Recent Trends and Applications of Artificial Intelligence

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 11645

Special Issue Editors


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Guest Editor
Department of Computer Science, Faculty of Sciences and Techniques Beni Mellal, University Sultan Moulay Slimane (USMS), Beni-Mellal 23000, Morocco
Interests: machine learning; deep learning; image processing; WSN; IoT; energy efficiency

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Guest Editor
Department of Mathematics and Computer Science, Polydisciplinary Faculty Beni Mellal, University Sultan Moulay Slimane (USMS), Beni-Mellal 23000, Morocco
Interests: artificial intelligence; cybersecurity; web security; network security; authentication protocols; applied cryptography

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Guest Editor
School of Computer Science, University of Galway, H91 TK33 Galway, Ireland
Interests: data mining; machine learning; data analytics; deep learning; anomaly detection; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer and Information Science (IDA), Linköpings Universitet, SE-581 83 Linköping, Sweden
Interests: cyber physical security; internet of things; blockchain; aviation security

Special Issue Information

Dear Colleagues,

In recent years, machine learning (ML) and artificial intelligence (AI) have profoundly impacted various fields, such as cybersecurity, big data analytics, digital twins, internet of things (IoT), image processing, cloud computing, agriculture, and industry 4.0. In particular, AI and ML techniques have achieved rapid technological improvement by overcoming various problems that traditional solutions were not able to address. However, the current AI and ML models have significant limits and difficulties, restricting their use to handling specific problems in limited areas. AI and ML solutions of the next generation will presumably eliminate the shortcomings of the current generation. Researchers and practitioners are working on a variety of areas to realize next-gen AI, including improved explainability, trust, and reliability; new AI paradigms, such as federated learning, bio-inspired AI models, neuro-symbolic AI, and quantum AI; specialized AI hardware, software, and data models; enhanced AI–human collaboration (collaborative intelligence); and efforts to move closer to artificial general intelligence (AGI).

As a result, this Special Issue aims to offer a distinctive academic forum for presenting recent advances in AI and ML techniques and applications across various fields. In addition to original research articles, we encourage surveys, experience reports, and case studies on the latest developments and applications of AI.

The topics of interest for this Special Issue include, but are not limited to:

  • Next-gen AI applications (in environmental science, green information and communication technologies, digital twins, industry, healthcare, automotive, financial services, manufacturing, agriculture, and other areas);
  • green artificial intelligence;
  • artificial intelligence for cybersecurity;
  • artificial intelligence for image processing;
  • artificial intelligence and blockchain technologies;
  • artificial intelligence and big data;
  • artificial intelligence and IoT;
  • artificial intelligence and cloud computing;
  • artificial intelligence and edge computing;
  • security and trustworthiness of AI.

Prof. Dr. Najlae Idrissi
Dr. Yassine Sadqi
Dr. Abdul Wahid
Dr. Gurjot Singh Gaba
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. Electronics 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

  • AI applications
  • machine learning
  • deep learning
  • federated learning
  • cybersecurity
  • blockchain
  • internet of things
  • green computing
  • artificial general intelligence
  • explainable AI

Published Papers (5 papers)

