Accuracy Performance Research Articles

Development and testing of hybrid (PNM–CFD) mathematical model and numerical algorithm for description of fluid flows in three-dimensional digital core models

Numerical simulation of fluid flow in porous and fractured rocks is an important task for many industrial applications. The most common approaches to constructing such models are direct (CFD or lattice Boltzmann) and porous network (PNM) modeling. Each approach has its own advantages and disadvantages. This paper presents a hybrid mathematical PNM–CFD model for describing fluid flows in three-dimensional digital core models, which has the high speed performance of PNM approaches and high accuracy of CFD models. Numerical technique has been developed for describing fluid flows in three-dimensional digital core models using a hybrid PNM–CFD model. The numerical technique links a one-dimensional pore network solver and a three-dimensional CFD solver into a combined model in original way by constructing a single pressure field for the entire computation domain. To validate the model, several tests were performed, including flow in straight channels and numerical simulation of fluid flow in a microfluidic chip. The test results have shown the adequacy of the hybrid model performance. The hybrid model for determining pressure drop in a branched network with three-dimensional chambers has an error of no more than 5 % when compared to experimental data. Similarly, the error in calculating velocity does not exceed 7 % when compared to the full three-dimensional calculation. The hybrid model has shown an almost twofold increase in calculation speed compared to the full three-dimensional model.

Deep Learning-Based Classification of Powerlifting Movements Using Mediapipe

The analysis of sports movements is of great importance for optimizing sports performance, minimizing injury risks, and ensuring that athletes work with correct techniques. Powerlifting is a power sport consisting of fundamental movements such as bench press, deadlift, and squat. These movements are inherently complex and challenging to execute. Therefore, it is of great importance to perform these movements with the correct technique and safely. The aim of this study was to classify these movements using deep learning methods to ensure that the basic movements in powerlifting sports (bench press, deadlift, and squat) are applied with correct techniques and to minimize the risk of injury. In this study, feature extraction was performed on powerlifting movements using the deep learning-based SqueezeNet model, followed by classification using machine learning methods. The dataset was compiled from 876 images of bench press, deadlift, and squat movements sourced from various online platforms. Additionally, the dataset was expanded through data augmentation techniques, and key points of posture estimation were added to the images using the Mediapipe library. The obtained datasets were classified using Neural Network, Logistic Regression, Support Vector Machine and Random Forest algorithms and model performances were evaluated using various metrics. The findings revealed that the Neural Network model demonstrated superior performance, achieving the highest accuracy (0.989). Additionally, the integration of pose estimation and data augmentation techniques significantly enhanced classification accuracy and overall model performance. The findings of this study show that deep learning methods are powerful tools in sports movement analysis and can make significant contributions to the evaluation of athletes' performance.

Speech Perception and Phonological Representation in 5 to 6 Year-Old Typically Developing Children

Objectives: This study aimed to examine the development of speech perception and phonological representation in typically developing children aged 5-6 years. It also explored the relationship between receptive vocabulary, consonant accuracy, nonword repetition performance, and speech perception and phonological representation. Methods: The study involved 77 typically developing children aged 5-6 years. The speech perception identification tasks included minimal pairs that reflected developmental error patterns. The phonological representation judgment tasks involved manipulating consonants and vowels, as well as the number of distinctive features, to examine performance differences under various speech sound manipulation conditions. Results: Performance on both speech perception and phonological representation tasks significantly increased with age. There were also significant differences in performance based on speech sound manipulation conditions. Specifically, in the speech perception task, performance was significantly higher when items focused on the stopping of fricatives rather than the fronting of velars. In the phonological representation task, performance was significantly better in vowel manipulation compared to consonant manipulation, and with two distinctive feature manipulations compared to one. Speech perception tasks showed a significant correlation only with receptive vocabulary, whereas phonological representation tasks showed significant positive correlations with receptive vocabulary, consonant accuracy, and nonword repetition performance. Conclusion: The study confirms that speech perception and phonological representation are developing between ages 5 and 6 in typically developing children. It also suggests that speech perception and phonological representation develop through a complex interplay with speech production abilities and receptive vocabulary.

