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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 (30 November 2023) | Viewed by 11987

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Special Issue Editors

Dr. Yunbo Rao

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Guest Editor

School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: video & image processing; virtual reality (based on HTC vive, oculus rift) and related applications; video content analysis, pattern recognition; artificial intelligence; 3D segmentation & reconstruction; video enhancement

Prof. Dr. Wu Yadong

E-Mail Website
Guest Editor

School of Computer Science and Engineering, Sichuan University of Science and Engineering, Zigong 643099, China
Interests: visualization and visual analysis; virtual reality and human–computer interaction; digital image processing

Dr. Zhihan Lv

E-Mail Website
Guest Editor

Department of Game Design, Uppsala University, SE-751 05 Uppsala, Sweden
Interests: virtual/augmented reality; multimedia; human–computer interaction; networks; geographic information; digital twins; Internet of Things; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is an interdisciplinary and emerging field based on computer science, and can be integrated with computer science, psychology, philosophy and other disciplines. AI constitutes a new technological science that researches and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Research in this field includes topics of computer vision, machine learning, robots, language recognition, image recognition, natural language processing, biometric recognition technology and expert systems.

AI will benefit many industries and fields in the future. This Special Issue aims to compile research from scholars in the research area of AI and present the application and technological innovation of AI in different industries. This Special Issue will also collect the best paper from PRAI2023: http:// http://www.prai.net.

Dr. Yunbo Rao
Prof. Dr. Wu Yadong
Dr. Zhihan Lv
Guest Editors

Manuscript Submission Information

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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.

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Keywords

robot perception and intelligent interaction
image analysis and smart healthcare
intelligent manufacturing and autonomous drive
intelligent recommendation and text analysis
metaverse, virtual reality and augmented reality
quantum computing and applications
internet of things and applications

Published Papers (11 papers)

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Research

22 pages, 7249 KiB

Open AccessArticle

An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study

by Rola R. Hassan, Manar Abu Talib, Fikri Dweiri and Jorge Roman

Appl. Sci. 2024, 14(6), 2569; https://doi.org/10.3390/app14062569 - 19 Mar 2024

Viewed by 663

Abstract

Implementing the European Foundation for Quality Management (EFQM) business excellence model in organizations is time- and cost-consuming. The integration of artificial intelligence (AI) into the EFQM business excellence model is a promising approach to improve the efficiency and effectiveness of excellence in organizations. This research paper’s integrated framework follows the ISO/IEC 23053 standard in addressing some of the concerns related to time and cost associated with the EFQM model, achieving higher EFQM scores, and hence operational excellence. A case study involving a UAE government organization serves as a sample to train the AI framework. Historical EFQM results from different years are used as training data. The AI framework utilizes the unsupervised machine learning technique known as k-means clustering. This technique follows the ISO/IEC 23053 standard to predict EFQM output total scores based on criteria and sub-criteria inputs. This research paper’s main output is a novel AI framework that can predict EFQM scores for organizations at an early stage. If the predicted EFQM score is not high enough, then the AI framework provides feedback to decision makers regarding the criteria that need reconsideration. Continuous use of this integrated framework helps organizations attain operational excellence. This framework is considered valuable for decision makers as it provides early predictions of EFQM total scores and identifies areas that require improvement before officially applying for the EFQM excellence award, hence saving time and cost. This approach can be considered as an innovative contribution and enhancement to knowledge body and organizational practices. Full article

(This article belongs to the Special Issue AI Technology and Application in Various Industries)

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20 pages, 5083 KiB

Open AccessArticle

Research on Multi-UAV Task Assignment Based on a Multi-Objective, Improved Brainstorming Optimization Algorithm

by Xiaofang Wang, Shi Yin, Lianyong Luo and Xin Qiao

Appl. Sci. 2024, 14(6), 2365; https://doi.org/10.3390/app14062365 - 11 Mar 2024

Viewed by 433

Abstract

In response to the practice of rescue channel blocking and a shortage of emergency materials in the event of sudden significant disasters, a multi-UAV collaborative distribution scheme was designed based on the demand for rapid and accurate distribution of materials. This paper constructed a multi-UAV collaborative task assignment and routing problem with simultaneous delivery and pick-up and time windows (MVTARPSDPTW), considering the factors of UAV load, energy consumption, cargo quality, and volume to minimize the total cost of UAV distribution and the full penalty of the task, as well as optimizing the balance of UAV efficiency. This paper proposes a multi-objective, improved brainstorming optimization algorithm based on Pareto dominance (MIBSO) to solve the MVTARPSDPTW problem. With DTLZ4, DTLZ5, and DTLZ6 benchmarks, this work tests the algorithm performance according to the characteristic attributes of the model sought, selecting the four indicators of GD, the Spacing metric, HV, and IGD, concerning convergence, solution distribution, and comprehensive performance. Case validation is based on a COVID-19 scenario in Changchun, China, and the results show that the model algorithm designed in this paper has good performance and feasibility in convergence and distribution of reconciliation. Finally, the multi-UAV emergency material distribution solution provides practical, theoretical support for rescue tasks in sudden significant disasters. Full article

