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Deep Network Learning and Its Applications
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Deep Network Learning and Its Applications

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 13569

Special Issue Editors

Dr. Guarino Alfonso

E-Mail Website
Guest Editor
Department of Law, Economics, Management and Quantitative Methods, University of Sannio, Piazza Arechi II, 82100 Benevento, BN, Italy
Interests: privacy techno-regulation; artificial intelligence and law; artificial intelligence for well-being; visual analytics; educational data mining; learning analytics
Dr. Rocco Zaccagnino

E-Mail Website
Guest Editor
Department of Computer Science, University of Salerno, Via Giovanni Paolo II, 130, 84084 Fisciano, SA, Italy
Interests: computer music; artificial intelligence; computational intelligence; formal languages; bioinformatics; techno-regulation
Special Issues, Collections and Topics in MDPI journals
Dr. Emiliano Del Gobbo

E-Mail Website
Guest Editor
Department of Economics, University of Foggia, 71122 Foggia, Italy
Interests: statistical learning; text mining; natural language processing; machine learning; opinion mining; learning analytics; deep learning; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning is becoming the leading approach in machine learning. Supervised, unsupervised, reinforcement, and hybrid, as well as single-view and multi-view learning approaches in deep learning, have been successfully applied in a variety of contexts, spanning from computer vision to natural language processing and data analytics. Deep learning techniques often require huge volumes of data, from which hidden knowledge can be extracted. Today, these data are generated by companies, public organizations, people (e.g., via smartphones), and machines (i.e., the Internet of Things) at a high pace. However, practical deployment is challenging, in a key part due to the high computational needs. Another drawback relies on the implementation of these deep learning applications in high-risk sectors where human oversights must be guaranteed, as recently highlighted by the EU Council. This issue is related to the challenge of putting humans at the center of the development and offering comprehensibility for end-users. The implementation and deployment in practice of deep learning applications is an active area of research from which a series of insights and clues can be captured so as to better shape the future of deep learning applications in our lives.

This Special Issue seeks papers dealing not only with the design of deep learning applications, but also (and with a special interest) the implementation, validation, testing, and/or management of such deep learning systems in simulated/operational environments. Moreover, we look for contributions providing practical guidelines in the development and management of these applications. Papers tackling issues related to the deployment of deep learning systems with final stakeholders/users are welcome. In addition, of particular interest in this Special Issue are deep learning applications conceived for beneficial and benign outcomes, i.e., to support people’s daily life, protect their interests, and enhance their well-being.

Thus, the primary contributions we expect concern not only algorithmic novelties, but also evidence from and the aspects involved when putting deep learning into practice.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Law and techno-regulation;
  • Critical data studies;
  • Computer sound and music;
  • Economics and finance;
  • Bioinformatics;
  • Learning analytics;
  • Internet of Things;
  • Information retrieval;
  • Knowledge extraction/discovery;
  • Decision support;
  • Human–machine cooperation/interaction;
  • Visual interfaces;
  • Human-centered AI (comprehensible AI).

Dr. Guarino Alfonso
Dr. Rocco Zaccagnino
Dr. Emiliano Del Gobbo
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

  • deep learning
  • deep learning applications
  • deep learning in practice
  • user study
  • deep learning implementation

Published Papers (7 papers)

