Big Data and Cognitive Computing

32 pages, 722 KB

Open AccessArticle

Adaptive Sensitivity-Aware Differential Privacy Accounting for Federated Smart-Meter Theft Detection

by Diego Labate, Dipanwita Thakur and Giancarlo Fortino

Big Data Cogn. Comput. 2026, 10(4), 113; https://doi.org/10.3390/bdcc10040113 - 8 Apr 2026

Smart-meter theft detection requires learning from fine-grained electricity consumption data, whose centralized processing poses significant privacy risks. Federated learning (FL) mitigates these risks by decentralizing training, but providing rigorous user-level differential privacy (DP) under non-IID data and heterogeneous client behavior remains challenging. Existing [...] Read more.

Smart-meter theft detection requires learning from fine-grained electricity consumption data, whose centralized processing poses significant privacy risks. Federated learning (FL) mitigates these risks by decentralizing training, but providing rigorous user-level differential privacy (DP) under non-IID data and heterogeneous client behavior remains challenging. Existing DP-FL approaches rely on fixed global clipping bounds for client updates, which substantially overestimate sensitivity when privacy loss is composed using Rényi Differential Privacy (RDP), zero-Concentrated DP (zCDP), or Moments Accountant (MA) frameworks, leading to excessive noise and degraded utility. This work proposes an adaptive clipping-based RDP accountant that incorporates empirical, round-wise update magnitudes into privacy accounting by rescaling each round’s RDP contribution according to the observed clipping ratio. The method is optimizer-agnostic and is evaluated with FedAvg, FedProx, and SCAFFOLD on the SGCC smart-meter theft dataset under IID and Dirichlet non-IID partitions. Experimental results show consistently tighter privacy bounds and improved model utility compared to classical DP accountants, demonstrating the effectiveness of sensitivity-aware privacy accounting for practical differentially private FL. Full article

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31 pages, 4926 KB

Open AccessArticle

Interpretable Optimized Extreme Gradient Boosting for Prediction of Higher Heating Value from Elemental Composition of Coal Resource to Energy Conversion

by Paulino José García-Nieto, Esperanza García-Gonzalo, José Pablo Paredes-Sánchez and Luis Alfonso Menéndez-García

Big Data Cogn. Comput. 2026, 10(4), 112; https://doi.org/10.3390/bdcc10040112 - 7 Apr 2026

Abstract

The higher heating value (HHV), sometimes referred to as the gross calorific value, is a crucial metric for determining a fuel’s primary energy potential in energy production systems. By combining extreme gradient boosting (XGBoost) with the differential evolution (DE) optimizer, an innovative machine [...] Read more.

The higher heating value (HHV), sometimes referred to as the gross calorific value, is a crucial metric for determining a fuel’s primary energy potential in energy production systems. By combining extreme gradient boosting (XGBoost) with the differential evolution (DE) optimizer, an innovative machine learning-based model was created in this study to forecast the HHV (dependent variable). As input variables, the model included the constituents of the coal’s ultimate analysis: carbon (C), oxygen (O), hydrogen (H), nitrogen (N), and sulfur (S). For comparative purposes, random forest regression (RFR), M5 model tree, multivariate linear regression (MLR), and previously reported empirical correlations were also applied to the experimental dataset. The results showed that the XGBoost strategy produced the most accurate predictions. An initial XGBoost analysis was carried out to identify the relative contribution of the input variables to coal HHV prediction. In particular, for coal HHV estimates reliant on experimental samples, the XGBoost regression produced a correlation coefficient of 0.9858 and a coefficient of determination of 0.9691. The excellent agreement between observed and anticipated values shows that the DE/XGBoost-based approximation performed satisfactorily. Lastly, a synopsis of the investigation’s key conclusions is provided. Full article

(This article belongs to the Special Issue Smart Manufacturing in the AI Era)

27 pages, 390 KB

Open AccessArticle

A Comparative Study of Federated Learning and Amino Acid Encoding with IoT Malware Detection as a Case Study

by Thaer AL Ibaisi, Stefan Kuhn, Muhammad Kazim, Ismail Kara, Turgay Altindag and Mujeeb Ur Rehman

Big Data Cogn. Comput. 2026, 10(4), 111; https://doi.org/10.3390/bdcc10040111 - 6 Apr 2026

Abstract

The increasing deployment of Internet of Things (IoT) devices introduces significant security challenges, while privacy concerns limit centralized data aggregation for intrusion detection. Federated learning (FL) offers a decentralized alternative, yet the interaction between feature representation, model architecture, and data heterogeneity remains insufficiently [...] Read more.

