Hybrid Prediction Model Research Articles

IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies.

When emergencies are widely discussed and shared, it may lead to conflicting opinions and negative emotions among internet users. Accurately predicting sudden network public opinion events is of great importance. Therefore, this paper constructs a hybrid forecasting model to solve this problem. First, this model introduces an improved inertia weight and an adaptive variation operation to enhance the Particle Swarm Optimization (PSO) algorithm. Then, the improved PSO (IPSO) algorithm optimizes the parameters of the Long Short-Term Memory (LSTM) neural network. Finally, the IPSO-LSTM hybrid prediction model is constructed to forecast and analyze emergency public opinion dissemination trends. The experimental outcomes indicate that the IPSO-LSTM model surpasses others and has high prediction accuracy. In the four emergency predictions we select, the MAPE value of IPSO-LSTM is 74.27% better than that of BP, 33.96% better than that of LSTM, and 13.59% better than that of PSO-LSTM on average. This study aims to assist authorities in quickly identifying potential public opinion crises, developing effective strategies, and promoting sustainable and positive growth in the network environment.

Novel framework based on ensemble classification and secure feature extraction for COVID-19 critical health prediction

The Covid outbreak necessitated the use of an automated method for treating patients with critical symptoms. Increasing use of the Internet of Things (IoT) and smart devices requires access to real-time data over robust and secure network infrastructures. During the lifetime of healthcare applications, the data must be transferred in a controlled manner. In this context, this study proposes, an IoT-based decision support system for the analysis of Covid-related data in healthcare systems. Through in-vehicle monitoring, it is intended to safely send important data and patient updates to attending physicians. The suggested approach uses feature extraction methods to identify patients' crucial states while protecting their private information. To improve feature extraction, two techniques are combined: ensemble classification and cipher substitution for secure Json Web Tokens (JWTs). By incorporating the Secure Hashing Algorithm-2 (SHA-2) methods into the Feature Extraction based Logistic Regression (FELR), security protections are substantially reinforced. The system uses the Message Queuing Telemetry Transport (MQTT) protocol to provide secure communication between devices and access to the extracted characteristics from rescue vehicles. The system analyzes passengers, traffic data, and medical issues, which are continually updated with cloud-based security, by using vehicle-based communication networks. The secure movement of patient data inside the hospital network is ensured by this cloud-based system. By using sophisticated regression analysis and ensemble learning approaches, our suggested methodology has shown significant advantages compared to traditional models such as Logistic Regression (LR) and Mixed Regression (MR). Our technique demonstrates exceptional performance with an accuracy rate of 96%, a recall rate of 91%, and an F-measure of 91%. We also precisely compared the performance of our proposed FELR algorithm with hybrid prediction model using deep learning, the extra tree convolutional neural network-based ensemble model, ensemble-based random forest, ensemble transformation learning, and machine learning with regression. Significantly, FELR algorithm has demonstrated exceptional superiority by consistently surpassing the predictive capabilities of all these algorithms. The system has exceptional performance and exhibits improved efficiency and secure transmission capabilities. The unique model that we propose offers a feasible and effective approach for detecting crucial states in patients within healthcare facilities. The success of our framework is shown via a well-planned approach that includes important processes such as data preprocessing, feature extraction, ensemble model construction, and evaluation. The proposed framework offers a promising avenue for aiding healthcare professionals in making informed decisions regarding the critical health trajectories of COVID-19 patients.

