Search Results - (( location prediction model algorithm ) OR ( panel classification system algorithm ))*

Extreme learning machine for user location prediction in mobile environment by Mantoro, Teddy, Olowolayemo, Akeem, Olatunji, Sunday O., Ayu, Media A., Abu Osman, Md. Tap

“…This work examines the location determination techniques by attempting to determine the location of mobile users by taking advantage of SS and SQ history data and modeling the locations using the ELM algorithm. …”
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Extreme learning machine for user location prediction in mobile environment by Mantoro, Teddy, Olowolayemo, Akeem, Olatunji, Sunday O., Ayu, Media Anugerah, Md. Tap, Abu Osman

“…This work examines the location determination techniques by attempting to determine the location of mobile users by taking advantage of SS and SQ history data and modeling the locations using the ELM algorithm. …”
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Identifying Damage Types in Solar Panels Through Surface Image Analysis with Naive Bayes by Wiliani, Ninuk, T.K.A, Rahman, Ramli, Suzaimah

“…Identifying and categorizing faults on solar panel surfaces is essential for maintenance, as these defects considerably affect energy output and system efficiency. …”
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Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion by Ahmed Osman A.I., Latif S.D., Wee Boo K.B., Ahmed A.N., Huang Y.F., El-Shafie A.

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Lightning damage assessment on solar panels using in-house portable infrared thermography camera with Convolutional Neural Network (CNN) algorithm by Chandrasegaran, Ormiila, Mustapha, Faizal, Abdullah, Mohd Na’im, Anwar, Murniwati, Mustapha, Mazli, Adzis, Zuraimy

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Classification model for hotspot occurrences using a decision tree method by Sitanggang, Imas Sukaesih, Ismail, Mohd Hasmadi

“…The model can be used to predict hotspot occurrences in new locations for fire prediction.…”
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Location-based Solar Energy Potential Prediction Algorithm for Mountainous Rural Landscapes by Onabajo, Olawale Olusegun, Tan, Chong Eng

“…Location-based Solar Energy Potential Prediction Algorithm (LOSEPPA) takes as input, the geographic latitude and longitude of the location of interest to compute the Solar Irradiance Factor (SIF). …”
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Implementation of machine learning algorithms for streamflow prediction of Dokan dam by Sarmad Dashti Latif, Mr.

“…The first scenario is to apply inflow data as model input for predicting inflow (output). The second scenario is to apply inflow and rainfall data as model inputs to predict inflow (output). …”

An artificial immune system model as talent performance predictor / Siti ‘Aisyah Sa’dan, Hamidah Jantan and Mohd Hanapi Abdul Latif by Sa’dan, Siti ‘Aisyah, Jantan, Hamidah, Abdul Latif, Mohd Hanapi

“…The objective of this study is to propose a prediction model based on bio-inspired algorithm for talent knowledge discovery through some experiments. …”
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Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid by Wahid, Muhammad Hazim

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Artificial neural network model for predicting windstorm intensity and the potential damages / Mohd Fatruz Bachok by Bachok, Mohd Fatruz

“…The other 11 prediction processes which utilised ANN model algorithms (fitting tool), gave R-values for all the algorithms higher than 0.900 except one algorithm equal to 0.8661, meanwhile MSE between 0.22 to 116.41. …”
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Enhancing reservoir simulation models with genetic algorithm optimized neural networks across diverse climatic zones / Saad Mawlood Saab by Saad Mawlood , Saab

“…The research improved the predictive models by integrating them with the Genetic Algorithm (GA). …”
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Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches by Latif S.D., Alyaa Binti Hazrin N., Hoon Koo C., Lin Ng J., Chaplot B., Feng Huang Y., El-Shafie A., Najah Ahmed A.

“…Using a comparison of three different major types, the best predictive model was determined. Statistical models and machine learning algorithms automatically learn and improve based on data. …”

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability by Hossain, Monowar, Mekhilef, Saad, Afifi, Firdaus, Halabi, Laith M., Olatomiwa, Lanre, Seyedmahmoudian, Mehdi, Horan, Ben, Stojcevski, Alex

“…For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. …”
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Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction by Jumin E., Zaini N., Ahmed A.N., Abdullah S., Ismail M., Sherif M., Sefelnasr A., El-Shafie A.

“…This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. …”

Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia by Jumin E., Basaruddin F.B., Yusoff Y.B.M., Latif S.D., Ahmed A.N.

Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China by Sattar Hanoon M., Najah Ahmed A., Razzaq A., Oudah A.Y., Alkhayyat A., Feng Huang Y., kumar P., El-Shafie A.

“…Therefore, this study investigates the capability of various machine learning algorithms in predicting the power production of a reservoir located in China using data from 1979 to 2016. …”

Investigating the application of artificial intelligence for earthquake prediction in Terengganu by Marhain S., Ahmed A.N., Murti M.A., Kumar P., El-Shafie A.

Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms by Lai V., Ahmed A.N., Malek M.A., Afan H.A., Ibrahim R.K., El-Shafie A., El-Shafie A.

Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration by Shafika Sultan Abdullah, M.A., Malek, Namiq Sultan Abdullah, A., Mustapha

“…The results of both models are promising, however the hybrid model shows higher efficiency in predicting ET0 and could be recommended for modeling of ET0 in arid and semiarid regions.…”
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