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Ensuring a secure online environment hinges on the timely detection of network attacks. Nevertheless, existing detection methods often grapple with the delicate balance between speed and accuracy. In this paper, we introduce a novel intrusion detection algorithm that marries quantum particle swarm optimization with an extreme learning machine (QPSO-ELM). Firstly, we present a feature selection algorithm grounded in partitioned gains to distill vital features from data samples, thereby diminishing feature count to amplify both model training speed and accuracy. Subsequently, we unveil an intrusion detection scheme underpinned by QPSO-ELM, capable of achieving exceptional levels of training and detection speed, all while maintaining high accuracy. Finally, we fine-tune the trained model using the proposed hidden layer node selection algorithm, reducing the detection model size without compromising detection accuracy, thus further elevating its speed. The experiment results indicate that compared to the current baseline, our proposed intrusion detection scheme achieves the best results in terms of accuracy, precision, recall, and detection latency. Furthermore, the ablation experiment results demonstrate the effectiveness of our proposed method in improving both detection speed and detection accuracy. [ABSTRACT FROM AUTHOR]

When solving multi-objective optimization problems, particle swarm optimization algorithms usually employ preset example selection methods and search strategies, which cannot be adjusted according to specific optimization states. In the face of different optimization problems, inappropriate search strategies cannot effectively guide the population, resulting in low search performance of the population. To solve the above problems, a multi-objective particle swarm optimization algorithm guided by extreme learning decision network (ELDN-PSO) is proposed. First of all, the multi-objective optimization problem is decomposed into several scalar subproblems, and an extreme learning decision network is constructed. The network takes the particle position as input, and selects appropriate search actions for each particle according to the optimization state. The fitness change of a particle on the subproblem is obtained as the training sample for the reinforcement learning, and the training speed is improved by extreme learning machine. In the process of optimization, the network is automatically adjusted according to the optimization states, and it selects the appropriate search strategy for the particles at different search stages. Secondly, the non-dominated solutions in the multi-objective optimization problem are difficult to compare. Thus, the leadership of each solution is quantified into a comparable value, so that the examples are more clearly selected for the particles. In addition, an external archive is used to store better particles to maintain the quality of the solutions and guide the population. Comparative experiments are carried out on the ZDT and DTLZ test functions. The results show that ELDN-PSO can effectively cope with different Pareto front shapes, improving the optimization speed as well as the convergence and diversity of the solutions.

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Accurate short-term electrical load forecasting is crucial for the stable operation of power systems. Given the nonlinear, periodic, and rapidly changing characteristics of short-term power load forecasts, this paper introduces a novel forecasting method employing an Extreme Learning Machine (ELM) enhanced by an improved Dwarf Mongoose Optimization Algorithm (Local escape Dwarf Mongoose Optimization Algorithm, LDMOA). This method addresses the significant prediction errors of conventional ELM models and enhances prediction accuracy. The enhancements to the Dwarf Mongoose Optimization Algorithm include three key modifications: initially, a dynamic backward learning strategy is integrated at the early stages of the algorithm to augment its global search capabilities. Subsequently, a cosine algorithm is employed to locate new food sources, thereby expanding the search scope and avoiding local optima. Lastly, a "madness factor" is added when identifying new sleeping burrows to further widen the search area and effectively circumvent local optima. Comparative analyses using benchmark functions demonstrate the improved algorithm's superior convergence and stability. In this study, the LDMOA algorithm optimizes the weights and thresholds of the ELM to establish the LDMOA-ELM prediction model. Experimental forecasts utilizing data from China's 2016 "The Electrician Mathematical Contest in Modeling" demonstrate that the LDMOA-ELM model significantly outperforms the original ELM model in terms of prediction error and accuracy. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have gained substantial prominence in various applications, like healthcare, and surveillance due to its data gathering ability. Effective data fusion in WSNs plays a pivotal role in removal of redundant information transmission and reliability of the collected information. A data fusion model utilizing extreme learning machine optimized by Artificial Cell Swarm Optimization for mobile heterogeneous wireless sensor networks is proposed for decreasing the redundant data transmission in the network. To drastically decrease the count of network data transmit to the sink node, the neural network of the extreme learning machine (ELM) gathers sensory data via mobile heterogeneous WSN and integrates the received sensor data to the clustering route. The output of ELM is uneven, that affect the efficacy of data fusion. Therefore, Artificial Cell Swarm Optimization is proposed to optimize ELM. The proposed model implemented on MATLAB. The efficiency of the proposed technique is analyzed to the existing techniques. The experimental result proves that the proposed method produce 92.8% in evaluation of energy consumption, 93.1% in evaluation of surviving node, 79.3% in evaluation of network in cluster head, 86.5% in evaluation of energy consumption in cluster head, 85.8% in evaluation of network node balance, and 84.3% in evaluation of data fusion in network compared with existing methods, such as data fusion method under ELM optimized by bat algorithm for mobile heterogeneous WSN (ELM-BA-WSN), Bug algorithm-based data fusion utilizing mobile element for WSN (ELM-EMESP-BA-WSN) and proficient data aggregation and node clustering with ELM for WSN (ELM-CN-WSN) respectively. [ABSTRACT FROM AUTHOR]

This research proposes a highly effective soft computing paradigm for estimating the compressive strength (CS) of metakaolin-contained cemented materials. The proposed approach is a combination of an enhanced grey wolf optimizer (EGWO) and an extreme learning machine (ELM). EGWO is an augmented form of the classic grey wolf optimizer (GWO). Compared to standard GWO, EGWO has a better hunting mechanism and produces an optimal performance. The EGWO was used to optimize the ELM structure and a hybrid model, ELM-EGWO, was built. To train and validate the proposed ELM-EGWO model, a sum of 361 experimental results featuring five influencing factors was collected. Based on sensitivity analysis, three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision. Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training (RMSE = 0.0959) and testing (RMSE = 0.0912) phases. The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks (DNN), k-nearest neighbors (KNN), long short-term memory (LSTM), and other hybrid ELMs constructed with GWO, particle swarm optimization (PSO), harris hawks optimization (HHO), salp swarm algorithm (SSA), marine predators algorithm (MPA), and colony predation algorithm (CPA). The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness. [ABSTRACT FROM AUTHOR]

