Object and Face Recognition, Ontologies, Reasoning Methods
  • Particle Swarm Optimisation
  • Planning and Resource Management
  • Planning and Scheduling

AIoT Track 2: AI Applications

AIoT Track 2: AI Applications

  • Autonomous and Ubiquitous Computing
  • Biomedical systems
  • Bioinformatics Coding
  • Collective Computational Intelligence
  • Color/Image Analysis
  • Computer Vision
  • Crisis and Risk Management
  • Data Fusion
  • Data Mining and Information Retrieval
  • Decision Support Systems
  • Deep Learning
  • Real Time Systems
  • Social Networks
  • eBusiness
  • eCommerce
  • eHealth
  • eLearning
  • Engineering and Industry
  • Expert Systems
  • Finance and AI
  • Fuzzy Logic and Systems
  • Genetic Algorithms and Programming
  • Human-Machine Interaction
  • Intelligent Real Time Monitoring and Control
  • Knowledge Management
  • Cybersecurity, ForensicsBioMedical
  • Medical Informatics and Biomedical
  • Movement and Motion
  • Multimedia Computing
  • Multimedia Ontologies
  • Multimedia
  • Political Decision Making
  • Project Management Recommendation Systems
  • Recurrent Neural Networks and Reservoir Computing
  • Robotics and Virtual Reality
  • Signal and Image Processing
  • Knowledge Extraction
  • Smart Graphics
  • Smart Grids
  • Social Media and AI
  • Speech and Natural Language Processing
  • Speech Synthesis
  • Time Series and Forecasting
  • Mining and Exploratory Analysis

AIoT Track 3: AI and Social Issues

AIoT Track 3: AI and Social Issues

  • AI and Ethical Issues
  • Cybersecurity and AI
  • Deep Learning and Big Data Analytics
  • Deep Learning and Cybersecurity
  • Deep Learning and Forensics
  • Forensic Science
  • Intelligent Profiling and Personalisation
  • Machine Learning and Social
  • Social Impact of AI

AIoT Track 4: Embedded Systems

AIoT Track 4: Embedded Systems

  • Multiprocessors
  • reconfigurable platforms
  • memory management support
  • communication
  • protocols
  • network-on-chip
  • real-time systems
  • embedded Microcontrollers
  • Real-time systems: real-time related aspects such as software, distributed real-time systems, real-time kernels, real-time OS, task scheduling, multitasking design
  • Embedded hardware support: System-on-a-chip, DSPs, hardware specification, synthesis, modeling, simulation and analysis at all levels for low power, power-aware, testable, reliable, verifiable systems, performance modeling, validation, security issues, real-time behavior and safety critical systems
  • Embedded software: memory management, object-oriented aspects, virtual machines, scheduling, concurrent software for SoCs, distributed/resource aware OS
  • OS and middleware support: Hardware/software
  • co-design: Methodologies, test and debug strategies, real-time systems, specification and modeling, design representation, synthesis, partitioning, estimation

AIoT Track 5: Information and Communication Technology

AIoT Track 5: Information and Communication Technology

  • Artificial Intelligence
  • Internet of Technology
  • Data Science, Telecommunication
  • Electrics and Electronics
  • Software and Hardware for ICT
  • ICT Policy/Strategy
  • Software Engineering
  • Semantic Web

AIoT Track 6: Internet of Things

AIoT Track 6: Internet of Things

  • All ranges of applications on embedded system
  • including speech processing, image processing
  • network computing
  • distributed computing
  • parallel computing and power conversion
  • Application-specific processors and devices: Network processors, real-time processor, media and signal processors, application specific hardware accelerators, reconfigurable processors, low power embedded processors, bio/fluidic processors, Bluetooth, hand-held devices
  • Industrial practices and benchmark suites: System design, processor design, software, tools, case studies, trends, emerging technologies, experience maintaining benchmark suites, representation, interchange format, tools, copyrights, maintenance, metrics, Curriculum issues, teaching tools and methods, New challenges for next generation embedded computing systems, arising from new technologies (e.g., nanotechnology), new applications (e.g., pervasive or ubiquitous computing, embedded internet tools) and new principle (e.g., embedded Engineering).