Granulation-based LSTM-RF combination model for hourly sea surface temperature prediction

ABSTRACT Accurate predictions of sea surface temperature (SST) are crucial due to the significant impact of SST on the global ocean-atmospheric system and its potential to trigger extreme weather events. Many existing machine-learning-based SST predictions adapt the traditional iterative point-wise prediction mechanism, whose predicting horizons and accuracy are limited owing to the high sensitivity to cumulative errors during iterative predictions. Therefore, this paper proposes a novel granulation-based long short-term memory (LSTM)-random forest (RF) combination model that can fully capture the feature dependencies involved in the fluctuation of SST sequences, reduce the cumulative error in the iteration process, and extend the prediction horizons, which includes two sub-models (adaptive granulation model and hybrid prediction model). They can restack the one-dimensional SST time-series into multidimensional feature variables, and achieve a strong forecasting ability. The analysis shows that the proposed model can achieve more accurate prediction-hours in nearly all prediction ranges from 1 to 125 h. The average prediction error of the proposed model in 25–125 h is 0.07 K, similar to that (0.067 K) in the first 24 h, which exhibits a high generalization performance and robustness and isthus a promising platform for the medium- and long-term forecasting of hourly SSTs.

Data-driven learning-based Model Predictive Control for energy-intensive systems

Efficient control of energy-intensive systems is essential for reducing energy consumption and realizing sustainable development. However, considering the complex inter-dependent energy-consumption devices, numerous control parameters, and dynamic environments, the energy-efficient control of energy-intensive system is always challenging. To address such problems, this paper proposes a data-driven learning-based Model Predictive Control (MPC) method for the integrated control of various devices in energy-intensive systems. Specifically, a hybrid prediction model based on two variants of RNN is integrated with the MPC scheme to learn and predict the system dynamics based on massive time-series sensing data. Then an efficient tree-based prioritized group control model for heterogeneous devices is developed with a rolling optimization and feedback correction mode. A real-life case study is provided to evaluate the performance of the proposed method, which demonstrates its superiority over existing methods on saving the energy consumption.

Cryptocurrency Price Prediction Using Frequency Decomposition and Deep Learning

Given the substantial volatility and non-stationarity of cryptocurrency prices, forecasting them has become a complex task within the realm of financial time series analysis. This study introduces an innovative hybrid prediction model, VMD-AGRU-RESVMD-LSTM, which amalgamates the disintegration–integration framework with deep learning techniques for accurate cryptocurrency price prediction. The process begins by decomposing the cryptocurrency price series into a finite number of subseries, each characterized by relatively simple volatility patterns, using the variational mode decomposition (VMD) method. Next, the gated recurrent unit (GRU) neural network, in combination with an attention mechanism, predicts each modal component’s sequence separately. Additionally, the residual sequence, obtained after decomposition, undergoes further decomposition. The resultant residual sequence components serve as input to an attentive GRU (AGRU) network, which predicts the residual sequence’s future values. Ultimately, the long short-term memory (LSTM) neural network integrates the predictions of modal components and residuals to yield the final forecasted price. Empirical results obtained for daily Bitcoin and Ethereum data exhibit promising performance metrics. The root mean square error (RMSE) is reported as 50.651 and 2.873, the mean absolute error (MAE) stands at 42.298 and 2.410, and the mean absolute percentage error (MAPE) is recorded at 0.394% and 0.757%, respectively. Notably, the predictive outcomes of the VMD-AGRU-RESVMD-LSTM model surpass those of standalone LSTM and GRU models, as well as other hybrid models, confirming its superior performance in cryptocurrency price forecasting.

Short-term wind power forecasting model based on temporal convolutional network and Informer

Wind power forecast remains challenging owing to the unpredictable peculiarity of wind. The accuracy of wind power predictions is critical to the stability of the whole system. This research proposes a hybrid prediction model based on a temporal convolutional network and an Informer to increase the accuracy of wind power forecasting. The hidden temporal features in the dataset are first extracted using TCN, and the Informer is then employed to predict wind power. Additionally, a cutting-edge AdaBelief optimizer is used to boost prediction accuracy even more. The validity of the model is verified by comparing with other wind speed prediction methods. The findings reveal that the proposed model has the highest prediction accuracy and the best forecast effect.

