Super learner ensemble model: A novel approach for predicting monthly copper price in future

In practice, the volatile organic compounds (VOCs) pollution can exist when refueling due to the properties of the gasoline, low viscosity and high saturated-vapor pressure. A new gasoline vapor recovery system involving frequency conversion technology and machine learning is developed to cope with this problem. In the proposed system, firstly, the pumping capacity of the vacuum pump is evaluated, and test shows an almost linear relationship between suction volume and frequency. Then, the Multi-Layer Perception (MLP) neural network and the support vector regression (SVR) are employed to predict the gas-liquid ratio, and the numerical examples are presented to prove the high prediction accuracy of the MLP and SVR, respectively, where the MLP neural network has better generalization ability. Finally, compared with the two gasoline vapor recovery systems based on the 1: 1 fixed control model and the PID control model, respectively, the gasoline vapor recovery efficiency is improved significantly by the new gasoline vapor recovery system.

Copper is an important mineral and fluctuations in copper prices can affect the stable functioning of some countries' economies. Policy makers, futures traders and individual investors are very concerned about copper prices. In a recent paper, we use an artificial intelligence model long short-term memory (LSTM) to predict copper prices. To improve the efficiency of long short-term memory (LSTM) model, we introduced a simulated annealing (SA) algorithm to find the best combination of hyperparameters. The feature engineering problem of the AI model is then solved by correlation analysis. Three economic indicators, West Texas Intermediate Oil Price, Gold Price and Silver Price, which are highly correlated with copper prices, were selected as inputs to be used in the training and forecasting model. Three different copper price time periods, namely 485, 363 and 242 days, were chosen for the model forecasts. The forecast errors are 0.00195, 0.0019 and 0.00097, respectively. Compared with the existing literature, the prediction results of this paper are more accurate and less error. The research in this paper provides a reliable reference for analyzing future copper price changes.

Estimating wastewater treatment plants’ (WWTPs) influent parameters such as 5-day biological oxygen demand (BOD5) and chemical oxygen demand (COD) is vital for optimizing electricity and energy consumption. Against this backdrop, the existing body of knowledge is bereft of a study employing Artificial Intelligence-based techniques for the prediction of BOD5 and COD. Thus, in this study, Gene expression programming (GEP), multilayer perception neural networks, multi-linear regression, k-nearest neighbors, gradient boosting, and regression trees -based models were trained for predicting BOD5 and COD, using monthly data collected from the inflow of 7 WWTPs over a three-year period in Hong Kong. Based on different statistical parameters, GEP provides more accurate estimations, with R2 values of 0.784 and 0.861 for BOD5 and COD respectively. Furthermore, results of sensitivity analysis undertaken by monte Carlo simulation revealed that both BOD5 and COD were mostly affected by concentrations of total suspended solids, and a 10% increase in the value of TSS resulted in a 7.94% and 7.92% increase in the values of BOD5 and COD, respectively. It is seen that the GEP modeling results complied with the fundamental chemistry of the wastewater quality parameters and can be further applied on other sewage sources such as industrial sewage and leachate. The promising results obtained pave the way for forecasting the operational parameters during sludge processing, leading to an extensive energy savings during the wastewater treatment processes.

The price of copper is unstable but it is considered an important indicator of the global economy. Changes in the price of copper point to higher global growth or an impending recession. In this work, the forecasting of the spot prices of copper from the New York Commodity Exchange is studied using a machine learning method, support vector regression coupled with different model schemas (recursive, direct and hybrid multi-step). Using these techniques, three different time series analyses are built and its performance are compared. The numerical results show that the hybrid direct-recursive obtains the best results.

Yarn tenacity directly affects the winding and knitting efficiency as well as warp and weft breakages during weaving process and therefore, is considered as the most important parameter to be controlled during yarn spinning process. Yarn tenacity is dependent on fiber properties and process parameters. Exploring the relationship between fiber properties, process parameters and yarn tenacity is very important to optimize the selection of raw materials and improve yarn quality. In this study, an efficient monarch butterfly optimization-based neural network simulator called MBONN was developed to predict the tenacity of siro-spun yarns from some process parameters and fiber properties. To this end, an experimental dataset was obtained with fiber fineness, yarn twist factor, yarn linear density and strand spacing as the input variables and yarn tenacity as the output parameter. In the proposed MBONN, a monarch butterfly optimization algorithm is applied as a global search method to evolve weights of a multilayer perception (MLP) neural network. The prediction accuracy of the MBONN was compared with that of a MLP neural network trained with back propagation algorithm, MLP neural network trained with genetic algorithms and linear regression model. The results indicated that the prediction accuracy of the proposed MBONN is statistically superior to that of other models. The effect of fiber fineness, yarn linear density, twist factor and strand spacing on yarn tenacity was investigated using the proposed MBONN. Additionally, the observed trends in variation of yarn tenacity with fiber and process parameters were discussed with reference to the yarn internal structure. It was established that higher migration parameters result in increasing the siro-spun yarn tenacity. It was found that the yarns with higher migration parameters benefit from a more coherent self-locking structure which severely restricts fiber slippage, thereby increasing the yarn tenacity.

