Optimization of Household Energy use Prediction Using Random Forest with Genetic Algorithm Feature Selection

One of the important practical applications of computer vision techniques is computer-aided diagnosis (CAD) in medical imaging. It has been shown that CAD can improve the accuracy of breast cancer detection and characterization by radiologists on mammograms. In this chapter, we discuss an important step — feature selection — in classifier design for CAD algorithms. Feature selection reduces the dimensionality of an available feature space and is therefore often used to prevent over-parameterization of a classifier. Many feature selection techniques have been proposed in the literature. We will illustrate the usefulness of genetic algorithms (GAs) for feature selection by comparing GA with a commonly used sequential selection method. A brief introduction to the GA is given and several examples using GA feature selection for the characterization of mammographic lesions are discussed. The examples illustrate the design of a fitness function for optimizing classification accuracy in terms of the receiver operating characteristics of the classifier, the dependence of GA performance on its evolution parameters, and the design of a fitness function tailored to a specific classification task.

A belt conveyor roller defect detection method based on genetic algorithm feature selection and stacking learning was proposed. Roller defect data from 1620 samples of three types of roller defects was first collected. To improve the detection efficiency of roller defects, a feature selection method based on a genetic algorithm for selecting roller defect features was proposed. The model classification efficiency of roller features after feature selection by the genetic algorithm was found to be higher. The feature subset with the highest fitness had a feature number of 35. A stacking learning model for detecting roller defects was also constructed. The stacking method enhanced the comprehensive performance of the base learner model for detecting roller defects. The stacking learning model finally achieved a classification accuracy of 94.86% on the roller defect dataset. The classification performance of the stacking learning model was better than that of its base learning model. The results showed that the stacking learning model was preferred for detecting roller defects. The proposed conveyor belt roller defect detection method, while achieving high accuracy, may be limited by potential overfitting and sensitivity to environmental conditions. The next steps in refining the conveyor belt roller defect detection method will focus on dataset expansion, algorithm parameter tuning, and environmental robustness testing.

Three dimensional energy profile:: A conceptual framework for assessing household energy use

To predict the potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into O. hupensis surveillance and control in Yunnan Province. The O. hupensis snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into O. hupensis snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population). Variables were screened with Pearson correlation test and variance inflation factor (VIF) test. The RF and MaxEnt models and the ensemble model were created using the biomod2 package of the software R 4.2.1, and the potential distribution of O. hupensis snails after 2016 was predicted in Yunnan Province. The predictive effects of models were evaluated through cross-validation and independent tests, and the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS) and Kappa statistics were used for model evaluation. In addition, the importance of environmental variables was analyzed, the contribution of environmental variables output by the models with AUC values of > 0.950 and TSS values of > 0.850 were selected for normalization processing, and the importance percentage of environmental variables was obtained to analyze the importance of environmental variables. Data of 148 O. hupensis snail distribution sites and 15 environmental variables were included in training sets of RF and MaxEnt models, and both RF and MaxEnt models had high predictive performance, with both mean AUC values of > 0.900 and all mean TSS values and Kappa values of > 0.800, and significant differences in the AUC (t = 19.862, P < 0.05), TSS (t = 10.140, P < 0.05) and Kappa values (t = 10.237, P < 0.05) between two models. The AUC, TSS and Kappa values of the ensemble model were 0.996, 0.954 and 0.920, respectively. Independent data verification showed that the AUC, TSS and Kappa values of the RF model and the ensemble model were all 1, which still showed high performance in unknown data modeling, and the MaxEnt model showed poor performance, with TSS and Kappa values of 0 for 24%(24/100) of the modeling results. The modeling results of 79 RF models, 38 MaxEnt models and their ensemble models with AUC values of > 0.950 and TSS values of > 0.850 were included in the evaluation of importance of environmental variables. The importance of annual sunshine duration (SSD) was 32.989%, 37.847% and 46.315% in the RF model, the MaxEnt model and their ensemble model, while the importance of annual mean relative humidity (RHU) was 30.947%, 15.921% and 28.121%, respectively. Important environment variables were concentrated in modeling results of the RF model, dispersed in modeling results of the MaxEnt model, and most concentrated in modeling results of the ensemble model. The potential distribution of O. hupensis snails after 2016 was predicted to be relatively concentrated in Yunnan Province by the RF model and relatively large by the MaxEnt model, and the distribution of O. hupensis snails predicted by the ensemble model was mostly the joint distribution of O. hupensis snails predicted by RF and MaxEnt models. Both RF and MaxEnt models are effective to predict the potential distribution of O. hupensis snails in Yunnan Province, which facilitates targeted O. hupensis snail control.

