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Advancements in Deep Learning and Deep Federated Learning Models
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Advancements in Deep Learning and Deep Federated Learning Models

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 22902

Special Issue Editors

Dr. Dilbag Singh

E-Mail Website
Guest Editor
NYU Grossman School of Medicine, New York University, New York, NY, USA
Interests: automated disease diagnosis; deep learning; machine learning; lightweight models; disease segmentation; federated learning; explainable AI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Heung-No Lee

E-Mail Website
Guest Editor
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
Interests: intelligent sensors; smart city projects
Special Issues, Collections and Topics in MDPI journals
Dr. Vijay Kumar

E-Mail Website
Guest Editor
Computer Science and Engineering Department, National Institute of Technology, Hamirpur, India
Interests: metaheuristic techniques; deep learning; artificial intelligence; big data analytics; cyber-physical systems; 5G networks; healthcare big data

Special Issue Information

Dear Colleagues,

With the advancements in multimedia technologies, artificial-intelligence-based imaging applications have gained significant attention from computational researchers. Many researchers have utilized deep learning techniques to obtain potential features of images and utilize these features to build artificial intelligence models. However, deep learning techniques still suffer from issues associated with over-fitting, data leakage, and hyper-parameters tuning. To overcome the problem of over-fitting, many researchers have utilized ensemble and federated (collaborative) learning techniques. However, federated learning suffers from the location privacy of the participants. Therefore, some researchers have utilized homomorphic encryption and blockchain techniques to provide security to the participants of federated learning models. Additionally, some researchers have utilized metaheuristic techniques to optimize the hyper-parameters of the deep learning and federated learning models. However, the selection of hyper-parameters is still an open area of research. Therefore, this Special Issue deals with those techniques that utilize imaging datasets to build artificial intelligence models. Advancements in deep learning and deep federated learning models will also be considered.

Dr. Dilbag Singh
Prof. Dr. Heung-No Lee
Dr. Vijay Kumar
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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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

  • image processing
  • computer vision
  • deep learning
  • deep federated learning

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Published Papers (5 papers)

