Learning Classification Models Research Articles

The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform.

Characterizing the kinome selectivity profiles of kinase inhibitors is essential in the early stages of novel small-molecule drug discovery. This characterization is critical for interpreting potential adverse events caused by off-target polypharmacology effects and provides unique pharmacological insights for drug repurposing development of existing kinase inhibitor drugs. However, experimental profiling of whole kinome selectivity is still time-consuming and resource-demanding. Here, we report a deep learning classification model using an in-house built data set of inhibitors against 191 well-representative kinases constructed based on a novel strategy by systematically cleaning and integrating six public data sets. This model, a multitask deep neural network, predicts the kinome selectivity profiles of compounds with novel structures. The model demonstrates excellent predictive performance, with auROC, prc-AUC, Accuracy, and Binary_cross_entropy of 0.95, 0.92, 0.90, and 0.37, respectively. It also performs well in a priori testing for inhibitors targeting different categories of proteins from internal compound collections, significantly improving over similar models on data sets from practical application scenarios. Integrated to subsequent machine learning-enhanced virtual screening workflow, novel CDK2 kinase inhibitors with potent kinase inhibitory activity and excellent kinome selectivity profiles are successfully identified. Additionally, we developed a free online web server, KinomePro-DL, to predict the kinome selectivity profiles and kinome-wide polypharmacology effects of small molecules (available on kinomepro-dl.pharmablock.com). Uniquely, our model allows users to quickly fine-tune it with their own training data sets, enhancing both prediction accuracy and robustness.

Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Analysis of Saliva as a Diagnostic Specimen for Rapid Classification of Oral Squamous Cell Carcinoma Using Chemometrics Methods

Background & Aim Recent advancements in analytical techniques have highlighted the potential of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy as a quick, cost-effective, non-invasive, and efficient tool for cancer diagnosis. This study aims to evaluate the effectiveness of ATR-FTIR spectroscopy in combination with supervised machine learning classification models for diagnosing OSCC using saliva samples. Methods & Materials Eighty unstimulated whole saliva samples from OSCC patients and healthy controls were collected. The ATR-FTIR spectroscopy was performed and spectral data were used to classify healthy and OSCC groups. The data were analyzed using machine learning classification methods such as Partial Least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine Classification (SVM-C). The classification performance of the models was evaluated by computing sensitivity, specificity, precision, and accuracy. Results The samples were classified into two classes based on their spectral data. The obtained results demonstrate a high level of accuracy in the prediction sets of the PLS-DA and SVM-C models, with accuracy values of 0.960 and 0.962, respectively. The OSCC group sensitivity values for both PLS-DA and SVM-C models was 1.00, respectively. Conclusion The study indicates that ATR-FTIR spectroscopy, combined with chemometrics, is a potential method for the non-invasive diagnosis of OSCC using saliva samples. This method achieved high accuracy and the findings of this study suggest that ATR-FTIR spectroscopy could be further developed for clinical applications in OSCC diagnosis.

A federated learning approach to automated and secure supplier selection in cyber manufacturing as-a-service

The emergence of cyber or platform-based manufacturing as-a-service is rapidly disrupting the way discrete parts are sourced and manufactured. However, the centralized business model of cyber manufacturing as-a-service platforms raises concerns about data ownership and access control of independent manufacturing suppliers. Contrary to centralized platforms, cyber manufacturing as-a-service aims to connect designers with geographically distributed manufacturers by serving as a broker who matches the query part design requirements with the manufacturing capabilities of candidate suppliers in its network. One of the key challenges in realizing the vision of cyber manufacturing as-a-service is the lack of a computationally efficient method for manufacturing capability search while maintaining data security of the proprietary datasets of the suppliers in the network. In this paper, we propose a federated learning approach that utilizes a deep unsupervised part retrieval model (FL-DUPR) to learn a federated embedding of suppliers’ manufacturing capabilities without directly accessing their proprietary datasets. We demonstrate through two case studies that this approach yields a supplier selection accuracy of 89 % when the manufacturing capabilities of the suppliers do not overlap, and a multi-label supplier selection accuracy of 87 % when there are significant overlaps in the suppliers’ manufacturing capabilities. We also show that our unsupervised learning approach outperforms the baseline supervised learning classification model trained under the same federated learning framework. The results demonstrate the promise of the proposed federated embedding approach for automated identification of the required manufacturing capabilities offered by various suppliers without directly accessing their proprietary data, thus paving the way for a more secure cyber manufacturing as-a-service business model.

