Information

32 pages, 4351 KiB

Open AccessArticle

Enhancing Brain Tumor Detection Through Custom Convolutional Neural Networks and Interpretability-Driven Analysis

by Kavinda Ashan Kulasinghe Wasalamuni Dewage, Raza Hasan, Bacha Rehman and Salman Mahmood

Information 2024, 15(10), 653; https://doi.org/10.3390/info15100653 - 18 Oct 2024

Brain tumor detection is crucial for effective treatment planning and improved patient outcomes. However, existing methods often face challenges, such as limited interpretability and class imbalance in medical-imaging data. This study presents a novel, custom Convolutional Neural Network (CNN) architecture, specifically designed to [...] Read more.

Brain tumor detection is crucial for effective treatment planning and improved patient outcomes. However, existing methods often face challenges, such as limited interpretability and class imbalance in medical-imaging data. This study presents a novel, custom Convolutional Neural Network (CNN) architecture, specifically designed to address these issues by incorporating interpretability techniques and strategies to mitigate class imbalance. We trained and evaluated four CNN models (proposed CNN, ResNetV2, DenseNet201, and VGG16) using a brain tumor MRI dataset, with oversampling techniques and class weighting employed during training. Our proposed CNN achieved an accuracy of 94.51%, outperforming other models in regard to precision, recall, and F1-Score. Furthermore, interpretability was enhanced through gradient-based attribution methods and saliency maps, providing valuable insights into the model’s decision-making process and fostering collaboration between AI systems and clinicians. This approach contributes a highly accurate and interpretable framework for brain tumor detection, with the potential to significantly enhance diagnostic accuracy and personalized treatment planning in neuro-oncology. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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15 pages, 6456 KiB

Open AccessArticle

Image Stitching of Low-Resolution Retinography Using Fundus Blur Filter and Homography Convolutional Neural Network

by Levi Santos, Maurício Almeida, João Almeida, Geraldo Braz, José Camara and António Cunha

Information 2024, 15(10), 652; https://doi.org/10.3390/info15100652 - 17 Oct 2024

Abstract

Great advances in stitching high-quality retinal images have been made in recent years. On the other hand, very few studies have been carried out on low-resolution retinal imaging. This work investigates the challenges of low-resolution retinal images obtained by the D-EYE smartphone-based fundus [...] Read more.

Great advances in stitching high-quality retinal images have been made in recent years. On the other hand, very few studies have been carried out on low-resolution retinal imaging. This work investigates the challenges of low-resolution retinal images obtained by the D-EYE smartphone-based fundus camera. The proposed method uses homography estimation to register and stitch low-quality retinal images into a cohesive mosaic. First, a Siamese neural network extracts features from a pair of images, after which the correlation of their feature maps is computed. This correlation map is fed through four independent CNNs to estimate the homography parameters, each specializing in different corner coordinates. Our model was trained on a synthetic dataset generated from the Microsoft Common Objects in Context (MSCOCO) dataset; this work added an important data augmentation phase to improve the quality of the model. Then, the same is evaluated on the FIRE retina and D-EYE datasets for performance measurement using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The obtained results are promising: the average PSNR was 26.14 dB, with an SSIM of 0.96 on the D-EYE dataset. Compared to the method that uses a single neural network for homography calculations, our approach improves the PSNR by 7.96 dB and achieves a 7.86% higher SSIM score. Full article

(This article belongs to the Special Issue Stitching, Alignment and Segmentation Applications in Biomedical Images)

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25 pages, 913 KiB

Open AccessArticle

Inference-Based Information Relevance Reasoning Method in Situation Assessment

by Shan Lu and Mieczyslaw Kokar

Information 2024, 15(10), 651; https://doi.org/10.3390/info15100651 - 17 Oct 2024

Abstract

The growing volume of information available to decision-makers makes it increasingly challenging to process all data during decision-making. As a result, a method for selecting only relevant information is highly desirable. Moreover, since the meaning of information depends on its context, the decision-making [...] Read more.