In the operation of medium-voltage distribution cables, the local insulation performance may degrade due to inherent defects, environmental influences, and external forces, leading to consecutive self-recovering latent faults in the cables. If not addressed promptly, these faults may escalate into permanent failures. To address this issue, this paper analyzes the development mechanism and characteristics of latent cable faults. A 10kV low-resistance cable latent fault model based on the Kizilcay arc model is built in the PSCAD/EMTDC platform. Furthermore, the paper analyzes and extracts the time-domain, frequency-domain, and time-frequency domain features of fault current samples. Effective fault feature vectors are constructed using multivariate analysis of variance (MANOVA) and Principal Component Analysis (PCA). Based on the fault feature vectors and Extreme Learning Machine (ELM), an intelligent fault identification model for cable latent faults is developed. The initial parameters of the ELM model are optimized using the Particle Swarm Optimization (PSO) algorithm. Finally, the superiority of the proposed model is validated in terms of classification accuracy, training time, and robustness compared to other machine learning algorithms. [ABSTRACT FROM AUTHOR]

A kernel based extreme learning machine (KELM) method was proposed for predicting joint moments of lower limb rehabilitation robots to address the problem that random initialization of input weights and bias affect the accuracy of the model when predicting lower limb joint moments by extreme learning machine (ELM). The method integrated Gaussian kernel function with ELM and used genetic algorithm ( GA) combined with particle swarm optimization ( PSO) of genetic algorithm-particle swarm optimization (GAPSO) to optimize the parameters of KELM. Firstly, the motion data of a patient with right lower limb hemiplegia walking on a treadmill at 5 different speeds of 0. 4, 0. 5, 0. 6, 0. 7 and 0. 8 m/ s were collected and preprocessed. Secondly, the KELM was optimized by GAPSO to obtain the optimal regularization coefficient C and kernel function width parameter S. The output joint moments were compared with the joints calculated by inverse biomechanical analysis. Finally, the root mean square (RMSE) and correlation coefficient (P) were used to evaluate the superiority of the algorithm. The experimental results show that the average maximum root mean square error of KELM algorithm based on GAPSO(GAPSO-KELM) optimization is 14%, 18% and 28% lower than that of PSO-KELM algorithm, KELM algorithm and ELM algorithm, respectively, and the minimum P is 0. 84 except for 0. 8 m/ s right ankle inversion, which is 0. 79. The GAPSO-KELM algorithm further improves the prediction accuracy, making it a more effective algorithm support for rehabilitation treatment. [ABSTRACT FROM AUTHOR]

This study investigated the compressibility of clay (Cc) for soft ground improvement and developed six optimized metaheuristic-based extreme learning machine (ELM) models (particle swarm optimization (PSO)-ELM, moth search optimization (MSO)-ELM, firefly optimization (FO)-ELM, cuckoo search optimization (CSO)-ELM, bees optimization (BO)-ELM, and ant colony optimization (ACO)-ELM) to predict Cc. A total of 739 laboratory tests were conducted to develop the models, and 517 datasets were used for training, while the remaining 222 samples were used for testing. The results showed that the accuracy of the developed models was improved by 3–5% compared to the original ELM model. The BO-ELM and MSO-ELM models were identified as the most effective models for predicting Cc, with accuracies ranging from 86.5% to 87%. The study suggests that the MSO-ELM model should be used if training time is critical. The developed models provide useful tools for predicting Cc, an essential parameter for soft ground improvement design, and can assist in the improvement of soft ground. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Vehicles, a large amount of vehicle network data is being generated. The large amount of data presents network communication security challenges. Although intrusion detection technology can assist in safeguarding the system from malicious attacks, the substantial data generated within the vehicle network poses time-consuming detection challenges. Thus, we propose an intrusion detection model for the Internet of Vehicles, utilizing Gaussian random incremental principal component analysis (GRIPCA) and optimal weighted extreme learning machine (OWELM). First, we utilize GRIPCA to reduce data redundancy by projecting high-dimensional data into a low-dimensional space, thus reducing storage costs. Then, we utilize the dynamic inertia weight particle swarm optimization (DPSO) to optimize the parameters of the weighted extreme learning machine (WELM) to achieve the best performance. We utilize the NSL-KDD and CIC-IDS-2017 datasets to perform experiments and compare the results with other techniques. The experimental results show the excellence of the proposed model, achieving an accuracy rate of 91.02% on the NSL KDD dataset and 94.67% on the CIC-IDS-2017 dataset.

AbstractPhotodynamic cancer therapy circumvents the major side effects associated with the conventional cancer treatment methods, such as chemotherapy, surgery and exposure to radiation. Experimental measurement of photosensitizer quantum yield (PQY) singlet production of oxygen through either sensitive laser spectroscopy or luminescence detection at the wavelength of 1270 nm is costly; time consuming and intensive while unreliability of chemical traps experimental approach is of serious concern. Quantitative structure–activity relationship (QSAR) computational method proposed in the literature for computing PQY of singlet oxygen production has characteristics deviation from the measured values. PQY singlet oxygen production of twenty-nine pteridines photosensitizer compounds is modeled and predicted in this present contribution using extreme learning machine (ELM) and support vector regression (SVR) with hybridized particle swarm optimization (PSO) method for ensuring combinatory parameter selection. The performances of the developed SVR-PSO computational method are assessed using mean absolute error (MAE), correlation coefficient (CC), root mean square error (RMSE) and mean absolute percentage deviation (MAPD). The developed SVR-PSO model outperforms QSAR (2016) model with performance superiority of 34.78%, 3.65%, 17.64% and 42.16% on the basis of RMSE, CC, MAE and MAPD performance measuring parameters, respectively. The developed ELM-SINE (with sine activation function) and ELM-SIG (with sigmoid activation function) respectively outperform the existing QSAR (2016) model with improvement of 6.54% and 4.70% using R-squared metric. The demonstrated outstanding performance of the present predictive models is immensely meritorious in strengthening the potentials of alternative cancer therapy and circumventing the experimental challenges of PQY singlet oxygen production determination.

