KTCCS

[Submission Due Date - 2023-10-30]
 

ICT technologies and convergence practices for Digital Transformation, DX

KIPS Transactions on Computer and Communication Systems

Description

Digital Transformation (DX) technology is leading innovation across society and creating a new ecosystem of industrial convergence. In particular, AI, data, cloud, edge computing, AIoT, and digital twin technologies are core technologies for digital transformation. Earlier digital transformation cases were simple digital transformation with single ICT technologies such as AI, cloud, and data, but now, various digital transformation is taking place in all areas such as smart city, digital finance, digital manufacturing, digital agriculture, and digital fishery by converging multiple ICT technologies in various industries. Therefore, this special issue solicits papers on AI, cloud, data, AIoT, and digital twin technologies that are being applied for digital transformation in various areas, as well as ICT applications and convergence technologies that are being applied for digital transformation in various areas. In addition, this special issue also solicits papers on the history of ICT technology changes in the past and present to mark the 30th anniversary of the Korea Information Processing Society.

Scope & Topics

Submission Guideline

Schedule

Guest Editors

Prof. YongGeun Hong (Daejeon University, Korea)

[Submission Due Date - 2020-09-15]
 

DNA (Data, Network, AI) based Intelligent Information Technology

KIPS Transactions on Computer and Communication Systems

Description

DNA (Data, Network, AI) is a relatively new term and has recently become a hot topic which combines three of the hottest acronyms. Recently, DNA-based intelligent services and applications are actively developed with IoT Technology. IoT consists of interconnected things with built-in sensors and has the potential to generate or collect a vast amount of data. Collected data can be then analyzed and utilized with AI for problem solving or decision making for Intelligent ICT service. Without AI, IoT and Data would have limited value. AI can multiply the value of Data collected by IoT; conversely, IoT can promote the learning and intelligence of AI.

There are many challenges while deploying DNA and DNA based intelligent applications in practice. For instance, the edge computing issues for supporting the low latency of transmitting huge data in networks and achieving real-time responses with high-performance AI for analyzing the huge data. Security issue is another challenge to provide privacy and safety in terms of data protection in personality applications based IoT system. Machine learning is one of the possible technologies to be utilized in DNA system protection. Besides, there are many other issues related to the increasing DNA systems and DNA based applications.

Since the combination of Data, Network and AI, namely DNA, has various potential technology to be able to develop Intelligent application in the real world, this Special Issue aims to solicit the submission of original research articles with a focus on challenging issues in the field of DNA technologies and Intelligent applications based on DNA. Both theoretical and experimental contributions containing novel architectures, systems, algorithms, approaches, techniques, or applications with new insights and findings in the field of DNA are welcome.
 

Scope & Topics

Submission Guideline

Schedule

Guest Editors

Prof. Joosang Youn (Dong-Eui University)

[Submission Due Date - 2019-01-20 ]
 

Machine Learning in IoT and Edge Computing

KIPS Transactions on Computer and Communication Systems

Description

The Internet of Things (IoT) advances and promotes a variety of wired and wireless technologies through embedded devices, smart objects, and smartphones. The integration of IoT along with Wireless Sensor Networks (WSN) based on new Bluetooth standards, such as Bluetooth Low Energy (BLE) 4.0 and ZigBee, unlocks new IoT applications and services in the future generation of networks. IoT devices generate a massive volume of data continuously. In general, cloud systems are used to store and compute such a large volume of IoT sensor data. This creates even more challenges in data storage and the integration between IoT devices. In order to overcome this problem, edge computing technology moves the collection, process, and analysis of data into the point of origin, rather than to a cloud-based data center, and it enables data processing and analytics as well as knowledge generation to occur in the vicinity of the source of the data. Thus, edge computing technology is becoming a significant platform for enabling consumer-centric IoT services and applications that demand real-world applications. In the meantime, machine learning is a branch of artificial intelligence (AI) that focuses on enabling machines to learn for themselves without the need for human intervention or to be explicitly programmed to do so. Machine learning on IoT devices allows for learning models directly on the devices themselves, removing the need to externalize data in any way. Machine learning algorithms have a capability to process and analyze a kind of streaming data generated at the edge of the network and IoT devices. This special issue focuses on challenges and problems in machine learning algorithms for processing, learning and discovering knowledge from IoT devices and edge computing technology.
 

Scope

Submission Guideline

Schedule

Guest Editors

Prof. Kyungbaek Kim (Chonnam National University)

[Submission Due Date - 2016-10-30 ]

Description

We plan to publish a new special issue. We are soliciting high quality manuscripts presenting original contributions for its special issue, and the manuscripts should be submitted by our online submission and review system. When submitting a new manuscript, please select 'Special Issue' track and then select 'Android Security' special issue, too.
 

Scope

Topics of interest include, but are not limited to:

• - Android system security
• - Android app security
• - Android hardware security
• - Android cryptography
• - Applying machine learning to android security
• - Android security in IoT Environment
• - Other android security study
   

Schedule

Guest Editors

KTSDE

[Submission Due Date - 2023-09-30  ]

TOPICS and SCOPE

Topics of KTSDE-Surveys & Reviews cover all aspects of software and data engineering fields. 

• - Artificial Intelligence and Machine Learning
• - Big Data and Cognitive Computing
• - Next-generation Communication
• - Edge and Cloud Computing
• - Internet of Things and Fog Computing
• - Vehicular and Mobility Technology
• - Augmented Reality and Virtual Reality
• - Metaverse and Web 3.0
• - Blockchains and Cryptography

Schedule

Guest Editor

[Submission Due Date - 2021-09-30  2021-10-07  ]

TOPICS and SCOPE

Topics of interest include, but are not limited to :

• - AI application and implementation technology
• - AI application and service technology
• - AI mobile application
• - AI platforms and frameworks
• - AI application training
• - Use cases of AI technology in industrial
• - AI-related research areas

Schedule

Guest Editor

[Submission Due Date - 2020-10-11 ]

TOPICS and SCOPE

Topics of interest include, but are not limited to :

• - Edutech
• - Untact education
• - AI-based education
• - MOOCs
• - Interaction and accessibility for e-Leaning
• - Industry policy for edutech and untact education
• - e-Learning Systems
• - Gamification in e-Learning
• - Learning Analytics

Schedule

Guest Editor

[Submission Due Date - 2016-10-30 ]

Scope

Topics of interest include, but are not limited to:

• - Android system security
• - Android app security
• - Android hardware security
• - Android cryptography
• - Applying machine learning to android security
• - Android security in IoT Environment
• - Other android security study

Schedule

Guest Editors