Big Data and Cognitive Computing

28 pages, 1742 KB

Open AccessArticle

Domestic Factors Influencing Perceived Interference in Distance Learning: A Machine Learning Approach in Residential Built Environments

by Virginia Puyana-Romero, Angela María Díaz-Márquez, Christiam Santiago Garzón-Pico and Giuseppe Ciaburro

Big Data Cogn. Comput. 2026, 10(5), 165; https://doi.org/10.3390/bdcc10050165 - 19 May 2026

Viewed by 552

Abstract

The change in learning methods to online/distance learning, catalyzed by recent health pandemics/social distancing requirements, has significantly changed how teaching occurs and what students experience in their learning spaces in regard to interference. New forms of interference exist, and they are related to [...] Read more.

The change in learning methods to online/distance learning, catalyzed by recent health pandemics/social distancing requirements, has significantly changed how teaching occurs and what students experience in their learning spaces in regard to interference. New forms of interference exist, and they are related to the domestic setting of the student’s life. This study examined how factors of domestic life influence what students find in regard to interference in their online learning spaces through a Likert-scale defined answer process to a 29-question predictor variable inventory that also includes two outcome variables that address the amount of acoustic interference experienced in learning spaces. Moreover, through regression models and various applications of machine learning science, this research aims to reveal crucial indicators that influence student experiences regarding disturbances. In this respect, these findings highlight crucial roles that housing density and internal interactive actions within residential contexts have on disturbances. Furthermore, this research reveals critical understandings of perceptual inequalities present within distance learning student populations and indicates significant cultural and social consequences related to digital technologies. This is crucial, understood within foundational perspectives that are necessary to address psychosocial challenges and human–building interaction present within distance learning science and policies aimed at reducing noise. Full article

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38 pages, 511 KB

Open AccessArticle

Similarity to a Single Set

by Lee Naish

Big Data Cogn. Comput. 2026, 10(5), 164; https://doi.org/10.3390/bdcc10050164 - 19 May 2026

Viewed by 366

Abstract

Identifying similarities in data is fundamental to discovery in science. Measuring or ranking similarity is a key way of reducing the dimensionality of data, is at the heart of many data intensive algorithms and can also be used directly for some applications. This [...] Read more.

Identifying similarities in data is fundamental to discovery in science. Measuring or ranking similarity is a key way of reducing the dimensionality of data, is at the heart of many data intensive algorithms and can also be used directly for some applications. This paper extends our understanding of a relatively simple similarity problem. Our primary application is spectral-based fault localisation (SBFL), in which a computer program is run with a large number of test cases and data is collected on which statements are executed in each test case. For each statement, the set of test cases in which it is executed is compared to the set of test cases that failed, and this is used to rank the statements to help locate bugs, an instance of what we call the similarity to a single set (STASS) problem. This paper is primarily theoretical but some contributions are validated with SBFL experiments. Set similarity is equivalent to similarity of binary vectors or two-by-two contingency tables. The problem is also equivalent to converting two-dimensional data with a “partial order”, such as points on a rectangular grid, to a one-dimensional total order. Even when the raw data is not binary, we are often interested in comparing binary classifiers for the data, such as diagnostic tests, and comparing binary classifiers is an instance of the STASS problem. More than a hundred set similarity measures have been proposed in the literature and hundreds of thousands have been evaluated for SBFL, but there is very little understanding of how best to choose a similarity measure for a given domain. This work discusses numerous properties and forms of symmetry that similarity measures can have. It refines previously identified properties so they are no longer incompatible, identifies new forms of symmetry, defines ordering relations over similarity measures, and proposes a new statistic that can be used to help choose a good similarity measure for a given domain. Full article

(This article belongs to the Section Data Mining and Machine Learning)

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20 pages, 3279 KB

Open AccessArticle

The Geometry of Privacy: A Two-Stage Analysis of Generative Membership Inference in Federated Learning

by Borja Arroyo Galende, Patricia A. Apellániz, Alejandro Almodóvar, Silvia Uribe, Federico Álvarez and Juan Parras

Big Data Cogn. Comput. 2026, 10(5), 163; https://doi.org/10.3390/bdcc10050163 - 19 May 2026

Viewed by 497

Abstract

We study Membership Inference Attack (MIA) risk in Federated Learning through a two-stage lens that separates (i) whether a target client’s contribution is detectable after aggregation and system noise (Stage I: Signal Survival) from (ii) whether a surviving contribution induces a generative membership [...] Read more.

We study Membership Inference Attack (MIA) risk in Federated Learning through a two-stage lens that separates (i) whether a target client’s contribution is detectable after aggregation and system noise (Stage I: Signal Survival) from (ii) whether a surviving contribution induces a generative membership score change attributable to the target’s private data (Stage II: Signal Attribution). Stage I models aggregation as a target–background decomposition and shows that detectability hinges on target–background alignment, which can induce cancellation. Stage II connects the surviving target component to a generative MIA score via a local path representation and Lipschitz/smoothness bounds, avoiding architecture-specific assumptions. Our analysis reveals that the leading attribution term is governed by the alignment between the target update and the score geometry of the target data at an appropriate baseline. We validate the theoretical bounds and illustrate risk trajectories across several scenarios. Full article

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26 pages, 396 KB

Open AccessEditor’s ChoiceArticle

Blockchains for Data Management: The DIGI4ECO Use Case and Practical Lessons Beyond Theory

by Andreas Polyvios Delladetsimas, Elias Iosif, Stamatis Papangelou and George Giaglis

Big Data Cogn. Comput. 2026, 10(5), 162; https://doi.org/10.3390/bdcc10050162 - 18 May 2026

Cited by 1 | Viewed by 854

Abstract

This article examines blockchain as an enabling technological component for data management tasks that are independent of currency-related functionality, a less-discussed aspect of a technology commonly associated with cryptocurrencies and decentralized finance (DeFi). Drawing on empirical findings from the DIGI4ECO project as a [...] Read more.

This article examines blockchain as an enabling technological component for data management tasks that are independent of currency-related functionality, a less-discussed aspect of a technology commonly associated with cryptocurrencies and decentralized finance (DeFi). Drawing on empirical findings from the DIGI4ECO project as a case study, we present a structured literature review and cross-domain analysis of blockchain-based data management systems (BDMSs), examine a representative permissioned BDMS implementation, and synthesize practical design guidelines and implementation insights for BDMS development. This perspective is motivated by core blockchain properties such as immutability and transparency, as well as by the observation that existing resources for BDMS development, including methods, tools, and best practices, remain fragmented and less developed than those available for more mature technologies. Full article

(This article belongs to the Section Big Data)

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28 pages, 3391 KB

Open AccessArticle

Enhanced Quantum-Inspired Deep Learning with Multi-Head Attention and Contrastive Learning for Text-Based Dialogue Sentiment Classification

by Fumin Zou, Lei Zou, Feng Guo, Xunhuang Wang, Jianqing Weng, Tao Fang, Haocai Jiang and Xueming Wu

Big Data Cogn. Comput. 2026, 10(5), 161; https://doi.org/10.3390/bdcc10050161 - 18 May 2026

Cited by 1 | Viewed by 534

Abstract

This study introduces the Quantum-inspired Pretrained Feature Embedding (ImprovedQPFE) model, a framework for dialogue sentiment classification. ImprovedQPFE integrates phase-pretrained complex embeddings, a bidirectional complex-valued GRU, a quantum-inspired attention mechanism, and supervised contrastive learning within a Transformer-based architecture, aiming to enhance feature discriminability under [...] Read more.