The necessity for personal data protection has become increasingly critical in recent years. The single trait-based authentication fails in many cases and hence multimodal biometric traits are utilized in this work. This research work focuses on the utilization of multimodal biometric traits for authentication. The face, fingerprint, and finger vein traits are considered in this work, feature extraction was done by local binary pattern and for the optimization of features, adaptive particle swarm optimization was employed. The extreme learning machine was proposed here for the classification and proficient results were generated. For the iteration count of 40 and population count of 30 of the optimization algorithm, the authentication model sensitivity and specificity are 95.69 and 94.29%. The outcome of this research work paves the way toward proficient security authentication. [ABSTRACT FROM AUTHOR]

The echo state network (ESN) is a recurrent neural network that has yielded state-of-the-art results in many areas owing to its rapid learning ability and the fact that the weights of input neurons and hidden neurons are fixed throughout the learning process. However, the setting procedure for initializing the ESN's recurrent structure may lead to difficulties in designing a sound reservoir that matches a specific task. This paper proposes an improved pre-training method to adjust the model's parameters and topology to obtain an adaptive reservoir for a given application. Two strategies, namely global random selection and ensemble training, are introduced to pre-train the randomly initialized ESN model. Specifically, particle swarm optimization is applied to optimize chosen fixed and global weight values within the network, and the reliability and stability of the pre-trained model are enhanced by employing the ensemble training strategy. In addition, we test the feasibility of the model for time series prediction on six benchmarks and two real-life datasets. The experimental results show a clear enhancement in the ESN learning results. Furthermore, the proposed global random selection and ensemble training strategies are also applied to pre-train the extreme learning machine (ELM), which has a similar training process to the ESN model. Numerical experiments are subsequently carried out on the above-mentioned eight datasets. The experimental findings consistently show that the performance of the proposed pre-trained ELM model is also improved significantly. The suggested two strategies can thus enhance the ESN and ELM models' prediction accuracy and adaptability. [ABSTRACT FROM AUTHOR]

Given the inherent volatility and intermittency of photovoltaic power generation, enhancing the precision of photovoltaic power predictions becomes imperative to ensure the stability of power systems and to elevate power quality. This article introduces an intelligent photovoltaic power prediction model based on the Extreme Learning Machine (ELM) with the Adaptive Spiral Dung Beetle Optimization (ASDBO) algorithm. The model aims to accurately predict photovoltaic power generation under multi-factor correlation conditions, including environmental temperature and solar irradiance. The computational efficiency in high-dimensional data feature conditions is enhanced by using the Pearson correlation analysis to determine the state input of the ELM. To address local optimization challenges in traditional Dung Beetle Optimization (DBO) algorithms, a spiral search strategy is implemented during the dung beetle reproduction and foraging stages, expanding the exploration capabilities. Additionally, during the dung beetle theft stage, dynamic adaptive weights update the optimal food competition position, and the levy flight strategy ensures search randomness. By balancing convergence accuracy and search diversity, the proposed algorithm achieves global optimization. Furthermore, eight benchmark functions are chosen for performance testing to validate the effectiveness of the ASDBO algorithm. By optimizing the input weights and implicit thresholds of the ELM through the ASDBO algorithm, a prediction model is established. Short-term prediction experiments for photovoltaic power generation are conducted under different weather conditions. The selected experimental results demonstrate an average prediction accuracy exceeding 93%, highlighting the effectiveness and superiority of the proposed methodology for photovoltaic power prediction. [ABSTRACT FROM AUTHOR]

Novel incremental regularized extreme learning machine (IR-ELM) based upon improved particle swarm optimization (IPSO-IR-ELM) is reported for the nonlinear multivariate calibration of visible/near-infrared (Vis/NIR) spectroscopy. IR-ELM is employed to construct a nonlinear calibration model for samples. Combined with IPSO, three parts of the IR-ELM algorithm are intelligently optimized. First, the regularization coefficient of the initial network in IR-ELM is optimized by IPSO. Second, IPSO is used again to select the optimal input weights and hidden biases while adding new hidden nodes in IR-ELM. Third, the 2-norm of the output matrix in IR-ELM is introduced as the conditional constraint in IPSO for updating the particle position. The performance of the reported method was tested with two Vis/NIR spectra datasets: blood hemoglobin and water pH. Key spectral variables were selected by successive projections algorithm and employed to establish the calibration models. Compared with partial least squares, ELM, error minimized extreme learning machine, and IR-ELM, IPSO-IR-ELM achieved the highest accuracy and best generalization. For the blood hemoglobin dataset, the RMSEP (root mean square error of prediction) was 0.210 g·dL−1, and the R p 2 (coefficient of determination of prediction) was 0.973. For the water pH dataset, the RMSEP was 0.825, and the R p 2 was 0.899. The results demonstrate that IPSO-IR-ELM is an alternative nonlinear multivariate calibration approach for Vis/NIR spectroscopy. [ABSTRACT FROM AUTHOR]

This research designs and develops a software innovation for Pattavia pineapple cultivation and productivity distribution planning to increase income for farmers. This research formulated and introduced an innovative machine learning (ML) model, the new model is called a new intelligent particle swarm optimization algorithm with extreme learning machine (NIPSO-ELM), to forecast Pattavia pineapple productivity with a notable degree of precision and dependability. In this work, the artificial neural network (ANN) and the standard ELM was built, and assessed for its ability to forecast the productivity of Pattavia pineapples. The findings indicate that the ELM neural network is an innovative model characterized by its straightforward architecture and exceptional performance. Moreover, the utilization of particle swarm optimization (PSO), ant colony optimization (ACO) and the NIPSO algorithms significantly enhanced ELM performance when forecasting the productivity of Pattavia pineapples. The NIPSO-ELM model emerged as the most optimal ML model for accurately, reliably and stably forecasting the productivity of Pattavia pineapples in practical scenarios. The most optimal NIPSO-ELM models for the Loei provinces, Thailand exhibit the following performance metrics: RMSE = 304.36389, MAE = 243.29531, MAPE = 0.03753 and MASE = 0.93157. The most optimal NIPSO-ELM models for the Nong Khai provinces, Thailand exhibit: RMSE = 304.57352, MAE = 244.67834, MAPE = 0.03756 and MASE = 0.93296, respectively. [ABSTRACT FROM AUTHOR]