AIoT Track 7: Management Technology

AIoT Track 7: Management Technology

  • Service science, management and engineering
  • Operations
  • Logistics and Supply chain management
  • Optimization, Probabilistic and Statistical Model
  • Economics
  • Occupational safety and health management
  • Ergonomics
  • Human Resource and Organization management
  • Environmental management

iSAI-NLP Track 1: Natural Language Processing

iSAI-NLP Track 1: Natural Language Processing

Natural Language Processing (NLP) Track Session, topics for the session include, but are not limited to:

  • Cognitive aspects of natural language processing
  • Corpus and Language Resources
  • Corpus-based language modeling
  • Dialog Systems
  • Information Retrieval
  • Language and Ontology Unifying
  • Language Engineering
  • Language Learning
  • Language processing in internet applications
  • Languages for Disability
  • Linguistic models of language
  • Linguistic Resources
  • Machine Translation
  • NLP Applications
  • NLP-based knowledge science
  • Ontology Engineering
  • Phonetics, phonology and morphology
  • Pragmatics and discourse
  • Semantics, syntax and lexicon
  • Speech Recognition and Synthesis
  • Tools and resources for NLP

Track Chair

  • Min Zhang, Soochow University, China
  • Masao Utiyama, NICT, Japan
  • Prachya Boonkwan, NECTEC, Thailand
  • Rachada Kongkachandra, Thammasat University, Thailand

Technical Committee

  • Krit Kossawat, NECTEC, Thailand
  • Boontawee Suntisrivaraporn, SCB, Thailand
  • Burasakorn Yoosooka, RMUTB, Thailand
  • Chaveevan Pechsiri, Dhurakij Pundit University, Thailand
  • Chengqing Zong, Institute of Automation, Chinese Academy of Sciences
  • Chi Mai Loung, Institute of Information Technology
  • Choochart Haruechaiyasak, NECTEC, Thailand
  • Chutima Beokhaimook, Rangsit University, Thailand
  • Chutiporn Anutariya, AIT, Thailand
  • Derek F. Wong, University of Macau
  • Ekawit Nantajeewarawat, SIIT Thammasat University, Thailand
  • Enya Kong Tang, Linton University College
  • Hitoshi Nishikawa, Tokyo Institute of Technology, Japan
  • Hutchatai Chanlekha, Kasetsart University, Thailand
  • Joel Ilao, De La Salle University
  • Kanzaki, Kyoko, Toyohashi University of Technology, Japan
  • Khin Mar Soe, University of Computer Studies, Yangon, Myanmar
  • Kiyota Hashimoto, Prince Songkla University, Thailand
  • Maomi Ueno, University of Electro-Communications, Japan
  • Michael Purwoadi, Badan Pengkajian dan Penerapan Teknologi, Indonesia
  • Monthika Boriboon, NECTEC, Thailand
  • Murata Masaki, Tottori University, Japan
  • Nichnan Kittiphattanabawon, Walailak University, Thailand
  • Nuttanart Facundes, KMUTT, Thailand
  • Pokpong Songmuong, Thammasat University, Thailand
  • Preslav Nakov, Qatar Computing Research Institute
  • Puttachart Potibal, Kasetsart University, Thailand
  • Qing Ma, Ryukoku University, Japan
  • Qun Liu, Dublin University, Ireland
  • Rapid Sun, National Institute of Posts, Telecoms and ICT, Cambodia
  • Sasiporn Usanavasin, SIIT Thammasat University, Thailand
  • Sebastian Stuker, Karlsruhe Institute of Technology
  • Shirai Kiyoaki, JAIST, Japan
  • Tasanawan Soonklang, Silpakorn University, Thailand
  • Thadpong Pongthawornkamol, KBTG, Thailand
  • Tokunaga Takenobu, Tokyo Institute of Technology, Japan
  • Vu Tat Thang, Institute of Information Technology, Vietnam
  • Wirote Aroonmanakun, Chulalongkorn University, Thailand
  • Yu-Ju LAN, National Taiwan Normal University, Taiwan

iSAI-NLP Track 2: Data Analytics and Machine Learning

iSAI-NLP Track 2: Data Analytics and Machine Learning

Decision-making is a crucial, yet challenging mission in enterprise management. It is still made based on a reactive approach rather than on facts and proactive approaches. This is often due to unknown correlation between data and goals, conflicting goals and weak defined strategy. Enterprise success depends on fast and well-defined decisions taken by relevant policy makers and business actors in their specific area. Open business intelligent systems can be seen as a collection of decision support technologies and tools for enterprises to enable knowledge workers such as executives, managers, and analysts to make better and faster decisions. With the emergence of big data, it possible to explore new opportunities that will revolutionize business intelligence. These include data warehouse based decision support, Hadoop (development environment), sensor data, social media, machine learning and crowd sourcing. The aim of this workshop is provide a forum to review open business intelligent systems as an open innovation strategy and address their importance in revolutionizing knowledge processing in economics and business sustainability. Topics for the workshop include, but are not limited to:

