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

Journal Menu

Journal Browser

Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Related Special Issue
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: 31 December 2024 | Viewed by 9316

Share This Special Issue

Special Issue Editors

Prof. Dr. Yujin Lim

E-Mail Website
Guest Editor

Department of IT Engineering, Sookmyung Women’s University, Seoul 04310, Republic of Korea
Interests: multi-agent system; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hideyuki Takahashi

E-Mail Website
Guest Editor

Department of Data Science, Faculty of Informatics, Tohoku Gakuin University, Miyagi 984-8588, Japan
Interests: Internet of Things; ubiquitous computing; multi-agent system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning and artificial intelligence have attracted great attention in almost every field in recent years. Applications of deep learning and artificial intelligence methods are now pervasive, being used into various fields beyond conventional computer engineering areas. Therefore, the goal of this Special Issue is to discuss new ideas and recent experimental results in the fields of the applications of deep learning and artificial intelligence methods.

Topics of interest include, but are not limited to, the following subjects:

Artificial intelligence tools and applications;
Automatic control;
Natural language processing;
Computer vision and speech understanding;
Data mining and analysis;
Heuristic and AI planning strategies;
Intelligent system;
Robotics.

Prof. Dr. Yujin Lim
Prof. Dr. Hideyuki Takahashi
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

machine learning
deel learning
artificial intelligence
natural language processing
computer vision
data mining
robotics

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Applications of Deep Learning and Artificial Intelligence Methods in Applied Sciences (48 articles)

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

43 pages, 2553 KiB

Open AccessArticle

DietNerd: A Nutrition Question-Answering System That Summarizes and Evaluates Peer-Reviewed Scientific Articles

by Shela Wu, Zubair Yacub and Dennis Shasha

Appl. Sci. 2024, 14(19), 9021; https://doi.org/10.3390/app14199021 - 6 Oct 2024

Viewed by 693

Abstract

DietNerd is a large language model-based system designed to enhance public health education in diet and nutrition. The system responds to user questions with concise, evidence-based summaries and assesses the quality and potential biases of cited research. This paper describes the system’s workflow, back-end implementation, and the prompts used. Accuracy and quality-of-response results are presented based on an automated comparison against systematic surveys and against the responses of similar state-of-the-art systems through human feedback from registered dietitians. DietNerd is among the highest-evaluated of these systems and is unique in combining safety features with sophisticated source analysis. Thus, DietNerd could be a tool to bridge the gap between complex scientific literature and public understanding. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)

► Show Figures

Figure 1

24 pages, 17595 KiB

Open AccessArticle

Revolutionizing Time Series Data Preprocessing with a Novel Cycling Layer in Self-Attention Mechanisms

by Jiyan Chen and Zijiang Yang

Appl. Sci. 2024, 14(19), 8922; https://doi.org/10.3390/app14198922 - 3 Oct 2024

Viewed by 590

Abstract

This paper introduces an innovative method for enhancing time series data preprocessing by integrating a cycling layer into a self-attention mechanism. Traditional approaches often fail to capture the cyclical patterns inherent to time series data, which affects the predictive model accuracy. The proposed method aims to improve models’ ability to identify and leverage these cyclical patterns, as demonstrated using the Jena Climate dataset from the Max Planck Institute for Biogeochemistry. Empirical results show that the proposed method enhances forecast accuracy and speeds up model fitting compared to the conventional techniques. This paper contributes to the field of time series analysis by providing a more effective preprocessing approach. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)

► Show Figures

Figure 1

13 pages, 9305 KiB

Open AccessArticle

Unveiling the Significance of Individual Level Predictions: A Comparative Analysis of GRU and LSTM Models for Enhanced Digital Behavior Prediction

by Burhan Y. Kiyakoglu and Mehmet N. Aydin

Appl. Sci. 2024, 14(19), 8858; https://doi.org/10.3390/app14198858 - 2 Oct 2024

Viewed by 631

Abstract

The widespread use of technology has led to a transformation of human behaviors and habits into the digital space; and generating extensive data plays a crucial role when coupled with forecasting techniques in guiding marketing decision-makers and shaping strategic choices. Traditional methods like autoregressive moving average (ARMA) can-not be used at predicting individual behaviors because we can-not create models for each individual and buy till you die (BTYD) models have limitations in capturing the trends accurately. Recognizing the paramount importance of individual-level predictions, this study proposes a deep learning framework, specifically uses gated recurrent unit (GRU), for enhanced behavior analysis. This article discusses the performance of GRU and long short-term memory (LSTM) models in this framework for forecasting future individual behaviors and presenting a comparative analysis against benchmark BTYD models. GRU and LSTM yielded the best results in capturing the trends, with GRU demonstrating a slightly superior performance compared to LSTM. However, there is still significant room for improvement at the individual level. The findings not only demonstrate the performance of GRU and LSTM models but also provide valuable insights into the potential of new techniques or approaches for understanding and predicting individual behaviors. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)

