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Big Data and Cognitive Computing in 2023

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A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 82245

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Prof. Dr. Domenico Ursino

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Department of Information Engineering, Polytechnic University of Marche, 60121 Ancona, Italy
Interests: social network analysis; databases; artificial intelligence; business intelligence; social IoT
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Miguel-Angel Sicilia

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Department of Computer Science, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Interests: business analytics; machine learning; distributed systems; knowledge representation
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Prof. Dr. Nik Bessis

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Data Science Research Centre, Graduate School, Edge Hill University, Ormskirk, Lancashire L39 4QP, UK
Interests: cloud computing; Internet of Things; social graphs; big data; future internet; resource provisioning; global challenges
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Prof. Dr. Marcello Trovati

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Department of Computer Science, Edge Hill University, St Helens Road, Ormskirk L39 4QP, Lancashire, UK
Interests: data science; AI; mathematical modelling
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Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue on “Big Data and Cognitive Computing in 2023”, which is part of the MDPI journal New Year Special Issue Series.

In recent years, we have cooperated with many excellent scholars/scientific groups and published high-level studies that have already been cited numerous times. This Special Issue will be a collection of high-quality reviews from Editorial Board Members, Guest Editors, Topical Advisory Panel Members, Reviewer Board Members, previous authors, and reviewers. We welcome articles from all authors.

Furthermore, our Journal has just been accepted into Ei Compendex in the Engineering Village, and we will receive the first Impact Factor in Web of Science in 2023.

You are welcome to send short proposals of Feature Papers to our Editorial Office ([email protected]) before submission.

These will first be evaluated by our Editors. Please note that the selected full papers will still be subject to a thorough and rigorous peer review.

Prof. Dr. Domenico Ursino
Prof. Dr. Miguel-Angel Sicilia
Prof. Dr. Nik Bessis
Prof. Dr. Marcello Trovati
Guest Editors

New Year Special Issue Series

This Special Issue is a part of Big Data and Cognitive Computing's New Year Special Issue Series. The series reflects on the achievements, scientific progress, and “hot topics” of the previous year in the journal. Submissions of articles whose lead authors are our Editorial Board Members are highly encouraged. However, we welcome articles from all authors.

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. Big Data and Cognitive Computing 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 1800 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

big data
cognitive computing
data science
data analytics
IoT applications
social IoT
machine and deep learning
natural language processing
augmented, virtual, and mixed reality
social network analysis

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Published Papers (13 papers)

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Research

19 pages, 342 KiB

Open AccessFeature PaperArticle

Inverse Firefly-Based Search Algorithms for Multi-Target Search Problem

by Ouarda Zedadra, Antonio Guerrieri, Hamid Seridi, Aymen Benzaid and Giancarlo Fortino

Big Data Cogn. Comput. 2024, 8(2), 18; https://doi.org/10.3390/bdcc8020018 - 19 Feb 2024

Viewed by 2079

Abstract

Efficiently searching for multiple targets in complex environments with limited perception and computational capabilities is challenging for multiple robots, which can coordinate their actions indirectly through their environment. In this context, swarm intelligence has been a source of inspiration for addressing multi-target search problems in the literature. So far, several algorithms have been proposed for solving such a problem, and in this study, we propose two novel multi-target search algorithms inspired by the Firefly algorithm. Unlike the conventional Firefly algorithm, where light is an attractor, light represents a negative effect in our proposed algorithms. Upon discovering targets, robots emit light to repel other robots from that region. This repulsive behavior is intended to achieve several objectives: (1) partitioning the search space among different robots, (2) expanding the search region by avoiding areas already explored, and (3) preventing congestion among robots. The proposed algorithms, named Global Lawnmower Firefly Algorithm (GLFA) and Random Bounce Firefly Algorithm (RBFA), integrate inverse light-based behavior with two random walks: random bounce and global lawnmower. These algorithms were implemented and evaluated using the ArGOS simulator, demonstrating promising performance compared to existing approaches. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

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20 pages, 740 KiB

Open AccessArticle

Fair-CMNB: Advancing Fairness-Aware Stream Learning with Naïve Bayes and Multi-Objective Optimization

by Maryam Badar and Marco Fisichella

Big Data Cogn. Comput. 2024, 8(2), 16; https://doi.org/10.3390/bdcc8020016 - 31 Jan 2024

