Random Forest Regression

One of the problems with new medications is their poor water solubility that is possible to be addressed by using supercritical method. This study aims to predict the solubility of raloxifene and the density of supercritical CO 2 using temperature and pressure as inputs to analyze the supercritical processing for production of drug nanoparticles. Three regression models, Extra Trees (ET), Random Forest (RF), and Gradient Boosting (GB) were proposed and optimized using Gradient-based optimization to predict density and solubility of drug. In predicting the density of supercritical CO₂, GB attained an R² value of 0.986, reflecting an excellent agreement between its estimates and the true measurements. The model exhibited an RMSE of 23.20, indicating high accuracy, with a maximum error of 33.06. Regarding the solubility of raloxifene, the ET model yielded the highest R-squared score of 0.949, indicating a good fit to the data. The model exhibited an RMSE of 0.41, with a maximum error of 0.90. Comparatively, the RF and GB models obtained slightly lower precision, for the solubility of raloxifene. The RF model exhibited an RMSE of 0.55, while the GB model had an RMSE of 0.72. The optimized models were found to be reliable in predicting solubility and density within the supercritical processing field.

The integration of deep learning techniques into smart power systems has gained significant attention due to their potential to optimize energy management, enhance grid reliability, and enable efficient utilization of renewable energy sources. This research article explores the enhanced application of deep learning techniques in smart power systems. It provides an overview of the key challenges faced by traditional power systems and presents various deep learning methodologies that can address these challenges. The results showed that the root mean square errors (RMSE) for the weekend power load forecast were 18.4 for the random forest and 18.2 for the long short-term memory (LSTM) algorithm, while 28.6 was predicted by the support vector machine (SVM) algorithm. The authors' approach provides the most accurate forecast (15.7). After being validated using realworld load data, this technique provides reliable power load predictions even when load oscillations are present. The article also discusses recent advancements, future research directions, and potential benefits of utilizing deep learning techniques in smart power systems.

Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Machine learning system also provides better customer service and safer automobile systems. In the present paper we discuss about the prediction of future housing prices that is generated by machine learning algorithm. For the selection of prediction methods we compare and explore various prediction methods. We utilize lasso regression as our model because of its adaptable and probabilistic methodology on model selection. Our result exhibit that our approach of the issue need to be successful, and has the ability to process predictions that would be comparative with other house cost prediction models. More over on other hand housing value indices, the advancement of a housing cost prediction tha...

This research investigates the predictive power of pre-release metadata in forecasting IMDb movie ratings. Unlike prior studies that rely on post-release variables such as box office income or audience reviews, our approach focuses exclusively on attributes available before release, including director quality, writer quality, actor scores, genre, budget, and actor popularity. Using datasets from IMDb, Kaggle TMDB, and a GitHub Social Media Dataset, we constructed two feature sets: EXP (experience-based, large-scale with imputed budgets) and POP (popularity-based, smaller-scale with verified budget and popularity metrics). Three machine learning models—Linear Regression, Random Forest, and Artificial Neural Networks—were evaluated using R², MSE, and RMSE. Results show that Random Forest consistently achieved the best performance (R² up to 0.909, RMSE ≈ 0.48), with director quality emerging as the most influential predictor. This study demonstrates the feasibility of accurate pre-release movie rating prediction and provides insights for production companies and streaming platforms in decision-making processes. Future work will explore gradient boosting and time-sensitive recalibration of features to further improve accuracy.

This study provides an in-depth study advanced machining processes and their optimization using various machine learning algorithms. The study focuses on key machining parameters such as cutting speed (m/min), feed rate (mm/rev), and cutting depth (mm), and rotation speed (RPM), investigating their effects on surface roughness (Ra) in manufacturing operations. This research addresses emerging challenges in modern manufacturing, particularly in the processing of advanced engineering materials for the aerospace, automotive, and precision industries. These algorithms were selected for their ability to manage complex and non-linear relationships in manufacturing data and for their proven performance in predictive modeling. The study explores how these methods can overcome traditional limitations in process planning and optimization, especially in situations where conventional empirical models are inadequate. Special attention is paid to the theoretical foundations of each algorithm, in which linear regression serves as a basic model, random forest regression provides improved predictive capabilities through ensemble learning, and support vector regression provides robust optimization through its ε-insensitive loss function approach. The research also explores the important relationship between machine parameter optimization and surface quality, emphasizing the importance of parameter optimization in achieving desired surface properties while maintaining production efficiency. This study advances the field by providing a structured methodology machine parameter optimization, particularly relevant to computer-aided process planning and advanced manufacturing processes. These findings have significant implications for industries requiring high-precision manufacturing, providing insights into How can machine learning methods be used effectively? optimize machining processes, reduce production costs, and improve surface quality in modern manufacturing operations.

ABSTRACTIt is generally agreed that models that better simulate historical and current features of climate should also be the ones that more reliably simulate future climate. This article describes the ability of a selection of global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to represent the historical and current mean climate and its variability over northeastern Argentina, a region that exhibits frequent extreme events. Two types of simulations are considered: Long‐term simulations for 1901–2005 in which the models respond to climate forcing (e.g. changes in atmospheric composition and land use) and decadal simulations for 1961–2010 that are initialized from observed climate states. Monthly simulations of precipitation and temperature are statistically evaluated for individual models and their ensembles.Subsets of models that best represent the region's climate are further examined. First, models that have a Nash–Sutcliffe efficiency o...

