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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 40789

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Special Issue Editors

Prof. Dr. Amar Ramdane-Cherif

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Guest Editor

LISV Laboratory, University of Paris Saclay, 10-12 Avenue of Europe, 78140 Velizy, France
Interests: software architecture; dynamic architecture; knowledge representation and reasoning; interaction machine-environment; ambient intelligence; robotic interaction; data fusion and fission; embedded system
Special Issues, Collections and Topics in MDPI journals

Dr. Ravi Tomar

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Guest Editor

School of Computer Science, University of Petroleum & Energy Studies, 248007 Dehradun, India
Interests: VANETs; big data; artificial intelligence

Dr. Thipendra P Singh

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Guest Editor

School of Computer Science & Technology, Bennett University, Greater Noida 201310, India
Interests: AIML; soft computing

Special Issue Information

Dear Colleagues,

This Special Issue will present extended versions of selected papers presented at the 3rd International Conference on Machine Intelligence and Data Science Applications (MIDAS-2022), which will be held on 7 and 8 December 2022, at the University of Versailles, Paris Saclay, France. MIDAS-2022 aims to promote and provide a platform for researchers, academics, and practitioners to meet and exchange ideas on recent theoretical and applied machine and artificial intelligence and data sciences research. The conference targets the theme of machine intelligence and its applications. A wide range of works with comprehensive information on image processing, natural language processing, computer vision, sentiment analysis, voice and gesture analysis, and other topics are invited to the conference. The latest works in multidisciplinary applications such as legal, healthcare, smart society, cyber physical systems, and smart agriculture, among others, are also invited. The conference will be of interest to computer science engineers, machine intelligence lecturers/researchers, and engineering graduates. The conference program consists of a wide range of sessions including distinguished lectures, paper presentations, and poster presentations, along with prominent keynote speakers and industrial workshops. The theme for the conference is apt to the present scenario as the world is currently driven by data, and human interference is being limited by using various AI technologies. Authors of invited papers should be aware that the final submitted manuscript must provide a minimum of 50% new content and not exceed 30% copy/paste from the proceedings paper.

Prof. Dr. Amar Ramdane-Cherif
Dr. Ravi Tomar
Dr. Thipendra P Singh
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. Information 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 1600 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

computational intelligence
cognitive intelligence
intelligent systems
ambient intelligence
deep learning
data analytics and optimization
data pre-processing
big data analytics
soft computing
evolutionary computing
predictive analysis

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Published Papers (13 papers)

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Research

23 pages, 6902 KiB

Open AccessArticle

Algorithm-Based Data Generation (ADG) Engine for Dual-Mode User Behavioral Data Analytics

by Iman I. M. Abu Sulayman, Peter Voege and Abdelkader Ouda

Information 2024, 15(3), 146; https://doi.org/10.3390/info15030146 - 6 Mar 2024

Viewed by 1410

Abstract

The increasing significance of data analytics in modern information analysis is underpinned by vast amounts of user data. However, it is only feasible to amass sufficient data for various tasks in specific data-gathering contexts that either have limited security information or are associated with older applications. There are numerous scenarios where a domain is too new, too specialized, too secure, or data are too sparsely available to adequately support data analytics endeavors. In such cases, synthetic data generation becomes necessary to facilitate further analysis. To address this challenge, we have developed an Algorithm-based Data Generation (ADG) Engine that enables data generation without the need for initial data, relying instead on user behavior patterns, including both normal and abnormal behavior. The ADG Engine uses a structured database system to keep track of users across different types of activity. It then uses all of this information to make the generated data as real as possible. Our efforts are particularly focused on data analytics, achieved by generating abnormalities within the data and allowing users to customize the generation of normal and abnormal data ratios. In situations where obtaining additional data through conventional means would be impractical or impossible, especially in the case of specific characteristics like anomaly percentages, algorithmically generated datasets provide a viable alternative. In this paper, we introduce the ADG Engine, which can create coherent datasets for multiple users engaged in different activities and across various platforms, entirely from scratch. The ADG Engine incorporates normal and abnormal ratios within each data platform through the application of core algorithms for time-based and numeric-based anomaly generation. The resulting abnormal percentage is compared against the expected values and ranges from 0.13 to 0.17 abnormal data instances in each column. Along with the normal/abnormal ratio, the results strongly suggest that the ADG Engine has successfully completed its primary task. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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24 pages, 2100 KiB

