Artificial Intelligence and Algorithms in Intelligent Systems for Augmented Human

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 6832

Special Issue Editors


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Guest Editor
Faculty of Mathematics and Computer Science, University of Bucharest, 030018 Bucharest, Romania
Interests: augmented reality; artificial intelligence; data science

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Guest Editor
Department of Computer Science, Open University of the Netherlands, 6419 AT Heerlen, The Netherlands
Interests: artificial intelligence; cyber security; cyber operations; information operations; military operations

Special Issue Information

Dear Colleagues,

Recent advances in artificial intelligence algorithms, the huge amounts of data available and the ongoing increase in computational resources have led to extraordinary results in difficult fields such as NLP, computer vision, and cyber security.

The focus of this Special Issue is on developing intelligent systems that use artificial intelligence or other advanced algorithms to interact with humans in order to improve the human understanding and physical/mental capabilities in various areas. Such intelligent systems can operate in real or virtual environments, or in mixed realities.

We strongly encourage interdisciplinary applications including, but not limited to, the following:

  • Collaborative systems that improve the situational awareness and presence of the users;
  • Medical systems to support decisions for diagnosis or rehabilitation systems with automatic adaptation to the patient’s mental/physical condition;
  • Digital Twin tools to offer a better insight for research in fields such as medicine, chemistry, biology, energy;
  • Intelligent systems in the (cyber) security field to provide context-related information in real time or to assess the impact of incidents;
  • Intelligent systems for education to adapt to each child’s performance.

We are inviting authors to submit research papers and review articles that fit the above-mentioned purpose.

Dr. Marina Anca Cidota
Dr. Clara Maathuis
Guest Editors

Manuscript Submission Information

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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

  • augmented human
  • machine learning
  • knowledge representation and reasoning
  • agent based modeling
  • decision support systems
  • recommendation systems
  • optimization algorithms
  • predictive models
  • augmented/virtual/mixed realities
  • digital twin
  • data science—multivariate analysis
  • situational awareness
  • presence
  • collaborative systems
  • (cyber) security
  • defence systems
  • law enforcement
  • education
  • health—rehabilitation

Published Papers (3 papers)