Photodynamic cancer therapy circumvents the major side effects associated with the conventional cancer treatment methods, such as chemotherapy, surgery and exposure to radiation. Experimental measurement of photosensitizer quantum yield (PQY) singlet production of oxygen through either sensitive laser spectroscopy or luminescence detection at the wavelength of 1270 nm is costly; time consuming and intensive while unreliability of chemical traps experimental approach is of serious concern. Quantitative structure-activity relationship (QSAR) computational method proposed in the literature for computing PQY of singlet oxygen production has characteristics deviation from the measured values. PQY singlet oxygen production of twenty-nine pteridines photosensitizer compounds is modeled and predicted in this present contribution using extreme learning machine (ELM) and support vector regression (SVR) with hybridized particle swarm optimization (PSO) method for ensuring combinatory parameter selection. The performances of the developed SVR-PSO computational method are assessed using mean absolute error (MAE), correlation coefficient (CC), root mean square error (RMSE) and mean absolute percentage deviation (MAPD). The developed SVR-PSO model outperforms QSAR (2016) model with performance superiority of 34.78%, 3.65%, 17.64% and 42.16% on the basis of RMSE, CC, MAE and MAPD performance measuring parameters, respectively. The developed ELM-SINE (with sine activation function) and ELM-SIG (with sigmoid activation function) respectively outperform the existing QSAR (2016) model with improvement of 6.54% and 4.70% using R-squared metric. The demonstrated outstanding performance of the present predictive models is immensely meritorious in strengthening the potentials of alternative cancer therapy and circumventing the experimental challenges of PQY singlet oxygen production determination. [ABSTRACT FROM AUTHOR]

In this study, a novel image‐based visual servo (IBVS) controller for robot manipulators is investigated using an optimized extreme learning machine (ELM) algorithm and an offline reinforcement learning (RL) algorithm. First of all, the classical IBVS method and its difficulties in accurately estimating the image interaction matrix and avoiding the singularity of pseudo‐inverse are introduced. Subsequently, an IBVS method based on ELM and RL is proposed to solve the problem of the singularity of the pseudo‐inverse solution and tune adaptive servo gain, improving the servo efficiency and stability. Specifically, the ELM algorithm optimized by particle swarm optimization (PSO) was used to approximate the pseudo‐inverse of the image interaction matrix to reduce the influence of camera calibration errors. Then, the RL algorithm was adopted to tune the adaptive visual servo gain in continuous space and improve the convergence speed. Finally, comparative simulation experiments on a 6‐DOF robot manipulator were conducted to verify the effectiveness of the proposed IBVS controller. [ABSTRACT FROM AUTHOR]

[Objective] Working at the maximum power point of photovoltaic cells can improve energy utilization efficiency and ensure sufficient power supply. However, most distributed photovoltaic power generation systems are located in natural environments, and the nonlinear changes in the environment in which photovoltaic panels operate make it very difficult to maintain their maximum power point. Traditional maximum power point tracking methods, such as perturbation observation, conductance increment, and particle swarm optimization, yield unsatisfactory results because of their different shortcomings. [Methods] To address the problem of maximum power point tracking in photovoltaics, this paper proposes a maximum power point tracking (MPPT) method for photovoltaics through research on neural network technology and is based on an extreme learning machine (ELM) neural network controller optimized using the sparrow search algorithm (SSA). Herein, a training database is established by analyzing and collecting data on the maximum power curve of photovoltaic cells at different temperatures and light intensities. The proposed neural network controller is trained to form a two-input and one-output controller. The voltage value corresponding to the maximum power point is calculated in real time based on the collected temperature and light intensity data and transmitted to the subsequent boost converter circuit controller. Further, a modulated wave is generated through PWM modulation, which controls the output voltage of the boost converter circuit to track the maximum power point voltage and builds a simulation experimental platform using MATLAB/Simulink. [Results] The simulation results show the following: 1 the proposed method has a high calculation speed and can rapidly track the reference voltage value, making the voltage value accurate. 2 The proposed method directly controls the voltage changes without oscillation or overshoot, resulting in a stable system. 3 The proposed method shows good search accuracy, convergence speed, and stability. The performance of the other three aforementioned mainstream tracking methods and the output voltage of the proposed method under sudden increase or decrease in light intensity are analyzed. [Conclusions] The results indicate that the proposed method can rapidly achieve maximum power point tracking without oscillation and overshoot issues. Compared with the traditional methods, the proposed method performs better in terms of search accuracy, convergence speed, and stability. [ABSTRACT FROM AUTHOR]

This study focused on enhancing the accuracy and efficiency of color detection in stained wood. Specifically, the research investigated the dyeing process of Chinese pine wood veneers. The spectral reflectance of the stained veneers was extracted and used as input data. The Extreme Learning Machine (ELM) model was employed as the predictive model to estimate the L*, a*, b* color parameters of the stained veneers. The ELM model was optimized using the Particle Swarm Optimization (PSO) algorithm, which addressed the issue of local optima by introducing non-linear inertia weights and a novel position-velocity update strategy. Evaluation of the models was based on the L*, a*, b* average absolute error metric. The results indicated that the optimized model achieved an average absolute error of 0.16, outperforming the baseline ELM model (0.68) and the ELM model optimized with the sparrow algorithm (0.37). This research contributed to improving the efficiency of wood staining production and holds implications for the field of color detection in wood materials. [ABSTRACT FROM AUTHOR]

In the operation of medium-voltage distribution cables, the local insulation performance may degrade due to inherent defects, environmental influences, and external forces, leading to consecutive self-recovering latent faults in the cables. If not addressed promptly, these faults may escalate into permanent failures. To address this issue, this paper analyzes the development mechanism and characteristics of latent cable faults. A 10kV low-resistance cable latent fault model based on the Kizilcay arc model is built in the PSCAD/EMTDC platform. Furthermore, the paper analyzes and extracts the time-domain, frequency-domain, and time-frequency domain features of fault current samples. Effective fault feature vectors are constructed using multivariate analysis of variance (MANOVA) and Principal Component Analysis (PCA). Based on the fault feature vectors and Extreme Learning Machine (ELM), an intelligent fault identification model for cable latent faults is developed. The initial parameters of the ELM model are optimized using the Particle Swarm Optimization (PSO) algorithm. Finally, the superiority of the proposed model is validated in terms of classification accuracy, training time, and robustness compared to other machine learning algorithms.