Data-driven rapid detection of Helicobacter pylori infection through machine learning with limited laboratory parameters in Chinese primary clinics

Diagnostic and prognostic value of diquat plasma concentration and complete blood count in patients with acute diquat poisoning based on random forest algorithms.

The goal of the feature selection process is, given a dataset described by n attributes (features), to find the minimum number m of relevant attributes which describe the data as well as the original set of attributes do. Genetic algorithms have been used to implement feature selection algorithms. Previous algorithms presented in the literature used the predictive accuracy of a specific learning algorithm as the fitness function to maximize over the space of possible feature subsets. Such an approach to feature selection requires a large amount of CPU time to reach a good solution on large datasets. This paper presents a genetic algorithm for feature selection which improves previous results presented in the literature for genetic-based feature selection. It is independent of a specific learning algorithm and requires less CPU time to reach a relevant subset of features. Reported experiments show that the proposed algorithm is at least ten times faster than a standard genetic algorithm for feature selection without a loss of predictive accuracy when a learning algorithm is applied to reduced data.

This contribution firstly proposed the concept of annual average power generation hours and analyzed per capita energy consumption, carbon emission, and the human development index from a macro perspective. On this basis, we compared the average household electrical energy consumption of urban and rural residents based on the data from CGSS-2015 from a micro perspective. The results show the positive correlation between carbon emissions per capita and the human development index and China’s regional imbalance characteristics between household electricity consumption and renewable energy distribution. Therefore, the distributed energy supply system is proposed as an effective complement to centralized power generation systems and is the key to synergizing human development and carbon emissions in China. Moreover, we analyzed the characteristics of distributed energy supply systems in the context of existing energy supply systems, pointing out the need to fully use solar energy and natural gas. Finally, two types of typical distributed energy supply systems are proposed for satisfying the household energy requirements in remote or rural areas of western and the eastern or coastal areas of China, respectively. Two typical distributed energy systems integrate high-efficiency energy conversion, storage, and transfer devices such as electric heat pumps, photovoltaic thermal, heat and electricity storage, and fuel cells.

&#x0D; &#x0D; &#x0D; &#x0D; Abstract&#x0D; The classification task known as MNIST digit recognition involves identifying handwritten numbers into their corresponding values. Although there are numerous approaches proposed for this type of task, they typically face issues in achieving high accuracy. One method that can improve single models' performance is through ensemble learning. The goal of this study is to explore the use of various learning techniques, such as boosting and bagging, in combination with random forest models and decision trees, to improve the performance of MNIST digit recognition with regard to accuracy. We then perform evaluations on these methods using various metrics, such as recall, precision, accuracy, and F1. The findings of this study provide valuable insight into the various advantages of ensemble methods for the MNIST digit recognition task. It also highlights the need to explore these techniques in the context of machine learning. The objective of this study is to investigate the use of ensembles in improving the accuracy of MNIST digit recognition. We performed evaluations on two popular methods, namely boosting and bagging, with random forest and decision tree models. The evaluation parameters included F1 score, recall, accuracy, and precision. The results of the evaluations revealed that both boosting and bagging methods performed well in terms of their evaluation metrics. In most cases, the decision tree performed better than the random forest. However, the random forest method was able to achieve the highest accuracy, which is 99 percent. The findings of the evaluation revealed that ensembles can help improve single models' accuracy in MNIST digit recognition. On the other hand, the random forest method is a promising option for this task. The exact results of the evaluations will vary depending on the evaluation and implementation metrics. More research is needed to confirm their generalizability. The study emphasizes the value of exploring ensembles in machine learning systems, as well as the potential advantages of performing MNIST digit recognition using them.&#x0D; &#x0D; &#x0D; &#x0D;

In recent years, detecting credit card fraud transactions has been a difficult task due to the high dimensions and imbalanced datasets. Selecting a subset of important features from a high-dimensional dataset has proven to be the most prominent approach for solving high-dimensional dataset issues, and the selection of features is critical for improving classification performance, such as the fraud transaction identification process. To contribute to the field, this paper proposes a novel feature selection (FS) approach based on a metaheuristic algorithm called Rock Hyrax Swarm Optimization Feature Selection (RHSOFS), inspired by the actions of rock hyrax swarms in nature, and implements supervised machine learning techniques to improve credit card fraud transaction identification approaches. This approach is used to select a subset of optimal relevant features from a high-dimensional dataset. In a comparative efficiency analysis, RHSOFS is compared with Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS) in a comparative efficiency analysis. The proposed RHSOFS outperforms existing approaches, such as DEFS, GAFS, PSOFS, and ACOFS, according to the experimental results. Various statistical tests have been used to validate the statistical significance of the proposed model.

