Search Results - (( developing learning lag algorithm ) OR ( java implication based algorithm ))

Groundwater level as an input to monthly predicting of water level using various machine learning algorithms by Sapitang M., Ridwan W.M., Ahmed A.N., Fai C.M., El-Shafie A.

Predicting sea levels using ML algorithms in selected locations along coastal Malaysia by Hazrin N.A., Chong K.L., Huang Y.F., Ahmed A.N., Ng J.L., Koo C.H., Tan K.W., Sherif M., El-shafie A.

“…In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. …”

Predicting sea levels using ML algorithms in selected locations along coastal Malaysia by Hazrin N.A, Chong K.L, Huang Y.F, Ahmed A.N, Ng J.L, Koo C.H, Tan K.W, Sherif M, El-shafie A

“…In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. …”

Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Pr... by Sammen S.S., Ehteram M., Sheikh Khozani Z., Sidek L.M.

“…Our study introduces a new model for predicting reservoir water levels. An extreme learning machine, the multi-kernel least square support vector machine model (MKLSSVM), is developed to predict the water level of a reservoir in Malaysia. …”

Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: a comparative assessment of regression and artificial neural network... by Ruslan, Siti Aisyah, Muharam, Farrah Melissa, Zulkafli, Zed, Omar, Dzolkhifli, Zambri, Muhammad Pilus

“…This study examined the relationship between the presence of Metisa plana at different time lags and remote sensing (RS) derived RH by using statistical and machine learning approaches. …”
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Implementation of machine learning algorithms for streamflow prediction of Dokan dam by Sarmad Dashti Latif, Mr.

“…This study aims at comparing the application of deep learning algorithms and conventional machine learning algorithms for predicting reservoir inflow. …”

The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Fine-scale predictive modeling of Aedes mosquito abundance and dengue risk indicators using machine learning algorithms with microclimatic variables by Che Dom, Nazri, Mohd Hardy Abdullah, Nur Athen, Dapari, Rahmat, Salleh, Siti Aekbal

“…Integrating time-lagged microclimatic variables into machine learning frameworks enhances the predictive accuracy of dengue risk indicators at a fine spatial scale. …”
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Application of Machine Learning for Daily Forecasting Dam Water Levels by Almubaidin, Ahmed, Winston C.A.A., El-Shajie A.

“…In this study, seven machine learning algorithms were developed to predict a dam water level daily based on the historical data of the dam water level. …”

Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq by Yaseen, Z.M., Jaafar, O., Deo, R.C., Kisi, O., Adamowski, J., Quilty, J., El-Shafie, A.

“…The motivation for exploring and developing expert predictive models is an ongoing endeavor for hydrological applications. …”
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Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations by Kumar M., Elbeltagi A., Pande C.B., Ahmed A.N., Chow M.F., Pham Q.B., Kumari A., Kumar D.

A coherent knowledge-driven deep learning model for idiomatic - aware sentiment analysis of unstructured text using Bert transformer by Bashar M. A., Tahayna

“…Therefore, we automated the development of such resources at scale to alleviate the lag time and the cost normally associated with their procurement. …”
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Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model by Megat Syahirul Amin, Megat Ali, Azlee, Zabidi, Nooritawati, Md Tahir, Ihsan, Mohd Yassin, Eskandari, Farzad, Azlinda, Saadon, Mohd Nasir, Taib, Abdul Rahim, Ridzuan

“…System Identification (SI), a methodology utilized in domains like engineering and mathematical modeling to construct or refine dynamic system models from captured data, relies significantly on the Nonlinear Auto-Regressive (NAR) model due to its reliability and capability of integrating nonlinear functions, complemented by contemporary machine learning strategies and computational algorithms to approximate complex system dynamics to address these limitations. …”
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