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BDCC
Special Issues
Machine Learning Applications and Big Data Challenges
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Machine Learning Applications and Big Data Challenges

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 4327

Special Issue Editors

Dr. Arunkumar Bagavathi

E-Mail Website
Guest Editor
Department of Computer Science, Oklahoma State University, Stillwater, OK 74078, USA
Interests: data science; applied machine learning; networks science; computational social science; natural language processing
Dr. Tao Hu

E-Mail Website
Guest Editor
Department of Geography, Oklahoma State University, Stillwater, OK 74074, USA
Interests: GIS; geospatial big data; health geography; health disparities
Special Issues, Collections and Topics in MDPI journals
Dr. Atriya Sen

E-Mail Website
Guest Editor
Department of Computer Science, Oklahoma State University, Stillwater, OK 74078, USA
Interests: artificial intelligence; AI explanation; computational logic; cognitive AI; philosophy of AI; automated scientific discovery; computational philosophy; biodiversity informatics; AI for sustainability and conservation biology

Special Issue Information

Dear Colleagues,

Machine learning (ML) has become a critical component in real-world application domains like industry, transportation, healthcare, manufacturing, and beyond. As organizations move towards digital environments, there will be a surge in data availability, which can introduce novel opportunities and challenges for any machine learning task. Big data, characterized by massive volumes, high velocity, and diverse varieties of data formats, can increase the power and performance of machine learning algorithms designed to solve downstream tasks. Although it introduces new problems with respect to scalability, efficiency, and complexity, the synergy between machine learning and big data can offer unprecedented capabilities to reveal complex patterns and trends. Understanding the applications of machine learning in the context of big data and mitigating any associated challenges still have the potential to advance the modeling of data-driven systems.

The scope of this Special Issue is to collect recent advancements in machine learning applications that are targeted towards tackling the challenges of big data. This Special Issue will also highly value interdisciplinary research to bring new challenges, research questions, approaches, and datasets.

This Special Issue invites new research contributions to machine learning tasks specifically tailored for big data challenges. The scope includes, but is not limited to, the following topics:

  • Information retrieval;
  • Computer vision;
  • Natural language processing;
  • Social network analysis;
  • Knowledge discovery;
  • Trustworthy and secure ML;
  • Multi-modal ML systems;
  • ML for big graphs;
  • Lightweight and efficient models;
  • Spatiotemporal and geospatial ML;
  • Distributed and parallel ML;
  • Applied research such as healthcare, industry, and manufacturing.

Dr. Arunkumar Bagavathi
Dr. Tao Hu
Dr. Atriya Sen
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