The increasing deployment of Internet of Things (IoT) devices introduces significant security challenges, while privacy concerns limit centralized data aggregation for intrusion detection. Federated learning (FL) offers a decentralized alternative, yet the interaction between feature representation, model architecture, and data heterogeneity remains insufficiently understood in IoT malware detection. This study provides a controlled comparative analysis of centralized and federated learning, optionally using amino acid encoding, under IID and Non-IID conditions using a 10,000-sample subset of the CTU–IoT–Malware–Capture dataset. First, we evaluate raw tabular features versus amino acid-based feature encoding, followed by a lightweight multi-layer perceptron (2882 parameters) versus a deeper residual network (70,532 parameters), across binary and multi-class classification tasks. In the binary setting, centralized training achieved up to 98.6% accuracy, while federated IID training reached 98.6%, with differences within statistical variance. Under Non-IID conditions, performance decreased modestly (0.1–0.5 percentage points), and accuracy was consistently lower when using encoded features compared with raw features. The degradation is smaller in deeper architectures and may offer improved stability under highly skewed federated conditions. In the four-class setting, the complex network achieved up to 97.8% accuracy with raw features, while amino acid encoding achieves up to 93.3%. The results show that federated learning can achieve performance comparable to centralized training under moderate heterogeneity, that lightweight architectures are sufficient for low-dimensional IoT traffic features, and that feature compression via amino acid encoding does not inherently mitigate Non-IID effects. These findings clarify the relative impact of representation, heterogeneity, and architectural capacity in practical FL-based IoT intrusion detection systems. Full article

(This article belongs to the Special Issue Application of Cloud Computing in Industrial Internet of Things)

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37 pages, 1919 KB

Open AccessArticle

LLMs for Integrated Business Intelligence: A Big Data-Driven Framework Integrating Marketing Optimization, Financial Performance, and Audit Quality

by Leonidas Theodorakopoulos, Aristeidis Karras, Alexandra Theodoropoulou and Christos Klavdianos

Big Data Cogn. Comput. 2026, 10(4), 110; https://doi.org/10.3390/bdcc10040110 - 5 Apr 2026

Abstract

Enterprise decision making in marketing, finance, and audit remains fragmented, leading to inefficient budget allocation and incomplete risk assessment. This study proposes an integrated, Big Data-driven decision-support framework that unifies Large Language Models (LLMs), attention-based marketing mix modeling, and multi-agent, game-theoretic optimization to [...] Read more.

Enterprise decision making in marketing, finance, and audit remains fragmented, leading to inefficient budget allocation and incomplete risk assessment. This study proposes an integrated, Big Data-driven decision-support framework that unifies Large Language Models (LLMs), attention-based marketing mix modeling, and multi-agent, game-theoretic optimization to coordinate cross-functional decisions. The architecture combines five modules: LLM-enhanced customer segmentation and customer lifetime value prediction, attention-weighted marketing mix modeling, multi-agent LLM systems for hierarchical budget optimization, attention-informed Markov multi-touch attribution, and LLM-augmented audit quality assessment. Empirical validation on a large-scale e-commerce dataset with 2.8 million customers and USD 156 million in marketing expenditure shows that marketing return on investment increases from 4.2 to 6.78 (61.4% relative improvement), financial forecasting error (MAPE) decreases from 12.8% to 4.7% (63.3% reduction), fraud detection accuracy improves by 29.8%, the Audit Quality Index reaches 0.951, and customer lifetime value prediction accuracy improves from 76.4% to 91.3%. By operationalizing the convergence of LLMs, attention mechanisms, and game-theoretic reasoning within a unified and empirically validated framework, the study delivers both theoretical advances and practically deployable tools for integrated business intelligence in digital economies. Full article

(This article belongs to the Section Large Language Models and Embodied Intelligence)

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24 pages, 2712 KB

Open AccessArticle

Stock Market Forecasting in Taiwan: A Radius Neighbors Regressor Approach

by Yu-Kai Huang, Chih-Hung Chen, Yun-Cheng Tsai and Shun-Shii Lin

Big Data Cogn. Comput. 2026, 10(4), 109; https://doi.org/10.3390/bdcc10040109 - 4 Apr 2026

Abstract

This study proposes a machine learning framework tailored to the institutional characteristics of Taiwan’s stock market, aiming to enhance forecasting accuracy for the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The model employs the Radius Neighbors Regressor with a dynamic radius-based similarity [...] Read more.