  • Artificial Intelligence tools & Applications
  • Big Data Mining and Analytics
  • Machine learning
  • Neural Networks
  • Probabilistic Reasoning
  • Evolutionary Computing
  • Pattern recognition
  • Heuristic Planning Strategies and Tools
  • Data Mining and Machine Learning Tools
  • Reactive Distributed AI
  • Hybrid Intelligent Systems
  • Intelligent System Architectures
  • Network Intelligence
  • Multimedia & Cognitive Informatics
  • Pervasive Computing and Ambient Intelligence
  • Semantic Web Techniques and Technologies
  • Web Intelligence Applications & Search
  • Deep Learning
  • Business Intelligent
  • Information Technology Management

Track Chair

  • Olarik Surinta, Mahasarakham University, Thailand
  • Akhilesh Kumar Sharma, Manipal University Jaipur, India
  • Worawut Yimyam, Phetchaburi Rajabhat University, Thailand

Technical Committee

  • Pokpong Songmuang, Thammasat University, Thailand
  • Rachada Kongkachandra, Thammasat University, Thailand
  • Wiwit Suksangaram, Phetchaburi Rajabhat University, Thailand
  • Narumol Chumuang, Muban Chombueng Rajabhat University, Thailand
  • Worawut Yimyam, Phetchaburi Rajabhat University, Thailand
  • Patiyuth Pramkeaw, King Mongkut’s University of Technology Thonburi, Thailand
  • Thadthong Bhrammanee,Phetchaburi Rajabhat University, Thailand
  • Sattarpoom Thaiparnit, Rajamangala University of Technology Suvarnabhumi, Thailand
  • Phayung Meesad, King Mongkut’s University of Technology North Bangkok, Thailand
  • Montean Rattanasiriwongwut, King Mongkut’s University of Technology North Bangkok, Thailand
  • Sakchai Tangwannawit, King Mongkut’s University of Technology North Bangkok, Thailand
  • Mahasak Ketcham, King Mongkut’s University of Technology North Bangkok, Thailand
  • Nattavee Utakrit, King Mongkut’s University of Technology North Bangkok, Thailand
  • Tanapon Jensuttiwetchakul, King Mongkut’s University of Technology North Bangkok, Thailand
  • Watchareewan Jitsakul, King Mongkut’s University of Technology North Bangkok, Thailand
  • Panana Tangwannawit, Phetchabun Rajabhat University, Thailand
  • Pol.Lt.Col. Peerapol Selarat, Commissioner General of the Royal Thai Police, Thailand
  • Tanapon Jensuttiwetchakul, King Mongkut’s University of Technology North Bangkok, Thailand
  • Thittaporn Ganokratanaa, Chulalongkorn University, Thailand
  • Chenyu Shi, University of Groningen, Netherlands
  • Jiapan Guo, University of Groningen, Netherlands
  • Nicola Strisciuglio, University of Groningen, Netherlands
  • Estefania Talavera, University of Groningen, Netherlands
  • Komal Batool, National University of Science and Technology, Pakistan
  • Laura_Fernandez-Robles,University of Leon, Spain
  • Ahmad Alsahaf, University of Groningen, Netherlands
  • Edison Muzenda, University of Science and Technology, South Africa
  • Estefania Talavera, University of Groningen, Netherlands
  • Hiroya Takamura, Tokyo Institute of Technology, Japan
  • Isao Echizen, National Institute of Informatics, Japan
  • Kei Eguchi, Fukuoka Institute of Technology, Japan
  • Kirk Scott, University of Alaska Anchorage, United States of America
  • Michael Pecht, University of Maryland, United States of America
  • Michel Plaisent, University of Quebec in Montreal, Canada
  • Ngoc Hong Tran, Vietnamese German University, Viet Nam
  • Nicola Strisciuglio, University of Groningen, Netherlands
  • Sven Wohlgemuth, Center for Advanced Security Research Darmstadt (CASED), Germany

iSAI-NLP Track 3: Signal, Image and Speech Processing

iSAI-NLP Track 3: Signal, Image & Speech Processing

The signal, image and speech processing (SIS) will focus on basic concepts, methodologies, and successful adoption of signal, image and speech processing and artificial intelligent technology. The conference will use technical papers, challenge papers, invited talks, and panel discussions to explore issues, methods, and lessons learned in the development and deployment of signal, image and speech processing, and AI applications; and to promote an interchange of ideas between basic and applied signal, image and speech processing.