The mortality of breast cancer is more among women besides lung cancer. However, the survival rates of breast cancer can be increased when there is a promising computer-aided diagnosis tool available for earlier detection and timely diagnosis. To tackle this, several research works are emerging with different methodologies but still accuracy and robustness are the key issues. Hence, a robust framework that incorporates the concept of Extreme Learning Machine (ELM) and Deep Transfer Learning is proposed and the performance of ELM is improved using an Iterative Flight-Length-Based Crow-Search Algorithm (iFLCSA) in this research work. Performance of ELM heavily depends on its parameters and to provide enhanced performance, the optimum parameters of ELM are found through the iFLCSA. When compared to the existing Crow Search Algorithm(CSA), the flight length parameter will be updated iteratively using an appropriate equation in iFLCSA to provide better balance between exploration and exploitation. Digital & full-field digital mammograms from the Mammographic Image Analysis Society (MIAS) and INbreast datasets are used for evaluation. The results obtained are then compared with the existing Support Vector Machine, ELM, Particle Swarm Optimization and CSA optimized ELM algorithms. The proposed iFLCSA-ELM provides a maximum classification accuracy of 98.292% and 98.171% for MIAS & INbreast datasets respectively. [ABSTRACT FROM AUTHOR]

Determining the bearing capacity of ring foundations on rock masses holds utmost importance within the framework of foundation design methodology. To examine the failure mechanism of ring foundations situated on Hock-Brown rock masses, the crucial bearing capacity factor (N σ) is analyzed. This analysis considered three dimensionless input parameters: the geological strength index (GSI), the yield parameter (mi), and the ratio of the internal and external radii (ri/ro). This study focuses on the development of a precise hybrid extreme learning machine (ELM) and least-square support vector machine (LSSVM) based on two swarm-based intelligence optimization algorithms, utilizing Harris hawks optimization (HHO) and particle swarm optimization (PSO). The primary objective of this study is to provide accurate predictions of the bearing capacity factor ( N σ ) for a ring foundation. Furthermore, the accuracy of the developed hybrid ELM-PSO, ELM-HHO, LSSVM-PSO, and LSSVM-HHO models was assessed through a comparison between the actual and predicted values of N σ using various performance metrics, uncertainty analysis, and rank analysis. The LSSVM-HHO and ELM-HHO outperformed the LSSVM-PSO and ELM-PSO models in predicting the N σ value. The proposed models can be used as soft computing tools to predict the N σ values in practical applications. [ABSTRACT FROM AUTHOR]

The purpose of this study is to use multivariate adaptive regression spline (MARS) and extreme learning machine (ELM) artificial intelligence models to estimate the compressive strength of the circular concrete columns wrapped with FRP sheets. In addition, to improve the accuracy of these models, the particle swarm optimization (PSO) algorithm was used in combination with these models, and the accuracy of the models was evaluated to estimate the strength values. The results indicate that in general, the artificial intelligence models estimate the compressive strength of FRP-wrapped columns more accurately than the existing analytical models. In particular, the combined MARS–PSO model offered a better performance compared to the other models, as this model has correlation coefficients of 0.9972 and 0.9961 in the training and testing phases, respectively. Moreover, a combination of the PSO algorithm with the two models, MARS and ELM, improved their accuracy by 6.13 and 4.68%, respectively. [ABSTRACT FROM AUTHOR]

Abstract Accurate measurements of available water resources play a key role in achieving a sustainable environment of a society. Precise river flow estimation is an essential task for optimal use of hydropower generation, flood forecasting, and best utilization of water resources in river engineering. The current paper presents the development and verification of the prediction abilities of new hybrid extreme learning machine (ELM)-based models coupling with metaheuristic methods, e.g., Particle swarm optimization (PSO), Mayfly optimization algorithm (MOA), Grey wolf optimization (GWO), and simulated annealing (SA) for monthly streamflow prediction. Prediction precision of standalone ELM model was compared with two-phase optimized state-of-the-arts models, e.g., ELM–PSO, ELM–MOA, ELM–PSOGWO, and ELM–SAMOA, respectively. Hydro-meteorological data acquired from Gorai and Padma Hardinge Bridge stations at Padma River Basin, northwestern Bangladesh, were utilized as inputs in this study to employ models in the form of seven different input combinations. The model’s performances are appraised using Nash–Sutcliffe efficiency, root-mean-square-error (RMSE), mean absolute error, mean absolute percentage error and determination coefficient. The tested results of both stations reported that the ELM–SAMOA and ELM–PSOGWO models offered the best accuracy in the prediction of monthly streamflows compared to ELM–PSO, ELM–MOA, and ELM models. Based on the local data, the ELM–SAMOA reduced the RMSE of ELM, ELM–PSO, ELM–MOA, and ELM–PSOGWO by 31%, 27%, 19%, and 14% for the Gorai station and by 29%, 27%, 19%, and 14% for Padma Hardinge bridge station, in the testing stage, respectively. In contrast, based on external data, ELM–PSOGWO improves in RMSE of ELM, ELM–PSO, ELM–MOA, and ELM–SAMOA by 20%, 5.1%, 6.2%, and 4.6% in the testing stage, respectively. The results confirmed the superiority of two-phase optimized ELM–SAMOA and ELM–PSOGWO models over a single ELM model. The overall results suggest that ELM–SAMOA and ELM–PSOGWO models can be successfully applied in modeling monthly streamflow prediction with either local or external hydro-meteorological datasets.

The accurate assessment of groundwater levels is critical to water resource management. With global warming and climate change, its significance has become increasingly evident, particularly in arid and semi-arid areas. This study compares new extreme learning machines (ELM) methods tuned with metaheuristic algorithms such as particle swarm optimization, grey wolf optimization, the whale optimization algorithm (WOA), Harris Hawks optimizer (HHO), and the jellyfish search optimizer (JFO) in groundwater level estimation. Daily precipitation and temperature datasets acquired from two stations in northern Bangladesh were used as inputs to the models, which were evaluated based on different quantitative statistics and assessed based on RMSE, MAE, R2, and some new graphical inspection methods. The outcomes of the applications revealed that the efficiency of ELM models was considerably improved by using metaheuristic algorithms. The ELM-JSO improved the RMSE of the standalone ELM model by 13% for the optimal precipitation, temperature, and groundwater level inputs in the testing stage. Among the implemented methods, the ELM-JFO performed the best in estimating the daily groundwater level, and the ELM-WOA and ELM-HHO, respectively, followed it. Viability of a new extreme machine learning (ELM) method tuned with Jellyfish search optimizer (JFO) is investigated in groundwater level estimation. The ELM-JFO is compared with hybrid ELM-PSO, ELM-WOA and ELM-HHO models using daily precipitation and temperature data acquired from two stations of Bangladesh. The ELM-JSO improves the root mean square error of the standalone ELM model by 13% for the optimal precipitation, temperature and groundwater level inputs.

