Artificial Neural Network Algorithm Research Articles

Experimental validation of a low-cost maximum power point tracking technique based on artificial neural network for photovoltaic systems

Maximum power point tracking (MPPT) is a technique involved in photovoltaic (PV) systems for optimizing the output power of solar panels. Traditional solutions like perturb and observe (P&O) and Incremental Conductance (IC) are commonly utilized to follow the MPP under various environmental circumstances. However, these algorithms suffer from slow tracking speed and low dynamics under fast-changing environment conditions. To cope with these demerits, a data-driven artificial neural network (ANN) algorithm for MPPT is proposed in this paper. By leveraging the learning capabilities of the ANN, the PV operating point can be adapted to dynamic changes in solar irradiation and temperature. Consequently, it offers promising solutions for MPPT in fast-changing environments as well as overcoming the limitations of traditional MPPT techniques. In this paper, simulations verification and experimental validation of a proposed data-driven ANN-MPPT technique are presented. Additionally, the proposed technique is analyzed and compared to traditional MPPT methods. The numerical and experimental findings indicate that, of the examined MPPT methods, the proposed ANN-MPPT approach achieves the highest MPPT efficiency at 98.16% and the shortest tracking time of 1.3 s.

Genetic algorithm-based optimization of combined supercritical CO2 power and flash-tank enhanced transcritical CO2 refrigeration cycle for shipboard waste heat recuperation

The shipping industry significantly contributes to global trade and economy, but is also responsible for approximately ∼3 % of global greenhouse gas (GHG) emissions. Harnessing waste heat from marine vessels to perform useful work is a potential solution to reduce these emissions. Although the Organic Rankine Cycle (ORC) and CO2 cycles have been studied separately for waste heat recovery, a combined power and refrigeration systems, utilizing both cycles have not been reported. Herein, a combined system, “Marine Energy Recovery System (MERS)”, that integrates the supercritical CO2 Brayton power cycle with a flash-tank-enhanced transcritical CO2 refrigeration cycle, is proposed, aiming to generate power and provide cooling simultaneously. The power cycle incorporates an ORC, and a flash-tank is introduced within the refrigeration cycle to improve system performance. Both cycles share a low-temperature recuperator and a gas cooler to further utilize the heat between them. The MERS is evaluated from both a 1st and 2nd law perspective against various performance metrics under different operating conditions, such as: gas cooler pressure, evaporation temperature, and turbine inlet temperature and pressure. Results elucidated that the MERS achieved energy and exergy efficiencies of 54.62 % and 54.90 %, respectively, representing significant improvements over similar systems. The net work output improved by 717.55 kW and the net cooling capacity by 515.7 kW relative to the reference system. Furthermore, a COP of 2.99 outlines its potential as a cooling system. To further enhance MERS’s performance, a multi-objective optimization approach is employed, utilizing Artificial Neural Network (ANN) and Genetic Algorithm (GA). Optimal operational conditions yielded an energy efficiency of 74.95 % while maintaining a net work output of 6890.55 kW with gas cooler pressure, turbine inlet temperature, and gas cooler outlet temperature being 9.5 MPa, 461.76°C and 50°C respectively. These findings support the reliability and improved design of the MERS for future marine applications.

Synthesis of benzoic acid from catalytic co-pyrolysis of waste wind turbine blades and biomass and their kinetic analysis

This work aims to study the catalytic co-pyrolysis of waste wind turbine blades (WTB: consists of fiberglass/unsaturated polyester resin) and woody biomass (WB) over ZSM-5 and Y-type zeolite catalysts for the production of benzoic acid. The experiments were performed on an equal combination of WTB and WB (WTB/WB) and a fixed amount of catalyst (50 wt%) using a thermogravimetric (TG) analyser at 10, 20 and 30 °C/min. The evolved products from the catalytic co-pyrolysis process were recognized using TG-FTIR and GC/MS. The kinetic and thermodynamic characteristics of catalytic co-pyrolysis was studied using various linear and nonlinear approaches. Also, the decomposition curves were predicted mathematically using an artificial neural network algorithm. The results revealed that the mixture was decomposed in the presence of both catalysts in the form of dual decomposition peaks at 361–374 °C and 428–443 °C, respectively. Based on TG-FTIR results, the CO stretching and carbon dioxide clusters were their main functional groups. While the GC-MS analysis revealed that the released vapours were completely free of toxic styrene (the main compound of resin) and the catalytic co-pyrolysis treatment succeeded in converting it into highly abundant benzoic acid, especially at 10 °C/ min, with an estimated 71.4 % (ZSM-5) and 64 % (Y-type). Whereas the activation energy was estimated at 262–295 kJ/mol (ZSM-5) and 319–357 kJ/mol (Y-type) with almost similar reaction complexity when compared to the case of absence of catalysts. Finally, the developed ANN algorithm showed high efficiency in predicting TG decomposition zones for both WTB/WB mixtures over zeolite catalysts with R2 more than 0.99. These results demonstrate that WB and zeolite catalysts can be used for upgrading the pyrolysis oil of WTB and eliminate its toxic styrene and convert it into benzoic acid and other aromatic hydrocarbons compounds (such as benzene, alanine, and 2-Propanamine).