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Research

20 pages, 17198 KiB  
Article
Graph-Based Semi-Supervised Deep Learning for Indonesian Aspect-Based Sentiment Analysis
by Ahmad Abdul Chamid, Widowati and Retno Kusumaningrum
Big Data Cogn. Comput. 2023, 7(1), 5; https://doi.org/10.3390/bdcc7010005 - 28 Dec 2022
Cited by 9 | Viewed by 3123
Abstract
Product reviews on the marketplace are interesting to research. Aspect-based sentiment analysis (ABSA) can be used to find in-depth information from a review. In one review, there can be several aspects with a polarity of sentiment. Previous research has developed ABSA, but it [...] Read more.
Product reviews on the marketplace are interesting to research. Aspect-based sentiment analysis (ABSA) can be used to find in-depth information from a review. In one review, there can be several aspects with a polarity of sentiment. Previous research has developed ABSA, but it still has limitations in detecting aspects and sentiment classification and requires labeled data, but obtaining labeled data is very difficult. This research used a graph-based and semi-supervised approach to improve ABSA. GCN and GRN methods are used to detect aspect and opinion relationships. CNN and RNN methods are used to improve sentiment classification. A semi-supervised model was used to overcome the limitations of labeled data. The dataset used is an Indonesian-language review taken from the marketplace. A small part is labeled manually, and most are labeled automatically. The experiment results for the aspect classification by comparing the GCN and GRN methods obtained the best model using the GRN method with an F1 score = 0.97144. The experiment for sentiment classification by comparing the CNN and RNN methods obtained the best model using the CNN method with an F1 score = 0.94020. Our model can label most unlabeled data automatically and outperforms existing advanced models. Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Deep Federated Learning Models)
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20 pages, 6697 KiB  
Article
Image Segmentation for Mitral Regurgitation with Convolutional Neural Network Based on UNet, Resnet, Vnet, FractalNet and SegNet: A Preliminary Study
by Linda Atika, Siti Nurmaini, Radiyati Umi Partan and Erwin Sukandi
Big Data Cogn. Comput. 2022, 6(4), 141; https://doi.org/10.3390/bdcc6040141 - 25 Nov 2022
Cited by 6 | Viewed by 3036
Abstract
The heart’s mitral valve is the valve that separates the chambers of the heart between the left atrium and left ventricle. Heart valve disease is a fairly common heart disease, and one type of heart valve disease is mitral regurgitation, which is an [...] Read more.
The heart’s mitral valve is the valve that separates the chambers of the heart between the left atrium and left ventricle. Heart valve disease is a fairly common heart disease, and one type of heart valve disease is mitral regurgitation, which is an abnormality of the mitral valve on the left side of the heart that causes an inability of the mitral valve to close properly. Convolutional Neural Network (CNN) is a type of deep learning that is suitable for use in image analysis. Segmentation is widely used in analyzing medical images because it can divide images into simpler ones to facilitate the analysis process by separating objects that are not analyzed into backgrounds and objects to be analyzed into foregrounds. This study builds a dataset from the data of patients with mitral regurgitation and patients who have normal hearts, and heart valve image analysis is done by segmenting the images of their mitral heart valves. Several types of CNN architecture were applied in this research, including U-Net, SegNet, V-Net, FractalNet, and ResNet architectures. The experimental results show that the best architecture is U-Net3 in terms of Pixel Accuracy (97.59%), Intersection over Union (86.98%), Mean Accuracy (93.46%), Precision (85.60%), Recall (88.39%), and Dice Coefficient (86.58%). Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Deep Federated Learning Models)
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19 pages, 4894 KiB  
Article
Detection and Classification of Human-Carrying Baggage Using DenseNet-161 and Fit One Cycle
by Mohamed K. Ramadan, Aliaa A. A. Youssif and Wessam H. El-Behaidy
Big Data Cogn. Comput. 2022, 6(4), 108; https://doi.org/10.3390/bdcc6040108 - 6 Oct 2022
Cited by 3 | Viewed by 3013
Abstract
In recent decades, the crime rate has significantly increased. As a result, the automatic video monitoring system has become increasingly important for researchers in computer vision. A person’s baggage classification is essential in knowing who has abandoned baggage. This paper proposes a model [...] Read more.
In recent decades, the crime rate has significantly increased. As a result, the automatic video monitoring system has become increasingly important for researchers in computer vision. A person’s baggage classification is essential in knowing who has abandoned baggage. This paper proposes a model for classifying humans carrying baggage. Two approaches are used for comparison using a deep learning technique. The first approach is based on categorizing human-containing image regions as either with or without baggage. The second approach classifies human-containing image regions based on the human position direction attribute. The proposed model is based on the pretrained DenseNet-161 architecture. It uses a "fit-one-cycle policy" strategy to reduce the training time and achieve better accuracy. The Fastai framework is used for implementation due to its super computational ability, simple workflow, and unique data cleansing functionalities. Our proposed model was experimentally validated, and the results show that the process is sufficiently precise, faster, and outperforms the existing methods. We achieved an accuracy of between 96% and 98.75% for the binary classification and 96.67% and 98.33% for the multi-class classification. For multi-class classification, the datasets, such as PETA, INRIA, ILIDS, and MSMT17, are re-annotated with one’s direction information about one’s stance to test the suggested approach’s efficacy. Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Deep Federated Learning Models)
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16 pages, 4437 KiB  
Article
Lightweight AI Framework for Industry 4.0 Case Study: Water Meter Recognition
by Jalel Ktari, Tarek Frikha, Monia Hamdi, Hela Elmannai and Habib Hmam
Big Data Cogn. Comput. 2022, 6(3), 72; https://doi.org/10.3390/bdcc6030072 - 1 Jul 2022
Cited by 24 | Viewed by 4849
Abstract
The evolution of applications in telecommunication, network, computing, and embedded systems has led to the emergence of the Internet of Things and Artificial Intelligence. The combination of these technologies enabled improving productivity by optimizing consumption and facilitating access to real-time information. In this [...] Read more.
The evolution of applications in telecommunication, network, computing, and embedded systems has led to the emergence of the Internet of Things and Artificial Intelligence. The combination of these technologies enabled improving productivity by optimizing consumption and facilitating access to real-time information. In this work, there is a focus on Industry 4.0 and Smart City paradigms and a proposal of a new approach to monitor and track water consumption using an OCR, as well as the artificial intelligence algorithm and, in particular the YoLo 4 machine learning model. The goal of this work is to provide optimized results in real time. The recognition rate obtained with the proposed algorithms is around 98%. Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Deep Federated Learning Models)
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22 pages, 2824 KiB  
Article
Startups and Consumer Purchase Behavior: Application of Support Vector Machine Algorithm
by Pejman Ebrahimi, Aidin Salamzadeh, Maryam Soleimani, Seyed Mohammad Khansari, Hadi Zarea and Maria Fekete-Farkas
Big Data Cogn. Comput. 2022, 6(2), 34; https://doi.org/10.3390/bdcc6020034 - 25 Mar 2022
Cited by 27 | Viewed by 6080
Abstract
This study evaluated the impact of startup technology innovations and customer relationship management (CRM) performance on customer participation, value co-creation, and consumer purchase behavior (CPB). This analytical study empirically tested the proposed hypotheses using structural equation modeling (SEM) and SmartPLS 3 techniques. Moreover, [...] Read more.
This study evaluated the impact of startup technology innovations and customer relationship management (CRM) performance on customer participation, value co-creation, and consumer purchase behavior (CPB). This analytical study empirically tested the proposed hypotheses using structural equation modeling (SEM) and SmartPLS 3 techniques. Moreover, we used a support vector machine (SVM) algorithm to verify the model’s accuracy. SVM algorithm uses four different kernels to check the accuracy criterion, and we checked all of them. This research used the convenience sampling approach in gathering the data. We used the conventional bias test method. A total of 466 respondents were completed. Technological innovations of startups and CRM have a positive and significant effect on customer participation. Customer participation significantly affects the value of pleasure, economic value, and relationship value. Based on the importance-performance map analysis (IPMA) matrix results, “customer participation” with a score of 0.782 had the highest importance. If customers increase their participation performance by one unit during the COVID-19 epidemic, its overall CPB increases by 0.782. In addition, our results showed that the lowest performance is related to the technological innovations of startups, which indicates an excellent opportunity for development in this area. SVM results showed that polynomial kernel, to a high degree, is the best kernel that confirms the model’s accuracy. Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Deep Federated Learning Models)
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