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Research

37 pages, 2323 KiB  
Article
Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach
by Mitra Pooyandeh and Insoo Sohn
Mathematics 2023, 11(23), 4865; https://doi.org/10.3390/math11234865 - 04 Dec 2023
Cited by 2 | Viewed by 1243
Abstract
This paper presents a transformative methodology that harnesses the power of digital twin (DT) technology for the advanced condition monitoring of lithium-ion batteries (LIBs) in electric vehicles (EVs). In contrast to conventional solutions, our approach eliminates the need to calibrate sensors or add [...] Read more.
This paper presents a transformative methodology that harnesses the power of digital twin (DT) technology for the advanced condition monitoring of lithium-ion batteries (LIBs) in electric vehicles (EVs). In contrast to conventional solutions, our approach eliminates the need to calibrate sensors or add additional hardware circuits. The digital replica works seamlessly alongside the embedded battery management system (BMS) in an EV, delivering real-time signals for monitoring. Our system is a significant step forward in ensuring the efficiency and sustainability of EVs, which play an essential role in reducing carbon emissions. A core innovation lies in the integration of the digital twin into the battery monitoring process, reshaping the landscape of energy storage and alternative power sources such as lithium-ion batteries. Our comprehensive system leverages a cloud-based IoT network and combines both physical and digital components to provide a holistic solution. The physical side encompasses offline modeling, where a long short-term memory (LSTM) algorithm trained with various learning rates (LRs) and optimized by three types of optimizers ensures precise state-of-charge (SOC) predictions. On the digital side, the digital twin takes center stage, enabling the real-time monitoring and prediction of battery activity. A particularly innovative aspect of our approach is the utilization of a time-series generative adversarial network (TS-GAN) to generate synthetic data that seamlessly complement the monitoring process. This pioneering use of a TS-GAN offers an effective solution to the challenge of limited real-time data availability, thus enhancing the system’s predictive capabilities. By seamlessly integrating these physical and digital elements, our system enables the precise analysis and prediction of battery behavior. This innovation—particularly the application of a TS-GAN for data generation—significantly contributes to optimizing battery performance, enhancing safety, and extending the longevity of lithium-ion batteries in EVs. Furthermore, the model developed in this research serves as a benchmark for future digital energy storage in lithium-ion batteries and comprehensive energy utilization. According to statistical tests, the model has a high level of precision. Its exceptional safety performance and reduced energy consumption offer promising prospects for sustainable and efficient energy solutions. This paper signifies a pivotal step towards realizing a cleaner and more sustainable future through advanced EV battery management. Full article
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29 pages, 3765 KiB  
Article
Using Probabilistic Models for Data Compression
by Iuliana Iatan, Mihăiţă Drăgan, Silvia Dedu and Vasile Preda
Mathematics 2022, 10(20), 3847; https://doi.org/10.3390/math10203847 - 17 Oct 2022
Cited by 4 | Viewed by 1739
Abstract
Our research objective is to improve the Huffman coding efficiency by adjusting the data using a Poisson distribution, which avoids the undefined entropies too. The scientific value added by our paper consists in the fact of minimizing the average length of the code [...] Read more.
Our research objective is to improve the Huffman coding efficiency by adjusting the data using a Poisson distribution, which avoids the undefined entropies too. The scientific value added by our paper consists in the fact of minimizing the average length of the code words, which is greater in the absence of applying the Poisson distribution. Huffman Coding is an error-free compression method, designed to remove the coding redundancy, by yielding the smallest number of code symbols per source symbol, which in practice can be represented by the intensity of an image or the output of a mapping operation. We shall use the images from the PASCAL Visual Object Classes (VOC) to evaluate our methods. In our work we use 10,102 randomly chosen images, such that half of them are for training, while the other half is for testing. The VOC data sets display significant variability regarding object size, orientation, pose, illumination, position and occlusion. The data sets are composed by 20 object classes, respectively: aeroplane, bicycle, bird, boat, bottle, bus, car, motorbike, train, sofa, table, chair, tv/monitor, potted plant, person, cat, cow, dog, horse and sheep. The descriptors of different objects can be compared to give a measurement of their similarity. Image similarity is an important concept in many applications. This paper is focused on the measure of similarity in the computer science domain, more specifically information retrieval and data mining. Our approach uses 64 descriptors for each image belonging to the training and test set, therefore the number of symbols is 64. The data of our information source are different from a finite memory source (Markov), where its output depends on a finite number of previous outputs. When dealing with large volumes of data, an effective approach to increase the Information Retrieval speed is based on using Neural Networks as an artificial intelligent technique. Full article
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20 pages, 2391 KiB  
Article
Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases
by Marwa M. Eid, El-Sayed M. El-Kenawy, Nima Khodadadi, Seyedali Mirjalili, Ehsaneh Khodadadi, Mostafa Abotaleb, Amal H. Alharbi, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Ghada M. Amer, Ammar Kadi and Doaa Sami Khafaga
Mathematics 2022, 10(20), 3845; https://doi.org/10.3390/math10203845 - 17 Oct 2022
Cited by 38 | Viewed by 2900
Abstract
Recent technologies such as artificial intelligence, machine learning, and big data are essential for supporting healthcare monitoring systems, particularly for monitoring Monkeypox confirmed cases. Infected and uninfected cases around the world have contributed to a growing dataset, which is publicly available and can [...] Read more.
Recent technologies such as artificial intelligence, machine learning, and big data are essential for supporting healthcare monitoring systems, particularly for monitoring Monkeypox confirmed cases. Infected and uninfected cases around the world have contributed to a growing dataset, which is publicly available and can be used by artificial intelligence and machine learning to predict the confirmed cases of Monkeypox at an early stage. Motivated by this, we propose in this paper a new approach for accurate prediction of the Monkeypox confirmed cases based on an optimized Long Short-Term Memory (LSTM) deep network. To fine-tune the hyper-parameters of the LSTM-based deep network, we employed the Al-Biruni Earth Radius (BER) optimization algorithm; thus, the proposed approach is denoted by BER-LSTM. Experimental results show the effectiveness of the proposed approach when assessed using various evaluation criteria, such as Mean Bias Error, which is recorded as (0.06) using BER-LSTM. To prove the superiority of the proposed approach, six different machine learning models are included in the conducted experiments. In addition, four different optimization algorithms are considered for comparison purposes. The results of this comparison confirmed the superiority of the proposed approach. On the other hand, several statistical tests are applied to analyze the stability and significance of the proposed approach. These tests include one-way Analysis of Variance (ANOVA), Wilcoxon, and regression tests. The results of these tests emphasize the robustness, significance, and efficiency of the proposed approach. Full article
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