Feature Subset Selection Research Articles

Effect of Green Transformational Leadership on Organizational Environmental Performance: The Role of Green Human Resource Management Practices (Study on SMEs in The Embroidery Sector)

Every organization must actively engage in environmental sustainability efforts by incorporating practices that prioritize the careful selection and maintenance of eco-friendly inputs, processes, and outputs within their business operations. However, empirical research on the specific influence of Green Transformational Leadership (GTL) on Organizational Environmental Performance is lacking, particularly within the Small Medium Enterprises (SMEs) of the embroidery sector in Indonesia. This study aims to address this gap by examining the direct and indirect effects of GTL, utilizing Green Human Resources Management (GHRM) practices as a potential mediator in the context of Indonesian SMEs specializing in embroidery. The research, conducted on 300 employees from 150 SMEs in the prominent embroidery industry of 20 city/regency areas in East Java, employs a Structural Equation Model of Partial Least Squares (SEM-PLS). Findings show a statistically significant positive influence of GTL on Organizational Environmental Performance directly, with GHRM practices partially mediating this relationship. Bridging this research gap not only contributes to academic knowledge but also offers practical insights for organizations seeking to enhance their environmental sustainability.

Improved Decision Support System for College Sports Training Based on Id3 Algorithm

A Decision Support System (DSS) is a computer-based tool designed to assist individuals and organizations in making informed decisions. It utilizes data analysis, modeling techniques, and interactive interfaces to provide users with relevant information and insights. DSSs help streamline decision-making processes by synthesizing large amounts of data from various sources, generating forecasts, and evaluating alternative courses of action. They are particularly valuable in complex and uncertain situations where decisions have significant implications. DSSs can be used in a wide range of domains, including business, healthcare, finance, and logistics, to support strategic, tactical, and operational decision-making processes. This paper introduces an Integrated Hybrid Feature Subset Decision Support System (HFS-DSS) designed to optimize decision-making processes in student performance evaluation within educational institutions. The HFS-DSS combines feature subset selection techniques with decision support algorithms to identify key attributes and make accurate predictions regarding student performance. Through experimental evaluation, the system demonstrates its effectiveness in accurately assessing student performance based on selected features, with predictions closely aligning with actual observations. The results underscore the system's reliability and effectiveness in supporting informed decision-making processes, offering valuable insights for educators and administrators to enhance academic outcomes. The adaptability and versatility of the HFS-DSS make it well-suited for various educational contexts, providing potential applications in curriculum planning, student support services, and performance monitoring. The present paper highlights the significant contributions of the Integrated HFS-DSS towards enhancing decision-making processes and improving academic outcomes in educational institutions.

Global horizontal irradiance prediction model for multi-site fusion under different aerosol types

Accurate prediction of global horizontal irradiance (GHI) is crucial for anticipating the volatility of solar power output. Under clear-sky conditions, aerosols play a significant role in influencing GHI, with different types of aerosols exhibiting distinct radiation effects. In this study, we employed the Planck mean aerosol optical depth (AOD) —Informer model to forecast future GHI, utilizing historical data of AOD, meteorological parameters, and GHI as input variables. Two data-fusion schemes were proposed, and the calculation results indicate that Scheme 1, organized in the order of Xianghe—Beijing—Shangdianzi (XBS) based on the day as a unit, demonstrates superior prediction performance. Additionally, the site information was blurred, allowing for the flexible selection of input and output lengths. The calculation results remained stable and reliable, suggesting that the inclusion of fuzzy site information does not degrade the prediction effect. Building upon this, we categorized aerosols into three types based on their time span and data volume. GHI predictions for different aerosol types revealed that the mean square error (0.145) for marine aerosols were significantly lower than those for the other types. The GHI predictions for continental aerosols were closest to the true values.

