Big Data and Cognitive Computing

35 pages, 4330 KiB

Open AccessArticle

Ship Typhoon Avoidance Route Planning Method Under Uncertain Typhoon Forecasts

by Zhengwei He, Junhong Guo, Weihao Ma and Jinfeng Zhang

Big Data Cogn. Comput. 2025, 9(6), 143; https://doi.org/10.3390/bdcc9060143 - 23 May 2025

Formulating effective typhoon avoidance routes is crucial for ensuring the safe navigation of ocean-going vessels. From a maritime safety perspective, this paper investigates ship route optimization under typhoon forecast uncertainty. Initially, the study calculates the probability of a ship encountering a typhoon based [...] Read more.

Formulating effective typhoon avoidance routes is crucial for ensuring the safe navigation of ocean-going vessels. From a maritime safety perspective, this paper investigates ship route optimization under typhoon forecast uncertainty. Initially, the study calculates the probability of a ship encountering a typhoon based on the distribution of historical typhoon data within the radius of seven-level winds and the distance between the ship and the typhoon. Subsequently, the minimum safe distance is quantified, and a multi-objective ship route optimization model for typhoon avoidance is established. A three-dimensional multi-objective ant colony algorithm is designed to solve this model. Finally, a typhoon avoidance simulation experiment is conducted using Typhoon TAMRI and a classic route in the South China Sea as a case study. The experimental results demonstrate that under adverse conditions of uncertain typhoon forecasts, the proposed multi-objective typhoon avoidance route optimization model can effectively avoid high wind and wave areas of the typhoon while balancing and optimizing multiple navigation indicators. This model can serve as a reference for shipping companies in formulating typhoon avoidance strategies. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Traffic Management)

20 pages, 932 KiB

Open AccessArticle

Predicting the Damage of Urban Fires with Grammatical Evolution

by Constantina Kopitsa, Ioannis G. Tsoulos, Andreas Miltiadous and Vasileios Charilogis

Big Data Cogn. Comput. 2025, 9(6), 142; https://doi.org/10.3390/bdcc9060142 - 22 May 2025

Abstract

Fire, whether wild or urban, depends on the triad of oxygen, fuel, and heat. Urban fires, although smaller in scale, have devastating impacts, as evidenced by the 2018 wildfire in Mati, Attica (Greece), which claimed 104 lives. The elderly and children are the [...] Read more.

Fire, whether wild or urban, depends on the triad of oxygen, fuel, and heat. Urban fires, although smaller in scale, have devastating impacts, as evidenced by the 2018 wildfire in Mati, Attica (Greece), which claimed 104 lives. The elderly and children are the most vulnerable due to mobility and cognitive limitations. This study applies Grammatical Evolution (GE), a machine learning method that generates interpretable classification rules to predict the consequences of urban fires. Using historical data (casualties, containment time, and meteorological/demographic parameters), GE produces classification rules in human-readable form. The rules achieve over 85% accuracy, revealing critical correlations. For example, high temperatures (>35 °C) combined with irregular building layouts exponentially increase fatality risks, while firefighter response time proves more critical than fire intensity itself. Applications include dynamic evacuation strategies (real-time adaptation), preventive urban planning (fire-resistant materials and green buffer zones), and targeted awareness campaigns for at-risk groups. Unlike “black-box” machine learning techniques, GE offers transparent human-readable rules, enabling firefighters and authorities to make rapid informed decisions. Future advancements could integrate real-time data (IoT sensors and satellites) and extend the methodology to other natural disasters. Protecting urban centers from fires is not only a technological challenge but also a moral imperative to safeguard human lives and societal cohesion. Full article

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42 pages, 1639 KiB

Open AccessArticle

A Comprehensive Evaluation of Embedding Models and LLMs for IR and QA Across English and Italian

by Ermelinda Oro, Francesco Maria Granata and Massimo Ruffolo

Big Data Cogn. Comput. 2025, 9(5), 141; https://doi.org/10.3390/bdcc9050141 - 21 May 2025

Abstract

This study presents a comprehensive evaluation of embedding techniques and large language models (LLMs) for Information Retrieval (IR) and question answering (QA) across languages, focusing on English and Italian. We address a significant research gap by providing empirical evidence of model performance across [...] Read more.