The adoption of signal, image and speech processing and artificial intelligent technology, and in particular of its most challenging components like information and intelligent which can constitute the basic building blocks for a variety of applications within the signal, image and speech processing world. The combination of the emerging information technologies such as information retrieval, computer vision, expert systems, chat bot, social network analysis, and big Data Analyticss lets us transform everyday information into smart knowledge applications.

This track will bring all signal, image and speech processing issues from a diverse group of people working on real world systems for commercial, industrial and academic applications. People from different background will share idea and experience by presenting the study and results leading to intelligent innovation, knowledge and applications.

Authors are solicited to contribute to this session by submitting papers that illustrate research results, projects, surveying works, and industrial experiences that describe significant advances in signal, image and speech processing, intelligent computing and business applications of information systems. Topics for the session include, but are not limited to:

  • AI in image and speech processing
  • Computer vision and virtual reality
  • Content-based image retrieval
  • Content-based indexing, search and retrieval
  • Document recognition
  • Evolution and fuzzy computation
  • Hardware implementation for signal processing
  • Image and video coding and compression
  • Image filtering, restoration, and enhancement
  • Image segmentation
  • Intelligent system and application
  • Multiple filtering and filter banks
  • Object and face detection
  • Pattern analysis and recognition
  • Super-resolution imaging
  • Time-frequency signal analysis
  • Video analysis and event recognition
  • Video compression and streaming
  • Visualization
  • Web intelligence application and search

Track Chair

  • Narit Hnoohom, Mahidol University, Thailand
  • Anuchit Jitpattanakul, King Mongkut ‘s University of Technology North Bangkok, Thailand
  • Ngoc Hong Tran, University College Dublin, Ireland
  • Sakorn Mekruksavanich, University of Phayao, Thailand
  • Thach-Thao Nguyen Duong, University of Burgundy, France

Technical Committee

  • Anantaporn Hanskunatai, King Mongkut’s Institute of Technology Ladkrabang, Thailand
  • Atsuo Yoshikata, JAIST, Japan
  • Catalin Tirnauca, University of cantabria, Spain
  • Colin De La Higuera University of Nantes, France
  • Cristina Tirnauca, University of cantabria, Spain
  • Jiradej Ponsawad, Khon Kaen University, Thailand
  • Kreangsak Tamee, Naresuan University, Thailand
  • Komate Amphawan, Burapha University, Thailand
  • Konlakorn Wongpatikaseree, Mahidol University, Thailand
  • Le-Minh Nguyen, JAIST, Japan
  • Philippe Lenca, University of Nantes, France
  • Saichon Jaiyen, King Mongkut’s Institute of Technology Ladkrabang, Thailand
  • Sakkayaphop Pravesjit, University of Phayao, Thailand
  • Sumeth Yuenyong, Mahidol University, Thailand
  • The-Bao Pham, University of Science, VNU-HCM, Vietnam
  • Trung-Hieu Huynh, Vietnamese-German University, Vietnam

iSAI-NLP Track 4: Robotics, IoT and Embedded System

iSAI-NLP Track 4: Robotics, IoT and Embedded System

High performance embedded computing has recently become more and more present in devices used in everyday life. A wide variety of applications require building up powerful yet cheap embedded devices. In this context, embedded software has turned out to be more and more complex, posing new challenging issues. Design of embedded systems must take into account a wide variety of constraints: performance, code size, power consumption, presence of real-time tasks, robustness, maintainability, security, and possibly scalability. Novel robotics applications is one of a good example in high performance embedded system that can driven by research, industry and society call for the development of systems of ever increasing complexity: systems with sliding autonomy. Software development for autonomous robots and to boost a smooth shifting of results from simulated to real-world applications is needed.

In recent years, Internet has become increasingly pervasive. Internet of Things (IoT) enables large numbers of previously unconnected devices to communicate and exchange data and deal with services that span areas from healthcare to transportation and much more, with the potential for significant benefits to people and quality of life. The IoT would also enable a range of new capabilities and services far beyond today’s offerings, which will radically change the life styles of future human generation. Areas of interest include, but not limited to:

  • Embedded Software and Compilers
  • Health and medical wireless applications
  • OS and middleware for mobile computing
  • Parallel architectures and computational models
  • Control algorithms and control systems
  • Manufacturing robotics
  • Computational methodologies in robotics
  • Human-Robot Interaction
  • Robotic cognition and emotion
  • Robotic perception and decision
  • Sensor integration, fusion, and perception
  • IoT Application and Services
  • IoT Mobility, localization, tracking & security