This study introduces the Quantum-inspired Pretrained Feature Embedding (ImprovedQPFE) model, a framework for dialogue sentiment classification. ImprovedQPFE integrates phase-pretrained complex embeddings, a bidirectional complex-valued GRU, a quantum-inspired attention mechanism, and supervised contrastive learning within a Transformer-based architecture, aiming to enhance feature discriminability under class imbalance. We evaluate ImprovedQPFE on the RECCON-DD and RECCON-IEM benchmarks under a unified and reproducible protocol, including standardized preprocessing and fixed data splits. To ensure reproducibility, all experiments were conducted using a fixed random seed of 42. The reported results are based on this single fixed-seed setting rather than averages over multiple repeated runs. The empirical results show that ImprovedQPFE achieves competitive performance and outperforms the compared baselines under the adopted experimental protocol. On the RECCON-DD dataset, ImprovedQPFE improves Macro-F1 from 80.08% to 83.75% compared with a strong non-quantum Transformer-based baseline equipped with contrastive learning. It also improves Pos-F1 while maintaining high performance for negative classes. On RECCON-IEM, ImprovedQPFE attains a leading Macro-F1 of 95.39% among the compared methods. These findings, together with an ablation analysis, support the effectiveness of the proposed quantum-inspired representation paradigm and its architectural components. However, further statistical validation with multiple repeated runs, standard deviations, confidence intervals, and significance testing remains an important direction for future work. Full article

(This article belongs to the Special Issue Advances in Natural Language Processing and Text Mining: 2nd Edition)

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29 pages, 25368 KB

Open AccessArticle

FedX: Privacy-Preserving Explainable Federated Ensemble Intrusion Detection System for Edge-Enabled Internet of Vehicles

by Nithya Nedungadi, Sriram Sankaran and Krishnashree Achuthan

Big Data Cogn. Comput. 2026, 10(5), 160; https://doi.org/10.3390/bdcc10050160 - 16 May 2026

Cited by 1 | Viewed by 1094

Abstract

The evolution from the Internet of Things (IoT) to the Internet of Vehicles (IoV) has expanded intelligent connectivity across embedded systems while increasing cybersecurity risks arising from large scale data exchange and device heterogeneity. As IoV environments become more dynamic and safety critical, [...] Read more.

The evolution from the Internet of Things (IoT) to the Internet of Vehicles (IoV) has expanded intelligent connectivity across embedded systems while increasing cybersecurity risks arising from large scale data exchange and device heterogeneity. As IoV environments become more dynamic and safety critical, centralized Intrusion Detection Systems (IDSs) face constraints related to latency, privacy exposure, and bandwidth overhead. These limitations motivate a transition to edge-enabled IoV architectures, where localized vehicular and anchor nodes supported by edge servers enable decentralized processing, enhanced privacy, and reduced communication load. To address these operational challenges, this paper proposes FedX (Federated Explainable Ensemble Intrusion Detection System), a privacy-preserving and explainable federated ensemble IDS that integrates XGBoost and LightGBM models across resource-constrained edge vehicles and roadside units (RSUs) to enable collaborative, low-latency anomaly detection without sharing raw data. By applying adaptive weighting based on model confidence and resource availability, FedX enhances robustness and efficiency while enabling explainable decisions via SHAP and LIME analysis, which highlights reliance on key features (flow duration, speed, RPM) for high-confidence (>97%) intrusion alerts grounded in domain-specific behavior. Privacy is further enforced through Gaussian differential privacy and secure aggregation to mitigate inference and inversion attacks. Experiments on the CICIoV2024 dataset show that FedX achieves 99.1% accuracy, outperforming existing federated ensemble IDS models by up to 2.1%. The system reduces communication overhead by 17% relative to full synchronization through adaptive weighted transmission and secure aggregation. It maintains negligible accuracy loss (<1.5%) under a strong privacy budget (

ϵ

= 1.1). The deployment of proposed IDS on Raspberry Pi 4 underscores its efficacy for edge computing. Experimental results indicate that adaptive weighting yields a 1.8% performance increase, while resource profiling shows 45% lower CPU utilization and over 50% lower power consumption compared with centralized baselines. The findings demonstrate that FedX, combined with explainable AI enables trustworthy, interpretable, and energy-efficient intrusion detection for secure next-generation Edge-enabled IoV networks. Full article

(This article belongs to the Special Issue Big Data Analytics with Machine Learning for Cyber Security)

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20 pages, 3389 KB

Open AccessEditor’s ChoiceArticle

Teaching AI to Decode Vaccine Hesitancy Narratives: A Few-Shot Learning and Topic Modeling Approach

by Md Enamul Kabir, Shakhawat H. Tanim, Deanna D. Sellnow, Geneva Lei P. Luteria and Lior Rennert

Big Data Cogn. Comput. 2026, 10(5), 159; https://doi.org/10.3390/bdcc10050159 - 16 May 2026

Cited by 1 | Viewed by 839

Abstract

Vaccine hesitancy—which can be defined as a delay in acceptance or the refusal to get vaccinated—has substantially increased over the past decade. This study introduces a computational and qualitative approach designed to efficiently classify stance and uncover narratives in social media discourse without [...] Read more.

Vaccine hesitancy—which can be defined as a delay in acceptance or the refusal to get vaccinated—has substantially increased over the past decade. This study introduces a computational and qualitative approach designed to efficiently classify stance and uncover narratives in social media discourse without relying on extensive manual annotation. Using 298,356 COVID-19 vaccine-related X posts geolocated to South Carolina (June 2021–May 2022), zero-shot and few-shot learning with instruction-tuned large language models (Mistral-7B, Meta-Llama-3.1, and DeepSeek-7B) was applied for stance detection while Latent Dirichlet Allocation (LDA) was used for topic modeling. The topic modeling identified five dominant themes in vaccine hesitant conversations: skepticism of vaccine efficacy, comparative framing, scientific justification, disapproval of regulations, and distrust. Temporal analysis revealed that skepticism peaked during late 2021, coinciding with booster campaigns and mandate debates. These findings suggest that vaccine hesitancy is influenced through complex rhetorical strategies rather than misinformation alone. These underlying narratives often frame skepticism as rational and evidence-based, using scientific language and statistical reasoning to challenge the effectiveness of vaccines. Full article

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47 pages, 1590 KB

Open AccessArticle

A Hybrid PoS–PoW Blockchain Framework for Secure Cyber Threat Intelligence Sharing: Design, Implementation, and Evaluation

by Ahmed El-Kosairy and Heba Kamal Aslan

Big Data Cogn. Comput. 2026, 10(5), 158; https://doi.org/10.3390/bdcc10050158 - 15 May 2026

Viewed by 1082

Abstract

Many blockchain-based cyber threat intelligence (CTI) sharing systems emphasize immutability and auditability, but often treat CTI submissions as ordinary blockchain transactions without explicitly separating content validation from publication anchoring. This paper presents CTIB, a proof-of-concept hybrid Proof-of-Stake (PoS) and Proof-of-Work (PoW) framework for [...] Read more.

