You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.

Search for Articles:

Title / Keyword

Author / Affiliation / Email

Journal

Article Type

Advanced Search

Section

Special Issue

Volume

Issue

Number

Page

Logical OperatorOperator

Search Text

Search Type

Journal Description

Big Data and Cognitive Computing

Big Data and Cognitive Computing is an international, peer-reviewed, open access journal on big data and cognitive computing published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, Inspec, Ei Compendex, and other databases.
Journal Rank: JCR - Q1 (Computer Science, Theory and Methods) / CiteScore - Q1 (Computer Science Applications)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 24.5 days after submission; acceptance to publication is undertaken in 4.6 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Journal Cluster of Artificial Intelligence: AI, AI in Medicine, Algorithms, BDCC, MAKE, MTI, Stats, Virtual Worlds and Computers.

Impact Factor: 4.4 (2024); 5-Year Impact Factor: 4.2 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2504-2289

Latest Articles

30 pages, 568 KB

Open AccessArticle

Demo-ToT: Enhancing the Reasoning Capabilities of AI Agent via Improved Demonstrations Retrieval Strategy

by Jiahui Li, Bangbang Ren, Mengmeng Zhang and Honghui Chen

Big Data Cogn. Comput. 2025, 9(11), 276; https://doi.org/10.3390/bdcc9110276 (registering DOI) - 2 Nov 2025

Innovative reasoning frameworks have been proposed to enhance the reasoning capabilities of AI agents, improving their performance in various tasks. However, most existing research has focused on enhancing designing frameworks for LLMs, with limited attention on leveraging in-context learning to boost their reasoning [...] Read more.

Innovative reasoning frameworks have been proposed to enhance the reasoning capabilities of AI agents, improving their performance in various tasks. However, most existing research has focused on enhancing designing frameworks for LLMs, with limited attention on leveraging in-context learning to boost their reasoning power. This paper proposes a novel approach, Demo-ToT, which enhances the Tree-of-Thought (ToT) reasoning framework by dynamically retrieving relevant demonstrations to improve reasoning accuracy. Various demonstration retrieval strategies, including vector similarity, sparse retrieval, and string similarity, were explored to identify the most effective methods for optimizing LLM performance. Experiments conducted across multiple benchmarks and language models of varying sizes demonstrated that Demo-ToT substantially enhanced the reasoning ability of smaller LLMs, achieving performance comparable to or even surpassing that of much larger models such as GPT-4. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Natural Language Processing (NLP))

18 pages, 2417 KB

Open AccessArticle

LizAI XT—AI-Accelerated Management Platform for Complex Healthcare Data at Scale, Beyond EMR/EHR and Dashboards

by Trung Tin Nguyen and David Raphael Elmaleh

Big Data Cogn. Comput. 2025, 9(11), 275; https://doi.org/10.3390/bdcc9110275 (registering DOI) - 1 Nov 2025

In this study, we present LizAI XT, an AI-powered platform designed to automate the structuring, anonymization, and semantic integration of large-scale healthcare data from diverse sources, into one comprehensive table or any designated forms, based on diseases, clinical variables, and/or other defined parameters, [...] Read more.

In this study, we present LizAI XT, an AI-powered platform designed to automate the structuring, anonymization, and semantic integration of large-scale healthcare data from diverse sources, into one comprehensive table or any designated forms, based on diseases, clinical variables, and/or other defined parameters, beyond the creation of a dashboard or visualization. We evaluate the platform’s performance on a cluster of 4x NVIDIA A30 GPU 24GB, with 16 diseases—from deathly cancer and COPD, to conventional ones—ear infections, including a total 16,000 patients, ∼115,000 medical files, and ∼800 clinical variables. LizAI XT structures data from thousands of files into sets of variables for each disease in one file, achieving > 95.0% overall accuracy, while providing exceptional outputs in complicated cases of cancers (99.1%), COPD (98.89%), and asthma (98.12%), without model-overfitting. Data retrieval is sub-second for a variable per patient with a minimal GPU power, which can significantly be improved on more powerful GPUs. LizAI XT uniquely enables fully client-controlled data, complying with strict data security and privacy regulations per region/nation. Our advances complement the existing EMR/EHR, AWS HealthLake, and Google Vertex AI platforms, for healthcare data management and AI development, with large-scalability and expansion at any levels of HMOs, clinics, pharma, and government. Full article

(This article belongs to the Special Issue Beyond Diagnosis: Machine Learning in Prognosis, Prevention, Healthcare, Neurosciences, and Precision Medicine)

► Show Figures

Figure 1

17 pages, 2642 KB

Open AccessArticle

RE-XswinUnet: Rotary Positional Encoding and Inter-Slice Contextual Connections for Multi-Organ Segmentation

by Hang Yang, Chuanghua Yang, Dan Yang, Xiaojing Hang and Wu Liu

Big Data Cogn. Comput. 2025, 9(11), 274; https://doi.org/10.3390/bdcc9110274 (registering DOI) - 31 Oct 2025

Medical image segmentation has been a central research focus in deep learning, but methods based on convolutions have limitations in modeling the long-range validity of images. To overcome this issue, hybrid CNN-Transformer architectures have been explored, with SwinUNet being a classic approach. However, [...] Read more.

