Deep Learning Models Research Articles

DEEP LEARNING-BASED ADAPTIVE ENSEMBLE LEARNING MODEL FOR CLASSIFICATION OF MONKEYPOX DISEASE

Monkeypox a viral disease resembling smallpox often transmitted via animal contact or human-to-human transmission. Symptoms include fever, rash, and respiratory issues. Healthcare experts initially may confuse it with chickenpox or measles due to its rarity, but swollen lymph nodes typically distinguish it. Diagnosis involves tissue sampling and polymerase chain reaction (PCR) testing, although PCR tests have limitations like time consumption and false negatives. Deep learning-based detection offers advantages over PCR, including reduced risk of exposure, quicker results, and improved accuracy. In this study, a novel adaptive ensemble learning (AEL)-based model for monkeypox diagnosis is proposed. This proposed ensemble learning model aims to enhance diagnosis accuracy by combining different deep learning models, leveraging an adaptive approach for model combination. Experimental studies using MSLD and MSID datasets show promising results, with ensemble models achieving high accuracy, precision, recall, and F1 scores. The ResNet101+VGG16 (92.46% accuracy, 92.75% precision, 93.22% recall, and 92.98% F1 score) ensemble model performs best for MSLD, while DenseNet121+Xception (97.58% accuracy, 96.57% precision, 95.74% recall, and 96.14% F1 score) excels for MSID. In addition, the proposed AEL model outperforms previous studies using the same datasets, showcasing its potential for improved monkeypox diagnosis.

Gluconeogenesis unraveled: A proteomic Odyssey with machine learning

The metabolic pathway known as gluconeogenesis, which produces glucose from non-carbohydrate substrates, is essential for maintaining balanced blood sugar levels while fasting. It’s extremely important to anticipate gluconeogenesis rates accurately to recognize metabolic disorders and create efficient treatment strategies. The implementation of deep learning and machine learning methods to forecast complex biological processes has been gaining popularity in recent years. The recognition of both the regulation of the pathway and possible therapeutic applications of proteins depends on accurate identification associated with their gluconeogenesis patterns. This article analyzes the uses of machine learning and deep learning models, to predict gluconeogenesis efficiency. The study also discusses the challenges that come with restricted data availability and model interpretability, as well as possible applications in personalized healthcare, metabolic disease treatment, and the discovery of drugs. The predictor utilizes statistics moments on the structures of gluconeogenesis and their enzymes, while Random Forest is utilized as a classifier to ensure the accuracy of this model in identifying the best outcomes. The method was validated utilizing the independent test, self-consistency, 10 k fold cross-validations, and jackknife test which achieved 92.33 %, 91.87 %, 87.88 %, and 87.02 %. An accurate prediction of gluconeogenesis has significant implications for understanding metabolic disorders and developing targeted therapies. This study contributes to the rising field of predictive biology by mixing algorithms for deep learning, and machine learning, with metabolic pathways.

Hybrid feature extraction and integrated deep learning for cloud-based malware detection

The escalating prevalence of malware necessitates a proactive and vigilant approach to its detection and mitigation. The ramifications of a successful malware attack on cloud services can be severe, underscoring the critical importance of effective malware detection mechanisms in cloud environments. To address this pressing need, we propose a comprehensive methodology for creating a novel cloud-based malware dataset, namely the CMD_2024 dataset. This dataset integrates static and dynamic attributes, providing a robust framework for malware analysis. The CMD_2024 dataset, comprising 20,850 samples meticulously labeled into various malware categories such as Virus, Trojan, Worm, Ransomware, Adware, Miner, PUA, and Downloader, is designed to facilitate the testing and evaluation of diverse analysis tools, machine learning models, deep learning models, and security systems. We enhance the dataset’s utility and effectiveness by focusing on dynamic features, particularly system calls within the cloud, in conjunction with static attributes. To address the challenges of the imbalance towards less common malware categories in the dataset, we employed the Conditional Tabular Generative Adversarial Network to generate synthetic data, significantly improving the detection capability for these rare malware samples. The application of various machine learning and deep learning classifiers, including our proposed integrated deep learning models, yielded remarkable results, achieving 99.42% accuracy in binary classification and 86.97% in multi-class classification. These outcomes demonstrate the CMD_2024 dataset’s substantial efficacy in supporting robust malware detection within cloud environments.

Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis

Accurate cooling consumption forecasts are crucial for optimizing energy management, storage, and overall efficiency in interconnected HVAC systems. Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. Since meteorological conditions highly influence cooling demand, leveraging external air data and user metrics offers a promising approach to estimate a building's hourly cooling energy usage. This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. By leveraging weather conditions, building characteristics, and operational parameters, we aim to predict cooling consumption across multiple systems (Cooling Ceiling, Ventilation, Free Cooling, and Total Cooling). Data from four weather stations, encompassing diverse features relevant to the European Central Bank (ECB) building's cooling consumption in Frankfurt, were employed. Our methodology includes the use of K-Nearest Neighbor, Decision Tree, Support Vector Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, Adaboost, Long-Short-Term Memory, and Gated Recurrent Unit. Models. The results consistently demonstrate the superiority of the Random Forest model across different weather stations and feature sets. This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. These findings contribute to improved building cooling load management, promoting insights into optimal energy utilization and sustainable building practices. Doi: 10.28991/ESJ-2024-08-06-01 Full Text: PDF

Multi-headed Ensemble Residual CNN: A Powerful Tool for Fibroblast Growth Factor Prediction

Fibroblast Growth Factor (FGF) performs a significant role in the repair, nervous system, development, and maintenance, making it a promising target for treating neurological diseases such as Parkinson's, stroke, Alzheimer's, and Huntington's disorders. However, the detection of FGF remains challenging due to the high cost and time required by traditional methods. To address this issue, we propose the first sequence-based computational method, FGF-MERCNN, for identifying FGFs using deep learning. Novel datasets were constructed by converting primary sequences into numerical representations through Enhanced Group Amino Acid Composition (EGAAC), Dipeptide Deviation from Expected Mean (DDE), and Group Tripeptide Composition (GTPC). These features were combined and analyzed using several deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), Gated Recurrent Units (GRU), and Multiheaded Ensemble Residual CNN (MERCNN). Among these, the MERCNN model achieved superior performance with accuracies of 88.60% on the training set and 83.68% on the test set, as validated by 5-fold cross-validation. The results of this study represent a significant advancement in FGF detection, offering a faster, cost-effective solution with the potential to improve diagnostic and therapeutic strategies for neurological diseases.

Multimodal Fused Deep Learning for Drug Property Prediction: Integrating Chemical Language and Molecular Graph

Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, mono-modal learning is inherently limited as it relies solely on a single modality of molecular representation, which restricts a comprehensive understanding of drug molecules. To overcome the limitations, we propose a multimodal fused deep learning (MMFDL) model to leverage information from different molecular representations. Specifically, we construct a triple-modal learning model by employing Transformer-Encoder, BiGRU, and graph convolutional network (GCN) to process three modalities of information from chemical language and molecular graph: SMILES-encoded vectors, ECFP fingerprints, and molecular graphs, respectively. We evaluate the proposed triple-modal model using five fusion approaches on six molecule datasets including Delaney, Llinas2020, Lipophilicity, SAMPL, BACE, and pKa from DataWarrior. The results show that the MMFDL model achieves the highest Pearson coefficients, and stable distribution of Pearson coefficients in a random splitting test, outperforming mono-modal models in accuracy and reliability. Furthermore, we validate the generalization ability of our model in the prediction of binding constants for protein-ligand complex molecules and assess the resilience capability against noise. Through analysis of feature distributions in chemical space and the assigned contribution of each modal model, we demonstrate that the MMFDL model shows the ability to acquire complementary information by using proper models and suitable fusion approaches. By leveraging diverse sources of bioinformatics information, multimodal deep learning models hold the potential for successful drug discovery.

Distinguishing the activity of flexor digitorum brevis and soleus across standing postures with deep learning models.

