Optimal Input Combinations Research Articles

Influence of WEDM input variables on the machinability of Ni–Cu superalloy

ABSTRACT The study investigates the influence of Wire-cut Electrical Discharge Machining (WEDM) input variables on the Ni–Cu superalloy, crucial in marine environments. Input variables include Input current (Ic), Pulse on Time (TON), Pulse off Time (TOFF), Servo Voltage (SV), and Wire Feed (WF) rate, while output variables are Machine Speed (Mc), Kerf Width (K), and Material Removal Rate (MRR). Utilizing an L16 orthogonal matrix, experiments were conducted. The Taguchi method examines single output variables, while the Grey Relational Grade Algorithm (GRA) assesses multi-objective outputs. GRA identifies optimal input combinations, crucial for maximizing the machining performance. ANOVA of GRA results highlights Pulse on Time (TON), Input current (Ic), and Servo Voltage (SV) as significant factors affecting Mc, K, and MRR. This analysis underscores their importance in optimizing the WEDM process for Ni–Cu (MONEL K-500 superalloy).

A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain

The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with the extreme gradient boosting (XGBoost) model to propose a novel NGO-XGBoost model. The performance of this model was evaluated using meteorological data from 30 stations in the North China Plain and compared with XGBoost, random forest (RF), and k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method combined with the results from RF, XGBoost, adaptive boosting (AdaBoost), and categorical boosting (CatBoost) models is used to obtain the importance of meteorological factors in estimating ETo, and thereby determine the optimal combination of inputs to the model. The results indicated that by using the top 3, 4, and 5 important factors as input combinations, all models achieved high ETo estimation accuracy. It is worth noting that there were significant spatial differences in the estimation precisions of the four models, but the NGO-XGBoost model exhibited consistently high estimation precisions, with global performance indicator (GPI) rankings of 1st, and the range of coefficient of determination (R2), nash efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) were 0.920–0.998, 0.902–0.998, 0.078–0.623 mm d−1, 0.058–0.430 mm d−1, and −0.254–0.062 mm d−1, respectively. Furthermore, the accuracy of the NGO-XGBoost model in estimating ETo varied across different seasons, which was more significantly affected by humidity and wind speed in winter. When the target station data was insufficient, the NGO-XGBoost model was trained by using the historical data from neighboring stations and still maintained a high precision. Overall, this study recommends a reliable method for estimating ETo, which provides a reference for accurately calculating ETo in the North China Plain in the absence of meteorological data.

Transparent objects segmentation based on polarization imaging and deep learning

Previous investigations relied on intensity, degree of linear polarization, and angle of linear polarization for transparent object (TO) segmentation. However, no research have examined TO segmentation using other polarization parameters. This paper presents an adaptive edge-enhanced TO segmentation network that is capable of selecting the most optimal combination of inputs from 12 polarization parameters in an identical epoch. This study employed a laboratory-built system with polarized illumination and polarization camera to build a multi-polarization cue-based TO dataset. The optimal polarization scheme has been determined based on segmentation accuracy, support by comparative studies. To verify the feasibility of this optimal network, several ablation and comparison experiments have been conducted. It provides a prospective TO recognition strategy for industrial detection.

Short-term forecasting of streamflow by integrating machine learning methods combined with metaheuristic algorithms