Application of Multilayer Perceptron Artificial Neural Network (MLP-ANN) Algorithm for PM2.5 Mass Concentration Estimation during Open Biomass Burning Episodes in Thailand

Application of Multilayer Perceptron Artificial Neural Network (MLP-ANN) Algorithm for PM2.5 Mass Concentration Estimation during Open Biomass Burning Episodes in Thailand

Prediction of Soil Field Capacity and Permanent Wilting Point Using Accessible Parameters by Machine Learning

The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this study, global-scale accessible soil data were retrieved from the world soil database called the World Soil Information Service (WoSIS), and artificial neural network (ANN) and gene-expression programming (GEP) algorithms were used to predict soil FC and PWP based on easily obtainable parameters from the database. The best-fit variable combination for FC (longitude, latitude, altitude, sand content, silt content, clay content, and electrical conductivity) and PWP (best-fit FC combination plus pH) modeling was determined. Both ANN and GEP showed greater accuracy than linear-based models in simulating the FC and PWP from the best-fit variables. The mean absolute error (MAE) was reduced by 51.54% for the FC and 56.38% for the PWP by the ANN model, compared with the linear model used in the previous literature. The normalized root mean square error (NRMSE) evaluation indicated that the ANN model performed best for PWP prediction (NRMSE of 19.9%), while the GEP model was superior for FC prediction (NRMSE of 29.9%). Between the ANN and GEP models, the ANN model showed a slightly higher model of interpretability; however, the GEP model exhibited a similar or better ability to avoid large error, based on the error distribution. Overall, our results demonstrated that machine learning is effective in predicting the FC and PWP from easily accessible data from WoSIS, and the GEP model is more preferable for FC and PWP modeling.

Artificial neural network-based sparse channel estimation for V2V communication systems

Abstract Artificial neural networks (ANNs) have gained a lot of attention from researchers in the past few years and have been employed on a large scale. They have also been gaining momentum in wireless communication systems. For efficient vehicle-to-vehicle (V2V) channel communication, a sparse multipath channel issue must be studied. To minimize the multipath effect, a time reversal (TR) operation and time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) have been appealing because of their fast synchronization and active spectral efficiency. To improve the transceiver's execution in a frequency-selective fading channel environment, an OFDM system is used to reduce inter- symbol interference (ISI). Simultaneous Orthogonal Matching Pursuit (SOMP) channel state estimator algorithm suffer from high computational cost and high computational complexity. The ANN algorithm has better performance than SOMP algorithm. The proposed neural network technologies have lower complexity than the SOMP algorithm. The application of ANN is capable of solving complex problems, such as those encountered in image, signal processing and have been implemented for channel estimation in OFDM. The proposed ANN outperformed the SOMP algorithm with regard to signal compensation. Overall, the ANN algorithm achieved the best performance. This study proposes an ANN-based sparse channel state estimator. Regarding the bit error rate (BER) metric, the proposed estimator outperforms the channel estimation approach based on the SOMP. The simulation results confirm the efficacy of the proposed approach.