Wind power forecasting technologies: A review

This study addresses the critical role of wind power forecasting in ensuring stable and reliable power system operations. Wind Power Forecasting is critical for the efficient operation of plant, time scheduling, and it’s balancing of power generation with grid integration systems. Due to its dependency on dynamic climatic conditions and associated factors, accurate wind power forecasting is challenging. The research delves into various aspects, including input data, input selection techniques, data pre-processing, and forecasting methods, with the aim of motivating researchers to design highly efficient online/offline models on weather-based data. The overarching goal is to enhance the reliability and stability of power systems while optimizing energy resource utilization. The analysis reveals that hybrid models offer more accurate results, highlighting their significance in the current era. This study investigates different Wind Power Forecasting (WPF) models from existing literature, focusing on input variables, time horizons, climatic conditions, pre-processing techniques, and sample sizes that affect model accuracy. It covers statistical models like ARMA and ARIMA, along with AI techniques including Deep Learning (DL), Machine Learning (ML), and neural networks, to estimate wind power.

An ensemble maximal feature subset selection for smartphone based human activity recognition

Smartphone-based Human Activity Recognition (HAR) uses spatiotemporal time series data collected from a smartphone’s in-built accelerometer, gyroscope, and magnetometer sensor. Real-time HAR datasets such as UCI-HAR and UCI-HAPT extract time and frequency domain statistical features for activity recognition from the collected time series signals. Various Machine Learning techniques have been proposed in the perspective of feature selection through ensemble, traditional, hybrid, and meta-heuristic-based techniques to improve the recognition performance. Though the feature selection techniques have shown promising results, the computational time and selection of valid significant features for better classification performance still need to be included. Moreover, these techniques are highly parameter-dependent and critical to threshold values used for experimentation. This research proposes a novel method of parameter-free ensemble feature selection by combining traditional feature selection algorithms with the concept of association rule mining to obtain the Maximal Feature Subset (MFS), which is used for training and testing the classification model for HAR. Mathematical association rule mining is framed rather than the traditional one to select the associated frequent feature(s) from the feature set evolved by traditional techniques. Experimentation has been carried out on UCI-HAR and UCI-HAPT, and the proposed method has increased the classification efficiency by selecting significant features and reduces computation time significantly.

Neural networks as effective surrogate models of radio-frequency quadrupole particle accelerator simulations

Radio-frequency quadrupoles (RFQs) are multi-purpose linear particle accelerators that simultaneously bunch and accelerate charged particle beams. They are ubiquitous in accelerator physics, especially as injectors to higher-energy machines, owing to their impressive efficiency. The design and optimization of these devices can be lengthy due to the need to repeatedly perform high-fidelity simulations. Several recent papers have demonstrated that machine learning can be used to build surrogate models (fast-executing replacements of computationally costly beam simulations) for order-of-magnitude computing time speedups. However, while these pilot studies are encouraging, there is room to improve their predictive accuracy. Particularly, beam summary statistics such as emittances (an important figure of merit in particle accelerator physics) have historically been challenging to predict. For the first time, we present a surrogate model trained on 200 000 samples that yields < 6% mean average percent error for the predictions of all relevant beam output parameters from defining RFQ design parameters, solving the problem of poor emittance predictions by identifying and including hidden variables which were not accounted for previously. These surrogate models were made possible by using the Julia language and GPU computing; we briefly discuss both. We demonstrate the utility of surrogate modeling by performing a multi-objective optimization using our best model as a callback in the objective function to select an optimal RFQ design. We consider trade-offs in RFQ performance for various choices of Pareto-optimal design variables—common issues for any multi-objective optimization scheme. Lastly, we make recommendations for input data preparation, selection, and neural network architectures that pave the way for future development of production-capable surrogate models for RFQs and other particle accelerators.

