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The main purpose is to construct the mapping relationship between garment flat and pattern. Particle swarm optimization–least-squares support vector machine (PSO-LSSVM), the data-driven model, is proposed for predicting the pattern design dimensions based on small sample sizes by digitizing the experience of the patternmakers.

For this purpose, the sleeve components were automatically localized and segmented from the garment flat by the Mask R-CNN. The sleeve flat measurements were extracted by the Douglas–Peucker algorithm. Then, the PSO algorithm was used to optimize the LSSVM parameters. PSO-LSSVM was trained by utilizing the experience of patternmakers.

The experimental results demonstrated that the PSO-LSSVM model can effectively improve the generation ability and prediction accuracy in pattern design dimensions, even with small sample sizes. The mean square error could reach 1.057 ± 0.06. The fluctuation range of absolute error was smaller than the others such as pure LSSVM, backpropagation and radial basis function prediction models.

By constructing the mapping relationship between sleeve flat and pattern, the problems of the garment flat objective recognition and pattern design dimensions accurate prediction were solved. Meanwhile, the proposed method overcomes the problem that the parameters are determined by PSO rather than empirically. This framework could be extended to other garment components.

This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO₂ prediction models. The purpose of this paper is to pre-process a relatively large data of NO₂ from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO₂ prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist.

The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO₂ pollution.

This paper's hybrid model provides a basis for giving an informed decision on the NO₂ pollutant avoidance system.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration.

In order to improve the accuracy of project cost prediction, considering the limitations of existing models, the construction cost prediction model based on SVM (Standard Support Vector Machine) and LSSVM (Least Squares Support Vector Machine) is put forward.

In the competitive growth and industries 4.0, the prediction in the cost plays a key role.

At the same time, the original data is dimensionality reduced. The processed data are imported into the SVM and LSSVM models for training and prediction respectively, and the prediction results are compared and analyzed and a more reasonable prediction model is selected.

The prediction result is further optimized by parameter optimization. The relative error of the prediction model is within 7%, and the prediction accuracy is high and the result is stable.

This paper aims to propose an odor sensing system based on wide spectrum for e-nose, based on comprehensive analysis on the merits and drawbacks of current e-nose.

The wide spectral light is used as the sensing medium in the e-nose system based on continuous wide spectrum (CWS) odor sensing, and the sensing response of each sensing element is the change of light intensity distribution.

Experimental results not only verify the feasibility and effectiveness of the proposed system but also show the effectiveness of least square support vector machine (LSSVM) in eliminating system errors.

Theoretical model of the system was constructed, and experimental tests were carried out by using NO₂ and SO₂. System errors in the test data were eliminated using the LSSVM, and the preprocessed data were classified by euclidean distance to centroids (EDC), k-nearest neighbor (KNN), support vector machine (SVM), LSSVM, respectively.

The system not only has the advantages of current e-nose but also realizes expansion of sensing array by means of light source and the spectrometer with their wide spectrum, high resolution characteristics which improve the detection accuracy and realize real-time detection.

The purpose of this paper is to solve the problems existing in topic popularity prediction in online social networks and advance a fine-grained and long-term prediction model for lack of sufficient data.

Based on GM(1,1) and neural networks, a co-training model for topic tendency prediction is proposed in this paper. The interpolation based on GM(1,1) is employed to generate fine-grained prediction values of topic popularity time series and two neural network models are considered to achieve convergence by transmitting training parameters via their loss functions.

The experiment results indicate that the integrated model can effectively predict dense sequence with higher performance than other algorithms, such as NN and RBF_LSSVM. Furthermore, the Markov chain state transition probability matrix model is used to improve the prediction results.

Fine-grained and long-term topic popularity prediction, further improvement could be made by predicting any interpolation in the time interval of popularity data points.

The paper succeeds in constructing a co-training model with GM(1,1) and neural networks. Markov chain state transition probability matrix is deployed for further improvement of popularity tendency prediction.

The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013).

A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies.

A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R,βS) (R, γO) and (R,γO,βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ).

This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy.

The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R,βS) (R,γO) and (R,γO,βS)inventory control policies are followed for replenishment.

This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R,βS) (R,γO) and (R,γO,βS) inventory control policies.

China is by far the world’s largest energy consumer and importer. Reasonably forecasting the trend of China’s total energy consumption (CTEC) is of great significance. The purpose of this paper is to propose a new-structure grey system model (NSGM (1, 1)) to forecast CTEC.

Two matrices for computing the parameters of NSGM (1, 1) were defined and the specific calculation formula was derived. Since the NSGM (1, 1) model increases the number of its background values, which improves the smoothness effect of the background value and weakens the effects of extreme values in the raw sequence on the model’s performance; hence it has better simulation and prediction performances than traditional grey models. Finally, NSGM (1, 1) was used to forecast China’s total energy consumption during 2016-2025. The forecast showed CTEC will grow rapidly in the next ten years.