This study proposes a machine learning framework tailored to the institutional characteristics of Taiwan’s stock market, aiming to enhance forecasting accuracy for the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The model employs the Radius Neighbors Regressor with a dynamic radius-based similarity measure and integrates domain-specific features including technical indicators, volume–price relationships, and Qualified Foreign Institutional Investor (QFII) activity. A custom 60-day input window with a 20-day forecast horizon is applied to capture medium-term market dynamics. The framework was evaluated through extensive backtesting and real-world validation with the TAIEX Futures. The results demonstrate that the model achieves a peak directional accuracy of 85.1% under optimal parameter settings. Moreover, trading simulations confirm its practical viability, yielding a cumulative return on investment (ROI) of approximately 1600% during the short-term evaluation period (2023–2025) and nearly 2000% in the long-term evaluation (2019–2025), even after accounting for transaction costs and stop-loss mechanisms. These findings indicate that combining historical pattern similarity with institutional investor behavior substantially improves predictive power and profitability. Nevertheless, the framework remains constrained by its reliance on Taiwan-specific institutional features and historical trading data, limiting generalizability. Future research should extend applications to other markets while incorporating macroeconomic variables, corporate fundamentals, and news-driven signals to enhance adaptability. Full article

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16 pages, 589 KB

Open AccessArticle

Exploring the Mechanisms Influencing Graduate Students’ Adoption of Generative AI: Insights from the Technology Acceptance Model

by Qing Chen, Yujie Xue, Jie Lin and Chang Zhu

Big Data Cogn. Comput. 2026, 10(4), 108; https://doi.org/10.3390/bdcc10040108 - 3 Apr 2026

Abstract

The rapid development of Generative Artificial Intelligence (GenAI) in graduate education has changed human–AI interaction within knowledge-intensive environments, leading to important questions about user-side cognitive adaptation in probabilistic AI systems. While many studies focus on ethical implications, limited attention has been paid to [...] Read more.

The rapid development of Generative Artificial Intelligence (GenAI) in graduate education has changed human–AI interaction within knowledge-intensive environments, leading to important questions about user-side cognitive adaptation in probabilistic AI systems. While many studies focus on ethical implications, limited attention has been paid to the cognitive mechanisms underlying graduate students’ adoption of GenAI. Drawing on the Technology Acceptance Model (TAM), this study explores the cognitive and interactional mechanisms shaping graduate students’ adoption and usage of GenAI. Using thematic analysis of in-depth interviews with 20 graduate students from diverse academic backgrounds, the study identifies seven interrelated constructs: perceived usefulness, perceived ease of use, external environment, risk perception, attitude, behavioral intention, and interaction subjectivity. This study demonstrates that the adoption of GenAI is not merely a result of perceived efficiency but is shaped by cognitive calibration between trust and risk evaluation. Moreover, interaction subjectivity emerges as a metacognitive factor that determines whether engagement results in human–AI collaboration or passive automation. By integrating external environment, risk perception, and interaction subjectivity, this study provides a cognitively grounded framework for understanding human–AI adoption and interaction dynamics. Practically, the findings provide design-relevant insights for developing GenAI systems that support calibrated trust, uncertainty awareness, and adaptive cognitive participation. Full article

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18 pages, 2329 KB

Open AccessArticle

Multi-Scale Optimal Transport Transformer for Efficient Exemplar-Based Image Translation

by Jinsong Zhang, Xiongzheng Li and Yuqin Lin

Big Data Cogn. Comput. 2026, 10(4), 107; https://doi.org/10.3390/bdcc10040107 - 1 Apr 2026

Abstract

Exemplar-based image translation generates an output image by transferring appearance from a reference exemplar to a content image. Existing works only consider the local correspondences between two modalities, and ignore the global distributions in each modality, struggling to obtain fine-grained details with efficient [...] Read more.

Exemplar-based image translation generates an output image by transferring appearance from a reference exemplar to a content image. Existing works only consider the local correspondences between two modalities, and ignore the global distributions in each modality, struggling to obtain fine-grained details with efficient computation. In this paper, we propose OTFormer, a multi-scale Optimal Transport transformer for exemplarbased image translation. We formulate cross-modal alignment as a multi-scale optimal transport problem, which progressively provides a globally coherent matching. In addition, we design a lightweight multi-scale fusion block to extract and fuse features efficiently. Experiments on CelebA-HQ and DeepFashion demonstrate that OTFormer improves both image fidelity and style adherence, while reducing model parameters by 62% and achieving faster inference compared with strong baselines. These results highlight OTguided global alignment as an effective and deployable solution for high-fidelity exemplarbased image translation. Full article

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

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13 pages, 1956 KB

Open AccessArticle

Multi-Modal Method for Candidate Interview Assessment Based on Computer Vision and Large Language Models

by Kenan Kassab, Alexey Kashevnik and Irina Shoshina

Big Data Cogn. Comput. 2026, 10(4), 106; https://doi.org/10.3390/bdcc10040106 - 1 Apr 2026

Abstract

Candidate interview assessment is primarily reliant on subjective human judgment, while existing AI-based methods rely on end-to-end predictions with no psychometric basis. In this paper, we propose an interpretable multi-modal framework that combines nonverbal behavior, LLM-based verbal analysis, and Big Five personality traits [...] Read more.