Comparative Analysis of Machine Learning Classifier Models for Predicting Student Cognitive Load and Performance Outcomes in Moodle Learning Environment

Comparative Analysis of Machine Learning Classifier Models for Predicting Student Cognitive Load and Performance Outcomes in Moodle Learning Environment

Multimodal Data-Driven Segmentation of Bone Metastasis Lesions in SPECT Bone Scans Using Deep Learning.

Patients with malignant tumors often develop bone metastases. SPECT bone scintigraphy is an effective tool for surveying bone metastases due to its high sensitivity, low-cost equipment, and radiopharmaceutical. However, the low spatial resolution of SPECT scans significantly hinders manual analysis by nuclear medicine physicians. Deep learning, a promising technique for automated image analysis, can extract hierarchal patterns from images without human intervention. To enhance the performance of deep learning-based segmentation models, we integrate textual data from diagnostic reports with SPECT bone scans, aiming to develop an automated analysis method that outperforms purely unimodal data-driven segmentation models. We propose a dual-path segmentation framework to extract features from bone scan images and diagnostic reports separately. In the first path, an encoder-decoder network is employed to learn hierarchical representations of features from SPECT bone scan images. In the second path, the Chinese version of the MacBERT model is utilized to develop a text encoder for extracting features from diagnostic reports. The extracted textual features are then fused with image features during the decoding stage in the first path, enhancing the overall segmentation performance. Experimental evaluation conducted on real-world clinical data demonstrated the superior performance of the proposed segmentation model. Our model achieved a 0.0209 increase in the DSC (Dice Similarity Coefficient) score compared to the well-known U-Net model. The proposed multimodal data-driven method effectively identifies and isolates metastasis lesions in SPECT bone scans, outperforming existing classical deep learning models. This study demonstrates the value of incorporating textual data in the deep learning-based segmentation of lowresolution SPECT bone scans.

Pengembangan Model Pembelajaran Kooperatif Jigsaw pada Materi Karya Ilmiah Kelas XI MA Al-Ma’tuq dalam Meningkatkan Kualitas Pemahaman Siswa

This study discusses the development of a jigsaw-type cooperative learning model in class XI Scientific Work material. This research is applied to Indonesian Language learning and is carried out at Madrasah 'Aliyah Al-Ma'tuq. The problem discussed was about the lack of understanding of students on the use of previous learning models that have been used in Scientific Work materials. This research is expected to confirm the usefulness of the jigsaw-type cooperative learning model for class XI Scientific Work material. This research uses a qualitative method with interview techniques. The results of this study suggest that jigsaw cooperative learning in the Scientific Work material of grade XI MA Al-Ma'tuq is proven to make the learning material easier for students to understand.

Leveraging machine learning to identify subgroups of misclassified patients in the emergency department: a multi-center proof-of-concept study.

Hospitals use triage systems to prioritize the needs of patients within available resources. Misclassification of a patient can lead to either adverse outcomes in a patient who did not receive appropriate care in the case of undertriage or waste of hospital resources in the case of overtriage. Recent advances in machine learning algorithms allow for the quantification of variables important to under- and overtriage. The aim of this study was to identify clinical features most strongly associated with triage misclassification using a machine learning classification model to capture non-linear relationships. Multicenter retrospective cohort data from two big regional hospitals in Norway was extracted. The South African Triage system is used at Bergen University Hospital and the Rapid Emergency Triage and Treatment System is used at Trondheim University Hospital. Variables included triage score, age, gendersex, arrival time, subject area affiliation, reason for emergency department contact, discharge location, level of care, and time of death were retrieved. Random forest classification models were used to identify features with the strongest association with overtriage and undertriage in clinical practice in Bergen and Trondheim. We reported variable importance as SHAP-values (SHapley Additive exPlanations). We collected data on 205,488 patient records from Bergen University Hospital and 304,997 patient records from Trondheim University Hospital. Overall, overtriage was very uncommon at both hospitals (all <0.1%), with undertriage differing between both locationlocations with 0.8% at Bergen and 0.2% at Trondheim University Hospital. Demographics were similar for both hospitals, however the percentage given a high priority triage score (red or orange) was higher in Bergen (24%) compared to 9% in Trondheim. Clinical referral department was found to be the variable with the strongest association with undertriage (mean SHAP +0.62 and +0.37 for Bergen and Trondheim, respectively). We identified subgroups of patients consistently undertriaged using two common triage systems. While the importance of clinical characteristics to triage misclassification varies by triage system and location, but we found consistent evidence between the two locations that clinical referral department is the most important variable associated with triage misclassification. Replication of this approach at other centers could help to further improve triage scoring systems and improve patient care worldwide.