A Hybrid Prediction Model for CatBoost Tomato Transpiration Rate Based on Feature Extraction

The growth and yield of crops are highly dependent on irrigation. Implementing irrigation plans that are tailored to the specific water requirements of crops can enhance crop yield and improve the quality of tomatoes. The mastery and prediction of transpiration rate (Tr) is of great significance for greenhouse crop water management. However, due to the influence of multiple environmental factors and the mutual coupling between environmental factors, it is challenging to construct accurate prediction models. This study focuses on greenhouse tomatoes and proposes a data-driven model configuration based on the Competitive adaptive reweighted sampling (CARS) algorithm, using greenhouse environmental sensors that collect six parameters, such as air temperature, relative humidity, solar radiation, substrate temperature, light intensity, and CO2 concentration. In response to the differences in crop transpiration changes at different growth stages and time stages, the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm was used to identify three characteristic intervals: florescence stage, fruiting stage daytime, and fruiting stage night-time. Based on this, a greenhouse tomato Tr prediction model (CARS-CatBoost model) based on the CatBoost machine learning algorithm was constructed. The experimental verification shows that the coefficient of determination (R2) of the constructed CARS-CatBoost single model for the whole growth stage is 0.92, which is higher than the prediction accuracy of the traditional single crop coefficient model (R2 = 0.54). Among them, the prediction accuracy at night during the fruiting stage is the highest, and the Root Mean Square Error (RMSE) drops to 0.427 g·m−2·h−1. This study provides an intelligent prediction method based on the zonal modeling of crop growth characteristics, which can be used to support precise irrigation regulation of greenhouse tomatoes.

A Hybrid Random Forest and Least Squares Support Vector Machine Model Based on Particle Swarm Optimization Algorithm for Slope Stability Prediction: A Case Study in Sichuan–Tibet Highway, China

Slope stability estimation is an engineering problem that involves several parameters. The problems of low accuracy of the model and blind data preprocessing are commonly existent in slope stability prediction research. To address these problems, 10 quantitative indicators are selected from 135 field cases to improve the accuracy of the model. These indicators were analyzed and visualized to examine their reliabilities after preprocessing. Combined with random forest (RF), particle swarm optimization (PSO), and least squares support vector machine (LSSVM) algorithms, a hybrid prediction model that the RF–PSO–LSSVM model is proposed for identifying slope stability, and its reliability is verified by other prediction models that SVM, logistic regression, decision trees, k-nearest neighbor, naive Bayes, and linear discriminant analysis. Besides, the importance score of each indicator in the prediction of slope stability is discussed by employing the RF algorithm. The research results show that the proposed hybrid model exhibits the best accuracy and superiority in slope stability prediction than other models in this paper, which its values of the best fitness, area under the curve, T-measure, and accuracy are 98.15%, 96.4%, 96.55%, and 95.82%, respectively. The most influential factors affecting slope stability are precipitation and gravity, and the slope type and pore water ratio are identified as the least significant factors in this paper. The results provide a novel approach toward slope stability prediction in the field of geotechnical engineering.

A novel hybrid approach to mooring tension prediction for semi-submersible offshore platforms

The accuracy of mooring tension prediction significantly affects the safety of semi-submersible offshore platform operations and the efficiency of production planning. Nevertheless, traditional single prediction models have difficulty accurately forecasting for complex measured mooring tension data. This work provides a hybrid method for predicting mooring tension on semi-submersible maritime platforms based on VMD, error correction, and the convolutional neural network–long short-term memory (CNN-LSTM) and convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM) models. The proposed hybrid prediction model was trained, validated, and tested using monitored mooring tension data acquired from the “Deepsea One” semi-submersible ocean platform in the South China Sea under different orientations and sea conditions. The results showed that the proposed hybrid prediction model for mooring tension on semi-submersible offshore platforms had lower values of the root mean square error, mean absolute error, and mean absolute percentage error and higher values of the coefficient of determination (R2) than almost all of the other comparison models when predicting complex time series data of the actual monitored mooring tension. This indicated that the proposed hybrid prediction model had better precision and stability than other models. Furthermore, this method can be used to anticipate nonlinear, nonstationary time series data in different fields of maritime engineering.

