Search Results - (( model evaluation bat algorithm ) OR ( defect classification _ algorithm ))*

Fuzzy modeling using Bat Algorithm optimization for classification by Noor Amidah, Ahmad Sultan

“…The purpose of this research is to apply and evaluate the performance of Bat Algorithm for classification. …”
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A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting by Mohammed, Athraa Jasim, Ghathwan, Khalil Ibrahim, Yusof, Yuhanis

“…Pre-determining the values of parameters will affect the results of the forecasting model; hence, to find the optimal value of these parameters, this study investigates the adaptation of Bat and Cuckoo Search algorithms to optimize LSSVM parameters. …”
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An improved defect classification algorithm for six printing defects and its implementation on real printed circuit board images by Ibrahim, I., Ibrahim, Z., Khalil, K., Mokji, M.M., Abu Bakar, S.A.R.S., Mokhtar, N., Ahmad, W.K.W.

“…The improved PCB defect classification algorithm has been applied to real PCB images to successfully classify all of the defects. …”
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Bat algorithm for dam–reservoir operation by Ethteram, Mohammad, Mousavi, Sayed Farhad, Karami, Hojat, Farzin, Saeed, Deo, Ravinesh, Othman, Faridah, Chau, Kwok Wing, Sarkamaryan, Saeed, Singh, Vijay P., El-Shafie, Ahmed

“…Hence, the bat algorithm with third-order rule curve can be considered as an appropriate optimization model for reservoir operation.…”
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Evaluation of the Transfer Learning Models in Wafer Defects Classification by Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund, Lim, Shi Xuen

“…The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train and test the algorithms. …”
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Using the bees algorithm to optimise a support vector machine for wood defect classification by Pham, D.T, Muhammad, Zaidi, Mahmuddin, Massudi, Ghanbarzadeh, Afshin, Koc, Ebubekir, Otri, Sameh

“…The objective of the work was to find the best combination of SVM parameters and data features to maximize defect classification accuracy. The paper presents the results obtained to demonstrate the strengths of the Bees Algorithm as an optimization tool.…”
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Software defect prediction framework based on hybrid metaheuristic optimization methods by Wahono, Romi Satria

“…The classification algorithm is a popular machine learning approach for software defect prediction. …”
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Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia by Wee W.J., Chong K.L., Ahmed A.N., Malek M.B.A., Huang Y.F., Sherif M., Elshafie A.

“…Only a few simulation systems, where previous techniques failed to anticipate SF data quickly, let alone cost-effectively, and took a long time to execute. The bat algorithm (BA), a meta-heuristic approach, was used in this study to optimize the weights and biases of the artificial neural network (ANN) model. …”

Classification Analysis Of High Frequency Stress Wave For Autonomous Detection Of Defect In Steel Tubes by Abd Halim, Zakiah, Jamaludin, Nordin, Junaidi, Syarif, Syed Yahya, Syed Yusaini

“…Interpretation of propagated high frequency stress wave signals in steel tubes is noteworthy for defect identification.This paper demonstrated a successful new approach for autonomous defect detection in steel tubes using classification analysis of high frequency stress waves.Classification analysis using Principal Component Analysis (PCA) algorithm involved feature extraction to reduce the dimensionality of the complex stress waves propagation path.Two defective tubes containing a slot defect of different orientation and a reference tube are inspected using Vibration Impact Acoustic Emission (VIAE) technique.The tubes are externally excited using impact hammer.The variation of stress wave transmission path are captured by high frequency Acoustic Emission sensor.The propagated stress waves in the steel tubes are classified using PCA algorithm.Classification results are graphically illustrated using a dendrogram that demonstrated the arrangement of the natural clusters of the stress wave signals.The inspection of steel tubes showed good recognition of defect in circumferential and longitudinal orientation.This approach successfully classified stress wave signals from VIAE testing and provide fast and accurate defect identification of defective steel tubes from non-defective tubes. …”
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The formulation of a transfer learning pipeline for the classification of the wafer defects by Lim, Shi Xuen

“…Automated processes have been used commonly in recent years, with the judgement done by using conventional image processing algorithm. However, limitations such as robustness and difficulty in setting up the parameters required for image processing algorithm encourages the investigation in using Deep learning classification in detecting the wafer defects. …”
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Bat algorithm and neural network for monthly streamflow prediction by Zaini N., Malek M.A., Yusoff M., Osmi S.F.C., Mardi N.H., Norhisham S.

“…Therefore, this study proposed on the development of streamflow prediction model AI techniques namely Bat algorithm (BA) and backpropagation neural network (BPNN). …”

The performance of Taguchi�s T-method with binary bat algorithm based on great value priority binarization for prediction by Marlan Z.M., Ramlie F., Jamaludin K.R., Harudin N.

“…This, however, leads to sub-optimality of prediction accuracy as the orthogonal array design lacks in offering higher-order variable interactions, in addition to its fixed and limited variable combinations to be assessed and evaluated. This paper proposes an optimization algorithm based on the Binary Bat algorithm methodology for replacing the conventional orthogonal array approach. …”

An improved defect classification algorithm for six printing defects and its implementation on real printed circuit board images by Ismail, Ibrahim, Zuwairie, Ibrahim, Kamal, Khalil, Musa, Mohd Mokji, Syed Abdul Rahman, Syed Abu Bakar, Norrima, Mokhtar, Wan Khairunizam, Wan Ahmad

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Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm by Farzin, Saeed, Singh, Vijay, Karami, Hojat, Farahani, Nazanin, Ehteram, Mohammad, Kisi, Ozgur, Allawi, Mohammed Falah, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

“…The present study attempted to develop a three-parameter Muskingum model considering lateral flow for flood routing, coupling with a new optimization algorithm namely, Improved Bat Algorithm (IBA). …”
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Performance evaluation of PID controller parameters gain optimization for wheel mobile robot based on bat algorithm and particle swarm optimization by Nur Aisyah Syafinaz, Suarin, Pebrianti, Dwi, Nurnajmin Qasrina, Ann, Luhur, Bayuaji, Muhammad, Syafrullah, Indra, Riyanto

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Performance evaluation of PID controller parameters gain optimization for wheel mobile robot based on bat algorithm and particle swarm optimization by Nur Aisyah Syafinaz, Suarin, Pebrianti, Dwi, Nurnajmin Qasrina, Ann, Bayuaji, Luhur, Syafrullah, Muhammad, Riyanto, Indra

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Evaluation of the machine learning classifier in wafer defects classification by Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund

“…The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train, test and validate the classifier. …”
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Neural network paradigm for classification of defects on PCB by Heriansyah, Rudi, Syed Al-Attas, Syed Abdul Rahman, Zabidi, Muhammad Mun'im Ahmad

“…The algorithms to segment the image into basic primitive patterns, enclosing the primitive patterns, patterns assignment, patterns normalization, and classification have been developed based on binary morphological image processing and Learning Vector Quantization (LVQ) neural network. …”
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CLASSIFICATION OF BEARING FAULTS USING EXTREME LEARNING MACHINE ALGORITHMS by TEH, CHOON KEONG

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Cross-project software defect prediction by Bala, Yahaya Zakariyau, Abdul Samat, Pathiah, Sharif, Khaironi Yatim, Manshor, Noridayu

“…In this work, five research questions covering the classification algorithms, dataset, independent variables, performance evaluation metrics used in CPDP studies, and as well as the performance of individual machine learning classification algorithms in predicting software defects across different software projects were addressed accordingly. …”
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