Medical image segmentation has been a central research focus in deep learning, but methods based on convolutions have limitations in modeling the long-range validity of images. To overcome this issue, hybrid CNN-Transformer architectures have been explored, with SwinUNet being a classic approach. However, SwinUNet still faces challenges such as insufficient modeling of relative position information, limited feature fusion capabilities in skip connections, and the loss of translational invariance caused by Patch Merging. To overcome these limitations, the architecture RE-XswinUnet is presented as a novel solution for medical image segmentation. In our design, relative position biases are replaced with rotary position embedding to enhance the model’s ability to extract detailed information. During the decoding stage, XskipNet is designed to improve cross-scale feature fusion and learning capabilities. Additionally, an SCAR Block downsampling module is incorporated to preserve translational invariance more effectively. The experimental results demonstrate that RE-XswinUnet achieves improvements of 2.65% and 0.95% in Dice coefficients on the Synapse multi-organ and ACDC datasets, respectively, validating its superiority in medical image segmentation tasks. Full article

► Show Figures

Figure 1

24 pages, 1237 KB

Open AccessArticle

Explainable Multi-Hop Question Answering: A Rationale-Based Approach

by Kyubeen Han, Youngjin Jang and Harksoo Kim

Big Data Cogn. Comput. 2025, 9(11), 273; https://doi.org/10.3390/bdcc9110273 (registering DOI) - 31 Oct 2025

Multi-hop question answering tasks involve identifying relevant supporting sentences from a given set of documents, which serve as the rationale for deriving answers. Most research in this area consists of two main components: a rationale identification module and a reader module. Since the [...] Read more.

Multi-hop question answering tasks involve identifying relevant supporting sentences from a given set of documents, which serve as the rationale for deriving answers. Most research in this area consists of two main components: a rationale identification module and a reader module. Since the rationale identification module often relies on retrieval models or supervised learning, annotated rationales are typically essential. This reliance on annotations, however, creates challenges when adapting to open-domain settings. Moreover, when models are trained on annotated rationales, explainable artificial intelligence (XAI) requires clear explanations of how the model arrives at these rationales. Consequently, traditional multi-hop question answering (QA) approaches that depend on annotated rationales are ill-suited for XAI, which demands transparency in the model’s reasoning process. To address this issue, we propose a rationale reasoning framework that can effectively infer rationales and clearly demonstrate the model’s reasoning process, even in open-domain environments without annotations. The proposed model is applicable to various tasks without structural constraints, and experimental results demonstrate its significantly improved rationale reasoning capabilities in multi-hop question answering, relation extraction, and sentence classification tasks. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainable Development)

► Show Figures

Figure 1

18 pages, 2239 KB

Open AccessArticle

AI–Big Data Analytics Platform for Energy Forecasting in Modern Power Systems

by Martin Santos-Dominguez, Nicasio Hernandez Flores, Isaac Alberto Parra-Ramirez and Gustavo Arroyo-Figueroa

Big Data Cogn. Comput. 2025, 9(11), 272; https://doi.org/10.3390/bdcc9110272 - 31 Oct 2025

Big Data Analytics is vital for power grids, as it empowers informed decision-making, anticipates potential operational and maintenance issues, optimizes grid management, supports renewable energy integration, ultimately reduces costs, improves customer service, monitors consumer behavior, and offers new services. This paper describes the [...] Read more.

Big Data Analytics is vital for power grids, as it empowers informed decision-making, anticipates potential operational and maintenance issues, optimizes grid management, supports renewable energy integration, ultimately reduces costs, improves customer service, monitors consumer behavior, and offers new services. This paper describes the AI–Big Data Analytics Architecture based on a data lake architecture that uses a reduced and customized set of Hadoop and Spark as a cost-effective, on-premises alternative for advanced data analytics in power systems. As a case study, a comparative analysis of electricity price forecasting models in the day-ahead market for nodes of the Mexican national electrical system using statistical, machine learning, and deep learning models, is presented. To build and select the best forecasting model, a data science and machine learning methodology is used. The results show that the Gradient Boosting and Support Vector Regression models presented the best performance, with a Mean Absolute Percentage Error (MAPE) between 1% and 4% for five-day-ahead electricity price forecasting. The implementation of the best forecasting model into the Big Data Analytics Platform allows the automation of the calculation of the local electricity price forecast per node (every 24, 72, or 120 h) and its display in a comparative dashboard with actual and forecasted data for decision-making on demand. The proposed architecture is a valuable tool that allows the future implementation of intelligent energy forecasting models in power grids, such as load demand, fuel prices, power generation, and consumption, among others. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainable Development)

► Show Figures

Figure 1

30 pages, 4273 KB

Open AccessArticle

Scalable Predictive Modeling for Hospitalization Prioritization: A Hybrid Batch–Streaming Approach

by Nisrine Berros, Youness Filaly, Fatna El Mendili and Younes El Bouzekri El Idrissi

Big Data Cogn. Comput. 2025, 9(11), 271; https://doi.org/10.3390/bdcc9110271 - 25 Oct 2025

Healthcare systems worldwide have faced unprecedented pressure during crises such as the COVID-19 pandemic, exposing limits in managing scarce hospital resources. Many predictive models remain static, unable to adapt to new variants, shifting conditions, or diverse patient populations. This work proposes a dynamic [...] Read more.