In this work, we propose a novel boosting-based machine learning algorithm called EvoBoost, invented by Sudip Barua. Gradient boosting has emerged as a cornerstone technique in machine learning, achieving state-of-the-art performance in both classification and regression tasks. While existing models such as XGBoost, LightGBM, and CatBoost are widely adopted, they present challenges including excessive hyperparameter tuning, high memory consumption, and suboptimal handling of imbalanced data. EvoBoost addresses these limitations through a streamlined boosting framework that is both effective and easy to implement. It introduces probabilistic residuals for classification and a clean, interpretable residual computation for regression. Extensive empirical evaluations across six benchmark datasets demonstrate that EvoBoost consistently outperforms or matches the performance of established models in terms of accuracy, R² score, and log loss, while maintaining superior interpretability and implementation simplicity.

Il presente report illustra l'applicazione del modello Random Forest Regressor per la previsione dei livelli idrici di cinque importanti bacini lacustri: il Lago Tana, il Lago Kainji, il Lago Nasser e i laghi Victoria e Turkana. L'implementazione e l'analisi del programma sono state realizzate da un modello di AI conversazionale, ovvero da Gemini AI. L'obiettivo primario di questo lavoro è stato quello di sfruttare le capacità di apprendimento automatico del Random Forest per identificare e catturare i complessi pattern temporali intrinseci nelle serie storiche dei livelli, al fine di generarne previsioni future affidabili. I risultati ottenuti mostrano prestazioni differenziate a seconda del lago analizzato. Per il Lago Tana e il Lago Kainji, il modello Random Forest ha prodotto grafici di previsione che replicano fedelmente il comportamento osservato nei dati di addestramento, suggerendo un'ottima capacità di catturare le loro dinamiche idrologiche. Al contrario, per il Lago Nasser e i laghi Victoria e Turkana, le previsioni hanno evidenziato un appiattimento significativo verso la media dei dati di addestramento. Questa discordanza indica una potenziale limitazione del Random Forest nell'estrapolare tendenze a lungo termine o nel replicare la specifica variabilità di questi ultimi due bacini, laddove potrebbero essere presenti dinamiche non-lineari o non-stazionarie più marcate non adeguatamente rappresentate dal modello. Questi risultati sottolineano l'importanza di una valutazione modello-specifica in base alle caratteristiche uniche di ciascuna serie temporale.

Il presente report illustra l'applicazione del modello Random Forest Regressor per la previsione dei livelli idrici di cinque importanti bacini lacustri: il Lago Tana, il Lago Kainji, il Lago Nasser e i laghi Victoria e Turkana. L'implementazione e l'analisi del programma sono state realizzate da un modello di AI conversazionale, ovvero da Gemini AI. L'obiettivo primario di questo lavoro è stato quello di sfruttare le capacità di apprendimento automatico del Random Forest per identificare e catturare i complessi pattern temporali intrinseci nelle serie storiche dei livelli, al fine di generarne previsioni future affidabili. I risultati ottenuti mostrano prestazioni differenziate a seconda del lago analizzato. Per il Lago Tana e il Lago Kainji, il modello Random Forest ha prodotto grafici di previsione che replicano fedelmente il comportamento osservato nei dati di addestramento, suggerendo un'ottima capacità di catturare le loro dinamiche idrologiche. Al contrario, per il Lago Nasser e i laghi Victoria e Turkana, le previsioni hanno evidenziato un appiattimento significativo verso la media dei dati di addestramento. Questa discordanza indica una potenziale limitazione del Random Forest nell'estrapolare tendenze a lungo termine o nel replicare la specifica variabilità di questi ultimi due bacini, laddove potrebbero essere presenti dinamiche non-lineari o non-stazionarie più marcate non adeguatamente rappresentate dal modello. Questi risultati sottolineano l'importanza di una valutazione modello-specifica in base alle caratteristiche uniche di ciascuna serie temporale.

Appropriate selection of gridded precipitation data is very important for the region where long-term precipitation observations are not available. An approach based on compromise programing (CP) is proposed to select the gridded precipitation data for Iraq, where precipitation gauges are very sparse. The performance of seven widely used gauge-based, reanalysis and remote sensing-based gridded monthly precipitation data were assessed using observed precipitation records of 41 stations located within and surrounding Iraq. Obtained results showed the superiority of different datasets in term of different statistical metrics, namely percentage of bias, normalized root-mean-square error, Nash-Sutcliff efficiency, modified index of agreement, volumetric efficiency, and skill score. However, combining all the indices using CP, the gauge-based Global Precipitation Climatology Centre (GPCC) precipitation data was found best in term of replicating observed precipitation at 21 out of 41 stations. Validation of results using different approaches revealed that the GPCC precipitation was able to replicate annual and seasonal mean and variability and probability distribution of observed precipitation. The study concludes that CP can be used to select appropriate gridded precipitation data by avoiding confusion arise from contradictory results obtained using different statistical metrics.

Planning of crops for the next season has been a tedious task for the farmers as it is a difficult prediction about metrics of prices that their crop will fetch in a particular season which will be typically based on dynamic weather conditions. This leads to inaccurate prediction of crops’’ prices by farmers, and they happen to wrongly select the crops or in haste they happen to sell their crops early without storing and thus earning less than what the same crop would have fetched them in the future. This problem could be addressed by an ML model which will predict the prices of crops in advance showing the proper analysis of the crop and presenting their future scenario so that farmers can select the right crops to strategize crop production which involves crop selection, time of sowing deciding crop pattern and storage of harvested crops providing enough insights for predicting the appropriate price in the markets.