► Show Figures

Figure 1

14 pages, 3011 KiB

Open AccessArticle

Deep Learning for Water Quality Prediction—A Case Study of the Huangyang Reservoir

by Jixuan Chen, Xiaojuan Wei, Yinxiao Liu, Chunxia Zhao, Zhenan Liu and Zhikang Bao

Appl. Sci. 2024, 14(19), 8755; https://doi.org/10.3390/app14198755 - 27 Sep 2024

Viewed by 761

Abstract

Water quality prediction is a fundamental prerequisite for effective water resource management and pollution prevention. Accurate predictions of water quality information can provide essential technical support and strategic planning for the protection of water resources. This study aims to enhance the accuracy of water quality prediction, considering the temporal characteristics, variability, and complex nature of water quality data. We utilized the LTSF-Linear model to predict water quality at the Huangyang Reservoir. Comparative analysis with three other models (ARIMA, LSTM, and Informer) revealed that the Linear model outperforms them, achieving reductions of 8.55% and 10.51% in mean square error (MSE) and mean absolute error (MAE), respectively. This research introduces a novel method and framework for predicting hydrological parameters relevant to water quality in the Huangyang Reservoir. These findings offer a valuable new approach and reference for enhancing the intelligent and sustainable management of the reservoir. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)

► Show Figures

Figure 1

25 pages, 494 KiB

Open AccessArticle

Research on the Application Maturity of Enterprises’ Artificial Intelligence Technology Based on the Fuzzy Evaluation Method and Analytic Network Process

by Yutong Liu and Peiyi Song

Appl. Sci. 2024, 14(17), 7804; https://doi.org/10.3390/app14177804 - 3 Sep 2024

Cited by 1 | Viewed by 685

Abstract

The aim of this study was to study the impact of artificial intelligence (AI) on enterprises in terms of strategy, technology, business operations, and organizational management. This study used grounded theory analysis to identify the influencing factors of AI technology application maturity in Chinese enterprises. Taking Chinese film and television enterprises as an example, this study constructed an AI technology application maturity evaluation index system for enterprises based on the analytic network process (ANP) and evaluated the application maturity of AI technology in enterprises in terms of enterprise strategy, technology, business operations, and organizational management. To comprehensively evaluate and empirically analyze the application maturity of enterprise AI technology, this study calculated the index weight based on the ANP, and combined it with the fuzzy comprehensive evaluation method to construct a comprehensive evaluation model. The research results showed that intelligence strategy was the element that was believed to be most affected by the maturity of enterprise AI technology. For technology, intelligence technology and equipment were the elements that were believed to be affected the most. For business operations, smart shooting was the element that was believed to be affected the most. With respect to organizational management, corporate culture was the element that was believed to be most affected. The results showed that the proposed methods for evaluating the application maturity of enterprise AI technology are scientific and effective. The results of this study provide a reference for promoting the application of AI, implementing the intelligence transformation, and enhancing the core competitiveness of enterprises. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)

► Show Figures

Figure 1

18 pages, 5207 KiB

Open AccessArticle

MAPPNet: A Multi-Scale Attention Pyramid Pooling Network for Dental Calculus Segmentation