Healthcare systems worldwide have faced unprecedented pressure during crises such as the COVID-19 pandemic, exposing limits in managing scarce hospital resources. Many predictive models remain static, unable to adapt to new variants, shifting conditions, or diverse patient populations. This work proposes a dynamic prioritization framework that recalculates severity scores in batch mode when new factors appear and applies them instantly through a streaming pipeline to incoming patients. Unlike approaches focused only on fixed mortality or severity risks, our model integrates dual datasets (survivors and non-survivors) to refine feature selection and weighting, enhancing robustness. Built on a big data infrastructure (Spark/Databricks), it ensures scalability and responsiveness, even with millions of records. Experimental results confirm the effectiveness of this architecture: The artificial neural network (ANN) achieved 98.7% accuracy, with higher precision and recall than traditional models, while random forest and logistic regression also showed strong AUC values. Additional tests, including temporal validation and real-time latency simulation, demonstrated both stability over time and feasibility for deployment in near-real-world conditions. By combining adaptability, robustness, and scalability, the proposed framework offers a methodological contribution to healthcare analytics, supporting fair and effective hospitalization prioritization during pandemics and other public health emergencies. Full article

► Show Figures

Figure 1

33 pages, 2850 KB

Open AccessReview

Network Traffic Analysis Based on Graph Neural Networks: A Scoping Review

by Ruonan Wang, Jinjing Zhao, Hongzheng Zhang, Liqiang He, Hu Li and Minhuan Huang

Big Data Cogn. Comput. 2025, 9(11), 270; https://doi.org/10.3390/bdcc9110270 - 24 Oct 2025

Network traffic analysis is crucial for understanding network behavior and identifying underlying applications, protocols, and service groups. The increasing complexity of network environments, driven by the evolution of the Internet, poses significant challenges to traditional analytical approaches. Graph Neural Networks (GNNs) have recently [...] Read more.

Network traffic analysis is crucial for understanding network behavior and identifying underlying applications, protocols, and service groups. The increasing complexity of network environments, driven by the evolution of the Internet, poses significant challenges to traditional analytical approaches. Graph Neural Networks (GNNs) have recently garnered considerable attention in network traffic analysis due to their ability to model complex relationships within network flows and between communicating entities. This scoping review systematically surveys major academic databases, employing predefined eligibility criteria to identify and synthesize key research in the field, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology. We present a comprehensive overview of a generalized architecture for GNN-based traffic analysis and categorize recent methods into three primary types: node prediction, edge prediction, and graph prediction. We discuss challenges in network traffic analysis, summarize solutions from various methods, and provide practical recommendations for model selection. This review also compiles publicly available datasets and open-source code, serving as valuable resources for further research. Finally, we outline future research directions to advance this field. This work offers an updated understanding of GNN applications in network traffic analysis and provides practical guidance for researchers and practitioners. Full article

(This article belongs to the Special Issue Machine Learning Methodologies and Applications in Cybersecurity Data Analysis)

► Show Figures

Figure 1

2 pages, 141 KB

Open AccessCorrection

Correction: Jiang et al. Assessing the Transformation of Armed Conflict Types: A Dynamic Approach. Big Data Cogn. Comput. 2025, 9, 123

by Dong Jiang, Jun Zhuo, Peiwei Fan, Fangyu Ding, Mengmeng Hao, Shuai Chen, Jiping Dong and Jiajie Wu

Big Data Cogn. Comput. 2025, 9(11), 269; https://doi.org/10.3390/bdcc9110269 - 24 Oct 2025

There was an error in the original publication [...] Full article

30 pages, 2440 KB

Open AccessArticle

Adaptive Segmentation and Statistical Analysis for Multivariate Big Data Forecasting

by Desmond Fomo and Aki-Hiro Sato

Big Data Cogn. Comput. 2025, 9(11), 268; https://doi.org/10.3390/bdcc9110268 - 24 Oct 2025

Forecasting high-volume, univariate, and multivariate longitudinal data streams is a critical challenge in Big Data systems, especially with constrained computational resources and pronounced data variability. However, existing approaches often neglect multivariate statistical complexity (e.g., covariance, skewness, kurtosis) of multivariate time series or rely [...] Read more.

