Results in Engineering最新文献

{"title":"Research on the coupling effect of lighting and thermal environment under learning behavior","authors":"Shiya Zhao ,&nbsp;Jianxin Diao ,&nbsp;Sheng Yao ,&nbsp;Yuting Cong ,&nbsp;Jingyu Yuan ,&nbsp;Zhenkun Wang","doi":"10.1016/j.rineng.2025.104658","DOIUrl":"10.1016/j.rineng.2025.104658","url":null,"abstract":"<div><div>Influence of multi-factor interaction on the comfort and learning efficiency are gradually receiving attention. Therefore, the interaction of PMV, color temperature and illuminance, and their effects on the lighting comfort, thermal comfort and learning efficiency under the learning behavior of college students was selected as the objective of study. Interestingly, 60 subjects were selected to conduct questionnaire survey and cognitive tests on the comfort of thermal and lighting environments. The comprehensive comfort factor model was developed to obtain the comprehensive comfort scores and rankings. Furthermore, the optimal learning environment suitable for college students were proposed. The results showed that the PMV has the greatest effect on the overall comfort perception of learning behavior, followed by color temperature and the lowest effect of illuminance. When the PMV is 0.87, it is appropriate to utilize a combination of high color temperature and high illuminance. Conversely, when the PMV is between 0.26 and -0.11, it is good to employ a combination of medium-high color temperature and medium-high illuminance. Conversely, in situations where the PMV is -0.54, a combination of low to medium color temperature and medium to high illuminance is acceptable. Similarly, a combination of low color temperature and medium illuminance is suitable for a PMV of -0.94. The most comfortable learning environment for college students is a combination of high color temperature 4400–4900 K and medium to high illuminance 500–800 lx at a PMV of 0.26.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104658"},"PeriodicalIF":6.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Entropy generation and heat transfer in MHD free convection within l-shaped cavities: A computational and AI-based approach","authors":"Noor Zeb Khan ,&nbsp;Neelam Tahir ,&nbsp;A.S. Shflot ,&nbsp;M.Y. Malik","doi":"10.1016/j.rineng.2025.104659","DOIUrl":"10.1016/j.rineng.2025.104659","url":null,"abstract":"<div><div>This study examines entropy generation and heat transfer in an <span>l</span>-shaped enclosure to optimize thermal system design by analyzing step aspect ratios (height vs. width). Governing equations (continuity, momentum, energy) are non-dimensionalized and solved via finite element simulations, with entropy production (thermal, viscous, total) evaluated across varying Rayleigh numbers (Ra), Hartmann number (Ha), aspect ratio (AR=h/w), and irreversibility ratios. Heat flux is predicted by an artificial neural network (ANN) that has been trained on simulation data, improving computing efficiency. As shown by previous research and grid sensitivity testing, the ANN model forecasts heat transport patterns with accuracy. These findings illustrate the usefulness of ANN in speeding up thermal research by elucidating entropy dynamics and optimizing geometric parameters, which are crucial for building energy-efficient devices. The main conclusions show that magnetic entropy generation can account for up to 44.8 % of total entropy generation under strong magnetic fields (Ha≥20). The average Nusselt number rises by 37.6 % as the Rayleigh number rises from 103 to 106, but it decreases slightly (0.25 %) under high Hartmann numbers, indicating MHD trade-offs. Kinetic energy has a 99.8 % increase with Ra, indicating convection dominance in taller geometries (h = 0.75, w = 0.25).</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104659"},"PeriodicalIF":6.