Applied Data Science for Social Good

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 9926

Special Issue Editors


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Guest Editor
Department of Applied Data Science, CPGE, San Jose State University, 1 Washington Sq., San Jose, CA 95192, USA
Interests: machine learning; artificial intelligence; social good

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Guest Editor
School of Creative Technologies, University of Bolton, Bolton BL3 5AB, UK
Interests: artificial intelligence; machine learning; Internet of Things; blockchain; wireless sensor networks
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Special Issue Information

Dear Colleagues,

Applied data science, particularly machine learning, has been driving innovation and the progress of civilization. Today, the world needs applied data science techniques more than ever in the field of social good. Data-based methods can help take civilization to the next level. Such a development should be sustainable, ethical, and tamper-proof. The journal Big Data and Cognitive Computing has been a great reference for high-end research that has the potential to serve some of the towering needs of society. Aligning with the profile of the journal, the aim of this Special Issue is to showcase cutting-edge research in applying data science to critical areas of social good. The United Nations has already identified 17 Sustainable Development Goals (SDGs). Applying data science to these goals is bound to accelerate the progress towards their achievement. Notably, though, with power comes great responsibility. The power that data science unleashes should be applied in accountable, ethical, and explainable ways.

In this Special Issue, original research articles and reviews that align with the above ideas are welcome. Research areas may include (but are not limited to) the application of data science to any of the following areas:

  • The 17 Sustainable Development Goals (SDGs) of the United Nations;
  • Ethical AI;
  • Urban computing;
  • Technology enablement for societal progress;
  • Explainability, interpretability, and accountability of AI models.

We look forward to receiving your contributions.

Dr. Vishnu S. Pendyala
Dr. Celestine Iwendi
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. 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

  • ethical AI
  • sustainability
  • big data analytics
  • data science applications

Published Papers (4 papers)