When dealing with specific tasks, the hidden layer output matrix of an extreme learning machine (ELM) may change, largely due to the random assigned weight matrix of the input layer and the threshold matrix of the hidden layer, which sequentially leads to the corresponding change to output weights. The unstable fluctuations of the output weights increase the structural risk and the empirical risk of ELM. This paper proposed a fuzzy adaptive particle swarm optimization (PSO) algorithm to solve this problem, which could nonlinearly control the inertia factor during the iteration by fuzzy control. Based on the fuzzy adaptive PSO-ELM algorithm, a sound quality prediction model was developed. The prediction results of this model were compared with the other three sound quality prediction models. The results showed that the fuzzy adaptive PSO-ELM model was more precise. In addition, in comparison with two other adaptive inertia factor algorithms, the fuzzy adaptive PSO-ELM model was the fastest model to reach goal accuracy. [ABSTRACT FROM AUTHOR]

Computer-aided diagnosis (CAD) can assist doctors with clinical diagnosis and improve diagnosis accuracy and efficiency further. It is significative and valuable for cancer detection by using machine learning. In this paper, a hybrid model based on optimization algorithm and machine learning with feature weighting is carried out to detect breast cancer. Firstly, to surmount the limitation of nonlinear and imbalanced data distribution, we apply feature weighting (FW) based on K-Means to make benign and malignant samples more separate. Then Particle Swarm Optimization (PSO) is used to enhance searching ability of Harris Hawks Optimization (HHO). Moreover, the HHO optimized by PSO (PHHO) is employed to optimize Extreme Learning Machine (ELM). Finally, in order to verify the availability of our proposed FW-PHHO-ELM model, experiments are implemented on Wisconsin Diagnosis Breast Cancer (WDBC) data set. The results indicate that the proposed model can achieve 98.76%, 97.37% and 99.46% on accuracy, sensitivity and specificity respectively. The comparison results demonstrate that the proposed model outperformed reported benchmark models and existing models on accuracy and sensitivity. Besides, the proposed model could also balance sensitivity and specificity well. [ABSTRACT FROM AUTHOR]

Accurate assessment of road conditions can effectively alleviate traffic congestion and guide people's travel plans, traffic control decisions of transportation departments, and formulation of traffic-related laws and regulations. This paper proposes a quantum particle swarm optimization (QPSO) and adaptive hybrid exponential smoothing with residual correction (AHES-RC) for the nonlinearity and randomness of traffic flow. In the proposed algorithm, the single–double-exponential smoothing method is mixed with adaptive weights to form AHES, a new method of mixing weights according to real-time traffic trend changes. After the residual correction of AHES is calculated by the extreme learning machine algorithm, the parameters of AHES-RC are optimized using QPSO to improve the prediction accuracy further. This paper comprehensively compares the QPSO-AHES-RC algorithm with other benchmark models through testing on 26 real-world data sets. The results show that the proposed algorithm can adaptive forecasting under various traffic conditions, yielding the best forecasting performance in terms of various forecast error metrics. Compared with advanced machine learning algorithms such as XGBoost and CatBoost, the mean RMSE and mean MAPE have been improved by over 20% on average. In addition, it is revealed that the real-time dynamic capture of traffic flow can effectively improve prediction accuracy. [ABSTRACT FROM AUTHOR]

It has been widely known that long non-coding RNA (lncRNA) plays an important role in gene expression and regulation. However, due to a few characteristics of lncRNA (e.g., huge amounts of data, high dimension, lack of noted samples, etc.), identifying key lncRNA closely related to specific disease is nearly impossible. In this paper, the authors propose a computational method to predict key lncRNA closely related to its corresponding disease. The proposed solution implements a BPSO based intelligent algorithm to select possible optimal lncRNA subset, and then uses ML-ELM based deep learning model to evaluate each lncRNA subset. After that, wrapper feature extraction method is used to select lncRNAs, which are closely related to the pathophysiology of disease from massive data. Experimentation on three typical open datasets proves the feasibility and efficiency of our proposed solution. This proposed solution achieves above 93% accuracy, the best ever. [ABSTRACT FROM AUTHOR]

In order to increase the market value and quality of wheat, it is important to separate different types and determine the amount of foreign matter using the visual properties of durum and bread wheat. In this study, the extreme learning machine (ELM) algorithm, which is often preferred in real-time applications, was used to make classifications using features obtained from images containing the wheat kernel and foreign matter. The feature selection process was applied to remove the irrelevant ones from the obtained 236 features. In addition, the Harris hawks' optimizer (HHO), a novel method in the literature, and the particle swarm optimizer (PSO), one of the well-known algorithms, were used to improve the ELM model. As part of this study, new models called HHO-ELM and PSO-ELM were created and compared with the original ELM model and other artificial neural networks (ANNs) studies published in the literature. As a result, in comparison with other models, the optimized ELM models demonstrated good stability and accuracy, having 99.32% in binary classification and 95.95% in multi-class classification. [ABSTRACT FROM AUTHOR]

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenyang Agricultural University is the property of Journal of Shenyang Agricultural University Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: The rapid update of computing power leads to exponential data traffic growth, and the incidence of network attacks is also increasing. It is significantly important to analyze and predict network traffic accurately in the early stage and take corresponding preventive measures. The existing network flow integrated forecasting models still have some bottlenecks that are difficult to solve, for example, the slow optimization speed of modal decomposition parameters, easy falling into local optimal solutions, the slow convergence speed of the training process, and poor generalization capability. In this paper, particle swarm optimization (PSO) is utilized to improve the parameters selection process of the variational mode decomposition (VMD) algorithm and the extreme learning machine (ELM) algorithm. First, the PSO‐VMD combined with multi‐scale permutation entropy (MPE) is utilized to decompose the original network flow, and multiple eigenmode components are obtained. Second, the PSO‐ELM is utilized to train the network traffic prediction model, and the PSO parameters in PSO‐ELM are updated through adaptive weight adjustment and synchronous learning factors to increase the training and prediction speed, and the component prediction results are reconstructed to get a high‐precision network flow forecasting result. Finally, through the prediction and verification of the public network flow data of the WIDE backbone, the result of this experiment indicates that the VMD‐PSO‐ELM can break through the bottlenecks of slow optimization speed of VMD decomposition parameters, reduce the computational complexity of ELM, accelerate the convergence speed, and increase the forecasting accuracy. [ABSTRACT FROM AUTHOR]