Electromyographic (EMG) recordings indicate that both the flexor digitorum brevis and soleus muscles contribute significantly to the control of standing balance, However, less is known about the adjustments in EMG activity of these two muscles across different postures. The purpose of our study was to use deep-learning models to distinguish between the EMG activity of the flexor digitorum brevis and soleus muscles across four standing postures. Deep convolutional neural networks were employed to classify standing postures based on the temporal and spatial features embedded in high-density surface EMG signals. The EMG recordings were obtained with grid electrodes placed over the flexor digitorum brevis and soleus muscles of healthy young men during four standing tasks: bipedal, tandem, one-leg, and tip-toe. Two-way repeated-measures analysis of variance demonstrated that the model achieved significantly greater classification accuracy, particularly during tandem stance, using EMG data from flexor digitorum brevis compared with soleus muscle. Average classification accuracy was 84.6 % for flexor digitorum brevis and 79.1 % for soleus. The classification accuracy of both muscles varied across the four postures. There were significant differences in classification accuracy for flexor digitorum brevis between bipedal and tandem stances compared with one-leg and tip-toe stances. In contrast, the EMG data for soleus were only significantly different between bipedal stance and one-leg stance. These findings indicate that flexor digitorum brevis exhibited more distinct adjustments than soleus in the temporo-spatial features of EMG activity across the four postures.

Concordance analysis between deep learning model predictions and electrocardiography thresholds from cardiology guidelines

Abstract Deep learning models for the classification of electrocardiograms (ECGs) are able to learn disease-specific patterns, but they are rarely implemented in medical practice due to their "black box" nature. Post-hoc explainable artificial intelligence (XAI) methods compute regions of interest (ROI) which are of importance for a model’s decision making. However, it needs to be further analyzed whether a model focuses on the morphological or rhythmical information within the ROIs. We evaluate a pre-trained ResNet for sinus bradycardia (SB) and sinus tachycardia (ST) classification on the PTBXL dataset using the XAI method Integrated Gradients. We compare the confidence of the model predictions to ECG features used by clinicians using correlation analysis. Correlation is highest for RR intervals (SB: 0.44) and atrial as well as ventricular heart rates (ST: 0.51), with the majority exceeding clinical thresholds for both disorders, indicating that the model learned rhythmical features. Except for QT intervals in ST classification, morphological features such as duration and amplitudes of P-/T-waves do not show any correlation.

Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning.

Breast density is a widely established independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate volumetric breast density (VBD) routinely. However, current available methods extrapolate VBD from two-dimensional (2D) images acquired using DBT and/or depend on the existence of raw DBT data, which is rarely archived by clinical centers because of storage constraints. We retrospectively analyzed 1,080 nonactionable three-dimensional (3D) reconstructed DBT screening examinations acquired between 2011 and 2016. Reference tissue segmentations were generated using previously validated software that uses 3D reconstructed slices and raw 2D DBT data. We developed a deep learning (DL) model that segments dense and fatty breast tissue from background. We then applied this model to estimate %VBD and absolute dense volume (ADV) in cm3 in a separate case-control sample (180 cases and 654 controls). We created two conditional logistic regression models, relating each model-derived density measurement to likelihood of contralateral breast cancer diagnosis, adjusted for age, BMI, family history, and menopausal status. The DL model achieved unweighted and weighted Dice scores of 0.88 (standard deviation [SD] = 0.08) and 0.76 (SD = 0.15), respectively, on the held-out test set, demonstrating good agreement between the model and 3D reference segmentations. There was a significant association between the odds of breast cancer diagnosis and model-derived VBD (odds ratio [OR], 1.41 [95 % CI, 1.13 to 1.77]; P = .002), with an AUC of 0.65 (95% CI, 0.60 to 0.69). ADV was also significantly associated with breast cancer diagnosis (OR, 1.45 [95% CI, 1.22 to 1.73]; P < .001) with an AUC of 0.67 (95% CI, 0.62 to 0.71). DL-derived density measures derived from 3D reconstructed DBT images are associated with breast cancer diagnosis.

The influence of cardiac substructure dose on survival in a large lung cancer stereotactic radiotherapy cohort using a robust personalized contour analysis