Compression is the method of compressing soil by decreasing the amount of air space within it. The level of compression needed for a particular soil is determined by its dry density, which reaches its highest point when the soil has the ideal moisture content. Nonetheless, accurately determining maximum dry density (MDD) through laboratory testing is time-draining and laborious. Therefore, combined forecasting schemes based on meta-heuristic optimization can reduce the required effort. The development of blended schemes was done using Random Forest Regression (RFR) along with three different meta-heuristic schemes, namely the FDA, GJO algorithm, and Gorilla Troops Optimizer (GTO). In this exploration, 30% of the blended schemes developed were deployed for training, and 70% were testing schemes. Many quantitative appraisal factors, including R2, RMSE, MDAPE, MAE, and U95, were deployed to examine the accuracy and effectiveness of the configured schemes. All the created schemes, particularly RFGJ—a hybrid of the RFR and the GJO algorithm—exhibited exceptional performance in predicting MDD values, attaining the top R² of 0.9966 and the least RMSE of 13.688.

Mobile network management and drive tests provide services that give a clear insight into the quality of mobile network coverage and other wireless networks including identifying areas of poor signal quality and identification of black spots. This research investigates the potential of supervised machine learning models to predict mobile network performance, focusing on Coventry University and Coventry City Center, United Kingdom. The measured download speed represented as Downlink bit rate (DL_bit rate) at these locations are used to obtain user performance data using Lebera mobile virtual network operator SIM card in order to assist with the visualization of the mobile network performance maps. The study explores the application of various machine learning models, including Random Forest, Linear Regression, Gradient Boosting, Support Vector Regression (SVR), and K-Nearest Neighbors (KNN), to forecast mobile network throughput speed (DL_Throughput). Among the models, the Random Forest Regression (RFR) model demonstrated the highest accuracy, with a Mean Absolute Error (MAE) of 6.79 and an R-squared (R²) value of 0.496, indicating its effectiveness in capturing network performance variability. The findings highlight the considerable promise of machine learning in optimizing mobile networks, enabling telecommunications providers to automate network management, enhance resource allocation, and improve the Quality of user Experience (QoE). The application of predictive analytics in this context offers significant advantages, including increased network efficiency, enhanced user satisfaction, and reduced operational costs. This study underscores the importance of integrating AI-driven predictive models into mobile network optimization strategies to meet the evolving demands of urban environments.

Forest fires are a significant environmental hazard with increasing frequency due to climate change. Early predictions and mitigation are essential for minimizing the damage caused by these fires. In this paper, we develop a machine-learning model that combines geospatial data from satellite imagery with real-time environmental variables to predict forest fires. Using satellite imagery, weather data, and historical fire records, we construct a predictive model capable of identifying fire-prone areas. The model outputs early warnings that can be used to implement mitigation strategies such as resource allocation and preventative measures. This paper also presents Python code used for data analysis and visualization

Exploiting Renewable energy to the maximum extent possible in an electric vehicle charging station (EVCS) is the key in supporting the anticipated carbon reduction from the electric vehicles (EVs). Knowing the expected load and the solar energy in advance at the EVCS can be crucial in framing a proper energy management strategy. Selection of suitable parameters associated with the participating EVs and EVCS are vital in utilizing them for predicting the probable EV load and expected solar energy for a given period under consideration. A prototype EVCS with smart communication infrastructure is developed considering solar pv as the energy source. Real time communication of the parameters between multiple agents has been established effectively using an interactive website, cloud server and an short message service (SMS) application programming interface (API). The data generated from the prototype models have been utilized in a random forest regression (RFR) classifier model in order...

Progress in sensor technologies has allowed real-time monitoring of soil water. It is a challenge to model soil water content based on remote sensing data. Here, we retrieved and modeled surface soil moisture (SSM) at the U.S. Climate Reference Network (USCRN) stations using Sentinel-1 backscatter data from 2016 to 2018 and ancillary data. Empirical machine learning models were established between soil water content measured at the USCRN stations with Sentinel-1 data from 2016 to 2017, the National Land Cover Dataset, terrain parameters, and Polaris soil data, and were evaluated in 2018 at the same USCRN stations. The Cubist model performed better than the multiple linear regression (MLR) and Random Forest (RF) model (R2 = 0.68 and RMSE = 0.06 m3 m-3 for validation). The Cubist model performed best in Shrub/Scrub, followed by Herbaceous and Cultivated Crops but poorly in Hay/Pasture. The success of SSM retrieval was mostly attributed to soil properties, followed by Sentinel-1 backsc...

Energy is one of the most critical and costly resources, playing a vital role in our daily lives. As technology advances, the demand for energy also increases. This work proposes a model for predicting energy consumption in smart homes, consisting of data preprocessing, performance evaluation, and application. Once the data is processed, it is fed into the prediction module, where various machine-learning algorithms are applied to forecast energy consumption. As smart home environments grow in complexity, selecting the most effective machine learning algorithm becomes increasingly crucial. The persistent challenge lies in manually discerning the best-performing algorithm, given their potential variance in efficacy across diverse use cases or datasets. In the dynamic landscape of energy conservation and cost-effective power generation, precise forecasting of energy consumption is essential, playing a pivotal role in advancing energy sustainability and bolstering economic stability. This introduction explores the intricate terrain of predicting energy utilization within smart homes, a domain that has seen increased interest due to the integration of machine learning algorithms. The primary focus of this exploration is the rigorous evaluation of these algorithms, using key performance metrics such as mean absolute error (MAE), mean squared error (MSE), and R-squared.

To predict physiological indicators, such as heart rate, blood pressure, and body heat sensors, this study develops an internet of things (IoT)-based healthcare approach performing on random forest regression models and mean square error (MSE). Machine learning approaches such as random forest design is trained to predict factors like age, heart rate, and recorded physiological measures using a dataset generated by sensors with Raspberry Pi. The precision and dependability of the models are assessed by contrasting the predictions with the physiological degrees produced by sensors. IoT-enabled models and sensors are useful for a variety of healthcare monitoring tasks, such as early anomaly detection and quick assistance for medical interventions. It is seen that the proposed model could provide appropriate predictions that are in line with common datasets demonstrated by the results. Moreover, there is strong agreement between the sensor readings and the predicted values for the considered parameters showcasing the outperformance of the proposed healthcare system.