Candidate interview assessment is primarily reliant on subjective human judgment, while existing AI-based methods rely on end-to-end predictions with no psychometric basis. In this paper, we propose an interpretable multi-modal framework that combines nonverbal behavior, LLM-based verbal analysis, and Big Five personality traits into three theory-based constructs: professional-cognitive competence, observed leadership behavior, and leadership disposition. The proposed method utilizes computer vision and larger language models to extract features from video interviews. Rather than targeting predictive accuracy, the proposed method prioritizes construct validity and transparent aggregation under severe label scarcity. The proposed method aggregates the constructs into a Top Potential Score that reflects the executive abilities of the candidate. Experiments on the method show its ability to significantly differentiate top candidates from others (Cliff’s delta = 0.91 for the composite Top Potential Score, permutation p = 0.0002). Leave-one-out analysis verifies robustness, while rank-based evaluation yields 100% recall of executive candidates in the top 20% of rated applications. The findings justify the use of the proposed multi-modal method as an interpretable decision-support tool for candidate interview assessment. Full article

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13 pages, 756 KB

Open AccessArticle

H²Avatar: Expressive Whole-Body Avatars from Monocular Video via Hierarchical Geometry and Hybrid Rendering

by Jinsong Zhang, Cheng Guan, Zhihua Lin and Yuqin Lin

Big Data Cogn. Comput. 2026, 10(4), 105; https://doi.org/10.3390/bdcc10040105 - 1 Apr 2026

Abstract

Reconstructing photorealistic and animatable whole-body avatars from monocular videos is a hot topic in computer vision and computer graphics. However, existing methods still face challenges due to the limited frequency response of single-scale geometry encodings and the instability of appearance modeling without an [...] Read more.

Reconstructing photorealistic and animatable whole-body avatars from monocular videos is a hot topic in computer vision and computer graphics. However, existing methods still face challenges due to the limited frequency response of single-scale geometry encodings and the instability of appearance modeling without an explicit surface anchor. In this paper, we present H²Avatar, a real-time framework that builds on a mesh-embedded 3D Gaussian representation guided by SMPL-X and disentangles geometry and appearance into hierarchical and hybrid components. For geometry, we propose a semantic-aware hierarchical encoding based on a multi-scale tri-plane pyramid, where features at different resolutions capture both global structure and high-frequency surface details such as clothing wrinkles. For appearance, we introduce a hybrid rendering strategy that anchors canonical colors using a learnable UV texture map, and complements it with a neural residual color branch conditioned on tri-plane features, pose embedding, and surface normals to model pose- and view-dependent shading variations. This design improves temporal stability and preserves identity details while enhancing photorealism under complex motions. Experiments on the NeuMan dataset demonstrate that H²Avatar consistently outperforms representative baselines across multiple sequences, outperforming ExAvatar by up to 0.66 dB in PSNR and reducing LPIPS by up to 16.3%. These results validate the effectiveness of hierarchical geometry encoding and texture-anchored hybrid appearance modeling. Full article

(This article belongs to the Special Issue Application of Pattern Recognition and Machine Learning)

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15 pages, 426 KB

Open AccessArticle

Enhanced Schema Linking with Large Language Models via Self-Verification and Value Hints

by Linfei Ma, Dexing Wei, Xiangpeng Li, Feng Wen and Haisu Zhang

Big Data Cogn. Comput. 2026, 10(4), 104; https://doi.org/10.3390/bdcc10040104 - 31 Mar 2026

Abstract

Schema linking, the task of identifying relevant database schema elements (tables and columns) for natural language queries, is a critical component in database-driven natural language interfaces. While existing approaches rely on question decomposition to handle complex queries, they often suffer from error propagation [...] Read more.

