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34 results for “Root Mean Square Error (RMSE)”
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Research ArticleOpen accessIn the abstractMultiple Linear Regression to Predict Electrical Energy Consumption Based on Meteorological Data: Application to Some Sites Supplied by the CEB in TogoApaloo Bara Komla Kpomonè et al.The prediction model developed in this article is based on the use of meteorological variables to estimate the consumption of electrical energy at the substations of the Electric Community of Benin. The objective is to predict this consumption in order to adapt production to it. The posts (Lomé Aflao, Légbasito, and Lomé port) are the targets that were used in … …American Journal of Applied Sciences, Vol. 21, Issue 1 · 10 Jun 2024 · 5,790 accessesView PDFCite
Research ArticleOpen accessIn the abstractMachine Learning Oceanographic Data for Prediction of the Potential of Marine ResourcesDenny Arbahri, Oky Dwi Nurhayati, Imam MuditaMarine data and information are very important for human survival, therefore this data and information is attractive to investors because of the potential economic value. This data and information has been difficult to obtain, the solution to overcome this is by analyzing oceanographic data for 2009-2019 collected from the marine database belonging to the Agency for the Study and Application … …Journal of Computer Science, Vol. 20, Issue 2 · 8 Jan 2024 · 5,975 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnalyze and Predict the 2022 World Happiness Report Based on the Past Year's DatasetYifei ZhangThrough the impact of the COVID-19, people around the world have been affected to various degrees. Thus, it is more interesting to compare the happiness reported between 2022/2021 and before 2019. This article concludes 5 years of happiness scores data including family, Gross Domestic Product (GDP), health, freedom, generosity trust, and dystopia residual. Happiness scores are considered appropriate indicators to … …Journal of Computer Science, Vol. 19, Issue 4 · 16 Mar 2023 · 6,920 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparison of Linear and Non-Linear Regression Models for Non-Invasive Blood Glucose MeasurementDorsaf Ghozzi, Yassine Manai, Khaled NouriThis paper deals with a new approach of non-invasive glucose monitoring based on near infrared spectroscopy. The proposed approach is coupled with a regression analysis in order to improve the predictive capacity of the designed device. Basic spectral data is a comparison that has been established between linear and non-linear machine learning regression algorithms. The experimental results show that feed … …Journal of Computer Science, Vol. 15, Issue 11 · 13 Nov 2019 · 7,869 accessesView PDFCite
Research ArticleOpen accessIn the abstractMapping of Temperature Trend Slope in Iran′s Zayanderud River Basin: A Comparison of Interpolation MethodsAli Zahraei, Saeid Eslamian, Ali Saeidi Rizi et al.The spatial distribution of daily, nightly and mean temperature trend in Iran′s Zayanderud river basin was carried out in this study by applying three approaches of interpolation including Inverse Distance Weighting (IDW), Multiple Linear Regression (MLR) and integration of these two methods (IDW+MLR). In this paper, t-test and statistical measures including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), … …American Journal of Engineering and Applied Sciences, Vol. 12, Issue 2 · 26 Jun 2019 · 7,479 accessesView PDFCite
Research ArticleOpen accessIn the abstractPerformance Comparison of Passive Fault Tolerant Control Strategy with PI and Fuzzy Control of Single-Tank Level Process with Sensor and System FaultHimanshukumar R. Patel, Vipul A. ShahFault-Tolerant Control (FTC) strategy has gain maximum attention in recent years in chemical industries due to economical and safety hazards perspective. Targeting at the decreasing control performance of the single-tank level control process under the constraint of system and sensor faults, this article presents model-based Passive Fault-Tolerant Control (PFTC) strategy which are based on conventional and artificial intelligence control. The … …American Journal of Engineering and Applied Sciences, Vol. 12, Issue 2 · 24 Jun 2019 · 7,606 accessesView PDFCite
Research ArticleOpen accessIn the abstractWeather Forecasting Using Merged Long Short-Term Memory Model (LSTM) and Autoregressive Integrated Moving Average (ARIMA) ModelAfan Galih Salman, Yaya Heryadi, Edi Abdurahman, Wayan SupartaWeather forecasting is an interesting research problem in flight navigation area. One of the important weather data in aviation is visibility. Visibility is an important factor in all phases of flight, especially when the aircraft is maneuvering on or close to the ground, i.e., during taxi-out, take-off and initial climb, approach and landing and taxi-in. The aim of these study … …Journal of Computer Science, Vol. 14, Issue 7 · 28 Jun 2018 · 11,437 accessesView PDFCite
Research ArticleOpen accessIn the abstractApplication of Time Series Modeling to Study River Water QualityMaryam Ghashghaie, Kaveh Ostad-Ali-Askari, Saeid Eslamian, Vijay P. SinghWater deficit problem originates from two factors: population increase and water pollution. However, studying and forecasting the quality of water are necessary to avoid serious problems in future through managerial works. In present study, using time series modeling, the quality of Madian Rood River is studied at Baraftab station using time series analysis. Nine parameters of water quality are studied … …American Journal of Engineering and Applied Sciences, Vol. 11, Issue 2 · 25 Apr 2018 · 8,938 accessesView PDFCite
Research ArticleOpen accessIn the abstractEstimating the Scouring Depth of Bridge Pier Using Self-Organizing Neural Networks (SOM)Abolfazl Rafat, Gholam Abbas Barani, Amineh NaseriScouring is caused as a result of erosion of river bed by water flow and materials carried by water. This research estimates the scouring depth using self-organizing neural network (SOM). The obtained findings were compared with findings of other models. It was found that self- organizing neural network (SOM) has higher correlation coefficient (0.98), compared to other methods. It was … …American Journal of Engineering and Applied Sciences, Vol. 10, Issue 4 · 27 Nov 2017 · 7,512 accessesView PDFCite
Research ArticleOpen accessIn the abstractBuilding Energy Modeling Using Artificial Neural NetworksMaya Arida, Nabil Nassif, Rand Talib, Taher Abu-LebdehAccurate modeling of total building energy is now vital to reduce energy consumption. This is especially true for buildings since they are considered as the largest energy consumer in the United States. This paper investigates modeling methods for building energy-systems using non-linear auto-regression artificial neural networks. The proposed model can forecast the whole building energy consumptions given the four input … …Energy Research Journal, Vol. 7, Issue 2 · 6 Jun 2017 · 7,131 accessesView PDFCite