In this paper, a digital twin of the network of heating systems for smart cities is developed using the example of the city of Almaty. The study used machine learning algorithms to estimate future thermal energy consumption and develop thermodynamic formulas. This work offers a thorough and in-depth analysis of thermal energy consumption. In addition, the paper identifies the relationship between thermal energy consumption and ambient temperature, and wind uncertainty in certain urban areas using machine learning methods to predict thermal energy consumption. Using both training and regression models, this interdependence is revealed. The obtained forecasts provide
useful information for studying the structure of heat consumption in Almaty and reducing heat losses by reducing overheating in the zones of heating networks. In addition, the study analyzes high-resolution spatial data collected from 385 homes and 62 heat transfer circuits located throughout the city during the heating season. The study examines the degree of relationship between the ambient temperature and the amount of heat energy used in the areas of Astana. A minor impact of wind speed is also estimated. These discoveries allow us to use machine learning algorithms to find the location of hot spots and inefficient zones with high losses.

Death investigations often include an effort to establish the postmortem interval (PMI) in cases in which the time of death is uncertain. The postmortem interval can lead to the identification of the deceased and the validation of witness statements and suspect alibis. Recent research has demonstrated that microbes provide an accurate clock that starts at death and relies on ecological change in the microbial communities that normally inhabit a body and its surrounding environment. Here, we explore how to build the most robust Random Forest regression models for prediction of PMI by testing models built on different sample types (gravesoil, skin of the torso, skin of the head), gene markers (16S ribosomal RNA (rRNA), 18S rRNA, internal transcribed spacer regions (ITS)), and taxonomic levels (sequence variants, species, genus, etc.). We also tested whether particular suites of indicator microbes were informative across different datasets. Generally, results indicate that the most acc...

A change-point (piecewise linear regression) model fitted to the pre-retrofit data as the counterfactual for the savings calculation, is considered to be the best approach to evaluating the energy savings of building retrofits (ASHRAE Guideline 14). However, when applied to a large portfolio savings analysis with substantial multi-year data, the change-point model does not fit the data well in some cases. The study thus aims to improve the accuracy of the changepoint model by: 1) using more advanced non-linear models, 2) incorporating additional input features, and 3) increasing the time resolution of input variables. We found that random forest regression (RF) models with an array of climate (humidity, wind, solar radiation, etc.), time (day of the week, season, holiday), and energy consumption of the immediate past 1-4 hours (energy lag terms) outperformed the change-point model, shallow neural networks, and support vector machine regression (SVR). Our result implies that high resolution smart meter data should be used in place of monthly utility bills to more accurately evaluate retrofit savings. We further explored the relative contribution of the input variables to the random forest regression model using Shapley Value, a game theoretic variable importance metric. We found that the most important input feature is the energy consumption of the immediate past (or energy lag terms). We also found that solar radiation and weekend day indicators are more important than outdoor temperature. The improved model could provide better insights to portfolio managers in planning future energy retrofits. Policy makers could also use such models to evaluate the average energy saving potential for energy policy changes, such as the requirement of minimum insulation level, and lighting equipment efficiency.

A change-point (piecewise linear regression) model fitted to the pre-retrofit data as the counterfactual for the savings calculation, is considered to be the best approach to evaluating the energy savings of building retrofits (ASHRAE Guideline 14). However, when applied to a large portfolio savings analysis with substantial multi-year data, the change-point model does not fit the data well in some cases. The study thus aims to improve the accuracy of the changepoint model by: 1) using more advanced non-linear models, 2) incorporating additional input features, and 3) increasing the time resolution of input variables. We found that random forest regression (RF) models with an array of climate (humidity, wind, solar radiation, etc.), time (day of the week, season, holiday), and energy consumption of the immediate past 1-4 hours (energy lag terms) outperformed the change-point model, shallow neural networks, and support vector machine regression (SVR). Our result implies that high resolution smart meter data should be used in place of monthly utility bills to more accurately evaluate retrofit savings. We further explored the relative contribution of the input variables to the random forest regression model using Shapley Value, a game theoretic variable importance metric. We found that the most important input feature is the energy consumption of the immediate past (or energy lag terms). We also found that solar radiation and weekend day indicators are more important than outdoor temperature. The improved model could provide better insights to portfolio managers in planning future energy retrofits. Policy makers could also use such models to evaluate the average energy saving potential for energy policy changes, such as the requirement of minimum insulation level, and lighting equipment efficiency.

A change-point (piecewise linear regression) model fitted to the pre-retrofit data as the counterfactual for the savings calculation, is considered to be the best approach to evaluating the energy savings of building retrofits (ASHRAE Guideline 14). However, when applied to a large portfolio savings analysis with substantial multi-year data, the change-point model does not fit the data well in some cases. The study thus aims to improve the accuracy of the changepoint model by: 1) using more advanced non-linear models, 2) incorporating additional input features, and 3) increasing the time resolution of input variables. We found that random forest regression (RF) models with an array of climate (humidity, wind, solar radiation, etc.), time (day of the week, season, holiday), and energy consumption of the immediate past 1-4 hours (energy lag terms) outperformed the change-point model, shallow neural networks, and support vector machine regression (SVR). Our result implies that high resolution smart meter data should be used in place of monthly utility bills to more accurately evaluate retrofit savings. We further explored the relative contribution of the input variables to the random forest regression model using Shapley Value, a game theoretic variable importance metric. We found that the most important input feature is the energy consumption of the immediate past (or energy lag terms). We also found that solar radiation and weekend day indicators are more important than outdoor temperature. The improved model could provide better insights to portfolio managers in planning future energy retrofits. Policy makers could also use such models to evaluate the average energy saving potential for energy policy changes, such as the requirement of minimum insulation level, and lighting equipment efficiency.