Uncovering Tourist Visit Intentions on Social Media through Sentence Transformers

The problem of understanding and predicting tourist behavior in choosing their destinations is a long-standing one. The first step in the process is to understand users’ intention to visit a country, which may later translate into an actual visit. Would-be tourists may express their intention to visit a destination on social media. Being able to predict their intention may be useful for targeted promotion campaigns. In this paper, we propose an algorithm to predict visit (or revisit) intentions based on the texts in posts on social media. The algorithm relies on a neural network sentence-transformer architecture using optimized embedding and a logistic classifier. Employing two real labeled datasets from Twitter (now X) for training, the algorithm achieved 90% accuracy and balanced performances over the two classes (visit intention vs. no-visit intention). The algorithm was capable of predicting intentions to visit with high accuracy, even when fed with very imbalanced datasets, where the posts showing the intention to visit were an extremely small minority.

Information hiding using approximate POSIT representation

AbstractPOSIT is a numerical system consists of sign, regime, exponential, and fraction bits to overcome some limitations of the IEEE‐754 floating point (FP) representation. This work proposes a technique to hide critical information in the least significant bits (LSBs) of the POSIT FP representation by exploiting approximate computing (AC). The proposed technique, called information hiding using POSIT and LSB (IHUPL), explores the opportunities offered by both the POSIT representation and the AC to hide information with a minimum loss in accuracy and other performance metrics. IHUPL offers two options for hiding information: either by embedding one digit of each character into each pixel or by embedding all the digits of the character at the same time into each pixel of the alpha‐red, green, blue, or black/white image. Experimental results are evaluated for benchmark images and showed that IHUPL enhanced the accuracy of embedding data into LSB of POSIT FP by an average of 5%, the image quality improvement rates of IHUPL are 19% and 16% for options 1 and 2, respectively. Besides the encoding method using IHUPL, the paper outlines an extraction decoding technique that saves the original replacement bits in a key‐image to recover the hidden security message.

DCNFYOLO: Dual-Convolution Network and Feature Fusion for High-Precision Smoke Detection

Fast, real-time, and accurate detection of smoke characteristics in the early stage of a fire is crucial for reducing fire losses. Existing smoke detection methods mainly rely on traditional algorithms and smoke sensors, and these approaches have limitations in false detection rates, accuracy, and real-time performance. Therefore, a novel DCNFYOLO network for smoke detection is proposed in this paper. Firstly, Switchable Atrous Convolution (SAConv) is introduced in the YOLOv5 backbone network to enhance the fusion extraction of smoke features by the Convolutional Neural Network (CNN). Secondly, both Distribution Shifts Convolution (DSConv) operator and Efficient Channel Attention (ECA) mechanisms are considered in the neck part to reduce the computational load of the model, and better capture the relationship between channels to improve the detection performance. Finally, to make low-quality examples less harmful to the gradients, the Wise-IoU (WIoU) loss function in the prediction part is used for reducing the competitiveness of high-quality anchor frames during model training, allowing the model to converge more quickly and stably. Experimental results show that the DCNFYOLO network can achieve a remarkable detection accuracy of 96.6%, which has a substantial improvement of 7.7% compared with the original YOLOv5 network performance, thereby validating the effectiveness of the proposed network.

Efficient nonlinear model predictive and active disturbance rejection control for trajectory tracking of unmanned vehicles

This article proposes an efficient trajectory tracking control strategy of unmanned vehicles. The method is based on nonlinear model predictive control (NMPC) and active disturbance reject control (ADRC). The designed control algorithm considers three challenges including nonlinear characteristics, multiple constraints, and external disturbance. First, NMPC method is presented for the nonlinear vehicle model with multiple constraints. To relax inequality constraints and reduce the heavy calculation burden, the penalty term with the variable factor is added to the cost function, an improved continuous/generalized minimum residual method is proposed to solve NMPC online optimization problem. Then, an ADRC scheme is designed to estimate the unknown disturbance via extended state observer, and compensate them by feedback control law in real time, the corresponding parameters are obtained by a novel particle swarm optimization algorithm to further improve the control precision. It ensures the stability to a certain extent. Finally, the results indicate the designed algorithm can raise the calculation efficiency and meet the real-time requirements, and obviously increase the tracking accuracy and robustness performance.

