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Application of Neural Computation in Artificial Intelligence
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Application of Neural Computation in Artificial Intelligence

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 4102

Special Issue Editors

Dr. Marta Chinnici

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Guest Editor
ICT Division-HPC Lab, Department of Energy Technologies and Renewable Energy Sources (TERIN), ENEA C.R. Casaccia, 00123 Roma, Italy
Interests: data science; artificial intelligence; machine learning; energy efficiency; digitalization; digital twin; data center; infrastructure; HPC
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pedro J. S. Cardoso

E-Mail Website
Guest Editor
NOVA LINCS and Instituto Superior de Engenharia (ISE), Universidade do Algarve , 8005-139 Faro, Portugal
Interests: operational research; machine learning; data science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. João M. F. Rodrigues

E-Mail Website
Guest Editor
NOVA LINCS and Instituto Superior de Engenharia (ISE) , University of the Algarve, 8005-139 Faro, Portugal
Interests: computer vision; human–computer interaction; human–machine cooperation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence, as a subset of artificial intelligence, is one of the most dynamically developing fields of computer science. This is due to the need to analyze increasingly larger data sets, which each time require the use of more efficient computer equipment and more advanced methods of data analysis and mining. One of the most powerful computational intelligence tools is artificial neural networks. These are structures inspired by the biological neural networks that make up the brain. A structure of this type learns to perform tasks thanks to given examples. At the present time, there is extensive interest in intelligent methods in application and theoretical fields. The above is visible by witnessing the continuous introduction of newer topological structures and teaching algorithms into this scientific domain. In particular, this dynamic is visible in the field of deep learning structures and procedures. Due to their specific plasticity and scalability, a tool called “Artificial Neural Networks” can be found to be employed in a very wide range of applications.

Artificial neural networks (ANNs) and their capability to address complex tasks with high efficiency when implemented on hardware have attracted remarkable interest for more than a decade now. The neuron models used in ANNs are highly simplified in function and operation to facilitate scaling to large networks.

This Special Issue will focus on “Neural Computation in Artificial Intelligence”, and we invite innovative and breakthrough ideas on implementing neural computation that goes beyond the limitations of the current ANN architectures. We aim to achieve features/functions not previously possible to perform new tasks and/or improve the performance in existing applications and thereby push the boundaries of what is possible.

Dr. Marta Chinnici
Prof. Dr. Pedro J. S. Cardoso
Prof. Dr. João M. F. Rodrigues
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 Sciences 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

  • neural networks
  • convolutional neural networks
  • logic for artificial intelligence
  • fuzzy logic
  • cloud computing
  • nature of artificial intelligence
  • nature-inspired predictions methods
  • model interoperability
  • reasoning about uncertainty
  • machine learning
  • data science
  • (deep) reinforcement learning
  • meta-learning
  • natural language processing
  • artificial neural network
  • hybrid intelligent systems
  • network architectures and learning paradigms
  • fully connected networks
  • affective computing

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Published Papers (5 papers)