Drivers spend an enormous amount of time searching for parking spots every year. Waste of time, emission of carbon and air pollution have been issues in hunting for parking spots without proper prediction. In this paper, we have proposed to build a framework based on Recurrent Neural Network (RNN) using Long Short Term Memory (LSTM) and Random Forest Regression model to provide prediction of parking availability and compared results afterwards. A real-world case of parking spots availability consisting of 5,500 parking spots in Kuala Lumpur City Centre (KLCC), Malaysia, has been used for regression implementation in this comparative analysis. The results showed that random forest outperformed LSTM approach based on performance metrics.

Storing carbon (C) within soils is significant for maintaining soil-health and reinforces the feedback loop of C loss from soils as CO2 to the atmosphere. Seasonal variation with increased temperatures and inconsistent precipitation as climate change consequences also affect the soil C-sequestration process globally. Soil-health management practices (SHMPs) such as cover crops, crop residues and manures increase organic components as well as soil-organic C (SOC) pool in an agroecosystem. While, soil microbial-biomass (SMB) which is considered as a soilhealth metric to understand microbial community response, is still not modelled to relate with SOC and seasonal impacts to identify suitable SHMPs. Cover crops followed by 100% residue addition and combinations of manure +organic-fertilizer were the SHMPs for a winter-wheat system in our field study. Seasonal data regarding SOC, nitrogen, SMB-C and labile-C content of soils were used to machine-learn the system and understand the influence of different drivers on SMB-C. The test models based on „Multivariate Linear Regression‟ could explain 70% of the variability and predicted seasonal-variation as a dominant variant followed by SHMPs and soil-moisture. AdaBoost and Random Forest Models performed better than others if "Ensemble Learning‟ was used. „Feature Importance‟ predicted labile-C and aboveground-biomass as the two most important drivers impacting SMB-C. "Ensemble Learning‟ method of Machine-Learning could be successfully implied to understand the SMB-C in an agroecosystem and set benchmark-strategies for soil-health improvement. 50% manure+ 50% fertilizer with crop-residue could be recommended for maximum labile-C and SOC in surface soil-layers.

Cars have become a necessity in this modern world. Every middle class family needs a vehicle or a mode of transport in order to move from one place to another. Not everyone is able to afford a new vehicle as they are costly and there’s an added cost of taxes and various other expenses by both the provider/company of the car as well as the government. Moreover, not every customer is sure of spending a sum of their wealth on a certain car. The product might not meet their needs. The solution to this problem of having a car despite not being able to afford one is met by buying and selling second hand cars. It has become its own market now. There are already numerous companies and websites and app based services that serve as a mediator or a platform for the dealing of second hand or used cars and other vehicles. Establishment of such places is easy but there is another problem that still remains- How to price the used car appropriately at a price comfortable for both the seller and the...

Drivers spend an enormous amount of time searching for parking spots every year. Waste of time, emission of carbon and air pollution have been issues in hunting for parking spots without proper prediction. In this paper, we have proposed to build a framework based on Recurrent Neural Network (RNN) using Long Short Term Memory (LSTM) and Random Forest Regression model to provide prediction of parking availability and compared results afterwards. A real-world case of parking spots availability consisting of 5,500 parking spots in Kuala Lumpur City Centre (KLCC), Malaysia, has been used for regression implementation in this comparative analysis. The results showed that random forest outperformed LSTM approach based on performance metrics.

The imaging spectroscopy mission EnMAP aims to assess the state and evolution of terrestrial and aquatic ecosystems, examine the multifaceted impacts of human activities, and support a sustainable use of natural resources. Once in operation (scheduled to launch in 2019), EnMAP will provide high-quality observations in the visible to near-infrared and shortwave-infrared spectral range. The scientific preparation of the mission comprises an extensive science program. This special issue presents a collection of research articles, demonstrating the potential of EnMAP for various applications along with overview articles on the mission and software tools developed within its scientific preparation.

Wind energy is one of the sustainable and clean energy resources that are uncertain because of its high fluctuation and stochastic volatility. Its uncertainty follows wind speed and occurs at multiple timescales. This fact necessitates the development of accurate wind speed prediction methods, especially for short-term periods. In this paper, we examine a two-step approach to improve the quality of ultra-short-term forecasting of wind speed using time series weather data and machine learning models. The Support Vector Regressor, Random Forests for regression, and Multi-layer perceptrons models for regression are the three machine learning models that are used in this work. The most relevant weather features are selected from original weather data using features selection methods and the predictions are obtained using separate machine-learning models. The simulation results identified the suitable combinations of feature selection techniques and machine learning models. They also showed that wrapper methods are the most suitable with support vector regressors and random forests in terms of performance and processing time.