Track Chair

  • Mahasak Ketcham, King Mongkut’s University of Technology North Bangkok, Thailand
  • Michael Pecht, University of Maryland, United States of America
  • Thaweesak Yingthawornsuk, King Mongkut’s University of Technology Thonburi, Thailand

Technical Committee

  • Vijay Kumar Banga, Amritsar College of Engineering & Technology, India.
  • Ahmad Alsahaf, University of Groningen, Netherlands.
  • Estefania Talavera, University of Groningen, Netherlands.
  • Gurjeet Singh, Amritsar College of Engineering & Technology, India.
  • Hiroya Takamura, Tokyo Institute of Technology, Japan.
  • Isao Echizen, National Institute of Informatics, Japan.
  • Kazuaki Maeda, Chubu University, Japan.
  • Kei Eguchi, Fukuoka Institute of Technology, Japan.
  • Michel Plaisent, University of Quebec in Montreal, Canada.
  • Montean Rattanasiriwongwut, King Mongkut’s University of Technology North Bangkok, Thailand.
  • Nattavee Utakrit, King Mongkut’s University of Technology North Bangkok, Thailand.
  • Nicola Strisciuglio, University of Groningen, Netherlands.
  • Patiyuth Pramkeaw, King Mongkut’s University of Technology Thonburi, Thailand.
  • Rakesh Jaitly, Amritsar College of Engineering & Technology, India.
  • Sakchai Tangwannawit, King Mongkut’s University of Technology North Bangkok, Thailand.
  • Tanapon Jensuttiwetchakul, King Mongkut’s University of Technology North Bangkok, Thailand.
  • Thittaporn Ganokratanaa, Chulalongkorn University, Thailand.

iSAI-NLP Track 5: Smart Industrial Technologies

iSAI-NLP Track 5: Smart Industrial Technologies

One science-related technology by applying technology to various tasks in the industry. In addition, including management quality control plant layout, industrial technician. There is knowledge in both smart technology and management will be able to work well with engineering science. Topics for the workshop include, but are not limited to:

  • Smart Home and Smart Building
  • Smart Material
  • Smart Transportation and Infrastructure
  • Smart Grid
  • Smart City and Technology Application
  • Smart Energy and Efficient-Networks
  • Autonomous Vehicles
  • Energy Storage Technology
  • Big Data, Machine Learning and Artificial intelligence for Industry Management
  • Condition Monitoring and Control for Intelligence Manufacturing
  • Smart Management for Industry
  • Smart and Technology in Tourism
  • Smart and Technology in Education
  • Learning Innovation Technology
  • Basic Research for Smart Industry
  • Relate topic in Smart Technology and Engineering

Track Chair

  • Narumol Chumuang, Muban Chombueng Rajabhat University, Thailand
  • Adil Farooq, University of Tartu, Estonia
  • Sumate Lipirodjanapong, Muban Chombueng Rajabhat University, Thailand

Technical Committee

  • Jiapan Guo, University of Groningen, Netherlands
  • Phadungath Chanokphat, Silapakorn University, Thailand
  • Ahmad Alsahaf, University of Groningen, Netherlands
  • Charin Namarak, Muban Chombueng Rajabhat University, Thailand
  • Laura_Fernandez-Robles,University of Leon, Spain
  • Estefania Talavera, University of Groningen, Netherlands
  • Satcha Kaisornrat, Muban Chombueng Rajabhat University, Thailand
  • Komal Batool, National university of Science and Technology, Pakistan
  • Krung Luewattana, Phranakorn Si Ayuthaya Rajabhat University, Thailand
  • Nicola Strisciuglio, University of Groningen, Netherlands
  • Burin Narin, Muban Chombueng Rajabhat University, Thailand
  • Isao Echizen, National Institute of Informatics, Japan
  • Phumin Sumalai, Muban Chombueng Rajabhat University, Thailand
  • Kirk Scott, University of Alaska Anchorage, United States of America
  • Surasak Inchan, Muban Chombueng Rajabhat University, Thailand
  • Michel Plaisent, University of Quebec in Montreal, Canada
  • Sven Wohlgemuth, Center for Advanced Security Research Darmstadt (CASED), Germany
  • Hiroya Takamura, Tokyo Institute of Technology, Japan
  • Kei Eguchi, Fukuoka Institute of Technology, Japan
  • Narumol Chumuang, Muban Chombueng Rajabhat University, Thailand
  • Sumate Lipirodjanapong, Muban Chombueng Rajabhat University, Thailand
  • Adil Farooq, University of Tartu, Estonia
  • Wasan Naksanee, Muban Chombueng Rajabhat University, Thailand