This paper aims to introduce an artificial intelligence approach to enhance short-term daily streamflow forecasts through the integration of machine learning (ML) techniques with meta-heuristic (MH) algorithms. To maximize performance, a comprehensive analysis involving the autocorrelation and partial autocorrelation functions of the observed streamflow was conducted to derive ten distinct input combinations, from which the most suitable set was selected for investigation. Subsequently, six distinct ML methods, namely, decision trees, support vector regression, long short-term memory, gated recurrent unit, multi-layer perceptron, and radial basis function (as the optimal input combination), were leveraged to predict one-day-ahead streamflow. The most promising outcomes from these six explored methods were synergistically amalgamated to enhance predictive precision. This amalgamation was then optimized by utilizing parameter values hyper-tuned by four different MH algorithms. The algorithms included the shuffled frog leaping algorithm, particle swarm optimization, ant colony optimization (ACO), and gray wolf optimization (GWO). In culmination, a case study centered around the “Wick” river in the UK was conducted to rigorously evaluate the performance of the proposed method, utilizing a range of statistical evaluation indices. The results underscored the efficacy of the proposed combined method in substantially refining streamflow predictions, exhibiting an impressive level of accuracy. Additionally, the outcomes emphasized that ACO and GWO stand out as the optimal algorithms for calibrating the parameters of the amalgamated model. For instance, the R2 values attained by the combined models optimized through the ACO and GWO algorithms were 0.69422 and 0.69444, respectively. These values notably surpass those observed in the initial models, substantiating the superiority of the applied optimization approaches.

Groundwater level forecasting using ensemble coactive neuro-fuzzy inference system

A modeling framework utilizing the coactive neuro-fuzzy inference system (CANFIS) has been developed for multi-lead time groundwater level (GWL) forecasting in four different wells located in Texas and Florida, USA. Various model input combinations, including GWL, precipitation, temperature, and surface water level variables, have been derived based on proposed correlation analysis using singular spectrum analysis (SSA) remainders. The models have been trained on data subsets of varying lengths to identify the optimal training data duration. Additionally, we have introduced the bagging ensemble learning method to enhance the performance of the CANFIS model. As part of a comprehensive model evaluation process, the best-performing CANFIS model for each forecasting scenario has undergone uncertainty analysis using bootstrap sampling. Our results reveal that the CANFIS model performs satisfactorily for daily forecasting but leaves room for improvement in monthly forecasting, particularly for two-month and three-month ahead forecasts. Moreover, we have identified several optimal input combinations, highlighting the significance of the temperature variable in monthly forecasting. Furthermore, our findings indicate that additional training data does not necessarily lead to improved performance. The ensemble CANFIS model has demonstrated significant performance enhancement, particularly for monthly forecasting. Finally, the CANFIS model uncertainty analysis has shown satisfactory results for daily forecasting scenarios, while monthly forecasting models exhibit higher uncertainties, particularly during periods with distinctly different GWL fluctuation patterns.

Bidirectional Long Short-Term Memory (BILSTM) - Support Vector Machine: A new machine learning model for predicting water quality parameters

Water pollution threatens human health, agriculture, and ecosystems. Accurate prediction of water quality parameters is crucial for effective protection. We suggest a novel hybrid deep learning model that enhances the efficiency of Support Vector Machines (SVMs) in predicting Electrical Conductivity (EC) and Total Dissolved Solids (TDS). Our model combines Bidirectional Long Short-Term Memory (BILSTM) and SVMs to extract essential features and predict output variables. We evaluated the models using input parameters (PH, Ca++, Mg++, Na+, K+, HCO3, SO4, and Cl) for one, two, and three-day predictions. Employing the Ali Baba and Forty Thieves (AFT) optimization algorithm, we identified optimal input combinations. The BILSTM-SVM model accurately estimated TDS values, with MAPE values of 2%, outperforming other models. Similarly, it successfully predicted EC values, exhibiting an R2 value of 0.94. Our proposed model processes complex relationships and captures crucial features from the data, contributing to improved water quality prediction.