Possible changes in rainfall extremes in Peninsular Malaysia were assessed in this study using an ensemble of four GCMs of CMIP5. The performance of four bias correction methods was compared, and the most suitable method was used for downscaling of GCM simulated daily rainfall to the spatial resolution (0.25°) of APHRODITE rainfall. The multi-model ensemble (MME) mean of the downscaled rainfall was developed using a random forest regression algorithm. The MME projected rainfall for four RCPs were compared with APHRODITE rainfall for the base year (1961–2005) to assess the annual and seasonal changes in eight extreme rainfall indices. The results showed power transformation as the most suitable bias correction method. The maximum changes in most of the annual and seasonal extreme rainfall indices were observed for RCP8.5 in the last part of this century. The maximum increase was observed for 1-day and 5 consecutive days' rainfall amount for RCP4.5. Spatial distribution of the cha...

x 2.3.4 Rising Temperature and Heat Waves in South Asia 2.3.5 Extreme Temperature and Heat Waves in Pakistan 2.3.6 Analysis of Temperature Trends 2.3.6.1 Non-Parametric Methods of Trend Analysis 2.3.6.2 Trends Under the Hypothesis of Scaling Effect 2.3.6.3 Trends Analysis in Data Scarce Region 2.3.7 Gridded Data for the Analysis of Temperature Changes 2.3.7.1 Gridded Temperature Data 2.3.7.2 Gridded Daily Temperature Data 2.4 Selection of GCM for Ensemble and Projection of Heat Waves 2.4.1.1 Global Climate Change 2.4.1.2 General Circulation Models 2.4.1.3 Climate Model Intercomparison Projects 2.4.1.4 Emission Scenarios 2.4.2 GCMs Selection to Address Uncertainties in Projection 2.4.2.1 GCM Selection Based on Pastperformance 2.4.2.2 GCM Selection Based on Envelope 2.4.2.3 Hybrid Approach of GCM Selection 2.4.2.4 Machine Learning in GCM Selection 2.4.3 Indices for Assessment of GCM Performance 2.4.3.1 Symmetric Uncertainty 2.4.3.2 Wavelets 2.4.3.3 Taylor Skill Score 2.4.4 Heat Wave Based GCM Selection 2.4.4.1 Projection of Global Heat Waves xi 2.4.4.2 Projection of Heat Waves in Pakistan and Neighbouring Countries 2.5 Forecasting Heat Waves 2.5.1 Temperature Forecasting Models 2.5.1.1 Types of Temperature Forecasting Models 2.5.2 Statistical Models for Temperature Forecasting 2.5.2.1 Physical-Empirical Models 2.5.3 Development of Statistical Models 2.5.3.1 Linear Models 2.5.3.2 Non-Linear Models 2.5.4 Machine Learning Model in Temperature Forecasting 2.5.5 Selection of Climatic Domain 2.5.6 Selection of Predictors 2.5.7 Forecasting Heat Waves 2.6 Climate Change Effect of Forecasting Models 2.7 Summary 3.1 xv 4.4.2.2 Selection of the Predictors 4.4.2.3 Relationship of Heat Wave Days with the NCEP Variables 4.4.2.4 Estimation of the Heat Wave Days 4.4.2.5 Sensitivity Analysis of Atmospheric Variables 4.5 Forecasting Model Incorporating Climate Change 4.6 Discussion 4.6.1 Definition OF Heat Wave 4.6.2 Trend in Temperature, Extreme Temperature and Heat Waves Indices 4.6.3 Selection of GCMs 4.6.3.1 GCMs for Climate Change Projections 4.6.3.2 GCMs for Heat Wave Projections 4.6.4 Forecasting Heat Waves 4.6.4.1 Forecasting Heat Wave Triggering Dates 4.6.4.2 Forecasting Annual Heat Wave Days 4.6.5 Forecasting Heat Waves by Incorporating Climate Change Effect 5.1

Possible changes in rainfall extremes in Peninsular Malaysia were assessed in this study using an ensemble of four GCMs of CMIP5. The performance of four bias correction methods was compared, and the most suitable method was used for downscaling of GCM simulated daily rainfall to the spatial resolution (0.25°) of APHRODITE rainfall. The multi-model ensemble (MME) mean of the downscaled rainfall was developed using a random forest regression algorithm. The MME projected rainfall for four RCPs were compared with APHRODITE rainfall for the base year (1961–2005) to assess the annual and seasonal changes in eight extreme rainfall indices. The results showed power transformation as the most suitable bias correction method. The maximum changes in most of the annual and seasonal extreme rainfall indices were observed for RCP8.5 in the last part of this century. The maximum increase was observed for 1-day and 5 consecutive days' rainfall amount for RCP4.5. Spatial distribution of the cha...

Possible changes in rainfall extremes in Peninsular Malaysia were assessed in this study using an ensemble of four GCMs of CMIP5. The performance of four bias correction methods was compared, and the most suitable method was used for downscaling of GCM simulated daily rainfall to the spatial resolution (0.25°) of APHRODITE rainfall. The multi-model ensemble (MME) mean of the downscaled rainfall was developed using a random forest regression algorithm. The MME projected rainfall for four RCPs were compared with APHRODITE rainfall for the base year (1961–2005) to assess the annual and seasonal changes in eight extreme rainfall indices. The results showed power transformation as the most suitable bias correction method. The maximum changes in most of the annual and seasonal extreme rainfall indices were observed for RCP8.5 in the last part of this century. The maximum increase was observed for 1-day and 5 consecutive days' rainfall amount for RCP4.5. Spatial distribution of the cha...

Rice is the primary staple food source for Indonesian people, with consumption increasing so that rice production needs to be increased. Rice drought is one of the problems that can hamper rice production. This research aims to determine the best extraction feature between the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) in describing rice fields' dryness. Moreover, using the random forest regression algorithm. This research compares NDVI with NDWI using data originating from Sentinel-2A and retrieved via the google earth engine. Regression algorithms are used in research to predict drought in paddy fields. This research shows that NDVI is better than NDWI in predicting drought using random forest regression algorithms and logistic regression algorithms. The random forest regression algorithm based on the results obtained shows that the average root mean square error (RMSE) on NDVI is 0.018, and NDWI is 0.012. Based on the logistic regression algorithm results, it was found that the average value of RMSE on NDVI was 0.346, and NDWI was 0.336. Based on the results of the RMSE, it shows that the forecasting ability of the random forest regression algorithm is better than the logistic regression.