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Research

24 pages, 2866 KiB  
Article
Cybercrime Risk Found in Employee Behavior Big Data Using Semi-Supervised Machine Learning with Personality Theories
by Kenneth David Strang
Big Data Cogn. Comput. 2024, 8(4), 37; https://doi.org/10.3390/bdcc8040037 - 29 Mar 2024
Viewed by 1055
Abstract
A critical worldwide problem is that ransomware cyberattacks can be costly to organizations. Moreover, accidental employee cybercrime risk can be challenging to prevent, even by leveraging advanced computer science techniques. This exploratory project used a novel cognitive computing design with detailed explanations of [...] Read more.
A critical worldwide problem is that ransomware cyberattacks can be costly to organizations. Moreover, accidental employee cybercrime risk can be challenging to prevent, even by leveraging advanced computer science techniques. This exploratory project used a novel cognitive computing design with detailed explanations of the action-research case-study methodology and customized machine learning (ML) techniques, supplemented by a workflow diagram. The ML techniques included language preprocessing, normalization, tokenization, keyword association analytics, learning tree analysis, credibility/reliability/validity checks, heatmaps, and scatter plots. The author analyzed over 8 GB of employee behavior big data from a multinational Fintech company global intranet. The five-factor personality theory (FFPT) from the psychology discipline was integrated into semi-supervised ML to classify retrospective employee behavior and then identify cybercrime risk. Higher levels of employee neuroticism were associated with a greater organizational cybercrime risk, corroborating the findings in empirical publications. In stark contrast to the literature, an openness to new experiences was inversely related to cybercrime risk. The other FFPT factors, conscientiousness, agreeableness, and extroversion, had no informative association with cybercrime risk. This study introduced an interdisciplinary paradigm shift for big data cognitive computing by illustrating how to integrate a proven scientific construct into ML—personality theory from the psychology discipline—to analyze human behavior using a retrospective big data collection approach that was asserted to be more efficient, reliable, and valid as compared to traditional methods like surveys or interviews. Full article
(This article belongs to the Special Issue Applied Data Science for Social Good)
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23 pages, 5698 KiB  
Article
Optimization of Cryptocurrency Algorithmic Trading Strategies Using the Decomposition Approach
by Sherin M. Omran, Wessam H. El-Behaidy and Aliaa A. A. Youssif
Big Data Cogn. Comput. 2023, 7(4), 174; https://doi.org/10.3390/bdcc7040174 - 14 Nov 2023
Viewed by 2247
Abstract
A cryptocurrency is a non-centralized form of money that facilitates financial transactions using cryptographic processes. It can be thought of as a virtual currency or a payment mechanism for sending and receiving money online. Cryptocurrencies have gained wide market acceptance and rapid development [...] Read more.
A cryptocurrency is a non-centralized form of money that facilitates financial transactions using cryptographic processes. It can be thought of as a virtual currency or a payment mechanism for sending and receiving money online. Cryptocurrencies have gained wide market acceptance and rapid development during the past few years. Due to the volatile nature of the crypto-market, cryptocurrency trading involves a high level of risk. In this paper, a new normalized decomposition-based, multi-objective particle swarm optimization (N-MOPSO/D) algorithm is presented for cryptocurrency algorithmic trading. The aim of this algorithm is to help traders find the best Litecoin trading strategies that improve their outcomes. The proposed algorithm is used to manage the trade-offs among three objectives: the return on investment, the Sortino ratio, and the number of trades. A hybrid weight assignment mechanism has also been proposed. It was compared against the trading rules with their standard parameters, MOPSO/D, using normalized weighted Tchebycheff scalarization, and MOEA/D. The proposed algorithm could outperform the counterpart algorithms for benchmark and real-world problems. Results showed that the proposed algorithm is very promising and stable under different market conditions. It could maintain the best returns and risk during both training and testing with a moderate number of trades. Full article
(This article belongs to the Special Issue Applied Data Science for Social Good)
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16 pages, 2139 KiB  
Article
Impulsive Aggression Break, Based on Early Recognition Using Spatiotemporal Features
by Manar M. F. Donia, Wessam H. El-Behaidy and Aliaa A. A. Youssif
Big Data Cogn. Comput. 2023, 7(3), 150; https://doi.org/10.3390/bdcc7030150 - 14 Sep 2023
Viewed by 1262
Abstract
The study of human behaviors aims to gain a deeper perception of stimuli that control decision making. To describe, explain, predict, and control behavior, human behavior can be classified as either non-aggressive or anomalous behavior. Anomalous behavior is any unusual activity; impulsive aggressive, [...] Read more.
The study of human behaviors aims to gain a deeper perception of stimuli that control decision making. To describe, explain, predict, and control behavior, human behavior can be classified as either non-aggressive or anomalous behavior. Anomalous behavior is any unusual activity; impulsive aggressive, or violent behaviors are the most harmful. The detection of such behaviors at the initial spark is critical for guiding public safety decisions and a key to its security. This paper proposes an automatic aggressive-event recognition method based on effective feature representation and analysis. The proposed approach depends on a spatiotemporal discriminative feature that combines histograms of oriented gradients and dense optical flow features. In addition, the principal component analysis (PCA) and linear discriminant analysis (LDA) techniques are used for complexity reduction. The performance of the proposed approach is analyzed on three datasets: Hockey-Fight (HF), Stony Brook University (SBU)-Kinect, and Movie-Fight (MF), with accuracy rates of 96.5%, 97.8%, and 99.6%, respectively. Also, this paper assesses and contrasts the feature engineering and learned features for impulsive aggressive event recognition. Experiments show promising results of the proposed method compared to the state of the art. The implementation of the proposed work is available here. Full article
(This article belongs to the Special Issue Applied Data Science for Social Good)
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24 pages, 3806 KiB  
Article
Geospatial Mapping of Suicide-Related Tweets and Sentiments among Malaysians during the COVID-19 Pandemic
by Noradila Rusli, Nor Zahida Nordin, Ak Mohd Rafiq Ak Matusin, Janatun Naim Yusof, Muhammad Solehin Fitry Rosley, Gabriel Hoh Teck Ling, Muhammad Hakimi Mohd Hussain and Siti Zalina Abu Bakar
Big Data Cogn. Comput. 2023, 7(2), 63; https://doi.org/10.3390/bdcc7020063 - 28 Mar 2023
Cited by 1 | Viewed by 3604
Abstract
The government enacted the Movement Control Order (MCO) to curb the spread of the COVID-19 pandemic in Malaysia, restricting movement and shutting down several commercial enterprises around the nation. The crisis, which lasted over two years and featured a few MCOs, had an [...] Read more.
The government enacted the Movement Control Order (MCO) to curb the spread of the COVID-19 pandemic in Malaysia, restricting movement and shutting down several commercial enterprises around the nation. The crisis, which lasted over two years and featured a few MCOs, had an impact on Malaysians’ mental health. This study aimed to understand the context of using the word “suicide” on Twitter among Malaysians during the pandemic. “Suicide” is a keyword searched for on Twitter when mining data with the NCapture plugin. Using NVivo 12 software, we used the content analysis approach to detect the theme of tweets discussed by tweeps. The tweet content was then analyzed using VADER sentiment analysis to determine if it was positive, negative, or neutral. We conducted a spatial pattern distribution of tweets, revealing high numbers from Kuala Lumpur, Klang, Subang Jaya, Kangar, Alor Setar, Chukai, Kuantan, Johor Bharu, and Kota Kinabalu. Our analysis of tweet content related to the word “suicide” revealed three (3) main themes: (i) criticism of the government of that day (CGD) (N = 218, 55.68%), (ii) awareness related to suicide (AS) (N = 162, 41.44%), and (iii) suicidal feeling or experience (SFE) (N = 12, 2.88%). The word “suicide” conveyed both negative and positive sentiments. Negative tweets expressed frustration and disappointment with the government’s response to the pandemic and its economic impact. In contrast, positive tweets spread hope, encouragement, and support for mental health and relationship building. This study highlights the potential of social-media big data to understand the users’ virtual behavior in an unprecedented pandemic situation and the importance of considering cultural differences and nuances in sentiment analysis. The spatial pattern information was useful in identifying areas that may require additional resources or interventions to address suicide risk. This study underscores the importance of timely and cost-effective social media data analysis for valuable insights into public opinion and attitudes toward specific topics. Full article
(This article belongs to the Special Issue Applied Data Science for Social Good)
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