Viewed by 2041

Abstract

Fairness-aware mining of data streams is a challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans in critical decision-making processes, e.g., hiring staff, assessing credit risk, etc. This calls for handling massive amounts of incoming information with minimal response delay while ensuring fair and high-quality decisions. Although deep learning has achieved success in various domains, its computational complexity may hinder real-time processing, making traditional algorithms more suitable. In this context, we propose a novel adaptation of Naïve Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance through multi-objective optimization (MOO). Class imbalance is an inherent problem in discrimination-aware learning paradigms. To deal with class imbalance, we propose a dynamic instance weighting module that gives more importance to new instances and less importance to obsolete instances based on their membership in a minority or majority class. We have conducted experiments on a range of streaming and static datasets and concluded that our proposed methodology outperforms existing state-of-the-art (SoTA) fairness-aware methods in terms of both discrimination score and balanced accuracy. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

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21 pages, 1133 KiB

Open AccessArticle

AI-Based User Empowerment for Empirical Social Research

by Thoralf Reis, Lukas Dumberger, Sebastian Bruchhaus, Thomas Krause, Verena Schreyer, Marco X. Bornschlegl and Matthias L. Hemmje

Big Data Cogn. Comput. 2024, 8(2), 11; https://doi.org/10.3390/bdcc8020011 - 23 Jan 2024

Viewed by 2458

Abstract

Manual labeling and categorization are extremely time-consuming and, thus, costly. AI and ML-supported information systems can bridge this gap and support labor-intensive digital activities. Since it requires categorization, coding-based analysis, such as qualitative content analysis, reaches its limits with large amounts of data and could benefit from AI and ML-based support. Empirical social research, its application domain, benefits from Big Data’s ability to create more extensive human behavior and development models. A range of applications are available for statistical analysis to serve this purpose. This paper aims to implement an information system that supports researchers in empirical social research in performing AI-supported qualitative content analysis. AI2VIS4BigData is a reference model that standardizes use cases and artifacts for Big Data information systems that integrate AI and ML for user empowerment. Thus, this work’s concepts and implementations try to achieve an AI2VIS4BigData-compliant information system that supports social researchers in categorizing text data and creating insightful dashboards. Thereby, the text categorization is based on an existing ML component. Furthermore, it presents two evaluations that were conducted for these concepts and implementations: a qualitative cognitive walkthrough assessing the system’s usability and a quantitative user study with 18 participants revealed that though the users perceive AI support as more efficient, they need more time to reflect on the recommendations. The research revealed that AI support increased the correctness of the users’ categorizations but also slowed down their decision-making. The assumption that this is due to the UI design and additional information for processing requires follow-up research. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

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12 pages, 918 KiB

Open AccessArticle

Contemporary Art Authentication with Large-Scale Classification

by Todd Dobbs, Abdullah-Al-Raihan Nayeem, Isaac Cho and Zbigniew Ras

Big Data Cogn. Comput. 2023, 7(4), 162; https://doi.org/10.3390/bdcc7040162 - 9 Oct 2023

Cited by 2 | Viewed by 3082

Abstract

Art authentication is the process of identifying the artist who created a piece of artwork and is manifested through events of provenance, such as art gallery exhibitions and financial transactions. Art authentication has visual influence via the uniqueness of the artist’s style in contrast to the style of another artist. The significance of this contrast is proportional to the number of artists involved and the degree of uniqueness of an artist’s collection. This visual uniqueness of style can be captured in a mathematical model produced by a machine learning (ML) algorithm on painting images. Art authentication is not always possible as provenance can be obscured or lost through anonymity, forgery, gifting, or theft of artwork. This paper presents an image-only art authentication attribute marker of contemporary art paintings for a very large number of artists. The experiments in this paper demonstrate that it is possible to use ML-generated models to authenticate contemporary art from 2368 to 100 artists with an accuracy of 48.97% to 91.23%, respectively. This is the largest effort for image-only art authentication to date, with respect to the number of artists involved and the accuracy of authentication. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

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18 pages, 1352 KiB

Open AccessArticle

EnviroStream: A Stream Reasoning Benchmark for Environmental and Climate Monitoring

by Elena Mastria, Francesco Pacenza, Jessica Zangari, Francesco Calimeri, Simona Perri and Giorgio Terracina

Big Data Cogn. Comput. 2023, 7(3), 135; https://doi.org/10.3390/bdcc7030135 - 31 Jul 2023

