A study on the oil palm fresh fruit bunch (FFB) ripeness detection by using Hue, Saturation and Intensity (HSI) approach by Shabdin, Muhammad Kashfi, Mohamed Shariff, Abdul Rashid, Johari, Mohd Nazrul Azlan, Saat, Nor Kamilah, Abbas, Zulkifly

“…The by linear regression in the data shows an average success rate at 45% accuracy for oil palm ripeness detection. Artificial Neural Network (ANN) however return a better accuracy result for both underipe and ripe categories which are 60% and 80% respectively. …”
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Identification of dorsal and ventral surface of rubber seed using image processing and machine learning approach by M. Johari, Siti Nurul ‘Afiah, Bejo, Siti Khairunniza, Wan Ismail, W. Ishak

“…Support Vector Machine (SVM) and Artificial Neural Network (ANN) were also used for the classification. …”
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Particle swarm optimization method in initialization of wavelet neural network model for fed-batch bioprocesses by Mamat, Nor Hana, Mohd Noor, Samsul Bahari, Che Soh, Azura, Taip, Farah Saleena, Ab Rashid, Ahmad Hazri, Jufika Ahmad, Nur Liyana, Mohd Yusuff, Ishak

“…Wavelet neural network is an alternative to artificial neural network in empirical modeling of industrial processes due to efficient initialization of network parameters that reduces training time. …”
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Easy to use remote sensing and GIS analysis for landslide risk assessment by Dibs, Hayder, Al-Janabi, Ahmed, Gomes, Gorakanage Arosha Chandima

“…We discussed different type of algorithms and factors for modeling the prediction of landslide risk assessment such as SVM (support vector machine), DT (decision tree), ANFIS (adaptive neural-fuzzy inference system), AHP (analytic hierarchy process), ANN (artificial neural network), probability frequency of landslides occurrence factors model and empirical model. …”
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Critical physical parameters for optimum recombinant protein production in yeast systems by Pallujam, Asha Devi, Zali, Siti Afiqah, Oslan, Siti Nur Hazwani, Salleh, Abu Bakar, Oslan, Siti Nurbaya

“…This paper reviews different optimization methods of the recombinant protein production for several factors such as pH, temperature, media, agitation rate, inducer, inoculum size and induction time using one factor at a time (OFAT), Response Surface Methodology (RSM) and Artificial Neural Network (ANN). This review highlights the current studies regarding the optimization of the recombinant proteins expressed in three different yeasts namely; Saccharomyces cerevisiae, Komagataella phaffii, and Yarrowia lipolytica. …”
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Classification of pesticide residues in cabbages based on spectral data by Che Mohammad Ishkandar El-Rahimin, Che Dini Maryani, Mat Nawi, Nazmi, Janius, Rimfiel, Mazlan, Norida, Ta, Te Lin, Li, Ta Chen

“…Three classification methods investigated in this study were artificial neural network (ANN), support vector machine (SVM) and logistic regression (LR). …”

Designing of Collagen Based Poly(3-hydroxybutyrate-co-4-hydroxybutyrate) Scaffolds for Tissue Engineering by S., Vigneswari, H. P. S., Abdul Khalil, A. A., Amirul

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Nonlinear Chemical Process Monitoring And Fault Detection Based On Modified Lstm Model by Zambri, Muhammad Ridzuan

“…The investigation the performance between the LSTM model with the Artificial Neural Network (ANN). The modification of the LSTM will be made by comparing different type of faults that will be used for the fault detection. …”
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Analisis Data Untuk Rekabentuk Sistem Pintar Bagi Pengelas Corak Aliran Minyak-Gas by Abdul Rahim, Adzrinna

“…In order to enable the simulated ECT data to be classified, Artificial Neural Network (ANN) is implemented. MLP Neural network and the Levenberg Marquardt algorithm is implemented to create a desirable network. …”
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Functional link neural network with modified bee-firefly learning algorithm for classification task by Mohmad Hassim, Yana Mazwin

“…Classification is one of the most frequent studies in the area of Artificial Neural Network (ANNs). The ANNs are capable of generating a complex mapping between the input and the output space to form arbitrarily complex nonlinear decision boundaries. …”
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A Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding by Sharman Sundarajoo, Sharman Sundarajoo, Dur Muhammad Soomro, Dur Muhammad Soomro, Non-members, Non-members

“…Hence, this article introduces an optimal UVLS method using a feedforward artificial neural network (ANN) model trained with the particle swarm optimization (PSO) algorithm to obtain the optimal load shedding amount for a distribution system. …”
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Voltage tracking of a DC-DC buck converter using neural network control by Mohamad Narsardin, Mohamad Adhar

“…The mathematical model of Buck converter and artificial neural network algorithm is derived. The dc-dc Buck converter is designed to tracking the output voltage with three variation. …”
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Predicting the capability of oxidized cnw adsorbents for the remediation of copper under optimal operating conditions using rsm and ann models by A. Hamid, Hazren, Harun‬, ‪Hairuddin, Mohamed Sunar, ‪Norshuhaila, Ahmad, Faridah Hanim, Jasmani, Latifah, Suleiman, Norhidayah

“…The aim of this work was to evaluate the capability of modified cellulose nanowhisker (CNW) adsorbents for the remediation of copper from water matrices under realistic conditions using response surface methodology (RSM) and artificial neural network (ANN) models. Considerations for design and application to remediate Cu(II) from wastewater by developing a continuous flow experiment are described in this study. …”

A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts by Masood, Ibrahim

“…Among the investigated designs, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network scheme gave superior performance, namely, average run lengths, ARL1 = 3.18 ~ 16.75 (for out-of-control process) and ARL0 = 452.13 (for in�control process), and recognition accuracy, RA = 89.5 ~ 98.5%. …”
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Pre-dispersive near-infrared light sensing in non-destructively classifying the brix of intact pineapples by Seng Chia, Kim, Jam, Mohamad Nur Hakim, Gan, Zeanne, Ismail, Nurlaila

“…After that, the diffuse reflectance NIR light of intact pineapples was non-destructively acquired using the developed NIR sensing device before their Brix values were conventionally measured using a digital refractometer. Next, an artificial neural network (ANN) was trained and optimized to classify the Brix values of pineapples using the acquired NIR light. …”

Prediction of tool wear using machine vision approach by Sim, Pei Chin, Lee, Woon Kiow, Abdullah, Haslina, Talib, Norfazillah, Ong, Pauline, Saleh, Aslinda, Ahmad, Said, Sung, Aun Naa

“…The objective of this study is to apply Artificial Neural Network (ANN) prediction model and machine vision system to predict flank wear in turning operation based on the texture images of machined surface captured by complementary metal oxide semiconductor (CMOS) camera in-cycle. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Ramadhan Saufi, Syahril, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Application of statistical and neural network model for oil palm yield study by Khamis, Azme

“…This thesis presents an exploratory study on modelling of oil palm (OP) yield using statistical and artificial neural network approach. Even though Malaysia is one of the largest producers of palm oil, research on modelling of OP yield is still at its infancy. …”
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