This study presents a comprehensive evaluation of embedding techniques and large language models (LLMs) for Information Retrieval (IR) and question answering (QA) across languages, focusing on English and Italian. We address a significant research gap by providing empirical evidence of model performance across linguistic boundaries. We evaluate 12 embedding models on diverse IR datasets, including Italian SQuAD and DICE, English SciFact, ArguAna, and NFCorpus. We assess four LLMs (GPT4o, LLama-3.1 8B, Mistral-Nemo, and Gemma-2b) for QA tasks within a retrieval-augmented generation (RAG) pipeline. We evaluate them on SQuAD, CovidQA, and NarrativeQA datasets, including cross-lingual scenarios. The results show multilingual models perform more competitively than language-specific ones. The embed-multilingual-v3.0 model achieves top nDCG@10 scores of 0.90 for English and 0.86 for Italian. In QA evaluation, Mistral-Nemo demonstrates superior answer relevance (0.91–1.0) while maintaining strong groundedness (0.64–0.78). Our analysis reveals three key findings: (1) multilingual embedding models effectively bridge performance gaps between English and Italian, though performance consistency decreases in specialized domains, (2) model size does not consistently predict performance, and (3) all evaluated QA systems exhibit a critical trade-off between answer relevance and factual groundedness. Our evaluation framework combines traditional metrics with innovative LLM-based assessment techniques. It establishes new benchmarks for multilingual language technologies while providing actionable insights for real-world IR and QA system deployment. Full article

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

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25 pages, 2733 KiB

Open AccessArticle

Polarity of Yelp Reviews: A BERT–LSTM Comparative Study

by Rachid Belaroussi, Sié Cyriac Noufe, Francis Dupin and Pierre-Olivier Vandanjon

Big Data Cogn. Comput. 2025, 9(5), 140; https://doi.org/10.3390/bdcc9050140 - 21 May 2025

Abstract

With the rapid growth in social network comments, the need for more effective methods to classify their polarity—negative, neutral, or positive—has become essential. Sentiment analysis, powered by natural language processing, has evolved significantly with the adoption of advanced deep learning techniques. Long Short-Term [...] Read more.

With the rapid growth in social network comments, the need for more effective methods to classify their polarity—negative, neutral, or positive—has become essential. Sentiment analysis, powered by natural language processing, has evolved significantly with the adoption of advanced deep learning techniques. Long Short-Term Memory networks capture long-range dependencies in text, while transformers, with their attention mechanisms, excel at preserving contextual meaning and handling high-dimensional, semantically complex data. This study compares the performance of sentiment analysis models based on LSTM and BERT architectures using key evaluation metrics. The dataset consists of business reviews from the Yelp Open Dataset. We tested LSTM-based methods against BERT and its variants—RoBERTa, BERTweet, and DistilBERT—leveraging popular pipelines from the Hugging Face Hub. A class-by-class performance analysis is presented, revealing that more complex BERT-based models do not always guarantee superior results in the classification of Yelp reviews. Additionally, the use of bidirectionality in LSTMs does not necessarily lead to better performance. However, across a diversity of test sets, transformer models outperform traditional RNN-based models, as their generalization capability is greater than that of a simple LSTM model. Full article

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

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17 pages, 1137 KiB

Open AccessArticle

Tri-Collab: A Machine Learning Project to Leverage Innovation Ecosystems in Portugal

by Ângelo Marujo, Bruno Afonso, Inês Martins, Lisandro Pires and Sílvia Fernandes

Big Data Cogn. Comput. 2025, 9(5), 139; https://doi.org/10.3390/bdcc9050139 - 20 May 2025

Abstract

This project consists of a digital platform named Tri-Collab, where investors, entrepreneurs, and other agents (mainly talents) can cooperate on their ideas and eventually co-create. It is a digital means for this triad of actors (among other potential ones) to better adjust their [...] Read more.

This project consists of a digital platform named Tri-Collab, where investors, entrepreneurs, and other agents (mainly talents) can cooperate on their ideas and eventually co-create. It is a digital means for this triad of actors (among other potential ones) to better adjust their requirements. It includes an app that easily communicates with a database of projects, innovation agents and their profiles, and the originality lies in the matching algorithm. Thus, co-creation can have better support through this assertive interconnection of players and their resources. This work also highlights the usefulness of the Canvas Business Model in structuring the idea and its dashboard, allowing a comprehensive view of channels, challenges and gains. Also, the potential of machine learning in improving matchmaking platforms is discussed, especially when technological advancements allow for forecasts and match people at scale. Full article

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29 pages, 4204 KiB

Open AccessArticle

A Comparative Study of Ensemble Machine Learning and Explainable AI for Predicting Harmful Algal Blooms

by Omer Mermer, Eddie Zhang and Ibrahim Demir

Big Data Cogn. Comput. 2025, 9(5), 138; https://doi.org/10.3390/bdcc9050138 - 20 May 2025

Abstract

Harmful algal blooms (HABs), driven by environmental pollution, pose significant threats to water quality, public health, and aquatic ecosystems. This study enhances the prediction of HABs in Lake Erie, part of the Great Lakes system, by utilizing ensemble machine learning (ML) models coupled [...] Read more.

Harmful algal blooms (HABs), driven by environmental pollution, pose significant threats to water quality, public health, and aquatic ecosystems. This study enhances the prediction of HABs in Lake Erie, part of the Great Lakes system, by utilizing ensemble machine learning (ML) models coupled with explainable artificial intelligence (XAI) for interpretability. Using water quality data from 2013 to 2020, various physical, chemical, and biological parameters were analyzed to predict chlorophyll-a (Chl-a) concentrations, which are a commonly used indicator of phytoplankton biomass and a proxy for algal blooms. This study employed multiple ensemble ML models, including random forest (RF), deep forest (DF), gradient boosting (GB), and XGBoost, and compared their performance against individual models, such as support vector machine (SVM), decision tree (DT), and multi-layer perceptron (MLP). The findings revealed that the ensemble models, particularly XGBoost and deep forest (DF), achieved superior predictive accuracy, with R² values of 0.8517 and 0.8544, respectively. The application of SHapley Additive exPlanations (SHAPs) provided insights into the relative importance of the input features, identifying the particulate organic nitrogen (PON), particulate organic carbon (POC), and total phosphorus (TP) as the critical factors influencing the Chl-a concentrations. This research demonstrates the effectiveness of ensemble ML models for achieving high predictive accuracy, while the integration of XAI enhances model interpretability. The results support the development of proactive water quality management strategies and highlight the potential of advanced ML techniques for environmental monitoring. Full article

(This article belongs to the Special Issue Machine Learning Applications and Big Data Challenges)

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21 pages, 1559 KiB

Open AccessArticle

The Development of Small-Scale Language Models for Low-Resource Languages, with a Focus on Kazakh and Direct Preference Optimization

by Nurgali Kadyrbek, Zhanseit Tuimebayev, Madina Mansurova and Vítor Viegas

Big Data Cogn. Comput. 2025, 9(5), 137; https://doi.org/10.3390/bdcc9050137 - 20 May 2025

Abstract

Low-resource languages remain underserved by contemporary large language models (LLMs) because they lack sizable corpora, bespoke preprocessing tools, and the computing budgets assumed by mainstream alignment pipelines. Focusing on Kazakh, we present a 1.94B parameter LLaMA-based model that demonstrates how strong, culturally aligned [...] Read more.

Low-resource languages remain underserved by contemporary large language models (LLMs) because they lack sizable corpora, bespoke preprocessing tools, and the computing budgets assumed by mainstream alignment pipelines. Focusing on Kazakh, we present a 1.94B parameter LLaMA-based model that demonstrates how strong, culturally aligned performance can be achieved without massive infrastructure. The contribution is threefold. (i) Data and tokenization—we compile a rigorously cleaned, mixed-domain Kazakh corpus and design a tokenizer that respects the language’s agglutinative morphology, mixed-script usage, and diacritics. (ii) Training recipe—the model is built in two stages: causal language modeling from scratch followed by instruction tuning. Alignment is further refined with Direct Preference Optimization (DPO), extended by contrastive and entropy-based regularization to stabilize training under sparse, noisy preference signals. Two complementary resources support this step: ChatTune-DPO, a crowd-sourced set of human preference pairs, and Pseudo-DPO, an automatically generated alternative that repurposes instruction data to reduce annotation cost. (iii) Evaluation and impact—qualitative and task-specific assessments show that targeted monolingual training and the proposed DPO variant markedly improve factuality, coherence, and cultural fidelity over baseline instruction-only and multilingual counterparts. The model and datasets are released under open licenses, offering a reproducible blueprint for extending state-of-the-art language modeling to other under-represented languages and domains. Full article

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15 pages, 1909 KiB

Open AccessArticle

Helium Speech Recognition Method Based on Spectrogram with Deep Learning

by Yonghong Chen, Shibing Zhang and Dongmei Li

Big Data Cogn. Comput. 2025, 9(5), 136; https://doi.org/10.3390/bdcc9050136 - 20 May 2025

Abstract

With the development of the marine economy and the increase in marine activities, deep saturation diving has gained significant attention. Helium speech communication is indispensable for saturation diving operations and is a critical technology for deep saturation diving, serving as the sole communication [...] Read more.

With the development of the marine economy and the increase in marine activities, deep saturation diving has gained significant attention. Helium speech communication is indispensable for saturation diving operations and is a critical technology for deep saturation diving, serving as the sole communication method to ensure the smooth execution of such operations. This study introduces deep learning into helium speech recognition and proposes a spectrogram-based dual-model helium speech recognition method. First, we extract the spectrogram features from the helium speech. Then, we combine a deep fully convolutional neural network with connectionist temporal classification (CTC) to form an acoustic model, in which the spectrogram features of helium speech are used as an input to convert speech signals into phonetic sequences. Finally, a maximum entropy hidden Markov model (MEMM) is employed as the language model to convert the phonetic sequences to word outputs, which is regarded as a dynamic programming problem. We use a Viterbi algorithm to find the optimal path to decode the phonetic sequences to word sequences. The simulation results show that the method can effectively recognize helium speech with a recognition rate of 97.89% for isolated words and 95.99% for continuous helium speech. Full article

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35 pages, 3235 KiB

Open AccessArticle

Applying Big Data for Maritime Accident Risk Assessment: Insights, Predictive Insights and Challenges

by Vicky Zampeta, Gregory Chondrokoukis and Dimosthenis Kyriazis

Big Data Cogn. Comput. 2025, 9(5), 135; https://doi.org/10.3390/bdcc9050135 - 19 May 2025

Abstract

Maritime safety is a critical concern for the transport sector and remains a key challenge for the international shipping industry. Recognizing that maritime accidents pose significant risks to both safety and operational efficiency, this study explores the application of big data analysis techniques [...] Read more.

Maritime safety is a critical concern for the transport sector and remains a key challenge for the international shipping industry. Recognizing that maritime accidents pose significant risks to both safety and operational efficiency, this study explores the application of big data analysis techniques to understand the factors influencing maritime transport accidents (MTA). Specifically, using extensive datasets derived from vessel performance measurements, environmental conditions, and accident reports, it seeks to identify the key intrinsic and extrinsic factors contributing to maritime accidents. The research examines more than 90 thousand incidents for the period 2014–2022. Leveraging big data analytics and advanced statistical techniques, the findings reveal significant correlations between vessel size, speed, and specific environmental factors. Furthermore, the study highlights the potential of big data analytics in enhancing predictive modeling, real-time risk assessment, and decision-making processes for maritime traffic management. The integration of big data with intelligent transportation systems (ITSs) can optimize safety strategies, improve accident prevention mechanisms, and enhance the resilience of ocean-going transportation systems. By bridging the gap between big data applications and maritime safety research, this work contributes to the literature by emphasizing the importance of examining both intrinsic and extrinsic factors in predicting maritime accident risks. Additionally, it underscores the transformative role of big data in shaping safer and more efficient waterway transportation systems. Full article

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20 pages, 1064 KiB

Open AccessArticle

Predicting Early Employability of Vietnamese Graduates: Insights from Data-Driven Analysis Through Machine Learning Methods

by Long-Sheng Chen, Thao-Trang Huynh-Cam, Van-Canh Nguyen, Tzu-Chuen Lu and Dang-Khoa Le-Huynh

Big Data Cogn. Comput. 2025, 9(5), 134; https://doi.org/10.3390/bdcc9050134 - 19 May 2025

Abstract

Graduate employability remains a crucial challenge for higher education institutions, especially in developing economies. This study investigates the key academic and vocational factors influencing early employment outcomes among recent graduates at a public university in Vietnam’s Mekong Delta region. By leveraging predictive analytics, [...] Read more.

Graduate employability remains a crucial challenge for higher education institutions, especially in developing economies. This study investigates the key academic and vocational factors influencing early employment outcomes among recent graduates at a public university in Vietnam’s Mekong Delta region. By leveraging predictive analytics, the research explores how data-driven approaches can enhance career readiness strategies. The analysis employed AI-driven models, particularly classification and regression trees (CARTs), using a dataset of 610 recent graduates from a public university in the Mekong Delta to predict early employability. The input factors included gender, field of study, university entrance scores, and grade point average (GPA) scores for four university years. The output factor was recent graduates’ (un)employment within six months after graduation. Among all input factors, third-year GPA, university entrance scores, and final-year academic performance are the most significant predictors of early employment. Among the tested models, CARTs achieved the highest accuracy (93.6%), offering interpretable decision rules that can inform curriculum design and career support services. This study contributes to the intersection of artificial intelligence and vocational education by providing actionable insights for universities, policymakers, and employers, supporting the alignment of education with labor market demands and improving graduate employability outcomes. Full article

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26 pages, 2125 KiB

Open AccessArticle

Adaptive Augmented Reality Architecture for Optimising Assistance and Safety in Industry 4.0

by Ginés Morales Méndez and Francisco del Cerro Velázquez

Big Data Cogn. Comput. 2025, 9(5), 133; https://doi.org/10.3390/bdcc9050133 - 19 May 2025

Abstract

The present study proposes adaptive augmented reality (AR) architecture, specifically designed to enhance real-time operator assistance and occupational safety in industrial environments, which is representative of Industry 4.0. The proposed system addresses key challenges in AR adoption, such as the need for dynamic [...] Read more.

The present study proposes adaptive augmented reality (AR) architecture, specifically designed to enhance real-time operator assistance and occupational safety in industrial environments, which is representative of Industry 4.0. The proposed system addresses key challenges in AR adoption, such as the need for dynamic personalisation of instructions based on operator profiles and the mitigation of technical and cognitive barriers. Architecture integrates theoretical modelling, modular design, and real-time adaptability to match instruction complexity with user expertise and environmental conditions. A working prototype was implemented using Microsoft HoloLens 2, Unity 3D, and Vuforia and validated in a controlled industrial scenario involving predictive maintenance and assembly tasks. The experimental results demonstrated statistically significant enhancements in task completion time, error rates, perceived cognitive load, operational efficiency, and safety indicators in comparison with conventional methods. The findings underscore the system’s capacity to enhance both performance and consistency while concomitantly bolstering risk mitigation in intricate operational settings. This study proposes a scalable and modular AR framework with built-in safety and adaptability mechanisms, demonstrating practical benefits for human–machine interaction in Industry 4.0. The present study is subject to certain limitations, including validation in a simulated environment, which limits the direct extrapolation of the results to real industrial scenarios; further evaluation in various operational contexts is required to verify the overall scalability and applicability of the proposed system. It is recommended that future research studies explore the long-term ergonomics, scalability, and integration of emerging technologies in decision support within adaptive AR systems. Full article

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25 pages, 11184 KiB

Open AccessArticle

Comparative Evaluation of Multimodal Large Language Models for No-Reference Image Quality Assessment with Authentic Distortions: A Study of OpenAI and Claude.AI Models

by Domonkos Varga

Big Data Cogn. Comput. 2025, 9(5), 132; https://doi.org/10.3390/bdcc9050132 - 16 May 2025

Abstract

This study presents a comparative analysis of several multimodal large language models (LLMs) for no-reference image quality assessment, with a particular focus on images containing authentic distortions. We evaluate three models developed by OpenAI and three models from Claude.AI, comparing their performance in [...] Read more.

This study presents a comparative analysis of several multimodal large language models (LLMs) for no-reference image quality assessment, with a particular focus on images containing authentic distortions. We evaluate three models developed by OpenAI and three models from Claude.AI, comparing their performance in estimating image quality without reference images. Our results demonstrate that these LLMs outperform traditional methods based on hand-crafted features. However, more advanced deep learning models, especially those based on deep convolutional networks, surpass LLMs in performance. Notably, we make a unique contribution by publishing the processed outputs of the LLMs, providing a transparent and direct comparison of their quality assessments based solely on the predicted quality scores. This work underscores the potential of multimodal LLMs in image quality evaluation, while also highlighting the continuing advantages of specialized deep learning approaches. Full article

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

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17 pages, 959 KiB

Open AccessArticle

Evaluating the Impact of Artificial Intelligence Tools on Enhancing Student Academic Performance: Efficacy Amidst Security and Privacy Concerns

by Jwern Tick Kiet Phua, Han-Foon Neo and Chuan-Chin Teo

Big Data Cogn. Comput. 2025, 9(5), 131; https://doi.org/10.3390/bdcc9050131 - 15 May 2025

Abstract

The rapid advancements in artificial intelligence (AI) have significantly transformed various domains, including education, by introducing innovative tools that reshape teaching and learning processes. This research investigates the perceptions and attitudes of students towards the use of AI tools in their academic activities, [...] Read more.

The rapid advancements in artificial intelligence (AI) have significantly transformed various domains, including education, by introducing innovative tools that reshape teaching and learning processes. This research investigates the perceptions and attitudes of students towards the use of AI tools in their academic activities, focusing on constructs such as perceived usefulness, the perceived ease of use, security and privacy concerns, and both positive and negative attitudes towards AI. On the basis of Technology Acceptance Model (TAM) and the General Attitudes towards Artificial Intelligence Scale (GAAIS), this research seeks to identify the factors influencing students’ behavioral intentions and actual adoption of AI tools in educational settings. A structured survey was administered to students at Multimedia University, Malaysia, capturing their experiences and opinions on widely used AI tools such as ChatGPT, Quillbot, Grammarly, and Perplexity. Hypothesis testing was used to evaluate the statistical significance of relationships between the constructs and behavioral intention and actual use of the AI tools. The findings reveal a high level of engagement with AI tools among University students, primarily driven by their perceived benefits in enhancing academic performance, improving efficiency, and facilitating personalized learning experiences. The findings also uncover significant concerns related to data security, privacy, and the potential over-reliance on AI tools, which may hinder the development of critical thinking and problem-solving skills. Full article

(This article belongs to the Special Issue Security, Privacy, and Trust in Artificial Intelligence Applications)

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20 pages, 5358 KiB

Open AccessArticle

Machine Learning-Based Classification of Sulfide Mineral Spectral Emission in High Temperature Processes

by Carlos Toro, Walter Díaz, Gonzalo Reyes, Miguel Peña, Nicolás Caselli, Carla Taramasco, Pablo Ormeño-Arriagada and Eduardo Balladares

Big Data Cogn. Comput. 2025, 9(5), 130; https://doi.org/10.3390/bdcc9050130 - 14 May 2025

Abstract

Accurate classification of sulfide minerals during combustion is essential for optimizing pyrometallurgical processes such as flash smelting, where efficient combustion impacts resource utilization, energy efficiency, and emission control. This study presents a deep learning-based approach for classifying visible and near-infrared (VIS-NIR) emission spectra [...] Read more.

Accurate classification of sulfide minerals during combustion is essential for optimizing pyrometallurgical processes such as flash smelting, where efficient combustion impacts resource utilization, energy efficiency, and emission control. This study presents a deep learning-based approach for classifying visible and near-infrared (VIS-NIR) emission spectra from the combustion of high-grade sulfide minerals. A one-dimensional convolutional neural network (1D-CNN) was developed and trained on experimentally acquired spectral data, achieving a balanced accuracy score of 99.0% in a test set. The optimized deep learning model outperformed conventional machine learning methods, highlighting the effectiveness of deep learning for spectral analysis in high-temperature environments. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to enhance model interpretability and identify key spectral regions contributing to classification decisions. The results demonstrated that the model successfully distinguished spectral features associated with different mineral species, offering insights into combustion dynamics. These findings support the potential integration of deep learning for real-time spectral monitoring in industrial flash smelting operations, thereby enabling more precise process control and decision-making. Full article

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

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21 pages, 9562 KiB

Open AccessArticle

Identifying Influential Nodes in Complex Networks via Transformer with Multi-Scale Feature Fusion

by Tingshuai Jiang, Yirun Ruan, Tianyuan Yu, Liang Bai and Yifei Yuan

Big Data Cogn. Comput. 2025, 9(5), 129; https://doi.org/10.3390/bdcc9050129 - 14 May 2025

Abstract

In complex networks, the identification of critical nodes is vital for optimizing information dissemination. Given the significant role of these nodes in network structures, researchers have proposed various identification methods. In recent years, deep learning has emerged as a promising approach for identifying [...] Read more.

In complex networks, the identification of critical nodes is vital for optimizing information dissemination. Given the significant role of these nodes in network structures, researchers have proposed various identification methods. In recent years, deep learning has emerged as a promising approach for identifying key nodes in networks. However, existing algorithms fail to effectively integrate local and global structural information, leading to incomplete and limited network understanding. To overcome this limitation, we introduce a transformer framework with multi-scale feature fusion (MSF-Former). In this framework, we construct local and global feature maps for nodes and use them as input. Through the transformer module, node information is effectively aggregated, thereby improving the model’s ability to recognize key nodes. We perform evaluations using six real-world and three synthetic network datasets, comparing our method against multiple baselines using the SIR model to validate its effectiveness. Experimental analysis confirms that MSF-Former achieves consistently high accuracy in the identification of influential nodes across real-world and synthetic networks. Full article

(This article belongs to the Special Issue Advances in Complex Networks)

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21 pages, 12662 KiB

Open AccessReview

Benchmarking of Anomaly Detection Methods for Industry 4.0: Evaluation, Ranking, and Practical Recommendations

by Aurélie Cools, Mohammed Amin Belarbi and Sidi Ahmed Mahmoudi

Big Data Cogn. Comput. 2025, 9(5), 128; https://doi.org/10.3390/bdcc9050128 - 13 May 2025

Abstract

Quality control and predictive maintenance are two essential pillars of Industry 4.0, aiming to optimize production, reduce operational costs, and enhance system reliability. Real-time visual inspection ensures early detection of manufacturing defects, assembly errors, or texture inconsistencies, preventing defective products from reaching customers. [...] Read more.

Quality control and predictive maintenance are two essential pillars of Industry 4.0, aiming to optimize production, reduce operational costs, and enhance system reliability. Real-time visual inspection ensures early detection of manufacturing defects, assembly errors, or texture inconsistencies, preventing defective products from reaching customers. Predictive maintenance leverages sensor data by analyzing vibrations, temperature, and pressure signals to anticipate failures and avoid production downtime. Image-based quality control has become critical in industries such as automotive, electronics, aerospace, and food processing, where visual appearance is a key quality indicator. Although advances in deep learning and computer vision have significantly improved anomaly detection, industrial deployments remain challenged by the scarcity of labeled anomalies and the variability of defects. These issues increasingly lead to the adoption of unsupervised methods and generative approaches, which, despite their effectiveness, introduce substantial computational complexity. We conduct a unified comparison of ten anomaly detection methods, categorizing them according to their reliance on synthetic anomaly generation and their detection strategy, either reconstruction-based or feature-based. All models are trained exclusively on normal data to mirror realistic industrial conditions. Our evaluation framework combines performance metrics such as recall, precision, and their harmonic mean, emphasizing the need to minimize false negatives that could lead to critical production failures. In addition, we assess environmental impact and hardware complexity to better guide method selection. Practical recommendations are provided to balance robustness, operational feasibility, and sustainability in industrial applications. Full article

(This article belongs to the Special Issue Fault Diagnosis and Detection Based on Deep Learning)

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18 pages, 11024 KiB

Open AccessArticle

Rail Surface Defect Diagnosis Based on Image–Vibration Multimodal Data Fusion

by Zhongmei Wang, Shenao Peng, Wenxiu Ao, Jianhua Liu and Changfan Zhang

Big Data Cogn. Comput. 2025, 9(5), 127; https://doi.org/10.3390/bdcc9050127 - 12 May 2025

Abstract

To address the challenges in existing multi-sensor data fusion methods for rail surface defect diagnosis, particularly their limitations in fully exploiting potential synergistic information among multimodal data and effectively bridging the semantic gap between heterogeneous multi-source data, this paper proposes a diagnostic approach [...] Read more.

To address the challenges in existing multi-sensor data fusion methods for rail surface defect diagnosis, particularly their limitations in fully exploiting potential synergistic information among multimodal data and effectively bridging the semantic gap between heterogeneous multi-source data, this paper proposes a diagnostic approach based on a Progressive Joint Representation Graph Attention Fusion Network (PJR-GAFN). The methodology comprises five principal phases: Firstly, shared and specific autoencoders are used to extract joint representations of multimodal features through shared and modality-specific representations. Secondly, a squeeze-and-excitation module is implemented to amplify defect-related features while suppressing non-essential characteristics. Thirdly, a progressive fusion module is introduced to comprehensively utilize cross-modal synergistic information during feature extraction. Fourthly, a source domain classifier and domain discriminator are employed to capture modality-invariant features across different modalities. Finally, the spatial attention aggregation properties of graph attention networks are leveraged to fuse multimodal features, thereby fully exploiting contextual semantic information. Experimental results on real-world rail surface defect datasets from domestic railway lines demonstrate that the proposed method achieves 95% diagnostic accuracy, confirming its effectiveness in rail surface defect detection. Full article

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28 pages, 11946 KiB

Open AccessArticle

Introducing a Novel Fast Neighbourhood Component Analysis–Deep Neural Network Model for Enhanced Driver Drowsiness Detection

by Sama Hussein Al-Gburi, Kanar Alaa Al-Sammak, Ion Marghescu, Claudia Cristina Oprea, Ana-Maria Claudia Drăgulinescu, George Suciu, Khattab M. Ali Alheeti, Nayef A. M. Alduais and Nawar Alaa Hussein Al-Sammak

Big Data Cogn. Comput. 2025, 9(5), 126; https://doi.org/10.3390/bdcc9050126 - 8 May 2025

Abstract

Driver fatigue is a key factor in road accidents worldwide, requiring effective real-time detection mechanisms. Traditional deep neural network (DNN)-based solutions have shown promising results in detecting drowsiness; however, they are often less suitable for real-time applications due to their high computational complexity, [...] Read more.

Driver fatigue is a key factor in road accidents worldwide, requiring effective real-time detection mechanisms. Traditional deep neural network (DNN)-based solutions have shown promising results in detecting drowsiness; however, they are often less suitable for real-time applications due to their high computational complexity, risk of overfitting, and reliance on large datasets. Hence, this paper introduces an innovative approach that integrates fast neighbourhood component analysis (FNCA) with a deep neural network (DNN) to enhance the detection of driver drowsiness using electroencephalogram (EEG) data. FNCA is employed to optimize feature representation, effectively highlighting critical features for drowsiness detection, which are then analysed using a DNN to achieve high accuracy in recognizing signs of driver fatigue. Our model has been evaluated on the SEED-VIG dataset and achieves state-of-the-art accuracy: 94.29% when trained on 12 subjects and 90.386% with 21 subjects, surpassing existing methods such as TSception, ConvNeXt LMDA-Net, and CNN + LSTM. Full article

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23 pages, 3804 KiB

Open AccessArticle

Quantifying Post-Purchase Service Satisfaction: A Topic–Emotion Fusion Approach with Smartphone Data

by Peijun Guo, Huan Li and Xinyue Mo

Big Data Cogn. Comput. 2025, 9(5), 125; https://doi.org/10.3390/bdcc9050125 - 8 May 2025

Abstract

Effectively identifying factors related to user satisfaction is crucial for evaluating customer experience. This study proposes a two-phase analytical framework that combines natural language processing techniques with hierarchical decision-making methods. In Phase 1, an ERNIE-LSTM-based emotion model (ELEM) is used to detect fake [...] Read more.

Effectively identifying factors related to user satisfaction is crucial for evaluating customer experience. This study proposes a two-phase analytical framework that combines natural language processing techniques with hierarchical decision-making methods. In Phase 1, an ERNIE-LSTM-based emotion model (ELEM) is used to detect fake reviews from 4016 smartphone evaluations collected from JD.com (accuracy: 84.77%, recall: 84.86%, F1 score: 84.81%). The filtered genuine reviews are then analyzed using Biterm Topic Modeling (BTM) to extract key satisfaction-related topics, which are weighted based on sentiment scores and organized into a multi-criteria evaluation matrix through the Analytic Hierarchy Process (AHP). These topics are further clustered into five major factors: user-centered design (70.8%), core performance (10.0%), imaging features (8.6%), promotional incentives (7.8%), and industrial design (2.8%). This framework is applied to a comparative analysis of two smartphone stores, revealing that Huawei Mate 60 Pro emphasizes performance, while Redmi Note 11 5G focuses on imaging capabilities. Further clustering of user reviews identifies six distinct user groups, all prioritizing user-centered design and core performance, but showing differences in other preferences. In Phase 2, a comparison of word frequencies between product reviews and community Q and A content highlights hidden user concerns often missed by traditional single-source sentiment analysis, such as screen calibration and pixel density. These findings provide insights into how product design influences satisfaction and offer practical guidance for improving product development and marketing strategies. Full article

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