Cited by 1 | Viewed by 2005

Abstract

Stream Reasoning (SR) focuses on developing advanced approaches for applying inference to dynamic data streams; it has become increasingly relevant in various application scenarios such as IoT, Smart Cities, Emergency Management, and Healthcare, despite being a relatively new field of research. The current lack of standardized formalisms and benchmarks has been hindering the comparison between different SR approaches. We proposed a new benchmark, called EnviroStream, for evaluating SR systems on weather and environmental data. The benchmark includes queries and datasets of different sizes. We adopted I-DLV-sr, a recently released SR system based on Answer Set Programming, as a baseline for query modelling and experimentation. We also showcased continuous online reasoning via a web application. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

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18 pages, 2466 KiB

Open AccessArticle

Transformational Entrepreneurship and Digital Platforms: A Combination of ISM-MICMAC and Unsupervised Machine Learning Algorithms

by Pejman Ebrahimi, Hakimeh Dustmohammadloo, Hosna Kabiri, Parisa Bouzari and Mária Fekete-Farkas

Big Data Cogn. Comput. 2023, 7(2), 118; https://doi.org/10.3390/bdcc7020118 - 13 Jun 2023

Cited by 8 | Viewed by 2925

Abstract

For many years, entrepreneurs were considered the change agents of their societies. They use their initiative and innovative minds to solve problems and create value. In the aftermath of the digital transformation era, a new group of entrepreneurs have emerged who are called transformational entrepreneurs. They use various digital platforms to create value. Surprisingly, despite their importance, they have not been sufficiently investigated. Therefore, this research scrutinizes the elements affecting transformational entrepreneurship in digital platforms. To do so, the authors have considered a two-phase method. First, interpretive structural modeling (ISM) and Matrices d’Impacts Croises Multiplication Appliqué a Un Classement (MICMAC) are used to suggest a model. ISM is a qualitative method to reach a visualized hierarchical structure. Then, four unsupervised machine learning algorithms are used to ensure the accuracy of the proposed model. The findings reveal that transformational leadership could mediate the relationship between the entrepreneurial mindset and thinking and digital transformation, interdisciplinary approaches, value creation logic, and technology diffusion. The GMM in the full type, however, has the best accuracy among the various covariance types, with an accuracy of 0.895. From the practical point of view, this paper provides important insights for practitioners, entrepreneurs, and public actors to help them develop transformational entrepreneurship skills. The results could also serve as a guideline for companies regarding how to manage the consequences of a crisis such as a pandemic. The findings also provide significant insight for higher education policymakers. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

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21 pages, 7048 KiB

Open AccessEditor’s ChoiceArticle

Molecular Structure-Based Prediction of Absorption Maxima of Dyes Using ANN Model

by Neeraj Tomar, Geeta Rani, Vijaypal Singh Dhaka, Praveen K. Surolia, Kalpit Gupta, Eugenio Vocaturo and Ester Zumpano

Big Data Cogn. Comput. 2023, 7(2), 115; https://doi.org/10.3390/bdcc7020115 - 8 Jun 2023

Cited by 4 | Viewed by 2723

Abstract

The exponentially growing energy requirements and, in turn, extensive depletion of non-restorable sources of energy are a major cause of concern. Restorable energy sources such as solar cells can be used as an alternative. However, their low efficiency is a barrier to their practical use. This provokes the research community to design efficient solar cells. Based on the study of efficacy, design feasibility, and cost of fabrication, DSSC shows supremacy over other photovoltaic solar cells. However, fabricating DSSC in a laboratory and then assessing their characteristics is a costly affair. The researchers applied techniques of computational chemistry such as Time-Dependent Density Functional Theory, and an ab initio method for defining the structure and electronic properties of dyes without synthesizing them. However, the inability of descriptors to provide an intuitive physical depiction of the effect of all parameters is a limitation of the proposed approaches. The proven potential of neural network models in data analysis, pattern recognition, and object detection motivated researchers to extend their applicability for predicting the absorption maxima (λ_max) of dye. The objective of this research is to develop an ANN-based QSPR model for correctly predicting the value of λ_max for inorganic ruthenium complex dyes used in DSSC. Furthermore, it demonstrates the impact of different activation functions, optimizers, and loss functions on the prediction accuracy of λ_max. Moreover, this research showcases the impact of atomic weight, types of bonds between constituents of the dye molecule, and the molecular weight of the dye molecule on the value of λ_max. The experimental results proved that the value of λ_max varies with changes in constituent atoms and types of bonds in a dye molecule. In addition, the model minimizes the difference in the experimental and calculated values of absorption maxima. The comparison with the existing models proved the dominance of the proposed model. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