by Tianyu Nie, Shihong Yao, Di Wang, Conger Wang and Yishi Zhao

Appl. Sci. 2024, 14(16), 7273; https://doi.org/10.3390/app14167273 - 19 Aug 2024

Viewed by 631

Abstract

Dental diseases are among the most prevalent diseases globally, and accurate segmentation of dental calculus images plays a crucial role in periodontal disease diagnosis and treatment planning. However, the current methods are not stable and reliable enough due to the variable morphology of dental calculus and the blurring of the boundaries between the dental edges and the surrounding tissues; therefore, our hope is to propose an accurate and reliable calculus segmentation algorithm to improve the efficiency of clinical detection. We propose a multi-scale attention pyramid pooling network (MAPPNet) to enhance the performance of dental calculus segmentation. The network incorporates a multi-scale fusion strategy in both the encoder and decoder, forming a model with a dual-ended multi-scale structure. This design, in contrast to employing a multi-scale fusion scheme at a single end, enables more effective capturing of features from diverse scales. Furthermore, the attention pyramid pooling module (APPM) reconstructs the features on this map by leveraging a spatial-first and channel-second attention mechanism. APPM enables the network to adaptively adjust the weights of different locations and channels in the feature map, thereby enhancing the perception of important regions and key features. Experimental evaluation of our collected dental calculus segmentation dataset demonstrates the superior performance of MAPPNet, which achieves an intersection-over-union of 81.46% and an accuracy rate of 98.35%. Additionally, on two publicly available datasets, ISIC2018 (skin lesion dataset) and Kvasir-SEG (gastrointestinal polyp segmentation dataset), MAPPNet achieved an intersection-over-union of 76.48% and 91.38%, respectively. These results validate the effectiveness of our proposed network in accurately segmenting lesion regions and achieving high accuracy rates, surpassing many existing segmentation methods. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)

► Show Figures

Figure 1

12 pages, 3508 KiB

Open AccessArticle

A Deep Learning Estimation for Probing Depth of Transient Electromagnetic Observation

by Lu Gan, Rongjiang Tang, Fusheng Li and Fengli Shen

Appl. Sci. 2024, 14(16), 7123; https://doi.org/10.3390/app14167123 - 14 Aug 2024

Cited by 1 | Viewed by 549

Abstract

The probing depth of the transient electromagnetic method (TEM) refers to the depth range at which the underground conductivity changes can be effectively detected. It typically ranges from tens of meters to several kilometers and is influenced by factors such as instrument parameters and the conductivity of the subsurface structure. Rapid and accurate probing depth is useful for the selection of appropriate inversion parameters and improving survey accuracy. However, mainstream methods suffer from issues such as low computational precision, large uncertainties, or high computational requirements, making them unsuitable for processing massive airborne electromagnetic data. In this study, we propose a prediction model based on deep learning that can directly compute the probing depth from the TEM responses, and its effectiveness and accuracy are validated through synthetic models and field measurements. We compared the performance of classic deep learning models, including CNN, RESNET, and RNN, and found that RNN performed the best overall on both synthetic and field data. Furthermore, we apply this algorithm to deep learning-based ATEM inversion by constraining the one-dimensional resistivity model depths in the training set, to reduce the non-uniqueness of the inversion, accelerate the convergence, and improve its prediction accuracy. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)

► Show Figures

Figure 1

28 pages, 6444 KiB

Open AccessArticle

Inventory Prediction Using a Modified Multi-Dimensional Collaborative Wrapped Bi-Directional Long Short-Term Memory Model

by Said Abualuroug, Ahmad Alzubi and Kolawole Iyiola

Appl. Sci. 2024, 14(13), 5817; https://doi.org/10.3390/app14135817 - 3 Jul 2024

Viewed by 738

Abstract

Inventory prediction is concerned with the forecasting of future demand for products in order to optimize inventory levels and supply chain management. The challenges include demand volatility, data quality, multi-dimensional interactions, lead time variability, seasonal trends, and dynamic pricing. Nevertheless, these models suffer from numerous shortcomings, and in this research, we propose a new model, MMCW-BiLSTM (modified multi-dimensional collaboratively wrapped BiLSTM), for inventory prediction. The MMCW-BiLSTM model reflects a considerable leap in inventory forecasting by combining a number of components in order to consider intricate temporal dependencies and incorporate feature interactions. The MMCW-BiLSTM makes use of BiLSTM layers, collaborative attention mechanisms, and a multi-dimensional attention approach to learn from augmented datasets consisting of the original features and the extracted time series data. Moreover, adding a Taylor series transformation allows for a more precise description of the features in the model, thus improving the prediction precision. The results show that the models make the least mistakes when they use the AV demand forecasting dataset, with MAE values of 1.75, MAPE values of 2.89, MSE values of 6.76, and RMSE values of 2.6. Similarly, when utilizing the product demand dataset, the model also achieves the lowest error values for these metrics at 1.97, 3.91, 8.76, and 2.96. Likewise, when utilizing the dairy goods sales dataset, the model also achieves the lowest error values for these metrics at 2.54, 3.69, 10.39, and 3.22. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)