A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms

Objective To evaluate the effectiveness of a machine learning based on computed tomography (CT) radiomics to distinguish nontuberculous mycobacterial pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB). Methods In this retrospective analysis, medical records of 99 individuals afflicted with NTM-PD and 285 individuals with PTB in Zhejiang Chinese and Western Medicine Integrated Hospital were examined. Random numbers generated by a computer were utilized to stratify the study cohort, with 80% designated as the training cohort and 20% as the validation cohort. A total of 2153 radiomics features were extracted using Python (Pyradiomics package) to analyse the CT characteristics of the large disease areas. The identification of significant factors was conducted through the least absolute shrinkage and selection operator (LASSO) regression. The following four supervised learning classifier models were developed: random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGBoost). For assessment and comparison of the predictive performance among these models, receiver-operating characteristic (ROC) curves and the areas under the ROC curves (AUCs) were employed. Results The Student’s t-test, Levene test, and LASSO algorithm collectively selected 23 optimal features. ROC analysis was then conducted, with the respective AUC values of the XGBoost, LR, SVM, and RF models recorded to be 1, 0.9044, 0.8868, and 0.7982 in the training cohort. In the validation cohort, the respective AUC values of the XGBoost, LR, SVM, and RF models were 0.8358, 0.8085, 0.87739, and 0.7759. The DeLong test results noted the lack of remarkable variation across the models. Conclusion The CT radiomics features can help distinguish between NTM-PD and PTB. Among the four classifiers, SVM showed a stable performance in effectively identifying these two diseases.

Peningkatan Kemampuan Analisis Fakta dan Opini Peserta Didik Kelas XI TEK 2 SMK Negeri 5 Surabaya dengan Model Pembelajaran Kooperatif

This study aims to describe the improvement of the ability to analyze facts and opinions in argumentation text of students of class XI TEK 2 SMK Negeri 5 Surabaya, as well as to describe the effectiveness of cooperative learning model in class XI TEK 2 SMK Negeri 5 Surabaya. This type of research is a class action research (PTK). This class action research was conducted in class XI TEK 2 of SMK Negeri 5 Surabaya in semester 1 of 2024/2025 academic year. The subjects of this study were students of class XI TEK 2 consisting of 31 students; 24 male students and 7 female students. Data collection techniques using direct observation and test methods. Based on the results and discussion, the following conclusions can be drawn. (1) There was an increase in the learning outcomes of students in class XI TEK 2 in learning to analyze facts and opinions in argumentation text. The increase in student learning outcomes was 37.5. In addition, the increase in learning outcomes did not only occur in students who had not met the minimum score in cycle I, but also in 4 groups at once. Even as many as three groups achieved maximum learning outcomes. (2) Cooperative learning model is effective in learning to analyze facts and opinions in argumentation text. The cooperative learning model encourages learners to actively discuss to determine facts and opinions in argumentation text in groups.

Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images.

Background/Objectives: To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. Methods: Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. Results: The model demonstrated an overall precision of 0.89 on the testing set, with an accuracy of 0.88, a recall of 0.88, and an F1-score of 0.885. It presented better results for multiple categories, particularly in the panesophageal pressurization category, with precision = 0.99 and recall = 0.99, yielding a balanced F1-score of 0.99. Conclusions: This study demonstrates the potential of artificial intelligence, particularly Gemini, in aiding the creation of robust deep learning models for medical image analysis, solving not just simple binary classification problems but more complex, multi-class image classification tasks.