(This article belongs to the Special Issue AI Technology and Application in Various Industries)

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14 pages, 4610 KiB

Open AccessArticle

3D Convolutional Neural Network with Dimension Reduction and Metric Learning for Crop Yield Prediction Based on Remote Sensing Data

by Ning Wang, Zhong Ma, Pengcheng Huo, Xi Liu, Zhao He and Kedi Lu

Appl. Sci. 2023, 13(24), 13305; https://doi.org/10.3390/app132413305 - 16 Dec 2023

Viewed by 659

Abstract

Crop yield prediction is essential for tasks like determining the optimal profile of crops to be planted, allocating government resources, effectively planning and preparing for aid distribution, making decisions about imports, and so on. Crop yield prediction using remote sensing data during the growing season is helpful to farm planning and management, which has received increasing attention. Information mining from multichannel geo-spatiotemporal data brings many benefits to crop yield prediction. However, most of the existing methods have not fully utilized the dimension reduction technology and the spatiotemporal feature of the data. In this paper, a new approach is proposed to predict the yield from multispatial images by using the dimension reduction method and a 3D convolutional neural network. In addition, regions with similar crop yields should have similar features learned by the network. Thus, metric learning and multitask learning are used to learn more discriminative features. We evaluated the proposed method on county-level soybean yield prediction in the United States, and the experimental results show the effectiveness of the proposed method. The proposed method provides new ideas for crop yield estimation and effectively improves the accuracy of crop yield estimation. Full article

(This article belongs to the Special Issue AI Technology and Application in Various Industries)

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15 pages, 6814 KiB

Open AccessArticle

Multi-Focus Microscopy Image Fusion Based on Swin Transformer Architecture

by Han Hank Xia, Hao Gao, Hang Shao, Kun Gao and Wei Liu

Appl. Sci. 2023, 13(23), 12798; https://doi.org/10.3390/app132312798 - 29 Nov 2023

Viewed by 799

Abstract

In this study, we introduce the U-Swin fusion model, an effective and efficient transformer-based architecture designed for the fusion of multi-focus microscope images. We utilized the Swin-Transformer with shifted window and path merging as the encoder for extracted hierarchical context features. Additionally, a Swin-Transformer-based decoder with patch expansion was designed to perform the un-sampling operation, generating the fully focused image. To enhance the performance of the feature decoder, the skip connections were applied to concatenate the hierarchical features from the encoder with the decoder up-sample features, like U-net. To facilitate comprehensive model training, we created a substantial dataset of multi-focus images, primarily derived from texture datasets. Our modulators demonstrated superior capability for multi-focus image fusion to achieve comparable or even better fusion images than the existing state-of-the-art image fusion algorithms and demonstrated adequate generalization ability for multi-focus microscope image fusion. Remarkably, for multi-focus microscope image fusion, the pure transformer-based U-Swin fusion model incorporating channel mix fusion rules delivers optimal performance compared with most existing end-to-end fusion models. Full article

(This article belongs to the Special Issue AI Technology and Application in Various Industries)

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13 pages, 3632 KiB

Open AccessArticle

Study on the Ion Mobility Spectrometry Data Classification and Application of Port Container Narcotics Using Machine Learning Algorithm

by Saeyong Park, Gualnaz Kemelbekova, Sungyoon Cho, Kiwon Kwon and Taeho Im

Appl. Sci. 2023, 13(23), 12769; https://doi.org/10.3390/app132312769 - 28 Nov 2023

Viewed by 500

Abstract

Narcotics should be strictly controlled as they can cause great disruption to society. Narcotics mostly flow into ports from major narcotic makers via transit points and through cargo containers. To prevent narcotic entry through smuggling, airports use animals or detect narcotics through X-rays. However, the use of animals in ports is not practical, and the method using X-rays sometimes does not detect substance narcotics with low atomic numbers. In this paper, we aimed to detect and classify narcotics using ion mobility spectrometry (IMS) data generated by inhaling air inside the container. To classify narcotic IMS data consisting of time-series data, the performance was improved using a time-series classification machine learning algorithm instead of the threshold method previously used. To this end, K-nearest neighbor, time-series forest, and random convolutional kernel algorithms were applied to the proposed algorithm considering the features of narcotic IMS data. The results demonstrate that the proposed algorithm outperforms the existing algorithm, and it reduces the classification performance processing time up to 5 s with more than 0.9 accuracy level. Full article