Background/Purpose: Radiation-induced cardiac toxicity in lung cancer patients has received increased attention since RTOG 0617. However, large cohort studies with accurate cardiac substructure (CS) contours are lacking, limiting our understanding of the potential influence of individual CSs. Here, we analyse the correlation between CS dose and overall survival (OS) while accounting for deep learning (DL) contouring uncertainty, a/b uncertainty and different modelling approaches. Materials/Methods: This single institution, retrospective cohort study includes 730 patients (early-stage tumours (I or II). All treated: 2009–2019), who received stereotactic body radiotherapy (≥5 Gy per fraction). A DL model was trained on 70 manually contoured patients to create 12 cardio-vascular structures. Structures with median dice score above 0.8 and mean surface distance (MSD) <2 mm during testing, were further analysed. Patientspecific CS dose was used to find the correlation between CS dose and OS with elastic net and random survival forest models (with and without confounding clinical factors). The influence of delineation-induced dose uncertainty on OS was investigated by expanding/contracting the DL-created contours using the MSD ± 2 standard deviations. Results: Eight CS contours met the required performance level. The left atrium (LA) mean dose was significant for OS and an LA mean dose of 3.3 Gy (in EQD2) was found as a significant dose stratum. Conclusion: Explicitly accounting for input parameter uncertainty in lung cancer survival modelling was crucial in robustly identifying critical CS dose parameters. Using this robust methodology, LA mean dose was revealed as the most influential CS dose parameter.

Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.

Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field. We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literature. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process.

Leveraging a Vision Transformer Model to Improve Diagnostic Accuracy of Cardiac Amyloidosis With Cardiac Magnetic Resonance.

Cardiac magnetic resonance (CMR) imaging is an important diagnostic tool for diagnosis of cardiac amyloidosis (CA). However, discrimination of CA from other etiologies of myocardial disease can be challenging. The aim of this study was to develop and rigorously validate a deep learning (DL) algorithm to aid in the discrimination of CA using cine and late gadolinium enhancement CMR imaging. A DL model using a retrospective cohort of 807 patients who were referred for CMR for suspicion of infiltrative disease or hypertrophic cardiomyopathy (HCM) was developed. Confirmed definitive diagnosis was as follows: 252 patients with CA, 290 patients with HCM, and 265 with neither CA or HCM (other). This cohort was split 70/30 into training and test sets. A vision transformer (ViT) model was trained primarily to identify CA. The model was validated in an external cohort of 157 patients also referred for CMR for suspicion of infiltrative disease or HCM (51 CA, 49 HCM, 57 other). The ViT model achieved a diagnostic accuracy (84.1%) and an area under the curve of 0.954 in the internal testing data set. The ViT model further demonstrated an accuracy of 82.8% and an area under the curve of 0.957 in the external testing set. The ViT model achieved an accuracy of 90% (n=55 of 61), among studies with clinical reports with moderate/high confidence diagnosis of CA, and 61.1% (n=22 of 36) among studies with reported uncertain, missing, or incorrect diagnosis of CA in the internal cohort. DL accuracy of this cohort increased to 79.1% when studies with poor image quality, dual pathologies, or ambiguity of clinically significant CA diagnosis were removed. A ViT model using only cine and late gadolinium enhancement CMR images can achieve high accuracy in differentiating CA from other underlying etiologies of suspected cardiomyopathy, especially in cases when reported human diagnostic confidence was uncertain in both a large single state health system and in an external CA cohort.

Robustness of a DenseNet-121 for the Classification of ARDS in Chest X-Rays

Abstract Research in the field of artificial intelligence (AI) in medicine is increasingly relying on algorithms based on deep learning (DL), especially for radiology. Despite producing promising results, DL models have a major drawback: their reliance on large training datasets. Especially in medicine, large, annotated datasets are hard to obtain, leading to low robustness and a performance loss when exposed to unseen, new data. To address this problem, our research evaluates how well data augmentation is able to expand the used dataset and thus improve a DL model. We employ 17 different augmentation methods to test the robustness of a DenseNet-121 trained to classify Acute Respiratory Distress Syndrome (ARDS) in chest X-rays. Our experiments show that while the model has low robustness for augmented test data when trained on unaugmented data, the general performance for ARDS classification can be improved by augmenting the training data. Overall, this demonstrates that data augmentation is beneficial in training AI models for ARDS classification in order to create more robust and generalizable models.

Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM

Quantifying and predicting driving risks for connected autonomous vehicles (CAVs) is critical to ensuring the safe operation of traffic in complex environments. This study first establishes a car-following model for CAVs based on molecular force fields. Subsequently, using a convolutional neural network and long short-term Memory (CNN-LSTM) deep-learning model, the future trajectory of the target vehicle is predicted. Risk is quantified by employing models that assess both the collision probability and collision severity, with deep-learning techniques applied for risk classification. Finally, the High-D dataset is used to predict the vehicle trajectory, from which the speed and acceleration of a target vehicle are derived to forecast driving risks. The results indicate that the CNN-LSTM model, when compared with standalone CNN and LSTM models, demonstrates a superior generalization performance, a higher sensitivity to risk changes, and an accuracy rate exceeding 86% for medium- and high-risk predictions. This improved accuracy and efficacy contribute to enhancing the overall safety of connected vehicle platoons.