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Research

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15 pages, 2801 KiB  
Article
Anomaly Detection of IoT Cyberattacks in Smart Cities Using Federated Learning and Split Learning
by Ishaani Priyadarshini
Big Data Cogn. Comput. 2024, 8(3), 21; https://doi.org/10.3390/bdcc8030021 - 22 Feb 2024
Viewed by 1666
Abstract
The swift proliferation of the Internet of Things (IoT) devices in smart city infrastructures has created an urgent demand for robust cybersecurity measures. These devices are susceptible to various cyberattacks that can jeopardize the security and functionality of urban systems. This research presents [...] Read more.
The swift proliferation of the Internet of Things (IoT) devices in smart city infrastructures has created an urgent demand for robust cybersecurity measures. These devices are susceptible to various cyberattacks that can jeopardize the security and functionality of urban systems. This research presents an innovative approach to identifying anomalies caused by IoT cyberattacks in smart cities. The proposed method harnesses federated and split learning and addresses the dual challenge of enhancing IoT network security while preserving data privacy. This study conducts extensive experiments using authentic datasets from smart cities. To compare the performance of classical machine learning algorithms and deep learning models for detecting anomalies, model effectiveness is assessed using precision, recall, F-1 score, accuracy, and training/deployment time. The findings demonstrate that federated learning and split learning have the potential to balance data privacy concerns with competitive performance, providing robust solutions for detecting IoT cyberattacks. This study contributes to the ongoing discussion about securing IoT deployments in urban settings. It lays the groundwork for scalable and privacy-conscious cybersecurity strategies. The results underscore the vital role of these techniques in fortifying smart cities and promoting the development of adaptable and resilient cybersecurity measures in the IoT era. Full article
(This article belongs to the Special Issue Deep Network Learning and Its Applications)
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21 pages, 1535 KiB  
Article
An Approach Based on Recurrent Neural Networks and Interactive Visualization to Improve Explainability in AI Systems
by William Villegas-Ch, Joselin García-Ortiz and Angel Jaramillo-Alcazar
Big Data Cogn. Comput. 2023, 7(3), 136; https://doi.org/10.3390/bdcc7030136 - 31 Jul 2023
Viewed by 1728
Abstract
This paper investigated the importance of explainability in artificial intelligence models and its application in the context of prediction in Formula (1). A step-by-step analysis was carried out, including collecting and preparing data from previous races, training an AI model to make predictions, [...] Read more.
This paper investigated the importance of explainability in artificial intelligence models and its application in the context of prediction in Formula (1). A step-by-step analysis was carried out, including collecting and preparing data from previous races, training an AI model to make predictions, and applying explainability techniques in the said model. Two approaches were used: the attention technique, which allowed visualizing the most relevant parts of the input data using heat maps, and the permutation importance technique, which evaluated the relative importance of features. The results revealed that feature length and qualifying performance are crucial variables for position predictions in Formula (1). These findings highlight the relevance of explainability in AI models, not only in Formula (1) but also in other fields and sectors, by ensuring fairness, transparency, and accountability in AI-based decision making. The results highlight the importance of considering explainability in AI models and provide a practical methodology for its implementation in Formula (1) and other domains. Full article
(This article belongs to the Special Issue Deep Network Learning and Its Applications)
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17 pages, 4460 KiB  
Article
An End-to-End Online Traffic-Risk Incident Prediction in First-Person Dash Camera Videos
by Hilmil Pradana
Big Data Cogn. Comput. 2023, 7(3), 129; https://doi.org/10.3390/bdcc7030129 - 06 Jul 2023
Cited by 3 | Viewed by 1673
Abstract
Predicting traffic risk incidents in first-person helps to ensure a safety reaction can occur before the incident happens for a wide range of driving scenarios and conditions. One challenge to building advanced driver assistance systems is to create an early warning system for [...] Read more.
Predicting traffic risk incidents in first-person helps to ensure a safety reaction can occur before the incident happens for a wide range of driving scenarios and conditions. One challenge to building advanced driver assistance systems is to create an early warning system for the driver to react safely and accurately while perceiving the diversity of traffic-risk predictions in real-world applications. In this paper, we aim to bridge the gap by investigating two key research questions regarding the driver’s current status of driving through online videos and the types of other moving objects that lead to dangerous situations. To address these problems, we proposed an end-to-end two-stage architecture: in the first stage, unsupervised learning is applied to collect all suspicious events on actual driving; in the second stage, supervised learning is used to classify all suspicious event results from the first stage to a common event type. To enrich the classification type, the metadata from the result of the first stage is sent to the second stage to handle the data limitation while training our classification model. Through the online situation, our method runs 9.60 fps on average with 1.44 fps on standard deviation. Our quantitative evaluation shows that our method reaches 81.87% and 73.43% for the average F1-score on labeled data of CST-S3D and real driving datasets, respectively. Furthermore, the proposed method has the potential to assist distribution companies in evaluating the driving performance of their driver by automatically monitoring near-miss events and analyzing driving patterns for training programs to reduce future accidents. Full article
(This article belongs to the Special Issue Deep Network Learning and Its Applications)
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17 pages, 617 KiB  
Article
Breaking Barriers: Unveiling Factors Influencing the Adoption of Artificial Intelligence by Healthcare Providers
by BM Zeeshan Hameed, Nithesh Naik, Sufyan Ibrahim, Nisha S. Tatkar, Milap J. Shah, Dharini Prasad, Prithvi Hegde, Piotr Chlosta, Bhavan Prasad Rai and Bhaskar K Somani
Big Data Cogn. Comput. 2023, 7(2), 105; https://doi.org/10.3390/bdcc7020105 - 30 May 2023
Cited by 8 | Viewed by 2980
Abstract
Artificial intelligence (AI) is an emerging technological system that provides a platform to manage and analyze data by emulating human cognitive functions with greater accuracy, revolutionizing patient care and introducing a paradigm shift to the healthcare industry. The purpose of this study is [...] Read more.