Block Chain and Machine Learning Models to Evaluate Faults in the Smart Manufacturing System

Smart Manufacturing Systems (SMS) have revolutionized industrial processes by incorporating automation, data analytics, and real-time monitoring to improve efficiency and quality. However, ensuring the reliability and fault tolerance of SMS remains a challenge. This paper proposes an innovative approach that combines Blockchain technology with Machine Learning (ML) models to evaluate faults in SMS. By leveraging the immutability and transparency of the blockchain and the predictive capabilities of ML, this approach enhances fault detection, facilitates traceability, and ultimately contributes to the resilience of smart manufacturing. The industrial sector's increase in data creation has made monitoring systems a crucial idea for management and decision-making. The Internet of Things (IoT), which is sensor-based and one of the most advanced and potent technologies today, can process appropriate ways to monitor the manufacturing process. The research's suggested method combines IoT, machine learning (ML), and monitoring of the industrial system. Temperature, humidity, gyroscope, and accelerometer IoT sensors are used to gather environmental data. Sensor data is produced in unstructured, enormous, and real-time data forms. Many big data approaches are used to process the data further. This system's hybrid prediction model employs the Random Forest classification approach to weed out outliers in the sensor data and aid in defect identification throughout the production process. The suggested approach was examined for South Korean vehicle production. This system uses a strategy to protect and strengthen data trust in order to prevent genuine data changes with fictitious data and system interactions. The efficacy of the suggested methodology in comparison to other methods is provided in the results section. Furthermore, compared to other inputs, the hybrid prediction model offers a respectable fault prediction. The suggested technique is anticipated to improve decision-making and decrease errors during the production process.

Stock index forecasting using DACLAMNN: A new intelligent highly accurate hybrid ACLSTM/Markov neural network predictor

AbstractThe authors present the investigation of a new hybrid predictive model of Duplex Attention‐based Coupled LSTM Markov Averaged Neural Network, known as DACLMANN. The financial field, particularly the stock market, heavily relies on accurate predictive models. DACLMANN comprises four essential components: two LSTM blocks, an Averagiser and a Markov Neural Network block. The first LSTM block is composed of two hidden layers, each containing 50 neurons and a dense layer with 25 neurons. The second LSTM block consists of two hidden layers, each with 100 neurons, and a dense layer with 50 neurons. The Averagiser plays a crucial role by averaging the closing prices and predicted values from the first LSTM block, resulting in a 90% gain. These averaged values are then fed into the second LSTM block for further prediction. Finally, the predictions undergo evaluation using the Markov model, yielding the final prediction. To assess the performance of DACLMANN, it was tested on 22 years of stock prices for the AMZN index. The evaluation metrics used by the authors include an R2 of 0.76, mean absolute error of 6.81216, root mean square error of 8.6040, Precision of 1, Accuracy of 1, Recall of 1 and F1 of 1. Additionally, DACLMANN achieved a Mean Absolute Percentage Error of less than 0.043% and an RMSPE of less than 2.1%. These results not only demonstrate the effectiveness of the proposed model but also authenticate the prediction outcomes. DACLMANN offers several advantages over traditional predictive models in the stock market. By combining the strengths of Duplex Attention‐based Coupled LSTM, Averagiser, and Markov Neural Network, DACLMANN leverages the power of deep learning, attention mechanisms, and sequential modelling. This hybrid approach enables DACLMANN to capture intricate patterns and dependencies present in stock market data, leading to more accurate and reliable predictions. The robust evaluation metrics further validate the superiority of DACLMANN in predicting stock prices.

Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia

Accurate prediction of short-term water demand, especially, in the case of extreme weather conditions such as flood, droughts and storms, is crucial information for the policy makers to manage the availability of freshwater. This study develops a hybrid model for the prediction of monthly water demand using the database of monthly urban water consumption in Melbourne, Australia. The dataset consisted of minimum, maximum, and mean temperature (°C), evaporation (mm), rainfall (mm), solar radiation (MJ/m2), maximum relative humidity (%), vapor pressure (hpa), and potential evapotranspiration (mm). The dataset was normalized using natural logarithm and denoized then by employing the discrete wavelet transform. Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. Post-optimization ANN model was trained using eleven different leaning algorithms. Models were trained several times with different configuration (nodes in hidden layers) to achieve better accuracy. The final optimum learning algorithm was selected based on the performance values (regression; mean absolute, relative and maximum error) and Taylor diagram.

Anomaly Detection for Agile Satellite Attitude Control System Using Hybrid Deep-Learning Technique

Agile low-Earth-orbit (LEO) observation satellites need a robust attitude control and determination system. It is a critical satellite subsystem, which stabilizes the satellite to different desired orientations during its mission using different actuators. The detection of satellite misorientation is a highly challenging problem because it requires continuous monitoring of data from hundreds of satellite sensors to guarantee healthy operability. In this paper, the authors propose a data-driven deep-learning framework for detecting satellite misorientation by analyzing attitude control subsystem telemetry data. The proposed approach combines a hybrid predictive deep-learning model that consists of long short-term memory and convolutional neural networks in two parallel paths to predict telemetry data and a robust isolation forest classifier for anomaly detection purposes that can classify output residuals as normal or anomalous. The hybrid model was optimized by the particle swarm optimization algorithm to ensure faster fitness function convergence with optimal model hyperparameters. The suggested data-driven model was validated using real telemetry datasets, including real anomalous case studies. The experimental results proved the suggested approach’s superiority for identifying satellite misorientation as well as helping satellite operators monitor the system’s health and deduce the causes of anomalies to aid in decision-making.

MRI-based Radiomics Model for Preoperative Prediction of Lateral Pelvic Lymph Node Metastasis in Locally Advanced Rectal Cancer

To develop a magnetic resonance imaging (MRI)-based radiomics model for preoperative prediction of lateral pelvic lymph node (LPLN) metastasis (LPLNM) in patients with locally advanced rectal cancer MATERIALS AND METHODS: We retrospectively enrolled 263 patients with rectal cancer who underwent total mesorectal excision and LPLN dissection. Radiomics features from the primary lesion and LPLNs on baseline MRI images were utilized to construct a radiomics model, and their radiomics scores were combined to develop a radiomics scoring system. A clinical prediction model was developed using logistic regression. A hybrid predicting model was created through multivariable logistic regression analysis, integrating the radiomics score with significant clinical risk factors (baseline Carcinoembryonic Antigen (CEA), clinical circumferential resection margin status, and the short axis diameter of LPLN). This hybrid model was presented with a hybrid clinical-radiomics nomogram, and its calibration, discrimination, and clinical usefulness were assessed. A total of 148 patients were included in the analysis and randomly divided into a training cohort (n=104) and an independent internal testing cohort (n=44). The hybrid clinical-radiomics model exhibited the highest discrimination, with an area under the receiver operating characteristic (AUC) of 0.843 [95% confidence interval (CI), 0.706-0.968]in the testing cohort compared to the clinical model [AUC (95% CI)=0.772 (0.589-0.856)]and radiomics model [AUC (95% CI)=0.731 (0.613-0.849)]. The hybrid prediction model also demonstrated good calibration, and decision curve analysis confirmed its clinical usefulness. This study developed a hybrid MRI-based radiomics model that incorporates a combination of radiomics score and significant clinical risk factors. The proposed model holds promise for individualized preoperative prediction of LPLNM in patients with locally advanced rectal cancer. The data presented in this study are available on request from the corresponding author.

