Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

Journal Browser

Advanced Artificial Intelligence Theories and Applications

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 17582

Share This Special Issue

Special Issue Editors

Prof. Dr. Huaqing Li

E-Mail Website
Guest Editor

College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Interests: multi-agent system; distributed collaborative control; distributed optimization theory and application; complex systems and complex networks

Dr. Qingguo Lü

E-Mail Website
Guest Editor

College of Computer Science, Chongqing University, Chongqing 400044, China
Interests: decentralized machine learning; optimization and control; privacy security; smart grids; swarm intelligence system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence has attracted great attention in almost every field and is one of the most promising fields today. Artificial intelligence theories and applications have now penetrated many fields beyond the traditional computer engineering field. As an important driving force of the new round of technological revolution and industrial change, artificial intelligence is widely used in medical, financial, and transportation fields, bringing huge economic and social benefits. However, with the deepening of artificial intelligence applications, its technical flaws and the problems brought about by decision bias and usage safety have triggered a crisis of trust. Thus, the research community is still investigating the most advanced artificial intelligence techniques to improve application reliability.

This special issue aims to propagate the latest research results and developments in artificial intelligence, with a special interest in its advanced theories and practical applications in computer science, industrial engineering, electronic information, control science, communication engineering, and other fields.

We kindly invite researchers and practitioners to contribute their high-quality original research or review articles discussing current cutting-edge research topics in artificial intelligence, such as big data and data analysis, deep learning, distributed computing, human action recognition, image classification and segmentation, information fusion, industrial automation, deep neural networks, signal processing, edge computing communications, natural language processing and applications, information security and privacy, networked systems, control and optimization, etc. Analytical, numerical, and experimental works which contribute to the development of theories and applications of artificial intelligence, are welcome.

Prof. Dr. Huaqing Li
Dr. Qingguo Lv
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
big data analytics
information processing
machine learning
human interaction
complex systems
control and optimization
computing approaches
theories and applications
communication mechanisms

Published Papers (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

15 pages, 1237 KiB

Open AccessArticle

φunit: A Lightweight Module for Feature Fusion Based on Their Dimensions

by Zhengyu Long, Rigui Zhou, Yaochong Li, Pengju Ren, Xue Yang and Shuo Cai

Appl. Sci. 2023, 13(23), 12621; https://doi.org/10.3390/app132312621 - 23 Nov 2023

Viewed by 581

Abstract

With the popularity of mobile devices, lightweight deep learning models have important value in various application scenarios. However, how to effectively fuse the feature information from different dimensions while ensuring the model’s lightness and high accuracy is a problem that has not been fully solved. In this paper, we propose a novel feature fusion module, called φunit, which can fuse the features extracted by different dimensional networks according to the order of feature information with a small computational cost, avoiding the problems of information fragmentation caused by simple feature stacking in traditional information fusion. Based on φunit, this paper further builds an extremely lightweight model φNet, which can achieve performance close to the highest accuracy on several public datasets under the condition of very limited parameter scale. The core idea of φunit is to use deconvolution to reduce the discrepancy among the features to be fused, and to lower the possibility of feature information fragmentation after fusion by fusing the features from different dimensions sequentially. φNet is a lightweight network composed of multiple φunits and bottleneck modules, with a parameter scale of only 1.24 M, much smaller than traditional lightweight models. This paper conducts experiments on public datasets, and φNet achieves an accuracy of 71.64% on the food101 dataset, and an accuracy of 75.31% on the random 50-category food101 dataset, both higher than or close to the highest accuracy. This paper provides a new idea and method for feature fusion of lightweight models, and also provides an efficient model selection for deep learning applications on mobile devices. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Theories and Applications)

► Show Figures

Figure 1

17 pages, 506 KiB

Open AccessArticle

Decentralized Coordination of DERs for Dynamic Economic Emission Dispatch

by Jingtong Dai and Zheng Wang

Appl. Sci. 2023, 13(22), 12431; https://doi.org/10.3390/app132212431 - 17 Nov 2023

Viewed by 607

Abstract

This paper focuses on the dynamic economic emission dispatch (DEED) problem, to coordinate the distributed energy resources (DERs) in a power system and achieve economical and environmental operation. Distributed energy storages (ESs) are introduced into problem formulation in which charging/discharging efficiency is taken into account. By relaxing the nonconvexity induced by the charging/discharging model of ESs and network losses, we convert the non-convex DEED problem into its convex equivalency. Then, through a Lagrangian duality reformulation, an equivalent unconstrained consensus optimization model is established—a novel consensus-based decentralized algorithm, where the incremental cost is chosen as the consensus variable. At each iteration, only one primal variable requires sub-optimization, and it is completely locally updated. This is different from the well-known alternating direction method of multiplier (ADMM)-based algorithms where more than one subproblem needs to be solved at each iteration. The results of the comparative experiments also reflect the algorithm’s advantage in terms of computational efficiency. The simulation results validate the effectiveness of the proposed algorithm, achieving a balance between emissions and economic considerations. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Theories and Applications)

