Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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

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 (31 December 2024) | Viewed by 21294

Special Issue Editors

Prof. Dr. Yujin Lim

E-Mail Website
Guest Editor
Division of Artificial Intelligence Engineering, Sookmyung Women’s University, Seoul 04310, Republic of Korea
Interests: multi-agent system; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Hideyuki Takahashi

E-Mail Website
Guest Editor
Department of Information Science, Faculty of Liberal Arts, Tohoku Gakuin University, Sendai 981-3193, 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 Issues

Published Papers (19 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 11152 KiB  
Article
Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar
by Jingqi Han, Yue Fan, Zheng He, Zhenhang You, Peng Zhang and Zhengliang Hu
Appl. Sci. 2025, 15(3), 1189; https://doi.org/10.3390/app15031189 - 24 Jan 2025
Viewed by 375
Abstract
Imaging sonar is a primary means of underwater detection, but it faces challenges of high false alarm rates in sonar image target detection due to factors such as reverberation, noise, and resolution. This paper proposes a method to improve the false alarm rate [...] Read more.
Imaging sonar is a primary means of underwater detection, but it faces challenges of high false alarm rates in sonar image target detection due to factors such as reverberation, noise, and resolution. This paper proposes a method to improve the false alarm rate by self-training a deep learning detector on sonar images. Self-training automatically generates proxy classification tasks based on the sonar image target detection dataset, and pre-trains the deep learning detector through these proxy classification tasks to enhance its learning effectiveness of target and background features. This, in turn, improves the detector’s ability to distinguish between targets and backgrounds, thereby reducing the false alarm rate. For the first time, this paper conducts target detection experiments based on deep learning using high-resolution synthetic aperture sonar images at two frequencies. The results show that, under the conditions of equal or higher recall rates, this method can reduce the false alarm rate by 3.91% and 18.50% on 240 kHz and 450 kHz sonar images, respectively, compared to traditional transfer learning methods. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)
►▼ Show Figures

Figure 1

23 pages, 3353 KiB  
Article
HyFusER: Hybrid Multimodal Transformer for Emotion Recognition Using Dual Cross Modal Attention
by Moung-Ho Yi, Keun-Chang Kwak and Ju-Hyun Shin
Appl. Sci. 2025, 15(3), 1053; https://doi.org/10.3390/app15031053 - 21 Jan 2025
Viewed by 497
Abstract
Emotion recognition is becoming increasingly important for accurately understanding and responding to user emotions, driven by the rapid proliferation of non-face-to-face environments and advancements in conversational AI technologies. Existing studies on multimodal emotion recognition, which utilize text and speech, have aimed to improve [...] Read more.
Emotion recognition is becoming increasingly important for accurately understanding and responding to user emotions, driven by the rapid proliferation of non-face-to-face environments and advancements in conversational AI technologies. Existing studies on multimodal emotion recognition, which utilize text and speech, have aimed to improve performance by integrating the information from both modalities. However, these approaches have faced limitations such as restricted information exchange and the omission of critical cues. To address these challenges, this study proposes a Hybrid Multimodal Transformer, which combines Intermediate Layer Fusion and Last Fusion. Text features are extracted using KoELECTRA, while speech features are extracted using HuBERT. These features are processed through a transformer encoder, and Dual Cross Modal Attention is applied to enhance the interaction between text and speech. Finally, the predicted results from each modality are aggregated using an average ensemble method to recognize the final emotion. The experimental results indicate that the proposed model achieves superior emotion recognition performance compared to existing models, demonstrating significant progress in improving both the accuracy and reliability of emotion recognition. In the future, incorporating additional modalities, such as facial expression recognition, is expected to further strengthen multimodal emotion recognition capabilities and open new possibilities for application across diverse fields. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)
►▼ Show Figures

Figure 1

27 pages, 10053 KiB  
Article
Part B: Innovative Data Augmentation Approach to Boost Machine Learning for Hydrodynamic Purposes—Computational Efficiency
by Hamed Majidiyan, Hossein Enshaei, Damon Howe and Eric Gubesch
Appl. Sci. 2025, 15(1), 346; https://doi.org/10.3390/app15010346 - 1 Jan 2025
Viewed by 706
Abstract
The increasing influence of AI across various scientific domains has prompted engineering to embark on new explorations. However, studies often overlook the foundational aspects of the maritime field, leading to over-optimistic or oversimplified outputs for real-world applications. We previously highlighted the sensitivity of [...] Read more.
The increasing influence of AI across various scientific domains has prompted engineering to embark on new explorations. However, studies often overlook the foundational aspects of the maritime field, leading to over-optimistic or oversimplified outputs for real-world applications. We previously highlighted the sensitivity of trained models to noise, the importance of computational efficiency, and the need for feature engineering/compactness in hydrodynamic models due to the stochastic nature of waves. A novel data analysis framework was introduced with two purposes to augment data for machine learning (ML) models: transferring features from high-fidelity to low-fidelity surrogates and enhancing simulation data and increasing computational efficiency. The current issue addresses the second objectives. Wave-induced response time series data from experiments on a spherical model under various wave conditions were analyzed using continuous wavelet transform to extract spectral-temporal features. These features were then reorganized into a new feature map and augmented with additional endogenous features to enhance their uniqueness. Different ML models were trained; the new framework substantially reduced training costs while maintaining fair accuracy, with training times slashed from hours to seconds. The significance of the current study extends beyond the maritime context and can be utilized for ML applications in intrinsically stochastic data. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)
►▼ Show Figures