Possible changes in rainfall extremes in Peninsular Malaysia were assessed in this study using an ensemble of four GCMs of CMIP5. The performance of four bias correction methods was compared, and the most suitable method was used for downscaling of GCM simulated daily rainfall to the spatial resolution (0.25°) of APHRODITE rainfall. The multi-model ensemble (MME) mean of the downscaled rainfall was developed using a random forest regression algorithm. The MME projected rainfall for four RCPs were compared with APHRODITE rainfall for the base year (1961–2005) to assess the annual and seasonal changes in eight extreme rainfall indices. The results showed power transformation as the most suitable bias correction method. The maximum changes in most of the annual and seasonal extreme rainfall indices were observed for RCP8.5 in the last part of this century. The maximum increase was observed for 1-day and 5 consecutive days' rainfall amount for RCP4.5. Spatial distribution of the cha...

Dirilis oleh WHO, penyakit jantung merupakan salah satu dari penyakit yang paling mematikan di dunia. Penyakit jantung biasanya diidap oleh seseorang yang berumur 30-70 tahun. Penyebabnya bisa bermacam-macam antara lain obesitas, tekanan darah yang tinggi, merokok, diabetes, riwayat keluarga, dan lain sebagainya. Pengecekan jantung secara berkala terutama pada orang yang memang telah berumur 30 tahun keatas karena sudah mulai memiliki resiko yang tinggi untuk terkena penyakit jantung. Pengecekan ini bertujuan untuk memeriksa kondisi jantung melalui aspek yang memungkinkan untuk mendiagnosa penyakit jantung seperti tekanan darah, denyut jantung, kolesterol,dst. Dari sekian banyaknya faktor tersebut, diagnosa tidak hanya dilakukan serta merta melihat satu aspek saja melainkan melalui berbagai aspek sekaligus. Maka dari itu, untuk mempermudah prediksi tersebut, diperlukan suatu sistem yang efektif. Salah satunya dapat diterapkan metode klasifikasi data, yaitu decision tree dan random forest. Pada hasilnya metode decision tree merupakan metode yang paling efektif dalam mendiagnosa penyakit jantung pada seseorang dengan akurasi yang didapatkan sebesar 83.02% serta sensitivity sebesar 83.33% dan specificity sebesar 82.61%. Sehingga untuk data ini disarankan untuk menggunakan model decision tree agar mempermudah proses diagnosa.

Drought is a slow developing phenomenon that accumulates over period and affecting various sectors. It is one of natural hazards that occurs each year, particularly in Indonesia over Australian Monsoon period. During drought event, vegetation's cover can be affected by water stress. Normalized Difference Water Index (NDWI) is a method for water resource assessment and known to be strongly related to the plant water content. NDWI is produced from MODIS bands Near-infrared (NIR) and Short Wave Infrared (SWIR). This research aims to monitor drought using NDWI in West Java during El Niño 2015 and its impact on rainfall variability. The result showed rainfall was decreased significantly starting from May-June, then increased in November. According to NDWI, it also showed that mostly West Java Region affected by drought during May-November. Very strong drought occurred on September-November. On December, areal extent of drought was decreasing significantly because rainfall had increased during November. Generally, areal extent of drought in West Java was dominated by strong and moderate drought. It implied that El Niño 2015, give great impact on increasing drought and decreasing rainfall in West Java. NDWI can be detected drought occurrence as it have good correlation with rainfall spatially.

Many elements affect the expenses of health insurance and it's far quite a tough project to analyze the sample from those capabilities. We use a regression version to recognize and study a complex sample that enables us to predict the price of medical insurance. In this paper, we used a dataset from Kaggle that consists of 6 capabilities and 1338 instances. We will be taking the assistance of diverse regression models and reading which model’s overall performance is excessive in predicting medical insurance. Regression is a statistical procedure for calculating the cost of a primarily based variable from an impartial variable. Regression measures the affiliation among variables. it's far a modeling method wherein a based variable is predicted primarily based on one or more unbiased variables. Regression evaluation is the maximum widely used of all statistical techniques. This text explains the primary concepts and explains how we can do regression calculations. INTRODUCTION ...

Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Machine learning system also provides better customer service and safer automobile systems. In the present paper we discuss about the prediction of future housing prices that is generated by machine learning algorithm. For the selection of prediction methods we compare and explore various prediction methods. We utilize lasso regression as our model because of its adaptable and probabilistic methodology on model selection. Our result exhibit that our approach of the issue need to be successful, and has the ability to process predictions that would be comparative with other house cost prediction models. More over on other hand housing value indices, the advancement of a housing cost prediction tha...

This paper proposes a fully automated atlas-based pancreas segmentation method from CT volumes utilizing atlas localization by regression forest and atlas generation using blood vessel information. Previous probabilistic atlas-based pancreas segmentation methods cannot deal with spatial variations that are commonly found in the pancreas well. Also, shape variations are not represented by an averaged atlas. We propose a fully automated pancreas segmentation method that deals with two types of variations mentioned above. The position and size of the pancreas is estimated using a regression forest technique. After localization, a patient-specific probabilistic atlas is generated based on a new image similarity that reflects the blood vessel position and direction information around the pancreas. We segment it using the EM algorithm with the atlas as prior followed by the graph-cut. In evaluation results using 147 CT volumes, the Jaccard index and the Dice overlap of the proposed method were 62.1% and 75.1%, respectively. Although we automated all of the segmentation processes, segmentation results were superior to the other state-of-the-art methods in the Dice overlap.

This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localized) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.