► Show Figures

Figure 1

19 pages, 3191 KiB

Open AccessArticle

A Multi-Target Identification and Positioning System Method for Tomato Plants Based on VGG16-UNet Model

by Xiaojing Li, Jiandong Fang and Yvdong Zhao

Appl. Sci. 2024, 14(7), 2804; https://doi.org/10.3390/app14072804 - 27 Mar 2024

Viewed by 908

Abstract

The axillary buds that grow between the main and lateral branches of tomato plants waste nutrients and lead to a decrease in yield, necessitating regular removal. Currently, these buds are removed manually, which requires substantial manpower and incurs high production costs, particularly on a large scale. Replacing manual labor with robots can lead to cost reduction. However, a critical challenge is the accurate multi-target identification of tomato plants and precise positioning for axillary bud removal. Therefore, this paper proposes a multi-target identification and localization method for tomato plants based on the VGG16-UNet model. The average intersection and pixel accuracies of the VGG16-UNet model after introducing the pretrained weights were 85.33% and 92.47%, respectively, which were 5.02% and 4.08% higher than those of the VGG16-UNet without pretrained weights, achieving the identification of main branches, side branches, and axillary bud regions. Then, based on the multi-objective segmentation of the tomato plants in the VGG16-UNet model, the regions of the axillary buds in the tomato plants were identified by HSV color space conversion and color threshold range selection. Morphological dilation and erosion operations were used to remove noise and connect adjacent regions of the same target. The endpoints and centroids of the axillary buds were identified using the feature point extraction algorithm. The left and right positions of the axillary buds were judged by the relationship between the position of the axillary bud centroid and the position of the main branch. Finally, the coordinate parameters of the axillary bud removal points were calculated using the feature points to determine the relationship between the position of the axillary bud and the position of the branch. Experimental results showed that the average accuracy of the axillary bud pruning point recognition was 85.5%. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)

► Show Figures

Figure 1

11 pages, 8566 KiB

Open AccessArticle

Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar

by Leon Ruddat, Laurenz Reichardt, Nikolas Ebert and Oliver Wasenmüller

Appl. Sci. 2024, 14(7), 2781; https://doi.org/10.3390/app14072781 - 26 Mar 2024

Viewed by 872

Abstract

Detecting vulnerable road users is a major challenge for autonomous vehicles due to their small size. Various sensor modalities have been investigated, including mono or stereo cameras and 3D LiDAR sensors, which are limited by environmental conditions and hardware costs. Radar sensors are a low-cost and robust option, with high-resolution 4D radar sensors being suitable for advanced detection tasks. However, they involve challenges such as few and irregularly distributed measurement points and disturbing artifacts. Learning-based approaches utilizing pillar-based networks show potential in overcoming these challenges. However, the severe sparsity of radar data makes detecting small objects with only a few points difficult. We extend a pillar network with our novel Sparsity-Robust Feature Fusion (SRFF) neck, which combines high- and low-level multi-resolution features through a lightweight attention mechanism. While low-level features aid in better localization, high-level features allow for better classification. As sparse input data are propagated through a network, the increasing effective receptive field leads to feature maps of different sparsities. The combination of features with different sparsities improves the robustness of the network for classes with few points. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)

► Show Figures

Figure 1

18 pages, 1514 KiB

Open AccessArticle

MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System

by Minseok Koo and Jaesung Park

Appl. Sci. 2024, 14(2), 605; https://doi.org/10.3390/app14020605 - 10 Jan 2024

Viewed by 1150

Abstract

Optimizing the energy consumption of an MEC (Multi-Access Edge Computing) system is a crucial challenge for operation cost reduction and environmental conservation. In this paper, we address an MECS (MEC Server) sleep control problem that aims to reduce the energy consumption of the system while providing users with a reasonable service delay by adjusting the number of active MECSs according to the load imposed on the system. To tackle the problem, we identify two crucial issues that influence the design of an effective sleep control technique and propose methods to address each of these issues. The first issue is accurately predicting the system load. Changes in system load are spatio-temporally correlated among MECSs. By leveraging such correlation information with STGCN (Spatio-Temporal Graph Convolutional Network), we enhance the prediction accuracy of task arrival rates for each MECS. The second issue is rapidly selecting MECSs to sleep when the load distribution over an MEC system is given. The problem of choosing sleep MECS is a combinatorial optimization problem with high time complexity. To address the issue, we employ a genetic algorithm and quickly determine the optimal sleep MECS with the predicted load information for each MECS. Through simulation studies, we verify that compared to the LSTM (Long Short-Term Memory)-based method, our method increases the energy efficiency of an MEC system while providing a compatible service delay. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)

► Show Figures

Journal Menu

Journal Browser

Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (11 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI