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	5.5	2011	16 Days	CHF 2400
Computers computers	4.2	7.5	2012	17.5 Days	CHF 1800
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400
Journal of Sensor and Actuator Networks jsan	4.2	9.4	2012	23.6 Days	CHF 2000
Technologies technologies	3.6	8.5	2013	19.1 Days	CHF 1800

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Published Papers (5 papers)

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All Applied Sciences Computers Electronics JSAN Technologies

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24 pages, 3277 KB  

Open AccessArticle

FT-iTransformer: A Stock Price Prediction Model Based on Time–Frequency Domain Collaborative Analysis

by Zheng Zou, Xi-Xi Zhou, Shi-Jian Liu and Chih-Yu Hsu

Technologies 2026, 14(1), 61; https://doi.org/10.3390/technologies14010061 - 14 Jan 2026

Viewed by 861

Abstract

The stock market serves as an important channel for investors to preserve and increase their assets and has attracted significant attention. However, stock price is affected by multiple factors and represents complex characteristics such as high volatility, nonlinearity, and non-stationarity, making accurate prediction [...] Read more.

The stock market serves as an important channel for investors to preserve and increase their assets and has attracted significant attention. However, stock price is affected by multiple factors and represents complex characteristics such as high volatility, nonlinearity, and non-stationarity, making accurate prediction highly challenging. To improve forecasting accuracy, this study proposes FT-iTransformer, a stock price prediction model based on time–frequency domain collaborative analysis. The model integrates a frequency domain feature extraction module and a multi-scale temporal convolution network module to comprehensively capture both time and frequency domain features, and then the extracted features are fused and input into iTransformer. It models the complex relationships among multiple variables through the self-attention mechanism, utilizes the feedforward network to capture temporal dependencies, and finally the prediction results are output through the projection layer. This study conducts both comparative and ablation experiments on six stock datasets to evaluate the proposed FT-iTransformer model. The results of comparative experiments show that, compared with seven mainstream baseline models, such as LSTM, Informer, and FEDformer, FT-iTransformer achieves superior performance on all evaluation metrics. Furthermore, the results of ablation experiments exhibit the contributions of each core module to the overall predictive performance, and confirming the validity of the model’s design. In summary, FT-iTransformer provides an effective framework for predicting stock price accurately. Full article

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

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13 pages, 436 KB  

Open AccessArticle

AI Training Data Management for Reliable Autonomous Vehicles Using Hashgraph

by Yeonsong Suh, Yoonseo Chung and Younghoon Park

Appl. Sci. 2025, 15(11), 6123; https://doi.org/10.3390/app15116123 - 29 May 2025

Cited by 1 | Viewed by 1647

Abstract

Autonomous vehicles have attracted considerable attention from researchers and organizations, with artificial intelligence (AI) playing a key role in this technology. For AI models in autonomous vehicles to be reliable, the integrity of the training data is crucial, resulting in the development of [...] Read more.

Autonomous vehicles have attracted considerable attention from researchers and organizations, with artificial intelligence (AI) playing a key role in this technology. For AI models in autonomous vehicles to be reliable, the integrity of the training data is crucial, resulting in the development of various blockchain-based management systems. However, conventional blockchain systems incur significant time delays when processing training data transactions, posing challenges in autonomous vehicle environments that require real-time processing. In this study, we propose a hashgraph-based training data management system for trusted AI. To validate our system, we conducted simulations using the CARLA simulator and compared its performance to a conventional blockchain-based system. The simulation results show that Hedera achieved significantly lower latencies and better scalability than Ethereum, confirming its suitability for secure and efficient AI data verification in autonomous systems. Full article

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

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18 pages, 1405 KB  

Open AccessArticle

Dual-Network Layered Network: A Method to Improve Reliability, Security, and Network Efficiency in Distributed Heterogeneous Network Transmission

by Shengyuan Qi, Lin Yang, Linru Ma, Shanqing Jiang and Guang Cheng

Electronics 2024, 13(23), 4749; https://doi.org/10.3390/electronics13234749 - 30 Nov 2024

Cited by 3 | Viewed by 1836

Abstract

This article delves into the routing architecture and reliable transmission service framework of dual-network layered networks, with a focus on analyzing their core design ideas and implementation strategies. In the context of increasing network complexity today, traditional single-network architectures are unable to meet [...] Read more.

This article delves into the routing architecture and reliable transmission service framework of dual-network layered networks, with a focus on analyzing their core design ideas and implementation strategies. In the context of increasing network complexity today, traditional single-network architectures are unable to meet diverse application needs. Therefore, dual-network layered networks, as an innovative solution, are gradually receiving attention from both academia and industry. This article first analyzes the key technical elements in the dual-network layered network architecture, including the optimization of routing algorithms, distributed consensus, reliability assurance mechanisms for packet transmission, and dynamic allocation strategies for network resources. Through in-depth research on these technologies, this article elaborates on the important role of dual-network layered networks in building efficient and stable transmission environments, providing important theoretical foundations and technical support for the construction and optimization of future network systems. Full article

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

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17 pages, 441 KB  

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

Cited by 3 | Viewed by 4078

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 KB  

Open AccessEditor’s ChoiceArticle

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

Cited by 1 | Viewed by 2676

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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