Schema linking, the task of identifying relevant database schema elements (tables and columns) for natural language queries, is a critical component in database-driven natural language interfaces. While existing approaches rely on question decomposition to handle complex queries, they often suffer from error propagation and low precision. In this paper, we propose a novel schema linking framework enhanced by self-verification (SV) and value hints (VHs) that significantly improves both precision and recall. Our approach introduces two key components: (1) self-verification (SV), an iterative refinement mechanism that validates and corrects initial predictions through explicit verification prompts, and (2) value hints (VHs), which explicitly guide the model to recognize database values mentioned in queries. We conduct comprehensive experiments on two benchmark datasets, Spider and BIRD, using two language models of 4B and 80B parameters. Our results demonstrate that SV + VH consistently improves performance across datasets, models, and method configurations, outperforming both decomposition-based approaches and compute-matched alternatives such as self-consistency under equivalent inference budgets. Full article

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35 pages, 4221 KB

Open AccessArticle

Semantic Agent-Based Intelligent Digital Twins Integrating Demand, Production and Product Through Asset Administration Shells

by Joel Lehmann, Tim Markus Häußermann and Julian Reichwald

Big Data Cogn. Comput. 2026, 10(4), 103; https://doi.org/10.3390/bdcc10040103 - 26 Mar 2026

Abstract

Complex products and production processes are intertwined and demand expressive, lifecycle-wide digital representations. The Asset Administration Shell emerged as a standard for Digital Twins (DTs), structuring heterogeneous data across cloud-based Industrial Internet of Things (IIoT) infrastructures. However, today’s deployments predominantly realize passive or [...] Read more.

Complex products and production processes are intertwined and demand expressive, lifecycle-wide digital representations. The Asset Administration Shell emerged as a standard for Digital Twins (DTs), structuring heterogeneous data across cloud-based Industrial Internet of Things (IIoT) infrastructures. However, today’s deployments predominantly realize passive or reactive DTs, while intelligent behavior remains underexploited. This paper addresses this gap, proposing an end-to-end architecture operationalizing the DT Reference Model through the integration of machine-interpretable granulated industrial skills, which are semantically accumulated into a knowledge graph enabling discovery and reasoning, while a multi-agent system provides autonomous, utility-based negotiation via machine-to-machine interactions within a federated marketplace. The approach is applied in a real smart manufacturing demonstrator, combining order processes, production orchestration, and lifecycle documentation into a unified execution pipeline spanning IIoT-connected shopfloor assets and cloud-based services. Quantitative experiments evaluating negotiation latency, renegotiation robustness, and utility variation demonstrate stable, predictable behavior even under concurrent demand and failure scenarios. The architecture lays a foundation for interoperable, sovereign collaboration across value chains to realize shared production. The results underline the effectiveness of the tightly coupled enabler technologies realizing proactive, reconfigurable, and semantically enriched intelligent DTs. Full article

(This article belongs to the Special Issue Application of Cloud Computing in Industrial Internet of Things—2nd Edition)

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28 pages, 502 KB

Open AccessArticle

Emotional Framing in Prompts Modulates Large Language Model Performance

by Manuel Gozzi and Francesca Fallucchi

Big Data Cogn. Comput. 2026, 10(4), 102; https://doi.org/10.3390/bdcc10040102 - 24 Mar 2026

Abstract

Large Language Models (LLMs) demonstrate remarkable performance across a variety of natural language understanding tasks, yet their sensitivity to emotional framing in user prompts remains underexplored. This paper presents an empirical study investigating how four emotional tones—joy, apathy, anger, and fear—affect LLM performance [...] Read more.

Large Language Models (LLMs) demonstrate remarkable performance across a variety of natural language understanding tasks, yet their sensitivity to emotional framing in user prompts remains underexplored. This paper presents an empirical study investigating how four emotional tones—joy, apathy, anger, and fear—affect LLM performance on the SuperGLUE benchmark. We evaluate five instruction-tuned, open-weight models across eight diverse tasks, systematically modulating input prompts with affective cues while keeping semantic content constant. Results reveal that prompts framed with joy and apathy lead to consistently higher accuracy, with gains of up to 4.5 percentage points compared to fear-framed inputs, which yield the lowest performance. These findings demonstrate that affective modulation in user prompts measurably impacts LLM reasoning and task outcomes, suggesting that emotional framing is not merely stylistic but functionally relevant to model behavior. Our study provides a reproducible experimental framework and an open-source prompt set, offering a foundation for future research on affect-aware prompting strategies and their implications in human–AI interaction. Full article

(This article belongs to the Special Issue Advances in Large Language Models for Biological and Medical Applications)

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15 pages, 8130 KB

Open AccessArticle

Integrative Machine Learning Framework for Epigenetic Biomarker Discovery and Disease Severity Prediction in Childhood Atopic Dermatitis

by Ding-Wei Chen and Yun-Nan Chang

Big Data Cogn. Comput. 2026, 10(4), 101; https://doi.org/10.3390/bdcc10040101 - 24 Mar 2026

Abstract

Atopic dermatitis (AD) is a chronic inflammatory skin disorder that is significantly contributed to by epigenetics. We developed a machine learning-based framework to identify DNA methylation biomarkers associated with AD classification and severity. Genome-wide methylation data from peripheral blood were processed using four [...] Read more.