Forecasting high-volume, univariate, and multivariate longitudinal data streams is a critical challenge in Big Data systems, especially with constrained computational resources and pronounced data variability. However, existing approaches often neglect multivariate statistical complexity (e.g., covariance, skewness, kurtosis) of multivariate time series or rely on recency-only windowing that discards informative historical fluctuation patterns, limiting robustness under strict resource budgets. This work makes two core contributions to big data forecasting. First, we establish a formal, multi-dimensional framework for quantifying “data bigness” across statistical, computational, and algorithmic complexities, providing a rigorous foundation for analyzing resource-constrained problems. Second, guided by this framework, we extend and validate the Adaptive High-Fluctuation Recursive Segmentation (AHFRS) algorithm for multivariate time series. By incorporating higher-order statistics such as covariance, skewness, and kurtosis, AHFRS improves predictive accuracy under strict computational budgets. We validate the approach in two stages. First, a real-world case study on a univariate Bitcoin time series provides a practical stress test using a Long Short-Term Memory (LSTM) network as a robust baseline. This validation reveals a significant increase in forecasting robustness, with our method reducing the Root Mean Squared Error (RMSE) by more than 76% in a challenging scenario. Second, its generalizability is established on synthetic multivariate data sets in Finance, Retail, and Healthcare using standard statistical models. Across domains, AHFRS consistently outperforms baselines; in our multivariate Finance simulation, RMSE decreases by up to 62.5% in Finance and Mean Absolute Percentage Error (MAPE) drops by more than 10 percentage points in Healthcare. These results demonstrate that the proposed framework and AHFRS advances the theoretical modeling of data complexity and the design of adaptive, resource-efficient forecasting pipelines for real-world, high-volume data ecosystems. Full article

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

► Show Figures

Figure 1

71 pages, 9523 KB

Open AccessArticle

Neural Network IDS/IPS Intrusion Detection and Prevention System with Adaptive Online Training to Improve Corporate Network Cybersecurity, Evidence Recording, and Interaction with Law Enforcement Agencies

by Serhii Vladov, Victoria Vysotska, Svitlana Vashchenko, Serhii Bolvinov, Serhii Glubochenko, Andrii Repchonok, Maksym Korniienko, Mariia Nazarkevych and Ruslan Herasymchuk

Big Data Cogn. Comput. 2025, 9(11), 267; https://doi.org/10.3390/bdcc9110267 - 22 Oct 2025

Thise article examines the reliable online detection and IDS/IPS intrusion prevention in dynamic corporate networks problems, where traditional signature-based methods fail to keep pace with new and evolving attacks, and streaming data is susceptible to drift and targeted “poisoning” of the training dataset. [...] Read more.

Thise article examines the reliable online detection and IDS/IPS intrusion prevention in dynamic corporate networks problems, where traditional signature-based methods fail to keep pace with new and evolving attacks, and streaming data is susceptible to drift and targeted “poisoning” of the training dataset. As a solution, we propose a hybrid neural network system with adaptive online training, a formal minimax false-positive control framework, and a robustness mechanism set (a Huber model, pruned learning rate, DRO, a gradient-norm regularizer, and a prioritized replay). In practice, the system combines modal encoders for traffic, logs, and metrics; a temporal GNN for entity correlation; a variational module for uncertainty assessment; a differentiable symbolic unit for logical rules; an RL agent for incident prioritization; and an NLG module for explanations and the preparation of forensically relevant artifacts. In this case, the applied components are connected via a cognitive layer (cross-modal fusion memory), a Bayesian-neural network fuser, and a single multi-task loss function. The practical implementation includes the pipeline “novelty detection → active labelling → incremental supervised update” and chain-of-custody mechanisms for evidential fitness. A significant improvement in quality has been experimentally demonstrated, since the developed system achieves an ROC AUC of 0.96, an F1-score of 0.95, and a significantly lower FPR compared to basic architectures (MLP, CNN, and LSTM). In applied validation tasks, detection rates of ≈92–94% and resistance to distribution drift are noted. Full article

(This article belongs to the Special Issue Internet Intelligence for Cybersecurity)

► Show Figures

Figure 1

20 pages, 1598 KB

Open AccessArticle

Importance of Data Preprocessing for Accurate and Effective Prediction of Breast Cancer: Evaluation of Model Performance in Novel Data

by Vekani Baloyi, Jamolbek Mattiev and Sello Mokwena

Big Data Cogn. Comput. 2025, 9(10), 266; https://doi.org/10.3390/bdcc9100266 - 21 Oct 2025

Breast cancer is one of the leading causes of mortality among women globally, and an early and accurate diagnosis is essential for effective treatment and improved survival rates. Traditional diagnostic techniques often struggle to differentiate between benign and malignant tumors due to overlapping [...] Read more.

Breast cancer is one of the leading causes of mortality among women globally, and an early and accurate diagnosis is essential for effective treatment and improved survival rates. Traditional diagnostic techniques often struggle to differentiate between benign and malignant tumors due to overlapping visual characteristics, resulting in false positives or delayed detection. For efficient breast cancer detection with machine learning, it is vital to identify the most significant features because those features play the most important roles in the treatment process. This study addresses this challenge by evaluating and comparing the performance of ten state-of-the-art machine learning classifiers for breast cancer detection using image-derived features. Initially, 30 features were extracted from a novel tertiary hospital dataset, and models were evaluated based on accuracy, precision, recall, and F-measure. To enhance model performance and reduce dimensionality, the Correlation-based Feature Selection (CFS) method was applied, leading to the identification of 11 highly informative features. Our experimental results demonstrate that, while models such as SVM and Logistic Regression achieved the highest accuracy (97.7%) on the full feature set, the Neural Network exhibited a superior performance (97.2%) on the reduced feature set, with a substantial reduction in training time. Most classifiers maintained comparable or improved accuracy with fewer features, indicating effective dimensionality reduction. Furthermore, pairwise statistical significance testing confirmed that ensemble and kernel-based classifiers achieved a statistically superior performance over simpler models. These findings highlight the importance of effective feature selection in developing accurate, efficient, and scalable breast cancer prediction systems. Full article