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Research

17 pages, 3855 KiB  
Article
Compressible Diagnosis of Membrane Fouling Based on Transfer Entropy
by Xiaolong Wu, Dongyang Hou, Hongyan Yang and Honggui Han
Appl. Sci. 2024, 14(18), 8176; https://doi.org/10.3390/app14188176 - 11 Sep 2024
Viewed by 270
Abstract
Membrane fouling caused by many direct and indirect triggering factors has become an obstacle to the application of membrane bioreactors (MBRs). The nonlinear relationship between those factors is subject to complex causality or affiliation, which is difficult to clarify for the diagnosis of [...] Read more.
Membrane fouling caused by many direct and indirect triggering factors has become an obstacle to the application of membrane bioreactors (MBRs). The nonlinear relationship between those factors is subject to complex causality or affiliation, which is difficult to clarify for the diagnosis of membrane fouling. To solve this problem, this paper proposes a compressible diagnosis model (CDM) based on transfer entropy to facilitate the fault diagnosis of the root cause for membrane fouling. The novelty of this model includes the following points: Firstly, a framework of a CDM between membrane fouling and causal variables is built based on a feature extraction algorithm and mechanism analysis. The framework can identify fault transfer scenarios following the changes in operating conditions. Secondly, the fault transfer topology of a CDM based on transfer entropy is constructed to describe the causal relationship between variables dynamically. Thirdly, an information compressible strategy is designed to simplify the fault transfer topology. This strategy can eliminate the repetitious affiliation relationship, which contributes to diagnosing the root causal variables speedily and accurately. Finally, the effectiveness of the proposed CDM is verified by the measured data from an actual MBR. The results of experiments demonstrate that the proposed CDM fulfills the diagnosis of membrane fouling. Full article
(This article belongs to the Special Issue Application of Neural Computation in Artificial Intelligence)
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26 pages, 1333 KiB  
Article
A Multi-Stage Automatic Method Based on a Combination of Fully Convolutional Networks for Cardiac Segmentation in Short-Axis MRI
by Italo Francyles Santos da Silva, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Marcelo Gattass and António Manuel Cunha
Appl. Sci. 2024, 14(16), 7352; https://doi.org/10.3390/app14167352 - 20 Aug 2024
Viewed by 378
Abstract
Magnetic resonance imaging (MRI) is a non-invasive technique used in cardiac diagnosis. Using it, specialists can measure the masses and volumes of the right ventricle (RV), left ventricular cavity (LVC), and myocardium (MYO). Segmenting these structures is an important step before this measurement. [...] Read more.
Magnetic resonance imaging (MRI) is a non-invasive technique used in cardiac diagnosis. Using it, specialists can measure the masses and volumes of the right ventricle (RV), left ventricular cavity (LVC), and myocardium (MYO). Segmenting these structures is an important step before this measurement. However, this process can be laborious and error-prone when done manually. This paper proposes a multi-stage method for cardiac segmentation in short-axis MRI based on fully convolutional networks (FCNs). This automatic method comprises three main stages: (1) the extraction of a region of interest (ROI); (2) MYO and LVC segmentation using a proposed FCN called EAIS-Net; and (3) the RV segmentation using another proposed FCN called IRAX-Net. The proposed method was tested with the ACDC and M&Ms datasets. The main evaluation metrics are end-diastolic (ED) and end-systolic (ES) Dice. For the ACDC dataset, the Dice results (ED and ES, respectively) are 0.960 and 0.904 for the LVC, 0.880 and 0.892 for the MYO, and 0.910 and 0.860 for the RV. For the M&Ms dataset, the ED and ES Dices are 0.861 and 0.805 for the LVC, 0.733 and 0.759 for the MYO, and 0.721 and 0.694 for the RV. These results confirm the feasibility of the proposed method. Full article
(This article belongs to the Special Issue Application of Neural Computation in Artificial Intelligence)
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26 pages, 6673 KiB  
Article
Out-of-Stock Prediction Model Using Buzzard Coney Hawk Optimization-Based LightGBM-Enabled Deep Temporal Convolutional Neural Network
by Ahmed Elghadghad, Ahmad Alzubi and Kolawole Iyiola
Appl. Sci. 2024, 14(13), 5906; https://doi.org/10.3390/app14135906 - 5 Jul 2024
Viewed by 648
Abstract
Out-of-stock prediction refers to the activity of forecasting the time when a product will not be available for purchase because of an inventory deficiency. Due to difficulties, out-of-stock forecasting models now face certain challenges. Incorrect demand forecasting may result in a lack or [...] Read more.
Out-of-stock prediction refers to the activity of forecasting the time when a product will not be available for purchase because of an inventory deficiency. Due to difficulties, out-of-stock forecasting models now face certain challenges. Incorrect demand forecasting may result in a lack or excess of goods in stock, a factor that affects client satisfaction and the profitability of companies. Accordingly, the new approach BCHO-TCN LightGBM, which is based on Buzzard Coney Hawk Optimization with a deep temporal convolutional neural network and the Light Gradient-Boosting Machine framework, is developed to deal with all challenges in the existing models in the field of out-of-stock prediction. The role that BCHO plays in the LightGBM-based deep temporal CNNis rooted in modifying the classifier to improve both accuracy and speed. Integrating BCHO into the model training process allows us to optimize and adjust the hyperparameters and the weights of the CNN linked with