Blood Pressure Predicted From Artificial Intelligence Analysis of Retinal Images Correlates With Future Cardiovascular Events

BackgroundHigh systolic blood pressure (SBP) is one of the leading modifiable risk factors for premature cardiovascular death. The retinal vasculature exhibits well-documented adaptations to high SBP and these vascular changes are known to correlate with atherosclerotic cardiovascular disease (ASCVD) events. ObjectivesThe purpose of this study was to determine whether using artificial intelligence (AI) to predict an individual’s SBP from retinal images would more accurately correlate with future ASCVD events compared to measured SBP. Methods95,665 macula-centered retinal images drawn from the 51,778 individuals in the UK Biobank who had not experienced an ASCVD event prior to retinal imaging were used. A deep-learning model was trained to predict an individual’s SBP. The correlation of subsequent ASCVD events with the AI-predicted SBP and the mean of the measured SBP acquired at the time of retinal imaging was determined and compared. ResultsThe overall ASCVD event rate observed was 3.4%. The correlation between SBP and future ASCVD events was significantly higher if the AI-predicted SBP was used compared to the measured SBP: 0.067 v 0.049, P = 0.008. Variability in measured SBP in UK Biobank was present (mean absolute difference = 8.2 mm Hg), which impacted the 10-year ASCVD risk score in 6% of the participants. ConclusionsWith the variability and challenges of real-world SBP measurement, AI analysis of retinal images may provide a more reliable and accurate biomarker for predicting future ASCVD events than traditionally measured SBP.

Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China

Changes in river runoff have a significant impact on the sustainable use of water resources in a watershed, and these changes are closely linked to variations in land use/land cover (LULC). This research explores an innovative approach in the Zhang River Basin (ZRB), China, by coupling a concept-based hydrological model, the Soil and Water Assessment Tool (SWAT), with a deep-learning model, the Bidirectional Long Short-Term Memory Network (Bi-LSTM), to improve the accuracy of river runoff simulations. By analyzing LULC changes in 2002, 2012, and 2022, this study developed three SWAT models and three coupled SWAT-BiLSTM models to quantitatively assess the impacts of these changes on river runoff through eight LULC scenarios. The findings revealed significant LULC changes from 2002 to 2022, with cropland and grassland areas decreasing while forest and urban land areas increased. The total area of grassland, forest, and cropland made up over 93 % of the basin, indicating active land type conversions. Calibration and validation results demonstrated that the SWAT-BiLSTM model outperformed the conventional SWAT model, yielding higher accuracy in runoff simulations. Specifically, the SWAT-BiLSTM model achieved R2 values of 0.89 and 0.90 during calibration and validation, compared to the SWAT model's R2 values of 0.76 and 0.79. Scenario analyses indicated that expansions in farmland, grassland, and urban areas were correlated with increased river runoff, while an expansion in forested areas led to reduced runoff. Notably, urban land changes had the most pronounced impact on runoff, emphasizing the need for careful runoff management and flood risk mitigation in urban planning. By combining SWAT and Bi-LSTM models, this study provides an innovative assessment of the impact of LULC changes on water resources in the ZRB. The results offer valuable insights for water resource management, LULC optimization, and flood risk management, highlighting the potential application of deep learning techniques in hydrological simulation. This research serves as a scientific basis for policy-making and sustainable land use planning in the ZRB and similar regions.

Multimodal Explainability Using Class Activation Maps and Canonical Correlation for MI-EEG Deep Learning Classification

Brain–computer interfaces (BCIs) are essential in advancing medical diagnosis and treatment by providing non-invasive tools to assess neurological states. Among these, motor imagery (MI), in which patients mentally simulate motor tasks without physical movement, has proven to be an effective paradigm for diagnosing and monitoring neurological conditions. Electroencephalography (EEG) is widely used for MI data collection due to its high temporal resolution, cost-effectiveness, and portability. However, EEG signals can be noisy from a number of sources, including physiological artifacts and electromagnetic interference. They can also vary from person to person, which makes it harder to extract features and understand the signals. Additionally, this variability, influenced by genetic and cognitive factors, presents challenges for developing subject-independent solutions. To address these limitations, this paper presents a Multimodal and Explainable Deep Learning (MEDL) approach for MI-EEG classification and physiological interpretability. Our approach involves the following: (i) evaluating different deep learning (DL) models for subject-dependent MI-EEG discrimination; (ii) employing class activation mapping (CAM) to visualize relevant MI-EEG features; and (iii) utilizing a questionnaire–MI performance canonical correlation analysis (QMIP-CCA) to provide multidomain interpretability. On the GIGAScience MI dataset, experiments show that shallow neural networks are good at classifying MI-EEG data, while the CAM-based method finds spatio-frequency patterns. Moreover, the QMIP-CCA framework successfully correlates physiological data with MI-EEG performance, offering an enhanced, interpretable solution for BCIs.

Predicting the Heat Capacity of Strontium-Praseodymium Oxysilicate SrPr4(SiO4)3O Using Machine Learning, Deep Learning, and Hybrid Models

Predicting the Heat Capacity of Strontium-Praseodymium Oxysilicate SrPr4(SiO4)3O Using Machine Learning, Deep Learning, and Hybrid Models

Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling.

Advancements in artificial intelligence (AI)-driven predictive modeling in dentistry are outpacing the clinical translation of research findings. Predictive modeling uses statistical methods to anticipate norms related to TMJ dynamics, complementing imaging modalities like cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI). Deep learning, a subset of AI, helps quantify and analyze complex hierarchical relationships in occlusion and TMJ function. This narrative review explores the application of predictive modeling and deep learning to identify clinical trends and associations related to occlusion and TMJ function. Debates persist regarding best practices for managing occlusal factors in temporomandibular joint (TMJ) function analysis while interpreting and quantifying findings related to the TMJ and occlusion and mitigating biases remain challenging. Data generated from noninvasive chairside tools such as jaw trackers, video tracking, and 3D scanners with virtual articulators offer unique insights by predicting variations in dynamic jaw movement, TMJ, and occlusion. The predictions help us understand the highly individualized norms surrounding TMJ function that are often required to address temporomandibular disorders (TMDs) in general practice. Normal TMJ function, occlusion, and the appropriate management of TMDs are complex and continue to attract ongoing debate. This review examines how predictive modeling and artificial intelligence aid in understanding occlusion and TMJ function and provides insights into complex dental conditions such as TMDs that may improve diagnosis and treatment outcomes with noninvasive techniques.

Landslide Susceptibility Zonation Mapping Using Machine Learning Algorithms and Statistical Prediction at Hunza Watershed Basin, Pakistan

The mountainous region of the Hunza River watershed basin, especially along the Karakorum highway, and also known as a third pole for the high accumulation of glaciers, which leads to huge devastating landslides occurring every year. Landslide susceptibility mapping was carried out using two deep machine learning techniques (DeeplabV3+ &amp; universal network U-Net) and two statistical models (Intuitionistic Fuzzy divergence IF-D &amp; Frequency ratio FR). The landslide susceptibility mapping is conducted using landslide inventory data and twelve conditional factors. The landslide susceptibility maps obtained from the two statistical models were compared with those generated by two deep machine learning models based on prediction accuracy measures, such as the Area Under the Curve (AUC) and Seed Cell Area Index (SCAI). The Success Rate Curve (SRC) was obtained using the training dataset, and the AUC values for the four models were as follows: 76.9% for IF-D, 76.9% for FR, 80.4% for DeeplabV3+, and 76.3% for U-Net. In terms of the Prediction Rate Curve (PRC) obtained from the validation dataset, the AUC values were found to be 80.8% for IF-D, 80.8% for FR, 81% for DeeplabV3+, and 77.8% for U-Net. To assess the classification ability of the models, the Seed Cell Area Index (SCAI) test was conducted. The results indicated that the SCAI (D-value) was 7.3 for U-Net, 10 for DeeplabV3+, 7.6 for IF-D, and 9.1 for FR. Overall, the findings revealed that DeeplabV3+ exhibited the highest prediction accuracy and classification ability, making it the most suitable choice for landslide susceptibility mapping in the relevant study area.