Many blockchain-based cyber threat intelligence (CTI) sharing systems emphasize immutability and auditability, but often treat CTI submissions as ordinary blockchain transactions without explicitly separating content validation from publication anchoring. This paper presents CTIB, a proof-of-concept hybrid Proof-of-Stake (PoS) and Proof-of-Work (PoW) framework for CTI publication. CTIB uses a sequential workflow in which a PoS committee first evaluates CTI submissions, and an accepted feed hash is then anchored through a PoW step to provide verifiable temporal binding. The prototype is evaluated in a controlled local Hardhat environment; therefore, the results should be interpreted as prototype-level feasibility evidence rather than production-scale deployment results. CTI content is represented using STIX 2.1, canonicalized, and hashed using SHA-256; only integrity-critical evidence is stored on-chain, while full CTI content remains off-chain. Experimental results demonstrate prototype-level feasibility, with measured throughput, latency, and success rate metrics under different PoW difficulty profiles. Across ten independent local runs, CTIB achieved an average throughput between 141.13 and 166.14 feeds/min, average p50 latency between 326.18 and 403.09 ms, and average p95 latency between 553.22 and 700.82 ms under the tested difficulty profiles. Security analysis uses analytical modeling, committee capture probability, and Monte Carlo simulation to evaluate majority-attack feasibility under stated assumptions. The results indicate that sequential compromise of both validation and anchoring layers increases the cost of coordinated manipulation. Full article

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Graphical abstract

18 pages, 1508 KB

Open AccessArticle

PRL-DAS: Robust Heliox Speech Recognition for Unaligned Low-Resource Data

by Yonghong Chen, Guoqi Zhang, Wanzhi Wen and Shibing Zhang

Big Data Cogn. Comput. 2026, 10(5), 157; https://doi.org/10.3390/bdcc10050157 - 15 May 2026

Viewed by 507

Abstract

Speech produced in helium–oxygen (heliox) environments in deep saturation diving exhibits pronounced spectral shifts and temporal distortions, which severely degrade automatic speech recognition (ASR) systems trained on normal-air corpora. Existing studies often adopt a restoration-then-recognition paradigm by training waveform mapping networks on paired [...] Read more.

Speech produced in helium–oxygen (heliox) environments in deep saturation diving exhibits pronounced spectral shifts and temporal distortions, which severely degrade automatic speech recognition (ASR) systems trained on normal-air corpora. Existing studies often adopt a restoration-then-recognition paradigm by training waveform mapping networks on paired heliox/air recordings. However, in realistic low-resource data collection, paired recordings are typically obtained by independent re-reading and are therefore not strictly time-aligned, which makes regression-style restoration more sensitive to pairing errors and increases the risk of front-end distortions. This paper proposes a robust recognition framework for heliox speech, termed PRL-DAS (Physics-informed Resampling and LoRA with Duration-Adaptive Speed). The framework consists of a physics-inspired linear resampling warm start (PhysSpeed), parameter-efficient Low-Rank Adaptation (LoRA), and duration-adaptive speed (DAS) inference enhancement. Specifically, we first apply physics-motivated linear resampling as a coarse warm start, and then perform mixed-domain LoRA fine-tuning of a Whisper foundation model to absorb residual non-linear differences. On a corpus of 1048 paired Chinese heliox utterances under leave-one-speaker-out (LOSO) evaluation, using Whisper-Medium as the base model, PhysSpeed followed by mixed-domain LoRA reduces the overall character error rate (CER) from 49.33% with PhysSpeed preprocessing only to 25.79%, while also improving performance on the normal domain. Furthermore, the full PRL-DAS framework applies Soft-DAS, a lightweight smooth schedule motivated by duration-dependent variation in the optimal resampling factor, and further reduces the overall CER to 24.37% without additional training cost. Full article

(This article belongs to the Section Data Mining and Machine Learning)

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24 pages, 5366 KB

Open AccessArticle

A Three-Tier Hybrid Architecture for an Admissions Dialogue Assistant with Graph-Aware Context Routing

by Nikita Stepanov, Anastasiya Radaeva, Peter Panfilov, Alexander Suleykin and Valery Pyatetsky

Big Data Cogn. Comput. 2026, 10(5), 156; https://doi.org/10.3390/bdcc10050156 - 15 May 2026

Viewed by 627

Abstract

University admissions services must answer large volumes of applicant questions that differ substantially in complexity, ranging from repetitive FAQ-type requests to multi-step questions involving programs, entrance exams, admission rules, passing scores, and temporal comparisons. Ungrounded large language model responses are risky in this [...] Read more.

University admissions services must answer large volumes of applicant questions that differ substantially in complexity, ranging from repetitive FAQ-type requests to multi-step questions involving programs, entrance exams, admission rules, passing scores, and temporal comparisons. Ungrounded large language model responses are risky in this domain because answers must be factually correct, source-based, and consistent with official institutional data. This paper presents a three-tier hybrid architecture for an admissions dialogue assistant that combines deterministic FAQ matching, hybrid retrieval-augmented generation, and graph-grounded retrieval for complex queries. The first tier, Hash-FAQ, returns verified answers for frequent intents using normalized keys, hash-based lookup, near-duplicate fingerprinting, and semantic similarity checks. The second tier applies hybrid RAG based on BM25 retrieval, vector search, rank fusion, and optional cross-encoder reranking. The third tier uses GraphRAG to extract a constrained k-hop subgraph from a Neo4j knowledge graph built from relational admissions data and document-derived facts. All tiers are synchronized through a versioned indexing pipeline with shadow collections and atomic switching across lexical, vector, FAQ, relational, and graph stores. The system was evaluated using real admissions-campaign traffic and a labeled subset of applicant queries. Tier 1 resolved 68.7% of requests with low latency, while the GraphRAG branch improved factual accuracy with attribution on multi-step queries from 0.55 to 0.91 compared with the non-graph baseline. The main contribution of the study is a production-oriented, cost-aware retrieval-and-generation architecture that links tiered routing, synchronized knowledge publication, source attribution, and operational evaluation for applicant-facing institutional dialogue systems. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)

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17 pages, 1159 KB

Open AccessEditor’s ChoiceArticle

Performance Trade-Offs of Optimizing Small Language Models for E-Commerce

by Josip Tomo Licardo, Nikola Tanković, Ivan Osman, Ivan Lorencin and Sandi Baressi Šegota

Big Data Cogn. Comput. 2026, 10(5), 155; https://doi.org/10.3390/bdcc10050155 - 14 May 2026

Cited by 1 | Viewed by 1232

Abstract

Large Language Models (LLMs) offer state-of-the-art performance in natural language understanding and generation tasks. However, the deployment of leading commercial models for specialized tasks, such as e-commerce, is often hindered by high computational costs, latency, and operational expenses. This paper investigates the viability [...] Read more.

Large Language Models (LLMs) offer state-of-the-art performance in natural language understanding and generation tasks. However, the deployment of leading commercial models for specialized tasks, such as e-commerce, is often hindered by high computational costs, latency, and operational expenses. This paper investigates the viability of smaller, open-weight models as a resource-efficient alternative. We present a methodology for optimizing a one-billion-parameter Llama 3.2 model for multilingual e-commerce intent recognition. The model was fine-tuned using Quantized Low-Rank Adaptation (QLoRA) on a synthetically generated dataset designed to mimic real-world user queries. Subsequently, we applied post-training quantization techniques, creating GPU-optimized (GPTQ) and CPU-optimized (GGUF) versions. Our results demonstrate that the specialized 1B model achieves 98.8% accuracy, approaching the performance of the significantly larger GPT-4.1 model. A detailed performance analysis revealed critical, hardware-dependent trade-offs: while 4-bit GPTQ reduced VRAM usage by 41%, it paradoxically slowed inference by 82% on an older GPU architecture (NVIDIA T4) due to dequantization overhead. Conversely, GGUF formats on a CPU achieved a speedup of up to

4.3 \times

in inference throughput and up to a 72% reduction in RAM consumption compared to the FP16 baseline. We conclude that small, properly optimized open-weight models are not just a viable but a more suitable alternative for domain-specific applications, offering state-of-the-art accuracy at a fraction of the computational cost. Full article

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28 pages, 3206 KB

Open AccessEditor’s ChoiceArticle

Consensus-Driven Framework for Data-Driven Optimization of Distributed Systems Through Blockchain Consensus Mechanism Selection

by Miljenko Švarcmajer, Mirko Kohler, Zdravko Krpić and Ivica Lukić

Big Data Cogn. Comput. 2026, 10(5), 154; https://doi.org/10.3390/bdcc10050154 - 13 May 2026

Cited by 1 | Viewed by 947

Abstract

Modern data-driven distributed systems increasingly rely on blockchain technologies to ensure trust, transparency, and decentralized coordination. However, the rapid proliferation of consensus mechanisms has created a complex design space, making the selection of an appropriate protocol a non-trivial architectural and decision-making challenge. Different [...] Read more.