the temporal DNN, which, in turn, makes the model perform better in the extraction of temporal features from time-series data. This optimization strategy, which derives from the cooperative behaviors and evasion tactics of BCHO, is a powerful source of information for the computational optimization agent. This leads to a faster convergence of the model towards optimal solutions and therefore improves the overall accuracy and predictive abilities of the temporal CNN with the LightGBM algorithm. The results indicate that when using data from Amazon India’s product listings, the model shows a high degree of accuracy, as well as excellent net present value (NPV), present discounted value (PDV), and threat scores, with values reaching 94.52%, 95.16%, 94.81%, and 95.76%, respectively. Likewise, in a k-fold 10 scenario, the model achieves values of 94.81%, 95.60%, 96.28%, and 95.86% for the same metrics. Full article
(This article belongs to the Special Issue Application of Neural Computation in Artificial Intelligence)
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37 pages, 29261 KiB  
Article
SNNtrainer3D: Training Spiking Neural Networks Using a User-Friendly Application with 3D Architecture Visualization Capabilities
by Sorin Liviu Jurj, Sina Banasaz Nouri and Jörg Strutwolf
Appl. Sci. 2024, 14(13), 5752; https://doi.org/10.3390/app14135752 - 1 Jul 2024
Viewed by 1155
Abstract
Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel [...] Read more.
Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel software application that addresses these challenges. The application provides an intuitive interface for designing Spiking Neural Networks architectures, with features such as dynamic architecture editing, allowing users to add, remove, and edit hidden layers in real-time. A key innovation is the integration of Three.js for three-dimensional visualization of the network structure, enabling users to inspect connections and weights and facilitating a deeper understanding of the model’s behavior. The application supports training on the Modified National Institute of Standards and Technology dataset and allows the downloading of trained weights for further use. Moreover, it lays the groundwork for future integration with physical memristor technology, positioning it as a crucial tool for advancing neuromorphic computing research. The advantages of the development process, technology stack, and visualization are discussed. The SNNtrainer3D represents a significant step in making Spiking Neural Networks more accessible, understandable, and easier for Artificial Intelligence researchers and practitioners. Full article
(This article belongs to the Special Issue Application of Neural Computation in Artificial Intelligence)
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35 pages, 4137 KiB  
Article
Decision Support System Driven by Thermo-Complexity: Scenario Analysis and Data Visualization
by Gerardo Iovane and Marta Chinnici
Appl. Sci. 2024, 14(6), 2387; https://doi.org/10.3390/app14062387 - 12 Mar 2024
Viewed by 761
Abstract
The present modelling aims to construct a computational information representation system useful for decision support system (DSS) solutions in the realization of intelligent systems or complex systems analysis solutions. Starting from an n-dimensional space (with n ≥ 7) represented by problem variables (referred [...] Read more.
The present modelling aims to construct a computational information representation system useful for decision support system (DSS) solutions in the realization of intelligent systems or complex systems analysis solutions. Starting from an n-dimensional space (with n ≥ 7) represented by problem variables (referred to as CSF—Critical Success Factors), a dimensional embedding procedure is used to transition to a two-dimensional space. In the two-dimensional space, thanks to new lattice motion algorithms, the decision support system can determine the optimal solution with a lower computational cost based on the decision-maker’s preferences. Finally, thanks to an algorithm that takes into account the hierarchical order of importance of the seven CSFs as per the expert’s liking or according to his optimization logics, a return is made to the n-dimensional space and the final solution in the original space. As we will see, the starting and ending states in the n-dimensional space (referred to as micro-states) when projected into the two-dimensional space generate states (referred to as macro-states) which are degenerate. In other words, the correspondence between micro-states and macro-states is not one-to-one, as multiple micro-states correspond to one macro-state. Therefore, in relation to the decision-maker’s preferences, it will be the responsibility of the decision support system to provide the decision-maker with the micro-state of interest in the n-dimensional space (dimensional emergence procedure), starting from the obtained optimal macro-state. This result can be achieved starting from a flat chain of sensors capable of measuring/emulating certain specific parameters of interest. As we will see, it emerges that by considering random–exhaustive rolling value paths in order to track and potentially intervene to rebalance a dynamic system representing the state of stress/sensing of a system of interest, we are using the most general and, therefore, complex hypotheses of ergodic theory. In this work, we will focus on the representation of information in n-dimensional and two-dimensional spaces, as well as construct evaluation scenarios. We will also show the results of the decision support system in some cases of specific interest, thanks to a specific lattice motion algorithm of the realized decision-making environment. Full article
(This article belongs to the Special Issue Application of Neural Computation in Artificial Intelligence)
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