Therefore, in order to meet the target of keeping CTEC under control at 4.8 billion tons of standard coal in 2020, Chinese government needs to take necessary measures such as transforming the economic development pattern and enhancing the energy utilization efficiency.

A new-structure grey forecasting model, NSGM (1, 1), is proposed in this paper, which improves the smoothness and weakens the effects of extreme values and has a better structure and performance than those of other grey models. The authors successfully employ the new model to simulate and forecast CTEC. The research findings could aid Chinese government in formulating energy policies and help energy exporters make rational energy yield plans.

The purpose of this paper is to propose a new linear-nonlinear data preprocessing-based hybrid model to achieve a more accurate result at a lower cost for wind power forecasting. For this purpose, a decomposed based series-parallel hybrid model (PKF-ARIMA-FMLP) is proposed which can model linear/nonlinear and certain/uncertain patterns in underlying data simultaneously.

To design the proposed model at first, underlying data are divided into two categories of linear and nonlinear patterns by the proposed Kalman filter (PKF) technique. Then, the linear patterns are modeled by the linear-fuzzy nonlinear series (LLFN) hybrid models to detect linearity/nonlinearity and certainty/uncertainty in underlying data simultaneously. This step is also repeated for nonlinear decomposed patterns. Therefore, the nonlinear patterns are modeled by the linear-fuzzy nonlinear series (NLFN) hybrid models. Finally, the weight of each component (e.g. KF, LLFN and NLFN) is calculated by the least square algorithm, and then the results are combined in a parallel structure. Then the linear and nonlinear patterns are modeled with the lowest cost and the highest accuracy.

The effectiveness and predictive capability of the proposed model are examined and compared with its components, based models, single models, series component combination based hybrid models, parallel component combination based hybrid models and decomposed-based single model. Numerical results show that the proposed linear-nonlinear data preprocessing-based hybrid models have been able to improve the performance of single, hybrid and single decomposed based prediction methods by approximately 66.29%, 52.10% and 38.13% for predicting wind power time series in the test data, respectively.

The combination of single linear and nonlinear models has expanded due to the theory of the existence of linear and nonlinear patterns simultaneously in real-world data. The main idea of the linear and nonlinear hybridization method is to combine the benefits of these models to identify the linear and nonlinear patterns in the data in series, parallel or series-parallel based models by reducing the limitations of the single model that leads to higher accuracy, more comprehensiveness and less risky predictions. Although the literature shows that the combination of linear and nonlinear models can improve the prediction results by detecting most of the linear and nonlinear patterns in underlying data, the investigation of linear and nonlinear patterns before entering linear and nonlinear models can improve the performance, which in no paper this separation of patterns into two classes of linear and nonlinear is considered. So by this new data preprocessing based method, the modeling error can be reduced and higher accuracy can be achieved at a lower cost.

Coal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction.

The proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging low-resolution and high-resolution data by adaptively combining both types of data and filling in missing gaps through interpolation for internal missing data and self-supervision for initiate/terminal missing data. The system employs self-supervised learning to complete the filling of complex missing data.

The ensemble model, which combines long short-term memory, XGBoost and support vector regression, demonstrated the best prediction performance among the tested models. It exhibited superior accuracy and stability across multiple indices in two datasets, namely the Bohai-Rim steam-coal price index and coal daily settlement price.

The proposed coal price forecasting system stands out as it integrates data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. Moreover, the system pioneers the use of self-supervised learning for filling in complex missing data, contributing to its originality and effectiveness.

This paper aims to evaluate the dynamic viscosity and thermal conductivity of halloysite nanotubes (HNTs) suspended in SAE 5W40 using machine learning methods (MLMs).

A two-step method with surfactant was selected to prepare nanolubricants in concentrations of 0.025, 0.05, 0.1 and 0.5 wt%. Thermal conductivity and dynamic viscosity of nanofluids were ascertained over the temperature range of 25–70 °C, with an increment step of 5 °C, using a KD2-Pro analyser device and a digital viscometer MRC VIS-8. Additionally, four different MLMs, including Gaussian process regression (GPR), artificial neural network (ANN), support vector machine (SVM) and decision tree (DT), were used for predicting dynamic viscosity and thermal conductivity by using nanoparticle concentration and temperature as input parameters.

According to the achieved results, the dynamic viscosity and thermal conductivity of nanolubricants mostly increased with the rise of nanoparticle concentration in the base oil. All the proposed models, especially GPR with root mean square error mean values of 0.0047 for dynamic viscosity and 0.0016 for thermal conductivity, basically showed superior ability and stability to estimate the viscosity and thermal conductivity of nanolubricants.

The results of this paper could contribute to optimising the cost and time required for modelling the thermophysical properties of lubricants.

To the best of the author’s knowledge, in this available literature, there is no paper dealing with experimental study and prediction of dynamic viscosity and thermal conductivity of HNTs-based nanolubricant using GPR, ANN, SVM and DT.