Genetic algorithm multiple linear regression and machine learning-driven QSTR modeling for the acute toxicity of sterol biosynthesis inhibitor fungicides

Sterol Biosynthesis Inhibitors (SBIs) are a major class of fungicides used globally. Their widespread application in agriculture raises concerns about potential harm and toxicity to non-target organisms, including humans. To address these concerns, a quantitative structure-toxicity relationship (QSTR) modeling approach has been developed to assess the acute toxicity of 45 different SBIs. The genetic algorithm (GA) was used to identify key molecular descriptors influencing toxicity. These descriptors were then used to build robust QSTR models using multiple linear regression (MLR), support vector regression (SVR), and artificial neural network (ANN) algorithms. The Cross-validation, Y-randomization test, applicability domain methods, and external validation were carried out to evaluate the accuracy and validity of the generated models. The MLR model exhibited satisfactory predictive performance, with an R2 of 0.72. The SVR and ANN models obtained R2 values of 0.7 and 0.8, respectively. ANN model demonstrated superior performance compared to other models, achieving R2cv and R2test values of 0.74 and 0.7, respectively. The models passed both internal and external validation, indicating their robustness. These models offer a valuable tool for risk assessment, enabling the evaluation of potential hazards associated with future applications of SBIs.

Application of Improved Class of Weighted Averaging Operators in Intuitionistic Fuzzy Ann Guided MAGDM E-Commerce Problems

Objectives: To propose a novel and improvised Artificial Neural Network (ANN) technique to solve Multiple Attribute Group Decision Making (MAGDM) problems and an improved class of Aggregation operators for combining Intuitionistic Fuzzy Set (IFS) matrices for the ANN algorithm. Methods: A novel class of improved aggregation operators namely the Improved Intuitionistic Fuzzy Weighted Arithmetic Averaging (IM-IFWAA) operator and the Improved Intuitionistic Fuzzy Ordered Weighted Averaging (IM-IFOWA) operator are proposed in this work. The proposed improved class of aggregation operators will aggregate the IFS matrix data sets appearing in the form of matrices and then the revised input vectors which is then fed into the ANN algorithm following Delta, Perceptron, and Hebb Learning Rule for the next phase. Findings: Aggregating the Intuitionistic Fuzzy set information or data in today’s digital world is a tedious task in the field of MAGDM problem-solving. In this work, two new classes of operators for aggregating the data sets are proposed namely the IM-IFWAA operator and the IM-IFOWA operator which will improve the performance of the ANN. Necessary theorems for the proposed operators are proved to provide consistency of the same. The input created using the aforementioned operators is then fed into the novel ANN algorithm for further computations. Varieties of learning rules are then engaged for ranking of the best alternative for the decision problem. The developed theory is supported by a numerical example which is computed using all the proposed techniques. Novelty: Most of the research done on Intuitionistic Fuzzy Artificial Neural Network models are based on learning rules or using some other calculations. The proposed methods of using novel and improvised aggregation operators for ANN are used to find the inputs for ANN where varieties of learning rules for ANN are employed for effective decision analysis. Keywords: ANN, Learning Rules, MAGDM, Intuitionistic Fuzzy Sets, Weighted Aggregation Operator

A Novel Machine-Learning Algorithm to Predict the Early Termination of Nutrition Support Team Follow-Up in Hospitalized Adults: A Retrospective Cohort Study.

For hospitalized adults, it is important to initiate the early reintroduction of oral food in accordance with nutrition support team guidelines. The aim of this study was to develop and validate a machine learning-based algorithm that predicts the early termination of medical nutritional therapy (the transition to oral feeding). This retrospective cohort study included consecutive adult patients admitted to the Hacettepe hospital (from 1 January 2018 to 31 December 2022). The outcome of the study was the prediction of an early transition to adequate oral feeding before discharge. The dataset was randomly (70/30) divided into training and test datasets. We used six ML algorithms with multiple features to construct prediction models. ML model performance was measured according to the accuracy, area under the receiver operating characteristic curve, and F1 score. We used the Boruta Method to determine the important features and interpret the selected features. A total of 2298 adult inpatients who were followed by a nutrition support team for medical nutritional therapy were included. Patients received parenteral nutrition (1471/2298, 64.01%), enteral nutrition (717/2298, 31.2%), or supplemental parenteral nutrition (110/2298, 4.79%). The median (interquartile range) Nutritional Risk Screening (NRS-2002) score was 5 (1). Six prediction algorithms were used, and the artificial neural network and elastic net models achieved the greatest area under the ROC in all outcomes (AUC = 0.770). Ranked by z-value, the 10 most important features in predicting an early transition to oral feeding in the artificial neural network and elastic net algorithms were parenteral nutrition, surgical wards, surgical outcomes, enteral nutrition, age, supplemental parenteral nutrition, digestive system diseases, gastrointestinal complications, NRS-2002, and impaired consciousness. We developed machine learning models for the prediction of an early transition to oral feeding before discharge. Overall, there was no discernible superiority among the models. Nevertheless, the artificial neural network and elastic net methods provided the highest AUC values. Since the machine learning model is interpretable, it can enable clinicians to better comprehend the features underlying the outcomes. Our study could support personalized treatment and nutritional follow-up strategies in clinical decision making for the prediction of an early transition to oral feeding in hospitalized adult patients.

Predicting the Risk of Severity and Readmission in Patients with Heart Failure in Indonesia: A Machine Learning Approach.

In Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient self-monitoring mobile application. In a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores. Of the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF. The ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.

Does the Exchange Rate Have a Pass-Through Effect on Domestic Food Prices in Türkiye? An Empirical Study Using a Combined ANN Algorithm and VAR Method

Exchange rate fluctuations have far-reaching consequences for national economies, companies and, above all, consumers. It is therefore important to understand the mechanisms by which these fluctuations affect food prices in domestic markets. In this context, we examine the complex relationship between exchange rates and domestic food prices and attempt to calculate the pass-through effect of the exchange rate on domestic prices. To examine the pass-through effect of the exchange rate on food prices, we use monthly USD/TRY exchange rate data from January 2003 to July 2023 as well as the global food price index, the export-import rate for food and the producer price index for food in Türkiye. We employ the vector autoregression (VAR) method to determine the optimal lags of the variables included in the analysis and the artificial neural network (ANN) algorithm to calculate the overall pass-through effect of the USD/TRY exchange rate on domestic food prices. The results show that the pass-through effect of the variable on food prices persists for three months: the current month, the previous month and the two previous months. The total pass-through effect of the exchange rate on food prices reaches up to 27%. Half of this impact comes from the previous month's food prices. In addition, the ratio of food exports to imports accounts for about 12% of the increase in domestic food prices. The results also show that the variable with the highest pass-through effect is the producer price index and that 90% of the increase in food prices is due to domestic dynamics, while the effect of price increases in foreign markets is only about 10%.

Adopting Machine Learning to Automatically Identify a Suitable Surgery Type for Refractive Error Patients

efractive error is a visual impairment that arises when the ocular anatomy hinders the proper focusing of light onto the retina, the light-sensitive tissue layer located at the posterior region of the eye. This condition poses difficulties in achieving clear vision. Refractive error stands as the prevailing kind of visual impairment. The objective of this study is to classify two surgical approaches utilized in the treatment of refractive defects. Two commonly performed refractive surgeries are Photo-Refractive Keratectomy (PRK) and Laser-Assisted In-Situ Keratomileusis (LASIK). Artificial Intelligence (AI) encompasses a specific branch known as Machine Learning (ML), which is the focal point of this investigation. ML is dedicated to the advancement and use of algorithms that possess the capacity to acquire knowledge from data and enhance their predictive capabilities without explicit programming. The present study employs sophisticated ML methods to classify different types of refractive defect surgeries using a dataset of 124 samples obtained from Al-Rabee Hospital in Iraq, specifically focusing on corneal topography data. Two ML approaches, namely K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN), are employed to predict the kind of refractive defect surgery. The findings produced from the experiment demonstrated an accuracy rate of 90.32% for the KNN algorithm and a perfect accuracy rate of 100% for the ANN algorithm. Additionally, the KNN algorithm exhibited a sensitivity of 90% and a specificity of 90.54%. The study’s findings indicate that the ANN classifier outperforms the KNN classifier.

Implementation of Artificial Neural Network in Predicting CPO Prices Using Backpropagation

This research examines the development of a forecasting model for Crude Palm Oil (CPO) prices using artificial neural network algorithms, particularly the backpropagation algorithm. CPO, as Indonesia's main export commodity, has significant economic impacts and affects the income of oil palm farmers. Data on CPO prices taken from CIF Rotterdam from January 2019 to December 2023 were used in this study. The research method involved several stages, including data collection, pre-processing, model design, and model implementation using Python programming. The results of training the model using the backpropagation algorithm showed an error value of 0.537829578 after 1000 epochs, while evaluation using Mean Squared Error (MSE) showed an MSE value of 0.022709 during the training process and 0.017604 during the testing process. The model also produced predictions for CPO prices in the next three months: 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model can predict future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.

Quantitative structure‐property relationship techniques for predicting carbon dioxide solubility in ionic liquids using machine learning methods

AbstractIonic liquids (ILs) are considered unique and attractive types of solvents with great potential to capture carbon dioxide (CO2) and reduce its emissions into the atmosphere. On the other hand, carrying out experimental measurements of CO2 solubility for each new IL is time‐consuming and expensive. Whereas, the possible combinations of cations and anions are numerous. Therefore, the preparation and design of such processes requires simple and accurate models to predict the solubility of CO2 as a greenhouse gas. In the present study, two different models, namely: artificial neural network (ANN) and support vector machine optimized with dragonfly algorithm (DA‐SVM) were used in order to establish a quantitative structure–property relationship (QSPR) between the molecular structures of cations and anions and the CO2 solubility. More than 10 116 CO2 solubility data measured in various ionic liquids (ILs) at different temperatures and pressures were collected. 13 significant PaDEL descriptors (E2M, MATS8S, TDB6I, TDB1S, ATSC4V, MATS8M, ATSC7V, Gats2S, Gats5S, Gats5C, ATSC6V, DE, and Lobmax), temperature and pressure were considered as the model input data. For the test set data (2023 data point), the estimated mean absolute error (MAE) and R2 for the ANN model are of 0.0195 and 0.9828 and 0.0219 and 0.9745 for the DA‐SVM model. The results obtained showed that both models can reliably predict the solubility of CO2 in ILs with a slight superiority of the ANN model. Examination of sensitivity and outlier diagnosis examinations confirmed that the QSPR model optimized using the ANN algorithm is better suited to correlate and predict this property.

Synergistic effect of cellulo-xylanolytic and laccase enzyme consortia for improved deinking of waste papers

This study reports the cellulo-xylanolytic cocktail production from Hypocrea lixii GGRK4 using multi-objective genetic algorithm-artificial neural network tool, resulting in 8.32 ± 1.07 IU/mL, 51.53 ± 3.78 IU/mL activity of CMCase and xylanase, respectively with more than 85 % residual activity at 60 °C and pH 6.0. Interestingly, metal ions viz. K+ and Ca2+ stimulated the enzyme activity, whereas Fe2+ and Cu2+ reduced the activity. Significant amounts of hydrophobic compounds, chromophores, and phenolics were released after wastepapers deinking. The deinking efficiency of 73.60 ± 2.45 % and 38.60 ± 1.34 % was obtained for photocopier paper and newspaper, respectively, whereas brightness of 89.90 ± 2.10 % ISO and 44.90 ± 1.63 % ISO was reported for both types of waste papers. The physical strength of deinked photocopier paper and newspapers, i.e., tensile index (3.10 and 0.50 %), tearing index (7.10 and 4.83 %), and burst factor (8.61) were enhanced whereas double fold property was decreased proving wastepaper reusability. This consortium showed effective and significant enzymatic deinking efficiency for recycled wastepapers.

A multi-Task Learning based applicable AI model simultaneously predicts stage, histology, grade and LNM for cervical cancer before surgery

PurposeTo build an Mult-Task Learning (MTL) based Artificial Intelligence(AI) model that can simultaneously predict clinical stage, histology, grade and LNM for cervical cancer before surgery.MethodsThis retrospective and prospective cohort study was conducted from January 2001 to March 2014 for the training set and from January 2018 to November 2021 for the validation set at Beijing Chaoyang Hospital, Capital Medical University. Preoperative clinical information of cervical cancer patients was used. An Artificial Neural Network (ANN) algorithm was used to build the MTL-based AI model. Accuracy and weighted F1 scores were calculated as evaluation indicators. The performance of the MTL model was compared with Single-Task Learning (STL) models. Additionally, a Turing test was performed by 20 gynecologists and compared with this AI model.ResultsA total of 223 cervical cancer cases were retrospectively enrolled into the training set, and 58 cases were prospectively collected as independent validation set. The accuracy of this cervical cancer AI model constructed with ANN algorithm in predicting stage, histology, grade and LNM were 75%, 95%, 86% and 76%, respectively. And the corresponding weighted F1 score were 70%, 94%, 86%, and 76%, respectively. The average time consumption of AI simultaneously predicting stage, histology, grade and LNM for cervical cancer was 0.01s (95%CI: 0.01–0.01) per 20 patients. The mean time consumption doctor and doctor with AI were 581.1s (95%CI: 300.0-900.0) per 20 patients and 534.8s (95%CI: 255.0-720.0) per 20 patients, respectively. Except for LNM, both the accuracy and F-score of the AI model were significantly better than STL AI, doctors and AI-assisted doctors in predicting stage, grade and histology. (P < 0.05) The time consumption of AI was significantly less than that of doctors’ prediction and AI-assisted doctors’ results. (P < 0.05ConclusionA multi-task learning AI model can simultaneously predict stage, histology, grade, and LNM for cervical cancer preoperatively with minimal time consumption. To improve the conditions and use of the beneficiaries, the model should be integrated into routine clinical workflows, offering a decision-support tool for gynecologists. Future studies should focus on refining the model for broader clinical applications, increasing the diversity of the training datasets, and enhancing its adaptability to various clinical settings. Additionally, continuous feedback from clinical practice should be incorporated to ensure the model’s accuracy and reliability, ultimately improving personalized patient care and treatment outcomes.

Structure optimization of intercooler bionic fins based on artificial neural network and genetic algorithms

The bionic fins inspired by the shark are proposed to enhance the thermal-hydraulic performance of the intercoolers in this work. The geometric parameters of the fins are investigated to examine the effects on thermal-hydraulic performance. The height and width of protrusions are chosen as optimization parameters based on the results of the geometric parameter analysis. Non-dominated genetic algorithms are combined with artificial neural networks to obtain the optimal solution for the geometry parameters. The artificial neural networks used in this study exhibit a reliable accuracy with errors below 10 % (with mostly remaining below 5 %). The optimal values for the height and width of the protrusions are determined to be 1.01 mm and 0.82 mm, which results in a Colburn factor of 0.01923 and a comprehensive index of 0.04305. Compared with the original design, the optimized structure yields 17.69 % and 4.77 % increments for the Colburn factor and comprehensive index. Compared with the original model, the intercooler with the optimal structure exhibits a 9.9 % increase in entropy generation and a 20.2 % enhancement in the exergy difference between the inlet and outlet.

Predicting depression in patients with heart failure based on a stacking model.

There is a lack of literature discussing the utilization of the stacking ensemble algorithm for predicting depression in patients with heart failure (HF). To create a stacking model for predicting depression in patients with HF. This study analyzed data on 1084 HF patients from the National Health and Nutrition Examination Survey database spanning from 2005 to 2018. Through univariate analysis and the use of an artificial neural network algorithm, predictors significantly linked to depression were identified. These predictors were utilized to create a stacking model employing tree-based learners. The performances of both the individual models and the stacking model were assessed by using the test dataset. Furthermore, the SHapley additive exPlanations (SHAP) model was applied to interpret the stacking model. The models included five predictors. Among these models, the stacking model demonstrated the highest performance, achieving an area under the curve of 0.77 (95%CI: 0.71-0.84), a sensitivity of 0.71, and a specificity of 0.68. The calibration curve supported the reliability of the models, and decision curve analysis confirmed their clinical value. The SHAP plot demonstrated that age had the most significant impact on the stacking model's output. The stacking model demonstrated strong predictive performance. Clinicians can utilize this model to identify high-risk depression patients with HF, thus enabling early provision of psychological interventions.

Design and Application of Artificial Neural Network Algorithm for Digital Speckle Correlation Method

Design and Application of Artificial Neural Network Algorithm for Digital Speckle Correlation Method

STRUCTURAL INSIGHTS AND ANTICANCER POTENTIAL OF MELITTIN IN CD147 INTERACTION

Objective: This study investigates the interaction between melittin (PDB ID: 2MLT), a bioactive peptide from honeybee venom, and CD147 (PDB ID: 5XF0), a glycosylated transmembrane protein implicated in tumor progression. Material and Method: Employing molecular docking and bioinformatics tools, our structural analysis reveals diverse binding features, including hydrogen bonds, salt bridges, and non-bonded contacts, between the CD147 complex and melittin. Result and Discussion: Non-bonded interactions between 2MLT and specific amino acids (Gly181 and Arg201) of CD147 are highlighted, resembling aspects of the CypA/CD147 binding mechanism (Pro180-Gly181 and Arg201). The elevated anticancer potential of 2MLT was substantiated by utilizing the AntiCP 2.0 server and the ENNAACT server, employing machine learning and artificial neural network algorithms. Additionally, hydrophobicity analysis aligns with characteristics associated with anticancer peptides. Notably, thermodynamic stability variations with temperature underscore the robust binding affinity of 2MLT to the 5XF0 receptor. While our study comprehensively explores molecular interactions and predictive analyses, further in vitro and in vivo investigations are crucial to validate these findings for potential therapeutic applications.