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Research

16 pages, 1052 KiB  
Article
FedSKF: Selective Knowledge Fusion via Optimal Transport in Federated Class Incremental Learning
by Minghui Zhou and Xiangfeng Wang
Electronics 2024, 13(9), 1772; https://doi.org/10.3390/electronics13091772 - 4 May 2024
Viewed by 656
Abstract
Federated learning has been a hot topic in the field of artificial intelligence in recent years due to its distributed nature and emphasis on privacy protection. To better align with real-world scenarios, federated class incremental learning (FCIL) has emerged as a new research [...] Read more.
Federated learning has been a hot topic in the field of artificial intelligence in recent years due to its distributed nature and emphasis on privacy protection. To better align with real-world scenarios, federated class incremental learning (FCIL) has emerged as a new research trend, but it faces challenges such as heterogeneous data, catastrophic forgetting, and inter-client interference. However, most existing methods enhance model performance at the expense of privacy, such as uploading prototypes or samples, which violates the basic principle of only transmitting models in federated learning. This paper presents a novel selective knowledge fusion (FedSKF) model to address data heterogeneity and inter-client interference without sacrificing any privacy. Specifically, this paper introduces a PIT (projection in turn) module on the server side to indirectly recover client data distribution information through optimal transport. Subsequently, to reduce inter-client interference, knowledge of the global model is selectively absorbed via knowledge distillation and an incomplete synchronization classifier at the client side, namely an SKS (selective knowledge synchronization) module. Furthermore, to mitigate global catastrophic forgetting, a global forgetting loss is proposed to distill knowledge from the old global model. Our framework can easily integrate various CIL methods, allowing it to adapt to application scenarios with varying privacy requirements. We conducted extensive experiments on CIFAR100 and Tiny-ImageNet datasets, and the performance of our method surpasses existing works. Full article
(This article belongs to the Special Issue Recent Trends and Applications of Artificial Intelligence)
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0 pages, 986 KiB  
Article
TXAI-ADV: Trustworthy XAI for Defending AI Models against Adversarial Attacks in Realistic CIoT
by Stephen Ojo, Moez Krichen, Meznah A. Alamro and Alaeddine Mihoub
Electronics 2024, 13(9), 1769; https://doi.org/10.3390/electronics13091769 - 3 May 2024
Viewed by 841
Abstract
Adversarial attacks are more prevalent in Consumer Internet of Things (CIoT) devices (i.e., smart home devices, cameras, actuators, sensors, and micro-controllers) because of their growing integration into daily activities, which brings attention to their possible shortcomings and usefulness. Keeping protection in the CIoT [...] Read more.
Adversarial attacks are more prevalent in Consumer Internet of Things (CIoT) devices (i.e., smart home devices, cameras, actuators, sensors, and micro-controllers) because of their growing integration into daily activities, which brings attention to their possible shortcomings and usefulness. Keeping protection in the CIoT and countering emerging risks require constant updates and monitoring of these devices. Machine learning (ML), in combination with Explainable Artificial Intelligence (XAI), has become an essential component of the CIoT ecosystem due to its rapid advancement and impressive results across several application domains for attack detection, prevention, mitigation, and providing explanations of such decisions. These attacks exploit and steal sensitive data, disrupt the devices’ functionality, or gain unauthorized access to connected networks. This research generates a novel dataset by injecting adversarial attacks into the CICIoT2023 dataset. It presents an adversarial attack detection approach named TXAI-ADV that utilizes deep learning (Mutli-Layer Perceptron (MLP) and Deep Neural Network (DNN)) and machine learning classifiers (K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Gaussian Naive Bayes (GNB), ensemble voting, and Meta Classifier) to detect attacks and avert such situations rapidly in a CIoT. This study utilized Shapley Additive Explanations (SHAP) techniques, an XAI technique, to analyze the average impact of each class feature on the proposed models and select optimal features for the adversarial attacks dataset. The results revealed that, with a 96% accuracy rate, the proposed approach effectively detects adversarial attacks in a CIoT. Full article
(This article belongs to the Special Issue Recent Trends and Applications of Artificial Intelligence)
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18 pages, 643 KiB  
Article
Using a Profiling System to Recommend Employees to Carry out a Project
by Abderrahim Rafae and Mohammed Erritali
Electronics 2023, 12(16), 3388; https://doi.org/10.3390/electronics12163388 - 9 Aug 2023
Cited by 1 | Viewed by 1679
Abstract
In this paper, we introduce the application of a profiling system to suggest appropriate employee profiles for project assignments based on task specifications. The primary objective of this system is to assist managers in gaining a comprehensive understanding of their employees’ profiles and [...] Read more.
