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Advancements in Deep Learning and Its Applications
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Advancements in Deep Learning and Its Applications

A special issue of Applied System Innovation (ISSN 2571-5577). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 17140

Special Issue Editors

Dr. Patrícia Ramos

E-Mail Website
Guest Editor
1. ISCAP, Polytechnic University of Porto, 4465-004 S. Mamede de Infesta, Portugal
2. INESC TEC – Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
Interests: statistical modelling; forecasting; optimization; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Jose Manuel Oliveira

E-Mail Website
Guest Editor
1. Faculty of Economics, University of Porto, 4200-464 Porto, Portugal
2. INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
Interests: time series forecasting; machine learning; deep learning; data science; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep Learning is a subfield of Machine Learning that has seen significant advancements over the past few years, thanks to the availability of large amounts of data, faster computing hardware, and improved algorithms. The advancements in Deep Learning have revolutionized several fields, including image recognition, speech recognition, natural language processing, robotics, and healthcare. The development of Convolutional Neural Networks, Recurrent Neural Networks, and Deep Reinforcement Learning has significantly improved the performance of Deep Learning models in these areas. As Deep Learning continues to grow, we can expect to see even more breakthroughs in various applications, which will have a profound impact on our lives.

Given this context, this Special Issue calls for a more critical discussion and perspective on the practical implementations of Artificial Intelligence and Deep Learning in real-world scenarios, as well as the recent advancements in leveraging these pioneering technologies, and to disseminate acquired knowledge. We encourage authors to submit original research articles that tackle crucial matters and contribute to the creation of innovative concepts, methodologies, applications, trends, and knowledge in the field. Additionally, review articles that present the current state of the art are warmly welcomed.

Dr. Patrícia Ramos
Dr. Jose Manuel Oliveira
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 System Innovation 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 1400 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.

Published Papers (7 papers)

