Search Results - (( based missing _ algorithm ) OR ( java implication based algorithm ))

Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment by Saeed, Gamil Abdulraqeb Abdullah, Chuan, Zun Liang, Roslinazairimah, Zakaria, Wan Nur Syahidah, Wan Yusoff

“…Based on the analysis, the proposed singular imputation algorithms, which treated missing data by geometric means, harmonic means and medians are more superior compared to the other imputation algorithms, irrespective of missing rates and rainfall stations. …”
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Determination of the best single imputation algorithm for missing rainfall data treatment by Gamil Abdulraqeb Abdullah Saeed, Chuan, Zun Liang, Roslinazairimah Zakaria, Wan Nur Syahidah Wan Yusoff, Mohd Zuki Salleh

“…Based on the analysis, the proposed singular imputation algorithms, which treated missing data by geometric means, harmonic means and medians are more superior compared to the other imputation algorithms, irrespective of missing rates and rainfall stations.…”
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Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm by Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad

“…Models were tested with both complete and missing output data to evaluate the robustness of the IAOA-based method. …”
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Missing tags detection algorithm for radio frequency identification (RFID) data stream by Zainudin, Nur 'Aifaa

“…Thus in this research, an AC complement algorithm with hashing algorithm and Detect False Negative Read algorithm (DFR) is used to developed the Missing Tags Detection Algorithm (MTDA). …”
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Restoration of missing data in old archives based on genetic algorithm by Khammar, Mohammad Reza, Saripan, M. Iqbal, Marhaban, Mohammad Hamiruce, Ishak, Asnor Juraiza, Zolfaghari, Fateme

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MISSING DAILY RAINFALL PREDICTION USING GREY WOLF OPTIMIZER-BASED NEURAL NETWORK by Lai, Wai Yan, Kuok, King Kuok, Chiu, Po Chan, Md. Rezaur, Rahman, Muhammad Khusairy, Bakri

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SINE COSINE ALGORITHM BASED NEURAL NETWORK FOR RAINFALL DATA IMPUTATION by Chiu, Po Chan, Ali, Selamat, Kuok, King Kuok

“…This chapter presents the ability of the sine cosine algorithm-based neural network (SCANN) to predict and optimize missing rainfall at different percentages of missing rates. …”
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An Evaluation of Machine Learning Algorithms for Missing Values Imputation by Kohbalan, Moorthy, Ali, Mohammed Hasan, Mohd Arfian, Ismail, Chan, Weng Howe, Mohd Saberi, Mohamad, Safaai, Deris

“…We considered the algorithms in a global, hybrid, local, and knowledge-based technique. …”
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The Effectiveness Of A Probabilistic Principal Component Analysis Model And Expectation Maximisation Algorithm In Treating Missing Daily Rainfall Data by Zun Liang, Chuan, Fam, Soo Fen, Mohd Deni, Sayang, Ismail, Noriszura

“…The proposed imputation algorithms are based on the M-component probabilistic principal component analysis model and an expectation maximisation algorithm (MPPCA-EM). …”
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Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset by Amirruddin, A., Aziz, I.A., Hasan, M.H.

“…The novel optimization-based artificial intelligence algorithm proposed in this paper implies an improved way to overcome a real engineering challenge i.e. handling missing values for better RUL prediction, hence bringing great opportunities for the domain area. …”
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An improved K-nearest neighbour with grasshopper optimization algorithm for imputation of missing data by Zainal Abidin, Nadzurah, Ismail, Amelia Ritahani

“…Thus, this paper proposes a novel method for imputation of missing data, named KNNGOA, which optimized the KNN imputation technique based on the grasshopper optimization algorithm. …”
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The effectiveness of a probabilistic principal component analysis model and expectation maximisation algorithm in treating missing daily rainfall data by Chuan, Zun Liang, Sayang, Mohd Deni, Fam, Soo-Fen, Noriszura, Ismail

“…The proposed imputation algorithms are based on the M-component probabilistic principal component analysis model and an expectation maximisation algorithm (MPPCA-EM). …”
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Missing-values imputation algorithms for microarray gene expression data by Moorthy, Kohbalan, Jaber, Aws Naser, Mohd Arfian, Ismail, Ernawan, Ferda, Mohd Saberi, Mohamad, Safaai, Deris

“…By using local and global correlation of the data, we were able to focus mostly on the differences between the algorithms. We classified the algorithms as global, hybrid, local, or knowledge-based techniques. …”
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Detection Of Misplaced And Missing Regions In Image Using Neural Network by Tan , Jin Siang

“…Therefore, it is necessary to develop an algorithm that is able to detect both misplaced and missing jigsaw puzzles. …”
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New Learning Models for Generating Classification Rules Based on Rough Set Approach by Al Shalabi, Luai Abdel Lateef

“…Classification rules were generated based on the best reduct. For the problem of missing data, a new approach was proposed based on data partitioning and function mode. …”
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Missing Value Imputation for PM10 Concentration in Sabah using Nearest Neighbour Method(NNM) and Expectation-Maximization (EM) Algorithm by Muhammad Izzuddin Rumaling, Chee, Fuei Pien, Jedol Dayou, Chang, Jackson Hian Wui, Steven Soon Kai Kong, Justin Sentian

“…The missing data is imputed by using both NNM and EM algorithm. …”
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Deep-learning-based detection of missing road lane markings using YOLOv5 algorithm by Sophian, Ali, Azmi, Nur Hanisah, Bawono, Ali Aryo

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A Rough Set-Based Approach for Identifying and Replacing Missing Concepts in Incomplete Sentences in Computer Domain Texts by Nur Faiqah ‘Aqilah binti Fuaad

Confidence intervals (CI) for concentration parameter in von Mises distribution and analysis of missing values for circular data / Siti Fatimah binti Hassan by Hassan, Siti Fatimah

“…Several methods in constructing the CI for the concentration parameter are proposed including CI based on circular population, CI based on the asymptotic distribution of ˆ , CI based on the distribution of 휃 and 푅 and also CI based on bootstrap-t method. …”
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Interpolation and extrapolation techniques based Neural Network in estimating the missing ionospheric TEC data by Jayapal V., Zain A.F.M.

“…The solar and magnetic indices, seasonal variation as well as diurnal variation are used as the input spaces in the NN to estimate the missing GPS TEC. The studies period is based on short term data during the medium solar activity period from 2005 to 2006. …”