In this paper, we introduce the application of a profiling system to suggest appropriate employee profiles for project assignments based on task specifications. The primary objective of this system is to assist managers in gaining a comprehensive understanding of their employees’ profiles and motivations. Our research introduces a recommendation system that relies on a profiling approach, analyzing messages and publications shared within a professional network. The proposed system is composed of two main components. The first component focuses on profiling, extracting relevant information from the company’s Human resources (HR) data, identifying interests, and establishing a psychological profile from publications exchanged within the professional platform. The second component is dedicated to recommending profiles that closely align with the specific requirements of each project. Our system yields promising results in predicting favored candidates for projects, achieving an accuracy of 0.92 and an F-score of 0.94. By integrating message-based profiling and leveraging data from professional networks, our approach proves to be effective in recommending well-suited candidates for various projects. Full article
(This article belongs to the Special Issue Recent Trends and Applications of Artificial Intelligence)
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17 pages, 1819 KiB  
Article
Digital Transformation Based on AI Technologies in European Union Organizations
by Florin Mihai, Ofelia Ema Aleca and Mirela Gheorghe
Electronics 2023, 12(11), 2386; https://doi.org/10.3390/electronics12112386 - 25 May 2023
Cited by 3 | Viewed by 2452
Abstract
This study aims to investigate the influence of emerging digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and cloud computing, on the digital intensity index (DII). The research method employed involves quantitative analysis of the indicators regarding DII and [...] Read more.
This study aims to investigate the influence of emerging digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and cloud computing, on the digital intensity index (DII). The research method employed involves quantitative analysis of the indicators regarding DII and emerging digital technologies, conducted based on data published by Eurostat for EU members in 2021. During our research, we formulated and tested hypotheses about the relationship between the DII and emerging digital technologies, and the effect on the DII of using AI-based technologies in various economic processes. The formulated hypotheses were validated via four regression models designed during this study, using the most relevant factors. Our research results demonstrate that the DII is positively influenced by emerging IoT and cloud computing digital technologies, as well as the use of AI technologies based on machine learning and AI-based robotic process automation (RPA) software. Furthermore, the same positive influence was identified in human resource management and recruitment processes compared to the intensity with which these technologies are used in other economic processes. Based on these findings, this study offers persuasive arguments for implementing emerging digital technologies at the EU organizational level to achieve significant increases in digitalization levels. Full article
(This article belongs to the Special Issue Recent Trends and Applications of Artificial Intelligence)
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23 pages, 3533 KiB  
Article
Efficient Multiclass Classification Using Feature Selection in High-Dimensional Datasets
by Ankur Kumar, Avinash Kaur, Parminder Singh, Maha Driss and Wadii Boulila
Electronics 2023, 12(10), 2290; https://doi.org/10.3390/electronics12102290 - 18 May 2023
Cited by 8 | Viewed by 3989
Abstract
Feature selection has become essential in classification problems with numerous features. This process involves removing redundant, noisy, and negatively impacting features from the dataset to enhance the classifier’s performance. Some features are less useful than others or do not correlate with the system’s [...] Read more.
Feature selection has become essential in classification problems with numerous features. This process involves removing redundant, noisy, and negatively impacting features from the dataset to enhance the classifier’s performance. Some features are less useful than others or do not correlate with the system’s evaluation, and their removal does not affect the system’s performance. In most cases, removing features with a monotonically decreasing impact on the system’s performance increases accuracy. Therefore, this research aims to propose a dimensionality reduction method using a feature selection technique to enhance accuracy. This paper proposes a novel feature-selection approach that combines filter and wrapper techniques to select optimal features using Mutual Information with the Sequential Forward Method and 10-fold cross-validation. Results show that the proposed algorithm can reduce features by more than 75% in datasets with large features and achieve a maximum accuracy of 97%. The algorithm outperforms or performs similarly to existing ones. The proposed algorithm could be a better option for classification problems with minimized features. Full article
(This article belongs to the Special Issue Recent Trends and Applications of Artificial Intelligence)
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