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30 pages, 3620 KiB

Open AccessArticle

Stroke Detection in Brain CT Images Using Convolutional Neural Networks: Model Development, Optimization and Interpretability

by Hassan Abdi, Mian Usman Sattar, Raza Hasan, Vishal Dattana and Salman Mahmood

Information 2025, 16(5), 345; https://doi.org/10.3390/info16050345 - 24 Apr 2025

Viewed by 109

Abstract

Stroke detection using medical imaging plays a crucial role in early diagnosis and treatment planning. In this study, we propose a Convolutional Neural Network (CNN)-based model for detecting strokes from brain Computed Tomography (CT) images. The model is trained on a dataset consisting [...] Read more.

Stroke detection using medical imaging plays a crucial role in early diagnosis and treatment planning. In this study, we propose a Convolutional Neural Network (CNN)-based model for detecting strokes from brain Computed Tomography (CT) images. The model is trained on a dataset consisting of 2501 images, including both normal and stroke cases, and employs a series of preprocessing steps, including resizing, normalization, data augmentation, and splitting into training, validation, and test sets. The CNN architecture comprises three convolutional blocks followed by dense layers optimized through hyperparameter tuning to maximize performance. Our model achieved a validation accuracy of 97.2%, with precision and recall values of 96%, demonstrating high efficacy in stroke classification. Additionally, interpretability techniques such as Local Interpretable Model-agnostic Explanations (LIME), occlusion sensitivity, and saliency maps were used to visualize the model’s decision-making process, enhancing transparency and trust for clinical use. The results suggest that deep learning models, particularly CNNs, can provide valuable support for medical professionals in detecting strokes, offering both high performance and interpretability. The model demonstrates moderate generalizability, achieving 89.73% accuracy on an external, patient-independent dataset of 9900 CT images, underscoring the need for further optimization in diverse clinical settings. Full article

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

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26 pages, 8945 KiB

Open AccessArticle

Benchmarking Methods for Pointwise Reliability

by Cláudio Correia, Simão Paredes, Teresa Rocha, Jorge Henriques and Jorge Bernardino

Information 2025, 16(4), 327; https://doi.org/10.3390/info16040327 - 20 Apr 2025

Viewed by 90

Abstract

The growing interest in machine learning in a critical domain like healthcare emphasizes the need for reliable predictions, as decisions based on these outputs can have significant consequences. This study benchmarks methods for assessing pointwise reliability, focusing on data-driven techniques based on the [...] Read more.

The growing interest in machine learning in a critical domain like healthcare emphasizes the need for reliable predictions, as decisions based on these outputs can have significant consequences. This study benchmarks methods for assessing pointwise reliability, focusing on data-driven techniques based on the density principle and the local fit principle. These methods evaluate the reliability of individual predictions by analyzing their similarity to training data and evaluating the performance of the model in local regions. Aiming to establish a standardized comparison, the study introduces a benchmark framework that combines error rate evaluations across reliability intervals with t-distributed Stochastic Neighbor Embedding visualizations to further validate the results. The results demonstrate that methods combining density and local fit principles generally outperform those relying on a single principle, achieving lower error rates for high-reliability predictions. Furthermore, the study identifies challenges such as the adjustment of method parameters and clustering limitations and provides insight into their impact on reliability assessments. Full article

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

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30 pages, 1749 KiB

Open AccessArticle

Deepfake Image Forensics for Privacy Protection and Authenticity Using Deep Learning

by Saud Sohail, Syed Muhammad Sajjad, Adeel Zafar, Zafar Iqbal, Zia Muhammad and Muhammad Kazim

Information 2025, 16(4), 270; https://doi.org/10.3390/info16040270 - 27 Mar 2025

Viewed by 766

Abstract

This research focuses on the detection of deepfake images and videos for forensic analysis using deep learning techniques. It highlights the importance of preserving privacy and authenticity in digital media. The background of the study emphasizes the growing threat of deepfakes, which pose [...] Read more.

This research focuses on the detection of deepfake images and videos for forensic analysis using deep learning techniques. It highlights the importance of preserving privacy and authenticity in digital media. The background of the study emphasizes the growing threat of deepfakes, which pose significant challenges in various domains, including social media, politics, and entertainment. Current methodologies primarily rely on visual features that are specific to the dataset and fail to generalize well across varying manipulation techniques. However, these techniques focus on either spatial or temporal features individually and lack robustness in handling complex deepfake artifacts that involve fused facial regions such as eyes, nose, and mouth. Key approaches include the use of CNNs, RNNs, and hybrid models like CNN-LSTM, CNN-GRU, and temporal convolutional networks (TCNs) to capture both spatial and temporal features during the detection of deepfake videos and images. The research incorporates data augmentation with GANs to enhance model performance and proposes an innovative fusion of artifact inspection and facial landmark detection for improved accuracy. The experimental results show near-perfect detection accuracy across diverse datasets, demonstrating the effectiveness of these models. However, challenges remain, such as the difficulty of detecting deepfakes in compressed video formats, the need for handling noise and addressing dataset imbalances. The research presents an enhanced hybrid model that improves detection accuracy while maintaining performance across various datasets. Future work includes improving model generalization to detect emerging deepfake techniques better. The experimental results reveal a near-perfect accuracy of over 99% across different architectures, highlighting their effectiveness in forensic investigations. Full article

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

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20 pages, 4390 KiB

Open AccessArticle

Application of VGG16 Transfer Learning for Breast Cancer Detection

by Tanjim Fatima and Hamdy Soliman

Information 2025, 16(3), 227; https://doi.org/10.3390/info16030227 - 14 Mar 2025

Viewed by 620

Abstract

Breast cancer is among the primary causes of cancer-related deaths globally, highlighting the critical need for effective and early diagnostic methods. Traditional diagnostic approaches, while valuable, often face limitations in accuracy and accessibility. Recent advancements in deep learning, particularly transfer learning, provide promising [...] Read more.

Breast cancer is among the primary causes of cancer-related deaths globally, highlighting the critical need for effective and early diagnostic methods. Traditional diagnostic approaches, while valuable, often face limitations in accuracy and accessibility. Recent advancements in deep learning, particularly transfer learning, provide promising solutions for enhancing diagnostic precision in breast cancer detection. Due to the limited capability of the BreakHis dataset, transfer learning was utilized to advance the training of our new model with the VGG16 neural network model, well trained on the rich ImageNet dataset. Moreover, the VGG16 architecture was carefully modified, including the fine-tuning of its layers, yielding our new model: M-VGG16. The new M-VGG16 model is designed to carry out the binary cancer/benign classification of breast samples effectively. The experimental results of our M-VGG16 model showed it achieved high validation accuracy (93.68%), precision (93.22%), recall (97.91%), and a high AUC (0.9838), outperforming other peer models in the same field. This study validates the VGG16 model’s suitability for breast cancer detection via transfer learning, providing an efficient, adaptable framework for improving diagnostic accuracy and potentially enhancing breast cancer detection. Key breast cancer detection challenges and potential M-VGG16 model refinements are also discussed. Full article

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

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13 pages, 1634 KiB

Open AccessArticle

Novel Machine Learning-Based Brain Attention Detection Systems

by Junbo Wang and Song-Kyoo Kim

Information 2025, 16(1), 25; https://doi.org/10.3390/info16010025 - 5 Jan 2025

Viewed by 843

Abstract

Electroencephalography (EEG) can reflect changes in brain activity under different states. The electrical signals of the brain are observed to exhibit varying amplitudes and frequencies. These variations are closely linked to different states of consciousness, influencing the internal and external behaviors, emotions, and [...] Read more.

Electroencephalography (EEG) can reflect changes in brain activity under different states. The electrical signals of the brain are observed to exhibit varying amplitudes and frequencies. These variations are closely linked to different states of consciousness, influencing the internal and external behaviors, emotions, and learning performance of humans. The assessment of personal level of attention, which refers to the ability to consciously focus on something, can also be facilitated by these signals. Research on brain attention aids in the understanding of the mechanisms underlying human cognition and behavior. Based on the characteristics of EEG signals, this research identifies the most effective method for detecting brain attention by adapting various preprocessing and machine learning techniques. The results of our analysis on a publicly available dataset indicate that KNN with the feature importance feature extraction method performed the best, achieving 99.56% accuracy, 99.67% recall, and 99.44% precision with a rapid training time. Full article

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

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25 pages, 2590 KiB

Open AccessArticle

Predictive Modeling of Water Level in the San Juan River Using Hybrid Neural Networks Integrated with Kalman Smoothing Methods

by Jackson B. Renteria-Mena and Eduardo Giraldo

Information 2024, 15(12), 754; https://doi.org/10.3390/info15120754 - 26 Nov 2024

Viewed by 732

Abstract

This study presents an innovative approach to predicting the water level in the San Juan River, Chocó, Colombia, by implementing two hybrid models: nonlinear auto-regressive with exogenous inputs (NARX) and long short-term memory (LSTM). These models combine artificial neural networks with smoothing techniques, [...] Read more.

This study presents an innovative approach to predicting the water level in the San Juan River, Chocó, Colombia, by implementing two hybrid models: nonlinear auto-regressive with exogenous inputs (NARX) and long short-term memory (LSTM). These models combine artificial neural networks with smoothing techniques, including the exponential, Savitzky–Golay, and Rauch–Tung–Striebel (RTS) smoothing filters, with the aim of improving the accuracy of hydrological predictions. Given the high rainfall in the region, the San Juan River experiences significant fluctuations in its water levels, which presents a challenge for accurate prediction. The models were trained using historical data, and various smoothing techniques were applied to optimize data quality and reduce noise. The effectiveness of the models was evaluated using standard regression metrics, such as Nash–Sutcliffe efficiency (NSE), mean square error (MSE), and mean absolute error (MAE), in addition to Kling–Gupta efficiency (KGE). The results show that the combination of neural networks with smoothing filters, especially the RTS filter and smoothed Kalman filter, provided the most accurate predictions, outperforming traditional methods. This research has important implications for water resource management and flood prevention in vulnerable areas such as Chocó. The implementation of these hybrid models will allow local authorities to anticipate changes in water levels and plan preventive measures more effectively, thus reducing the risk of damage from extreme events. In summary, this study establishes a solid foundation for future research in water level prediction, highlighting the importance of integrating advanced technologies in water resources management. Full article

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

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12 pages, 509 KiB

Open AccessArticle

Test–Retest Reliability of Deep Learning Analysis of Brain Volumes in Adolescent Brain

by Anna-Maria Kasparbauer, Heidrun Lioba Wunram, Fabian Abuhsin, Friederike Körber, Eckhard Schönau, Stephan Bender and Ibrahim Duran

Information 2024, 15(12), 748; https://doi.org/10.3390/info15120748 - 25 Nov 2024

Viewed by 913

Abstract

Magnetic resonance imaging (MRI) is essential for studying brain development and psychiatric disorders in adolescents. However, the imaging consistency remains challenging, highlighting the need for advanced methodologies to improve the diagnostic and research reliability in this unique developmental period. Adolescence is marked by [...] Read more.

Magnetic resonance imaging (MRI) is essential for studying brain development and psychiatric disorders in adolescents. However, the imaging consistency remains challenging, highlighting the need for advanced methodologies to improve the diagnostic and research reliability in this unique developmental period. Adolescence is marked by significant neuroanatomical changes, distinguishing adolescent brains from those of adults and making age-specific imaging research crucial for understanding the neuropsychiatric conditions in youth. This study examines the test–retest reliability of anatomical brain MRI scans in adolescents diagnosed with depressive disorders, emphasizing a developmental perspective on neuropsychiatric disorders. Using a sample of 42 adolescents, we assessed the consistency of structural imaging metrics across 95 brain regions with deep learning-based neuroimaging analysis pipelines. The results demonstrated moderate to excellent reliability, with the intraclass correlation coefficients (ICC) ranging from 0.57 to 0.99 across regions. Notably, regions such as the pallidum, amygdala, entorhinal cortex, and white matter hypointensities showed moderate reliability, likely reflecting the challenges in the segmentation or inherent anatomical variability unique to this age group. This study highlights the necessity of integrating advanced imaging technologies to enhance the accuracy and reliability of the neuroimaging data specific to adolescents. Addressing the regional variability and strengthening the methodological rigor are essential for advancing the understanding of brain development and psychiatric disorders in this distinct developmental stage. Future research should focus on larger, more diverse samples, multi-site studies, and emerging imaging techniques to further validate the neuroimaging biomarkers. Such advancements could improve the clinical outcomes and deepen our understanding of the neuropsychiatric conditions unique to adolescence. Full article

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

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26 pages, 951 KiB

Open AccessArticle

Maternal Nutritional Factors Enhance Birthweight Prediction: A Super Learner Ensemble Approach

by Muhammad Mursil, Hatem A. Rashwan, Pere Cavallé-Busquets, Luis A. Santos-Calderón, Michelle M. Murphy and Domenec Puig

Information 2024, 15(11), 714; https://doi.org/10.3390/info15110714 - 6 Nov 2024

Cited by 2 | Viewed by 1253

Abstract

Birthweight (BW) is a widely used indicator of neonatal health, with low birthweight (LBW) being linked to higher risks of morbidity and mortality. Timely and precise prediction of LBW is crucial for ensuring newborn health and well-being. Despite recent machine learning advancements in [...] Read more.

Birthweight (BW) is a widely used indicator of neonatal health, with low birthweight (LBW) being linked to higher risks of morbidity and mortality. Timely and precise prediction of LBW is crucial for ensuring newborn health and well-being. Despite recent machine learning advancements in BW classification based on physiological traits in the mother and ultrasound outcomes, maternal status in essential micronutrients for fetal development is yet to be fully exploited for BW prediction. This study aims to evaluate the impact of maternal nutritional factors, specifically mid-pregnancy plasma concentrations of vitamin B12, folate, and anemia on BW prediction. This study analyzed data from 729 pregnant women in Tarragona, Spain, for early BW prediction and analyzed each factor’s impact and contribution using a partial dependency plot and feature importance. Using a super learner ensemble method with tenfold cross-validation, the model achieved a prediction accuracy of 96.19% and an AUC-ROC of 0.96, outperforming single-model approaches. Vitamin B12 and folate status were identified as significant predictors, underscoring their importance in reducing LBW risk. The findings highlight the critical role of maternal nutritional factors in BW prediction and suggest that monitoring vitamin B12 and folate levels during pregnancy could enhance prenatal care and mitigate neonatal complications associated with LBW. Full article

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

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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

Cited by 5 | Viewed by 1224

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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17 pages, 4057 KiB

Open AccessArticle

A Comparative Analysis of Automated Machine Learning Tools: A Use Case for Autism Spectrum Disorder Detection

by Rana Tuqeer Abbas, Kashif Sultan, Muhammad Sheraz and Teong Chee Chuah

Information 2024, 15(10), 625; https://doi.org/10.3390/info15100625 - 11 Oct 2024

Cited by 1 | Viewed by 1396

Abstract

Automated Machine Learning (AutoML) enhances productivity and efficiency by automating the entire process of machine learning model development, from data preprocessing to model deployment. These tools are accessible to users with varying levels of expertise and enable efficient, scalable, and accurate classification across [...] Read more.

Automated Machine Learning (AutoML) enhances productivity and efficiency by automating the entire process of machine learning model development, from data preprocessing to model deployment. These tools are accessible to users with varying levels of expertise and enable efficient, scalable, and accurate classification across different applications. This paper evaluates two popular AutoML tools, the Tree-Based Pipeline Optimization Tool (TPOT) version 0.10.2 and Konstanz Information Miner (KNIME) version 5.2.5, comparing their performance in a classification task. Specifically, this work analyzes autism spectrum disorder (ASD) detection in toddlers as a use case. The dataset for ASD detection was collected from various rehabilitation centers in Pakistan. TPOT and KNIME were applied to the ASD dataset, with TPOT achieving an accuracy of 85.23% and KNIME achieving 83.89%. Evaluation metrics such as precision, recall, and F1-score validated the reliability of the models. After selecting the best models with optimal accuracy, the most important features for ASD detection were identified using these AutoML tools. The tools optimized the feature selection process and significantly reduced diagnosis time. This study demonstrates the potential of AutoML tools and feature selection techniques to improve early ASD detection and outcomes for affected children and their families. Full article

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

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20 pages, 3585 KiB

Open AccessArticle

A Study of Exergame System Using Hand Gestures for Wrist Flexibility Improvement for Tenosynovitis Prevention

by Yanqi Xiao, Nobuo Funabiki, Irin Tri Anggraini, Cheng-Liang Shih and Chih-Peng Fan

Information 2024, 15(10), 622; https://doi.org/10.3390/info15100622 - 10 Oct 2024

Viewed by 1031

Abstract

Currently, as an increasing number of people have been addicted to using cellular phones, smartphone tenosynovitis has become common from long-term use of fingers for their operations. Hand exercise while playing video games, which is called exergame, can be a good solution [...] Read more.

Currently, as an increasing number of people have been addicted to using cellular phones, smartphone tenosynovitis has become common from long-term use of fingers for their operations. Hand exercise while playing video games, which is called exergame, can be a good solution to provide enjoyable daily exercise opportunities for its prevention, particularly, for young people. In this paper, we implemented a simple exergame system with a hand gesture recognition program made in Python using the Mediapipe library. We designed three sets of hand gestures to control the key operations to play the games as different exercises useful for tenosynovitis prevention. For evaluations, we prepared five video games running on a web browser and asked 10 students from Okayama and Hiroshima Universities, Japan, to play them and answer 10 questions in the questionnaire. Their playing results and System Usability Scale (SUS) scores confirmed the usability of the proposal, although we improved one gesture set to reduce its complexity. Moreover, by measuring the angles for maximum wrist movements, we found that the wrist flexibility was improved by playing the games, which verifies the effectiveness of the proposal. Full article

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

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29 pages, 6970 KiB

Open AccessArticle

Traumatic Brain Injury Structure Detection Using Advanced Wavelet Transformation Fusion Algorithm with Proposed CNN-ViT

by Abdullah, Ansar Siddique, Zulaikha Fatima and Kamran Shaukat

Information 2024, 15(10), 612; https://doi.org/10.3390/info15100612 - 6 Oct 2024

Viewed by 1569

Abstract

Detecting Traumatic Brain Injuries (TBI) through imaging remains challenging due to limited sensitivity in current methods. This study addresses the gap by proposing a novel approach integrating deep-learning algorithms and advanced image-fusion techniques to enhance detection accuracy. The method combines contextual and visual [...] Read more.

Detecting Traumatic Brain Injuries (TBI) through imaging remains challenging due to limited sensitivity in current methods. This study addresses the gap by proposing a novel approach integrating deep-learning algorithms and advanced image-fusion techniques to enhance detection accuracy. The method combines contextual and visual models to effectively assess injury status. Using a dataset of repeat mild TBI (mTBI) cases, we compared various image-fusion algorithms: PCA (89.5%), SWT (89.69%), DCT (89.08%), HIS (83.3%), and averaging (80.99%). Our proposed hybrid model achieved a significantly higher accuracy of 98.78%, demonstrating superior performance. Metrics including Dice coefficient (98%), sensitivity (97%), and specificity (98%) verified that the strategy is efficient in improving image quality and feature extraction. Additional validations with “entropy”, “average pixel intensity”, “standard deviation”, “correlation coefficient”, and “edge similarity measure” confirmed the robustness of the fused images. The hybrid CNN-ViT model, integrating curvelet transform features, was trained and validated on a comprehensive dataset of 24 types of brain injuries. The overall accuracy was 99.8%, with precision, recall, and F1-score of 99.8%. The “average PSNR” was 39.0 dB, “SSIM” was 0.99, and MI was 1.0. Cross-validation across five folds proved the model’s “dependability” and “generalizability”. In conclusion, this study introduces a promising method for TBI detection, leveraging advanced image-fusion and deep-learning techniques, significantly enhancing medical imaging and diagnostic capabilities for brain injuries. Full article

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

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17 pages, 4471 KiB

Open AccessArticle

Machine Learning Applications in Prediction Models for COVID-19: A Bibliometric Analysis

by Hai Lv, Yangyang Liu, Huimin Yin, Jingzhi Xi and Pingmin Wei

Information 2024, 15(9), 575; https://doi.org/10.3390/info15090575 - 18 Sep 2024

Viewed by 2007

Abstract

The COVID-19 pandemic has had a profound impact on global health, inspiring the widespread use of machine learning in combating the disease, particularly in prediction models. This study aimed to assess academic publications utilizing machine learning prediction models to combat COVID-19. We analyzed [...] Read more.

The COVID-19 pandemic has had a profound impact on global health, inspiring the widespread use of machine learning in combating the disease, particularly in prediction models. This study aimed to assess academic publications utilizing machine learning prediction models to combat COVID-19. We analyzed 2422 original articles published between 2020 and 2023 with bibliometric tools such as Histcite Pro 2.1, Bibliometrix, CiteSpace, and VOSviewer. The United States, China, and India emerged as the most prolific countries, with Stanford University producing the most publications and Huazhong University of Science and Technology receiving the most citations. The National Natural Science Foundation of China and the National Institutes of Health have made significant contributions to this field. Scientific Reports is the most frequent journal for publishing these articles. Current research focuses on deep learning, federated learning, image classification, air pollution, mental health, sentiment analysis, and drug repurposing. In conclusion, this study provides detailed insights into the key authors, countries, institutions, funding agencies, and journals in the field, as well as the most frequently used keywords. Full article

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

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15 pages, 506 KiB

Open AccessArticle

Understanding Telehealth Adoption among the Elderly: An Empirical Investigation

by Urvashi Tandon, Myriam Ertz, Muhammed Sajid and Mehrdad Kordi

Information 2024, 15(9), 552; https://doi.org/10.3390/info15090552 - 9 Sep 2024

Cited by 1 | Viewed by 2375

Abstract

The adoption of telemedicine among the elderly is vital due to their unique healthcare needs and growing engagement with technology. This study explores the factors influencing their adoption behaviors, identifying both facilitating and inhibiting elements. While previous research has examined these factors, few [...] Read more.

The adoption of telemedicine among the elderly is vital due to their unique healthcare needs and growing engagement with technology. This study explores the factors influencing their adoption behaviors, identifying both facilitating and inhibiting elements. While previous research has examined these factors, few have empirically assessed the simultaneous influence of barriers and enablers using a sample of elderly individuals. Using behavioral reasoning theory (BRT), this research investigates telehealth adoption behaviors of the elderly in India. A conceptual model incorporates both “reasons for” and “reasons against” adopting telehealth, capturing the nuanced dynamics of adoption behaviors. Data from 375 elderly individuals were collected to validate the model through structural equation modeling. The findings reveal that openness to change significantly enhances attitudes towards telehealth and “reasons for” adoption, influencing behaviors. This research contributes to the healthcare ecosystem by improving the understanding of telehealth adoption among the elderly. It validates the impact of openness to change alongside reasons for and against adoption, refining the understanding of behavior. By addressing impediments and leveraging facilitators, this study suggests strategies to maximize telehealth usage among the elderly, particularly those who are isolated, improving their access to medical services. Full article

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

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17 pages, 1115 KiB

Open AccessArticle

From GPT-3.5 to GPT-4.o: A Leap in AI’s Medical Exam Performance

by Markus Kipp

Information 2024, 15(9), 543; https://doi.org/10.3390/info15090543 - 5 Sep 2024

Cited by 5 | Viewed by 5753

Abstract

ChatGPT is a large language model trained on increasingly large datasets to perform diverse language-based tasks. It is capable of answering multiple-choice questions, such as those posed by diverse medical examinations. ChatGPT has been generating considerable attention in both academic and non-academic domains [...] Read more.

ChatGPT is a large language model trained on increasingly large datasets to perform diverse language-based tasks. It is capable of answering multiple-choice questions, such as those posed by diverse medical examinations. ChatGPT has been generating considerable attention in both academic and non-academic domains in recent months. In this study, we aimed to assess GPT’s performance on anatomical multiple-choice questions retrieved from medical licensing examinations in Germany. Two different versions were compared. GPT-3.5 demonstrated moderate accuracy, correctly answering 60–64% of questions from the autumn 2022 and spring 2021 exams. In contrast, GPT-4.o showed significant improvement, achieving 93% accuracy on the autumn 2022 exam and 100% on the spring 2021 exam. When tested on 30 unique questions not available online, GPT-4.o maintained a 96% accuracy rate. Furthermore, GPT-4.o consistently outperformed medical students across six state exams, with a statistically significant mean score of 95.54% compared with the students’ 72.15%. The study demonstrates that GPT-4.o outperforms both its predecessor, GPT-3.5, and a cohort of medical students, indicating its potential as a powerful tool in medical education and assessment. This improvement highlights the rapid evolution of LLMs and suggests that AI could play an increasingly important role in supporting and enhancing medical training, potentially offering supplementary resources for students and professionals. However, further research is needed to assess the limitations and practical applications of such AI systems in real-world medical practice. Full article

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

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18 pages, 1789 KiB

Open AccessArticle

Multivariate Hydrological Modeling Based on Long Short-Term Memory Networks for Water Level Forecasting

by Jackson B. Renteria-Mena, Douglas Plaza and Eduardo Giraldo

Information 2024, 15(6), 358; https://doi.org/10.3390/info15060358 - 15 Jun 2024

Cited by 4 | Viewed by 1790

Abstract

In the Department of Chocó, flooding poses a recurrent and significant challenge due to heavy rainfall and the dense network of rivers characterizing the region. However, the lack of adequate infrastructure to prevent and predict floods exacerbates this situation. The absence of early [...] Read more.

In the Department of Chocó, flooding poses a recurrent and significant challenge due to heavy rainfall and the dense network of rivers characterizing the region. However, the lack of adequate infrastructure to prevent and predict floods exacerbates this situation. The absence of early warning systems, the scarcity of meteorological and hydrological monitoring stations, and deficiencies in urban planning contribute to the vulnerability of communities to these phenomena. It is imperative to invest in flood prediction and prevention infrastructure, including advanced monitoring systems, the development of hydrological prediction models, and the construction of hydraulic infrastructure, to reduce risk and protect vulnerable communities in Chocó. Additionally, raising public awareness of the associated risks and encouraging the adoption of mitigation and preparedness measures throughout the population are essential. This study introduces a novel approach for the multivariate prediction of hydrological variables, specifically focusing on water level forecasts for two hydrological stations along the Atrato River in Colombia. The model, utilizing a specialized type of recurrent neural network (RNN) called the long short-term memory (LSTM) network, integrates data from hydrological variables, such as the flow, precipitation, and level. With a model architecture featuring four inputs and two outputs, where flow and precipitation serve as inputs and the level serves as the output for each station, the LSTM model is adept at capturing the complex dynamics and cross-correlations among these variables. Validation involves comparing the LSTM model’s performance with linear and nonlinear Autoregressive with Exogenous Input (NARX) models, considering factors such as the estimation error and computational time. Furthermore, this study explores different scenarios for water level prediction, aiming to utilize the proposed approach as an effective flood early warning system. Full article

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

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25 pages, 766 KiB

Open AccessArticle

A Comparison of Bias Mitigation Techniques for Educational Classification Tasks Using Supervised Machine Learning

by Tarid Wongvorachan, Okan Bulut, Joyce Xinle Liu and Elisabetta Mazzullo

Information 2024, 15(6), 326; https://doi.org/10.3390/info15060326 - 4 Jun 2024

Cited by 2 | Viewed by 3121

Abstract

Machine learning (ML) has become integral in educational decision-making through technologies such as learning analytics and educational data mining. However, the adoption of machine learning-driven tools without scrutiny risks perpetuating biases. Despite ongoing efforts to tackle fairness issues, their application to educational datasets [...] Read more.

Machine learning (ML) has become integral in educational decision-making through technologies such as learning analytics and educational data mining. However, the adoption of machine learning-driven tools without scrutiny risks perpetuating biases. Despite ongoing efforts to tackle fairness issues, their application to educational datasets remains limited. To address the mentioned gap in the literature, this research evaluates the effectiveness of four bias mitigation techniques in an educational dataset aiming at predicting students’ dropout rate. The overarching research question is: “How effective are the techniques of reweighting, resampling, and Reject Option-based Classification (ROC) pivoting in mitigating the predictive bias associated with high school dropout rates in the HSLS:09 dataset?" The effectiveness of these techniques was assessed based on performance metrics including false positive rate (FPR), accuracy, and F1 score. The study focused on the biological sex of students as the protected attribute. The reweighting technique was found to be ineffective, showing results identical to the baseline condition. Both uniform and preferential resampling techniques significantly reduced predictive bias, especially in the FPR metric but at the cost of reduced accuracy and F1 scores. The ROC pivot technique marginally reduced predictive bias while maintaining the original performance of the classifier, emerging as the optimal method for the HSLS:09 dataset. This research extends the understanding of bias mitigation in educational contexts, demonstrating practical applications of various techniques and providing insights for educators and policymakers. By focusing on an educational dataset, it contributes novel insights beyond the commonly studied datasets, highlighting the importance of context-specific approaches in bias mitigation. Full article

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

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18 pages, 1241 KiB

Open AccessArticle

Harnessing Artificial Intelligence for Automated Diagnosis

by Christos B. Zachariadis and Helen C. Leligou

Information 2024, 15(6), 311; https://doi.org/10.3390/info15060311 - 27 May 2024

Cited by 1 | Viewed by 3093

Abstract

The evolving role of artificial intelligence (AI) in healthcare can shift the route of automated, supervised and computer-aided diagnostic radiology. An extensive literature review was conducted to consider the potential of designing a fully automated, complete diagnostic platform capable of integrating the current [...] Read more.

The evolving role of artificial intelligence (AI) in healthcare can shift the route of automated, supervised and computer-aided diagnostic radiology. An extensive literature review was conducted to consider the potential of designing a fully automated, complete diagnostic platform capable of integrating the current medical imaging technologies. Adjuvant, targeted, non-systematic research was regarded as necessary, especially to the end-user medical expert, for the completeness, understanding and terminological clarity of this discussion article that focuses on giving a representative and inclusive idea of the evolutional strides that have taken place, not including an AI architecture technical evaluation. Recent developments in AI applications for assessing various organ systems, as well as enhancing oncology and histopathology, show significant impact on medical practice. Published research outcomes of AI picture segmentation and classification algorithms exhibit promising accuracy, sensitivity and specificity. Progress in this field has led to the introduction of the concept of explainable AI, which ensures transparency of deep learning architectures, enabling human involvement in clinical decision making, especially in critical healthcare scenarios. Structure and language standardization of medical reports, along with interdisciplinary collaboration between medical and technical experts, are crucial for research coordination. Patient personal data should always be handled with confidentiality and dignity, while ensuring legality in the attribution of responsibility, particularly in view of machines lacking empathy and self-awareness. The results of our literature research demonstrate the strong potential of utilizing AI architectures, mainly convolutional neural networks, in medical imaging diagnostics, even though a complete automated diagnostic platform, enabling full body scanning, has not yet been presented. Full article

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

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30 pages, 2732 KiB

Open AccessArticle

Exploiting Properties of Student Networks to Enhance Learning in Distance Education

by Rozita Tsoni, Evgenia Paxinou, Aris Gkoulalas-Divanis, Dimitrios Karapiperis, Dimitrios Kalles and Vassilios S. Verykios

Information 2024, 15(4), 234; https://doi.org/10.3390/info15040234 - 19 Apr 2024

Cited by 2 | Viewed by 1929

Abstract

Distance Learning has become the “new normal”, especially during the pandemic and due to the technological advances that are incorporated into the teaching procedure. At the same time, the augmented use of the internet has blurred the borders between distance and conventional learning. [...] Read more.

Distance Learning has become the “new normal”, especially during the pandemic and due to the technological advances that are incorporated into the teaching procedure. At the same time, the augmented use of the internet has blurred the borders between distance and conventional learning. Students interact mainly through LMSs, leaving their digital traces that can be leveraged to improve the educational process. New knowledge derived from the analysis of digital data could assist educational stakeholders in instructional design and decision making regarding the level and type of intervention that would benefit learners. This work aims to propose an analysis model that can capture the students’ behaviors in a distance learning course delivered fully online, based on the clickstream data associated with the discussion forum, and additionally to suggest interpretable patterns that will support education administrators and tutors in the decision-making process. To achieve our goal, we use Social Network Analysis as networks represent complex interactions in a meaningful and easily interpretable way. Moreover, simple or complex network metrics are becoming available to provide valuable insights into the students’ social interaction. This study concludes that by leveraging the imprint of these actions in an LMS and using metrics of Social Network Analysis, differences can be spotted in the communicational patterns that go beyond simple participation recording. Although HITS and PageRank algorithms were created with completely different targeting, it is shown that they can also reveal methodological features in students’ communicational approach. Full article

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

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20 pages, 540 KiB

Open AccessArticle

Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts

by George Papageorgiou, Vangelis Sarlis and Christos Tjortjis

Information 2024, 15(1), 61; https://doi.org/10.3390/info15010061 - 20 Jan 2024

Cited by 8 | Viewed by 4169

Abstract

This study utilized advanced data mining and machine learning to examine player injuries in the National Basketball Association (NBA) from 2000–01 to 2022–23. By analyzing a dataset of 2296 players, including sociodemographics, injury records, and financial data, this research investigated the relationships between [...] Read more.

This study utilized advanced data mining and machine learning to examine player injuries in the National Basketball Association (NBA) from 2000–01 to 2022–23. By analyzing a dataset of 2296 players, including sociodemographics, injury records, and financial data, this research investigated the relationships between injury types and player recovery durations, and their socioeconomic impacts. Our methodology involved data collection, engineering, and mining; the application of techniques such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), isolation forest, and the Z score for anomaly detection; and the application of the Apriori algorithm for association rule mining. Anomaly detection revealed 189 anomalies (1.04% of cases), highlighting unusual recovery durations and factors influencing recovery beyond physical healing. Association rule mining indicated shorter recovery times for lower extremity injuries and a 95% confidence level for quick returns from “Rest” injuries, affirming the NBA’s treatment and rest policies. Additionally, economic factors were observed, with players in lower salary brackets experiencing shorter recoveries, pointing to a financial influence on recovery decisions. This study offers critical insights into sports injuries and recovery, providing valuable information for sports professionals and league administrators. This study will impact player health management and team tactics, laying the groundwork for future research on long-term injury effects and technology integration in player health monitoring. Full article

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

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14 pages, 5209 KiB

Open AccessTechnical Note

Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving Data

by Natthida Sukkam, Tossapon Katongtung, Pana Suttakul, Yuttana Mona, Witsarut Achariyaviriya, Korrakot Yaibuathet Tippayawong and Nakorn Tippayawong

Information 2024, 15(9), 553; https://doi.org/10.3390/info15090553 - 9 Sep 2024

Cited by 3 | Viewed by 1878

Abstract

Electric vehicles (EVs) are alternatives to traditional combustion engine-powered vehicles. This work focuses on a thermal management system for battery EVs using liquid cooling and a machine learning (ML) model to predict their thermal-related health. Real-world data of EV operation, battery and cooling [...] Read more.

Electric vehicles (EVs) are alternatives to traditional combustion engine-powered vehicles. This work focuses on a thermal management system for battery EVs using liquid cooling and a machine learning (ML) model to predict their thermal-related health. Real-world data of EV operation, battery and cooling conditions were collected. Key influencing factors on the thermal-related health of batteries were identified. The ML model’s effectiveness was evaluated against experimental test data. The ML model proved effective in predicting and analyzing battery thermal health, suggesting its potential for use with the thermal management system. Full article

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

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