► Show Figures

Figure 1

14 pages, 586 KB

Open AccessArticle

Complex Table Question Answering with Multiple Cells Recall Based on Extended Cell Semantic Matching

by Hainan Chen and Dongqi Shen

Big Data Cogn. Comput. 2025, 9(10), 265; https://doi.org/10.3390/bdcc9100265 - 20 Oct 2025

Tables, as a form of structured or semi-structured data, are widely found in documents, reports, and data manuals. Table-based question answering (TableQA) plays a key role in table document analysis and understanding. Existing approaches to TableQA can be broadly categorized into content-matching methods [...] Read more.

Tables, as a form of structured or semi-structured data, are widely found in documents, reports, and data manuals. Table-based question answering (TableQA) plays a key role in table document analysis and understanding. Existing approaches to TableQA can be broadly categorized into content-matching methods and end-to-end generation methods based on encoder–decoder deep neural networks. Content-matching methods return one or more table cells as answers, thereby preserving the original data and making them more suitable for downstream tasks. End-to-end methods, especially those leveraging large language models (LLMs), have achieved strong performance on various benchmarks. However, the variability in LLM-generated expressions and their heavy reliance on prompt engineering limit their applicability where answer fidelity to the source table is critical. In this work, we propose CBCM (Cell-by-Cell semantic Matching), a fine-grained cell-level matching method that extends the traditional row- and column-matching paradigm to improve accuracy and applicability in TableQA. Furthermore, based on the public IM-TQA dataset, we construct a new benchmark, IM-TQA-X, specifically designed for the multi-row and multi-column cell recall task, a scenario underexplored in existing state-of-the-art content-matching methods. Experimental results show that CBCM improves overall accuracy by 2.5% over the latest row- and column-matching method RGCNRCI (Relational Graph Convolutional Networks based Row and Column Intersection), and boosts accuracy in the multi-row and multi-column recall task from 4.3% to 34%. Full article

► Show Figures

Figure 1

35 pages, 1642 KB

Open AccessArticle

Adopting Generative AI in Higher Education: A Dual-Perspective Study of Students and Lecturers in Saudi Universities

by Doaa M. Bamasoud, Rasheed Mohammad and Sara Bilal

Big Data Cogn. Comput. 2025, 9(10), 264; https://doi.org/10.3390/bdcc9100264 - 18 Oct 2025

The integration of Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, into higher education has introduced new opportunities and challenges for students and lecturers alike. This study investigates the psychological, ethical, and institutional factors that shape the adoption of GenAI tools in Saudi [...] Read more.

The integration of Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, into higher education has introduced new opportunities and challenges for students and lecturers alike. This study investigates the psychological, ethical, and institutional factors that shape the adoption of GenAI tools in Saudi Arabian universities, drawing on an extended Technology Acceptance Model (TAM) that incorporates constructs from Self-Determination Theory (SDT) and ethical decision-making. A cross-sectional survey was administered to 578 undergraduate students and 309 university lecturers across three major institutions in Southern Saudi Arabia. Quantitative analysis using Structural Equation Modelling (SmartPLS 4) revealed that perceived usefulness, intrinsic motivation, and ethical trust significantly predicted students’ intention to use GenAI. Perceived ease of use influenced intention both directly and indirectly through usefulness, while institutional support positively shaped perceptions of GenAI’s value. Academic integrity and trust-related concerns emerged as key mediators of motivation, highlighting the ethical tensions in AI-assisted learning. Lecturer data revealed a parallel set of concerns, including fear of overreliance, diminished student effort, and erosion of assessment credibility. Although many faculty members had adapted their assessments in response to GenAI, institutional guidance was often perceived as lacking. Overall, the study offers a validated, context-sensitive model for understanding GenAI adoption in education and emphasises the importance of ethical frameworks, motivation-building, and institutional readiness. These findings offer actionable insights for policy-makers, curriculum designers, and academic leaders seeking to responsibly integrate GenAI into teaching and learning environments. Full article

► Show Figures

Figure 1

27 pages, 3065 KB

Open AccessArticle

Chinese Financial News Analysis for Sentiment and Stock Prediction: A Comparative Framework with Language Models

by Hsiu-Min Chuang, Hsiang-Chih He and Ming-Che Hu

Big Data Cogn. Comput. 2025, 9(10), 263; https://doi.org/10.3390/bdcc9100263 - 16 Oct 2025

Financial news has a significant impact on investor sentiment and short-term stock price trends. While many studies have applied natural language processing (NLP) techniques to financial forecasting, most have focused on single tasks or English corpora, with limited research in non-English language contexts [...] Read more.