  • big data
  • data science
  • machine learning
  • artificial intelligence

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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24 pages, 7001 KiB  
Article
Appendicitis Diagnosis: Ensemble Machine Learning and Explainable Artificial Intelligence-Based Comprehensive Approach
by Mohammed Gollapalli, Atta Rahman, Sheriff A. Kudos, Mohammed S. Foula, Abdullah Mahmoud Alkhalifa, Hassan Mohammed Albisher, Mohammed Taha Al-Hariri and Nazeeruddin Mohammad
Big Data Cogn. Comput. 2024, 8(9), 108; https://doi.org/10.3390/bdcc8090108 - 4 Sep 2024
Viewed by 1130
Abstract
Appendicitis is a condition wherein the appendix becomes inflamed, and it can be difficult to diagnose accurately. The type of appendicitis can also be hard to determine, leading to misdiagnosis and difficulty in managing the condition. To avoid complications and reduce mortality, early [...] Read more.
Appendicitis is a condition wherein the appendix becomes inflamed, and it can be difficult to diagnose accurately. The type of appendicitis can also be hard to determine, leading to misdiagnosis and difficulty in managing the condition. To avoid complications and reduce mortality, early diagnosis and treatment are crucial. While Alvarado’s clinical scoring system is not sufficient, ultrasound and computed tomography (CT) imaging are effective but have downsides such as operator-dependency and radiation exposure. This study proposes the use of machine learning methods and a locally collected reliable dataset to enhance the identification of acute appendicitis while detecting the differences between complicated and non-complicated appendicitis. Machine learning can help reduce diagnostic errors and improve treatment decisions. This study conducted four different experiments using various ML algorithms, including K-nearest neighbors (KNN), DT, bagging, and stacking. The experimental results showed that the stacking model had the highest training accuracy, test set accuracy, precision, and F1 score, which were 97.51%, 92.63%, 95.29%, and 92.04%, respectively. Feature importance and explainable AI (XAI) identified neutrophils, WBC_Count, Total_LOS, P_O_LOS, and Symptoms_Days as the principal features that significantly affected the performance of the model. Based on the outcomes and feedback from medical health professionals, the scheme is promising in terms of its effectiveness in diagnosing of acute appendicitis. Full article
(This article belongs to the Special Issue Machine Learning Applications and Big Data Challenges)
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18 pages, 2336 KiB  
Article
Performance and Board Diversity: A Practical AI Perspective
by Lee-Wen Yang, Thi Thanh Binh Nguyen and Wei-Ju Young
Big Data Cogn. Comput. 2024, 8(9), 106; https://doi.org/10.3390/bdcc8090106 - 4 Sep 2024
Viewed by 889
Abstract
The face of corporate governance is changing as new technologies in the scope of artificial intelligence and data analytics are used to make better future-oriented decisions on performance management. This study attempts to provide empirical results to analyze when the impact of diversity [...] Read more.
The face of corporate governance is changing as new technologies in the scope of artificial intelligence and data analytics are used to make better future-oriented decisions on performance management. This study attempts to provide empirical results to analyze when the impact of diversity on the board of directors is most evident through the multi-breaks model and artificial neural networks. The input data for the simulation includes 853 electronic companies listed on the Taiwan Stock Exchange from 2000 to 2021. The empirical results show that the higher the percentage of female board members, the more influential the company’s performance is, which is only evident when the company is in good business condition. By integrating ANNs with multi-breakpoint regression, this study introduces a novel approach to management research, providing a detailed perspective on how board diversity impacts firm performance across different conditions. The ANN results show that using the number of business board members for predicting Return on Assets yields the highest accuracy, with female board members following closely in predictive effectiveness. The presence of women on the board contributes positively to ROA, particularly when the company is experiencing favorable business conditions and high profitability. Our analysis also reveals that a higher percentage of male board members improves company performance, but this benefit is observed only in highly favorable and unfavorable business conditions. Conversely, a higher percentage of business members tends to affect performance during periods of high profitability negatively. The power of the board of directors and significant shareholders is positively correlated with performance, whereas CEO power positively impacts performance only when it is not extremely low. Independent board members generally do not have a significant effect on profits. Additionally, the company’s asset value positively influences performance primarily when the return on assets is high, and increased financial leverage is associated with reduced profitability. Full article
(This article belongs to the Special Issue Machine Learning Applications and Big Data Challenges)
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13 pages, 2451 KiB  
Systematic Review
The State of the Art of Artificial Intelligence Applications in Eosinophilic Esophagitis: A Systematic Review
by Martina Votto, Carlo Maria Rossi, Silvia Maria Elena Caimmi, Maria De Filippo, Antonio Di Sabatino, Marco Vincenzo Lenti, Alessandro Raffaele, Gian Luigi Marseglia and Amelia Licari
Big Data Cogn. Comput. 2024, 8(7), 76; https://doi.org/10.3390/bdcc8070076 - 9 Jul 2024
Viewed by 1375
Abstract
Introduction: Artificial intelligence (AI) tools are increasingly being integrated into computer-aided diagnosis systems that can be applied to improve the recognition and clinical and molecular characterization of allergic diseases, including eosinophilic esophagitis (EoE). This review aims to systematically evaluate current applications of AI, [...] Read more.
Introduction: Artificial intelligence (AI) tools are increasingly being integrated into computer-aided diagnosis systems that can be applied to improve the recognition and clinical and molecular characterization of allergic diseases, including eosinophilic esophagitis (EoE). This review aims to systematically evaluate current applications of AI, machine learning (ML), and deep learning (DL) methods in EoE characterization and management. Methods: We conducted a systematic review using a registered protocol published in the International Prospective Register of Systematic Reviews (CRD42023451048). The risk of bias and applicability of eligible studies were assessed according to the prediction model study risk of bias assessment tool (PROBAST). We searched PubMed, Embase, and Web of Science to retrieve the articles. The literature review was performed in May 2023. We included original research articles (retrospective or prospective studies) published in English in peer-reviewed journals, studies whose participants were patients with EoE, and studies assessing the application of AI, ML, or DL models. Results: A total of 120 articles were found. After removing 68 duplicates, 52 articles were reviewed based on the title and abstract, and 34 were excluded. Eleven full texts were assessed for eligibility, met the inclusion criteria, and were analyzed for the systematic review. The AI models developed in three studies for identifying EoE based on endoscopic images showed high score performance with an accuracy that ranged from 0.92 to 0.97. Five studies developed AI models that histologically identified EoE with high accuracy (87% to 99%). We also found two studies where the AI model identified subgroups of patients according to their clinical and molecular features. Conclusions: AI technologies could promote more accurate evidence-based management of EoE by integrating the results of molecular signature, clinical, histology, and endoscopic features. However, the era of AI application in medicine is just beginning; therefore, further studies with model validation in the real-world environment are required. Full article
(This article belongs to the Special Issue Machine Learning Applications and Big Data Challenges)
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