In order to apply machine vision technology replacing human vision to identify the plant germplasm resources. This paper select 5 different types of Diospyros lotus seeds, 7 different appearance features and 6 color features were extracted by machine vision technology based on traditional identification method. One input, seven hidden layers and one output has been used for the multilayer perception (MLP) in our system. K-fold cross Validation was used for the modeling and classified of Diospyros lotus seeds. The results showed that the average identification rate of 5 types seeds was reached 91.8%, which indicated that the established seed model could be used as an effective method for the accurate classification of the seeds

Copper price forecasted by hybrid neural network with Bayesian Optimization and wavelet transform

This paper aims to evaluate the performance of Electroencephalographic (EEG) emotion recognition system (EEG-ER) using source-temporal domain with Takagi- Sugeno-Kang (TSK) fuzzy model. Ten healthy subjects aged 5-6 years were participated in this study. Emotion elicitation procedure has done using the Radbound faces database (RafD). The selected emotions were happy, sad, and neutral and fear. The results were compared with wavelet coefficients (WC) as feature extraction method and Regularized Least Square (RLS) and Multi-Layer Perception (MLP) neural network classifiers from our previous work. Another comparison was done between affective model of Russell and RafD. The results show the efficiency of using source-temporal features in emotion recognition system hence there was a slight difference in accuracy among different classifier; MLP, RLS and TSK however MLP and TSK results were with high accurate and stable. Moreover Russell model which is based on positive-negative dimensions shows high accuracy than RafD model that has positive dimensions. The accuracy was around 97% using Russell model.

Transient states modeling of industrial grinding process with significant accuracy is extremely essential to run these energy intensive processes in optimal conditions ensuring sustainability. Traditional modeling using physics-based approach not only demands extensive process knowledge but also results in time-expensive models difficult to use during iterative processes like optimization. Proposing Support Vector Regression (SVR) as an alternative data driven tool, such a surrogate building task has been performed under an optimization framework. Minimizing square root of mean square error (RMSE) between ground truth and model predictions, optimal hyper-parameter combination is achieved using a novel genetic algorithm-based formulation indicating a paradigm shift as opposed to the usual practice of determining them heuristically. The RMSE of the grinding model obtained using the proposed formulation is reported as 0.00496. When compared with another model obtained using conventional approach, prediction plots indicate the effectiveness of the novel algorithm. Comparison with coefficient of correlation leads similar conclusion, where the least RMSE model has 99.88% and the conventional model has 71.40% correlation value. Such dynamic surrogates with optimal hyper-parameter settings can be extremely useful for control and optimization of grinding processes and can be easily extendable to design of experiment-based response surface modeling.

Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia

The primary goal of investors who include Real Estate Investment Trusts (REITs) in their portfolios is to achieve better returns while reducing the overall risk of their investments. REITs are entities responsible for owning and managing real estate properties. To achieve greater returns while reducing risk, it is essential to accurately predict future REIT prices. This study explores the predictive capability of five different machine learning algorithms used to predict REIT prices. These algorithms include Ordinary Least Squares Linear Regression, Support Vector Regression, k-Nearest Neighbours Regression, Extreme Gradient Boosting, and Long/Short-Term Memory Neural Networks. Additionally, historical REIT prices are supplemented with Technical Analysis indicators (TAIs) to aid in price predictions. While TA indicators are commonly used in stock market forecasting, their application in the context of REITs has remained relatively unexplored. The study applied these algorithms to predict future prices for 30 REITs from the United States, United Kingdom, and Australia, along with 30 stocks and 30 bonds. After obtaining our price predictions, we employ a Genetic Algorithm (GA) to optimise weights of a diversified portfolio. Our results reveal several key findings: (i) all machine learning algorithms demonstrated low average and standard deviation values in the error rate distributions, outperforming commonly used statistical benchmarks such as Holt’s Linear Trend Method (HLTM), Trigonometric Box-Cox Autoregressive Time Series (TBATS), and Autoregressive Integrated Moving Average (ARIMA); (ii) incorporating Technical Analysis indicators in the ML algorithms resulted in a significant reduction in prediction errors, up to 60% in some cases; and (iii) a multi-asset portfolio constructed using predictions that incorporated Technical Analysis indicators outperformed a portfolio based solely on predictions derived from past prices. Furthermore, this study employed Shapley Value-based techniques, specifically SHAP and SAGE, to analyse the importance of the features used in the analysis. These techniques provided additional evidence of the value added by Technical Analysis indicators in this context.