Financial news has a significant impact on investor sentiment and short-term stock price trends. While many studies have applied natural language processing (NLP) techniques to financial forecasting, most have focused on single tasks or English corpora, with limited research in non-English language contexts such as Taiwan. This study develops a joint framework to perform sentiment classification and short-term stock price prediction using Chinese financial news from Taiwan’s top 50 listed companies. Five types of word embeddings—one-hot, TF-IDF, CBOW, skip-gram, and BERT—are systematically compared across 17 traditional, deep, and Transformer models, as well as a large language model (LLaMA3) fully fine-tuned on the Chinese financial texts. To ensure annotation quality, sentiment labels were manually assigned by annotators with finance backgrounds and validated through a double-checking process. Experimental results show that a CNN using skip-gram embeddings achieves the strongest performance among deep learning models, while LLaMA3 yields the highest overall F1-score for sentiment classification. For regression, LSTM consistently provides the most reliable predictive power across different volatility groups, with Bayesian Linear Regression remaining competitive for low-volatility firms. LLaMA3 is the only Transformer-based model to achieve a positive

R^{2}

under high-volatility conditions. Furthermore, forecasting accuracy is higher for the five-day horizon than for the fifteen-day horizon, underscoring the increasing difficulty of medium-term forecasting. These findings confirm that financial news provides valuable predictive signals for emerging markets and that short-term sentiment-informed forecasts enhance real-time investment decisions. Full article

(This article belongs to the Special Issue Natural Language Processing Applications in Big Data)

► Show Figures

Figure 1

25 pages, 1360 KB

Open AccessArticle

Source Robust Non-Parametric Reconstruction of Epidemic-like Event-Based Network Diffusion Processes Under Online Data

by Jiajia Xie, Chen Lin, Xinyu Guo and Cassie S. Mitchell

Big Data Cogn. Comput. 2025, 9(10), 262; https://doi.org/10.3390/bdcc9100262 - 16 Oct 2025

Temporal network diffusion models play a crucial role in healthcare, information technology, and machine learning, enabling the analysis of dynamic event-based processes such as disease spread, information propagation, and behavioral diffusion. This study addresses the challenge of reconstructing temporal network diffusion events in [...] Read more.

Temporal network diffusion models play a crucial role in healthcare, information technology, and machine learning, enabling the analysis of dynamic event-based processes such as disease spread, information propagation, and behavioral diffusion. This study addresses the challenge of reconstructing temporal network diffusion events in real time under conditions of missing and evolving data. A novel non-parametric reconstruction method by simple weights differentiationis proposed to enhance source detection robustness with provable improved error bounds. The approach introduces adaptive cost adjustments, dynamically reducing high-risk source penalties and enabling bounded detours to mitigate errors introduced by missing edges. Theoretical analysis establishes enhanced upper bounds on false positives caused by detouring, while a stepwise evaluation of dynamic costs minimizes redundant solutions, resulting in robust Steiner tree reconstructions. Empirical validation on three real-world datasets demonstrates a 5% improvement in Matthews correlation coefficient (MCC), a twofold reduction in redundant sources, and a 50% decrease in source variance. These results confirm the effectiveness of the proposed method in accurately reconstructing temporal network diffusion while improving stability and reliability in both offline and online settings. Full article

(This article belongs to the Special Issue Advances in Graph Learning and Representation Models for Complex Network Analysis)

► Show Figures

Figure 1

24 pages, 13667 KB

Open AccessArticle

Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems

by Marvin Niederhaus, Nico Migenda, Julian Weller, Martin Kohlhase and Wolfram Schenck

Big Data Cogn. Comput. 2025, 9(10), 261; https://doi.org/10.3390/bdcc9100261 - 15 Oct 2025

Making time-critical decisions with serious consequences is a daily aspect of work environments. To support the process of finding optimal actions, data-driven approaches are increasingly being used. The most advanced form of data-driven analytics is prescriptive analytics, which prescribes actionable recommendations for users. [...] Read more.

Making time-critical decisions with serious consequences is a daily aspect of work environments. To support the process of finding optimal actions, data-driven approaches are increasingly being used. The most advanced form of data-driven analytics is prescriptive analytics, which prescribes actionable recommendations for users. However, the produced recommendations rely on complex models and optimization techniques that are difficult to understand or justify to non-expert users. Currently, there is a lack of platforms that offer easy integration of domain-specific prescriptive analytics workflows into production environments. In particular, there is no centralized environment and standardized approach for implementing such prescriptive workflows. To address these challenges, large language models (LLMs) can be leveraged to improve interpretability by translating complex recommendations into clear, context-specific explanations, enabling non-experts to grasp the rationale behind the suggested actions. Nevertheless, we acknowledge the inherent black-box nature of LLMs, which may introduce limitations in transparency. To mitigate these limitations and to provide interpretable recommendations based on real user knowledge, a knowledge graph is integrated. In this paper, we present and validate a prescriptive analytics platform that integrates ontology-based graph retrieval-augmented generation (GraphRAG) to enhance decision making by delivering actionable and context-aware recommendations. For this purpose, a knowledge graph is created through a fully automated workflow based on an ontology, which serves as the backbone of the prescriptive platform. Data sources for the knowledge graph are standardized and classified according to the ontology by employing a zero-shot classifier. For user-friendly presentation, we critically examine the usability of GraphRAG in prescriptive analytics platforms. We validate our prescriptive platform in a customer clinic with industry experts in our IoT-Factory, a dedicated research environment. Full article

► Show Figures

Figure 1

15 pages, 296 KB

Open AccessArticle

Cognitive Computing Frameworks for Scalable Deception Detection in Textual Data

by Faiza Belbachir

Big Data Cogn. Comput. 2025, 9(10), 260; https://doi.org/10.3390/bdcc9100260 - 14 Oct 2025

Detecting deception in emotionally grounded natural language remains a significant challenge due to the subtlety and context dependence of deceptive intent. In this work, we use a structured behavioral dataset in which participants produce truthful and deceptive statements under emotional and social constraints. [...] Read more.

Detecting deception in emotionally grounded natural language remains a significant challenge due to the subtlety and context dependence of deceptive intent. In this work, we use a structured behavioral dataset in which participants produce truthful and deceptive statements under emotional and social constraints. To maintain label accuracy and semantic consistency, we propose a multilayer validation pipeline combining selfconsistency prompting with feedback-guided revision, implemented through the CoTAM (Chain-of-Thought Assisted Modification) method. Our results demonstrate that this framework enhances deception detection by leveraging a sentence decomposition strategy that highlights subtle emotional and strategic cues, improving interpretability for both models and human annotators. Full article

► Show Figures

Figure 1

38 pages, 913 KB

Open AccessArticle

Towards the Adoption of Recommender Systems in Online Education: A Framework and Implementation

by Alex Martínez-Martínez, Águeda Gómez-Cambronero, Raul Montoliu and Inmaculada Remolar

Big Data Cogn. Comput. 2025, 9(10), 259; https://doi.org/10.3390/bdcc9100259 - 14 Oct 2025

The rapid expansion of online education has generated large volumes of learner interaction data, highlighting the need for intelligent systems capable of transforming this information into personalized guidance. Educational Recommender Systems (ERS) represent a key application of big data analytics and machine learning, [...] Read more.

The rapid expansion of online education has generated large volumes of learner interaction data, highlighting the need for intelligent systems capable of transforming this information into personalized guidance. Educational Recommender Systems (ERS) represent a key application of big data analytics and machine learning, offering adaptive learning pathways that respond to diverse student needs. For widespread adoption, these systems must align with pedagogical principles while ensuring transparency, interpretability, and seamless integration into Learning Management Systems (LMS). This paper introduces a comprehensive framework and implementation of an ERS designed for platforms such as Moodle. The system integrates big data processing pipelines to support scalability, real-time interaction, and multi-layered personalization, including data collection, preprocessing, recommendation generation, and retrieval. A detailed use case demonstrates its deployment in a real educational environment, underlining both technical feasibility and pedagogical value. Finally, the paper discusses challenges such as data sparsity, learner model complexity, and evaluation of effectiveness, offering directions for future research at the intersection of big data technologies and digital education. By bridging theoretical models with operational platforms, this work contributes to sustainable and data-driven personalization in online learning ecosystems. Full article

► Show Figures

Figure 1

36 pages, 2906 KB

Open AccessReview

Data Organisation for Efficient Pattern Retrieval: Indexing, Storage, and Access Structures

by Paraskevas Koukaras and Christos Tjortjis

Big Data Cogn. Comput. 2025, 9(10), 258; https://doi.org/10.3390/bdcc9100258 - 13 Oct 2025

The increasing scale and complexity of data mining outputs, such as frequent itemsets, association rules, sequences, and subgraphs have made efficient pattern retrieval a critical, yet underexplored challenge. This review addresses the organisation, indexing, and access strategies, which enable scalable and responsive retrieval [...] Read more.

The increasing scale and complexity of data mining outputs, such as frequent itemsets, association rules, sequences, and subgraphs have made efficient pattern retrieval a critical, yet underexplored challenge. This review addresses the organisation, indexing, and access strategies, which enable scalable and responsive retrieval of structured patterns. We examine the underlying types of data and pattern outputs, common retrieval operations, and the variety of query types encountered in practice. Key indexing structures are surveyed, including prefix trees, inverted indices, hash-based approaches, and bitmap-based methods, each suited to different pattern representations and workloads. Storage designs are discussed with attention to metadata annotation, format choices, and redundancy mitigation. Query optimisation strategies are reviewed, emphasising index-aware traversal, caching, and ranking mechanisms. This paper also explores scalability through parallel, distributed, and streaming architectures, and surveys current systems and tools, which integrate mining and retrieval capabilities. Finally, we outline pressing challenges and emerging directions, such as supporting real-time and uncertainty-aware retrieval, and enabling semantic, cross-domain pattern access. Additional frontiers include privacy-preserving indexing and secure query execution, along with integration of repositories into machine learning pipelines for hybrid symbolic–statistical workflows. We further highlight the need for dynamic repositories, probabilistic semantics, and community benchmarks to ensure that progress is measurable and reproducible across domains. This review provides a comprehensive foundation for designing next-generation pattern retrieval systems, which are scalable, flexible, and tightly integrated into analytic workflows. The analysis and roadmap offered are relevant across application areas including finance, healthcare, cybersecurity, and retail, where robust and interpretable retrieval is essential. Full article