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21 pages, 722 KiB

Open AccessArticle

Semantic Hierarchical Indexing for Online Video Lessons Using Natural Language Processing

by Marco Arazzi, Marco Ferretti and Antonino Nocera

Big Data Cogn. Comput. 2023, 7(2), 107; https://doi.org/10.3390/bdcc7020107 - 31 May 2023

Cited by 1 | Viewed by 1834

Abstract

Huge quantities of audio and video material are available at universities and teaching institutions, but their use can be limited because of the lack of intelligent search tools. This paper describes a possible way to set up an indexing scheme that offers a smart search modality, that combines semantic analysis of video/audio transcripts with the exact time positioning of uttered words. The proposal leverages NLP methods for topic modeling with lexical analysis of lessons’ transcripts and builds a semantic hierarchical index into the corpus of lessons analyzed. Moreover, using abstracting summarization, the system can offer short summaries on the subject semantically implied by the search carried out. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

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23 pages, 1484 KiB

Open AccessEditor’s ChoiceArticle

Unsupervised Deep Learning for Structural Health Monitoring

by Roberto Boccagna, Maurizio Bottini, Massimo Petracca, Alessia Amelio and Guido Camata

Big Data Cogn. Comput. 2023, 7(2), 99; https://doi.org/10.3390/bdcc7020099 - 17 May 2023

Cited by 4 | Viewed by 3217

Abstract

In the last few decades, structural health monitoring has gained relevance in the context of civil engineering, and much effort has been made to automate the process of data acquisition and analysis through the use of data-driven methods. Currently, the main issues arising in automated monitoring processing regard the establishment of a robust approach that covers all intermediate steps from data acquisition to output production and interpretation. To overcome this limitation, we introduce a dedicated artificial-intelligence-based monitoring approach for the assessment of the health conditions of structures in near-real time. The proposed approach is based on the construction of an unsupervised deep learning algorithm, with the aim of establishing a reliable method of anomaly detection for data acquired from sensors positioned on buildings. After preprocessing, the data are fed into various types of artificial neural network autoencoders, which are trained to produce outputs as close as possible to the inputs. We tested the proposed approach on data generated from an OpenSees numerical model of a railway bridge and data acquired from physical sensors positioned on the Historical Tower of Ravenna (Italy). The results show that the approach actually flags the data produced when damage scenarios are activated in the OpenSees model as coming from a damaged structure. The proposed method is also able to reliably detect anomalous structural behaviors of the tower, preventing critical scenarios. Compared to other state-of-the-art methods for anomaly detection, the proposed approach shows very promising results. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

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18 pages, 543 KiB

Open AccessArticle

Managing and Optimizing Big Data Workloads for On-Demand User Centric Reports

by Alexandra Băicoianu and Ion Valentin Scheianu

Big Data Cogn. Comput. 2023, 7(2), 78; https://doi.org/10.3390/bdcc7020078 - 18 Apr 2023

Cited by 1 | Viewed by 2286

Abstract

The term “big data” refers to the vast amount of structured and unstructured data generated by businesses, organizations, and individuals on a daily basis. The rapid growth of big data has led to the development of new technologies and techniques for storing, processing, and analyzing these data in order to extract valuable information. This study examines some of these technologies, compares their pros and cons, and provides solutions for handling specific types of reporting using big data tools. In addition, this paper discusses some of the challenges associated with big data and suggests approaches that could be used to manage and analyze these data. The findings demonstrate the benefits of efficiently managing the datasets and choosing the appropriate tools, as well as the efficiency of the proposed solution with hands-on examples. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

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16 pages, 355 KiB

Open AccessEditor’s ChoiceArticle

The Role of ChatGPT in Data Science: How AI-Assisted Conversational Interfaces Are Revolutionizing the Field

by Hossein Hassani and Emmanuel Sirmal Silva

Big Data Cogn. Comput. 2023, 7(2), 62; https://doi.org/10.3390/bdcc7020062 - 27 Mar 2023