Continuous blood pressure monitoring using photoplethysmography and electrocardiogram signals by random forest feature selection and GWO-GBRT prediction model

Blood pressure (BP) is an important indicator of a person's cardiovascular status, but accurate, continuous BP monitoring remains a challenge. To improve the monitoring performance of continuous BP using electrocardiogram (ECG) and photoplethysmography (PPG) signals, a continuous BP monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model is developed in this study. First, features in time, frequency, and time–frequency domains are extracted from PPG and ECG signals to supply comprehensive characteristics for BP monitoring. Then, the RFFS method is employed to select sensitive features highly correlated with BP from the candidate feature sets, which reduced redundant information and improved monitoring efficiency. Next, a hybrid prediction model of the gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm is established to learn the dependency relationship between BP and sensitive features. And a new fitness function of GWO is designed to balance the monitoring accuracy and consistency. Finally, ablation and comparative experiments demonstrate the effectiveness and advancement of the proposed method, using the ECG and PPG signals of 150 people downloaded from the MIMIC-III database. The mean absolute error (MAE) and standard deviation (STD) of the proposed method in predicting systolic blood pressure were 2.91 mmHg and 3.97 mmHg, and those of diastolic blood pressure were 1.71 mmHg and 2.35 mmHg. Its monitoring accuracy has surpassed the Association for Advancement of Medical Instrumentation (AAMI) standard and reached the “A” level standard of the British Hypertension Society (BHS) protocol.

A hybrid coal prediction model based on grey Markov optimized by GWO – A case study of Hebei province in China

Accurate prediction of coal consumption is crucial to the structural adjustment and high-quality development of the coal industry, and provides an effective basis for the government to formulate energy strategies. To enhance the prediction accuracy, a hybrid prediction model combining grey wolf optimization algorithm and grey Markov model (GWO_Markov_DNGM) is proposed in this study, which introduces the idea of Markov interval division in order to make up the defect of obtaining interval by experience in the past. In this research, coal consumption in Hebei Province from 2001 to 2020 is used as an example to verify the accuracy of the model and the results show that the proposed model has higher accuracy than the comparison models, including ARIMA, GM (1, 1), DGM (1, 1), DNGM (1, 1) and the grey Markov model with equidistant division of state interval. In addition, this study also discusses the influence of numbers of state intervals on the prediction accuracy of the proposed model, which further illustrates the high prediction accuracy of the proposed hybrid model. Finally, the proposed model is employed to predict the coal consumption of Hebei Province under three scenarios from 2021 to 2025, and several suggestions are put forward.

Next activity prediction of ongoing business processes based on deep learning

AbstractNext activity prediction of business processes (BPs) provides valid execution information of ongoing (i.e., unfinished) process instances, which enables process executors to rationally allocate resources and detect process deviations in advance. Current researches on next activity prediction, however, concentrate mostly on model construction without in‐depth analysis of historical event logs. In this article, we are dedicated to proposing an approach to forecast the next activity effectively in BPs. After in‐depth analysis of historical event logs, three types of candidate activity attributes are defined and calculated as additional input for the prediction based on three essential elements, that is, frequent activity patterns, trace similarity and position information. Furthermore, we construct an effective hybrid prediction model combining the popular convolutional neural network (CNN) and bidirectional long short‐term memory (Bi‐LSTM) with self‐attention mechanism. Specifically, CNN is used to extract the temporal features before importing into Bi‐LSTM for accurate prediction, and self‐attention mechanism is applied to strengthen features that have decisive effects on the prediction results. Comparison experiments on four real‐life datasets demonstrate that our hybrid model with selected attributes achieves better performance on next activity prediction than single models, and improves the prediction accuracy by 2.98%, 6.05%, 2.70% and 5.26% on Helpdesk, Sepsis, BPIC2013 Incidents and BPIC2012O datasets than the state‐of‐the‐art methods, respectively.