Figure 1

29 pages, 8238 KiB  
Article
Part A: Innovative Data Augmentation Approach to Enhance Machine Learning Efficiency—Case Study for Hydrodynamic Purposes
by Hamed Majidiyan, Hossein Enshaei, Damon Howe and Eric Gubesch
Appl. Sci. 2025, 15(1), 158; https://doi.org/10.3390/app15010158 - 27 Dec 2024
Cited by 1 | Viewed by 632
Abstract
These days, AI and machine learning (ML) have become pervasive in numerous fields. However, the maritime industry has faced challenges due to the dynamic and unstructured nature of environmental inputs. Hydrodynamic models, vital for predicting ship responses and estimating sea states, rely on [...] Read more.
These days, AI and machine learning (ML) have become pervasive in numerous fields. However, the maritime industry has faced challenges due to the dynamic and unstructured nature of environmental inputs. Hydrodynamic models, vital for predicting ship responses and estimating sea states, rely on diverse data sources of varying fidelities. The effectiveness of ML models in real-world applications hinges on the diversity, range, and quality of the data. Linear simulation techniques, chosen for their simplicity and cost-effectiveness, produce unrealistic and overly optimistic results. Conversely, high-fidelity experiments are prohibitively expensive. To address this, the study introduces an innovative feature engineering that incorporates uncertainty into features of linear models derived from higher fidelity modeling. This enhances productive data entropy, positively enhancing feature classification and improving the accuracy and feasibility of ML models in hydrodynamic responses of floating vessels. Tested with data from a known geometrical shape exposed to regular and irregular waves, the technique employs Ansys Aqwa for linear models. The results demonstrate the efficiency of the proposed technique, expanding the applicability of ML models in realistic scenarios. The application of the proposed approach extends beyond and can be further applied to any stochastic process, which expands the ML application for realistic use cases. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)
►▼ Show Figures

Figure 1

23 pages, 1151 KiB  
Article
Language Models for Predicting Organic Synthesis Procedures
by Mantas Vaškevičius and Jurgita Kapočiūtė-Dzikienė
Appl. Sci. 2024, 14(24), 11526; https://doi.org/10.3390/app142411526 - 11 Dec 2024
Viewed by 746
Abstract
In optimizing organic chemical synthesis, researchers often face challenges in efficiently generating viable synthesis procedures that conserve time and resources in laboratory settings. This paper systematically analyzes multiple approaches to efficiently generate synthesis procedures for a wide variety of organic synthesis reactions, aiming [...] Read more.
In optimizing organic chemical synthesis, researchers often face challenges in efficiently generating viable synthesis procedures that conserve time and resources in laboratory settings. This paper systematically analyzes multiple approaches to efficiently generate synthesis procedures for a wide variety of organic synthesis reactions, aiming to decrease time and resource consumption in laboratory work. We investigated the suitability of different sizes of BART, T5, FLAN-T5, molT5, and classic sequence-to-sequence transformer models for our text-to-text task and utilized a large dataset prepared specifically for the task. Experimental investigations demonstrated that a fine-tuned molT5-large model achieves a BLEU score of 47.75. The results demonstrate the capability of LLMs to predict chemical synthesis procedures involving 24 possible distinct actions, many of which include various parameters like solvents, reaction agents, temperature, duration, solvent ratios, and other specific parameters. Our findings show that only when the core reactants are used as input, the models learn to correctly predict what ancillary components need to be included in the resulting procedure. These results are valuable for AI researchers and chemists, suggesting that curated datasets and large language model fine-tuning techniques can be tailored for specific reaction classes and practical applications. This research contributes to the field by demonstrating how deep-learning-based methods can be customized to meet the specific requirements of chemical synthesis, leading to more intelligent and resource-efficient laboratory processes. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)
►▼ Show Figures