Lightweight monocular depth estimation using a fusion-improved transformer

The existing deep estimation networks often overlook the issue of computational efficiency while pursuing high accuracy. This paper proposes a lightweight self-supervised network that combines convolutional neural networks (CNN) and Transformers as the feature extraction and encoding layers for images, enabling the network to capture both local geometric and global semantic features for depth estimation. First, depth-separable convolution is used to construct a dilated convolution residual module based on a shallow network to improve the shallow CNN feature extraction receptive field. In the transformer, a multidepth separable convolution head transposed attention module is proposed to reduce the computational burden of spatial self-attention. In the feedforward network, a two-step gating mechanism is proposed to improve the nonlinear representation ability of the feedforward network. Finally, the CNN and transformer are integrated to implement a depth estimation network with a local-global context interaction function. Compared with other lightweight models, this model has fewer model parameters and higher estimation accuracy. It also has better generalizability for different outdoor datasets. Additionally, the inference speed can reach 87 FPS, achieving better real-time performance and accounting for both inference speed and estimation accuracy.

Eyes and hand are both reliable at localizing somatosensory targets.

Body representations (BR) for action are critical to perform accurate movements. Yet, behavioral measures suggest that BR are distorted even in healthy people. However, the upper limb has mostly been used as a probe so far, making difficult to decide whether BR are truly distorted or whether this depends on the effector used as a readout. Here, we aimed to assess in healthy humans the accuracy of the eye and hand effectors in localizing somatosensory targets, to determine whether they may probe BR similarly. Twenty-six participants completed two localization tasks in which they had to localize an unseen target (proprioceptive or tactile) with either their eyes or hand. Linear mixed model revealed in both tasks a larger horizontal (but not vertical) localization error for the ocular than for the manual localization performance. However, despite better hand mean accuracy, manual and ocular localization performance positively correlated to each other in both tasks. Moreover, target position also affected localization performance for both eye and hand responses: accuracy was higher for the more flexed position of the elbow in the proprioceptive task and for the thumb than for the index finger in the tactile task, thus confirming previous results of better performance for the thumb. These findings indicate that the hand seems to beat the eyes along the horizontal axis when localizing somatosensory targets, but the localization patterns revealed by the two effectors seemed to be related and characterized by the same target effect, opening the way to assess BR with the eyes when upper limb motor control is disturbed.

Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm

BackgroundImbalanced datasets pose significant challenges in predictive modeling, leading to biased outcomes and reduced model reliability. This study addresses data imbalance in diabetes prediction using machine learning techniques. Utilizing data from the Fasa Adult Cohort Study (FACS) with a 5-year follow-up of 10,000 participants, we developed predictive models for Type 2 diabetes.MethodsWe employed various data-level and algorithm-level interventions, including SMOTE, ADASYN, SMOTEENN, Random Over Sampling and KMeansSMOTE, paired with Random Forest, Gradient Boosting, Decision Tree and Multi-Layer Perceptron (MLP) classifier. We evaluated model performance using F1 score, AUC, and G-means—metrics chosen to provide a comprehensive assessment of model accuracy, discrimination ability, and overall balance in performance, particularly in the context of imbalanced datasets.Resultsour study uncovered key factors influencing diabetes risk and evaluated the performance of various machine learning models. Feature importance analysis revealed that the most influential predictors of diabetes differ between males and females. For females, the most important factors are triglyceride (TG), basal metabolic rate (BMR), and total cholesterol (CHOL), whereas for males, the key predictors are body Mass Index (BMI), serum glutamate Oxaloacetate Transaminase (SGOT), and Gamma-Glutamyl (GGT). Across the entire dataset, BMI remains the most important variable, followed by SGOT, BMR, and energy intake. These insights suggest that gender-specific risk profiles should be considered in diabetes prevention and management strategies. In terms of model performance, our results show that ADASYN with MLP classifier achieved an F1 score of 82.17 ± 3.38, AUC of 89.61 ± 2.09, and G-means of 89.15 ± 2.31. SMOTE with MLP followed closely with an F1 score of 79.85 ± 3.91, AUC of 89.7 ± 2.54, and G-means of 89.31 ± 2.78. The SMOTEENN with Random Forest combination achieved an F1 score of 78.27 ± 1.54, AUC of 87.18 ± 1.12, and G-means of 86.47 ± 1.28.ConclusionThese combinations effectively address class imbalance, improving the accuracy and reliability of diabetes predictions. The findings highlight the importance of using appropriate data-balancing techniques in medical data analysis.

Incidence trends, overall survival, and metastasis prediction using multiple machine learning and deep learning techniques in pediatric and adolescent population with osteosarcoma and Ewing's sarcoma: nomogram and webpage.

The objective of this study was to analyze the incidence and overall survival (OS) of osteosarcoma (OSC) and Ewing's sarcoma (EWS) in a pediatric and adolescent population, employing machine learning (ML) and deep learning (DL) models to predict the likelihood of metastasis. Involving 2465 OSC and 1373 EWS patients aged 0-19 years, from 2004 to 2020. ML techniques-Lasso, Ridge Regression, Elastic Net, and Random Forest-were used alongside a deep learning model based on TensorFlow and Keras, to construct predictive models for metastasis. These models were optimized using grid search with cross-validation and evaluated on their performance metrics, including AUC, sensitivity, and accuracy. The variables' importance in metastasis prediction was determined using SHAP values. Statistical analysis was performed using R software, and an online nomogram was developed for clinical use. The age-adjusted incidence of OSC and EWS from 2004 to 2020 showed a significant uptrend. The deep learning model, iterated 50 times, outperformed the Random Forest model in both loss and accuracy stabilization. The nomogram created demonstrated accurate survival predictions, as evidenced by its calibration curves and the distinction between high and low-risk groups. The increasing trend in age-adjusted incidence of OSC and EWS highlights the need for continued research and improved therapeutic strategies in this domain. The study employed ML and DL models to predict distant metastasis in pediatric and adolescent patients with OSC and EWS, providing a valuable tool for prognosis. The online nomogram developed as a part of this research enhances the models' clinical utility, offering an accessible means for clinicians to predict survival outcomes effectively.

Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database.

Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple machine learning (ML) models with the traditional Cox proportional hazards (CoxPH) model in predicting prognosis and explored the potential of ML models in clinical decision-making. From 2000 to 2018, 1243 patients with osteosarcoma were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Three ML methods were chosen for model development (DeepSurv, neural multi-task logistic regression [NMTLR]) and random survival forest [RSF]) and compared them with the traditional CoxPH model and TNM staging systems. 871 samples were used for model training, and the rest were used for model validation. The models' overall performance and predictive accuracy for 3- and 5-year survival were assessed by several metrics, including the concordance index (C-index), the Integrated Brier Score (IBS), receiver operating characteristic curves (ROC), area under the ROC curves (AUC), calibration curves, and decision curve analysis. The efficacy of personalized recommendations by ML models was evaluated by the survival curves. The performance was highest in the DeepSurv model (C-index, 0.77; IBS, 0.14; 3-year AUC, 0.80; 5-year AUC, 0.78) compared with other methods (C-index, 0.73-0.74; IBS, 0.16-0.17; 3-year AUC, 0.73-0.78; 5-year AUC, 0.72-0.78). There are also significant differences in survival outcomes between patients who align with the treatment option recommended by the DeepSurv model and those who do not (hazard ratio, 1.88; P < .05). The DeepSurv model is available in an approachable web app format at https://survivalofosteosarcoma.streamlit.app/. We developed ML models capable of accurately predicting the survival of osteosarcoma, which can provide useful information for decision-making regarding the appropriate treatment.

Integration of deep learning in the diagnosis, chemical analysis, and therapeutic approaches for neurodegenerative disorders

Neurodegenerative disorders (NDD) are a group of progressive conditions that primarily affect neurons in the brain. The diseases gradually impair cognitive function, movement, and other neurological processes, leading to a decline in the individual’s quality of life. Reliable biomarkers that accurately detect and track the growth of neurological disorders are crucial for the development of effective therapeutics. People with NDDs have damage to the brainneurons, which causes strange walking patterns. To overcome this problem we proposed Chemical reaction optimization based improved generative adversarial network (CRO-IGAN) method is to improve the patient’s conditions that are affected in neurodegenerative diseases. The patient’s dataset is gathered, here we utilized min max normalization for data preprocessing is used to clean the data. Principal component analysis (PCA) is employed for feature extraction to extract the pre-processed data and remove the unwanted data. The appropriate data is selected using linear discriminant analysis (LDA) for feature selection. The parameter metrics used in this study are recall, sensitivity and specificity. The suggest techniques CRO-IGAN provides high performance of accuracy for diagnosing NDD to improve the patient health which provides a superior performance than other existing methods.

Applying Machine Learning and SHAP Method to Identify Key Influences on Middle-School Students' Mathematics Literacy Performance.

The PISA 2022 literacy assessment highlights a significant decline in math performance among most OECD countries, with the magnitude of this decline being approximately three times that of the previous round. Remarkably, Hong Kong, Macao, Taipei, Singapore, Japan, and Korea ranked in the top six among all participating countries or economies, with Taipei, Singapore, Japan, and Korea also demonstrating improved performance. Given the widespread concern about the factors influencing secondary-school students' mathematical literacy, this paper adopts machine learning and the SHapley Additive exPlanations (SHAP) method to analyze 34,968 samples and 151 features from six East Asian education systems within the PISA 2022 dataset, aiming to pinpoint the crucial factors that affect middle-school students' mathematical literacy. First, the XGBoost model has the highest prediction accuracy for math literacy performance. Second, 15 variables were identified as significant predictors of mathematical literacy across the student population, particularly variables such as mathematics self-efficacy (MATHEFF) and expected occupational status (BSMJ). Third, mathematics self-efficacy was determined to be the most influential factor. Fourth, the factors influencing mathematical literacy vary among individual students, including the key influencing factors, the direction (positive or negative) of their impact, and the extent of this influence. Finally, based on our findings, four recommendations are proffered to enhance the mathematical literacy performance of secondary-school students.

CLASSIFICATION OF ACUTE LYMPHOBLASTIC LEUKEMIA CELLS USING ARTIFICIAL INTELLIGENCE

Due to the morphological similarity between immature lymphoblasts (cancerous cells) to lymphocytes (non-cancerous cells), detecting Acute Lymphoblastic Leukemia poses a significant challenge for pathologists. These cells, which exhibit a similar pattern, can lead to various errors during the diagnosis of the disease. In this study, the cancerous and non-cancerous cells were classified using 3 different artificial intelligence approaches. In the first approach, the classification process was carried out by training Convolutional Neural Networks in 4 different architectures. In the second approach, a hybrid approach was proposed by combining the convolution layer of the CNN model as the feature extractor with the Support Vector Machine, Naive Bayes and Random Forest algorithms as the classifier. The classification processes were carried out by training the proposed second approach. In the third approach, the classification process was performed using transfer learning process and ResNet50 and VGG16 networks. In all experiments, the effects of hyper-parameter and dataset changes on model performance were also examined. The results obtained by these three approaches were compared using the Accuracy, Precision, Recall, F-score, and AUC performance measures. It was determined that the most successful results were obtained with the 1st approach using the Dataset3.

DCSS-UNet: UNet based on State Space Model for Polyp Segmentation

Early and accurate segmentation of medical images can provide valuable information for medical treatment. In recent years, the automatic and accurate segmentation of polyps in colonoscopy images has received extensive attention from the research community of artificial intelligence and computer vision. Many researchers have conducted in-depth research on models based on CNN and Transformer. However, CNN have limited ability to model remote dependencies, which makes it challenging to fully utilize semantic information in images. On the other hand, the complexity of the secondary computation poses a challenge to the transformer. Recently, state-space models (SSMS), such as Mamba, have been recognized as a promising approach. They not only show superior performance in remote interaction, but also maintain linear computational complexity. Inspired by Mamba, we propose DCSS-UNet, where we utilize visual state space (VSS) blocks in VMamba to capture a wide range of contextual information. In the Skip connection phase, we propose Skip Connects Feature Attention modules(SFA) to better communicate information from the encoder. In the decoder stage, we innovatively combined the Temporal Fusion Attention Module(TFAM) to effectively fuse the feature information. In addition, we introduced a custom Loss calculation method, Tversky Loss, for the model to achieve faster convergence and improve segmentation along polyp boundaries. Our model was trained on the Kvasir-SEG and CVC-ClinicDB datasets, and validated on datasets Kvasir-SEG, CVC-ColonDB, CVC-300, and ETIS. The results show that the model achieves good segmentation accuracy and generalization performance with a low number of parameters. We are 6.1% ahead in the Kavirs-SEG dataset and 3.1% ahead in the CVC-ClinicDB dataset compared to VM-UNet.

DEEP LEARNING BASED NETWORK INTRUSION DETECTION

As a direct consequence of the unrelenting march of technological innovation, the use of the Internet has become an unavoidable condition for the life of modern humans. The Internet has increased both the quantity and range of situations in which information products can be useful or non-useful. It’s no surprise that as the number of different systems and users has grown, so have the number of different ways to exploit those systems. A security issue has arisen with such diversity and growth. Its diversity and increase in quantity introduce new system weaknesses and thus new attack strategies. Methods for detecting both internal and external attacks are suggested as a solution to this issue. The purpose of this research, a Convolutional Neural Network was utilized to identify intrusions, also known as attacks for the imbalanced class distribution in the NF-BoT-IoT data set, Synthetic Minority Over Sampling Technique, Random Over Sampling and Random Under Sampling methods were used. K-Fold Cross Validation, one of the strategies for splitting the data set, was utilized to evaluate the performance of classification models and to train the developed model. The model’s performance was evaluated using the accuracy, precision, recall, and F1-score performance criteria.

Robust Model Predictive Control for an Ion Beam Shepherd in a large-debris removal mission

The increasing accumulation of space debris poses significant risks to spacecraft, making the development of effective debris mitigation technologies necessary. This paper explores the Ion Beam Shepherd (IBS) method as a potential contactless solution for deorbiting large debris objects. The IBS system concept involves a spacecraft equipped with an ion thruster to direct a controlled ion beam at the debris, generating a small force that gradually lowers its orbit. A proposed configuration of the chaser’s actuator system integrates radial and out-of-plane cold-gas thrusters along with in-track ion thrusters to enhance control and safety while maintaining low mission costs. A robust Model Predictive Control (MPC) strategy is implemented, using the theory of MPC for Tracking to ensure accurate positioning and effective deorbiting. This theoretical approach addresses uncertainties and perturbations to robustly guarantee safe distances between the chaser and the debris. Additionally, a new ray-marching-based algorithm is introduced to estimate the force and torque exerted by the ion beam on the target, considered as a 6 degrees of freedom object, improving simulation accuracy and control performance assessment. A comprehensive simulation of the deorbit of a large debris object is performed, demonstrating the potential of the IBS technology for future large-debris removal missions. This research advances the conceptual framework and control techniques for the IBS technology, advancing towards its future implementation in space debris mitigation.

Adaptive optimal observer for real-time state of charge estimation of lithium-ion batteries in robotic systems

Purpose This study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan. Design/methodology/approach The authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation. Findings The proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates. Originality/value This study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

A comparative analysis of the accuracy of cardiac surgery risk scores in tricuspid valve procedures undertaken in the republic of Georgia

Background: Tricuspid valve (TV) surgeries present significant challenges marked by elevated risk and mortality rates. Despite the application of established risk scores like ACEF-II, EuroSCORE-II, and Hannan EL et al's risk stratification (HRS), their accuracy in predicting mortality rates for these procedures in a third-world setting remains under-researched. This study sought to assess the predictive performance and overall accuracy of these risk scores in the context of TV surgeries performed in the Republic of Georgia. Methods: A cohort of 63 patients who underwent TV procedures was retrospectively analyzed. ACEF-II, EuroSCORE-II, and HRS were applied and run through statistical analysis to assess their predictive capabilities. Results: ACEF-II demonstrated an accuracy rate of 58.7%, an area under the curve (AUC) of 0.819, and an expected-to-observed mortality ratio of 0.16. EuroSCORE-II exhibited an accuracy rate of 61.9%, an AUC of 0.866, and an expected-to-observed mortality ratio of 0.17. The HRS demonstrated an accuracy rate of 65.1%, an AUC of 0.882, and an expected-to-observed mortality ratio of 0.35. Conclusion: While the risk scores show promise, our study highlights their limitations in accurately predicting mortality rates of TV surgeries by underestimating them and emphasizes the need for refinement or the development of risk scores tailored specifically to TV procedures in third-world countries.