Modern data-driven distributed systems increasingly rely on blockchain technologies to ensure trust, transparency, and decentralized coordination. However, the rapid proliferation of consensus mechanisms has created a complex design space, making the selection of an appropriate protocol a non-trivial architectural and decision-making challenge. Different consensus mechanisms rely on distinct security resources, validator admission models, and agreement architectures, leading to diverse trade-offs between scalability, decentralization, performance, and governance. Existing studies primarily focus on classification or performance comparison of consensus mechanisms, while the problem of systematic, requirement-driven selection remains insufficiently addressed. In particular, there is a lack of structured approaches that integrate multiple system requirements into a unified decision framework suitable for real-world environments. To address this gap, this paper proposes a consensus-driven, layered framework for blockchain consensus mechanism selection, formulated as a multi-criteria decision problem. The framework organizes the consensus design space across key architectural dimensions and analyzes 32 consensus mechanisms, enabling systematic comparison and supporting data-driven decision-making. The approach is further demonstrated through five representative use-case scenarios, showing its applicability in optimizing distributed system design. Full article

(This article belongs to the Special Issue AI and Blockchain for Trustworthy Social Computing)

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29 pages, 12420 KB

Open AccessArticle

A Dueling DQN-Based Hyper-Heuristic Framework for Learning Path Optimization

by Yong-Wei Zhang, Ming-Yang Zhu, Wen-Kai Xia, Xin-Yang Zhang and Jin-Di Liu

Big Data Cogn. Comput. 2026, 10(5), 153; https://doi.org/10.3390/bdcc10050153 - 13 May 2026

Viewed by 961

Abstract

Learning path optimization is crucial in intelligent educational systems, with the core challenge of efficient multi-objective sequential decision-making under complex prerequisite constraints. To address the poor generalization of existing methods relying on fixed operator scheduling or handcrafted heuristics, this paper proposes a hyper-heuristic [...] Read more.

Learning path optimization is crucial in intelligent educational systems, with the core challenge of efficient multi-objective sequential decision-making under complex prerequisite constraints. To address the poor generalization of existing methods relying on fixed operator scheduling or handcrafted heuristics, this paper proposes a hyper-heuristic framework based on Dueling Deep Q-Network (Dueling DQN-HH), formulating operator selection as a sequential decision-making process for dynamic adaptive scheduling of low-level operators. The framework adopts priority-based encoding to unify learning path representation (decoupling the hyper-heuristic layer from the problem domain) and designs a composite reward mechanism integrating reward shaping, exploration incentives, and computational cost awareness to balance solution quality and efficiency. Additionally, it employs a dueling network architecture with prioritized experience replay to enhance policy learning stability. Experimental results show the proposed method outperforms representative baseline algorithms in solution quality, convergence stability, and computational efficiency. The framework demonstrates superior performance across multiple objectives, particularly in minimizing the total learning time (F_time), as validated on two heterogeneous datasets: MOOCCube (Computer Science) and PsyDataset (Psychology). Further ablation studies and operator evolution analyses verify its adaptive scheduling capability under different objectives and knowledge graph structures, demonstrating strong objective independence and cross-dataset generalization. Full article

(This article belongs to the Section Data Mining and Machine Learning)

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24 pages, 1787 KB

Open AccessEditor’s ChoiceArticle

Data-Driven Peak Demand Identification in Commercial Electricity Consumption for Load Curve Flattening

by Michał Gostkowski, Tomasz Ząbkowski and Krzysztof Gajowniczek

Big Data Cogn. Comput. 2026, 10(5), 152; https://doi.org/10.3390/bdcc10050152 - 12 May 2026

Viewed by 839

Abstract

Effective peak load management enables utilities to mitigate increased electricity demand and optimize the use of available resources during periods of maximum consumption. Accurate forecasting of the peak load is essential for ensuring the reliability, efficiency, and resilience of contemporary power systems. In [...] Read more.

Effective peak load management enables utilities to mitigate increased electricity demand and optimize the use of available resources during periods of maximum consumption. Accurate forecasting of the peak load is essential for ensuring the reliability, efficiency, and resilience of contemporary power systems. In this study, commercial customer-level data were employed to identify electricity peak demand within the Polish power system, drawing upon historical records of both energy consumption and meteorological variables. Departing from conventional time series forecasting approaches, the problem was intentionally reformulated as a pattern recognition task. Three classification techniques were systematically evaluated to identify individual customers’ peak load events, thereby offering a basis for demand-side management strategies and incentive mechanisms aimed at flattening load profiles and improving grid stability. The proposed approach demonstrates how data-driven analytics can support utilities in extracting actionable knowledge from large-scale energy datasets and enabling proactive demand response programs. Empirical results indicate that the proposed methods are capable of predicting up to 90% of electricity peak occurrences, with a forecasting horizon of 24 h leading to significant shifts in the load curve. Full article

(This article belongs to the Section Data Mining and Machine Learning)

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15 pages, 812 KB

Open AccessArticle

A Comparative Evaluation of AI Approaches to Large-Scale Scientific Subject Classification

by Roland Tanácsi and András Micsik

Big Data Cogn. Comput. 2026, 10(5), 151; https://doi.org/10.3390/bdcc10050151 - 11 May 2026

Cited by 1 | Viewed by 538

Abstract

Background: The Hungarian Science Bibliography applies the OECD Frascati Fields of Science and Technology taxonomy for subject classification; however, approximately 80% of its records lack assigned categories. Automated large-scale classification could support retrospective completion and improve the quality of bibliographic data. Methods: We [...] Read more.

Background: The Hungarian Science Bibliography applies the OECD Frascati Fields of Science and Technology taxonomy for subject classification; however, approximately 80% of its records lack assigned categories. Automated large-scale classification could support retrospective completion and improve the quality of bibliographic data. Methods: We evaluated multiple artificial intelligence approaches to classifying publications into level 4 Frascati categories using only titles and keywords. Training datasets were compiled from bibliographic records and subjected to heuristic and large-language-model-based filtering to reduce noise and ambiguity. The approaches tested included statistical methods, classical machine learning classifiers, fine-tuned SciBERT models, zero-shot prompting with large language models, and a Mixture-of-Experts architecture. Results: Data quality had a stronger impact on performance than model complexity. Large-language-model-based filtering substantially improved classification results. The best-performing model, a Support Vector Classifier, achieved a weighted F1 score of 0.83, which is an outstanding result relative to state-of-the-art approaches from the literature. Conclusions: Our findings contribute new insights into classification research and may assist others in selecting appropriate solutions for real-world, large-scale bibliographic classification tasks. Full article

(This article belongs to the Topic Generative AI and Interdisciplinary Applications)

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24 pages, 1976 KB

Open AccessArticle

BDERL: A Reinforcement Learning-Enhanced Differential Evolution for the Earliness–Tardiness RCPSP

by Hao Nguyen Thi, Loc Nguyen The and Huu Dang Quoc

Big Data Cogn. Comput. 2026, 10(5), 150; https://doi.org/10.3390/bdcc10050150 - 11 May 2026

Viewed by 705

Abstract

This paper introduces the ETMS-RCPSP (Earliness–Tardiness Multi-Skill Resource-Constrained Scheduling Problem)—a novel problem derived from the MS-RCPSP by adding constraints on project completion time or actual production contracts. The goal of the new problem is to control the project completion time as closely as [...] Read more.