Machine learning-based prediction of scour depth around different-shaped bridge abutments

Accurate assessment of scour depth around bridge abutments is crucial to reasonable design of abutment structures. In this study, machine learning (ML) models are implemented, including M5′ model tree (M5′MT), multivariate adaptive regression spline (MARS), locally weighted polynomial regression (LWPR) and multigene genetic programming (MGGP) to predict scour depth around vertical-wall, 45° wing-wall and semicircular bridge abutments. Published experimental data are adopted, with four input parameters considered for the prediction of relative scour depth. The optimal input combination for each model is first determined using correlation and sensitivity analyses; results reveal that MGGP exhibits the best agreement with experimental data for vertical-wall and semicircular abutments, whereas LWPR outperforms the other models for the 45° wing-wall abutment. In addition, compared with the empirical equations and ML models employed in the literature, the accuracy of scour depth prediction is significantly improved with the ML models used in this study. Considering the comprehensive performance for all types of abutments in terms of accuracy, reliability and interpretability, MGGP is recommended as the representative of the implemented ML models with its mean absolute percentage error of 2.40% for a vertical-wall abutment, 3.95% for a 45° wing-wall abutment and 3.85% for a semicircular abutment.

Predicting stream water temperature with artificial neural networks based on open‐access data

AbstractPredictions of stream water temperature are an important tool for assessing potential impacts of climate warming on aquatic ecosystems and for prioritizing targeted adaptation and mitigation measures. Since predictions require reliable baseline data, we assessed whether open‐access data can serve as a suitable resource for accurate and reliable water temperature prediction using artificial neural networks (ANNs). For this purpose, we trained and tested ANNs in 16 small () headwater streams of major types located in Bavaria, Germany. Between four and eight different combinations of input parameters were trained and tested for each stream ANN, based on data availability. These were air temperature (mean, minimum and maximum), day of the year, discharge, water level and sunshine duration per day. We found that the input combination with the highest accuracy (lowest RMSE) was stream‐specific, suggesting that the optimal input combination cannot be generalized across streams. Using a reasonable, but random, input combination resulted in an increase in error (RMSE) of up to >100% compared to the stream‐specific optimal combination. Hence, we conclude that the accuracy of water temperature prediction strongly depends on the availability of open‐access input data. We also found that environmental parameters such as hydrological characteristics and the proportion of land use in the 5 m riparian strip and the entire catchment are important drivers, affecting the accuracy and reliability of ANNs. ANNs' prediction accuracy was strongly negatively related to river length, total catchment area and water level. High proportions of semi‐natural and forested land cover correlated with a higher accuracy, while open‐canopy land use types such as grassland were negatively associated with ANN accuracy. In conclusion, open‐access data were found to be suitable for accurate and reliable predictions of water temperature using ANNs. However, we recommend incorporating stream‐specific environmental information and tailor the combination of input parameters to individual streams in order to obtain optimal results.

Optimal combinations of parameters for seismic response prediction of high-speed railway bridges using machine learnings

This study aims to determine the optimal number and combination of input parameters from machine learning (ML) techniques, encompassing both earthquake and bridge parameters, for effectively and efficiently predicting the seismic response of high-speed railway bridges (HSRBs). 14 earthquake parameters and 37 bridge parameters were carefully selected. To mitigate multicollinearity among earthquake parameters and identify the most influential bridge parameters, correlation matrix analysis and the “Weight” method were employed, respectively. ML models were trained using data obtained from finite element analyses of 1000 bridge-ground motion pairs. The forward floating selection algorithm sequentially (FFSAS) and the backward floating selection algorithm sequentially (BFSAS) were utilized to determine the optimal combination of ML inputs for HSRBs. The analysis results demonstrate that the ML model, when utilizing the optimal input combination, yields accurate predictions of seismic response for HSRBs under various ground motion types. The predictive performance of machine learning models improves with more input parameters, but this particular model achieves stability with only six parameters. The optimization of this parameter combination is significantly influenced by factors such as the model type, evaluation criteria, and characteristics of the bridge components. FFSAS and BFSAS methods are both applicable for identifying the optimal combination of input parameters in machine learning models.

Determination of optimal combination of pumping and probing inputs in vibro-acoustic modulation for sensitive BVID detection of CFRP

In this work, the potential of using defect resonance-based vibro-acoustic modulation (VAM) spectroscopy to determine pumping and probing frequencies to maximize the nonlinear VAM response of carbon fiber reinforced polymer (CFRP) plates is investigated. An integrated 3D finite element (FE) model which implements the barely visible impact damage (BVID) due to impact force and the loading of ultrasonic signals is proposed. Based on this model, the scaling subtraction method (SSM) is used to determine the low-frequency global plate resonance that leading strongest defect excitation for pumping input (GPRpump) and high-frequency local defect resonance for probing input (LDRprobe). A scanning laser Doppler vibrometer (SLDV) is adopted to experimentally validated the proposed numerical methodology. Then, the maximum response amplitude (MRA) of nonlinear VAM response is proposed to evaluate the influence of defect resonance on the nonlinear VAM response. The numerical and experimental results are in good agreement and show that MRA of the VAM sideband can be improved when the GPRpump frequency is taken as the pumping input and the LDRprobe frequency is taken as the probing input, respectively. The best BVID detection effect is obtained when the pumping input of the VAM test is selected as the GPRpump frequency and the probing input is selected as sum or difference of the GPRpump frequency and LDRprobe frequency.

A hybrid SVR-BO model for predicting the soil thermal conductivity with uncertainty

This study proposes a generalised framework for developing a hybrid machine learning (ML) model that combines support vector regression (SVR) with hyperparameter optimisation to predict thermal conductivity ( k) with uncertainty. The framework contains four phases: data pre-processing, determining the best-performing hybrid model, selecting the optimal input combination, and uncertainty implementation. A database containing 2197 data points is first compiled to train the ML model. Three hyperparameter optimisation algorithms are adopted to tune hyperparameters, and their performance is evaluated by model evaluation metrics. Results show that SVR with Bayesian optimisation (SVR-BO) is the best-performing model since it produces more accurate predictions for k than models that employ grid and random searches. Given the sample insufficiency issue encountered in practice, the SVR-BO models with 144 input combinations are analysed. The compassion among models under various input combinations indicates that incorporating temperature as an additional input can provide moderate improvement in the accuracy and generalisability of the hybrid model. Based on the comparison, a five-input model is selected as the best candidate to implement the uncertainty evaluation for k. Results demonstrate that the predicted k possesses higher reliability for denser datasets and shows promising potential for applications in k with uncertainty assessments.

A hybrid ensemble machine learning model for discharge coefficient prediction of side orifices with different shapes

Side orifices are widely applied for flow control and regulation in channel systems. Accurate estimation of the discharge coefficient of the side orifice is significant for water management. The main objective of current research is to accurately predict the discharge coefficients of circular and rectangular side orifices. Considering that traditional empirical regressions are hard to estimate the discharge coefficient precisely due to the complex nonlinear relationship between the discharge coefficient and relevant parameters, a new hybrid boosting ensemble machine learning model, BO-XGBoost, is developed, which combines the advantages of the boosting ensemble model (XGBoost) and Bayesian Optimization. To further evaluate the proposed hybrid model, it is also compared with other tree-based machine learning models, including standalone XGBoost, Random Forest (RF) and Decision Tree (DT). Literature experimental data of the flow and geometric parameters relevant to the discharge coefficients of circular and rectangular side orifices are collected and applied to develop the models. Four dimensionless parameters of the relative channel width (B/L), the relative bottom height (W/L), the relative upstream depth (Y/L) and the upstream Froude number (Fr) are taken into consideration for the prediction of discharge coefficient (Cd). Furthermore, four different input combinations are designed and then compared to determine the best one on the basis of RMSE. By using the optimal input combination, our results demonstrate that BO-XGBoost provides the best comprehensive performance among all the involved machine learning models in the discharge coefficient prediction for both types of side orifices. Besides, the uncertainty analysis also reveals that BO-XGBoost shows the narrowest uncertainty bandwidth and gives the highest prediction reliability.

A data‐driven approach for flood prediction using grid‐based meteorological data

AbstractEstablishing a physically‐based hydrological model for flood prediction in ungauged or data‐limited catchments has always been a difficult problem. In this study, a data‐driven approach based on the NASA Global Land Data Assimilation System (GLDAS) data is proposed for flood prediction with the assistance of the Gamma Test. A runoff generation model together with a routing model based on the most advanced deep learning model, i.e. Long Short‐Term Memory (LSTM) network is established. By calculating the noise of the input data, Gamma Test can effectively avoid the overfitting phenomenon with the LSTM network. Taking the a small‐scale mountainous catchment from northern China as an example, Gamma Test in this study is used to help select the optimal combination of the GLDAS inputs for the runoff generation model, and meanwhile the input grids involved in the routing model. The established models are then verified based on the Nash‐Sutcliffe Efficiency coefficient (NSE), and it is found that the surface runoff generation model shows a good performance, with an average NSE value of 0.8708, while the baseflow‐groundwater runoff generation model also results in an acceptable performance with an average NSE value of 0.6320. After the involvement of the observed rainfall data, the model performance (NSE) of the routing model has increased from 0.5738 to 0.7144. Finally, the runoff generation models and the routing model are integrated, and the grid‐based GLDAS meteorological data are used directly to simulate the streamflow at the catchment outlet. The integrated model performs well with an NSE value of 0.7909, which indicates the feasibility of this data‐driven approach for flood prediction using the gird‐based meteorological data. The methodology adopted in this study provides a reference for flood prediction using data‐driven models in ungauged or data‐limited catchments.

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

The thermal comfort of passengers in the carriage cannot be ignored. Thus, this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote (PMV) index. Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro. Support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were used to build four models. This research aims to select the most appropriate input variables for the predictive model. All possible combinations of 11 input variables were used to determine the most accurate model, with variable selection for each model comprising 102 350 iterations. In the PMV prediction, the RF model was the best when using the correlation coefficients square (R2) as the evaluation indicator (R2: 0.7680, mean squared error (MSE): 0.2868). The variables include clothing temperature (CT), convective heat transfer coefficient between the surface of the human body and the environment (CHTC), black bulb temperature (BBT), and thermal resistance of clothes (TROC). The RF model with MSE as the evaluation index also had the highest accuracy (R2: 0.7676, MSE: 0.2836). The variables include clothing surface area coefficient (CSAC), CT, BBT, and air velocity (AV). The results show that the RF model can efficiently predict the PMV of the subway car environment.

The Causality Relationship Between R&D Expenditure, Efficiency Levels and Total Factor Productivity of Firms (A Review on Firms Operating in BIST)

Küresel ekonomik koşulların geçerli olduğu günümüzde artan rekabet koşullarının da etkisiyle firma etkinliği kavramı firmaların ulusal ve uluslararası piyasalarda rekabet güçlerinin belirlenmesi açısından önem arz etmektedir. Genel olarak firma etkinliği bir firmanın veri girdilerle optimal çıktı bileşimini sağlaması olarak ifade edilmektedir. Firmaların etkinlik performanslarının artırılmasında ise ar-ge harcamaları önemli rol oynamaktadır. İşletmelerin yapmış oldukları ar-ge harcamaları firma büyüklüğünü, karlılığını ve rekabet gücünü artırmaktadır. Bu çerçevede çalışmada Türkiye’de 2009-2020 yılları arasında BİST’ te faaliyet gösteren ve kesintisiz ar-ge harcaması yapan imalat sanayii firmalarının ar-ge harcamaları, firma etkinliği ve toplam faktör verimliliği arasındaki ilişki incelenmiştir. Yapılan analizlerden nedensellik testi sonuçlarına göre teknik etkinlikten ar-ge harcamalarına doğru ve ar-ge harcamalarından toplam faktör verimliliğine doğru tek yönlü bir nedensellik ilişkisine rastlanmıştır. Ayrıca yapılan Panel ARDL testi sonuçlarına göre ise ar-ge harcamaları ile teknik etkinlik düzeyi ve toplam faktör verimliliği arasında uzun dönemli bir ilişki bulunmaktadır.

Sustainable practices to reduce environmental impact of industry using interaction aggregation operators under interval-valued Pythagorean fuzzy hypersoft set

<abstract><p>Optimization techniques can be used to find the optimal combination of inputs and parameters and help identify the most efficient solution. Aggregation operators (AOs) play a prominent role in discernment between two circulations of prospect and pull out anxieties from that insight. The most fundamental objective of this research is to extend the interaction AOs to the interval-valued Pythagorean fuzzy hypersoft set (IVPFHSS), the comprehensive system of the interval-valued Pythagorean fuzzy soft set (IVPFSS). The IVPFHSS adroitly contracts with defective and ambagious facts compared to the prevalent Pythagorean fuzzy soft set and interval-valued intuitionistic fuzzy hypersoft set (IVIFHSS). It is the dominant technique for enlarging imprecise information in decision-making (DM). The most important intention of this exploration is to intend interactional operational laws for IVPFHSNs. We extend the AOs to interaction AOs under IVPFHSS setting such as interval-valued Pythagorean fuzzy hypersoft interactive weighted average (IVPFHSIWA) and interval-valued Pythagorean fuzzy hypersoft interactive weighted geometric (IVPFHSIWG) operators. Also, we study the significant properties of the proposed operators, such as Idempotency, Boundedness, and Homogeneity. Still, the prevalent multi-criteria group decision-making (MCGDM) approaches consistently carry irreconcilable consequences. Meanwhile, our proposed MCGDM model is deliberate to accommodate these shortcomings. By utilizing a developed mathematical model and optimization technique, Industry 5.0 can achieve digital green innovation, enabling the development of sustainable processes that significantly decrease environmental impact. The impacts show that the intentional model is more operative and consistent in conducting inaccurate data based on IVPFHSS.</p></abstract>

Multi-Encoder Transformer for Korean Abstractive Text Summarization

In this paper, we propose a Korean abstractive text summarization approach that uses a multi -encoder transformer. Recently, in many natural language processing (NLP) tasks, the use of the pre-trained language models (PLMs) for transfer learning has achieved remarkable performance. In particular, transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) are used for pre-training and applied to downstream tasks, showing state-of-the-art performance including abstractive text summarization. However, existing text summarization models usually use one pre-trained model per model architecture, meaning that it becomes necessary to choose one PLM at a time. For PLMs applicable to Korean abstractive text summarization, there are publicly available BERT-based pre-trained Korean models that offer different advantages such as Multilingual BERT, KoBERT, HanBERT, and KorBERT. We assume that if these PLMs could be leveraged simultaneously, better performance would be obtained. We propose a model that uses multiple encoders which are capable of leveraging multiple pre-trained models to create an abstractive summary. We evaluate our method using three benchmark Korean abstractive summarization datasets, each named Law (AI-Hub), News (AI-Hub), and News (NIKL) datasets. Experimental results show that the proposed multi-encoder model variations outperform single -encoder models. We find the empirically best summarization model by determining the optimal input combination when leveraging multiple PLMs with the multi-encoder method.

Experimental analysis, statistical modeling, and parametric optimization of quinary-(CoCrFeMnNi)100 –x/TiCx high-entropy-alloy (HEA) manufactured by laser additive manufacturing

For additional strength increase, 5, 10, and 15% TiC was added to the quinary CoCrFeMnNi high entropy alloy (HEA) at laser powers of 100, 400, and 700 watts while selective laser melting method was engaged in the fabrication. Microstructure, porosity, density, yield and tensile strengths, elongation, and microhardness are among the parameters analyzed. As TiC appreciated from 5 to 15%, the microstructure revealed that the particles were dispersed within the matrix. Also, the addition ensued grain size refinement with increasing particle proportion. Meanwhile, 15% caused an increase in porosity, 0–10% TiC dosage and 100–700 watts laser power led to a decrease in porosity. The same dosage of TiC resulted in a linear improvement in microhardness even as 0–15% TiC ensued gradual reductions in density and elongation Increases in laser power between 100 and 700 watts were detrimental to elongation but beneficial to density and microhardness enhancement. For composites produced at 100–700 watts laser power, 5–10% TiC increased yield and ultimate tensile strengths whereas 15% TiC decreased strength. For every TiC addition, laser power 100 - 400 watts generally showed an improvement in strength and microhardness, whereas 700 watts depicted a decrease in strength and microhardness. The optimal input combination was predicted by the developed models to be 15% TiC and 504 watts laser power. Since the deviation between anticipated outcome and validation values for the responses is < 0.05, the models are certified for future prediction of the responses. In conclusion, with 504 watt laser power, the entropy alloy's optimum composition is (CoCrFeMnNi)85/TiC15.

Influencing parameter optimisation of CRDI engine fuelled with lemongrass biodiesel blends

Based on the central composite design of the experiment of 50 input combinations, the behaviour of common rail direct injection compressed ignition engine was examined, and maximum engine performance and minimal emission were found using multi-objective RSM. The optimal input combination was identified after thorough testing. It consisted of 12.3 kg engine load, 60% lemongrass biodiesel, 1000 bar FIP, 6obTDC FIT, and 0% exhaust gas recirculation. A second order quadratic model with R 2 values of 0.99, 0.99, 0.84, 0.98, 0.82, 0.93 and 0.94 for Torque, BMEP, BTE, mechanical efficiency, BSFC, CO and NOx, respectively, was developed by taking into account all of the input parameters for all of the investigated responses. This model can be used to predict output responses within the experimental analysis ranges, with a maximum combined desirability of 0.863. ANN model may be successfully implemented for better prediction of the engine each output response than RSM-based prediction. Highlights CCD-based DOE was made in the design expert 13 version by considering major influencing CRDI engine parameters. RSM was used to create a second-order polynomial quadratic model for forecasting torque, BMEP, mechanical efficiency, BTE, BSFC, CO and NOx. Furthermore, there was no apparent difference between the fitted values and the experimental data. The optimisation was done to enhance the BMEP, Torque, BTHE, and mechanical efficiency and minimise the BSFC, CO and NOx. The optimum combination for the best performance is obtained when running the engine at the following settings such as 100% EL, 60% fuel blend, 6obTDC of FIT, 1000 bar FIP and 0% EGR. The ANOVA study's findings indicate that EL has a 99% impact on BMEP and torque. The effect of EL on BTHE is 60.3%. EL affects 94.4% of mechanical efficiency. EL controls 55.9% of SFC. CO is influenced by EL, EGR and IT, respectively, by 60.58%, 10% and 5.3%. EGR and IT both affect NOx by 50.5% and 23.9%, respectively. CRDI engine output response prediction accuracy of ANN is better than RSM

Smart ECG Biosensor Design with an Improved ANN Performance Based on the Taguchi Optimizer.

This paper aims to design a smart biosensor to predict electrocardiogram (ECG) signals in a specific auscultation site from other ECG signals measured from other measurement sites. The proposed design is based on a hybrid architecture using the Artificial Neural Networks (ANNs) model and Taguchi optimizer to avoid the ANN issues related to hyperparameters and to improve its accuracy. The proposed approach aims to optimize the number and type of inputs to be considered for the ANN model. Indeed, different combinations are considered in order to find the optimal input combination for the best prediction quality. By identifying the factors that influence a model’s prediction and their degree of importance via the modified Taguchi optimizer, the developed biosensor improves the prediction accuracy of ECG signals collected from different auscultation sites compared to the ANN-based biosensor. Based on an actual database, the simulation results show that this improvement is significant; it can reach more than 94% accuracy.