Atopic dermatitis (AD) is a chronic inflammatory skin disorder that is significantly contributed to by epigenetics. We developed a machine learning-based framework to identify DNA methylation biomarkers associated with AD classification and severity. Genome-wide methylation data from peripheral blood were processed using four feature selection algorithms: coarse approximation linear function (CALF), elastic net (EN), minimum redundancy maximum relevance (mRMR), and recursive feature elimination with cross-validation (RFECV). The integrative framework identified a central panel of 8 CpG sites that achieved an area under the curve (AUC) of 1.00 in the test set. This panel demonstrated high disease specificity, showing poor classification performance for systemic lupus erythematosus (AUC = 0.46), Crohn’s disease (AUC = 0.50), and oral squamous cell carcinoma (AUC = 0.58). Severity prediction using RFECV-selected 63 CpG sites (RFE63) achieved high accuracy across classifiers, with Random Forest (accuracy = 0.94) outperforming the others. The functional enrichment of CpG-associated genes highlighted key immune-related transcriptional regulators, including STAT5A, RUNX1, MEIS1, and PAX4. These genes are linked to chromatin remodeling, T helper cell differentiation, and interleukin-2 regulation, which are critical in AD pathogenesis and severity. Our findings demonstrate the utility of machine learning-integrated epigenomics in identifying robust, disease-specific biomarkers for AD diagnosis and monitoring, offering new insights into the molecular mechanisms underlying childhood AD. However, further validation in large-scale independent cohorts is required to confirm their clinical robustness and generalizability. Full article

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

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32 pages, 1203 KB

Open AccessArticle

An Experimental Study on Harassment Moderation in Llama and Alpaca

by Henrique Tostes de Sousa and Leo Natan Paschoal

Big Data Cogn. Comput. 2026, 10(4), 100; https://doi.org/10.3390/bdcc10040100 - 24 Mar 2026

Abstract

The growing integration of chatbots and large language models (LLMs) into society raises important concerns about their potential to reproduce toxic human behaviors. As a result, it is essential to investigate these models to mitigate or eliminate such risks. This paper presents an [...] Read more.

The growing integration of chatbots and large language models (LLMs) into society raises important concerns about their potential to reproduce toxic human behaviors. As a result, it is essential to investigate these models to mitigate or eliminate such risks. This paper presents an experimental study evaluating the responses of the Llama and Alpaca models to scenarios involving verbal harassment. The methodology involved using harassment dialogues generated by an LLM as prompts to elicit responses from both models. The responses were then analyzed for levels of toxicity, sexually explicit content, and flirtatiousness. The results indicate that although both models reduce explicit offensive terms, they exhibit limitations in identifying and intercepting abusive behavior from users. Statistical analysis reveals that general-purpose instruction tuning in Alpaca does not provide a robust safety barrier compared to the Llama base model for most variables investigated in the experiment. However, a significant difference was observed concerning flirting, where Llama proved more prone to validation and encouragement than Alpaca. Furthermore, the study identifies critical vulnerabilities, such as a “self-deprecation” bias in Llama and “mirroring” behavior in Alpaca. We also report a complementary triangulation with GPT-family models as a secondary point of reference. This paper discusses and contains content that can be offensive or upsetting. Full article

(This article belongs to the Special Issue Artificial Intelligence in Digital Humanities)

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26 pages, 5635 KB

Open AccessArticle

A Multi-Feature Transition-Aware Framework for Next POI Recommendation

by Oraya Sooknit, Jakkarin Suksawatchon and Ureerat Suksawatchon

Big Data Cogn. Comput. 2026, 10(3), 99; https://doi.org/10.3390/bdcc10030099 - 23 Mar 2026

Abstract

Next Point-of-Interest (POI) recommendation focuses on predicting a user’s subsequent location based on historical check-in data. In practice, however, check-in logs frequently contain uncertain records in which ambiguous spatial, temporal, or behavioral information obscures the underlying mobility regularities, thereby degrading prediction performance. To [...] Read more.