This paper introduces the ETMS-RCPSP (Earliness–Tardiness Multi-Skill Resource-Constrained Scheduling Problem)—a novel problem derived from the MS-RCPSP by adding constraints on project completion time or actual production contracts. The goal of the new problem is to control the project completion time as closely as possible to reality—this differs from the original MS-RCPSP, which aimed to minimize project execution time. The objective of the problem is of greater practical significance in ensuring project completion on schedule while also addressing related issues, such as the ability to receive finished products on time as stipulated in the contract. The ETMS-RCPSP is an NP-hard problem whose result can be used for resource allocation in project execution or for resource arrangement in production lines to fulfill economic contracts. To address the ETMS-RCPSP, the paper proposes a new evolutionary algorithm, BDERL (Balanced Differential Evolution with Reinforcement Learning), that combines differential evolution with a problem-specific decoding mechanism and an adaptive parameter control strategy based on reinforcement learning (Q-learning). The proposed algorithm is evaluated on benchmark instances derived from the iMOPSE dataset and the TNG company dataset—a real-world dataset from manufacturing and contract-driven environments. Experimental results demonstrate that the approach consistently reduces total production costs compared to baseline heuristics while maintaining competitive computational efficiency. The findings underscore the efficacy of adaptive hybrid optimization techniques in solving intricate production scheduling problems characterized by limited resources and varied skill competencies. Full article

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

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23 pages, 2131 KB

Open AccessArticle

MDA-Net: A Segmentation Network for Kidney Tumor Based on Enhanced Multi-Scale Feature Extraction and Attention Refinement

by Shaofu Lin, Yumiao Chang, Jianhui Chen and Lianfang Ma

Big Data Cogn. Comput. 2026, 10(5), 149; https://doi.org/10.3390/bdcc10050149 - 8 May 2026

Viewed by 620

Abstract

Accurate kidney tumor segmentation from abdominal CT is essential for quantitative assessment and treatment planning. However, indistinct tumor boundaries and substantial inter-patient shape variability render traditional hand-crafted feature-based methods unreliable for precise delineation. Although deep learning has advanced this task, these methods still [...] Read more.

Accurate kidney tumor segmentation from abdominal CT is essential for quantitative assessment and treatment planning. However, indistinct tumor boundaries and substantial inter-patient shape variability render traditional hand-crafted feature-based methods unreliable for precise delineation. Although deep learning has advanced this task, these methods still struggle with multi-scale tumor characteristics, complex morphological variations, and background noise in medical images. To address these challenges, we propose MDA-Net, an end-to-end segmentation method based on enhanced multi-scale feature extraction and attention refinement. Specifically, we introduce a Multi-Scale Feature Extraction (MSFE) module into encoder–decoder skip connections to aggregate dilated features across multiple receptive fields and learn branch-wise weights for adaptive refinement and fusion, thereby enhancing boundary details and semantic cues to reduce tumor-tissue ambiguity. At the bottleneck, a Deformable Pyramid Feature Refinement (DPFR) module combines deformable sampling with pyramid contextual modeling, thereby improving adaptability to variations in tumor shape and scale while preserving feature resolution. Moreover, a Channel and Spatial Attention (CASA) module is embedded in the decoder to suppress background interference and enhance boundary-sensitive structures during upsampling via coordinated channel and spatial reweighting, thereby improving the reconstruction of fine-grained tumor morphology and contours. Experiments on both KiTS19 and KiTS21 show that MDA-Net consistently improves tumor boundary delineation, lesion localization, and mask reconstruction, demonstrating stronger robustness and cross-dataset generalizability than representative baseline methods. Ablation studies further confirm the complementary effects of MSFE, DPFR, and CASA. In addition, Grad-CAM visualizations improve the clinical transparency and interpretability of the model. Overall, this method advances deep learning for medical image analysis and supports precise diagnosis and treatment of renal tumors. Full article

(This article belongs to the Special Issue Deep Learning for Advanced Visual Representation and Analysis)

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86 pages, 13619 KB

Open AccessArticle

Adaptive Neural Network System for Preventing Violations of Personal Digital Rights as a National Security Factor

by Serhii Vladov, Oksana Mulesa, Maryana Marusinets, Tiberiy Chegi, Victoria Vysotska, Anton Kazakov, Iryna Kirieieva, Maksym Korniienko and Tetiana Morhunova

Big Data Cogn. Comput. 2026, 10(5), 148; https://doi.org/10.3390/bdcc10050148 - 8 May 2026

Viewed by 1306

Abstract

The article develops a hybrid multimodal neural network for the automatic prevention of personal digital rights violations, focusing on improving security through anomaly detection and ensuring data confidentiality. The main aim is to integrate several innovative methods, such as federated learning, gating, latent [...] Read more.

The article develops a hybrid multimodal neural network for the automatic prevention of personal digital rights violations, focusing on improving security through anomaly detection and ensuring data confidentiality. The main aim is to integrate several innovative methods, such as federated learning, gating, latent competitive learning, and a variational autoencoder, to improve violation detection accuracy. The key contribution is the development of a training mixture that combines a probabilistic anomaly detector and an autoencoder reconstruction signal, which allows for effective detection of typical incidents and hidden anomalies. The experimental evaluation results showed high-performance indicators, with ROC-AUC at 0.96 and accuracy at 0.94, confirming the system’s effectiveness on anonymized data. The results obtained have a significant practical contribution, as they can be integrated into national information security systems, including SOC and forensic reports, which will ensure a higher level of personal data protection and reduce privacy breach risks. The scope of the proposed system simultaneously covers cybersecurity, personal data protection, national security, SOC systems, and forensic analysis. Full article

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

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26 pages, 24884 KB

Open AccessArticle

Federated Learning-Based Adaptive Multi-Head Attention Model for Wind Power Forecasting

by Yihua Zhu, Chao Luo, Ke Wu, Jiawei Yu, Binjiang Hu, Lei Huang and Bitao Xiao

Big Data Cogn. Comput. 2026, 10(5), 147; https://doi.org/10.3390/bdcc10050147 - 7 May 2026

Viewed by 416

Abstract

Enhancing the accuracy of short-term wind power forecasting helps mitigate the adverse impacts of prediction errors on grid dispatch. Wind power exhibits a significantly nonlinear dependence on multiple influencing factors. However, existing methods struggle to effectively resolve multi-dimensional feature redundancy and multi-scale non-stationary [...] Read more.

Enhancing the accuracy of short-term wind power forecasting helps mitigate the adverse impacts of prediction errors on grid dispatch. Wind power exhibits a significantly nonlinear dependence on multiple influencing factors. However, existing methods struggle to effectively resolve multi-dimensional feature redundancy and multi-scale non-stationary evolutionary characteristics inherent in far-offshore wind power forecasting tasks. This leads to bottlenecks such as insufficient feature discriminability and temporal dependency focus shift under complex marine environments, ultimately limiting further improvements in prediction accuracy. To address these challenges, this paper proposes a federated learning-based adaptive multi-head attention model for wind power forecasting (Fed-AMHA). The proposed framework operates as follows: First, each wind farm client utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network to model input sequences bidirectionally, capturing long-term temporal dependencies. Subsequently, linear projection and parallel one-dimensional convolution operations are introduced to mine multi-scale local temporal features from each time step and its neighborhood. Building upon this, channel attention and multi-head temporal feature attention mechanisms are stacked. The model adaptively adjusts the weights of different time slices and feature channels by learning the importance of each channel to the forecasting task. The central server then aggregates the model parameters uploaded by the clients via averaging, enabling cross-site collaborative training without directly sharing raw data. Simulation results based on public datasets and actual wind farm data under various short-term forecasting scenarios demonstrate that the proposed model consistently achieves lower prediction errors and superior stability compared to existing forecasting models under identical settings. Full article

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16 pages, 284 KB

Open AccessEditor’s ChoiceReview

Talent Identification and AI-Driven Decision Tools in Sport: A Policy-Oriented Perspective on Algorithmic Bias, Data Privacy, and Digital Determinism in Player Evaluation

by Elia Morgulev and Ofer H. Azar

Big Data Cogn. Comput. 2026, 10(5), 146; https://doi.org/10.3390/bdcc10050146 - 7 May 2026

Cited by 1 | Viewed by 2795

Abstract

Big-data analytics are increasingly used in scouting and talent identification, with machine learning (ML) tools applied to evaluate and predict player performance based on match statistics, video tracking, physical and anthropometric tests, psychological assessments, social media data, and qualitative scouting reports. Advances in [...] Read more.

Big-data analytics are increasingly used in scouting and talent identification, with machine learning (ML) tools applied to evaluate and predict player performance based on match statistics, video tracking, physical and anthropometric tests, psychological assessments, social media data, and qualitative scouting reports. Advances in computer vision, together with the emergence of affordable automated broadcasting and data collection systems, have extended the deployment of ML-driven scouting from professional to youth sport. The use of algorithms in educational, employment, and healthcare settings has been shown to introduce biases and discrimination while wrongly assuming accuracy and objectivity because the decisions are made automatically and quantitatively. In this respect, we briefly describe the development of data-driven performance analysis and how ML-based technologies are currently applied for early screening and comparison of large player populations. Based on a narrative overview of the literature, we draw on evidence from education, employment, and healthcare to identify risks that may also emerge in ML-driven player evaluation, including algorithmic bias, non-representative training data, privacy concerns, and the persistence of model-based labels over time, especially in youth sport. Our main contribution is translating these threats into governance principles and operational safeguards for responsible use of AI in scouting and talent identification. Full article

(This article belongs to the Special Issue AI and Data Science in Sports Analytics)

21 pages, 1348 KB

Open AccessArticle

AI-Driven Generation of Old English: A Framework for Low-Resource Languages

by Rodrigo Gabriel Salazar Alva, Matías Núñez, Cristian López Del Alamo and Javier Martín Arista

Big Data Cogn. Comput. 2026, 10(5), 145; https://doi.org/10.3390/bdcc10050145 - 6 May 2026

Cited by 1 | Viewed by 1317

Abstract

Preserving ancient languages is essential for understanding the cultural and linguistic heritage of humanity. Old English, however, remains critically under-resourced, which limits its accessibility to modern natural language processing (NLP) techniques. We present a scalable framework that uses advanced large language models (LLMs) [...] Read more.

Preserving ancient languages is essential for understanding the cultural and linguistic heritage of humanity. Old English, however, remains critically under-resourced, which limits its accessibility to modern natural language processing (NLP) techniques. We present a scalable framework that uses advanced large language models (LLMs) to generate high-quality Old English texts to address this gap. In this study, we specifically employ state-of-the-art models, including Llama-3.1-8B and Mistral-7B, as our foundation models, which are then adapted to the unique characteristics of Old English. Our approach combines parameter-efficient fine-tuning (Low-Rank Adaptation (LoRA)), data augmentation via back-translation, and a dual-agent pipeline that separates content generation (in English) and translation (into Old English). Evaluation with automated metrics (BLEU, METEOR, and CHRF) shows improvements over baseline models, with BLEU scores increasing from 26 to over 65 for English-to-Old English translation. Expert human assessment confirms high grammatical accuracy and stylistic fidelity in the generated texts, with average scores of 9.0/10 for inflection and word order, 9.1/10 for lexical authenticity, and 7.8 for semantic coherence. These results demonstrate that the framework can reliably expand limited historical corpora while maintaining linguistic integrity, with immediate practical applications in digital humanities research, computational philology, and the development of educational resources for Old English study. Beyond expanding the Old English corpus, our method offers a practical blueprint for revitalizing other endangered languages, thus linking AI innovation with the goals of cultural preservation. Full article

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23 pages, 625 KB

Open AccessArticle

HYSARD: A Hybrid Feature-Fusion Model for Sarcasm Detection Using RoBERTa Embeddings and Linguistic Features

by Ismail Jabri, Zine Eddine Louriga, Aziza El Ouaazizi and Abdelaziz Ahaitouf

Big Data Cogn. Comput. 2026, 10(5), 144; https://doi.org/10.3390/bdcc10050144 - 6 May 2026

Viewed by 943

Abstract

Sarcasm detection remains a challenging task in natural language processing because sarcastic expressions often convey meanings that contradict their literal wording. Although transformer-based encoders such as RoBERTa capture contextual semantics effectively, sparse linguistic signals common in sarcastic user-generated text, such as exaggerated punctuation, [...] Read more.

Sarcasm detection remains a challenging task in natural language processing because sarcastic expressions often convey meanings that contradict their literal wording. Although transformer-based encoders such as RoBERTa capture contextual semantics effectively, sparse linguistic signals common in sarcastic user-generated text, such as exaggerated punctuation, elongated words, capitalization, and sentiment contrast, may not always remain explicitly accessible in the final sentence representation. To address this limitation, we propose HYSARD, a hybrid feature-fusion model that combines RoBERTa-based sentence embeddings with complementary linguistic features, including sentiment polarity, stylistic markers, syntactic patterns, and TF-IDF lexical cues. The resulting feature space is refined through Random Forest-based feature selection to reduce redundancy and improve robustness, while SMOTE mitigates class imbalance during training. We evaluate HYSARD on the SemEval-2022 iSarcasmEval dataset and the balanced Main and Political subsets of SARC 2.0. Results show strong and consistent performance across datasets, with an F1-score of 0.80 on iSarcasmEval, while held-out test-set error analysis further highlights strong class-wise discrimination. The ablation study further confirms that combining contextual embeddings with explicit linguistic cues improves sarcasm detection over reduced feature configurations. These findings show that hybrid feature fusion remains an effective and practical strategy for sarcasm detection in noisy social media text. Full article

(This article belongs to the Special Issue Natural Language Processing and Text Analysis in Social Media)

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19 pages, 496 KB

Open AccessEditor’s ChoiceArticle

Evaluating Computational Approaches for Harmful Content Analysis: Promise, Pitfalls and Tools for Responsible Research

by Itai Himelboim and Mudit Baid

Big Data Cogn. Comput. 2026, 10(5), 143; https://doi.org/10.3390/bdcc10050143 - 2 May 2026

Viewed by 877

Abstract

This manuscript develops and demonstrates a practical framework for evaluating automated classifiers used in communication research, using harmful language detection as an illustrative case. We combine (a) a structured review of documentation practices for 27 publicly available classifiers and their associated annotation processes [...] Read more.

This manuscript develops and demonstrates a practical framework for evaluating automated classifiers used in communication research, using harmful language detection as an illustrative case. We combine (a) a structured review of documentation practices for 27 publicly available classifiers and their associated annotation processes with (b) a cross-dataset evaluation that re-tests each model beyond its original training context. Across 27 datasets, we extract and compare reporting on construct definitions, annotator instructions, and inter-annotator agreement, and we quantify generalization by applying each model to multiple out-of-domain test sets. We also benchmark a contemporary large language model (GPT-5) under a consistent prompting protocol to illustrate how LLM-based classification compares to fine-tuned classifiers. Results show that documentation is uneven and often insufficient for theory-driven measurement, inter-annotator agreement varies widely across datasets, and cross-dataset performance frequently drops substantially relative to within-dataset evaluations. Building on these findings and existing validation guidance, we provide a reusable checklist and decision flow to help researchers select, justify, and report classifier-based measures in ways that support transparency and cumulative science. Recommendations for researchers, reviewers, and journal editors stress aligning model selection with standards of validity, reliability, and transparency. Full article

(This article belongs to the Section Data Mining and Machine Learning)

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20 pages, 17426 KB

Open AccessEditor’s ChoiceArticle

Towards Improved Clinical Adoption of AI Segmentation Models: Benchmarking High-Performance Models for Resource-Constrained Settings

by Emmanuel Chibuikem Nnadozie, Susana Merino-Caviedes, Daniel A. de Luis-Román, Marcos Martín-Fernández and Carlos Alberola-López

Big Data Cogn. Comput. 2026, 10(5), 142; https://doi.org/10.3390/bdcc10050142 - 2 May 2026

Viewed by 845

Abstract

High-performance medical segmentation models are often benchmarked on high-end GPUs. Such benchmarks do not provide useful performance insights for point-of-care low-end devices. This work, firstly, posits that to achieve improved clinical adoption of AI-powered segmentation models, especially in reduced manpower settings like rural [...] Read more.

High-performance medical segmentation models are often benchmarked on high-end GPUs. Such benchmarks do not provide useful performance insights for point-of-care low-end devices. This work, firstly, posits that to achieve improved clinical adoption of AI-powered segmentation models, especially in reduced manpower settings like rural hospitals, we need benchmarks that provide actionable insights on the degree to which high-performance models address five deployment constraints viz: resource-effectiveness for low-end computing devices, clinically acceptable accuracy, clinically compatible execution times, localization of user data, and user-based finetuning. In this work, five state-of-the-art foundation segmentation models and one target-specific model were systematically evaluated on three multi-organ medical datasets. Furthermore, the best-ranking foundation model and target-specific model were benchmarked on three low-end devices. Our findings show that lightweight foundation models provided the best performance trade-off and are easily user-fine-tuned on custom datasets. Target-specific models provide high accuracy out-of-the-box, but may require significant optimisation to deliver comparably fast execution times and user-based finetuning on low-end devices. The methods and results from this research provide actionable insights on high-performance medical segmentation models for low-end computing devices, as a necessary step towards improved adoption in resource-limited clinical settings. Full article

(This article belongs to the Special Issue Next-Generation Medical Image Analysis: Multimodal, Decentralized, Fair and Reasoning-Centric Approaches)

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22 pages, 5847 KB

Open AccessEditor’s ChoiceArticle

BERT-Based Models for Normalization of Adverse Drug Event Expressions in Social Media to Standard Medical Terminology for Drug Safety Analysis

by Fan Dong, Wenjing Guo, Jie Liu, Ann Varghese, Weida Tong, Tucker A. Patterson and Huixiao Hong

Big Data Cogn. Comput. 2026, 10(5), 141; https://doi.org/10.3390/bdcc10050141 - 2 May 2026

Viewed by 811

Abstract

Social media platforms host abundant and timely descriptions of medication experiences that can complement traditional pharmacovigilance systems. Yet the linguistic informality of these data presents a major challenge for mapping adverse drug event (ADE) expressions to standardized medical terminology. In this study, we [...] Read more.

Social media platforms host abundant and timely descriptions of medication experiences that can complement traditional pharmacovigilance systems. Yet the linguistic informality of these data presents a major challenge for mapping adverse drug event (ADE) expressions to standardized medical terminology. In this study, we developed BERT-based language models to classify ADE mentions from social media into MedDRA System Organ Classes (SOCs). Using the SMM4H and CADEC corpora, as well as their combination, we performed 20 iterations of 20% holdout validation for 3-, 6-, 22-, and 25-SOC classification tasks with a selected fixed training configuration (learning rate, batch size, and training epochs) based on training-loss convergence. The models achieved accuracies ranging from 75% to 94%, demonstrating strong performance for SOC-level classification of noisy and informal ADE expressions under the evaluated settings. These results are based on a controlled mention-level evaluation using deduplicated adverse drug event strings and do not establish document-level or real-world deployment generalization. This work provides a systematic evaluation of BERT-based models for SOC-level classification of ADEs and demonstrates consistent performance within the evaluated datasets and label granularities. While direct comparison with prior studies is limited by differences in datasets and evaluation protocols, the results demonstrate that transformer-based models can effectively classify ADEs into SOCs. These findings support the use of transformer-based normalization for SOC-level aggregation of user-reported adverse events and their integration into large-scale social media pharmacovigilance pipelines as a downstream component under controlled conditions. Full article

(This article belongs to the Section Data Mining and Machine Learning)

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29 pages, 1779 KB

Open AccessEditor’s ChoiceArticle

BWT-Enhanced Compression for GIS Raster Data: A Hybrid AV1-Inspired Approach with Burrows–Wheeler Transform

by Yair Wiseman

Big Data Cogn. Comput. 2026, 10(5), 140; https://doi.org/10.3390/bdcc10050140 - 1 May 2026

Viewed by 959

Abstract

The AVIF (AV1 Image File Format) is a modern, royalty-free image format that leverages the AV1 video codec for superior compression efficiency, supporting both lossy and lossless modes. Its entropy encoding relies on a multi-symbol context-adaptive arithmetic coder (range coding with adaptive cumulative [...] Read more.

The AVIF (AV1 Image File Format) is a modern, royalty-free image format that leverages the AV1 video codec for superior compression efficiency, supporting both lossy and lossless modes. Its entropy encoding relies on a multi-symbol context-adaptive arithmetic coder (range coding with adaptive cumulative distribution functions (CDFs)), which is effective for general imagery but may not optimally exploit the repetitive structures common in Geographic Information System (GIS) maps/data. This paper proposes replacing AVIF’s entropy encoder with the Burrows–Wheeler Transform (BWT), a reversible preprocessing algorithm that rearranges data to create runs of similar symbols, enhancing subsequent compression. We detail the technical steps for modification, drawing from AV1’s open-source implementation, and explain why BWT is advantageous for GIS raster maps/data, which often feature large uniform areas, limited color palettes, and spatial redundancies. Empirical evidence from related studies on BWT-based image compression shows improvements in lossless scenarios, potentially considerably reducing file sizes over standard methods while preserving data integrity critical for geospatial analysis. This swap could improve storage, transmission, and processing efficiency in GIS applications, such as remote sensing and cartography. The discussion includes challenges like computational overhead and compatibility, with recommendations for implementations. The resulting BWT-AVIF hybrid produces a non-standard AV1 bit-stream that is not compliant with the AV1 or AVIF specifications and therefore requires custom decoders. It is presented here as a research prototype for GIS-specific compression rather than a compliant AVIF extension. Full article

(This article belongs to the Special Issue Intelligent Communication and Sensor Networks for Advanced Signal Processing)

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36 pages, 5579 KB

Open AccessArticle

A Hybrid Artificial Intelligence Framework for Reliable and Seamless Vertical Handover in Next-Generation Heterogeneous Networks

by Sunisa Kunarak

Big Data Cogn. Comput. 2026, 10(5), 139; https://doi.org/10.3390/bdcc10050139 - 29 Apr 2026

Viewed by 719

Abstract

Next-generation heterogeneous wireless networks (HetNets) comprising LTE macro-cells, 5G New Radio (NR) small cells, and WiFi 6 access points aim to provide seamless connectivity under diverse mobility scenarios. However, vertical handover (VHO) remains a performance bottleneck because of the highly variable radio environments, [...] Read more.