Figure 1

15 pages, 1195 KiB  
Article
Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
by Wei He, Haoxuan Li, Tao Wang and Nan Wang
Appl. Sci. 2024, 14(22), 10350; https://doi.org/10.3390/app142210350 - 11 Nov 2024
Viewed by 877
Abstract
In automatic driving, accurate prediction of vehicle trajectory is the key to achieve automatic driving, and multi-vehicle joint trajectory prediction has become an important part of modern human-computer interaction systems such as automatic driving. In order to better predict vehicle trajectories, we propose [...] Read more.
In automatic driving, accurate prediction of vehicle trajectory is the key to achieve automatic driving, and multi-vehicle joint trajectory prediction has become an important part of modern human-computer interaction systems such as automatic driving. In order to better predict vehicle trajectories, we propose a new residual diffusion model to infer the joint distribution of future multi-vehicle trajectories. This approach has several major advantages. First, the model is able to learn multiple probability distributions from trajectory data to obtain potential outcomes for vehicles to multiple future trajectories. Secondly, in order to integrate the motion characteristics of multiple vehicles in the same scene, we use the method of combining the reference denoising and multiple residual denoising to improve the model performance and prediction speed. Finally, on this basis, a general trajectory constraint function is introduced, so that the generated trajectories of multiple vehicles will not collide with each other. We perform a rich experimental comparison of various existing methods on the NGSIM dataset and demonstrate that the proposed algorithm achieves a 26% improvement on mAP. 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, 6618 KiB  
Article
Exploring Cutout and Mixup for Robust Human Activity Recognition on Sensor and Skeleton Data
by Hiskias Dingeto and Juntae Kim
Appl. Sci. 2024, 14(22), 10286; https://doi.org/10.3390/app142210286 - 8 Nov 2024
Viewed by 871
Abstract
Human Activity Recognition (HAR) is an essential area of research in Artificial Intelligence and Machine Learning, with numerous applications in healthcare, sports science, and smart environments. While several advancements in the field, such as attention-based models and Graph Neural Networks, have made great [...] Read more.
Human Activity Recognition (HAR) is an essential area of research in Artificial Intelligence and Machine Learning, with numerous applications in healthcare, sports science, and smart environments. While several advancements in the field, such as attention-based models and Graph Neural Networks, have made great strides, this work focuses on data augmentation methods that tackle issues like data scarcity and task variability in HAR. In this work, we investigate and expand the use of mixup and cutout data augmentation methods to sensor-based and skeleton-based HAR datasets. These methods were first widely used in Computer Vision and Natural Language Processing. We use both augmentation techniques, customized for time-series and skeletal data, to improve the robustness and performance of HAR models by diversifying the data and overcoming the drawbacks of having limited training data. Specifically, we customize mixup data augmentation for sensor-based datasets and cutout data augmentation for skeleton-based datasets with the goal of improving model accuracy without adding more data. Our results show that using mixup and cutout techniques improves the accuracy and generalization of activity recognition models on both sensor-based and skeleton-based human activity datasets. This work showcases the potential of data augmentation techniques on transformers and Graph Neural Networks by offering a novel method for enhancing time series and skeletal HAR tasks. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)
►▼ Show Figures

Figure 1

26 pages, 3370 KiB  
Article
Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark
by Marios C. Gkikas, Dimitris C. Gkikas, Gerasimos Vonitsanos, John A. Theodorou and Spyros Sioutas
Appl. Sci. 2024, 14(22), 10112; https://doi.org/10.3390/app142210112 - 5 Nov 2024
Viewed by 1202
Abstract
The current study evaluates the performance of three machine learning models—Decision Trees, Random Forest, and Linear Regression—applied to aquaculture data to mitigate risks in aquaculture management. The performances of these models are analyzed and properly demonstrated using metrics including the Mean Squared Error [...] Read more.
The current study evaluates the performance of three machine learning models—Decision Trees, Random Forest, and Linear Regression—applied to aquaculture data to mitigate risks in aquaculture management. The performances of these models are analyzed and properly demonstrated using metrics including the Mean Squared Error (MSE), R-squared (R2), Root Mean Squared Error (RMSE), and Concordance Index (C-index). The Random Forest model achieved the highest prediction accuracy among all machine learning models, followed by Linear Regression and the Decision Trees. The scatter plot for Linear Regression demonstrates good predictive accuracy for mid-range values. However, it shows significant deviations at the extremes, indicating that the model struggles to capture the full range of variability in the data. The bar chart of coefficients pinpoints the variables with the greatest impact on the predictions, providing suggestions for potential areas that can be improved and providing model interpretability. Future work could incorporate more predictive statistics models focusing on improving the models for extreme values by assessing non-linear models, feature engineering methods, and expanding research into less influential variables. The results greatly impact several sections, including aquaculture management, policy-making, and operational strategies, providing valuable insights for stakeholders and decision-makers. Apache Spark was used for data processing and machine learning model implementation; Apache Cassandra was also used for data storage, ensuring efficient large dataset management and SQL tools for structured data handling; Oracle VM VirtualBox for cross-platform virtualization; and Spark Connector was also used. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)
►▼ Show Figures

Figure 1

43 pages, 2553 KiB  
Article
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 1446
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, [...] Read more.
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  
Article
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 1055
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 [...] Read more.
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  
Article
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 1090
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 [...] Read more.
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  
Article
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 1511
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 [...] Read more.
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  
Article
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 1243
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 [...] Read more.
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  
Article
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
Cited by 2 | Viewed by 1028
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 [...] Read more.
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  
Article
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 2 | Viewed by 826
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 [...] Read more.
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  
Article
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 1009
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 [...] Read more.
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  
Article
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 1156
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 [...] Read more.
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  
Article
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
Cited by 1 | Viewed by 1155
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 [...] Read more.
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  
Article
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
Cited by 1 | Viewed by 1415
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 [...] Read more.
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

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-19
Back to TopTop