Cited by 138 | Viewed by 45838

Abstract

ChatGPT, a conversational AI interface that utilizes natural language processing and machine learning algorithms, is taking the world by storm and is the buzzword across many sectors today. Given the likely impact of this model on data science, through this perspective article, we seek to provide an overview of the potential opportunities and challenges associated with using ChatGPT in data science, provide readers with a snapshot of its advantages, and stimulate interest in its use for data science projects. The paper discusses how ChatGPT can assist data scientists in automating various aspects of their workflow, including data cleaning and preprocessing, model training, and result interpretation. It also highlights how ChatGPT has the potential to provide new insights and improve decision-making processes by analyzing unstructured data. We then examine the advantages of ChatGPT’s architecture, including its ability to be fine-tuned for a wide range of language-related tasks and generate synthetic data. Limitations and issues are also addressed, particularly around concerns about bias and plagiarism when using ChatGPT. Overall, the paper concludes that the benefits outweigh the costs and ChatGPT has the potential to greatly enhance the productivity and accuracy of data science workflows and is likely to become an increasingly important tool for intelligence augmentation in the field of data science. ChatGPT can assist with a wide range of natural language processing tasks in data science, including language translation, sentiment analysis, and text classification. However, while ChatGPT can save time and resources compared to training a model from scratch, and can be fine-tuned for specific use cases, it may not perform well on certain tasks if it has not been specifically trained for them. Additionally, the output of ChatGPT may be difficult to interpret, which could pose challenges for decision-making in data science applications. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

23 pages, 4426 KiB

Open AccessArticle

Textual Feature Extraction Using Ant Colony Optimization for Hate Speech Classification

by Shilpa Gite, Shruti Patil, Deepak Dharrao, Madhuri Yadav, Sneha Basak, Arundarasi Rajendran and Ketan Kotecha

Big Data Cogn. Comput. 2023, 7(1), 45; https://doi.org/10.3390/bdcc7010045 - 6 Mar 2023

Cited by 17 | Viewed by 3595

Abstract

Feature selection and feature extraction have always been of utmost importance owing to their capability to remove redundant and irrelevant features, reduce the vector space size, control the computational time, and improve performance for more accurate classification tasks, especially in text categorization. These feature engineering techniques can further be optimized using optimization algorithms. This paper proposes a similar framework by implementing one such optimization algorithm, Ant Colony Optimization (ACO), incorporating different feature selection and feature extraction techniques on textual and numerical datasets using four machine learning (ML) models: Logistic Regression (LR), K-Nearest Neighbor (KNN), Stochastic Gradient Descent (SGD), and Random Forest (RF). The aim is to show the difference in the results achieved on both datasets with the help of comparative analysis. The proposed feature selection and feature extraction techniques assist in enhancing the performance of the machine learning model. This research article considers numerical and text-based datasets for stroke prediction and detecting hate speech, respectively. The text dataset is prepared by extracting tweets consisting of positive, negative, and neutral sentiments from Twitter API. A maximum improvement in accuracy of 10.07% is observed for Random Forest with the TF-IDF feature extraction technique on the application of ACO. Besides, this study also highlights the limitations of text data that inhibit the performance of machine learning models, justifying the difference of almost 18.43% in accuracy compared to that of numerical data. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

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23 pages, 736 KiB

Open AccessArticle

Performing Wash Trading on NFTs: Is the Game Worth the Candle?

by Gianluca Bonifazi, Francesco Cauteruccio, Enrico Corradini, Michele Marchetti, Daniele Montella, Simone Scarponi, Domenico Ursino and Luca Virgili

Big Data Cogn. Comput. 2023, 7(1), 38; https://doi.org/10.3390/bdcc7010038 - 21 Feb 2023

Cited by 12 | Viewed by 3800

Abstract

Wash trading is considered a highly inopportune and illegal behavior in regulated markets. Instead, it is practiced in unregulated markets, such as cryptocurrency or NFT (Non-Fungible Tokens) markets. Regarding the latter, in the past many researchers have been interested in this phenomenon from an “ex-ante” perspective, aiming to identify and classify wash trading activities before or at the exact time they happen. In this paper, we want to investigate the phenomenon of wash trading in the NFT market from a completely different perspective, namely “ex-post”. Our ultimate goal is to analyze wash trading activities in the past to understand whether the game is worth the candle, i.e., whether these illicit activities actually lead to a significant profit for their perpetrators. To the best of our knowledge, this is the first paper in the literature that attempts to answer this question in a “structured” way. The efforts to answer this question have enabled us to make some additional contributions to the literature in this research area. They are: (i) a framework to support future “ex-post” analyses of the NFT wash trading phenomenon; (ii) a new dataset on wash trading transactions involving NFTs that can support further future investigations of this phenomenon; (iii) a set of insights of the NFT wash trading phenomenon extracted at the end of an experimental campaign. Full article

(This article belongs to the Special Issue Big Data and Cognitive Computing in 2023)

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