Artificial intelligence (AI) is an emerging technological system that provides a platform to manage and analyze data by emulating human cognitive functions with greater accuracy, revolutionizing patient care and introducing a paradigm shift to the healthcare industry. The purpose of this study is to identify the underlying factors that affect the adoption of artificial intelligence in healthcare (AIH) by healthcare providers and to understand “What are the factors that influence healthcare providers’ behavioral intentions to adopt AIH in their routine practice?” An integrated survey was conducted among healthcare providers, including consultants, residents/students, and nurses. The survey included items related to performance expectancy, effort expectancy, initial trust, personal innovativeness, task complexity, and technology characteristics. The collected data were analyzed using structural equation modeling. A total of 392 healthcare professionals participated in the survey, with 72.4% being male and 50.7% being 30 years old or younger. The results showed that performance expectancy, effort expectancy, and initial trust have a positive influence on the behavioral intentions of healthcare providers to use AIH. Personal innovativeness was found to have a positive influence on effort expectancy, while task complexity and technology characteristics have a positive influence on effort expectancy for AIH. The study’s empirically validated model sheds light on healthcare providers’ intention to adopt AIH, while the study’s findings can be used to develop strategies to encourage this adoption. However, further investigation is necessary to understand the individual factors affecting the adoption of AIH by healthcare providers. Full article
(This article belongs to the Special Issue Deep Network Learning and Its Applications)
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18 pages, 9202 KiB  
Article
Data-Driven Fracture Morphology Prognosis from High Pressured Modified Proppants Based on Stochastic-Adam-RMSprop Optimizers; tf.NNR Study
by Dennis Delali Kwesi Wayo, Sonny Irawan, Alfrendo Satyanaga and Jong Kim
Big Data Cogn. Comput. 2023, 7(2), 57; https://doi.org/10.3390/bdcc7020057 - 24 Mar 2023
Viewed by 1388
Abstract
Data-driven models with some evolutionary optimization algorithms, such as particle swarm optimization (PSO) and ant colony optimization (ACO) for hydraulic fracturing of shale reservoirs, have in recent times been validated as one of the best-performing machine learning algorithms. Log data from well-logging tools [...] Read more.
Data-driven models with some evolutionary optimization algorithms, such as particle swarm optimization (PSO) and ant colony optimization (ACO) for hydraulic fracturing of shale reservoirs, have in recent times been validated as one of the best-performing machine learning algorithms. Log data from well-logging tools and physics-driven models is difficult to collate and model to enhance decision-making processes. The study sought to train, test, and validate synthetic data emanating from CMG’s numerically propped fracture morphology modeling to support and enhance productive hydrocarbon production and recovery. This data-driven numerical model was investigated for efficient hydraulic-induced fracturing by using machine learning, gradient descent, and adaptive optimizers. While satiating research curiosities, the online predictive analysis was conducted using the Google TensorFlow tool with the Tensor Processing Unit (TPU), focusing on linear and non-linear neural network regressions. A multi-structured dense layer with 1000, 100, and 1 neurons was compiled with mean absolute error (MAE) as loss functions and evaluation metrics concentrating on stochastic gradient descent (SGD), Adam, and RMSprop optimizers at a learning rate of 0.01. However, the emerging algorithm with the best overall optimization process was found to be Adam, whose error margin was 101.22 and whose accuracy was 80.24% for the entire set of 2000 synthetic data it trained and tested. Based on fracture conductivity, the data indicates that there was a higher chance of hydrocarbon production recovery using this method. Full article
(This article belongs to the Special Issue Deep Network Learning and Its Applications)
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16 pages, 2013 KiB  
Article
A Hybrid Deep Learning Framework with Decision-Level Fusion for Breast Cancer Survival Prediction
by Nermin Abdelhakim Othman, Manal A. Abdel-Fattah and Ahlam Talaat Ali
Big Data Cogn. Comput. 2023, 7(1), 50; https://doi.org/10.3390/bdcc7010050 - 16 Mar 2023
Cited by 5 | Viewed by 2522
Abstract
Because of technological advancements and their use in the medical area, many new methods and strategies have been developed to address complex real-life challenges. Breast cancer, a particular kind of tumor that arises in breast cells, is one of the most prevalent types [...] Read more.
Because of technological advancements and their use in the medical area, many new methods and strategies have been developed to address complex real-life challenges. Breast cancer, a particular kind of tumor that arises in breast cells, is one of the most prevalent types of cancer in women and is. Early breast cancer detection and classification are crucial. Early detection considerably increases the likelihood of survival, which motivates us to contribute to different detection techniques from a technical standpoint. Additionally, manual detection requires a lot of time and effort and carries the risk of pathologist error and inaccurate classification. To address these problems, in this study, a hybrid deep learning model that enables decision making based on data from multiple data sources is proposed and used with two different classifiers. By incorporating multi-omics data (clinical data, gene expression data, and copy number alteration data) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, the accuracy of patient survival predictions is expected to be improved relative to prediction utilizing only one modality of data. A convolutional neural network (CNN) architecture is used for feature extraction. LSTM and GRU are used as classifiers. The accuracy achieved by LSTM is 97.0%, and that achieved by GRU is 97.5, while using decision fusion (LSTM and GRU) achieves the best accuracy of 98.0%. The prediction performance assessed using various performance indicators demonstrates that our model outperforms currently used methodologies. Full article
(This article belongs to the Special Issue Deep Network Learning and Its Applications)
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Review

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26 pages, 1404 KiB  
Review
Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape
by Divya Garikapati and Sneha Sudhir Shetiya
Big Data Cogn. Comput. 2024, 8(4), 42; https://doi.org/10.3390/bdcc8040042 - 07 Apr 2024
Viewed by 696
Abstract
The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of [...] Read more.
The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects. Full article
(This article belongs to the Special Issue Deep Network Learning and Its Applications)
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