Upconversion and NIR-II luminescent rare earth nanoparticles combined with machine learning for cancer theranostics.

How to develop contrast agents for cancer theranostics is a meaningful and challenging endeavor, and rare earth nanoparticles (RENPs) may provide a possible solution. In this study, we initially modified RENPs through the application of photodynamic agents (ZnPc) and targeted the bevacizumab antibody for cancer theranostics, which was aimed at improving the therapeutic targeting and efficacy. Subsequently, we amalgamated anthocyanin with the modified RENPs, creating a potential cancer diagnosis platform. When the spectral data were obtained from the composite of cells, the crucial information was extracted through a competitive adaptive reweighted sampling feature algorithm. Then, we employed a machine learning classification model and classified both the individual spectral data and fused spectral data to accurately predict distinctions between breast cancer and normal tissue. The results indicate that the amalgamation of fusion techniques with machine learning algorithms provides highly precise predictions for molecular-level breast cancer detection. Finally, in vitro and in vivo experiments were carried out to validate the near-infrared luminescence and therapeutic effectiveness of the modified nanomedicine. This research not only underscores the targeted effects of nanomedicine but also demonstrates the potent synergy between optical spectral technology and machine learning. This innovative approach offers a comprehensive strategy for the integrated treatment of breast cancer.

Improving Student Learning Outcomes in Science and Science Subjects Through a Problem-Based Learning Model for Class V.A SD Negeri 27 Palembang

This study aims to improve the learning outcomes of students in the subject of Natural and Social Sciences (IPAS) by applying the Problem-Based Learning (PBL) learning model in class V.A SD Negeri 27 Palembang. This research used the Classroom Action Research (CAR) method which was carried out in three cycles. Each cycle involved planning, implementation, observation, and reflection. For data analysis, both quantitative and qualitative approaches were employed. The results showed a significant increase in students' learning outcomes in Chapter 2 “Harmony in Ecosystems”. The average score of students increased from 62.6 in cycle I with 62% learning completeness, to 70.1 in cycle II with 70% learning completeness, and reached 83.3 in cycle III with 83% learning completeness. Thus, the PBL learning model proved effective in improving student learning outcomes in IPAS subjects in class V.A SD Negeri 27 Palembang.

Application of Types of Learning Models Problem Based Learning (PBL) in Improving History Learning Results for Class XII MIA 9 SMA Negeri 3 Medan

This research uses classroom action research in its implementation. The subjects in this research are students of class XII MIA 9 SMA Negeri 3 Medan, and the population in this research are all students of class learning model problem based learning (PBL). The stages carried out in this research consisted of 2 cycles. This research was carried out using documentation techniques and providing tests in the form of pre-tests and post-tests, with the average score in cycle I being 89 then in cycle II being 94, and for learning completeness in cycle I 88.5% then learning completeness in cycle II 97.2%.

Penerapan Model Pembelajaran Team Games Tournament untuk Meningkatkan Hasil Pembelajaran Menganalisis Unsur Intrinsik Cerita Pendek Siswa Kelas XI-D3 SMA Negeri 9 Surabaya

This study aims to improve students' learning outcomes in analyzing the intrinsic elements of short stories through the application of Team Games Tournament (TGT) learning model in class XI-D3 SMAN 9 Surabaya. This research was conducted in two cycles, with each cycle including four stages: planning, action, observation, and reflection. Data were collected through classroom observation, field notes, learning outcome tests, and analysis of intrinsic elements of short stories by students. The results showed that the application of the TGT learning model was effective in improving students' learning outcomes in analyzing the intrinsic elements of short stories. This improvement can be seen from the increase in students' test scores from cycle 1 to cycle 2. This study suggests that the TGT learning model can be used as an effective alternative learning strategy to improve students' learning outcomes in analyzing the intrinsic elements of short stories. In addition, this study also shows that short stories are the right learning media to develop students' analytical skills.

Fault diagnosis method of rotating machinery based on MSResNet feature fusion and CAM

To solve the problem of noise interference, it is difficult to extract multi-scale information from complex vibration signals in fault diagnosis with the single-scale convolution kernel of classical deep learning model convolutional neural network (CNN). Therefore, a fault diagnosis method of rotating machinery based on MSResNet feature fusion and CAM is proposed. The residual network (ResNet) and multi-scale convolutional neural network (MSCNN) are combined to extract multi-scale feature information according to convolution kernels with different sizes, so as to avoid the loss of single-scale feature extraction. Make full use of the advantages of the residual network to skip the connection and prevent the feature information extracted by the multi-scale convolution kernel from being lost when the convolution layer propagates forward. In addition, in order to avoid the interference of invalid features after multi-scale information feature fusion, a channel attention mechanism module (CAM) is introduced to screen important features adaptively. The effectiveness of MSResNet-CAM is verified by the bearing data set of Western Reserve University (CWRU) and the data set of QPZZ-II gearbox, and the anti-noise ability is verified by adding noise to the two data sets. The experimental results show that MSResNet-CAM has the characteristics of high fault classification accuracy, good robustness and strong anti-noise ability.

Performance Evaluation of Deep Learning Models for Dental Caries Classification via Panoramic Radiograph Images

Objective: The purpose of this study is to evaluate the ability of deep learning models to classify mandibular molar teeth according to the presence and proximity of caries to the dental pulp. This research summarizes the progress of artificial intelligence and potential dental problems in diagnosis, treatment, and disease prediction in medicine. It discusses data limitations, computational power, ethical considerations, and their implications for dentists. This can lay the groundwork for future research in this rapidly expanding field. Methods: The dataset used in this study consists of 1200 panoramic radiographs, which have been evaluated and classified into three categories: free of dental caries, coded as (H); enamel-dentin caries lesions treated with restorative filling, coded as (R); and deep dental caries that underwent root canal treatment, coded as (E). The images are prepared for the training-testing process using the k-fold crossevaluation technique and then fed into the pre-trained deep learning models for classification. Results: The VGG-19 model achieved superior results compared to the other models, with macro-average scores of 0.9111 for precision, 0.9127 for recall, and 0.9115 for f1-score, respectively. Conclusion: The promising results obtained in this study give confidence in endorsing the use of deep learning models in the dental treatments sector.

Defining of the nasal anthropometry on facial computed tomography in subjects with epilepsy and healthy subjects.

Epilepsy is a frequent chronic and genetic brain disease. In diagnose of genetic and craniofacial disease, the face is one of the gold standard phenotypic features. This study was carried out to determine the angle and linear measurements of face region in Turkish healthy and epilepsy subjects, and to apply a feature selection method to identify the most important attributes that affect epilepsy decision. The retrospective and observational study was conducted with 120 subjects with epilepsy aged between 18 to 60 years (56 males; 64 females) and 60 healthy subjects aged between 18 and 55 years (29 males, 31 females). Pyramidal angle performed from 3 reference points, nasal bone length, the width of piriform aperture, nasofrontal angle, nasolabial angle, distance between glabella and nasion were measured on computed tomography. Also, we used supervised machine learning to learn classification models to detect epilepsy as our dataset has class label where 1 means epilepsy, 2 means healthy. The well-known classification model learning algorithms implemented in Weka (with version 3.8.6) machine learning toolkit were applied. All parameters excluding nasal bone length were higher in epilepsy patients than in healthy subjects. Also, there was a significant difference in nasal pyramidal angle nasal bone, nasal pyramidal angle nasal tip, piriform aperture, and nasal bone lengths between epilepsy and healthy subjects. However, age related changes for healthy subjects were no seen in healthy subjects. In epilepsy subjects there was a significant difference in 6 measurements all nasal pyramidal angles, piriform aperture width, nasofrontal angle, and nasolabial angle. Gender related changes were found in only nasal pyramidal angle nasal root and nasal bone of healthy subjects, in nasal pyramidal angle nasal bone, nasal bone length, nasofrontal angle of epilepsy subjects. We can say epilepsy may affect the some facial parameters and these, although anthropometric measurements are affected by age and gender parameters. Comprehensive knowledge of this region's normal references ranges is essential for planning, proper selection of silicone implants or osteotomy determining the limitations of the surgical field and minimizing the risk of complication and performing aesthetic facial surgery or rhinoplasty in epilepsy patients.

Pengaruh Model Pembelajaran Collaborative Learning terhadap Motivasi Belajar PAK Siswa Kelas VII SMP Negeri 1 Sipoholon Tahun Pembelajaran 2023/2024

The aim of this research is to determine the differences in student learning motivation in PAK learning before and after it is taught using the Collaborative Learning learning model for class VII students at SMP Negeri 1 Sipoholon for the 2023/2024 academic year. The research hypothesis is: "There is a difference in students' learning motivation in PAK learning before and after learning with the Collaborative Learning learning model for Class VII Students of SMP Negeri 1 Sipoholon for the 2023/2024 Learning Year." The population is all class VII students of SMP Negeri 1 Sipoholon, totaling 163 people. The research sample was determined using the Purposive Sampling technique, namely 32 students in class VII-3 as the experimental class which used a collaborative learning model and 32 students in class VII-4 as the control class which used normal treatment (conventional class). This research method is the Quasi Experimental Design method using a Nonequivalent Control Group Design. The instrument used in this research was a 22 item questionnaire. Research data for the questionnaire was analyzed using the average difference test formula (N-Gain Score). It was found that the average value for the experimental class was 0.7283 in the high category. And the average value for the control class is 0.1548, which is in the low category. Then carrying out an independent test using the t test obtained a value of t = 11.648 &gt; t table (α = 0.05; df = 62) = 2,000. Based on the research results, it is stated that Ha is accepted and H0 is rejected. Thus, it can be concluded that there are differences in students' learning motivation in PAK learning before and after being taught using the Collaborative Learning learning model.

Predicting compressed earth blocks compressive strength by means of machine learning models

Compressed Earth Blocks (CEB) are an interesting alternative to conventional masonry units. They are unfired and provide a thermal comfort in the constructions. However, their compressive strength needs to be assessed to ensure a mechanical stability. The latter depends on the soil variability, as well as several manufacturing parameters such as water content, compaction pressure, stabilizer type and proportion. This is challenging as it requires time and effort to adapt the parameters to achieve satisfactory results. In this study, machine learning classification models were trained using historical data for predicting CEB compressive strength. Voting Classifier (VC) provided the highest performance with an accuracy of 78 %. SHapley Additive exPlanations (SHAP) were used to identify and prioritize the features in the model's decision-making process. The compaction pressure and soil granularity were the most decisive parameters. VC was also tested to assess the compressive strength of sediment-based CEB manufactured at the laboratory scale.

Optimized Tiny Machine Learning and Explainable AI for Trustable and Energy-Efficient Fog-Enabled Healthcare Decision Support System

The Internet of things (IoT)-based healthcare decision support system plays a crucial role in modern medicine, especially with the rise in chronic illnesses and an aging population necessitating continuous remote health monitoring. Current healthcare decision support systems struggle to deliver timely and accurate decisions with minimal latency due to limited real-time healthcare data and inefficient computational resources. There is a critical need for an optimized, energy-efficient machine learning model that reliably supports remote health monitoring within IoT and fog computing environments. Our study proposes an Optimized Tiny Machine Learning (TinyML) and Explainable AI (XAI) binary classification model for a trustable and energy-efficient healthcare decision support system, leveraging fog computing to optimize performance. The fog-based approach improves response times and enhances bandwidth usage, addressing critical needs such as reduced latency, higher bandwidth utilization, and decreased packet loss. To further improve efficiency, we incorporate the innovative mLZW data compression technique, significantly enhancing data communication efficiency and reducing response time to critical health alerts. However, limited real-time healthcare data records challenge machine learning classification performance. By implementing a TinyML algorithm, our system demonstrates superior performance to other machine learning models. The proposed optimized TinyML model achieves an impressive F1 score of 0.93 for health abnormalities detection, emphasizing its robustness and effectiveness. This paper highlights the potential of TinyML and XAI in delivering robust, trustworthy, and energy-aware healthcare solutions, making significant contributions toward effective remote health monitoring and decision support in fog-enabled IoT networks.Graphical abstract