► Show Figures

Figure 1

23 pages, 888 KiB

Open AccessArticle

A Meta Reinforcement Learning Approach for SFC Placement in Dynamic IoT-MEC Networks

by Shuang Guo, Yarong Du and Liang Liu

Appl. Sci. 2023, 13(17), 9960; https://doi.org/10.3390/app13179960 - 03 Sep 2023

Viewed by 972

Abstract

In order to achieve reliability, security, and scalability, the request flow in the Internet of Things (IoT) needs to pass through the service function chain (SFC), which is composed of series-ordered virtual network functions (VNFs), then reach the destination application in multiaccess edge computing (MEC) for processing. Since there are usually multiple identical VNF instances in the network and the network environment of IoT changes dynamically, placing the SFC for the IoT request flow is a significant challenge. This paper decomposes the dynamic SFC placement problem of the IoT-MEC network into two subproblems: VNF placement and path determination of routing. We first formulate these two subproblems as Markov decision processes. We then propose a meta reinforcement learning and fuzzy logic-based dynamic SFC placement approach (MRLF-SFCP). The MRLF-SFCP contains an inner model that focuses on making SFC placement decisions and an outer model that focuses on learning the initial parameters considering the dynamic IoT-MEC environment. Specifically, the approach uses fuzzy logic to pre-evaluate the link status information of the network by jointly considering available bandwidth, delay, and packet loss rate, which is helpful for model training and convergence. In comparison to existing algorithms, simulation results demonstrate that the MRLF-SFCP algorithm exhibits superior performance in terms of traffic acceptance rate, throughput, and the average reward. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Theories and Applications)

► Show Figures

Figure 1

14 pages, 920 KiB

Open AccessArticle

Distributed GNE Seeking under Global-Decision and Partial-Decision Information over Douglas-Rachford Splitting Method

by Jingran Cheng, Menggang Chen, Huaqing Li, Yawei Shi, Zhongzheng Wang and Jialong Tang

Appl. Sci. 2023, 13(12), 7058; https://doi.org/10.3390/app13127058 - 12 Jun 2023

Viewed by 758

Abstract

This paper develops an algorithm for solving the generalized Nash equilibrium problem (GNEP) in non-cooperative games. The problem involves a set of players, each with a cost function that depends on their own decision as well as the decisions of other players. The goal is to find a decision vector that minimizes the cost for each player. Unlike most of the existing algorithms for GNEP, which require full information exchange among all players, this paper considers a more realistic scenario where players can only communicate with a subset of players through a connectivity graph. The proposed algorithm enables each player to estimate the decisions of other players and update their own and others’ estimates through local communication with their neighbors. By introducing a network Lagrangian function and applying the Douglas-Rachford splitting method (DR), the GNEP is reformulated as a zero-finding problem. It is shown that the DR method can find the generalized Nash equilibrium (GNE) of the original problem under some mild conditions. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Theories and Applications)

► Show Figures

Figure 1

20 pages, 1270 KiB

Open AccessArticle

Artificial Bee Colony Algorithm with Pareto-Based Approach for Multi-Objective Three-Dimensional Single Container Loading Problems

by Suriya Phongmoo, Komgrit Leksakul, Nivit Charoenchai and Chawis Boonmee

Appl. Sci. 2023, 13(11), 6601; https://doi.org/10.3390/app13116601 - 29 May 2023

Cited by 4 | Viewed by 1152

Abstract

The ongoing container shortage crisis has presented significant challenges for the freight forwarding industry, requiring companies to implement adaptive measures in order to maintain peak operational efficiency. This article presents a novel mathematical model and artificial bee colony algorithm (ABC) with a Pareto-based approach to solve single-container-loading problems. The goal is to fit a set of boxes with strongly heterogeneous boxes into a container with a specific dimension to minimize the broken space and maximize profits. Furthermore, the proposed algorithm incorporates the bottom-left fill method, which is a heuristic strategy for packing containers. We conducted numerical testing to identify optimal parameters using the

