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BDCC
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Big Data and Cognitive Computing in 2026
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Big Data and Cognitive Computing in 2026

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

Deadline for manuscript submissions: 31 March 2027 | Viewed by 1442

Editors

Prof. Dr. Min Chen

grade E-Mail Website
Guest Editor
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China
Interests: cognitive computing; 5G networks; wearable computing; big data analytics; robotics; machine learning; deep learning; emotion detection; mobile edge computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giancarlo Fortino

E-Mail Website
Guest Editor
Computer Engineering, DIMES—Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, 87036 Rende, Italy
Interests: wearable computing systems; e-Health; Internet of Things; agent-based computing
Special Issues, Collections and Topics in MDPI journals
Dr. Wenjing Xiao

E-Mail Website
Guest Editor
Guangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
Interests: applied ML/DL; edge intelligence; distributed learning; cognitive computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Happy New Year! We hope you enjoyed a fruitful 2025, and we extend our best wishes for a successful 2026.

We are pleased to announce a new Special Issue, titled “Big Data and Cognitive Computing in 2026”, as part of the MDPI journal New Year Special Issue Series. This Special Issue will feature a collection of high-quality reviews and original research articles contributed by Advisory Board Members, Editors-in-Chief, Editorial Board Members, Guest Editors, Topical Advisory Panel Members, Reviewer Board Members, as well as authors and reviewers affiliated with learned societies.

If you are interested in contributing, you are welcome to submit a short proposal for a contribution to our Editorial Office at bdcc@mdpi.com before full submission.

All proposals will be evaluated by our Editors. Please note that selected full papers will still undergo a thorough and rigorous peer-review process. 

We look forward to your contributions and to another year of excellent research together.

Prof. Dr. Min Chen
Prof. Dr. Giancarlo Fortino
Dr. Wenjing Xiao
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • large language models
  • multimodal learning
  • explainable AI
  • edge computing
  • privacy-preserving computing
  • knowledge graphs
  • embodied intelligence
  • blockchain
  • agentic AI
  • big data
  • cloud computing
  • machine learning
  • deep learning
  • cognitive computing
  • IoT
  • data mining
  • human-machine interaction
  • digital twin

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

Published Papers (2 papers)

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Research

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46 pages, 2150 KB  
Article
The Fragility of Phishing Detection Models: Evidence from Cross-Corpus Transfer, Prevalence Shift, Artifact Learning, and Evasion Risk
by Istiaque Bhuiyan and Tanvir Bhuiyan
Big Data Cogn. Comput. 2026, 10(7), 211; https://doi.org/10.3390/bdcc10070211 - 29 Jun 2026
Viewed by 511
Abstract
Phishing detection models often report strong benchmark performance, yet their reliability under realistic deployment conditions remains uncertain. This study examines this problem by investigating three failure modes of cross-dataset phishing email detection: corpus generalization failure, asymmetric prevalence-shift failure, and artifact-driven spurious learning. Using [...] Read more.
Phishing detection models often report strong benchmark performance, yet their reliability under realistic deployment conditions remains uncertain. This study examines this problem by investigating three failure modes of cross-dataset phishing email detection: corpus generalization failure, asymmetric prevalence-shift failure, and artifact-driven spurious learning. Using six public email corpora, CEAS_08, Enron, Ling, Nazario, Nigerian Fraud, and SpamAssassin, the study evaluates Term Frequency (TF) and Inverse Document Frequency (IDF)-based Logistic Regression and Linear Support Vector Classifier (SVC) models across pooled baseline testing, single-corpus cross-dataset transfer, leave-one-corpus-out pooled training, prevalence-shift simulation, training prevalence manipulation, dataset-identification analysis, top-feature inspection, artifact-removal ablation, and targeted feature-sensitivity masking. The findings show that single-corpus models are unstable under cross-dataset transfer, with F1-scores varying substantially across source–target combinations. In contrast, leave-one-corpus-out pooled training improves robustness, with Logistic Regression achieving sustained F1-scores between 0.8201 and 0.8994, and Linear SVC achieving F1-scores between 0.7607 and 0.8910 across unseen corpora. Prevalence-shift experiments reveal that failure is asymmetric and threshold-dependent. High-prevalence-trained models maintain high recall under fixed thresholds but suffer sharp recall degradation when operational alert-budget constraints are imposed. Conversely, low-prevalence-trained models become overly conservative in high-threat environments, producing high precision but substantially lower recall and poorer calibration. Artifact analyses further show that source corpus identity is highly learnable, with dataset-identification accuracy reaching 0.9722 for Logistic Regression and 0.9806 for Linear SVC. Top-feature and masking analyses indicate that models rely partly on corpus markers, date tokens, URL/domain terms, headers, and other artifact-like features rather than only general phishing indicators. The study contributes a deployment-aware and adversary-aware evaluation framework for phishing detection. It shows that benchmark accuracy alone is insufficient for assessing real-world robustness and that reliable phishing detection requires cross-corpus validation, prevalence-aware thresholding, and systematic testing for artifact-driven spurious learning. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing in 2026)
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Review

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23 pages, 472 KB  
Review
A Review of Human-AI Complementarities Across Multiple Dimensions of Organisational Complexity
by Ganesh Sankaran, Marco A. Palomino and Guido Siestrup
Big Data Cogn. Comput. 2026, 10(8), 268; https://doi.org/10.3390/bdcc10080268 - 11 Aug 2026
Viewed by 87
Abstract
The growing capabilities of artificial intelligence (AI) have not translated straightforwardly into organisational value. A persistent disconnect—the “last-mile problem”—arises from structural gaps between idealised AI tasks and real-world organisational contexts. Synthesising insights from organisational theory, cognitive science, and computer science, we have developed [...] Read more.
The growing capabilities of artificial intelligence (AI) have not translated straightforwardly into organisational value. A persistent disconnect—the “last-mile problem”—arises from structural gaps between idealised AI tasks and real-world organisational contexts. Synthesising insights from organisational theory, cognitive science, and computer science, we have developed a five-dimensional diagnostic framework that maps the challenges of human-AI collaboration across Integration, Representation, Scale, Temporality, and Adequacy gaps. These gaps illuminate how socio-technical complexity, contextualised problem representations, interdependencies among agents, dynamic environments, and limitations in current AI reasoning collectively constrain full automation and demand human judgement. By reviewing the historical evolution of AI—from symbolic systems to machine learning, generative models, and emerging agentic approaches—we show that augmentation remains the dominant and most viable mode of use in complex environments. An illustrative system-dynamics example demonstrates how improvements in algorithmic performance do not automatically yield proportional system-level gains. Overall, our framework provides researchers with a conceptual lens and practitioners with a diagnostic tool for assessing complementarities and informing the design of human-AI collaborations. The framework is offered as a conceptual synthesis and diagnostic instrument rather than an empirically validated model. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing in 2026)
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