The growing volume of information available to decision-makers makes it increasingly challenging to process all data during decision-making. As a result, a method for selecting only relevant information is highly desirable. Moreover, since the meaning of information depends on its context, the decision-making process requires mechanisms to identify the context of specific scenarios. In this paper, we propose a conceptual framework that utilizes Situation Theory to formalize the concept of context and analyze information relevance. Building on this framework, we introduce an inference-based reasoning process that automatically identifies the information necessary to characterize a given situation. We evaluate our approach in a cybersecurity scenario where computer agents respond to queries by utilizing available information and sharing relevant facts with other agents. The results show that our method significantly reduces the time required to infer answers to situation-specific queries. Additionally, we demonstrate that using only relevant information provides the same answers as using the entire knowledge base. Finally, we show that the method can be applied to a limited set of training queries, allowing the reuse of relevant facts to address new queries effectively. Full article

(This article belongs to the Special Issue Feature Papers in Information in 2023)

21 pages, 7429 KiB

Open AccessArticle

A Method for Single-Phase Ground Fault Section Location in Distribution Networks Based on Improved Empirical Wavelet Transform and Graph Isomorphic Networks

by Chen Wang, Lijun Feng, Sizu Hou, Guohui Ren and Wenyao Wang

Information 2024, 15(10), 650; https://doi.org/10.3390/info15100650 - 17 Oct 2024

Abstract

When single-phase ground faults occur in distribution systems, the fault characteristics of zero-sequence current signals are not prominent. They are quickly submerged in noise, leading to difficulties in fault section location. This paper proposes a method for fault section location in distribution networks [...] Read more.

When single-phase ground faults occur in distribution systems, the fault characteristics of zero-sequence current signals are not prominent. They are quickly submerged in noise, leading to difficulties in fault section location. This paper proposes a method for fault section location in distribution networks based on improved empirical wavelet transform (IEWT) and GINs to address this issue. Firstly, based on kurtosis, EWT is optimized using the N-point search method to decompose the zero-sequence current signal into modal components. Noise is filtered out through weighted permutation entropy (WPE), and signal reconstruction is performed to obtain the denoised zero-sequence current signal. Subsequently, GINs are employed for graph classification tasks. According to the topology of the distribution network, the corresponding graph is constructed as the input to the GIN. The denoised zero-sequence current signal is the node input for the GIN. The GIN autonomously explores the features of each graph structure to achieve fault section location. The experimental results demonstrate that this method has strong noise resistance, with a fault section location accuracy of up to 99.95%, effectively completing fault section location in distribution networks. Full article

(This article belongs to the Section Information Processes)

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31 pages, 628 KiB

Open AccessArticle

A Dynamic Event-Triggered Secure Monitoring and Control for a Class of Discrete-Time Markovian Jump Systems: A Plug-and-Play Architecture

by Yi Gao, Yunji Li, Ziyan Hua, Junjie Chen and Yajun Wu

Information 2024, 15(10), 649; https://doi.org/10.3390/info15100649 - 17 Oct 2024

Abstract

In modern industrial applications, production quality, system performance, process reliability, and safety have received considerable attention. This article proposes a dynamic event-triggered attack estimator for Markovian jump stochastic systems susceptible to actuator deception attacks. Utilizing the developed estimator, the presented attack-tolerant control strategy [...] Read more.

In modern industrial applications, production quality, system performance, process reliability, and safety have received considerable attention. This article proposes a dynamic event-triggered attack estimator for Markovian jump stochastic systems susceptible to actuator deception attacks. Utilizing the developed estimator, the presented attack-tolerant control strategy can tolerate the effects of such attacks and ensure the mean-square convergence of the overall closed-loop system. A dynamic event-triggered mechanism is implemented on the sensor side to optimize communication efficiency. To address the potential threat of deception attacks, a plug-and-play (PnP) secure monitoring and control architecture is introduced. This architecture facilitates the seamless integration of the designed attack-tolerant controller with the nominal feedback controller, thereby enhancing system security without requiring significant modifications to the existing control structure. The practicality and effectiveness of the proposed approaches are demonstrated through experimental results on a switched boost converter circuit. Full article

(This article belongs to the Special Issue Developments in Cyber-Physical Systems and Cyber-Physical-Human Systems)

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20 pages, 2499 KiB

Open AccessArticle

The Localization of Software and Video Games: Current State and Future Perspectives

by Marco Pirrone and Arianna D’Ulizia

Information 2024, 15(10), 648; https://doi.org/10.3390/info15100648 - 17 Oct 2024

Abstract

The study of linguistics applied to computer science is a much-discussed topic today. In this area, particularly relevant is the software localization process describing the linguistic and cultural adaptation of software products to a specific market scenario. Software localization is going through a [...] Read more.

The study of linguistics applied to computer science is a much-discussed topic today. In this area, particularly relevant is the software localization process describing the linguistic and cultural adaptation of software products to a specific market scenario. Software localization is going through a phase of strong development due to the great market demand and the current trend of making the computer more human-like in the way it interacts with the user. This paper focuses on “linguistic” localization by addressing the language translation process from the perspective of translation studies. In particular, the process of translating the language assets in a game and making the game linguistically and culturally appropriate for the target market will be explored. The study provides a systematic literature review of the main localization methods developed over the last four decades, along with the major issues and challenges mainly related to the main linguistic and cultural aspects of videogames. The review results are integrated with the results of a qualitative analysis conducted through a focus group with the participation of both academic and professional experts in software and videogame localization. The results of this study are worthwhile for many academics and industry professionals as they provide an in-depth overview of the localization process in software and videogames as well as potential directions for future research. Full article

(This article belongs to the Special Issue Machine Translation for Conquering Language Barriers)

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15 pages, 3155 KiB

Open AccessArticle

Machine Learning Ensemble Methodologies for the Prediction of the Failure Mode of Reinforced Concrete Beam–Column Joints

by Martha Karabini, Ioannis Karampinis, Theodoros Rousakis, Lazaros Iliadis and Athanasios Karabinis

Information 2024, 15(10), 647; https://doi.org/10.3390/info15100647 - 16 Oct 2024

Abstract

One of the most critical aspects in the seismic behavior or reinforced concrete (RC) structures pertains to beam–column joints. Modern seismic design codes dictate that, if failure is to occur, then this should be the ductile yielding of the beam and not brittle [...] Read more.

One of the most critical aspects in the seismic behavior or reinforced concrete (RC) structures pertains to beam–column joints. Modern seismic design codes dictate that, if failure is to occur, then this should be the ductile yielding of the beam and not brittle shear failure of the joint, which can lead to sudden collapse and loss of human lives. To this end, it is imperative to be able to predict the failure mode of RC joints for a large number of structures in a building stock. In this research effort, various ensemble machine learning algorithms were employed to develop novel, robust classification models. A dataset comprising 486 measurements from real experiments was utilized. The performance of the employed classifiers was assessed using Precision, Recall, F1-Score, and overall Accuracy indices. N-fold cross-validation was employed to enhance generalization. Moreover, the obtained models were compared to the available engineering ones currently adopted by many international organizations and researchers. The novel ensemble models introduced in this research were proven to perform much better by improving the obtained accuracy by 12–18%. The obtained metrics also presented small variability among the examined failure modes, indicating unbiased models. Overall, the results indicate that the proposed methodologies can be confidently employed for the prediction of the failure mode of RC joints. Full article

(This article belongs to the Special Issue Real-World Applications of Machine Learning Techniques)

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19 pages, 7484 KiB

Open AccessArticle

Archaeogenetic Data Mining Supports a Uralic–Minoan Homeland in the Danube Basin

by Peter Z. Revesz

Information 2024, 15(10), 646; https://doi.org/10.3390/info15100646 - 16 Oct 2024

Abstract

Four types of archaeogenetic data mining are used to investigate the origin of the Minoans and the Uralic peoples: (1) six SNP mutations related to eye, hair, and skin phenotypes; (2) whole-genome admixture analysis using the G25 system; (3) an analysis of the [...] Read more.

Four types of archaeogenetic data mining are used to investigate the origin of the Minoans and the Uralic peoples: (1) six SNP mutations related to eye, hair, and skin phenotypes; (2) whole-genome admixture analysis using the G25 system; (3) an analysis of the history of the U5 mitochondrial DNA haplogroup; and (4) an analysis of the origin of each currently known Minoan mitochondrial and y-DNA haplotypes. The uniform result of these analyses is that the Minoans and the Uralic peoples had a common homeland in the lower and middle Danube Basin, as well as the Black Sea coastal regions. This new result helps to reconcile archaeogenetics with linguistics, which have shown that the Minoan language belongs to the Uralic language family. Full article

(This article belongs to the Special Issue International Database Engineered Applications)

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26 pages, 2812 KiB

Open AccessReview

Integrating Knowledge Graphs into Autonomous Vehicle Technologies: A Survey of Current State and Future Directions

by Swe Nwe Nwe Htun and Ken Fukuda

Information 2024, 15(10), 645; https://doi.org/10.3390/info15100645 - 16 Oct 2024

Abstract

Autonomous vehicles (AVs) represent a transformative innovation in transportation, promising enhanced safety, efficiency, and sustainability. Despite these promises, achieving robustness, reliability, and adherence to ethical standards in AV systems remains challenging due to the complexity of integrating diverse technologies. This survey reviews literature [...] Read more.

Autonomous vehicles (AVs) represent a transformative innovation in transportation, promising enhanced safety, efficiency, and sustainability. Despite these promises, achieving robustness, reliability, and adherence to ethical standards in AV systems remains challenging due to the complexity of integrating diverse technologies. This survey reviews literature from 2017 to 2023, analyzing over 90 papers to explore the integration of knowledge graphs (KGs) into AV technologies. Our findings indicate that KGs significantly enhance AV systems by providing structured semantic understanding, improving real-time decision-making, and ensuring compliance with regulatory standards. The paper identifies that while KGs contribute to better environmental perception and contextual reasoning, challenges remain in their seamless integration with existing systems and in maintaining processing speed. We also address the ethical dimensions of AV decision-making, advocating for frameworks that prioritize safety and transparency. This review underscores the potential of KGs to address critical challenges in AV technologies, offering a hopeful and optimistic outlook for the development of robust, reliable, and socially responsible autonomous transportation solutions. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)

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29 pages, 8573 KiB

Open AccessReview

Blockchain Consensus Mechanisms: A Bibliometric Analysis (2014–2024) Using VOSviewer and R Bibliometrix

by Joongho Ahn, Eojin Yi and Moonsoo Kim

Information 2024, 15(10), 644; https://doi.org/10.3390/info15100644 - 16 Oct 2024

Abstract

Blockchain consensus mechanisms play a critical role in ensuring the security, decentralization, and integrity of distributed networks. As blockchain technology expands beyond cryptocurrencies into broader applications such as supply chain management and healthcare, the importance of efficient and scalable consensus algorithms has grown [...] Read more.

Blockchain consensus mechanisms play a critical role in ensuring the security, decentralization, and integrity of distributed networks. As blockchain technology expands beyond cryptocurrencies into broader applications such as supply chain management and healthcare, the importance of efficient and scalable consensus algorithms has grown significantly. This study provides a comprehensive bibliometric analysis of blockchain and consensus mechanism research from 2014 to 2024, using tools such as VOSviewer and R’s Bibliometrix package. The analysis traces the evolution from foundational mechanisms like Proof of ork (PoW) to more advanced models such as Proof of Stake (PoS) and Byzantine Fault Tolerance (BFT), with particular emphasis on Ethereum’s “The Merge” in 2022, which marked the historic shift from PoW to PoS. Key findings highlight emerging themes, including scalability, security, and the integration of blockchain with state-of-the-art technologies like artificial intelligence (AI), the Internet of Things (IoT), and energy trading. The study also identifies influential authors, institutions, and countries, emphasizing the collaborative and interdisciplinary nature of blockchain research. Through thematic analysis, this review uncovers the challenges and opportunities in decentralized systems, underscoring the need for continued innovation in consensus mechanisms to address efficiency, sustainability, scalability, and privacy concerns. These insights offer a valuable foundation for future research aimed at advancing blockchain technology across various industries. Full article

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27 pages, 5316 KiB

Open AccessArticle

Phishing and the Human Factor: Insights from a Bibliometric Analysis

by Meltem Mutlutürk, Martin Wynn and Bilgin Metin

Information 2024, 15(10), 643; https://doi.org/10.3390/info15100643 (registering DOI) - 15 Oct 2024

Abstract

Academic research on the human element in phishing attacks is essential for developing effective prevention and detection strategies and guiding policymakers to protect individuals and organizations from cyber threats. This bibliometric study offers a comprehensive overview of international research on phishing and human [...] Read more.

Academic research on the human element in phishing attacks is essential for developing effective prevention and detection strategies and guiding policymakers to protect individuals and organizations from cyber threats. This bibliometric study offers a comprehensive overview of international research on phishing and human factors from 2006 to 2024. Analysing 308 articles from the Web of Science database, a significant increase in publications since 2015 was identified, highlighting the growing importance of this field. The study revealed influential authors such as Vishwanath and Rao, leading journals like Computers & Security, and key contributing institutions including Carnegie Mellon University. The analysis uncovered strong collaborations between institutions and countries, with the USA being the most prolific and collaborative. Emerging research themes focus on psychological factors influencing phishing susceptibility, user-centric security measures, and the integration of technological solutions with human behaviour insights. The findings highlight the need for increased collaboration between academia and non-academic organizations and the exploration of industry-specific challenges. These insights offer valuable guidance for researchers, practitioners, and policymakers to advance their understanding of phishing attacks, human factors, and resource allocation in this critical aspect of digitalisation, which continues to have significant impacts across business and society at large. Full article

(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)

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21 pages, 4210 KiB

Open AccessArticle

Preliminary Studies to Bridge the Gap: Leveraging Informal Software Architecture Artifacts for Structured Model Creation

by Joshua Kaplan and Luis Rabelo

Information 2024, 15(10), 642; https://doi.org/10.3390/info15100642 (registering DOI) - 15 Oct 2024

Abstract

This study addresses the prevalent gap between structured models and informal architectural methodologies in software engineering. Recognizing the potential of informal architecture artifacts in analytical processes, we introduce a methodology that efficiently transforms these informal components into structured models. This method facilitates understanding [...] Read more.

This study addresses the prevalent gap between structured models and informal architectural methodologies in software engineering. Recognizing the potential of informal architecture artifacts in analytical processes, we introduce a methodology that efficiently transforms these informal components into structured models. This method facilitates understanding and utilizing informal diagrams and enhances analytical capabilities through graph analysis techniques. By leveraging user-friendly tools such as Draw.io, the methodology democratizes the modeling process, making sophisticated architectural analyses accessible to a broader spectrum of professionals without requiring deep expertise in formal methods. The innovative aspects of this methodology lie in its ability to streamline the transformation process, significantly improving both the efficiency and effectiveness of model creation and analysis. These enhancements are demonstrated through a practical application involving a sample architecture diagram, where the resulting model is thoroughly analyzed using advanced graph analysis tools. This approach bridges the theoretical and practical divides in software architecture. Full article

(This article belongs to the Special Issue Optimization and Methodology in Software Engineering, 2nd Edition)

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19 pages, 4794 KiB

Open AccessArticle

An Efficient Ensemble Approach for Brain Tumors Classification Using Magnetic Resonance Imaging

by Zubair Saeed, Tarraf Torfeh, Souha Aouadi, (Jim) Xiuquan Ji and Othmane Bouhali

Information 2024, 15(10), 641; https://doi.org/10.3390/info15100641 (registering DOI) - 15 Oct 2024

Abstract

Tumors in the brain can be life-threatening, making early and precise detection crucial for effective treatment and improved patient outcomes. Deep learning (DL) techniques have shown significant potential in automating the early diagnosis of brain tumors by analyzing magnetic resonance imaging (MRI), offering [...] Read more.

Tumors in the brain can be life-threatening, making early and precise detection crucial for effective treatment and improved patient outcomes. Deep learning (DL) techniques have shown significant potential in automating the early diagnosis of brain tumors by analyzing magnetic resonance imaging (MRI), offering a more efficient and accurate approach to classification. Deep convolutional neural networks (DCNNs), which are a sub-field of DL, have the potential to analyze rapidly and accurately MRI data and, as such, assist human radiologists, facilitating quicker diagnoses and earlier treatment initiation. This study presents an ensemble of three high-performing DCNN models, i.e., DenseNet169, EfficientNetB0, and ResNet50, for accurate classification of brain tumors and non-tumor MRI samples. Our proposed ensemble model demonstrates significant improvements over various evaluation parameters compared to individual state-of-the-art (SOTA) DCNN models. We implemented ten SOTA DCNN models, i.e., EfficientNetB0, ResNet50, DenseNet169, DenseNet121, SqueezeNet, ResNet34, ResNet18, VGG16, VGG19, and LeNet5, and provided a detailed performance comparison. We evaluated these models using two learning rates (LRs) of 0.001 and 0.0001 and two batch sizes (BSs) of 64 and 128 and identified the optimal hyperparameters for each model. Our findings indicate that the ensemble approach outperforms individual models, having 92% accuracy, 90% precision, 92% recall, and an F1 score of 91% at a 64 BS and 0.0001 LR. This study not only highlights the superior performance of the ensemble technique but also offers a comprehensive comparison with the latest research. Full article

(This article belongs to the Special Issue Detection and Modelling of Biosignals)

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20 pages, 5100 KiB

Open AccessArticle

Neurophysiological Approach for Psychological Safety: Enhancing Mental Health in Human–Robot Collaboration in Smart Manufacturing Setups Using Neuroimaging

by Arshia Arif, Zohreh Zakeri, Ahmet Omurtag, Philip Breedon and Azfar Khalid

Information 2024, 15(10), 640; https://doi.org/10.3390/info15100640 (registering DOI) - 15 Oct 2024

Abstract

Human–robot collaboration (HRC) has become increasingly prevalent due to innovative advancements in the automation industry, especially in manufacturing setups. Although HRC increases productivity and efficacy, it exposes human workers to psychological stress while interfacing with collaborative robotic systems as robots may not provide [...] Read more.

Human–robot collaboration (HRC) has become increasingly prevalent due to innovative advancements in the automation industry, especially in manufacturing setups. Although HRC increases productivity and efficacy, it exposes human workers to psychological stress while interfacing with collaborative robotic systems as robots may not provide visual or auditory cues. It is crucial to comprehend how HRC impacts mental stress in order to enhance occupational safety and well-being. Though academics and industrial interest in HRC is expanding, safety and mental stress problems are still not adequately studied. In particular, human coworkers’ cognitive strain during HRC has not been explored well, although being fundamental to sustaining a secure and constructive workplace environment. This study, therefore, aims to monitor the mental stress of factory workers during HRC using behavioural, physiological and subjective measures. Physiological measures, being objective and more authentic, have the potential to replace conventional measures i.e., behavioural and subjective measures, if they demonstrate a good correlation with traditional measures. Two neuroimaging modalities including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used as physiological measures to track neuronal and hemodynamic activity of the brain, respectively. Here, the correlation between physiological data and behavioural and subjective measurements has been ascertained through the implementation of seven different machine learning algorithms. The results imply that the EEG and fNIRS features combined produced the best results for most of the targets. For subjective measures being the target, linear regression has outperformed all other models, whereas tree and ensemble performed the best for predicting the behavioural measures. The outcomes indicate that physiological measures have the potential to be more informative and often substitute other skewed metrics. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

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15 pages, 14407 KiB

Open AccessArticle

A Feature-Weighted Support Vector Regression Machine Based on Hilbert–Schmidt Independence Criterion Least Absolute Shrinkage and Selection Operator

by Xin Zhang, Tinghua Wang and Zhiyong Lai

Information 2024, 15(10), 639; https://doi.org/10.3390/info15100639 (registering DOI) - 15 Oct 2024

Abstract

Support vector regression (SVR) is a powerful kernel-based regression prediction algorithm that performs excellently in various application scenarios. However, for real-world data, the general SVR often fails to achieve good predictive performance due to its inability to assess feature contribution accurately. Feature weighting [...] Read more.

Support vector regression (SVR) is a powerful kernel-based regression prediction algorithm that performs excellently in various application scenarios. However, for real-world data, the general SVR often fails to achieve good predictive performance due to its inability to assess feature contribution accurately. Feature weighting is a suitable solution to address this issue, applying correlation measurement methods to obtain reasonable weights for features based on their contributions to the output. In this paper, based on the idea of a Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC LASSO) for selecting features with minimal redundancy and maximum relevance, we propose a novel feature-weighted SVR that considers the importance of features to the output and the redundancy between features. In this approach, the HSIC is utilized to effectively measure the correlation between features as well as that between features and the output. The feature weights are obtained by solving a LASSO regression problem. Compared to other feature weighting methods, our method takes much more comprehensive consideration of weight calculation, and the obtained weighted kernel function can lead to more precise predictions for unknown data. Comprehensive experiments on real datasets from the University of California Irvine (UCI) machine learning repository demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Section Artificial Intelligence)

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19 pages, 2994 KiB

Open AccessArticle

Voltage Deviation Improvement in Microgrid Operation through Demand Response Using Imperialist Competitive and Genetic Algorithms

by Mahdi Ghaffari and Hamed H. Aly

Information 2024, 15(10), 638; https://doi.org/10.3390/info15100638 (registering DOI) - 14 Oct 2024

Abstract

In recent decades, with the expansion of distributed energy generation technologies and the increasing need for more flexibility and efficiency in energy distribution systems, microgrids have been considered a promising innovative solution for local energy supply and enhancing resilience against network fluctuations. One [...] Read more.

In recent decades, with the expansion of distributed energy generation technologies and the increasing need for more flexibility and efficiency in energy distribution systems, microgrids have been considered a promising innovative solution for local energy supply and enhancing resilience against network fluctuations. One of the basic challenges in the operation of microgrids is the optimal management of voltage and frequency in the network, which has been the subject of extensive research in the field of microgrid operational optimization. The energy demand is considered a crucial element for energy management due to its fluctuating nature over the day. The use of demand response strategies for energy management is one of the most important factors in dealing with renewables. These strategies enable better energy management in microgrids, thereby improving system efficiency and stability. Given the complexity of optimization problems related to microgrid management, evolutionary optimization algorithms such as the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) have gained great attention. These algorithms enable solving high-complexity optimization problems by considering various constraints and multiple objectives. In this paper, both ICA and GA, as well as their hybrid application, are used to significantly enhance the voltage regulation in microgrids. The integration of optimization techniques with demand response strategies improves the overall system efficiency and stability. The results proved that the hybrid method provides valuable insights for optimizing energy management systems. Full article

(This article belongs to the Special Issue Emerging Research in Optimization Algorithms in the Era of Big Data)

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21 pages, 5183 KiB

Open AccessArticle

Sustainable Mobility as a Service: A Scientometric Review in the Context of Agenda 2030

by Antonio Comi, Francis M. M. Cirianni and Lorenzo Cabras

Information 2024, 15(10), 637; https://doi.org/10.3390/info15100637 (registering DOI) - 14 Oct 2024

Abstract

City planners are facing an emergency to develop, promote, and implement actions that allow the mobility needs of people and businesses in cities (and their surroundings) to be satisfied to assure a better quality of life. Among the different actions to promote, there [...] Read more.

City planners are facing an emergency to develop, promote, and implement actions that allow the mobility needs of people and businesses in cities (and their surroundings) to be satisfied to assure a better quality of life. Among the different actions to promote, there is public transport, which should be the cornerstone of sustainable urban mobility. The only way to achieve the transition from private to public is by integrating services into a multimodal network and then encouraging interchange between different modes of transportation. In this context, the development of mobility as a service (MaaS) solutions is significant, and different studies have been developed in recent years. The paper thus introduces a scientometric review of such a topic in relation to the Sustainable Development Goals of Agenda 2030. The study focuses on the current state of MaaS implementation, trends, and research gaps, with an in-depth analysis of emerging themes, based on 819 documents selected from the WoS and Scopus databases. Introducing the database, and research methodology, an accurate interpretation of the data generated by the bibliometric analysis, and the primary evaluation parameters are outlined. The identified studies have been then categorized into three thematic groups with the intention of offering a comprehensive study that identifies the shortcomings and difficulties in the research carried out in these areas up to today. Particular attention is paid to how this research relates to the Sustainable Development Goals. Full article

(This article belongs to the Special Issue Mobility as a Service: Opportunities and Challenges for the Sustainable Mobility)

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19 pages, 1452 KiB

Open AccessArticle

Electronic Health (eHealth) Literacy and Self-Care Behaviors—Results from a Survey of University Students in a Developing Country

by Salman Bin Naeem, Anthony Faiola, Aziz Ur-Rehman and Maged N. Kamel Boulos

Information 2024, 15(10), 636; https://doi.org/10.3390/info15100636 (registering DOI) - 14 Oct 2024

Abstract

eHealth literacy (eHL) is directly linked to disease prevention, health promotion, and improved healthcare outcomes. The objectives of this study are to assess undergraduate university students’ knowledge and perceived skills of finding, appraising, and applying electronic health information to health-related problems, as well [...] Read more.

eHealth literacy (eHL) is directly linked to disease prevention, health promotion, and improved healthcare outcomes. The objectives of this study are to assess undergraduate university students’ knowledge and perceived skills of finding, appraising, and applying electronic health information to health-related problems, as well as to assess the association of eHL with physical, psychological, and emotional self-care. Methods: The measurement model, comprising four correlated factors based on the 28 valid items from two reliable and valid tests, the ‘eHealth literacy scale (eHEALS)’ and ‘the self-care assessment tool (SCAT)’, was estimated using confirmatory factor analysis (CFA) among a sample of 1557 undergraduate university students in Pakistan. Results: The mean value of the eHEALS ranges between 2.90 and 3.33, indicating that the majority of the respondents had moderate levels of eHL skills. Female respondents and respondents from urban areas have greater levels of perceived eHL skills compared with their male and rural counterparts. The CFA model fit indices show that the goodness of fit values are acceptable: x² = 7.727, p = 0.000; RMSEA = 0.065; TLI = 0.930, CFI = 0.936, IFI = 0.936, GFI = 0.890, NFI = 0.928, RFI = 0.920, PGFI = 0.754. Conclusion: Electronic health (eHealth) literacy has a strong positive association with physical, psychological, and emotional self-care. However, perceived eHL skills among undergraduate university students are moderate, making them potentially susceptible to health risks. Implications: Our study has several practical implications. Its findings can be used to devise eHealth literacy programs for developing relevant skills among undergraduate university students based on their identified needs. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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18 pages, 8011 KiB

Open AccessArticle

Intelligent Vision System with Pruning and Web Interface for Real-Time Defect Detection on African Plum Surfaces

by Arnaud Nguembang Fadja, Sain Rigobert Che and Marcellin Atemkemg

Information 2024, 15(10), 635; https://doi.org/10.3390/info15100635 (registering DOI) - 14 Oct 2024

Abstract

Agriculture stands as the cornerstone of Africa’s economy, supporting over 60% of the continent’s labor force. Despite its significance, the quality assessment of agricultural products remains a challenging task, particularly at a large scale, consuming valuable time and resources. The African plum is [...] Read more.

Agriculture stands as the cornerstone of Africa’s economy, supporting over 60% of the continent’s labor force. Despite its significance, the quality assessment of agricultural products remains a challenging task, particularly at a large scale, consuming valuable time and resources. The African plum is an agricultural fruit that is widely consumed across West and Central Africa but remains underrepresented in AI research. In this paper, we collected a dataset of 2892 African plum samples from fields in Cameroon representing the first dataset of its kind for training AI models. The dataset contains images of plums annotated with quality grades. We then trained and evaluated various state-of-the-art object detection and image classification models, including YOLOv5, YOLOv8, YOLOv9, Fast R-CNN, Mask R-CNN, VGG-16, DenseNet-121, MobileNet, and ResNet, on this African plum dataset. Our experimentation resulted in mean average precision scores ranging from 88.2% to 89.9% and accuracies between 86% and 91% for the object detection models and the classification models, respectively. We then performed model pruning to reduce model sizes while preserving performance, achieving up to 93.6% mean average precision and 99.09% accuracy after pruning YOLOv5, YOLOv8 and ResNet by 10–30%. We deployed the high-performing YOLOv8 system in a web application, offering an accessible AI-based quality assessment tool tailored for African plums. To the best of our knowledge, this represents the first such solution for assessing this underrepresented fruit, empowering farmers with efficient tools. Our approach integrates agriculture and AI to fill a key gap. Full article

(This article belongs to the Special Issue Advanced Technologies in Intelligent Detection of Biological Information)

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