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Mathematics
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Artificial Intelligence and Algorithms with Their Applications
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Artificial Intelligence and Algorithms with Their Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 7755

Special Issue Editors

Dr. Gang Hu

E-Mail Website
Guest Editor
Department of Computer Information Systems, State University of New York at Buffalo State, Buffalo, NY 14222, USA
Interests: image processing; computer vision; deep learning; pattern recognition; natural language processing
Dr. Lan Cheng

E-Mail Website
Guest Editor
Department of Mathematical Science, State University of New York at Fredonia, Fredonia, NY 14063, USA
Interests: mathematical modeling; differential equations; actuarial science; financial mathematics
Dr. Guanqiu Qi

E-Mail Website
Guest Editor
Department of Computer Information Systems, State University of New York at Buffalo State, Buffalo, NY 14222, USA
Interests: computer vision; image processing; pattern recognition; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has undergone several generations of development, from the Turing test and logic theory machine, to the expert system and automated driving. In the current generation, we have witnessed the success of artificial intelligence in many areas, including industry, business, manufacture, research, and education. Advanced AI frameworks, with their related algorithms, augment the performance and capacities of many applications. There are still some limitations in current approaches that have not been fully addressed, such as approximation errors, gradient vanishing or explosion, difficulties in converging, and the lack of robustness. A deep mathematical understanding of AI models is necessary to overcome those obstacles. Comprehensive theoretical mathematical methods such as numerical analysis, differential geometry, operations research, learning theory, probability theory, statistics, parameter estimation method, and entropy are required in AI and algorithms.

This Special Issue of Mathematics is devoted to topics in artificial intelligence and algorithms, including theory and applications, as well as applied mathematics. The focus will be on techniques for improving the performance of the AI framework in different application domains.

Dr. Gang Hu
Dr. Lan Cheng
Dr. Guanqiu Qi
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. Mathematics 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 2600 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
  • statistical analysis
  • pattern recognition
  • natural language processing
  • computer vision
  • image processing
  • network security
  • Internet of Things (IoT)
  • blockchain
  • model visualization
  • optimization
  • differential equations.

Published Papers (5 papers)

