Emerging AI+X Technologies and Applications

Topic Information

Dear Colleagues,

The 12th International Conference on Green and Human Information Technology (ICGHIT 2024) will be held on 23–25 January, 2024, in Hanoi, Vietnam (http://icghit.org/).

The 12th International Conference on Green and Human Information Technology is a unique global conference for researchers, industry professionals, and academics who are interested in the latest green and human information technologies. The main theme of ICGHIT this year is “Towards of Artificial Super Intelligence”. The latest Artificial Super Intelligence technologies are used by the general population and present us with major challenges and great opportunities at the same time.

Centering around the main theme, ICGHIT offers an exciting program: hands-on experience-based tutorial sessions and special sessions covering research issues and directions with theoretical and practical applications. The conference will also include plenary, special, and technical sessions, and workshops.

Highly qualified papers selected from ICGHIT 2024 will be included in this Topic. However, this Topic also welcomes submissions from general researchers that fit within the scope of the SI, as shown below.

In this Topic, original research articles and reviews are welcome. The research areas may include (but are not limited to) the following:

Emerging AI+X technologies
Deep Neural Network;
Artificial Super Intelligence;
AI-based Applications in any area (Robotics, HCI, Smart City, Autonomous Driving, etc.)

Green Information Technology
Green Technology and Energy Saving;
Green Computing and Green IT Convergence and Applications;
Energy-Harvest-based Communications and Networking;
Technologies for Network Sustainability.

Communications and Networks for IoT and 5/6G-based network
Communications and Networks for Massive IoT and 5G;
Optical and Visual Light Communication;
Distributed/De-centralized Networks;
M2M/IoT and Ubiquitous Computing;
NFV, SDN, ICN, Network Slicing;
AI- and ML-based Technologies.

Network Security
Block-Chain-based Networking and Applications;
Distributed PKI;
Applied Cryptography;
Security in Big Data and Cloud Computing;
Security for Future Internet Architecture (SDN, ICN, etc.).

Network SW/HW Design, Architecture and Applications
Architecture and Protocols;
Sustainable Networks;
Information-centric Networks;
Blockchain-based Secure Networks;
AI-based Self-evolving networks;
Sensor/RFID Circuit Design;
System on Chip (SoC);
IC System for Communication.

We look forward to receiving your contributions.

Prof. Dr. Byung-Seo Kim
Prof. Dr. Hyunsik Ahn
Dr. Kyu-Tae Lee
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Computers computers	2.6	5.4	2012	17.2 Days	CHF 1800	Submit
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400	Submit
Journal of Sensor and Actuator Networks jsan	3.3	7.9	2012	22.6 Days	CHF 2000	Submit
Technologies technologies	4.2	6.7	2013	24.6 Days	CHF 1600	Submit

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

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Published Papers (2 papers)

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All Applied Sciences Computers Electronics JSAN Technologies

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17 pages, 441 KiB  

Open AccessReview

A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education

by Haseeb Ali Khan, Sonain Jamil, Md. Jalil Piran, Oh-Jin Kwon and Jong-Weon Lee

Technologies 2024, 12(5), 72; https://doi.org/10.3390/technologies12050072 - 19 May 2024

Viewed by 1613

Abstract

Machine learning (ML) is enabling augmented reality (AR) to gain popularity in various fields, including gaming, entertainment, healthcare, and education. ML enhances AR applications in education by providing accurate visualizations of objects. For AR systems, ML algorithms facilitate the recognition of objects and [...] Read more.

Machine learning (ML) is enabling augmented reality (AR) to gain popularity in various fields, including gaming, entertainment, healthcare, and education. ML enhances AR applications in education by providing accurate visualizations of objects. For AR systems, ML algorithms facilitate the recognition of objects and gestures from kindergarten through university. The purpose of this survey is to provide an overview of various ways in which ML techniques can be applied within the field of AR within education. The first step is to describe the background of AR. In the next step, we discuss the ML models that are used in AR education applications. Additionally, we discuss how ML is used in AR. Each subgroup’s challenges and solutions can be identified by analyzing these frameworks. In addition, we outline several research gaps and future research directions in ML-based AR frameworks for education. Full article

(This article belongs to the Topic Emerging AI+X Technologies and Applications)

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18 pages, 3587 KiB  

Open AccessArticle

Extended-Window Algorithms for Model Prediction Applied to Hybrid Power Systems

by Fu-Cheng Wang and Hsiao-Tzu Huang

Technologies 2024, 12(1), 6; https://doi.org/10.3390/technologies12010006 - 5 Jan 2024

Viewed by 1982

Abstract

This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of [...] Read more.

This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of prediction models and corresponding changes in system parts and power management based on the accumulated data. We first develop a hybrid power model to evaluate system responses under different conditions. We then build prediction models using five artificial intelligence algorithms. Among them, the light gradient boosting machine and extreme gradient boosting methods achieve the highest accuracies for predicting solar radiation and load responses, respectively. Therefore, we apply these two models to forecast solar and load responses. Third, we introduce extended-window algorithms and investigate the effects of window sizes and replacement costs on system performance. The results show that the optimal window size is one week, and the system cost is 13.57% lower than the cost of the system that does not use the extended-window algorithms. The proposed method also tends to make fewer component replacements when the replacement cost increases. Finally, we design experiments to demonstrate the feasibility and effectiveness of systems using extended-window model prediction. Full article

(This article belongs to the Topic Emerging AI+X Technologies and Applications)

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