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Pelvic classification based on deep learning algorithm on clinical CT scans in Malaysian population by Yahaya, Yasmin Arijah Che

“…The Phenice sex estimation method provides 98% of mean precision while 88.3% and 95% for mean sensitivity and mean specificity respectively. …”
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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application by Hossain Lipu M.S., Hannan M.A., Hussain A.

A study on the application of discrete curvature feature extraction and optimization algorithms to battery health estimation by Goh, Hui Hwang, An, Zhen, Zhang, Dongdong, Dai, Wei, Kurniawan, Tonni Agustiono, Goh, Kai Chen

“…The error evaluation metrics used are mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). …”
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Analysis Of Feature Reduction Algorithms To Estimate Human Stress Conditions by Arasu, Darshan Babu

“…Therefore, this study aimed to present analyse of the performance of feature classify when combining with feature selection algorithm to estimate human stress based on the facial feature of thermal imaging. …”
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Improved measurement of blood pressure by extraction of characteristic features from the cuff oscillometric waveform by Lim, P.K., Ng, S.C., Jassim, W.A., Redmond, S.J., Zilany, M., Avolio, A., Lim, E., Tan, M.P., Lovell, N.H.

“…Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean +/- SD = -0.3 +/- 5.8 mmHg; SVR and -0.6 +/- 5.4 mmHg) with only two features, i.e., Ratio(2) and Area(3), as compared to the conventional maximum amplitude algorithm (MAA) method (mean +/- SD = -1.6 +/- 8.6 mmHg). …”
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Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection by Hossain Lipu M.S., Hannan M.A., Hussain A., Saad M.H.M.

A study on the application of discrete curvature feature extraction and optimization algorithms to battery health estimation by Hui Hwang Goh, Hui Hwang Goh, Zhen An, Zhen An, Dongdong Zhang, Dongdong Zhang, Wei Dai, Wei Dai, Tonni Agustiono Kurniawan, Tonni Agustiono Kurniawan, Kai Chen Goh, Kai Chen Goh

“…The error evaluation metrics used are mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). …”
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Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm by Dalatu, Paul Inuwa

“…It is attained successfully by combining the mean in K-Means algorithm, minimum and maximum in K-Midranges algorithm and compute their average as mean cluster of Hybrid mean. …”
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Estimation of elbow flexion torque using equilibrium optimizer on feature selection of NMES MMG signals and hyperparameter tuning of random forest regression by Raphael, Uwamahoro, Sundaraj, Kenneth, Feroz, Farah Shahnaz

“…The performance of the GLEO-coupled with the RFR model was compared with the standard Equilibrium Optimizer (EO) and other state-of-the-art algorithms in physical and physiological function estimation using biological signals. …”
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A comparative effectiveness of hierarchical and non-hierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions by Chuan, Zun Liang, Wan Nur Syahidah, Wan Yusoff, Azlyna, Senawi, Mohd Akramin, Mohd Romlay, Fam, Soo-Fen, Wendy Ling, Shinyie, Tan Lit, Ken

“…The results of the analysis show that Forgy K-means non-hierarchical (FKNH), Hartigan- Wong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions by Zun, Liang Chuan, Wan Yusof, Wan Nur Syahidah, Senawi, Azlyna, Mohd Akramin, Mohd Romlay, Soo, Fen Fam, Wendy, Ling Shinyie, Tan, Lit Ken

“…The results of the analysis show that Forgy K-means non-hierarchical (FKNH), HartiganWong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions by Chuan, Zun Liang, Wan Nur Syahidah, Wan Yusoff, Azlyna, Senawi, Mohd Akramin, Mohd Romlay, Fam, Soo-Fen, Shinyie, Wendy Ling, Ken, Tan Lit

“…The results of the analysis show that Forgy K-means non-hierarchical (FKNH), HartiganWong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

“…Moreover, instead of concatenating feature vectors together and send to classifier, sparse coding and dictionary learning methods are used and instead of considering all features as one view (visual feature), K-SVD algorithm that is one of the famous algorithms for sparse representation is optimized and developed to multi-view model.The experimental results prove that the proposed methods has improved accuracy by 53.77% compared to concatenating features and classic K-SVD dictionary learning model as well.…”
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A comparative effectiveness of hierarchical and non-hierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions by Zun, Liang Chuan, Fam, Soo Fen, Wan Yusof, Wan Nur Syahidah, Senawi, Azlyna, Mohd Akramin, Mohd Romlay, Wendy, Ling Shinyie, Tan, Lit Ken

“…The results of the analysis show that Forgy K-means non-hierarchical (FKNH), Hartigan-Wong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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Eye-state analysis using an interdependence and adaptive scale mean shift (IASMS) algorithm by Mat Ibrahim, Masrullizam

“…The design is based on an interdependence and adaptive scale mean shift (IASMS) algorithm. IASMS uses moment features to track and estimate the iris area in order to quantify the state of the eye. …”
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Estimation of the state of health (SOH) of batteries using discrete curvature feature extraction by Hui, Hwang Goh, Lan, Zhentao, Zhang, Dongdong, Wei Dai, Wei Dai, Kurniawan, Tonni Agustiono, Kai, Chen Goh

“…To demonstrate the superiority of the proposed model, several well-known machine learning algorithms are employed to estimate SOH using extracted attributes. …”
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A decision support system using Neurofuzzy algorithm methods in selecting lecturer's teaching work load for bachelor in multimedia / Asmaa Musa by Musa, Asmaa

“…A key stage in the system process is the selection of features. This paper discuss about a decision support system using Neurofuzzy algorithm methods in selecting lecturer's teaching work load for Bachelor in Multimedia. …”
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Novel chewing cycle approach for peak detection algorithm of chew count estimation by Selamat, Nur Asmiza, Md Ali, Sawal Hamid, Ismail, Ahmad Ghadafi, Ahmad, Siti Anom, Minhad, Khairun Nisa'

“…The chewing dataset comprises signals collected from 20 participants consuming eight different food types, with proximity sensors (PSs) detecting temporalis muscle activity. The peak detection algorithm identifies key signal features, while PSO optimizes the peak prominence and width parameters to minimize the mean absolute error (MAE) in chew count estimation. …”
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Novel chewing cycle approach for peak detection algorithm of chew count estimation by Selamat, Nur Asmiza, Md Ali, Sawal Hamid, Ismail, Ahmad Ghadafi, Ahmad, Siti Anom, Minhad, Khairun Nisa’

“…The chewing dataset comprises signals collected from 20 participants consuming eight different food types, with proximity sensors (PSs) detecting temporalis muscle activity. The peak detection algorithm identifies key signal features, while PSO optimizes the peak prominence and width parameters to minimize the mean absolute error (MAE) in chew count estimation. …”
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