Next Point-of-Interest (POI) recommendation focuses on predicting a user’s subsequent location based on historical check-in data. In practice, however, check-in logs frequently contain uncertain records in which ambiguous spatial, temporal, or behavioral information obscures the underlying mobility regularities, thereby degrading prediction performance. To address this challenge, this study first infers user preferences from historical trajectories and reweights transition importance based on temporal and spatial proximity. It then models transition relationships using three complementary feature dimensions: POI category, spatial area, and routine versus non-routine behavioral patterns. Using transition probability analysis, feature-level dependencies in user mobility are systematically investigated. The findings demonstrate that these transition features contribute unevenly to predictive performance, with area-based transitions yielding the strongest results when used in isolation. Nonetheless, their joint integration consistently achieves the highest accuracy, underscoring the critical role of transition-aware modeling. Across two real-world datasets, the proposed framework consistently achieves state-of-the-art performance in top-ranked accuracy (Recall@1) and ranking quality (NDCG@1), while delivering competitive effectiveness at higher cutoff values (

k = 3

and

k = 5

). Notably, on the NYC dataset, MTF-POI achieves the highest Recall@1 (+19.01% over the strongest baseline) with a marginal trade-off at Recall@3, reflecting the framework’s design emphasis on precise next-step prediction. Full article

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18 pages, 7435 KB

Open AccessArticle

A Comparative Analysis of Deep-Learning-Based Speech Enhancement Models: Assessing Biometric Speaker Verification in Real-World Noisy Environments

by Md Jahangir Alam Khondkar, Ajan Ahmed, Stephanie Schuckers and Masudul H. Imtiaz

Big Data Cogn. Comput. 2026, 10(3), 98; https://doi.org/10.3390/bdcc10030098 - 23 Mar 2026

Abstract

Speech enhancement through denoising is essential for maintaining signal intelligibility and quality in biometric speaker verification pipelines that operate in acoustically adverse conditions. Despite the proliferation of deep learning (DL) architectures for speech denoising, simultaneously optimizing noise attenuation, perceptual fidelity, and speaker-identity preservation [...] Read more.

Speech enhancement through denoising is essential for maintaining signal intelligibility and quality in biometric speaker verification pipelines that operate in acoustically adverse conditions. Despite the proliferation of deep learning (DL) architectures for speech denoising, simultaneously optimizing noise attenuation, perceptual fidelity, and speaker-identity preservation remains an open problem. We address this gap by benchmarking three architecturally distinct DL-based enhancement models—Wave-U-Net, CMGAN, and U-Net—on three independent, domain-diverse corpora (SpEAR, VPQAD, and Clarkson) that the models never encountered during training and by introducing commercial-grade VeriSpeak speaker-verification scores as a biometric evaluation dimension absent from prior comparative studies. Our experiments reveal a clear three-way trade-off: U-Net achieves the highest signal-to-noise ratio (SNR) gains (+61.44% on SpEAR, +67.05% on VPQAD, +235.3% on Clarkson) but sacrifices naturalness; CMGAN yields the best perceptual evaluation of speech quality (PESQ) values (3.33, 1.35, and 2.50, respectively), favoring listening-comfort applications; and Wave-U-Net delivers the strongest biometric fidelity (VeriSpeak improvements of +11.63%, +30.22%, and +29.24%) while offering competitive perceptual quality. These results highlight that model selection must be driven by the target deployment scenario and provide actionable guidance for improving biometric verification robustness under real-world noise. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques for Audio, Image, and Multisensory Signal Processing)

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17 pages, 746 KB

Open AccessArticle

Predicting Mortality and Readmission in Obstructive Sleep Apnea via LLM-Expanded Clinical Concepts

by Awwal Ahmed, Anthony Rispoli, Carrie Wasieloski, Ifrah Khurram, Rafael Zamora-Resendiz, Destinee Morrow, Aijuan Dong and Silvia Crivelli

Big Data Cogn. Comput. 2026, 10(3), 97; https://doi.org/10.3390/bdcc10030097 - 21 Mar 2026

Abstract

Obstructive Sleep Apnea (OSA) is a common sleep disorder associated with serious health risks. This study leverages large language models (LLMs) to process and interpret clinical narratives in electronic health records. It develops clinically meaningful lexicons for predicting mortality and readmission risk, as [...] Read more.

Obstructive Sleep Apnea (OSA) is a common sleep disorder associated with serious health risks. This study leverages large language models (LLMs) to process and interpret clinical narratives in electronic health records. It develops clinically meaningful lexicons for predicting mortality and readmission risk, as well as for multiclass diagnostic classification in OSA patients. Using LLM-expanded lexicons, logistic regression models achieved ROC–AUC scores of 0.844 for 6-month all-cause post-discharge mortality, 0.817 for 1-year all-cause post-discharge mortality, and 0.729 for all-cause hospital readmissions following the first discharge. Diagnostic performance was highest with smaller n-gram representations, indicating that additional contextual length did not improve performance. Compared with frequency-based n-gram models, LLM-expanded lexicons yielded sparser feature sets with lower computational cost and comparable performance. Our findings highlight the potential of LLM-expanded lexicons to enhance OSA diagnosis and clinical risk stratification. Full article