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Research

13 pages, 5379 KiB  
Article
A Comparative Analysis of Oak Wood Defect Detection Using Two Deep Learning (DL)-Based Software
by Branimir Jambreković, Filip Veselčić, Iva Ištok, Tomislav Sinković, Vjekoslav Živković and Tomislav Sedlar
Appl. Syst. Innov. 2024, 7(2), 30; https://0-doi-org.brum.beds.ac.uk/10.3390/asi7020030 - 15 Apr 2024
Viewed by 410
Abstract
The world’s expanding population presents a challenge through its rising demand for wood products. This requirement contributes to increased production and, ultimately, the high-quality and efficient utilization of basic materials. Detecting defects in wood elements, which are inevitable when working with a natural [...] Read more.
The world’s expanding population presents a challenge through its rising demand for wood products. This requirement contributes to increased production and, ultimately, the high-quality and efficient utilization of basic materials. Detecting defects in wood elements, which are inevitable when working with a natural material such as wood, is one of the difficulties associated with the issue above. Even in modern times, people still identify wood defects by visually scrutinizing the sawn surface and marking the defects. Industrial scanners equipped with software based on convolutional neural networks (CNNs) allow for the rapid detection of defects and have the potential to accelerate production and eradicate human subjectivity. This paper evaluates the suitability of defect recognition software in industrial scanners against software specifically designed for this task within a research project conducted using Adaptive Vision Studio, focusing on feature detection techniques. The research revealed that the software installed as part of the industrial scanner is more effective for analyzing knots (77.78% vs. 70.37%), sapwood (100% vs. 80%), and ambrosia wood (60% vs. 20%), while the software derived from the project is more effective for analyzing cracks (70% vs. 65%), ingrown bark (42.86% vs. 28.57%), and wood rays (81.82% vs. 27.27%). Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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19 pages, 8245 KiB  
Article
Deep Learning Method to Detect Missing Welds for Joist Assembly Line
by Hamed Raoofi, Asa Sabahnia, Daniel Barbeau and Ali Motamedi
Appl. Syst. Innov. 2024, 7(1), 16; https://0-doi-org.brum.beds.ac.uk/10.3390/asi7010016 - 13 Feb 2024
Viewed by 1368
Abstract
Traditional methods of supervision in the construction industry are time-consuming and costly, requiring significant investments in skilled labor. However, with advancements in artificial intelligence, computer vision, and deep learning, these methods can now be automated, resulting in time and cost savings, as well [...] Read more.
Traditional methods of supervision in the construction industry are time-consuming and costly, requiring significant investments in skilled labor. However, with advancements in artificial intelligence, computer vision, and deep learning, these methods can now be automated, resulting in time and cost savings, as well as improvements in product quality. This research focuses on the application of computer vision approaches to monitor the quality of welding in prefabricated steel elements. A high-performance network was designed, consisting of a video capturing station, a customized classifier based on a YOLOv4 detector and an IoU tracker, and a user interface software for any interaction with quality control workers. The network demonstrated over 98% accuracy in identifying steel connection types and detecting missed welds on the assembly line in real-time. Extensive validation was conducted using a large dataset from a real production environment. The proposed framework aims to reduce rework, minimize hazards, and enhance product quality. This research contributes to the automation of quality control processes in the construction industry. Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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20 pages, 1720 KiB  
Article
Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer
by Weijun Li, Jintong Liu, Yuxiao Gao, Xinyong Zhang and Jianlai Gu
Appl. Syst. Innov. 2024, 7(1), 8; https://0-doi-org.brum.beds.ac.uk/10.3390/asi7010008 - 08 Jan 2024
Viewed by 1610
Abstract
The task of named entity recognition (NER) is to identify entities in the text and predict their categories. In real-life scenarios, the context of the text is often complex, and there may exist nested entities within an entity. This kind of entity is [...] Read more.
The task of named entity recognition (NER) is to identify entities in the text and predict their categories. In real-life scenarios, the context of the text is often complex, and there may exist nested entities within an entity. This kind of entity is called a nested entity, and the task of recognizing entities with nested structures is referred to as nested named entity recognition. Most existing NER models can only handle flat entities, and there has been limited research progress in Chinese nested named entity recognition, resulting in relatively few models in this direction. General NER models have limited semantic extraction capabilities and cannot capture deep semantic information between nested entities in the text. To address these issues, this paper proposes a model that uses the GlobalPointer module to identify nested entities in the text and constructs the IDCNNLR semantic extraction module to extract deep semantic information. Furthermore, multiple-head self-attention mechanisms are incorporated into the model at multiple positions to achieve data denoising, enhancing the quality of semantic features. The proposed model considers each possible entity boundary through the GlobalPointer module, and the IDCNNLR semantic extraction module and multi-position attention mechanism are introduced to enhance the model’s semantic extraction capability. Experimental results demonstrate that the proposed model achieves F1 scores of 69.617% and 79.285% on the CMeEE Chinese nested entity recognition dataset and CLUENER2020 Chinese fine-grained entity recognition dataset, respectively. The model exhibits improvement compared to baseline models, and each innovation point shows effective performance enhancement in ablative experiments. Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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21 pages, 4029 KiB  
Article
Stock Market Prediction Using Deep Reinforcement Learning
by Alamir Labib Awad, Saleh Mesbah Elkaffas and Mohammed Waleed Fakhr
Appl. Syst. Innov. 2023, 6(6), 106; https://0-doi-org.brum.beds.ac.uk/10.3390/asi6060106 - 10 Nov 2023
Cited by 2 | Viewed by 5741
Abstract
Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has introduced advanced predictive algorithms, reshaping investment strategies. Essential to this [...] Read more.
Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has introduced advanced predictive algorithms, reshaping investment strategies. Essential to this transformation is the profound reliance on historical data analysis, driving the automation of decisions, particularly in individual stock contexts. Recent strides in deep reinforcement learning algorithms have emerged as a focal point for researchers, offering promising avenues in stock market predictions. In contrast to prevailing models rooted in artificial neural network (ANN) and long short-term memory (LSTM) algorithms, this study introduces a pioneering approach. By integrating ANN, LSTM, and natural language processing (NLP) techniques with the deep Q network (DQN), this research crafts a novel architecture tailored specifically for stock market prediction. At its core, this innovative framework harnesses the wealth of historical stock data, with a keen focus on gold stocks. Augmented by the insightful analysis of social media data, including platforms such as S&P, Yahoo, NASDAQ, and various gold market-related channels, this study gains depth and comprehensiveness. The predictive prowess of the developed model is exemplified in its ability to forecast the opening stock value for the subsequent day, a feat validated across exhaustive datasets. Through rigorous comparative analysis against benchmark algorithms, the research spotlights the unparalleled accuracy and efficacy of the proposed combined algorithmic architecture. This study not only presents a compelling demonstration of predictive analytics but also engages in critical analysis, illuminating the intricate dynamics of the stock market. Ultimately, this research contributes valuable insights and sets new horizons in the realm of stock market predictions. Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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20 pages, 2302 KiB  
Article
Personalized E-Learning Recommender System Based on Autoencoders
by Lamyae El Youbi El Idrissi, Ismail Akharraz and Abdelaziz Ahaitouf
Appl. Syst. Innov. 2023, 6(6), 102; https://0-doi-org.brum.beds.ac.uk/10.3390/asi6060102 - 27 Oct 2023
Cited by 1 | Viewed by 2381
Abstract
Through the Internet, learners can access available information on e-learning platforms to facilitate their studies or to acquire new skills. However, finding the right information for their specific needs among the numerous available choices is a tedious task due to information overload. Recommender [...] Read more.
Through the Internet, learners can access available information on e-learning platforms to facilitate their studies or to acquire new skills. However, finding the right information for their specific needs among the numerous available choices is a tedious task due to information overload. Recommender systems are a good solution to personalize e-learning by proposing useful and relevant information adapted to each learner using a set of techniques and algorithms. Collaborative filtering (CF) is one of the techniques widely used in such systems. However, the high dimensions and sparsity of the data are major problems. Since the concept of deep learning has grown in popularity, various studies have emerged to improve this form of filtering. In this work, we used an autoencoder, which is a powerful model in data dimension reduction, feature extraction and data reconstruction, to learn and predict student preferences in an e-learning recommendation system based on collaborative filtering. Experimental results obtained using the database created by Kulkarni et al. show that this model is more accurate and outperforms models based on K-nearest neighbor (KNN), singular value decomposition (SVD), singular value decomposition plus plus (SVD++) and non-negative matrix factorization (NMF) in terms of the root-mean-square error (RMSE) and mean absolute error (MAE). Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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19 pages, 1307 KiB  
Article
Application of Deep Learning in the Early Detection of Emergency Situations and Security Monitoring in Public Spaces
by William Villegas-Ch and Jaime Govea
Appl. Syst. Innov. 2023, 6(5), 90; https://0-doi-org.brum.beds.ac.uk/10.3390/asi6050090 - 08 Oct 2023
Cited by 1 | Viewed by 1680
Abstract
This article addresses the need for early emergency detection and safety monitoring in public spaces using deep learning techniques. The problem of discerning relevant sound events in urban environments is identified, which is essential to respond quickly to possible incidents. To solve this, [...] Read more.
This article addresses the need for early emergency detection and safety monitoring in public spaces using deep learning techniques. The problem of discerning relevant sound events in urban environments is identified, which is essential to respond quickly to possible incidents. To solve this, a method is proposed based on extracting acoustic features from captured audio signals and using a deep learning model trained with data collected both from the environment and from specialized libraries. The results show performance metrics such as precision, completeness, F1-score, and ROC-AUC curve and discuss detailed confusion matrices and false positive and negative analysis. Comparing this approach with related works highlights its effectiveness and potential in detecting sound events. The article identifies areas for future research, including incorporating real-world data and exploring more advanced neural architectures, and reaffirms the importance of deep learning in public safety. Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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13 pages, 541 KiB  
Article
Robust Sales forecasting Using Deep Learning with Static and Dynamic Covariates
by Patrícia Ramos and José Manuel Oliveira
Appl. Syst. Innov. 2023, 6(5), 85; https://0-doi-org.brum.beds.ac.uk/10.3390/asi6050085 - 28 Sep 2023
Viewed by 1474
Abstract
Retailers must have accurate sales forecasts to efficiently and effectively operate their businesses and remain competitive in the marketplace. Global forecasting models like RNNs can be a powerful tool for forecasting in retail settings, where multiple time series are often interrelated and influenced [...] Read more.
Retailers must have accurate sales forecasts to efficiently and effectively operate their businesses and remain competitive in the marketplace. Global forecasting models like RNNs can be a powerful tool for forecasting in retail settings, where multiple time series are often interrelated and influenced by a variety of external factors. By including covariates in a forecasting model, we can often better capture the various factors that can influence sales in a retail setting. This can help improve the accuracy of our forecasts and enable better decision making for inventory management, purchasing, and other operational decisions. In this study, we investigate how the accuracy of global forecasting models is affected by the inclusion of different potential demand covariates. To ensure the significance of the study’s findings, we used the M5 forecasting competition’s openly accessible and well-established dataset. The results obtained from DeepAR models trained on different combinations of features indicate that the inclusion of time-, event-, and ID-related features consistently enhances the forecast accuracy. The optimal performance is attained when all these covariates are employed together, leading to a 1.8% improvement in RMSSE and a 6.5% improvement in MASE compared to the baseline model without features. It is noteworthy that all DeepAR models, both with and without covariates, exhibit a significantly superior forecasting performance in comparison to the seasonal naïve benchmark. Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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