► Show Figures

Figure 1

24 pages, 1550 KB

Open AccessArticle

Tester-Guided Graph Learning with End-to-End Detection Certificates for Triangle-Based Anomalies

by Manuel J. C. S. Reis

Big Data Cogn. Comput. 2025, 9(10), 257; https://doi.org/10.3390/bdcc9100257 - 12 Oct 2025

We investigate anomaly detection in complex networks through a property-testing-guided graph neural model (PT-GNN) that provides an end-to-end miss-probability certificate

(δ + α)

. The method combines (i) a wedge-sampling tester that estimates triangle-closure frequency and derives a concentration bound [...] Read more.

We investigate anomaly detection in complex networks through a property-testing-guided graph neural model (PT-GNN) that provides an end-to-end miss-probability certificate

(δ + α)

. The method combines (i) a wedge-sampling tester that estimates triangle-closure frequency and derives a concentration bound

(δ)

via Bernstein’s inequality, with (ii) a lightweight classifier over structural features whose validation error contributes

(α)

. The overall certificate is given by the sum

(δ + α)

, quantifying the probability of missed anomalies under bounded sampling. On synthetic communication graphs with n = 1000, edge probability p = 0.01, and anomalous subgraph size k = 120, PT-GNN achieves perfect detection performance (AUC = 1.0, F1 = 1.0) across all tested regimes. Moreover, the miss-probability certificate tightens systematically as the tester budget m increases (e.g., for

ε

= 0.06, enlarging m from 2000 to 8000 reduces

(δ + α)

from ≈0.87 to ≈0.49). These results demonstrate that PT-GNN effectively couples graph learning with property testing, offering both strong empirical detection and formally verifiable guarantees in anomaly detection tasks. Full article

(This article belongs to the Special Issue Advances in Graph Learning and Representation Models for Complex Network Analysis)

► Show Figures

Figure 1

More Articles...

Submit to BDCC Review for BDCC

Journal Menu

Journal Browser

► Journal Browser

Highly Accessed Articles

View More...

Latest Books

More Books and Reprints...

E-Mail Alert

News

1 October 2025
2024 MDPI Top 1000 Reviewers

15 October 2025
MDPI’s Newly Launched Journals in September 2025

15 October 2025
Global Media and Information Literacy Week—“Minds over AI - MIL in Digital Spaces”, 24–31 October 2025

More News & Announcements...

Topics

Propose a Topic

Topic in Actuators, Algorithms, BDCC, Future Internet, JMMP, Machines, Robotics, Systems

Smart Product Design and Manufacturing on Industrial Internet Topic Editors: Pingyu Jiang, Jihong Liu, Ying Liu, Jihong Yan
Deadline: 31 December 2025

Topic in Computers, Information, AI, Electronics, Technologies, BDCC

Graph Neural Networks and Learning Systems Topic Editors: Huijia Li, Jun Hu, Weichen Zhao, Jie Cao
Deadline: 31 January 2026

Topic in AI, BDCC, Fire, GeoHazards, Remote Sensing

AI for Natural Disasters Detection, Prediction and Modeling Topic Editors: Moulay A. Akhloufi, Mozhdeh Shahbazi
Deadline: 31 March 2026

Topic in Applied Sciences, Electronics, J. Imaging, MAKE, Information, BDCC, Signals

Applications of Image and Video Processing in Medical Imaging Topic Editors: Jyh-Cheng Chen, Kuangyu Shi
Deadline: 30 April 2026

More Topics

Conferences

Propose a Conference Collaboration

2–7 August 2026 The 3rd International Conference on AI Sensors and Transducers

25–27 November 2025 First International Conference On Big Data Analytics & Applications

18–20 December 2025 The 5th International Conference on Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC 2025)

More Conferences...

Special Issues

Propose a Special Issue

Special Issue in BDCC

Artificial Intelligence in Digital Humanities Guest Editor: George Pavlidis
Deadline: 18 November 2025

Special Issue in BDCC

Generative AI and Large Language Models Guest Editors: Fabrizio Marozzo, Riccardo Cantini
Deadline: 30 November 2025

Special Issue in BDCC

Knowledge Graphs in the Big Data Era: Navigating the Confluence of Distribution, Visualization, and Advanced Computational Models Guest Editors: Amna Dridi, Edlira Kalemi Vakaj, Mohamed Medhat Gaber
Deadline: 30 November 2025

Special Issue in BDCC

Applied Data Science for Social Good: 2nd Edition Guest Editors: Vishnu S. Pendyala, Celestine Iwendi
Deadline: 15 December 2025

More Special Issues

Back to TopTop