\tilde{C}

metric method. Subsequently, we evaluated the performance of our proposed algorithm by comparing it to other heuristics and meta-heuristic approaches using the relative improvement (RI) value. Our analysis showed that our algorithm outperformed the other approaches and achieved the best results. These results demonstrate the effectiveness of the proposed algorithm in solving real-world single-container-loading problems for freight forwarding companies. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Theories and Applications)

► Show Figures

Figure 1

17 pages, 624 KiB

Open AccessArticle

Distributed GNE-Seeking under Partial Information Based on Preconditioned Proximal-Point Algorithms

by Zhongzheng Wang, Huaqing Li, Menggang Chen, Jialong Tang, Jingran Cheng and Yawei Shi

Appl. Sci. 2023, 13(11), 6405; https://doi.org/10.3390/app13116405 - 24 May 2023

Viewed by 824

Abstract

This paper proposes a distributed algorithm for games with shared coupling constraints based on the variational approach and the proximal-point algorithm. The paper demonstrates the effectiveness of the proximal-point algorithm in distributed computing of generalized Nash equilibrium (GNE) problems using local data and communication with neighbors in any networked game. The algorithm achieves the goal of reflecting local decisions in the Nash–Cournot game under partial-decision information while maintaining the distributed nature and convergence of the algorithm. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Theories and Applications)

► Show Figures

Figure 1

11 pages, 3327 KiB

Open AccessArticle

Label-Free Model Evaluation with Out-of-Distribution Detection

by Fangzhe Zhu, Ye Zhao, Zhengqiong Liu and Xueliang Liu

Appl. Sci. 2023, 13(8), 5056; https://doi.org/10.3390/app13085056 - 18 Apr 2023

Cited by 1 | Viewed by 1029

Abstract

In recent years, label-free model evaluation has been developed to estimate the performance of models on unlabeled test sets. However, we find that existing methods perform poorly in environments with out-of-distribution (OOD) data. To address this issue, we propose a novel automatic model evaluation method using OOD detection to reduce the impact of OOD data on model evaluation. Specifically, we use the representation of datasets to train a neural network for accuracy prediction and employ energy-based OOD detection to exclude OOD data during testing. We conducted experiments on several benchmark datasets with varying amounts of OOD data (SVHN, ISUN, ImageNet, and LSUN) and demonstrated that our method reduces the RMSE compared to existing methods by at least 1.27%. Additionally, we tested our method on transformed datasets and datasets with a high proportion of OOD data, and the results show its robustness. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Theories and Applications)

► Show Figures

Figure 1

18 pages, 4532 KiB

Open AccessArticle

Vectorized Representation of Commodities by Fusing Multisource Heterogeneous User-Generated Content with Multiple Models

by Guangyi Man, Xiaoyan Sun and Weidong Wu

Appl. Sci. 2023, 13(7), 4217; https://doi.org/10.3390/app13074217 - 27 Mar 2023

Viewed by 971

Abstract

In the field of personalized recommendation, user-generated content (UGC) such as videos, images, and product comments are becoming increasingly important, since they implicitly represent the preferences of users. The vectorized representation of a commodity with multisource and heterogeneous UGC is the key for sufficiently mining the preference information to make a recommendation. Existing studies have mostly focused on using one type of UGC, e.g., images, to enrich the representation of a commodity, ignoring other contents. When more UGC are fused, complicated models with heavy computation cost are often designed. Motivated by this, we proposed a low-computational-power model for vectorizing multisource and recommendation UGC to achieve accurate commodity representations. In our method, video description keyframes, commodities’ attribute text, and user comments were selected as the model’s input. A multi-model fusion framework including feature extraction, vectorization, fusion, and classification based on MobileNet and multilayer perceptrons was developed. In this UGC fusion framework, feature correlations between images and product comments were extracted to design the loss function to improve the precision of vectorized representation. The proposed algorithm was applied to an actual representation of a commodity described by UGC, and the effectiveness of the proposed algorithm was demonstrated by the classification accuracy of the commodity represented. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Theories and Applications)