Open AccessArticle

Predicting COVID-19 Hospital Stays with Kolmogorov–Gabor Polynomials: Charting the Future of Care

by Hamidreza Marateb, Mina Norouzirad, Kouhyar Tavakolian, Faezeh Aminorroaya, Mohammadreza Mohebbian, Miguel Ángel Mañanas, Sergio Romero Lafuente, Ramin Sami and Marjan Mansourian

Information 2023, 14(11), 590; https://doi.org/10.3390/info14110590 - 31 Oct 2023

Viewed by 1801

Abstract

Optimal allocation of ward beds is crucial given the respiratory nature of COVID-19, which necessitates urgent hospitalization for certain patients. Several governments have leveraged technology to mitigate the pandemic’s adverse impacts. Based on clinical and demographic variables assessed upon admission, this study predicts the length of stay (LOS) for COVID-19 patients in hospitals. The Kolmogorov–Gabor polynomial (a.k.a., Volterra functional series) was trained using regularized least squares and validated on a dataset of 1600 COVID-19 patients admitted to Khorshid Hospital in the central province of Iran, and the five-fold internal cross-validated results were presented. The Volterra method provides flexibility, interactions among variables, and robustness. The most important features of the LOS prediction system were inflammatory markers, bicarbonate (HCO₃), and fever—the adj. R² and Concordance Correlation Coefficients were 0.81 [95% CI: 0.79–0.84] and 0.94 [0.93–0.95], respectively. The estimation bias was not statistically significant (p-value = 0.777; paired-sample t-test). The system was further analyzed to predict “normal” LOS ≤ 7 days versus “prolonged” LOS > 7 days groups. It showed excellent balanced diagnostic accuracy and agreement rate. However, temporal and spatial validation must be considered to generalize the model. This contribution is hoped to pave the way for hospitals and healthcare providers to manage their resources better. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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22 pages, 7281 KiB

Open AccessArticle

A New Social Media Analytics Method for Identifying Factors Contributing to COVID-19 Discussion Topics

by Fahim Sufi

Information 2023, 14(10), 545; https://doi.org/10.3390/info14100545 - 5 Oct 2023

Cited by 2 | Viewed by 1807

Abstract

Since the onset of the COVID-19 crisis, scholarly investigations and policy formulation have harnessed the potent capabilities of artificial intelligence (AI)-driven social media analytics. Evidence-driven policymaking has been facilitated through the proficient application of AI and natural language processing (NLP) methodologies to analyse the vast landscape of social media discussions. However, recent research works have failed to demonstrate a methodology to discern the underlying factors influencing COVID-19-related discussion topics. In this scholarly endeavour, an innovative AI- and NLP-based framework is deployed, incorporating translation, sentiment analysis, topic analysis, logistic regression, and clustering techniques to meticulously identify and elucidate the factors that are relevant to any discussion topics within the social media corpus. This pioneering methodology is rigorously tested and evaluated using a dataset comprising 152,070 COVID-19-related tweets, collected between 15th July 2021 and 20th April 2023, encompassing discourse in 58 distinct languages. The AI-driven regression analysis revealed 37 distinct observations, with 20 of them demonstrating a higher level of significance. In parallel, clustering analysis identified 15 observations, including nine of substantial relevance. These 52 AI-facilitated observations collectively unveil and delineate the factors that are intricately linked to five core discussion topics that are prevalent in the realm of COVID-19 discourse on Twitter. To the best of our knowledge, this research constitutes the inaugural effort in autonomously identifying factors associated with COVID-19 discussion topics, marking a pioneering application of AI algorithms in this domain. The implementation of this method holds the potential to significantly enhance the practice of evidence-based policymaking pertaining to matters concerning COVID-19. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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19 pages, 544 KiB