Accurate measurements of available water resources play a key role in achieving a sustainable environment of a society. Precise river flow estimation is an essential task for optimal use of hydropower generation, flood forecasting, and best utilization of water resources in river engineering. The current paper presents the development and verification of the prediction abilities of new hybrid extreme learning machine (ELM)-based models coupling with metaheuristic methods, e.g., Particle swarm optimization (PSO), Mayfly optimization algorithm (MOA), Grey wolf optimization (GWO), and simulated annealing (SA) for monthly streamflow prediction. Prediction precision of standalone ELM model was compared with two-phase optimized state-of-the-arts models, e.g., ELM–PSO, ELM–MOA, ELM–PSOGWO, and ELM–SAMOA, respectively. Hydro-meteorological data acquired from Gorai and Padma Hardinge Bridge stations at Padma River Basin, northwestern Bangladesh, were utilized as inputs in this study to employ models in the form of seven different input combinations. The model's performances are appraised using Nash–Sutcliffe efficiency, root-mean-square-error (RMSE), mean absolute error, mean absolute percentage error and determination coefficient. The tested results of both stations reported that the ELM–SAMOA and ELM–PSOGWO models offered the best accuracy in the prediction of monthly streamflows compared to ELM–PSO, ELM–MOA, and ELM models. Based on the local data, the ELM–SAMOA reduced the RMSE of ELM, ELM–PSO, ELM–MOA, and ELM–PSOGWO by 31%, 27%, 19%, and 14% for the Gorai station and by 29%, 27%, 19%, and 14% for Padma Hardinge bridge station, in the testing stage, respectively. In contrast, based on external data, ELM–PSOGWO improves in RMSE of ELM, ELM–PSO, ELM–MOA, and ELM–SAMOA by 20%, 5.1%, 6.2%, and 4.6% in the testing stage, respectively. The results confirmed the superiority of two-phase optimized ELM–SAMOA and ELM–PSOGWO models over a single ELM model. The overall results suggest that ELM–SAMOA and ELM–PSOGWO models can be successfully applied in modeling monthly streamflow prediction with either local or external hydro-meteorological datasets. [ABSTRACT FROM AUTHOR]

With the rapid development of the national economy, the demand for electricity is also growing. Thermal power generation accounts for the highest proportion of power generation, and coal is the most commonly used combustion material. The massive combustion of coal has led to serious environmental pollution. It is significant to improve energy conversion efficiency and reduce pollutant emissions effectively. In this paper, an extreme learning machine model based on improved Kalman particle swarm optimization (ELM-IKPSO) is proposed to establish the boiler combustion model. The proposed modeling method is applied to the combustion modeling process of a 300 MWe pulverized coal boiler. The simulation results show that compared with the same type of modeling method, ELM-IKPSO can better predict the boiler thermal efficiency and NOx emission concentration and also show better generalization performance. Finally, multi-objective optimization is carried out on the established model, and a set of mutually non-dominated boiler combustion solutions is obtained. [ABSTRACT FROM AUTHOR]

Multiple classifier system exhibits strong classification capacity compared with single classifiers, but they require significant computational resources. Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers. However, current methods fail to identify precise solutions for constructing an ensemble classifier. In this study, we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm (ECDPB). Considering that extreme learning machines (ELMs) have rapid learning rates and good generalization ability, they can serve as the basic classifier for creating multiple candidates while using fewer computational resources. Meanwhile, we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account; it is used to identify the ELMs that have good diversity and low error. In addition, we propose an ECDPB with powerful optimizing ability; it is employed to find the optimal subset of ELMs. The selected ELMs can then be used to form an ensemble classifier. Experiments on 10 benchmark datasets have been conducted, and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenyang Agricultural University is the property of Journal of Shenyang Agricultural University Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many columns that have been built in the past need to be repaired and strengthened for various reasons such as errors during construction, design errors, changes in the use of the building, changes in regulations, strong beam-weak column conditions and also injuries from accidents. Today, various methods for strengthening and improving structures, especially concrete columns, have become popular. One of the most common methods for reinforcing columns is to confine them using polymer fiber composites. The advantages of this method include increasing the axial and lateral bearing capacity of the column, increasing the compressive and tensile strength of the concrete member and increasing the ductility of the member. The mechanical properties of the concrete confined with FRP polymer fibers may be required for the purpose of design and rehabilitation. Artificial intelligence methods are among the modeling methods that have shown great power to coordinate with engineering problems. The aim of this study is to use multivariate adaptive regression spline (MARS) and extreme learning machine (ELM) artificial intelligence models to estimate the compressive strength of concrete in circular concrete columns confined with FRP polymer fibers. In addition, in order to improve the accuracy of these models, the particle swarm Optimization algorithm (PSO) is used in combination with these models and the accuracy of the models is evaluated to estimate the resistance. The results show that in overall, the used artificial intelligence models estimate the compressive strength of FRP- confined columns more accurately than the existing analytical models. In particular, the integrated MARS-PSO model has better performance compared to other models used, so that this model has a correlation coefficient of 0.9972 in the training stage and 0.9961 in the experimental stage. Also, the combination of PSO algorithm with MARS and ELM models improves their accuracy by 6.13 and 4.68 percent, respectively.

The aim of this paper is to study the performance of a composite floor system at different heat stages using artificial intelligence to derive a sustainable design and to select the most critical factors for a sustainable floor system at elevated temperatures. In a composite floor system, load bearing is due to composite action between steel and concrete materials which is achieved by using shear connectors. Although shear connectors play an important role in the performance of a composite floor system by transferring shear force from the concrete to the steel profile, if the composite floor system is exposed to high temperature conditions excessive deformations may reduce the shear-bearing capacity of the composite floor system. Therefore, in this paper, the slip response of angle shear connectors is evaluated by using artificial intelligence techniques to determine the performance of a composite floor system during high temperatures. Accordingly, authenticated experimental data on monotonic loading of a composite steel-concrete floor system in different heat stages were employed for analytical assessment. Moreover, an artificial neural network was developed with a fuzzy system (ANFIS) optimized by using a genetic algorithm (GA) and particle swarm optimization (PSO), namely the ANFIS-PSO-GA (ANPG) method. In addition, the results of the ANPG method were compared with those of an extreme learning machine (ELM) method and a radial basis function network (RBFN) method. The mechanical and geometrical properties of the shear connectors and the temperatures were included in the dataset. Based on the results, although the behavior of the composite floor system was accurately predicted by the three methods, the RBFN and ANPG methods represented the most accurate values for split-tensile load and slip prediction, respectively. Based on the numerical results, since the slip response had a rational relationship with the load and geometrical parameters, it was dramatically predictable. In addition, slip response and temperature were determined as the most critical factors affecting the shear-bearing capacity of the composite floor system at elevated temperatures. [ABSTRACT FROM AUTHOR]