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photoreduction of methylene blue using nanoporous composite materials","authors":"K.M. Álvarez ,&nbsp;M.A. Hernández ,&nbsp;J.J. Alvarado ,&nbsp;R. Portillo","doi":"10.1016/j.rineng.2025.104662","DOIUrl":"10.1016/j.rineng.2025.104662","url":null,"abstract":"<div><div>Currently, the application of semiconductor materials for environmental remediation is very common, especially in the area of photocatalysis. However, these materials are very deficient because they do not have a large surface area. Therefore, it is important to generate advanced materials that combine their properties, as is the case of combining semiconductor materials with porous materials such as zeolites. In this work Titanium Oxide (TiO₂₎ nanoparticles were synthesized by the Sol-Gel method, later natural Zeolites (Erionite, Clinoptilolite and Mordenite) were added to obtain TitaniumOxide /Zeolites (TiO₂/Zeolites) compounds. Furthermore, these composite materials were subjected to heat treatment at 500 °C. Furthermore, the compounds were characterized by X-ray diffraction (XRD), energy dispersive spectroscopy (EDS) and Nitrogen (N₂) adsorption. Through XRD the different crystallographic structures were determined, being anatase (TiO₂), hexagonal (Erionite), monoclinic (Clinoptilolite) and orthorhombic (Mordenite). In addition, thanks to EDS, the elemental composition of each compound in relation to oxides was determined. While pore diameters and pore volume were determined in N₂ adsorption by various methods (eg, BJH (Barret, Joiner and Halenda)). As a result, the average pore size ranges from 0.8 to 5.08 nm and the pore volumes are 0.0007–0.2614 cm³/g. In addition, the surface area was determined by BET (Brunauer-Emmett-Teller) and Langmuir, where the sample with the lowest surface area was Titanium Oxide -Clinoptilolite (25-75) (TiO₂-Cli (25-75)) with a value of 14.1 m²/g and the one with the highest surface area was Titanium Oxide -Mordenite (25-75) (TiO₂-Mor (25-75)) with a value of 456 m²/g, which means that this sample is the one with the highest adsorption. Indicating with these results that the aggregation of porous materials for the formation of new compounds generates an increase in the surface area, thereby improving the material. Resulting in a wide range of applications such as: sensors, photocatalysis and irreversible adsorption of polluting gases among other applications.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104662"},"PeriodicalIF":6.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Redox mediator system: Expanding the potential of laccase-like nanozymes towards pollutant remediation","authors":"E. Hermosilla ,&nbsp;M. Diaz ,&nbsp;M.J. Pérez-Vélez ,&nbsp;S. Leiva ,&nbsp;A.M.R. Ramírez ,&nbsp;M.R.V. Lanza ,&nbsp;O. Rubilar","doi":"10.1016/j.rineng.2025.104656","DOIUrl":"10.1016/j.rineng.2025.104656","url":null,"abstract":"<div><div>This study investigates the use of a redox mediator system to enhance the pollutant remediation capabilities of laccase-like nanozymes. A three-phase magnetic nanocomposite, composed of MnFe₂O₄, Mn₃O₄, and CuO in various precursor ratios, was synthesized. The 1:2:2 nanocomposite demonstrated the highest specific laccase-like activity, with values of 8.9 U mg⁻¹ at pH 4 and 9.2 U mg⁻¹ at pH 5. In contrast, single-phase nanozymes exhibited much lower activities. Cyclic voltammetry (CV) revealed that the 1:2:2 composite had a MnFe₂O₄ core and a Mn₃O₄|CuO alloy shell. The study also evaluated natural and synthetic redox mediators—acetosyringone, syringaldehyde, vanillin, and 1-hydroxybenzotriazole (HBT)—for their effect on methyl orange degradation. Acetosyringone was the most effective, achieving near-complete degradation at 50 µM and above, resulting in a 6.3-fold increase in degradation rate compared to treatments without mediators. Syringaldehyde and vanillin also enhanced degradation, with syringaldehyde reaching near-complete degradation at 75 µM and 100 µM, while vanillin required higher concentrations for 80 % degradation. CV was used to analyze the electrochemical properties of reactions involving the 1:2:2 nanocomposite and redox mediators. These findings highlight the potential of combining nanozymes with natural redox mediators for improved degradation of harmful compounds in environmental remediation. While laccase-like nanozymes have typically been used for sensor applications, this study opens new possibilities for their use in environmental challenges and catalytic systems.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104656"},"PeriodicalIF":6.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-driven approach with explainable artificial intelligence for customer churn prediction in the telecommunications industry","authors":"Daniyal Asif ,&nbsp;Muhammad Shoaib Arif ,&nbsp;Aiman Mukheimer","doi":"10.1016/j.rineng.2025.104629","DOIUrl":"10.1016/j.rineng.2025.104629","url":null,"abstract":"<div><div>In the competitive telecommunications industry (TCI), retaining clients is crucial for profitability, as customer churn remains a significant challenge. Traditional machine learning (ML) models often lack the predictive power needed for complex telecom data, while black-box models provide limited transparency, reducing trust and actionable insights. This study introduces XAI-Churn TriBoost, an interpretable and explainable data-driven model developed using a dataset of over 2 million records. The model combines extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM) in a soft voting ensemble to enhance churn prediction. Data preprocessing included handling missing values through iterative imputation with a Bayesian ridge. Sequential data scaling was implemented by combining robust, standard, and min-max scaling methods to ensure feature consistency. Feature selection was conducted using the Boruta technique with a random forest (RF), and class imbalance in the training data was addressed using the synthetic minority oversampling technique (SMOTE). XAI-Churn TriBoost achieved high predictive performance, with an accuracy of 96.44%, precision of 92.82%, recall of 87.82%, and F1 score of 90.25%. To enhance model transparency, we incorporated explainable artificial intelligence (AI) techniques, specifically local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP), to interpret individual predictions and identify critical features affecting churn. Key factors impacting churn include regularity and montant, offering TCI valuable insights for targeted retention strategies. XAI-Churn TriBoost thus provides both robust performance and interpretability, highlighting its potential to support customer retention efforts in the TCI.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104629"},"PeriodicalIF":6.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges and developments in wire arc additive manufacturing of steel: A review","authors":"Mohamed Dekis ,&nbsp;Mahmoud Tawfik ,&nbsp;Mohamed Egiza ,&nbsp;Montaser Dewidar","doi":"10.1016/j.rineng.2025.104657","DOIUrl":"10.1016/j.rineng.2025.104657","url":null,"abstract":"<div><div>Wire Arc Additive Manufacturing (WAAM) offers significant potential for enhancing steel component production. This review addresses critical challenges associated with WAAM, including defect formation, process optimization, and property enhancement. The review categorizes defects, such as porosity, cracks, and lack of fusion, and correlating them with process parameters including wire feed rate and travel speed, to identify key factors influencing component quality. The existing literature reveal that porosity can reduce tensile strength by 15–20 % and fatigue life by up to 50 %, while cracks can lead to a strength reduction of up to 30 %. Strategies for mitigating these defects, including process optimization, post-processing techniques, and emerging technologies, are discussed. Specifically, strategies to control heat input, reduce residual stresses, and refine microstructure have been shown to significantly improve build quality, mechanical properties, and overall performance of WAAM-produced steel component. This review provides valuable insights for industry practitioners for overcoming existing challenges and advancing the application of WAAM in steel production.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104657"},"PeriodicalIF":6.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating age-related muscle force adaptations in males and females during sit-to-walk transition motion using EMG-informed modeling","authors":"Zakia Hussain ,&nbsp;Alpha Agape Gopalai ,&nbsp;Siti Anom Ahmad ,&nbsp;Mazatulfazura Sf Binti Salim ,&nbsp;Darwin Gouwanda ,&nbsp;Pei-Lee Teh","doi":"10.1016/j.rineng.2025.104660","DOIUrl":"10.1016/j.rineng.2025.104660","url":null,"abstract":"<div><h3>Background</h3><div>The sit-to-walk (STW) transition is essential for mobility but deteriorates with age due to declining muscle strength, balance, and postural control. This study hypothesizes that age- and sex-related variations in muscle forces during STW lead to altered muscle recruitment strategies, reflecting compensatory mechanisms. When these compensations are no longer adequate, mobility limitations may occur.</div></div><div><h3>Methods</h3><div>This study involved 65 healthy adults (32 males and 33 females) from three age groups. Motion capture and surface electromyography (sEMG) data were used to develop an EMG-informed neuromusculoskeletal model for estimating muscle forces. Age and sex-related variations in muscle forces were analyzed using Generalized Linear Mixed Modeling (GLMM).</div></div><div><h3>Results</h3><div>The findings reveal consistent recruitment of primary STW muscles across all age-sex subgroups. The <em>vasti, gluteus maximus, gluteus medius, dorsiflexors,</em> and <em>soleus</em> generated the highest average muscle forces during STW. However, older adults consistently generated lower forces in these muscles during rising (except for the <em>soleus</em>). Despite this, phase durations were like other groups, with increased ankle plantarflexor (except <em>gastrocnemius</em>) and hip abductors (<em>gluteus medius</em>) forces to support gait transition. These findings indicate compensation in healthy aging involves variations in muscle force production rather than altered muscle recruitment strategies. Moreover, females exhibited more pronounced age-related muscle force changes than males during gait initiation.</div></div><div><h3>Conclusion</h3><div>Significant age- and sex-specific variations in muscle forces across STW phases highlight the need for biomechanically informed interventions to preserve muscle health and reduce functional decline. Targeted interventions should focus on strengthening STW muscles and not just knee extensors to enhance mobility. For example, exercises like Tai Chi, known to improve dynamic stability during gait initiation, can benefit females across age groups.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104660"},"PeriodicalIF":6.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flow boiling of HFE-7100 over graphene coated sintered porous copper surfaces in a minichannel","authors":"Tayfun Guler ,&nbsp;Vahid Ebrahimpour Ahmadi ,&nbsp;Ilker Alagozoglu ,&nbsp;Saifa Amin ,&nbsp;Ahmet Muhtar Apak ,&nbsp;Alper Apak ,&nbsp;Murat Parlak ,&nbsp;Umur Tastan ,&nbsp;Ismet Inonu Kaya ,&nbsp;Ali Sadaghiani ,&nbsp;Ali Koşar","doi":"10.1016/j.rineng.2025.104653","DOIUrl":"10.1016/j.rineng.2025.104653","url":null,"abstract":"<div><div>High power dense electronic devices demand efficient heat removal and thermal management. Phase change heat transfer with the application of graphene coating offers superior heat dissipation. In this study, the effects of sintered copper powders and monolayer graphene coating on flow boiling of HFE-7100 were investigated for a minichannel. Bare copper surface and surfaces with additional sintered layers of thicknesses of 0.5 mm, 1.0 mm, and 2.0 mm were compared in terms of flow boiling heat transfer. Additionally, graphene coatings were applied to each surface, and the effects of graphene coating on flow boiling heat transfer were assessed at atmospheric pressure. The experiments were conducted at different heating fluxes and two different mass fluxes (120 kg/m²s and 180 kg/m²s) for each surface. Novec HFE-7100, a dielectric fluid having a high potential for the use in electronics cooling applications, was used as the working fluid in flow boiling experiments. The results indicated that the sintered layer improved the flow boiling heat transfer performance. The sintered layer thickness of 0.5 mm offered the best heat transfer performance with an enhancement up to 145 % relative to the bare surface at high heat fluxes. It was also observed that graphene coatings further enhanced the heat transfer performance of the sintered surfaces up to 34 %. When 0.5 mm sinter thickness and graphene coating were combined, the maximum heat transfer enhancement was recorded as 227 % compared to the bare surface. In the light of high-speed camera images, flow boiling characteristics and effects of graphene coating on flow patterns were displayed. Accordingly, the graphene coating increased the nucleation site density, improved the stability of bubble formation and led to HTC enhancement for the sintered surfaces.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104653"},"PeriodicalIF":6.0,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating and parametrically improving the microstructure, hardness, and wear resistance of SiC-CeO2 reinforcements on hot rolled Al7075 hybrid composites","authors":"Ravi Kumar M ,&nbsp;Vijay Kumar S ,&nbsp;C.Durga Prasad ,&nbsp;G Sridevi ,&nbsp;Aprameya C R ,&nbsp;Ashish Kumar ,&nbsp;Saravana Bavan ,&nbsp;Adem Abdirkadir Aden","doi":"10.1016/j.rineng.2025.104634","DOIUrl":"10.1016/j.rineng.2025.104634","url":null,"abstract":"<div><div>This research employed stir casting to fabricate hybrid aluminum matrix composites (MMC) by mixing different weight proportions of silicon carbide (SiC) with a fixed weight percentage of cerium oxide (CeO₂) and adding it to Al7075 alloy. Hot rolling process was carried out for the developed hybrid composites and mechanical and wear behavior were studied. The effect of wear parameters like applied load (N), sliding distance (m) and wt. % of SiC were studied using statistical approach. The obtained results indicate that, significant improvement was obtained in the grain refinements with minimum porous structures. Similarly, increases of toughness (80–120 KJ/m2), tensile strength (115–136 N/mm2) and hardness (55–71 VHN) with increasing in 0–6 wt. % of SiC reinforcements were obtained. The statistical analysis results indicate that, SiC reinforcements significantly influence the wear resistance of the hybrid composites followed by applied load and sliding distance. Lastly, a feed-forward &amp; backward propagation neural network employing the Levenberg-Marquardt algorithm was used to study COF &amp; wear loss based on three input parameters. For both combinations, the coefficient of correlation was found to be 0.9516 &amp; 0.9956 for training &amp; 0.9907 &amp; 0.9736 for testing, with a confidence interval of 95 %. The mean square error performance achieved was 1.6010^-5 &amp; 1.3210^-5, respectively.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104634"},"PeriodicalIF":6.0,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing voting classifiers for enhanced analysis and diagnosis of cardiac conditions","authors":"Mohamed S. Elgendy ,&nbsp;Hossam El-Din Moustafa ,&nbsp;Hala B. Nafea ,&nbsp;Warda M. Shaban","doi":"10.1016/j.rineng.2025.104636","DOIUrl":"10.1016/j.rineng.2025.104636","url":null,"abstract":"<div><div>Identifying heart disease based on initial symptoms poses a considerable difficulty in the modern era. Untimely diagnosis may lead to fatality. An accurate decision support system is essential for timely identification of heart diseases. The model proposed is named Heart Disease Prediction Model (HDPM) and comprises three primary components; which are; (i) data collection and preprocessing, (ii) feature selection, and (iii) Disease Prediction. In the first part, the used heart disease dataset is preprocessed and the heart disease features are extracted. Then, these extracted features are fed to the second part (i.e. feature selection). This paper presents a novel approach to feature selection using the Sand Cat Swarm Optimization (SCSO) algorithm. An enhanced methodology has been implemented in the SCSO system to improve its effectiveness in identifying and categorizing the most crucial and impactful features for predicting and classifying patients with heart disease. The proposed methodology is called Dynamic SCSO (DSCSO). DSCSO is combination method between SCSO and Dynamic Opposite Learning (DOL). Ultimately, the chosen features are inputted into the voting classifiers to arrive at the ultimate determination. The proposed voting classifiers is based on using multiple classifiers which are, Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Extreme Gradient Boost (EGB), Decision Tree (DT), and Support Vector Machine (SVM). At the end, the proposed model (i.e., HDPM) trained and tested using heart disease data and it performs well.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104636"},"PeriodicalIF":6.0,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}