This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected ...

Construction projects are often subject to scheduling errors caused by uncertainty and systematic planning fallacies. This paper evaluates the prediction accuracy of CatBoost expert intuition predict duration public construction in predesign phase. The authors use a dataset city New York (USA) with 367 projects. Both compared performance indicator Mean Absolute Error (MAE) absolute preferrence....

The air temperature is an essential variable in many applications related to Earth science. Sporadic spatial distribution of weather stations causes a low spatial resolution of measured air temperatures. This study focused on modeling the air diurnal temperature cycle (DTC) based on the land surface temperature (LST) DTC. The air DTC model parameters were estimated from LST DTC model parameters...

common heat transfer fluids such as water, ethylene glycol, and engine oil have limited heat transfer capabilities due to their low heat transfer properties. nanofluids are suspensions of nanoparticles in base fluids, a new challenge for thermal sciences provided by nanotechnology. in this study, we are to optimize and report the effects of various parameters such as the ratio of the thermal co...

This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories analysis: Price, Volume, Turnover. Various error metrics like Mean Absolute Error (MAE), Squared (MSE), Percentage (MAPE), Root-Mean-Squared-Error...

Robust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasonal climate forecasts. The neural network model architecture of standard NLCCA is kept intact, but the cost functions used to set the model parameters are replaced with more robust variants. The Pear...

This is the second paper of a two part tutorial on the fundamentals of univariate time series ltering using order statistics where both temporal and rank orderings are considered jointly This second paper focuses on order statistic selection lters where the lter output is restricted to be one of the input samples In particular we treat class of Weighted Order Statistic WOS lters and the more ge...

PURPOSE Respiratory motion prediction using an artificial neural network (ANN) was integrated with pseudocontinuous arterial spin labeling (pCASL) MRI to allow free-breathing perfusion measurements in the kidney. In this study, we evaluated the performance of the ANN to accurately predict the location of the kidneys during image acquisition. METHODS A pencil-beam navigator was integrated with...

This study investigated the predictive ability of neural networks in the estimation of methane yield (MY) and effluent substrate (Se as mg/L COD) produced by two anaerobic filters, one mesophilic (35°C) and one thermophilic (55°C), which were operated to treat paper-mill wastewater at varying organic loadings. An artificial neural network (ANN) architecture was optimized to obtain a three-layer...

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link,...