Open AccessArticle

The Impact of Digital Business on Energy Efficiency in EU Countries

by Aleksy Kwilinski, Oleksii Lyulyov and Tetyana Pimonenko

Information 2023, 14(9), 480; https://doi.org/10.3390/info14090480 - 29 Aug 2023

Cited by 24 | Viewed by 2827

Abstract

Digital business plays a crucial role in driving energy efficiency and sustainability by enabling innovative solutions such as smart grid technologies, data analytics for energy optimization, and remote monitoring and control systems. Through digitalization, businesses can streamline processes, minimize energy waste, and make informed decisions that lead to more efficient resource utilization and reduced environmental impact. This paper aims at analyzing the character of digital business’ impact on energy efficiency to outline the relevant instruments to unleash EU countries’ potential for attaining sustainable development. The study applies the panel-corrected standard errors technique to check the effect of digital business on energy efficiency for the EU countries in 2011–2020. The findings show that digital business has a significant negative effect on energy intensity, implying that increased digital business leads to decreased energy intensity. Additionally, digital business practices positively contribute to reducing CO₂ emissions and promoting renewable energy, although the impact on final energy consumption varies across different indicators. The findings underscore the significance of integrating digital business practices to improve energy efficiency, lower energy intensity, and advance the adoption of renewable energy sources within the EU. Policymakers and businesses should prioritize the adoption of digital technologies and e-commerce strategies to facilitate sustainable energy transitions and accomplish environmental objectives. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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18 pages, 2424 KiB

Open AccessArticle

Health Monitoring Apps: An Evaluation of the Persuasive System Design Model for Human Wellbeing

by Asif Hussian, Abdul Mateen, Farhan Amin, Muhammad Ali Abid and Saeed Ullah

Information 2023, 14(7), 412; https://doi.org/10.3390/info14070412 - 16 Jul 2023

Cited by 1 | Viewed by 4216

Abstract

In the current era of ubiquitous computing and mobile technology, almost all human beings use various self-monitoring applications. Mobile applications could be the best health assistant for safety and adopting a healthy lifestyle. Therefore, persuasive designing is a compulsory element for designing such apps. A popular model for persuasive design named the Persuasive System Design (PSD) model is a generalized model for whole persuasive technologies. Any type of persuasive application could be designed using this model. Designing any special type of application using the PSD model could be difficult because of its generalized behavior which fails to provide moral support for users of health applications. There is a strong need to propose a customized and improved persuasive system design model for each category to overcome the issue. This study evaluates the PSD model and finds persuasive gaps in users of the Mobile Health Monitoring application, developed by following the PSD model. Furthermore, this study finds that users misunderstand health-related problems when using such apps. A misunderstanding of this nature can have serious consequences for the user’s life in some cases. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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15 pages, 579 KiB

Open AccessArticle

Tokenized Markets Using Blockchain Technology: Exploring Recent Developments and Opportunities

by Angel A. Juan, Elena Perez-Bernabeu, Yuda Li, Xabier A. Martin, Majsa Ammouriova and Barry B. Barrios

Information 2023, 14(6), 347; https://doi.org/10.3390/info14060347 - 17 Jun 2023

Cited by 4 | Viewed by 4922

Abstract

The popularity of blockchain technology stems largely from its association with cryptocurrencies, but its potential applications extend beyond this. Fungible tokens, which are interchangeable, can facilitate value transactions, while smart contracts using non-fungible tokens enable the exchange of digital assets. Utilizing blockchain technology, tokenized platforms can create virtual markets that operate without the need for a central authority. In principle, blockchain technology provides these markets with a high degree of security, trustworthiness, and dependability. This article surveys recent developments in these areas, including examples of architectures, designs, challenges, and best practices (case studies) for the design and implementation of tokenized platforms for exchanging digital assets. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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16 pages, 1984 KiB

Open AccessArticle

A Robust Hybrid Deep Convolutional Neural Network for COVID-19 Disease Identification from Chest X-ray Images

by Theodora Sanida, Irene-Maria Tabakis, Maria Vasiliki Sanida, Argyrios Sideris and Minas Dasygenis

Information 2023, 14(6), 310; https://doi.org/10.3390/info14060310 - 29 May 2023

Cited by 7 | Viewed by 2164

Abstract

The prompt and accurate identification of the causes of pneumonia is necessary to implement rapid treatment and preventative approaches, reduce the burden of infections, and develop more successful intervention strategies. There has been an increase in the number of new pneumonia cases and diseases known as acute respiratory distress syndrome (ARDS) as a direct consequence of the spread of COVID-19. Chest radiography has evolved to the point that it is now an indispensable diagnostic tool for COVID-19 infection pneumonia in hospitals. To fully exploit the technique, it is crucial to design a computer-aided diagnostic (CAD) system to assist doctors and other medical professionals in establishing an accurate and rapid diagnosis of pneumonia. This article presents a robust hybrid deep convolutional neural network (DCNN) for rapidly identifying three categories (normal, COVID-19 and pneumonia (viral or bacterial)) using X-ray image data sourced from the COVID-QU-Ex dataset. The proposed approach on the test set achieved a rate of 99.25% accuracy, 99.10% Kappa-score, 99.43% AUC, 99.24% F1-score, 99.25% recall, and 99.23% precision, respectively. The outcomes of the experiments demonstrate that the presented hybrid DCNN mechanism for identifying three categories utilising X-ray images is robust and effective. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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16 pages, 1253 KiB

Open AccessArticle

A Double-Stage 3D U-Net for On-Cloud Brain Extraction and Multi-Structure Segmentation from 7T MR Volumes

by Selene Tomassini, Haidar Anbar, Agnese Sbrollini, MHD Jafar Mortada, Laura Burattini and Micaela Morettini

Information 2023, 14(5), 282; https://doi.org/10.3390/info14050282 - 10 May 2023

Cited by 2 | Viewed by 2490

Abstract

The brain is the organ most studied using Magnetic Resonance (MR). The emergence of 7T scanners has increased MR imaging resolution to a sub-millimeter level. However, there is a lack of automatic segmentation techniques for 7T MR volumes. This research aims to develop a novel deep learning-based algorithm for on-cloud brain extraction and multi-structure segmentation from unenhanced 7T MR volumes. To this aim, a double-stage 3D U-Net was implemented in a cloud service, directing its first stage to the automatic extraction of the brain and its second stage to the automatic segmentation of the grey matter, basal ganglia, white matter, ventricles, cerebellum, and brain stem. The training was performed on the 90% (the 10% of which served for validation) and the test on the 10% of the Glasgow database. A mean test Dice Similarity Coefficient (DSC) of 96.33% was achieved for the brain class. Mean test DSCs of 90.24%, 87.55%, 93.82%, 85.77%, 91.53%, and 89.95% were achieved for the brain structure classes, respectively. Therefore, the proposed double-stage 3D U-Net is effective in brain extraction and multi-structure segmentation from 7T MR volumes without any preprocessing and training data augmentation strategy while ensuring its machine-independent reproducibility. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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13 pages, 395 KiB

Open AccessArticle

Improving Semantic Information Retrieval Using Multinomial Naive Bayes Classifier and Bayesian Networks

by Wiem Chebil, Mohammad Wedyan, Moutaz Alazab, Ryan Alturki and Omar Elshaweesh

Information 2023, 14(5), 272; https://doi.org/10.3390/info14050272 - 3 May 2023

Cited by 11 | Viewed by 2875

Abstract

This research proposes a new approach to improve information retrieval systems based on a multinomial naive Bayes classifier (MNBC), Bayesian networks (BNs), and a multi-terminology which includes MeSH thesaurus (Medical Subject Headings) and SNOMED CT (Systematized Nomenclature of Medicine of Clinical Terms). Our approach, which is entitled improving semantic information retrieval (IMSIR), extracts and disambiguates concepts and retrieves documents. Relevant concepts of ambiguous terms were selected using probability measures and biomedical terminologies. Concepts are also extracted using an MNBC. The UMLS (Unified Medical Language System) thesaurus was then used to filter and rank concepts. Finally, we exploited a Bayesian network to match documents and queries using a conceptual representation. Our main contribution in this paper is to combine a supervised method (MNBC) and an unsupervised method (BN) to extract concepts from documents and queries. We also propose filtering the extracted concepts in order to keep relevant ones. Experiments of IMSIR using the two corpora, the OHSUMED corpus and the Clinical Trial (CT) corpus, were interesting because their results outperformed those of the baseline: the P@50 improvement rate was +36.5% over the baseline when the CT corpus was used. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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14 pages, 2277 KiB

Open AccessArticle

The Process of Identifying Automobile Joint Failures during the Operation Phase: Data Analytics Based on Association Rules