► Show Figures

Figure 1

17 pages, 30513 KiB

Open AccessArticle

A Novel Small Target Detection Strategy: Location Feature Extraction in the Case of Self-Knowledge Distillation

by Gaohua Liu, Junhuan Li, Shuxia Yan and Rui Liu

Appl. Sci. 2023, 13(6), 3683; https://doi.org/10.3390/app13063683 - 14 Mar 2023

Viewed by 1066

Abstract

Small target detection has always been a hot and difficult point in the field of target detection. The existing detection network has a good effect on conventional targets but a poor effect on small target detection. The main challenge is that small targets have few pixels and are widely distributed in the image, so it is difficult to extract effective features, especially in the deeper neural network. A novel plug-in to extract location features of the small target in the deep network was proposed. Because the deep network has a larger receptive field and richer global information, it is easier to establish global spatial context mapping. The plug-in named location feature extraction establishes the spatial context mapping in the deep network to obtain the global information of scattered small targets in the deep feature map. Additionally, the attention mechanism can be used to strengthen attention to the spatial information. The comprehensive effect of the above two can be utilized to realize location feature extraction in the deep network. In order to improve the generalization of the network, a new self-distillation algorithm was designed for pre-training that could work under self-supervision. The experiment was conducted on the public datasets (Pascal VOC and Printed Circuit Board Defect dataset) and the self-made dedicated small target detection dataset, respectively. According to the diagnosis of the false-positive error distribution, the location error was significantly reduced, which proved the effectiveness of the plug-in proposed for location feature extraction. The mAP results can prove that the detection effect of the network applying the location feature extraction strategy is much better than the original network. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Theories and Applications)

► Show Figures

Figure 1

26 pages, 3192 KiB

Open AccessArticle

Analysis of Factors Affecting Purchase of Self-Defense Tools among Women: A Machine Learning Ensemble Approach

by Rianina D. Borres, Ardvin Kester S. Ong, Tyrone Wyeth O. Arceno, Allyza R. Padagdag, Wayne Ralph Lee B. Sarsagat, Hershey Reina Mae S. Zuñiga and Josephine D. German

Appl. Sci. 2023, 13(5), 3003; https://doi.org/10.3390/app13053003 - 26 Feb 2023

Cited by 2 | Viewed by 6332

Abstract

Street crime is one of the world’s top concerns and a surge in cases has alarmed people, particularly women. Related studies and recent news have provided proof that women are the target for crimes and violence at home, outdoors, and even in the workplace. To guarantee protection, self-defense tools have been developed and sales are on the rise in the market. The current study aimed to determine factors influencing women’s intention to purchase self-defense tools by utilizing the Protection Motivation Theory (PMT) and the Theory of Planned Behavior (TPB). The study applied multiple data analyses, Machine Learning Algorithms (MLAs): Decision Tree (DT), Random Forest Classifier (RFC), and Deep Learning Neural Network (DLNN), to predict purchasing and consumer behavior. A total of 553 Filipino female respondents voluntarily completed a 46-item questionnaire which was distributed online, yielding 22,120 data points. The MLAs output showed that attitude, perceived risk, subjective norm, and perceived behavioral control were the most significant factors influencing women’s intention to purchase self-defense tools. Environment, hazardous surroundings, relatives and peers, and thinking and control, all influenced the women’s intention to buy self-defense tools. The RFC and DLNN analyses proved effective, resulting in 96% and 97.70% accuracy rates, respectively. Finally, the MLA analysis in this research can be expanded and applied to predict and assess factors affecting human behavior in the context of safety. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Theories and Applications)

► Show Figures

Figure 1

15 pages, 3318 KiB

Open AccessArticle

Robust and High-Fidelity 3D Face Reconstruction Using Multiple RGB-D Cameras

by Haocheng Peng, Li Yang and Jinhui Li

Appl. Sci. 2022, 12(22), 11722; https://doi.org/10.3390/app122211722 - 18 Nov 2022

Cited by 1 | Viewed by 2264

Abstract

In this paper, we propose a robust and high-fidelity 3D face reconstruction method that uses multiple depth cameras. This method automatically reconstructs high-quality 3D face models from aligned RGB-D image pairs using multi-view consumer-grade depth cameras. To this end, we mainly analyze the problems in existing traditional and classical multi-view 3D face reconstruction systems and propose targeted improvement strategies for the issues related. In particular, we propose a fast two-stage point cloud filtering method that combines coarse filtering and fine filtering to rapidly extract the reconstructed subject point cloud with high purity. Meanwhile, in order to improve the integrity and accuracy of the point cloud for reconstruction, we propose a depth data restoration and optimization method based on the joint space–time domain. In addition, we also propose a method of multi-view texture alignment for the final texture fusion session that is more conducive for fusing face textures with better uniformity and visual performance. The above-proposed methods are reproducible and can be extended to the 3D reconstruction of any subject. The final experimental results show that the method is able to robustly generate 3D face models having high geometric and visual quality. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Theories and Applications)

► Show Figures

Journal Menu

Journal Browser

Advanced Artificial Intelligence Theories and Applications

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (11 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI