Big Data and Cognitive Computing

30 pages, 3938 KiB

Open AccessArticle

Cognitive Method for Synthesising a Fuzzy Controller Mathematical Model Using a Genetic Algorithm for Tuning

by Serhii Vladov

Big Data Cogn. Comput. 2025, 9(1), 17; https://doi.org/10.3390/bdcc9010017 - 20 Jan 2025

Viewed by 571

In this article, a fuzzy controller mathematical model synthesising method that uses cognitive computing and a genetic algorithm for automated tuning and adaptation to changing environmental conditions has been developed. The technique consists of 12 stages, including creating the control objects’ mathematical model [...] Read more.

In this article, a fuzzy controller mathematical model synthesising method that uses cognitive computing and a genetic algorithm for automated tuning and adaptation to changing environmental conditions has been developed. The technique consists of 12 stages, including creating the control objects’ mathematical model and tuning the controller coefficients using classical methods. The research pays special attention to the error parameters and their derivative fuzzification, which simplifies the development of logical rules and helps increase the stability of the systems. The fuzzy controller parameters were tuned using a genetic algorithm in a computational experiment based on helicopter flight data. The results show an increase in the integral quality criterion from 85.36 to 98.19%, which confirms an increase in control efficiency by 12.83%. The fuzzy controller use made it possible to significantly improve the helicopter turboshaft engines’ gas-generator rotor speed control performance, reducing the first and second types of errors by 2.06…12.58 times compared to traditional methods. Full article

► Show Figures

Figure 1

19 pages, 1914 KiB

Open AccessArticle

AI-Driven Mental Health Surveillance: Identifying Suicidal Ideation Through Machine Learning Techniques

by Hesham Allam, Chris Davison, Faisal Kalota, Edward Lazaros and David Hua

Big Data Cogn. Comput. 2025, 9(1), 16; https://doi.org/10.3390/bdcc9010016 - 20 Jan 2025

Viewed by 634

Abstract

As suicide rates increase globally, there is a growing need for effective, data-driven methods in mental health monitoring. This study leverages advanced artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), to identify suicidal ideation from Twitter data. A predictive [...] Read more.

As suicide rates increase globally, there is a growing need for effective, data-driven methods in mental health monitoring. This study leverages advanced artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), to identify suicidal ideation from Twitter data. A predictive model was developed to process social media posts in real time, using NLP and sentiment analysis to detect textual and emotional cues associated with distress. The model aims to identify potential suicide risks accurately, while minimizing false positives, offering a practical tool for targeted mental health interventions. The study achieved notable predictive performance, with an accuracy of 85%, precision of 88%, and recall of 83% in detecting potential suicide posts. Advanced preprocessing techniques, including tokenization, stemming, and feature extraction with term frequency–inverse document frequency (TF-IDF) and count vectorization, ensured high-quality data transformation. A random forest classifier was selected for its ability to handle high-dimensional data and effectively capture linguistic and emotional patterns linked to suicidal ideation. The model’s reliability was supported by a precision–recall AUC score of 0.93, demonstrating its potential for real-time mental health monitoring and intervention. By identifying behavioral patterns and triggers, such as social isolation and bullying, this framework provides a scalable and efficient solution for mental health support, contributing significantly to suicide prevention strategies worldwide. Full article

► Show Figures

Figure 1

18 pages, 29962 KiB

Open AccessArticle

Eliciting Emotions: Investigating the Use of Generative AI and Facial Muscle Activation in Children’s Emotional Recognition

by Manuel A. Solis-Arrazola, Raul E. Sanchez-Yanez, Ana M. S. Gonzalez-Acosta, Carlos H. Garcia-Capulin and Horacio Rostro-Gonzalez

Big Data Cogn. Comput. 2025, 9(1), 15; https://doi.org/10.3390/bdcc9010015 - 20 Jan 2025

Viewed by 623

Abstract

This study explores children’s emotions through a novel approach of Generative Artificial Intelligence (GenAI) and Facial Muscle Activation (FMA). It examines GenAI’s effectiveness in creating facial images that produce genuine emotional responses in children, alongside FMA’s analysis of muscular activation during these expressions. [...] Read more.

This study explores children’s emotions through a novel approach of Generative Artificial Intelligence (GenAI) and Facial Muscle Activation (FMA). It examines GenAI’s effectiveness in creating facial images that produce genuine emotional responses in children, alongside FMA’s analysis of muscular activation during these expressions. The aim is to determine if AI can realistically generate and recognize emotions similar to human experiences. The study involves generating a database of 280 images (40 per emotion) of children expressing various emotions. For real children’s faces from public databases (DEFSS and NIMH-CHEFS), five emotions were considered: happiness, angry, fear, sadness, and neutral. In contrast, for AI-generated images, seven emotions were analyzed, including the previous five plus surprise and disgust. A feature vector is extracted from these images, indicating lengths between reference points on the face that contract or expand based on the expressed emotion. This vector is then input into an artificial neural network for emotion recognition and classification, achieving accuracies of up to 99% in certain cases. This approach offers new avenues for training and validating AI algorithms, enabling models to be trained with artificial and real-world data interchangeably. The integration of both datasets during training and validation phases enhances model performance and adaptability. Full article

(This article belongs to the Special Issue Perception and Detection of Intelligent Vision)

► Show Figures

Figure 1

20 pages, 17747 KiB

Open AccessArticle

A Secure Learned Image Codec for Authenticity Verification via Self-Destructive Compression

by Chen-Hsiu Huang and Ja-Ling Wu

Big Data Cogn. Comput. 2025, 9(1), 14; https://doi.org/10.3390/bdcc9010014 - 15 Jan 2025

Viewed by 496

Abstract

In the era of deepfakes and AI-generated content, digital image manipulation poses significant challenges to image authenticity, creating doubts about the credibility of images. Traditional image forensics techniques often struggle to detect sophisticated tampering, and passive detection approaches are reactive, verifying authenticity only [...] Read more.

In the era of deepfakes and AI-generated content, digital image manipulation poses significant challenges to image authenticity, creating doubts about the credibility of images. Traditional image forensics techniques often struggle to detect sophisticated tampering, and passive detection approaches are reactive, verifying authenticity only after counterfeiting occurs. In this paper, we propose a novel full-resolution secure learned image codec (SLIC) designed to proactively prevent image manipulation by creating self-destructive artifacts upon re-compression. Once a sensitive image is encoded using SLIC, any subsequent re-compression or editing attempts will result in visually severe distortions, making the image’s tampering immediately evident. Because the content of an SLIC image is either original or visually damaged after tampering, images encoded with this secure codec hold greater credibility. SLIC leverages adversarial training to fine-tune a learned image codec that introduces out-of-distribution perturbations, ensuring that the first compressed image retains high quality while subsequent re-compressions degrade drastically. We analyze and compare the adversarial effects of various perceptual quality metrics combined with different learned codecs. Our experiments demonstrate that SLIC holds significant promise as a proactive defense strategy against image manipulation, offering a new approach to enhancing image credibility and authenticity in a media landscape increasingly dominated by AI-driven forgeries. Full article

► Show Figures

Figure 1

31 pages, 4560 KiB

Open AccessArticle

Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model

by A. M. Mutawa

Big Data Cogn. Comput. 2025, 9(1), 13; https://doi.org/10.3390/bdcc9010013 - 14 Jan 2025

Viewed by 571

Abstract

Intensive Care Units (ICUs) have been in great demand worldwide since the COVID-19 pandemic, necessitating organized allocation. The spike in critical care patients has overloaded ICUs, which along with prolonged hospitalizations, has increased workload for medical personnel and lead to a significant shortage [...] Read more.

Intensive Care Units (ICUs) have been in great demand worldwide since the COVID-19 pandemic, necessitating organized allocation. The spike in critical care patients has overloaded ICUs, which along with prolonged hospitalizations, has increased workload for medical personnel and lead to a significant shortage of resources. The study aimed to improve resource management by quickly and accurately identifying patients who need ICU admission. We designed an intelligent decision support system that employs machine learning (ML) to anticipate COVID-19 ICU admissions in Kuwait. Our algorithm examines several clinical and demographic characteristics to identify high-risk individuals early in illness diagnosis. We used 4399 patients to identify ICU admission with predictors such as shortness of breath, high D-dimer values, and abnormal chest X-rays. Any data imbalance was addressed by employing cross-validation along with the Synthetic Minority Oversampling Technique (SMOTE), the feature selection was refined using backward elimination, and the model interpretability was improved using Shapley Additive Explanations (SHAP). We employed various ML classifiers, including support vector machines (SVM). The SVM model surpasses all other models in terms of precision (0.99) and area under curve (AUC, 0.91). This study investigated the healthcare process during a pandemic, facilitating ML-based decision-making solutions to confront healthcare problems. Full article

(This article belongs to the Special Issue Revolutionizing Healthcare: Exploring the Latest Advances in Digital Health Technology)

► Show Figures

Figure 1

22 pages, 12031 KiB

Open AccessArticle

Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations

by Milan Maksimovic and Ivan S. Maksymov

Big Data Cogn. Comput. 2025, 9(1), 12; https://doi.org/10.3390/bdcc9010012 - 14 Jan 2025

Viewed by 542

Abstract

Contemporary machine learning (ML) systems excel in recognising and classifying images with remarkable accuracy. However, like many computer software systems, they can fail by generating confusing or erroneous outputs or by deferring to human operators to interpret the results and make final decisions. [...] Read more.

Contemporary machine learning (ML) systems excel in recognising and classifying images with remarkable accuracy. However, like many computer software systems, they can fail by generating confusing or erroneous outputs or by deferring to human operators to interpret the results and make final decisions. In this paper, we employ the recently proposed quantum tunnelling neural networks (QT-NNs) inspired by human brain processes alongside quantum cognition theory to classify image datasets while emulating human perception and judgment. Our findings suggest that the QT-NN model provides compelling evidence of its potential to replicate human-like decision-making. We also reveal that the QT-NN model can be trained up to 50 times faster than its classical counterpart. Full article

► Show Figures

Figure 1

24 pages, 6478 KiB

Open AccessArticle

The Data Heterogeneity Issue Regarding COVID-19 Lung Imaging in Federated Learning: An Experimental Study

by Fatimah Alhafiz and Abdullah Basuhail

Big Data Cogn. Comput. 2025, 9(1), 11; https://doi.org/10.3390/bdcc9010011 - 14 Jan 2025

Viewed by 407

Abstract

Federated learning (FL) has emerged as a transformative framework for collaborative learning, offering robust model training across institutions while ensuring data privacy. In the context of making a COVID-19 diagnosis using lung imaging, FL enables institutions to collaboratively train a global model without [...] Read more.

Federated learning (FL) has emerged as a transformative framework for collaborative learning, offering robust model training across institutions while ensuring data privacy. In the context of making a COVID-19 diagnosis using lung imaging, FL enables institutions to collaboratively train a global model without sharing sensitive patient data. A central manager aggregates local model updates to compute global updates, ensuring secure and effective integration. The global model’s generalization capability is evaluated using centralized testing data before dissemination to participating nodes, where local assessments facilitate personalized adaptations tailored to diverse datasets. Addressing data heterogeneity, a critical challenge in medical imaging, is essential for improving both global performance and local personalization in FL systems. This study emphasizes the importance of recognizing real-world data variability before proposing solutions to tackle non-independent and non-identically distributed (non-IID) data. We investigate the impact of data heterogeneity on FL performance in COVID-19 lung imaging across seven distinct heterogeneity settings. By comprehensively evaluating models using generalization and personalization metrics, we highlight challenges and opportunities for optimizing FL frameworks. The findings provide valuable insights that can guide future research toward achieving a balance between global generalization and local adaptation, ultimately enhancing diagnostic accuracy and patient outcomes in COVID-19 lung imaging. Full article

► Show Figures

Figure 1

20 pages, 3578 KiB

Open AccessArticle

SqueezeMaskNet: Real-Time Mask-Wearing Recognition for Edge Devices

by Gibran Benitez-Garcia, Lidia Prudente-Tixteco, Jesus Olivares-Mercado and Hiroki Takahashi

Big Data Cogn. Comput. 2025, 9(1), 10; https://doi.org/10.3390/bdcc9010010 - 10 Jan 2025

Viewed by 429

Abstract

This paper presents SqueezeMaskNet, a lightweight convolutional neural network designed for real-time recognition of proper and improper mask usage. The model classifies four categories: masks worn correctly, masks covering only the mouth, masks not covering, and no mask. SqueezeMaskNet integrates seamlessly with existing [...] Read more.

This paper presents SqueezeMaskNet, a lightweight convolutional neural network designed for real-time recognition of proper and improper mask usage. The model classifies four categories: masks worn correctly, masks covering only the mouth, masks not covering, and no mask. SqueezeMaskNet integrates seamlessly with existing face detection systems, removing the need for retraining. We propose using Fire modules for efficiency, along with attention mechanisms like efficient channel attention (ECA) and squeeze-and-excitation (SE) blocks for improved feature refinement. SqueezeMaskNet achieved 96.7% accuracy on the challenging FineFM test set and ran at 297 FPS on a GPU and up to 96 FPS on edge devices like a Jetson Orin NX. We also introduced ImproperTFM, a subset of real-world images focusing on improper mask usage, which enhanced the model accuracy when combined with FineFM data. Comparative experiments demonstrated SqueezeMaskNet’s superior performance, efficiency, and adaptability compared to MobileNet and EfficientNet, making it a practical solution for mask-wearing recognition across various devices and settings. Full article

► Show Figures

Figure 1

25 pages, 5594 KiB

Open AccessArticle

DRCCT: Enhancing Diabetic Retinopathy Classification with a Compact Convolutional Transformer

by Mohamed Touati, Rabeb Touati, Laurent Nana, Faouzi Benzarti and Sadok Ben Yahia

Big Data Cogn. Comput. 2025, 9(1), 9; https://doi.org/10.3390/bdcc9010009 - 9 Jan 2025

Viewed by 637

Abstract

Diabetic retinopathy, a common complication of diabetes, is further exacerbated by factors such as hypertension and obesity. This study introduces the Diabetic Retinopathy Compact Convolutional Transformer (DRCCT) model, which combines convolutional and transformer techniques to enhance the classification of retinal images. The DRCCT [...] Read more.

Diabetic retinopathy, a common complication of diabetes, is further exacerbated by factors such as hypertension and obesity. This study introduces the Diabetic Retinopathy Compact Convolutional Transformer (DRCCT) model, which combines convolutional and transformer techniques to enhance the classification of retinal images. The DRCCT model achieved an impressive average F1-score of 0.97, reflecting its high accuracy in detecting true positives while minimizing false positives. Over 100 training epochs, the model demonstrated outstanding generalization capabilities, achieving a remarkable training accuracy of 99% and a validation accuracy of 95%. This consistent improvement underscores the model’s robust learning process and its effectiveness in avoiding overfitting. On a newly evaluated dataset, the model attained precision and recall scores of 96.93% and 98.89%, respectively, indicating a well-balanced handling of false positives and false negatives. The model’s ability to classify retinal images into five distinct diabetic retinopathy categories demonstrates its potential to significantly improve automated diagnosis and aid in clinical decision-making. Full article

(This article belongs to the Special Issue Recent Advances in Big Data-Driven Prescriptive Analytics)

► Show Figures

Figure 1

19 pages, 14471 KiB

Open AccessArticle

Efficient Data Augmentation Methods for Crop Disease Recognition in Sustainable Environmental Systems

by Saebom Lee and Sokjoon Lee

Big Data Cogn. Comput. 2025, 9(1), 8; https://doi.org/10.3390/bdcc9010008 - 8 Jan 2025

Viewed by 7344

Abstract

Crop diseases significantly threaten agricultural productivity, leading to unstable food supply and economic losses. The current approaches to automated crop disease recognition face challenges such as limited datasets, restricted coverage of disease types, and inefficient feature extraction, which hinder their generalization across diverse [...] Read more.

Crop diseases significantly threaten agricultural productivity, leading to unstable food supply and economic losses. The current approaches to automated crop disease recognition face challenges such as limited datasets, restricted coverage of disease types, and inefficient feature extraction, which hinder their generalization across diverse crops and disease patterns. To address these challenges, we propose an efficient data augmentation method to enhance the performance of deep learning models for crop disease recognition. By constructing a new large-scale dataset comprising 24 different classes, including both fruit and leaf samples, we intend to handle a variety of disease patterns and improve model generalization capabilities. Geometric transformations and color space augmentation techniques are applied to validate the efficiency of deep learning models, specifically convolution and transformer models, in recognizing multiple crop diseases. The experimental results show that these augmentation techniques improve classification accuracy, achieving F1 scores exceeding 98%. Feature map analysis further confirms that the models effectively capture key disease characteristics. This study underscores the importance of data augmentation in developing automated, energy-efficient, and environmentally sustainable crop disease detection solutions, contributing to more sustainable agricultural practices. Full article

(This article belongs to the Special Issue Application of Semantic Technologies in Intelligent Environment)

► Show Figures

Figure 1

28 pages, 3289 KiB

Open AccessArticle

Topic Analysis of the Literature Reveals the Research Structure: A Case Study in Periodontics

by Carlo Galli, Maria Teresa Colangelo, Marco Meleti, Stefano Guizzardi and Elena Calciolari

Big Data Cogn. Comput. 2025, 9(1), 7; https://doi.org/10.3390/bdcc9010007 - 5 Jan 2025

Viewed by 513

Abstract

Periodontics is a complex field characterized by a constantly growing body of research, which poses a challenge for researchers and stakeholders striving to stay abreast of the evolving literature. Traditional bibliometric surveys, while accurate, are labor-intensive and not scalable to meet the demands [...] Read more.

Periodontics is a complex field characterized by a constantly growing body of research, which poses a challenge for researchers and stakeholders striving to stay abreast of the evolving literature. Traditional bibliometric surveys, while accurate, are labor-intensive and not scalable to meet the demands of such rapidly expanding domains. In this study, we employed BERTopic, a transformer-based topic modeling framework, to map the thematic landscape of periodontics research published in MEDLINE from 2009 to 2024. We identified 31 broad topics encompassing four major thematic axes—patient management, periomedicine, oral microbiology, and implant-related surgery—thereby illuminating core areas and their semantic relationships. Compared with a conventional Latent Dirichlet Allocation (LDA) approach, BERTopic yielded more contextually nuanced clusters and facilitated the isolation of distinct, smaller research niches. Although some documents remained unlabeled, potentially reflecting either semantic ambiguity or niche topics below the clustering threshold, our results underscore the flexibility, interpretability, and scalability of neural topic modeling in this domain. Future refinements—such as domain-specific embedding models and optimized granularity levels—could further enhance the precision and utility of this method, ultimately guiding researchers, educators, and policymakers in navigating the evolving landscape of periodontics. Full article

(This article belongs to the Special Issue Application of Semantic Technologies in Intelligent Environment)

► Show Figures

Figure 1

18 pages, 2535 KiB

Open AccessArticle

A Recursive Attribute Reduction Algorithm and Its Application in Predicting the Hot Metal Silicon Content in Blast Furnaces

by Zhanqi Li, Pan Cheng, Linzi Yin and Yuyin Guan

Big Data Cogn. Comput. 2025, 9(1), 6; https://doi.org/10.3390/bdcc9010006 - 3 Jan 2025

Viewed by 521

Abstract

For many complex industrial applications, traditional attribute reduction algorithms are often inefficient in obtaining optimal reducts that align with mechanistic analyses and practical production requirements. To solve this problem, we propose a recursive attribute reduction algorithm that calculates the optimal reduct. First, we [...] Read more.

For many complex industrial applications, traditional attribute reduction algorithms are often inefficient in obtaining optimal reducts that align with mechanistic analyses and practical production requirements. To solve this problem, we propose a recursive attribute reduction algorithm that calculates the optimal reduct. First, we present the notion of priority sequence to describe the background meaning of attributes and evaluate the optimal reduct. Next, we define a necessary element set to identify the “individually necessary” characteristics of the attributes. On this basis, a recursive algorithm is proposed to calculate the optimal reduct. Its boundary logic is guided by the conflict between the necessary element set and the core attribute set. The experiments demonstrate the proposed algorithm’s uniqueness and its ability to enhance the prediction accuracy of the hot metal silicon content in blast furnaces. Full article

► Show Figures

Figure 1

26 pages, 1002 KiB

Open AccessArticle

Training Neural Networks with a Procedure Guided by BNF Grammars

by Ioannis G. Tsoulos and Vasileios Charilogis

Big Data Cogn. Comput. 2025, 9(1), 5; https://doi.org/10.3390/bdcc9010005 - 2 Jan 2025

Viewed by 465

Abstract

Artificial neural networks are parametric machine learning models that have been applied successfully to an extended series of classification and regression problems found in the recent literature. For the effective identification of the parameters of the artificial neural networks, a series of optimization [...] Read more.

Artificial neural networks are parametric machine learning models that have been applied successfully to an extended series of classification and regression problems found in the recent literature. For the effective identification of the parameters of the artificial neural networks, a series of optimization techniques have been proposed in the relevant literature, which, although they present good results in many cases, either the optimization method used is not efficient and the training error of the network is trapped in sub-optimal values, or the neural network exhibits the phenomenon of overfitting which means that it has poor results when applied to data that was not present during the training. This paper proposes an innovative technique for constructing the weights of artificial neural networks based on appropriate BNF grammars, used in the evolutionary process of Grammatical Evolution. The new procedure locates an interval of values for the parameters of the artificial neural network, and the optimization method effectively locates the network parameters within this interval. The new technique was applied to a wide range of data classification and adaptation problems covering a number of scientific areas and the experimental results were more than promising. Full article

► Show Figures

Figure 1

21 pages, 5647 KiB

Open AccessArticle

Face-to-Face Interactions Estimated Using Mobile Phone Data to Support Contact Tracing Operations

by Silvino Pedro Cumbane, Gyözö Gidófalvi, Osvaldo Fernando Cossa, Afonso Madivadua Júnior, Nuno Sousa and Frederico Branco

Big Data Cogn. Comput. 2025, 9(1), 4; https://doi.org/10.3390/bdcc9010004 - 30 Dec 2024

Viewed by 1004

Abstract

Understanding people’s face-to-face interactions is crucial for effective infectious disease management. Traditional contact tracing, often relying on interviews or smartphone applications, faces limitations such as incomplete recall, low adoption rates, and privacy concerns. This study proposes utilizing anonymized Call Detail Records (CDRs) as [...] Read more.

Understanding people’s face-to-face interactions is crucial for effective infectious disease management. Traditional contact tracing, often relying on interviews or smartphone applications, faces limitations such as incomplete recall, low adoption rates, and privacy concerns. This study proposes utilizing anonymized Call Detail Records (CDRs) as a substitute for in-person meetings. We assume that when two individuals engage in a phone call connected to the same cell tower, they are likely to meet shortly thereafter. Testing this assumption, we evaluated two hypotheses. The first hypothesis—that such co-located interactions occur in a workplace setting—achieved 83% agreement, which is considered a strong indication of reliability. The second hypothesis—that calls made during these co-location events are shorter than usual—achieved 86% agreement, suggesting an almost perfect reliability level. These results demonstrate that CDR-based co-location events can serve as a reliable substitute for in-person interactions and thus hold significant potential for enhancing contact tracing and supporting public health efforts. Full article

► Show Figures

Figure 1

32 pages, 11502 KiB

Open AccessArticle

DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography

by Ismael Santarrosa-López, Giner Alor-Hernández, Maritza Bustos-López, Jonathan Hernández-Capistrán, Laura Nely Sánchez-Morales, José Luis Sánchez-Cervantes and Humberto Marín-Vega

Big Data Cogn. Comput. 2025, 9(1), 3; https://doi.org/10.3390/bdcc9010003 - 30 Dec 2024

Viewed by 695

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is often challenging due to subjective assessments and symptom variability, which can delay accurate detection and treatment. To address these limitations, this study introduces DETEC-ADHD, a web-based application that combines machine learning (ML) techniques with multi-source data [...] Read more.

Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is often challenging due to subjective assessments and symptom variability, which can delay accurate detection and treatment. To address these limitations, this study introduces DETEC-ADHD, a web-based application that combines machine learning (ML) techniques with multi-source data to enhance diagnostic accuracy. Unlike traditional approaches, DETEC-ADHD primarily utilizes extensive personal, medical, and psychological information for its initial classification. DETEC-ADHD further refines diagnoses by identifying ADHD subtypes (inattentive, hyperactive, combined) through theta/beta wave ratio analysis from EEG data, offering neurophysiological insights that complement its classification process. Logistic Regression, selected for its validated accuracy and reliability, served as the ML model for the app. The case studies demonstrated DETEC-ADHD’s effectiveness, achieving 100% accuracy in children and 90% in adults. By integrating diverse data sources with real-time EEG analysis, DETEC-ADHD provides a scalable, cost-effective, and accessible solution for ADHD detection and subtype identification, addressing diagnostic challenges and supporting healthcare providers, particularly in resource-limited environments. Full article

(This article belongs to the Special Issue Revolutionizing Healthcare: Exploring the Latest Advances in Digital Health Technology)

► Show Figures

Figure 1

19 pages, 1128 KiB

Open AccessArticle

MobileNet-HeX: Heterogeneous Ensemble of MobileNet eXperts for Efficient and Scalable Vision Model Optimization

by Emmanuel Pintelas, Ioannis E. Livieris, Vasilis Tampakas and Panagiotis Pintelas

Big Data Cogn. Comput. 2025, 9(1), 2; https://doi.org/10.3390/bdcc9010002 - 27 Dec 2024

Viewed by 341

Abstract

Efficient and accurate vision models are essential for real-world applications such as medical imaging and deepfake detection, where both performance and computational efficiency are critical. While recent vision models achieve high accuracy, they often come with the trade-off of increased size and computational [...] Read more.

Efficient and accurate vision models are essential for real-world applications such as medical imaging and deepfake detection, where both performance and computational efficiency are critical. While recent vision models achieve high accuracy, they often come with the trade-off of increased size and computational demands. In this work, we propose MobileNet-HeX, a new ensemble model based on Heterogeneous MobileNet eXperts, designed to achieve top-tier performance while minimizing computational demands in real-world vision tasks. By utilizing a two-step Expand-and-Squeeze mechanism, MobileNet-HeX first expands a MobileNet population through diverse random training setups. It then squeezes the population through pruning, selecting the top-performing models based on heterogeneity and validation performance metrics. Finally, the selected Heterogeneous eXpert MobileNets are combined via sequential quadratic programming to form an efficient super-learner. MobileNet-HeX is benchmarked against state-of-the-art vision models in challenging case studies, such as skin cancer classification and deepfake detection. The results demonstrate that MobileNet-HeX not only surpasses these models in performance but also excels in speed and memory efficiency. By effectively leveraging a diverse set of MobileNet eXperts, we experimentally show that small, yet highly optimized, models can outperform even the most powerful vision networks in both accuracy and computational efficiency. Full article

► Show Figures

Figure 1

21 pages, 1865 KiB

Open AccessArticle

Integrating Social Relationships and Personality into MAS-Based Group Recommendations

by Ariel Monteserin, Daiana Elin Madsen, Daniela Godoy and Silvia Schiaffino

Big Data Cogn. Comput. 2025, 9(1), 1; https://doi.org/10.3390/bdcc9010001 - 24 Dec 2024

Viewed by 463

Abstract

Recommender systems aim to predict the preferences of users and suggest items of interest to them in various domains. While traditional recommendation techniques consider users as individuals, some approaches aim to satisfy the needs of a group of people. Multi-agent systems can be [...] Read more.

Recommender systems aim to predict the preferences of users and suggest items of interest to them in various domains. While traditional recommendation techniques consider users as individuals, some approaches aim to satisfy the needs of a group of people. Multi-agent systems can be used to develop such recommendations, where multiple intelligent agents interact with each other to achieve a common goal, i.e., deciding which item to recommend. Particularly, negotiation techniques can be used to find a decision that aims at maximizing the satisfaction of all group members. The proposed approach introduces a multi-agent recommender system for a group of users by considering their personality traits, relationships and social interactions during the negotiation process that leads to the generation of recommendations. While traditional recommendation techniques do not take into account the effects of personality traits and relationships between individuals, our approach demonstrates that personality traits, especially personality types in the context of conflict management, and social relationships can significantly impact on the group recommendation. The results indicate that the opinion of an individual can be influenced when she is part of a group that cooperates towards a shared goal. Overall, the proposed approach shows that recommender systems can benefit from considering that factors. This work contributes to understanding the impact of personality traits and social relationships on group recommendations and suggests potential directions for future research. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Big Data Cogn. Comput., Volume 9, Issue 1 (January 2025) – 17 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI