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Integration of discriminant analysis with Artificial neural networks to decision analytic framework for enhancing automated visual IC inspection accuracy
(2026-05-15)
Kiatcharoenpol, Tossapol
;
Klongboonjit, Sakon
This study aims to enhance the accuracy and reliability of automated visual inspection (AVI) in semiconductor manufacturing by integrating Linear Discriminant Analysis (LDA) and an Optimization Layer by Layer Neural Network (OLLNN). Unlike prior LDA-ANN hybrid approaches that primarily emphasize classification accuracy, this study formalizes a decision-analytic inspection loop that explicitly links discriminant centroids, feasible lighting regions, surrogate nonlinear modeling, and production level validation. A two-stage decision analytic framework is developed. In the first stage, LDA is applied to classify and identify discriminant boundaries and centroids between acceptable and defective image features under three lighting setups: coaxial ring, high ring, and low ring lights. In the second stage, OLLNN is trained using these features to capture nonlinear dependencies between greyscale intensity and lighting parameters, and then a surface response plot is used to ease the optimal parameter selection. The integrating model is validated using experimental IC marking inspection data to evaluate improvements in accuracy, especially false positive rates (Type I error). It was found that for the validation state, the false positive rates are reduced from 5.8% to below 4.6%, and classification accuracy improves significantly across variable illumination conditions. After implementation in mass production, the yield is increased to 99.6% with zero false positive found. This significant development of the integrating model enhances a foundation for adaptive, data-driven control of AVI parameters in smart factory environments that support real-time learning and improvement.
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Evaluating the slope behavior for geophysical flow prediction with advanced machine learning combinations
(2025-12-01)
Onyelowe, Kennedy C.
;
Ebid, Ahmed M.
;
Hanandeh, Shadi
;
Kamchoom, Viroon
Ensuring safety in geotechnical engineering has consistently posed challenges due to the inherent variability of soil. In the case of slope stability problems, performing on-site tests is both costly and time-intensive due to the need for sophisticated equipment (to acquire and move) and logistics. Consequently, the analysis of simulation models based on soft computing proves to be a practical and invaluable alternative. In this research work, learning abilities of the Class Noise Two (CN2), Stochastic Gradient Descent (SGD), Group Method of Data Handling (GMDH) and artificial neural network (ANN) have been investigated in the prediction of the factor of safety (FOS) of slopes. This has been successfully done through literature search, data curation and data sorting. A total of three hundred and forty-nine (349) data entries on the FOS of slopes were collected from literature and sorted to remove odd values and unlogic results, which had been used together in a previous research work. After the sorting process, the remainder of the realistic data entries was 296. The previous work which had included unrealistic data entries had unit weight, γ (kN/m<sup>3</sup>), cohesion, C (kPa),angle of internal friction (Φ°), slope angle (°), slope height H (m), and pore water pressure ratio, r<inf>u</inf> as the studied parameters, which formed the independent variables. After careful checks, the initial results showed poor correlation with the individual factors and the factors were collected into three non-dimensional parameters based on the understanding of the physics of flows, which are: C/γ.h-Cohesion/unit weight x slope height, tan(ϕ)/tan(β)-the tangent of internal friction angle/Tangent of slope angle, and ρ/γ.h-Water pressure/unit weight x slope height, which are deployed as inputs and FOS-the safety factor of the slope as the output. At the end of the exercise, the ANN outclassed the other techniques with SSE of 62%, MAE of 0.27, MSE of 0.21, RMSE of 0.46, average total error of 24%, and R<sup>2</sup> of 0.946 thereby becoming the decisive intelligent model in this exercise. However, there is an advantage the deployment of GMDH, which comes second in order of superiority, has over the ANN. This is the development of a closed-form equation that allows its model to be applied manually in the design of slope stability problems. Overall, the present research models outperformed the eleven (11) models of the previous work due to sorting and elimination of unrealistic data entries deposited in the literature, the application of dimensionless combination of the studied slope stability parameters and the superiority of the selected machine learning techniques.
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Effect of conical air distributors on drying of peppercorns in a fluidized bed dryer: Prediction using an artificial neural network
(2022-08-01)
Chuwattanakul, V.
;
Wongcharee, K.
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Pimsarn, M.
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Chokphoemphun, S.
;
Chamoli, S.
The effect of conical air distributors on the drying of peppercorns in a fluidized bed dryer was experimentally studied. A flat perforated sheet was installed in the column at the base of the bed. Conical air distributors consisted of two parts. The first was a solid cone located below an air duct, while the second part was a perforated metal cone placed on the flat perforated sheet. Experiments were carried out using perforated metal cones with three different height to base diameter ratios, (h/H) values of 0.5, 1.0, and 1.5 and three different air velocities, 1.2Umf, 1.6Umf, and 2.0Umf. An air distributor, consisting of a solid cone and a perforated metal cone with h/H = 1.0 and an air velocity of 2.0Umf, showed the best drying performance. It was also discovered that increasing the air velocity accelerated the drying process. A neural network was created to predict the moisture content of peppercorns during the drying process. The split, sample type, spilt ratio, momentum, and learning rate, as well as the numbers of hidden layers, hidden nodes, and training cycles all had an impact. A maximum coefficient of determination of 0.996 was found for the best model.
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FORECASTING MODELS FOR FIRST YEAR PREMIUM OF LIFE INSURANCE
(2022-01-01)
Banditvilai, Somsri
;
Kuharattanachai, Choojai
The objective of this research is to study forecasting models for the first year premium of life insurance. The premium data are gathered from the Office of Insurance Commission (OIC) during January 2003 to November, 2021. The data are divided into 2 sets. The first set from January, 2003 to December 2020 is used for constructing and selection the forecasting models. The second one from January 2021 to November 2021 is used for computing the accuracy of the forecasting model. The forecasting models are chosen by considering the minimum Root Mean Square Error (RMSE). The Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the model. The results showed that the multiplicative model with initial values from 18 years Decomposition method give the appropriate model for the first year premium of life insurance and yields the MAPE = 17.29%
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Using machine learning-based algorithms to analyze erosion rates of a watershed in Northern Taiwan
(2020-03-01)
Nguyen, Kieu Anh
;
Chen, Walter
;
Lin, Bor Shiun
;
Seeboonruang, Uma
This study continues a previous study with further analysis of watershed-scale erosion pin measurements. Three machine learning (ML) algorithms-Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN)-were used to analyze depth of erosion of a watershed (Shihmen reservoir) in northern Taiwan. In addition to three previously used statistical indexes (Mean Absolute Error, Root Mean Square of Error, and R-squared), Nash-Sutcliffe Effciency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, theWilcoxon signed-rank test was used. The research utilized 14 environmental attributes as the input predictors of the ML algorithms. They are distance to river, distance to road, type of slope, sub-watershed, slope direction, elevation, slope class, rainfall, epoch, lithology, and the amount of organic content, clay, sand, and silt in the soil. Additionally, measurements of a total of 550 erosion pins installed on 55 slopes were used as the target variable of the model prediction. The dataset was divided into a training set (70%) and a testing set (30%) using the stratified random sampling with sub-watershed as the stratification variable. The results showed that the ANFIS model outperforms the other two algorithms in predicting the erosion rates of the study area. The average RMSE of the test data is 2.05 mm/yr for ANFIS, compared to 2.36 mm/yr and 2.61 mm/yr for ANN and SVM, respectively. Finally, the results of this study (ANN, ANFIS, and SVM) were compared with the previous study (Random Forest, Decision Tree, and multiple regression). It was found that Random Forest remains the best predictive model, and ANFIS is the second-best among the six ML algorithms.

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