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19 pages, 992 KB

Open AccessArticle

Hybrid Music Similarity with Hypergraph and Siamese Network

by Sera Kim, Youngjun Kim, Jaewon Lee and Dalwon Jang

Big Data Cogn. Comput. 2026, 10(3), 96; https://doi.org/10.3390/bdcc10030096 - 21 Mar 2026

Abstract

This paper proposes a novel method for measuring music similarity. Existing music similarity measurements have often been used for music appreciation, but this paper proposes a method for measuring the similarity between music samples which are used for music production. Conventional music recommendation [...] Read more.

This paper proposes a novel method for measuring music similarity. Existing music similarity measurements have often been used for music appreciation, but this paper proposes a method for measuring the similarity between music samples which are used for music production. Conventional music recommendation approaches often rely on either metadata-based similarity or audio-based feature similarity in isolation, which limits their effectiveness in sample-based recommendation scenarios where both compositional context and acoustic characteristics are important. To address this limitation, the proposed framework combines a hypergraph-based information similarity module with a feature-based similarity module learned using Siamese networks and triplet loss. In the information-based module, metadata attributes such as beats per minute (BPM), genre, chord, key, and instrument are modeled as vertices in a hypergraph, and Random Walk–Word2Vec embeddings are learned to capture structural relationships between music samples and their attributes. In parallel, the feature-based module employs vertex-specific Siamese networks trained on instrument and key classification tasks to learn perceptual similarity directly from audio signals. The two modules are trained independently and jointly utilized at the recommendation stage to provide attribute-specific similarity results for a given query sample. Results show that the proposed system achieves high Precision@k across multiple attributes and forms stable similarity structures in the embedding space, even without relying on user interaction data. These results reflect embedding consistency evaluated over the entire dataset where training and retrieval are performed on the same sample pool, rather than generalization to unseen samples. These results demonstrate that the proposed hybrid framework effectively captures both structural and perceptual similarity among music samples and is well suited for sample-based music recommendation in music production environments. Full article

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25 pages, 6493 KB

Open AccessArticle

A Dynamic Prompt-Based Logic-Aided Compliance Checker

by Wenxi Sheng, Chi Wei, Yinuo Zhang, Bowen Zhang and Jingyun Sun

Big Data Cogn. Comput. 2026, 10(3), 95; https://doi.org/10.3390/bdcc10030095 - 21 Mar 2026

Abstract

Text-based automatic compliance checking (ACC) employs natural language processing technologies to scrutinize a corporation’s business documents, ensuring adherence to related normative texts. The current methods fall into two primary categories: symbol-based and embedding-based approaches. Symbol-based methods, noted for their accuracy and transparent processing, [...] Read more.

Text-based automatic compliance checking (ACC) employs natural language processing technologies to scrutinize a corporation’s business documents, ensuring adherence to related normative texts. The current methods fall into two primary categories: symbol-based and embedding-based approaches. Symbol-based methods, noted for their accuracy and transparent processing, suffer from limited versatility. Conversely, embedding-based methods operate independently of expert knowledge yet often yield challenging-to-interpret results and require substantial volumes of annotated data. While both types of methods exhibit advantages in different aspects, the current research fails to combine these advantages effectively. Therefore, the existing methods fail to balance interpretability, generalization ability, and accuracy, which are key requirements for practical compliance systems. To address this problem, we introduce a novel approach termed the Dynamic Prompt-based Logic-Aided Compliance Checker (DPLACC), which is grounded in the prompt learning framework. This method initially parses target texts, transforming the results into first-order logical expressions. It subsequently retrieves pertinent knowledge from a knowledge graph, converting the knowledge into analogous first-order logical expressions. These expressions are then encoded into a global semantic vector via a pre-trained first-order logistic encoder. Ultimately, the semantics of expressions and initial texts are amalgamated within the prompt template, facilitating the logical knowledge enhancement of model reasoning. Experiments on Chinese and English datasets demonstrate that DPLACC comprehensively outperforms existing methods based solely on symbols or embeddings in terms of accuracy, precision, recall, and F1 score and significantly surpasses current mainstream large language models. Furthermore, DPLACC exhibits enhanced interpretability and reduced data dependence, maintaining 70% checking accuracy with as few as ten training samples. This capability allows DPLACC to be rapidly deployed in data-scarce real-world scenarios with minimal annotation overhead, thus offering a practical pathway toward the scalable implementation of compliance inspection systems. Full article

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

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