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Research

20 pages, 3724 KiB  
Article
Get Spatial from Non-Spatial Information: Inferring Spatial Information from Textual Descriptions by Conceptual Spaces
by Omid Reza Abbasi, Ali Asghar Alesheikh and Seyed Vahid Razavi-Termeh
Mathematics 2023, 11(24), 4917; https://doi.org/10.3390/math11244917 - 11 Dec 2023
Cited by 1 | Viewed by 743
Abstract
With the rapid growth of social media, textual content is increasingly growing. Unstructured texts are a rich source of latent spatial information. Extracting such information is useful in query processing, geographical information retrieval (GIR), and recommender systems. In this paper, we propose a [...] Read more.
With the rapid growth of social media, textual content is increasingly growing. Unstructured texts are a rich source of latent spatial information. Extracting such information is useful in query processing, geographical information retrieval (GIR), and recommender systems. In this paper, we propose a novel approach to infer spatial information from salient features of non-spatial nature in text corpora. We propose two methods, namely DCS and RCS, to represent place-based concepts. In addition, two measures, namely the Shannon entropy and the Moran’s I, are proposed to calculate the degree of geo-indicativeness of terms in texts. The methodology is compared with a Latent Dirichlet Allocation (LDA) approach to estimate the accuracy improvement. We evaluated the methods on a dataset of rental property advertisements in Iran and a dataset of Persian Wikipedia articles. The results show that our proposed approach enhances the relative accuracy of predictions by about 10% in case of the renting advertisements and by 13% in case of the Wikipedia articles. The average distance error is about 13.3 km for the advertisements and 10.3 km for the Wikipedia articles, making the method suitable to infer the general region of the city in which a property is located. The proposed methodology is promising for inferring spatial knowledge from textual content that lacks spatial terms. Full article
(This article belongs to the Special Issue Artificial Intelligence and Algorithms with Their Applications)
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17 pages, 3210 KiB  
Article
Towards Efficient and Trustworthy Pandemic Diagnosis in Smart Cities: A Blockchain-Based Federated Learning Approach
by Mohamed Abdel-Basset, Ibrahim Alrashdi, Hossam Hawash, Karam Sallam and Ibrahim A. Hameed
Mathematics 2023, 11(14), 3093; https://doi.org/10.3390/math11143093 - 13 Jul 2023
Cited by 2 | Viewed by 1121
Abstract
In the aftermath of the COVID-19 pandemic, the need for efficient and reliable disease diagnosis in smart cities has become increasingly serious. In this study, we introduce a novel blockchain-based federated learning framework tailored specifically for the diagnosis of pandemic diseases in smart [...] Read more.
In the aftermath of the COVID-19 pandemic, the need for efficient and reliable disease diagnosis in smart cities has become increasingly serious. In this study, we introduce a novel blockchain-based federated learning framework tailored specifically for the diagnosis of pandemic diseases in smart cities, called BFLPD, with a focus on COVID-19 as a case study. The proposed BFLPD takes advantage of the decentralized nature of blockchain technology to design collaborative intelligence for automated diagnosis without violating trustworthiness metrics, such as privacy, security, and data sharing, which are encountered in healthcare systems of smart cities. Cheon–Kim–Kim–Song (CKKS) encryption is intelligently redesigned in BFLPD to ensure the secure sharing of learning updates during the training process. The proposed BFLPD presents a decentralized secure aggregation method that safeguards the integrity of the global model against adversarial attacks, thereby improving the overall efficiency and trustworthiness of our system. Extensive experiments and evaluations using a case study of COVID-19 ultrasound data demonstrate that BFLPD can reliably improve diagnostic accuracy while preserving data privacy, making it a promising tool with which smart cities can enhance their pandemic disease diagnosis capabilities. Full article
(This article belongs to the Special Issue Artificial Intelligence and Algorithms with Their Applications)
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18 pages, 2480 KiB  
Article
Hybrid Fuzzy Rule Algorithm and Trust Planning Mechanism for Robust Trust Management in IoT-Embedded Systems Integration
by Nagireddy Venkata Rajasekhar Reddy, Pydimarri Padmaja, Miroslav Mahdal, Selvaraj Seerangan, Vrince Vimal, Vamsidhar Talasila and Lenka Cepova
Mathematics 2023, 11(11), 2546; https://doi.org/10.3390/math11112546 - 01 Jun 2023
Viewed by 1136
Abstract
The Internet of Things (IoT) is rapidly expanding and becoming an integral part of daily life, increasing the potential for security threats such as malware or cyberattacks. Many embedded systems (ESs), responsible for handling sensitive data or facilitating secure online activities, must adhere [...] Read more.
The Internet of Things (IoT) is rapidly expanding and becoming an integral part of daily life, increasing the potential for security threats such as malware or cyberattacks. Many embedded systems (ESs), responsible for handling sensitive data or facilitating secure online activities, must adhere to stringent security standards. For instance, payment processors employ security-critical components as distinct chips, maintaining physical separation from other network components to prevent the leakage of sensitive information such as cryptographic keys. Establishing a trusted environment in IoT and ESs, where interactions are based on the trust model of communication nodes, is a viable approach to enhance security in IoT and ESs. Although trust management (TM) has been extensively studied in distributed networks, IoT, and ESs, significant challenges remain for real-world implementation. In response, we propose a hybrid fuzzy rule algorithm (FRA) and trust planning mechanism (TPM), denoted FRA + TPM, for effective trust management and to bolster IoT and ESs reliability. The proposed system was evaluated against several conventional methods, yielding promising results: trust prediction accuracy (99%), energy consumption (53%), malicious node detection (98%), computation time (61 s), latency (1.7 ms), and throughput (9 Mbps). Full article
(This article belongs to the Special Issue Artificial Intelligence and Algorithms with Their Applications)
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14 pages, 1171 KiB  
Article
Enhancing Data Security in IoT Networks with Blockchain-Based Management and Adaptive Clustering Techniques
by Ajmeera Kiran, Prasad Mathivanan, Miroslav Mahdal, Kanduri Sairam, Deepak Chauhan and Vamsidhar Talasila
Mathematics 2023, 11(9), 2073; https://doi.org/10.3390/math11092073 - 27 Apr 2023
Cited by 2 | Viewed by 2720
Abstract
The rapid proliferation of smart devices in Internet of Things (IoT) networks has amplified the security challenges associated with device communications. To address these challenges in 5G-enabled IoT networks, this paper proposes a multi-level blockchain security architecture that simplifies implementation while bolstering network [...] Read more.
The rapid proliferation of smart devices in Internet of Things (IoT) networks has amplified the security challenges associated with device communications. To address these challenges in 5G-enabled IoT networks, this paper proposes a multi-level blockchain security architecture that simplifies implementation while bolstering network security. The architecture leverages an adaptive clustering approach based on Evolutionary Adaptive Swarm Intelligent Sparrow Search (EASISS) for efficient organization of heterogeneous IoT networks. Cluster heads (CH) are selected to manage local authentication and permissions, reducing overhead and latency by minimizing communication distances between CHs and IoT devices. To implement network changes such as node addition, relocation, and deletion, the Network Efficient Whale Optimization (NEWO) algorithm is employed. A localized private blockchain structure facilitates communication between CHs and base stations, providing an authentication mechanism that enhances security and trustworthiness. Simulation results demonstrate the effectiveness of the proposed clustering algorithm compared to existing methodologies. Overall, the lightweight blockchain approach presented in this study strikes a superior balance between network latency and throughput when compared to conventional global blockchain systems. Further analysis of system under test (SUT) behavior was accomplished by running many benchmark rounds at varying transaction sending speeds. Maximum, median, and lowest transaction delays and throughput were measured by generating 1000 transactions for each benchmark. Transactions per second (TPS) rates varied between 20 and 500. Maximum delay rose when throughput reached 100 TPS, while minimum latency maintained a value below 1 s. Full article
(This article belongs to the Special Issue Artificial Intelligence and Algorithms with Their Applications)
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20 pages, 1322 KiB  
Article
Local Property of Depth Information in 3D Images and Its Application in Feature Matching
by Erbing Yang, Fei Chen, Meiqing Wang, Hang Cheng and Rong Liu
Mathematics 2023, 11(5), 1154; https://doi.org/10.3390/math11051154 - 26 Feb 2023
Cited by 2 | Viewed by 1190
Abstract
In image registration or image matching, the feature extracted by using the traditional methods does not include the depth information which may lead to a mismatch of keypoints. In this paper, we prove that when the camera moves, the ratio of the depth [...] Read more.
In image registration or image matching, the feature extracted by using the traditional methods does not include the depth information which may lead to a mismatch of keypoints. In this paper, we prove that when the camera moves, the ratio of the depth difference of a keypoint and its neighbor pixel before and after the camera movement approximates a constant. That means the depth difference of a keypoint and its neighbor pixel after normalization is invariant to the camera movement. Based on this property, all the depth differences of a keypoint and its neighbor pixels constitute a local depth-based feature, which can be used as a supplement of the traditional feature. We combine the local depth-based feature with the SIFT feature descriptor to form a new feature descriptor, and the experimental results show the feasibility and effectiveness of the new feature descriptor. Full article
(This article belongs to the Special Issue Artificial Intelligence and Algorithms with Their Applications)
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