Accurate prediction of dynamic production of oil and gas wells is of great significance to the efficient development of oil fields, and is the key to the prediction of cumulative oil production of single wells and optimization of deployment policies. In the actual production process, the production of oil and gas wells is influenced by various factors such as reservoir properties and fracturing process parameters, etc. Traditional production prediction methods and numerical simulation methods have limited consideration of the influencing factors and poor applicability of the prediction methods. Based on the understanding of production characteristics, the six main control factors including formation thickness, formation pressure, total sand volume, permeability, number of fracture clusters and oil saturation were selected by using principal component analysis(PCA), and the input weights and hidden layer bias were optimized by using particle swarm optimization-extreme learning machine(PSO-ELM) to establish a horizontal well production prediction model in Mahu oilfield. The prediction results show that PSO-ELM has the advantages of fast calculation speed, strong generalization ability and high prediction accuracy compared with the traditional prediction model, and the single well production of five horizontal wells was predicted by the method with an average error of 2. 14% ~5. 28%, which is in good agreement with the actual production. [Keywords]　multi-stage; yield prediction; principal component analysis; particle swarm optimization; extreme learning dicted by the method with an average error of 2. 14% ~5. 28%, which is in good agreement with the actual production. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The accurate assessment of groundwater levels is critical to water resource management. With global warming and climate change, its significance has become increasingly evident, particularly in arid and semi-arid areas. This study compares new extreme learning machines (ELM) methods tuned with metaheuristic algorithms such as particle swarm optimization, grey wolf optimization, the whale optimization algorithm (WOA), Harris Hawks optimizer (HHO), and the jellyfish search optimizer (JFO) in groundwater level estimation. Daily precipitation and temperature datasets acquired from two stations in northern Bangladesh were used as inputs to the models, which were evaluated based on different quantitative statistics and assessed based on RMSE, MAE, R2, and some new graphical inspection methods. The outcomes of the applications revealed that the efficiency of ELM models was considerably improved by using metaheuristic algorithms. The ELM-JSO improved the RMSE of the standalone ELM model by 13% for the optimal precipitation, temperature, and groundwater level inputs in the testing stage. Among the implemented methods, the ELM-JFO performed the best in estimating the daily groundwater level, and the ELM-WOA and ELM-HHO, respectively, followed it. Viability of a new extreme machine learning (ELM) method tuned with Jellyfish search optimizer (JFO) is investigated in groundwater level estimation. The ELM-JFO is compared with hybrid ELM-PSO, ELM-WOA and ELM-HHO models using daily precipitation and temperature data acquired from two stations of Bangladesh. The ELM-JSO improves the root mean square error of the standalone ELM model by 13% for the optimal precipitation, temperature and groundwater level inputs. [ABSTRACT FROM AUTHOR]

With the increasingly severe problem of PM2.5 environmental pollution, the threat to human health is gradually increasing. Therefore, accurate prediction of PM2.5 concentration is of great significance to the healthy life of human beings. To make up for the deficiencies of previous studies on PM2.5 concentration prediction, a spatiotemporal model (Spatiotemporal prediction model of label distribution, LDSPM) for PM2.5 concentration prediction based on the K‐Core algorithm concept and label distribution learning was proposed. Leveraging K‐Core ideas and the label distribution support vector regression model of the label distribution paradigm, the influence weight of each meteorological factor on PM2.5 concentration in each piece of data was obtained with the decomposition of meteorological factors using the complete ensemble empirical mode decomposition of adaptive noise. Using a long short‐term memory neural network to predict each decomposed signal and obtain the forecast data of meteorological factors. Finally, according to the expected weight and meteorological factor data, a particle swarm optimization extreme learning machine is used to train the prediction, and the predicted value of PM2.5 is obtained. The experimental results show that the forecasting model performs better than other combined and single forecasting models. It provides new directions and ideas for PM2.5 concentration prediction. [ABSTRACT FROM AUTHOR]

The open carbon trading market is an important means to reduce carbon emissions, develop a low-carbon economy, and promote environmental protection. Accurate carbon price projections have far-reaching implications for economic and environmental policymaking. In this paper, a new combination model of carbon price prediction is proposed, which can effectively improve the accuracy of prediction. First, adaptive variational mode decomposition (AVMD) was used to decompose the original carbon price sequence into several less complex intrinsic mode functions (IMF). Secondly, the partial autocorrelation function (PACF) is used to determine the input variables for each IMF forecasting model. Finally, the extreme learning machine (ELM) optimized by the dynamic adaptive inertial factor particle swarm optimization (DAIFPSO) algorithm was used for prediction. The proposed combination model is applied to the carbon price prediction of some carbon markets in China (Beijing, Guangdong, Hubei) and compared with the other 11 models. Finally, it is found that the model has the best performance. The results show that the model is effective and stable. Therefore, this model can be used as an effective tool for government and carbon market participants to predict carbon prices and provide a reference for their decision-making. [ABSTRACT FROM AUTHOR]

Based on machine-learning (ML) and analytical methods, a hybrid method is developed herein to predict the ground-displacement field (GDF) caused by tunneling. The extreme learning machine (ELM), as a single hidden layer feedforward neural network, is used as an ML model to predict maximum settlement smax of the ground surface. The particle swarm optimization (PSO) algorithm is applied to optimize the parameters for the ELM method, namely, weight and bias values from the input layer to the hidden layer. The mean square error of the k-fold cross validation sets is considered the fitness function of the PSO algorithm. For 38 data samples from published papers, 30 samples are used as the training set, and 8 samples are used as the test set. For the test samples, the error of five samples ranges between −5 and 5 mm. The error of only one sample is slightly greater than 10 mm. The proposed PSO-ELM method demonstrates good prediction performance of smax. A deformation parameter of the nonuniform displacement mode for the tunnel cross-section is calibrated based on predicted smax. When the determined nonuniform displacement mode is used as the boundary condition of the tunnel cross-section, the GDF of a shallow circular tunnel is analytically predicted based on the complex-variable method prior to tunnel excavation. For a specific engineering case, i.e., the Heathrow Express tunnel, the proposed PSO-ELM-analytical method can well predict the surface-settlement trough curve, horizontal displacements at different depths, and vertical displacements above the tunnel. [ABSTRACT FROM AUTHOR]