Enriching and updating maps are among the most important tasks of any urban management organization for informed decision making. Urban cadastral map enrichment is a time-consuming and costly process, which needs an expert’s opinion for quality control. This research proposes a smart framework to enrich a cadastral base map using a more up-to-date map automatically by machine learning algorithms. The proposed framework has three main steps, including parcel matching, parcel change detection and base map enrichment. The matching step is performed by checking the center point of each parcel in the other map parcels. Support vector machine and random forest classification algorithms are used to detect the changed parcels in the base map. The proposed models employ the genetic algorithm for feature selection and grey wolf optimization and Harris hawks optimization for hyperparameter optimization to improve accuracy and performance. By assessing the accuracies of the models, the random forest model with feature selection and grey wolf optimization, with an F1-score of 0.9018, was selected for the parcel change detection method. Finally, the detected changed parcels in the base map are deleted and relocated automatically with corresponding parcels in the more up-to-date map by the affine transformation.

Nowadays, IT systems are everywhere like in an organization, enterprise, and institution. Among organizational assets, IT assets are the hardest to nail down as it requires many repetitive technical works to identify intangible things like data, software, and configurations. To protect dangerous impacts on the IT infrastructure, this research focuses on the development of a network intrusion detection model that performs accurate detection at a low false-positive rate. We consider the significant role of the standard protocols used to launch an attack, and therefore developed the detection model based on the protocol datasets. We utilized the research methods of the random forest as the classifier and genetic algorithm for feature selection. Three protocol datasets, which represent subsets of the NSL-KDD dataset, are used for the experiment. The finding is sufficient to prove that our protocol random forest (Protocol-RF) model detects the attacks with the highest precision of 0.907 and a low false-positive rate of 0.001. KeywordsIntrusion detection systemFeature selectionGenetic algorithmRandom forest

Predicting financial trouble effectively is now crucial as businesses face an increasing variety of financial threats. This research utilizes a dataset to predict a company's financial difficulties using GBDT and Random Forest models. The objective is to assess how well these models handle nonlinear interactions, capture data properties, and prevent overfitting. Firstly, data preprocessing ensures data quality, and then random forest and GBDT models are applied for analysis. Random forests perform outstandingly in feature selection and avoiding overfitting, while GBDT has significant advantages in capturing nonlinear relationships. The evaluation results show that the single model has limitations. Therefore, this article proposes to integrate the random forest and GBDT model to comprehensively leverage their respective advantages. The experimental results of the integrated model show a significant improvement in predictive performance. In summary, the model integration strategy effectively improves the accuracy of financial distress prediction, provides a more reliable tool for enterprise risk management, and also offers new directions for future research.

Over the last two decades, the residential building sector has been one of the largest energy consumption sectors in New Zealand. The relationship between that sector and household energy consumption should be carefully studied in order to optimize the energy consumption structure and satisfy energy demands. Researchers have made efforts in this field; however, few have concentrated on the association between household energy use and the cost of residential buildings. This study examined the correlation between household energy use and residential building cost. Analysis of the data indicates that they are significantly correlated. Hence, this study proposes time series methods, including the exponential smoothing method and the autoregressive integrated moving average (ARIMA) model for forecasting residential building costs of five categories of residential buildings (one-storey house, two-storey house, townhouse, residential apartment and retirement village building) in New Zealand. Moreover, the artificial neutral networks (ANNs) model was used to forecast the future usage of three types of household energy (electricity, gas and petrol) using the residential building costs. The t-test was used to validate the effectiveness of the obtained ANN models. The results indicate that the ANN models can generate acceptable forecasts. The primary contributions of this paper are twofold: (1) Identify the close correlation between household energy use and residential building costs; (2) provide a new clue for optimizing energy management.

Inter-regional environmental inequality under lasting pandemic exacerbated by residential response