A Hybrid Prediction Model for Local Resistance Coefficient of Water Transmission Tunnel Maintenance Ventilation Based on Machine Learning

Multiple ducts in the working shaft and main body of tunnels form a combined tee structure. An efficient and accurate prediction method for the local resistance coefficient is the key to the design and optimization of the maintenance ventilation scheme. However, most existing studies use numerical simulations and model experiments to analyze the local resistance characteristics of specific structures and calculate the local resistance coefficient under specific ventilation conditions. Therefore, there are shortcomings of low efficiency and high cost in the ventilation scheme optimization when considering the influence of the local resistance. This paper proposes a hybrid prediction model for the local resistance coefficient of water transmission tunnel maintenance ventilation based on machine learning. The hybrid prediction model introduces the hybrid kernel into a relevance vector machine to build the hybrid kernel relevance vector machine model (HKRVM). The improved artificial jellyfish search algorithm (IAJS), which utilizes Fuch chaotic mapping, lens-imaging reverse learning, and adaptive hybrid mutation strategies to improve the algorithm performance, is applied to the kernel parameter optimization of the HKRVM model. The results of a case study show that the method proposed in this paper can achieve the efficient and accurate prediction of the local resistance coefficient of maintenance ventilation and improve the prediction accuracy and prediction efficiency to a certain extent. The method proposed in this paper provides a new concept for the prediction of the ventilation local resistance coefficient and can further provide an efficient prediction method for the design and optimization of maintenance ventilation schemes.

Hybrid Majority Voting: Prediction and Classification Model for Obesity.

Because it is associated with most multifactorial inherited diseases like heart disease, hypertension, diabetes, and other serious medical conditions, obesity is a major global health concern. Obesity is caused by hereditary, physiological, and environmental factors, as well as poor nutrition and a lack of exercise. Weight loss can be difficult for various reasons, and it is diagnosed via BMI, which is used to estimate body fat for most people. Muscular athletes, for example, may have a BMI in the obesity range even when they are not obese. Researchers from a variety of backgrounds and institutions devised different hypotheses and models for the prediction and classification of obesity using different approaches and various machine learning techniques. In this study, a majority voting-based hybrid modeling approach using a gradient boosting classifier, extreme gradient boosting, and a multilayer perceptron was developed. Seven distinct machine learning algorithms were used on open datasets from the UCI machine learning repository, and their respective accuracy levels were compared before the combined approaches were chosen. The proposed majority voting-based hybrid model for prediction and classification of obesity that was achieved has an accuracy of 97.16%, which is greater than both the individual models and the other hybrid models that have been developed.

River-dust induced airborne particulate matter forecasting using a hybrid model of improved complete ensemble empirical mode decomposition with adaptive noise and radial basis function neural network

In the western plains of Taiwan, several rivers originate from the central Taiwanese mountains. Turbulent flow transports large quantities of river sediment that accumulates in the riverbed downstream. River-dust events are extreme dust events that are occasionally caused by drought along riverbeds. The Choushui and Kaoping Rivers demonstrate the highest potential for river-dust events in central and southern Taiwan, respectively. During monsoons, the accumulated river sediment in the estuary can easily become airborne. Consequently, vulnerable populations living along riverbanks are exposed to high PM10 concentrations, which may exert adverse health effects. To reduce the significant health risks associated with river-dust events, a hybrid pmodel composed of improved complete ensemble empirical mode decomposition with adaptive noise and radial basis function neural network (ICEEMDAN-RBFNN) is proposed. The hybrid model is compared with the RBFNN and Multilayer Perceptron (MLP) models for forecasting 3-h PM10 concentrations. In the hybrid prediction model, temperatures, relative humidity, wind speeds, wind direction indices, and previous hourly PM10 concentrations are accounted for. The results indicate that the hybrid model is more accurate than the RBFNN and MLP models in forecasting high and low PM10 concentrations in 3 h advance. The hybrid model achieves a reduction of up to 50% in both the MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Squared Error) compared to the RBFNN and MLP prediction models. Therefore, the hybrid ICEEMDAN-RBFNN model can forecast PM10 efficiently and can serve as an early-warning system for river-dust events. Finally, risk assessments are conducted to exemplify the application of the proposed hybrid model.