Modeling of pressure drop in fluidized dense phase conveying (FDP) of powders is a tough work as the flow comprises of various interactions among solid, gas and pipe wall. It is difficult to incorporate these interactions into a model. The pressure drop depends on flow, material and geometrical parameters. The existing models show high error when applied to other pipeline configurations of varying pipeline lengths or diameters. The current study investigates the capability of machine learning (ML) techniques to estimate the drop in pressure in FDP conveying of powders. Pneumatic conveying experimental data were used for training the network and then for predicting the pressure drop. For estimating the pressure drop, four distinct ML algorithms light gradient boosting machine (LighGBM)), multilayer perception (MLP), K-nearerst neighbors (KNN), extreme gradient boosting (XGBoost), and were selected. XGBoost model performed better than other models chosen for the study with ±5% error margin while training and testing the data, and ±10% error margin in validating the data. MLP, XGBoost, KNN, and LightGBM models predicted the data of pressure drop with MAE of 5.05, 1.19, 5.72, and 2.85, respectively, for training as well as testing data. Among the four models considered, the model using XGBoost algorithm performed the best, whereas the model using KNN algorithm performed poorly in predicting the FDP conveying pressure drop.

Internet of Things (IoT) systems are rightly receiving considerable interest for many real-world applications, from in-body networks to satellite networks. Such a massive-scale system generates a considerable amount of traffic data, making IoT systems a distributed data source generator. For many reasons, such as the functionality of IoT applications and Quality of Service (QoS) provisioning, classifying these traffic data is of high importance. In the last few years, widespread interest has been expressed in applying Machine Learning (ML)-based techniques for Network Traffic Classification (NTC) tasks. However, the traditional centralized learning-based traffic classifiers pose serious challenges, especially in IoT networks. The centralized ML techniques call for collecting a large amount of data from various IoT devices, which in turn introduces data governance and privacy challenges. Furthermore, in the centralized ML, training data need to be transferred to the Cloud, which increases communication cost and latency. To address these problems, we propose Federated Learning (FL) Internet of Things (IoT) Traffic Classifier (FLITC)-a Federated Learning (FL)-based IoT traffic classification method which is based on the Multi-Layer Perception (MLP) neural network and holds the local data unimpaired on IoT devices by sending only the learned parameters to the aggregation server. Our experimental results show that the FLITC beats centralized learning in preserving the privacy of sensitive data and offers a better degree of accuracy at the cost of a longer training time.

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

Travel planning is a process that allows users to obtain maximum benefit from their time, cost and energy. When planning a route from one place to another, it is an important option to present alternative travel areas on the route. This study proposes a travel route planning (TRP) architecture using a Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) model to improve both travel efficiency and environmental sustainability in route selection. This model incorporates carbon emissions directly into the route planning process by unifying user preferences, location recommendations, route optimization, and multimodal vehicle selection within a comprehensive framework. By merging environmental sustainability with user-focused travel planning, it generates personalized, practical, and low-carbon travel routes. The carbon emissions observed with TRP’s artificial intelligence (AI) recommendation route are presented comparatively with those of the user-determined route. XGBoost, Random Forest (RF), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), (Extra Trees Regressor) ETR, and Multi-Layer Perception (MLP) models are applied to the TRP model. LSTM is compared with Recurrent Neural Networks (RNNs) and Gated Recurrent Unit (GRU) models. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Normalized Root Mean Square Error (NRMSE) error measurements of these models are carried out, and the best result is obtained using XGBoost and LSTM. TRP enhances environmental responsibility awareness within travel planning by integrating sustainability-oriented parameters into the decision-making process. Unlike conventional reservation systems, this model encourages individuals and organizations to prioritize eco-friendly options by considering not only financial factors but also environmental and socio-cultural impacts. By promoting responsible travel behaviors and supporting the adoption of sustainable tourism practices, the proposed approach contributes significantly to the broader dissemination of environmentally conscious travel choices.