(This article belongs to the Special Issue AI Technology and Application in Various Industries)

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17 pages, 10146 KiB

Open AccessArticle

DP-YOLO: Effective Improvement Based on YOLO Detector

by Chao Wang, Qijin Wang, Yu Qian, Yating Hu, Ying Xue and Hongqiang Wang

Appl. Sci. 2023, 13(21), 11676; https://doi.org/10.3390/app132111676 - 25 Oct 2023

Cited by 1 | Viewed by 1581

Abstract

YOLOv5 remains one of the most widely used real-time detection models due to its commendable performance in accuracy and generalization. However, compared to more recent detectors, it falls short in label assignment and leaves significant room for optimization. Particularly, recognizing targets with varying shapes and poses proves challenging, and training the detector to grasp such features requires expert verification or collective discussion during the dataset labeling process, especially in domain-specific contexts. While deformable convolutions offer a partial solution, their extensive usage can enhance detection capabilities but at the expense of increased computational effort. We introduce DP-YOLO, an enhanced target detector that efficiently integrates the YOLOv5s backbone network with deformable convolutions. Our approach optimizes the positive sample selection during label assignment, resulting in a more scientifically grounded process. Notably, experiments on the COCO benchmark validate the efficacy of DP-YOLO, which utilizes an image size of [640, 640], achieves a remarkable 41.2 AP, and runs at an impressive 69 fps on an RTX 3090. Comparatively, DP-YOLO outperforms YOLOv5s by 3.2 AP, with only a small increase in parameters and GFLOPSs. These results demonstrate the significant advancements made by our proposed method. Full article

(This article belongs to the Special Issue AI Technology and Application in Various Industries)

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23 pages, 6130 KiB

Open AccessArticle

Mitigating Class Imbalance in Sentiment Analysis through GPT-3-Generated Synthetic Sentences

by Cici Suhaeni and Hwan-Seung Yong

Appl. Sci. 2023, 13(17), 9766; https://doi.org/10.3390/app13179766 - 29 Aug 2023

Cited by 4 | Viewed by 1811

Abstract

In this paper, we explore the effectiveness of the GPT-3 model in tackling imbalanced sentiment analysis, focusing on the Coursera online course review dataset that exhibits high imbalance. Training on such skewed datasets often results in a bias towards the majority class, undermining the classification performance for minority sentiments, thereby accentuating the necessity for a balanced dataset. Two primary initiatives were undertaken: (1) synthetic review generation via fine-tuning of the Davinci base model from GPT-3 and (2) sentiment classification utilizing nine models on both imbalanced and balanced datasets. The results indicate that good-quality synthetic reviews substantially enhance sentiment classification performance. Every model demonstrated an improvement in accuracy, with an average increase of approximately 12.76% on the balanced dataset. Among all the models, the Multinomial Naïve Bayes achieved the highest accuracy, registering 75.12% on the balanced dataset. This study underscores the potential of the GPT-3 model as a feasible solution for addressing data imbalance in sentiment analysis and offers significant insights for future research. Full article

(This article belongs to the Special Issue AI Technology and Application in Various Industries)

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27 pages, 10672 KiB

Open AccessArticle

A Novel Autonomous Landing Method for Flying–Walking Power Line Inspection Robots Based on Prior Structure Data

by Yujie Zeng, Xinyan Qin, Bo Li, Jin Lei, Jie Zhang, Yanqi Wang and Tianming Feng

Appl. Sci. 2023, 13(17), 9544; https://doi.org/10.3390/app13179544 - 23 Aug 2023

Cited by 1 | Viewed by 1135

Abstract

Hybrid inspection robots have been attracting increasing interest in recent years, and are suitable for inspecting long-distance overhead power transmission lines (OPTLs), combining the advantages of flying robots (e.g., UAVs) and climbing robots (e.g., multiple-arm robots). Due to the complex work conditions (e.g., power line slopes, complex backgrounds, wind interference), landing on OPTL is one of the most difficult challenges faced by hybrid inspection robots. To address this problem, this study proposes a novel autonomous landing method for a developed flying–walking power line inspection robot (FPLIR) based on prior structure data. The proposed method includes three main steps: (1) A color image of the target power line is segmented using a real-time semantic segmentation network, fusing the depth image to estimate the position of the power line. (2) The safe landing area (SLA) is determined using prior structure data, applying the trajectory planning method with geometric constraints to generate the dynamic landing trajectory. (3) The landing trajectory is tracked using real-time model predictive control (MPC), controlling FPLIR to land on the OPTL. The feasibility of the proposed method was verified in the ROS Gazebo environment. The RMSE values of the position along three axes were

