Search Results - (( using classification using algorithm ) OR ( variable extraction learning algorithm ))

Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms by Rachmad, Iqbal, Tri Basuki, Kurniawan, Misinem, ., Edi Surya, Negara, Tata, Sutabri

“…The classification algorithm used in this research is the Convolutional Neural Network (CNN) algorithm. …”
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

“…Therefore, this research has designed fuzzy learning algorithm that is able to classify fruits based on their shape and size features using Harumanis dataset. …”
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Classification of students' performance in computer programming course according to learning style by Norwawi N.M., Abdusalam S.F., Hibadullah C.F., Shuaibu B.M.

Classification of Students' Performance in Computer Programming Course According to Learning Style by Norwawi, NM, Abdusalam, SF, Hibadullah, CF, Shuaibu, BM

“…The critical point of this study is the use of classification algorithm to extract patterns which are examined from the cognitive factor specific learning style. …”

The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers by Zhang, Xiaolei, Iqbal bin Saripan, M., Wu, Yanjun, Wang, Zhongxiao, Wen, Dong, Cao, Zhendong, Wang, Bingzhen, Xu, Shiqi, Liu, Yanli, Marhaban, Mohammad Hamiruce, Dong, Xianling

“…Conclusions: The Combat algorithm has reduced variability in radiomic features from different scanners. …”
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Classification model for chlorophyll content using CNN and aerial images by Wagimin, Mohd Nazuan, Ismail, Mohammad Hafiz, Mohd Fauzi, Shukor Sanim, Seng, Chuah Tse, Abd Latif, Zulkiflee, Muharam, Farrah Melissa, Mohd Zaki, Nurul Ain

“…Besides that, the starting point of the Digitization Footprint for this study site across the development stages of the classification model was 308.5756 MB/ha. Finally, the overall accuracy performances for the classification models that used the transfer learning algorithms, which were InceptionV3, DenseNet121, and ResNet50, and trained using the images of the mango plant infected with pest were 96.49 %, 92.98 %, and 89.47 %, respectively, and for using the images of the mango plant not infected with pest were 88.10 %, 78.57 %, and 69.05 %, respectively.…”
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

“…A novel feature extraction algorithm was developed to extract the feature vectors. …”
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Mortality prediction in critically ill patients using machine learning score by Dzaharudin, Fatimah, Md Ralib, Azrina, Jamaludin, Ummu Kulthum, Mat Nor, Mohd Basri, Tumian, Afidalina, Har, Lim Chiew, Ceng, T C

“…Various types of classification algorithms in machine learning were investigated using common clinical variables extracted from patient records obtained from four major ICUs in Malaysia to predict mortality and assign patient mortality risk scores. …”
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Mortality prediction in critically ill patients using machine learning score by Fatimah, Dzaharudin, Azrina, Md Ralib, Ummu Kulthum, Jamaludin, Mohd Basri, Mat Nor, Afidalina, Tumian, Har, Lim Chiew, Ceng, T. C.

“…Various types of classification algorithms in machine learning were investigated using common clinical variables extracted from patient records obtained from four major ICUs in Malaysia to predict mortality and assign patient mortality risk scores. …”
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Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using k-Nearest Neighbour (k-NN) by Mohamad Hushnie, Haron, Nur Azzimah, Zamri, Khairunisa, Muthusamy

“…The data from this process were first transformed using min-max normalization and then, analysed using exploratory and descriptive analysis to discover patterns between input variables and concrete grades. Next, the grades of concrete were classified using a machine learning algorithm named k-Nearest Neighbour (k-NN). …”
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Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm by Acharya, U.R., Faust, O., Molinari, F., Sree, S.V., Junnarkar, S.P., Sudarshan, V.

“…We then review the state-of-the-art US-based CAD techniques that utilize a range of image texture based features like entropy, Local Binary Pattern (LBP), Haralick textures and run length matrix in several automated decision making algorithms. These classification algorithms are trained using the features extracted from the patient data in order for them to learn the relationship between the features and the end-result (FLD present or absent). …”
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Analysis using data mining techniques: the exploration and review data of diabetes patients / Syarifah Adilah Mohamed Yusoff ... [et al.] by Mohamed Yusoff, Syarifah Adilah, Othman, Jamal, Johan, Elly Johana, Mohd Mydin, Azlina, Wan Mohamad, Wan Anisha

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A hybrid serendipity social recommender model / Ahmad Subhi Zolkafly and Rahayu Ahmad by Zolkafly, Ahmad Subhi, Ahmad, Rahayu

“…In the next stage, the classification model is used to classify the data into subclasses by using a deep learning algorithm. …”
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Recommendation System Model For Decision Making in the E-Commerce Application by Mohammed Mahmudur Rahman

Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using kNearest Neighbour (k-NN) by Mohamad Hushnie, Haron, Nur Azzimah, Zamri, Khairunisa, Muthusamy

“…The data from this process were first transformed using min-max normalization and then, analysed using exploratory and descriptive analysis to discover patterns between input variables and concrete grades. Next, the grades of concrete were classified using a machine learning algorithm named k-Nearest Neighbour (k-NN). …”
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ECG-based driving fatigue detection using heart rate variability analysis with mutual information by Halomoan, Junartho, Ramli, Kalamullah, Sudiana, Dodi, Gunawan, Teddy Surya, Salman, Muhammad

“…For feature extraction, we employ heart rate fragmentation to extract non-linear features to analyze the driver’s cognitive status. …”
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Tree species and aboveground biomass estimation using machine learning, hyperspectral and LiDAR data / Nik Ahmad Faris Nik Effendi by Nik Effendi, Nik Ahmad Faris

“…Besides, Artificial Neural Network (ANN) and Random Forest (RF) algorithm was used to predicted the AGB using different combination of variables. …”
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Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease by Che Hashim, Izrahayu

“…The temperature variation for each thermal image was examined using FLIR ResearchIR Max, the camera manufacturer's software, and feature extraction for each thermal image was extracted using FLIR Tools in the FLIR ResearcherIR environment software. …”
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Oil palm female inflorescences anthesis stages identification using selected emissivities through thermal imaging and Machine Learning by Yousefidashliboroun, Mamehgol

“…Different ML algorithms such as Random Forest (RF), k Nearest Neighbor (kNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) as well as an ensemble method are used on data extracted from thermal images collected during infield oil palms pollination stages monitoring. …”
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Evaluation of multiple In Situ and remote sensing system for early detection of Ganoderma boninense infected oil palm by Ahmadi, Seyedeh Parisa

“…In the next phase, the SVM classifier was trained to achieve the best classification using training data and test data integrated with selected features. …”
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