This paper presents a colon deformation estimation method, which can be used to estimate colon deformations during colonoscope insertions. Colonoscope tracking or navigation system that navigates a physician to polyp positions during a colonoscope insertion is required to reduce complications such as colon perforation. A previous colonoscope tracking method obtains a colonoscope position in the colon by registering a colonoscope shape and a colon shape. The colonoscope shape is obtained using an electromagnetic sensor, and the colon shape is obtained from a CT volume. However, large tracking errors were observed due to colon deformations occurred during colonoscope insertions. Such deformations make the registration difficult. Because the colon deformation is caused by a colonoscope, there is a strong relationship between the colon deformation and the colonoscope shape. An estimation method of colon deformations occur during colonoscope insertions is necessary to reduce tracking errors. We propose a colon deformation estimation method. This method is used to estimate a deformed colon shape from a colonoscope shape. We use the regression forests algorithm to estimate a deformed colon shape. The regression forests algorithm is trained using pairs of colon and colonoscope shapes, which contains deformations occur during colonoscope insertions. As a preliminary study, we utilized the method to estimate deformations of a colon phantom. In our experiments, the proposed method correctly estimated deformed colon phantom shapes.

Rice is the primary staple food source for Indonesian people, with consumption increasing so that rice production needs to be increased. Rice drought is one of the problems that can hamper rice production. This research aims to determine the best extraction feature between the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) in describing rice fields' dryness. Moreover, using the random forest regression algorithm. This research compares NDVI with NDWI using data originating from Sentinel-2A and retrieved via the google earth engine. Regression algorithms are used in research to predict drought in paddy fields. This research shows that NDVI is better than NDWI in predicting drought using random forest regression algorithms and logistic regression algorithms. The random forest regression algorithm based on the results obtained shows that the average root mean square error (RMSE) on NDVI is 0.018, and NDWI is 0.012. Based on the logistic regression algorithm results, it was found that the average value of RMSE on NDVI was 0.346, and NDWI was 0.336. Based on the results of the RMSE, it shows that the forecasting ability of the random forest regression algorithm is better than the logistic regression.

Human activity has dramatically altered the environment around the world. Most of these modifications occurred in natural habitats due to human activities. On the other hand, the variations in climatic conditions required to sustain appropriately performing ecosystems are becoming increasingly challenging for managers and policymakers. Often, pollution associated with human activities is diffuse, occurs over a wide geographic area, and accumulates over time. Assessing, monitoring, and eventually connecting relevant environmental variables with landscape development is crucial to overcoming these problems. Remote monitoring and evaluation of agroecosystem changes, using sensors, global positioning systems, and satellitebased platforms, provides for constant monitoring and assessment of field environment dynamics on a large scale. This study reviewed critical advances in remote sensing for the sustainable development of the agroecosystem.

Land use classification is the basis for making further policy in many fields including agriculture. Effective methods in landuse/landcover (LULC) classification are essential for later application in policy making. The development of remote sensing technology has been increasing rapidly. The use of Earth Observing (EO) Sentinel-2 imagery can greatly help LULC mapping over large area. As the basic input on the assessment of land availability and suitability, it is important to perform LULC in such way that it is objective, replicable, and accurate. This study aim to performed state-of-the-art Random Forest algorithm on multitemporal Sentinle-2 imagery on LULC extraction over Enrekang Region. With its 10 m spatial resolution as well as multitemporal information, acquired on December as a representation of the rainy season and in July as a representation of the dry season, it is expected to produce a more optimal LULC maps. Confusion matrix were then performed using visually interpret...

Land use classification is the basis for making further policy in many fields including agriculture. Effective methods in landuse/landcover (LULC) classification are essential for later application in policy making. The development of remote sensing technology has been increasing rapidly. The use of Earth Observing (EO) Sentinel-2 imagery can greatly help LULC mapping over large area. As the basic input on the assessment of land availability and suitability, it is important to perform LULC in such way that it is objective, replicable, and accurate. This study aim to performed state-of-the-art Random Forest algorithm on multitemporal Sentinle-2 imagery on LULC extraction over Enrekang Region. With its 10 m spatial resolution as well as multitemporal information, acquired on December as a representation of the rainy season and in July as a representation of the dry season, it is expected to produce a more optimal LULC maps. Confusion matrix were then performed using visually interpret...

Background and Aims: Storing carbon (C) within soils is significant for maintaining soil-health and reinforces the feedback loop of C loss from soils as CO 2 to the atmosphere. Seasonal variation with increased temperatures and inconsistent precipitation as climate change consequences also affect the soil C-sequestration process globally. Soil-health management practices (SHMPs) such as cover crops, crop residues and manures increase organic components as well as soil-organic C (SOC) pool in an agroecosystem. While, soil microbial-biomass (SMB) which is considered as a soil-health metric to understand microbial community response, is still not modelled to relate with SOC and seasonal impacts to identify suitable SHMPs. Methods: Cover crops followed by 100% residue addition and combinations of manure +organic-fertilizer were the SHMPs for a winter-wheat system in our field study. Seasonal data regarding SOC, nitrogen, SMB-C and labile-C content of soils were used to machine-learn the system and understand the influence of different drivers on SMB-C. Results: The test models based on 'Multivariate Linear Regression' could explain 70% of the variability and predicted seasonal-variation as a dominant variant followed by SHMPs and soil-moisture. AdaBoost and Random Forest Models performed better than others if 'Ensemble Learning' was used. 'Feature Importance' predicted labile-C and aboveground-biomass as the two most important drivers impacting SMB-C. Conclusions: Ensemble Learning' method of Machine-Learning could be successfully implied to understand the SMB-C in an agroecosystem and set benchmark-strategies for soil-health improvement. 50% manure+ 50% fertilizer with crop-residue could be recommended for maximum labile-C and SOC in surface soil-layers.