by Polina Buyvol, Irina Makarova, Aleksandr Voroshilov and Alla Krivonogova

Information 2023, 14(5), 257; https://doi.org/10.3390/info14050257 - 25 Apr 2023

Cited by 1 | Viewed by 2043

Abstract

The increasing complexity of vehicle design, the use of new engine types and fuels, and the increasing intelligence of automobiles are making it increasingly difficult to ensure trouble-free operation. Finding faulty parts quickly and accurately is becoming increasingly difficult, as the diagnostic process requires analyzing a great amount of information. Therefore, we propose an approach based on association rules, a machine learning technique, to simplify the defect detection process. To facilitate its use in a real repair company environment, we have developed a web service that allows a repairman to simultaneously identify nodes with a high probability of failure. We have described the structure and working principles of the developed web service, as well as the procedure for its application, which resulted in the discovery of several useful non-trivial rules. We have presented several rules resulting from the use of this interactive tool, which allow repairers to detect possible defects in the relevant components, during the diagnostic process, quickly and easily. These rules are also well supported and can be used by procurement departments to make tactical decisions when selecting the most promising suppliers and manufacturers. The methodology developed allows the evaluation of the effectiveness of changes in the design and technology for the manufacture and operation of individual vehicle components, analyzing the change in the composition of parts combinations over time. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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20 pages, 5402 KiB

Open AccessArticle

FedUA: An Uncertainty-Aware Distillation-Based Federated Learning Scheme for Image Classification

by Shao-Ming Lee and Ja-Ling Wu

Information 2023, 14(4), 234; https://doi.org/10.3390/info14040234 - 10 Apr 2023

Cited by 2 | Viewed by 2605

Abstract

Recently, federated learning (FL) has gradually become an important research topic in machine learning and information theory. FL emphasizes that clients jointly engage in solving learning tasks. In addition to data security issues, fundamental challenges in this type of learning include the imbalance and non-IID among clients’ data and the unreliable connections between devices due to limited communication bandwidths. The above issues are intractable to FL. This study starts from the uncertainty analysis of deep neural networks (DNNs) to evaluate the effectiveness of FL, and proposes a new architecture for model aggregation. Our scheme improves FL’s performance by applying knowledge distillation and the DNN’s uncertainty quantification methods. A series of experiments on the image classification task confirms that our proposed model aggregation scheme can effectively solve the problem of non-IID data, especially when affordable transmission costs are limited. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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17 pages, 931 KiB

Open AccessArticle

Graph Neural Networks and Open-Government Data to Forecast Traffic Flow

by Petros Brimos, Areti Karamanou, Evangelos Kalampokis and Konstantinos Tarabanis

Information 2023, 14(4), 228; https://doi.org/10.3390/info14040228 - 7 Apr 2023

Cited by 9 | Viewed by 3604

Abstract

Traffic forecasting has been an important area of research for several decades, with significant implications for urban traffic planning, management, and control. In recent years, deep-learning models, such as graph neural networks (GNN), have shown great promise in traffic forecasting due to their ability to capture complex spatio–temporal dependencies within traffic networks. Additionally, public authorities around the world have started providing real-time traffic data as open-government data (OGD). This large volume of dynamic and high-value data can open new avenues for creating innovative algorithms, services, and applications. In this paper, we investigate the use of traffic OGD with advanced deep-learning algorithms. Specifically, we deploy two GNN models—the Temporal Graph Convolutional Network and Diffusion Convolutional Recurrent Neural Network—to predict traffic flow based on real-time traffic OGD. Our evaluation of the forecasting models shows that both GNN models outperform the two baseline models—Historical Average and Autoregressive Integrated Moving Average—in terms of prediction performance. We anticipate that the exploitation of OGD in deep-learning scenarios will contribute to the development of more robust and reliable traffic-forecasting algorithms, as well as provide innovative and efficient public services for citizens and businesses. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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13 pages, 1703 KiB

Open AccessArticle

An Attention-Based Deep Convolutional Neural Network for Brain Tumor and Disorder Classification and Grading in Magnetic Resonance Imaging

by Ioannis D. Apostolopoulos, Sokratis Aznaouridis and Mpesi Tzani

Information 2023, 14(3), 174; https://doi.org/10.3390/info14030174 - 9 Mar 2023

Cited by 10 | Viewed by 3269

Abstract

This study proposes the integration of attention modules, feature-fusion blocks, and baseline convolutional neural networks for developing a robust multi-path network that leverages its multiple feature-extraction blocks for non-hierarchical mining of important medical image-related features. The network is evaluated using 10-fold cross-validation on large-scale magnetic resonance imaging datasets involving brain tumor classification, brain disorder classification, and dementia grading tasks. The Attention Feature Fusion VGG19 (AFF-VGG19) network demonstrates superiority against state-of-the-art networks and attains an accuracy of 0.9353 in distinguishing between three brain tumor classes, an accuracy of 0.9565 in distinguishing between Alzheimer’s and Parkinson’s diseases, and an accuracy of 0.9497 in grading cases of dementia. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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