Next-generation heterogeneous wireless networks (HetNets) comprising LTE macro-cells, 5G New Radio (NR) small cells, and WiFi 6 access points aim to provide seamless connectivity under diverse mobility scenarios. However, vertical handover (VHO) remains a performance bottleneck because of the highly variable radio environments, dynamic user mobility, stringent quality of service (QoS) requirements, and the coexistence of multi-tier access technologies. Existing handover approaches based on deep learning and deep reinforcement learning (DRL) suffer from limitations: deep learning models lack decision-making capabilities, whereas DRL models, particularly deep Q-network (DQN)-based policies, face Q-value overestimation and unstable convergence. To overcome these limitations, this paper introduces a Hybrid deep double-Q networks (DDQN)–bidirectional long short-term memory (Bi-LSTM) Framework that integrates bi-directional mobility prediction and DRL-based adaptive decision-making. The Bi-LSTM module captures forward and backward temporal dependencies and predicts future Received Signal Strength (RSS) trajectories, mobility dynamics, and cell-edge transitions. The DDQN module stabilizes the action value estimation, mitigates overestimation bias, and enables context-aware handover decisions. A multi-tier simulation environment consisting of LTE, 5G NR, and WiFi 6 networks was developed using realistic path loss, shadowing, interference, and mobility models. Extensive evaluations demonstrated substantial improvements in mobility prediction accuracy, handover stability, radio link reliability, throughput efficiency, and latency reduction compared to conventional RSS-based and DQN-based schemes. The findings highlight the effectiveness of integrating predictive intelligence with reinforcement learning for reliable mobility management in 5G-Advanced and emerging 6G networks. Full article

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31 pages, 2220 KB

Open AccessArticle

GPU-TOPSIS: A Complete Vectorized and Parallel Reformulation of the TOPSIS Method for Large-Scale Multi-Criteria Decision Making

by Latifa Boubekri, Hassnae Aberkane, Mohammed Chaouki Abounaima and Loubna Lamrini

Big Data Cogn. Comput. 2026, 10(5), 138; https://doi.org/10.3390/bdcc10050138 - 28 Apr 2026

Cited by 1 | Viewed by 986

Abstract

The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is one of the most widely used multi-criteria decision-making (MCDM) approaches in industrial, financial, and scientific fields. However, its sequential computational cost of O(m × n), where m denotes the number [...] Read more.

The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is one of the most widely used multi-criteria decision-making (MCDM) approaches in industrial, financial, and scientific fields. However, its sequential computational cost of O(m × n), where m denotes the number of alternatives and n the number of criteria, becomes prohibitive when decision matrices have several million rows. Despite its geometric interpretability and simplicity, classical TOPSIS faces two key computational bottlenecks at scale: (i) Euclidean distance calculations O(m × n) dominating the total cost, and (ii) the O(m × log m) sorting step, both inherently sequential and memory-bound on CPUs. To overcome these limitations, we propose GPU-TOPSIS, a fully vectorized and parallel reformulation of TOPSIS based on tensor execution on graphics processing units (GPUs), whose main contributions are: (i) a formally correct reformulation of TOPSIS as a GPU tensor pipeline preserving mathematical fidelity to the original method; (ii) a two-pass fragment-processing algorithm guaranteeing exact mathematical equivalence with monolithic TOPSIS, while reducing the memory footprint from O(m × n) to O(m_t × n), where m_t < m is the size of each independently processed fragment; (iii) three independent implementations on CuPy, PyTorch, and TensorFlow, ensuring the framework’s portability and genericity. Experimental evaluations on real data from the Amazon Products 2023 dataset, using matrices of up to 200 million alternatives (via the 2-pass formulation), demonstrate speedups of up to 4.75× compared to the reference CPU implementation (NumPy), with inter-backend score differences below 5 × 10⁻⁸ and 100% ranking overlap across all tested Top-K thresholds. A perturbation sensitivity analysis of the criteria weights and cross-backend consistency tests confirms that GPU acceleration fully preserves robustness and decision reliability, making GPU-TOPSIS a practical, open, and reproducible solution for large-scale multi-criteria decision making in Big Data environments. Full article

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25 pages, 1483 KB

Open AccessReview

A Review of Key Technologies for Systems Based on Non-Volatile Memory

by Yuhan Zhang, Zehang Wang, Yuanfang Chen, Chunfeng Du and Jing Chen

Big Data Cogn. Comput. 2026, 10(5), 137; https://doi.org/10.3390/bdcc10050137 - 27 Apr 2026

Viewed by 1110

Abstract

With the continuous growth of data-intensive applications and artificial intelligence workloads, traditional dynamic random access memory (DRAM) is increasingly struggling to meet demands in terms of capacity scale, energy consumption constraints, and data retention after power failure. Consequently, non-volatile memory (NVM) has emerged [...] Read more.

With the continuous growth of data-intensive applications and artificial intelligence workloads, traditional dynamic random access memory (DRAM) is increasingly struggling to meet demands in terms of capacity scale, energy consumption constraints, and data retention after power failure. Consequently, non-volatile memory (NVM) has emerged as a crucial technology for bridging the gap between the memory and storage layers. However, due to inherent differences in write life, read–write performance variations, and consistency guarantee after failure, the systematic application of NVM still faces a series of challenges. Addressing these issues, this paper takes as its starting point the adaptation of medium characteristics and system design, and summarizes the research progress in aspects such as write optimization, consistency and security coordination mechanisms, data structure modification under hybrid memory architecture, and cross-layer resource collaboration. It also conducts an in-depth analysis of representative solutions and evaluation methods. The review results show that current research has shifted from improving a single performance bottleneck to multi-mechanism collaborative optimization. Various technical approaches have proven complementary in alleviating write amplification, enhancing persistence efficiency, and optimizing access patterns. This paper demonstrates that achieving stable and scalable application of NVM requires establishing a more systematic collaborative design concept between durability, security, and performance. As AI training workloads and big data analytics place increasing demands on memory bandwidth and persistence, the techniques surveyed here provide a foundational basis for next-generation memory-centric computing infrastructures. Full article

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

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30 pages, 12283 KB

Open AccessArticle

A Robust Ensemble Learning Approach to URL-Based Phishing Webpage Detection

by Abdellah Rezoug and Mohamed Bader-el-den

Big Data Cogn. Comput. 2026, 10(5), 136; https://doi.org/10.3390/bdcc10050136 - 27 Apr 2026

Viewed by 1327

Abstract

The proliferation of online fraud has resulted in substantial financial damage to individuals and organizations alike, with web phishing emerging as one of the most pervasive and harmful attack vectors. In response, this paper proposes the Stacking Ensemble Models Generator (SEMG), a URL-based [...] Read more.

The proliferation of online fraud has resulted in substantial financial damage to individuals and organizations alike, with web phishing emerging as one of the most pervasive and harmful attack vectors. In response, this paper proposes the Stacking Ensemble Models Generator (SEMG), a URL-based phishing detection approach that leverages a multi-objective Genetic Algorithm to jointly optimize Precision and Recall in the selection and configuration of stacking ensemble models. An initial pool of base learners is trained on labeled datasets and subsequently evolved through genetic operators toward a globally optimal ensemble. Experimental evaluation across five datasets sourced from Mendeley and UCI repositories demonstrates that SEMG consistently surpasses individual base learners and compares favorably against existing methods, attaining

99.2 %

performance across all metrics on D2 while matching or exceeding state-of-the-art results on the remaining benchmarks. These outcomes underscore the framework’s robustness and its potential for deployment in real-world phishing detection systems. Full article

(This article belongs to the Section Data Mining and Machine Learning)

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