0.1205, 0.0976

and

0.0953

, respectively, while the RMSE values of the velocity along these axes were 0.0426, 0.0345 and 0.0781. Additionally, experiments in a real environment using FPLIR were performed to verify the validity of the proposed method. The experimental results showed that the errors of position and velocity for the FPLIR landing on the lines were

6.18 \times 10^{- 2} m

and

2.16 \times 10^{- 2} m / s

. The simulation results as well as the experimental findings both satisfy the practical requirements. The proposed method provides a foundation for the intelligent inspection of OPTL in the future. Full article

(This article belongs to the Special Issue AI Technology and Application in Various Industries)

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20 pages, 4803 KiB

Open AccessArticle

Near-Optimal Active Learning for Multilingual Grapheme-to-Phoneme Conversion

by Dezhi Cao, Yue Zhao and Licheng Wu

Appl. Sci. 2023, 13(16), 9408; https://doi.org/10.3390/app13169408 - 19 Aug 2023

Viewed by 896

Abstract

The construction of pronunciation dictionaries relies on high-quality and extensive training data in data-driven way. However, the manual annotation of corpus for this purpose is both costly and time consuming, especially for low-resource languages that lack sufficient data and resources. A multilingual pronunciation dictionary includes some common phonemes or phonetic units, which means that these phonemes or units have similarities in the pronunciation of different languages and can be used in the construction process of pronunciation dictionaries for low-resource languages. By using a multilingual pronunciation dictionary, knowledge can be shared among different languages, thus improving the quality and accuracy of pronunciation dictionaries for low-resource languages. In this paper, we propose using shared articulatory features among multiple languages to construct a universal phoneme set, which is then used to label words for multiple languages. To achieve this, we first developed a grapheme−phoneme (G2P) model based on an encoder−decoder deep neural network. We then adopted a near-optimal active learning method in the process of building the pronunciation dictionary to select informative samples from a large, unlabeled corpus and had them labeled by experts. Our experiments demonstrate that this method selected about 1/5 of the unlabeled data and achieved an even higher conversion accuracy than the results of the large data training method. By selectively labeling samples with a high uncertainty in the model, while avoiding labeling samples that were accurately predicted by the current model, our method greatly enhances the efficiency of pronunciation dictionary construction. Full article

(This article belongs to the Special Issue AI Technology and Application in Various Industries)

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21 pages, 4778 KiB

Open AccessArticle

Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots

by Bao Pang, Ziqi Zhang, Yong Song, Xianfeng Yuan and Qingyang Xu

Appl. Sci. 2023, 13(16), 9107; https://doi.org/10.3390/app13169107 - 10 Aug 2023

Viewed by 675

Abstract

In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. Firstly, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. This paper completes the simulation and physical experiments, respectively, and the performance of the proposed method is evaluated using three commonly used performance indexes: the average deviation of food, the energy efficiency, and the number of obstacle avoidance events. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in all three indexes. Full article

(This article belongs to the Special Issue AI Technology and Application in Various Industries)

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22 pages, 3523 KiB

Open AccessArticle

A Data Feature Extraction Method Based on the NOTEARS Causal Inference Algorithm

by Hairui Wang, Junming Li and Guifu Zhu

Appl. Sci. 2023, 13(14), 8438; https://doi.org/10.3390/app13148438 - 21 Jul 2023

Viewed by 1077

Abstract

Extracting effective features from high-dimensional datasets is crucial for determining the accuracy of regression and classification models. Model predictions based on causality are known for their robustness. Thus, this paper introduces causality into feature selection and utilizes Feature Selection based on NOTEARS causal discovery (FSNT) for effective feature extraction. This method transforms the structural learning algorithm into a numerical optimization problem, enabling the rapid identification of the globally optimal causality diagram between features and the target variable. To assess the effectiveness of the FSNT algorithm, this paper evaluates its performance by employing 10 regression algorithms and 8 classification algorithms for regression and classification predictions on six real datasets from diverse fields. These results are then compared with three mainstream feature selection algorithms. The results indicate a significant average decline of 54.02% in regression prediction achieved by the FSNT algorithm. Furthermore, the algorithm exhibits exceptional performance in classification prediction, leading to an enhancement in the precision value. These findings highlight the effectiveness of FSNT in eliminating redundant features and significantly improving the accuracy of model predictions. Full article

(This article belongs to the Special Issue AI Technology and Application in Various Industries)

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