Enhancing multi-scenario data-driven energy consumption prediction in campus buildings by selecting appropriate inputs and improving algorithms with attention mechanisms

Effective building energy prediction is vital for sustainable development, especially with an increasing focus on flexibility and elasticity in building energy usage. However, challenges persist in input and algorithm selection including suitable input selection and the balance of calculation time consumption and prediction precision. This study proposed a method for suitable input selection and introduced a CNN-LSTM-SE model for enhancing prediction precision. The CNN-LSTM-SE model integrates a convolutional neural network (CNN) process and a squeeze-and-excitation (SE) block attention mechanism into a long short-term memory (LSTM) neural network. Experiments across seven buildings demonstrate that selecting inputs improves the prediction performance with a 13.94 % decrease in MAPE (mean absolute percent error), a 3.36 % increase in R2 (coefficient of determination), and an 11.20 % decrease in CV-RMSE (coefficient-of-variation of root mean square error) on average compared to not selecting inputs. Moreover, implementing CNN-LSTM-SE yields a 21.44 % MAPE decrease, a 6.25 % R2 increase, and a 21.82 % CV-RMSE decrease compared to alternative algorithms. Simultaneously employing suitable inputs and CNN-LSTM-SE balances time consumption and prediction precision optimally.

Simultaneous Fresh Water Collection and Li+ Selective Adsorption Enabled by A Salt‐Resistant Separated Solar Evaporator

AbstractSolar‐driven interfacial evaporation (SIE) of brine may solve the fresh water shortage issue but suffers from salt‐fouling. Meanwhile, adsorption can extract valuable lithium (Li) from brine but is hampered by low adsorption capacity/rate, additional energy input and low selectivity, etc. Here, as a proof‐of‐concept, the design of a separated solar evaporator (S‐evaporator) is reported for simultaneously efficient fresh water collection and Li+ selective adsorption by SIE of brine, accomplishing their complementation using only sunlight. The S‐evaporator consists of a tilted n‐shaped H2TiO3‐modified fabric and a photothermal sheet on it. The superhydrophilic fabric transports brine to the photothermal sheet and provides affluent sites for Li+ adsorption. The photothermal sheet promotes SIE and enhances Li+ adsorption by significantly increasing the fabric's temperature. Consequently, simultaneous fresh water collection and Li+ selective adsorption are realized by the S‐evaporator. Under 1 kW m−2 illumination, the S‐evaporator shows long‐term stable evaporation rate (1.51 kg m−2 h−1) for 20 wt% brine, high Li+ adsorption capacity (20.09 mg g−1), good Li+ adsorption selectivity from real brine and good cycle stability. The S‐evaporator has great application potential for efficiently extracting fresh water and Li+ from brine as demonstrated by the large SIE setup in real outdoor conditions.

An overlapping conscious relief-based feature subset selection method

Feature selection is considered as a fundamental prepossessing step in various data mining and machine learning based works. The quality of features is essential to achieve good classification performance and to have better data analysis experience. Among several feature selection methods, distance-based methods are gaining popularity because of their eligibility in capturing feature interdependency and relevancy with the endpoints. However, most of the distance-based methods only rank the features and ignore the class overlapping issues. Features with class overlapping data work as an obstacle during classification. Therefore, the objective of this research work is to propose a method named overlapping conscious MultiSURF (OMsurf) to handle data overlapping and select a subset of informative features discarding the noisy ones. Experimental results over 20 benchmark dataset demonstrates the superiority of OMsurf over six existing state-of-the-art methods

Optimal feature subset selection for MRI brain tumor classification using improved ant-lion optimization

Optimal feature subset selection for MRI brain tumor classification using improved ant-lion optimization

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection.

This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, undergo a scaled continuous wavelet transform (S-CWT) to generate scalogram images. These images are subsequently utilized as inputs for pretrained deep learning models. The experiments encompass various fault scenarios, spanning distinct fault types, locations, times, and resistance values. A remarkable feature of the proposed work is the attainment of 100% classification accuracy, obviating the need for additional algorithmic enhancements. The foundation of this achievement is the deliberate selection of the right input. The decision to employ an identical number of samples as the number of scales for the CWT emerges as a pivotal factor. This approach underpins the high accuracy and renders supplementary algorithms superfluous. Furthermore, this research underscores the versatility of this approach, showcasing its effectiveness across diverse networks and scenarios. Wavelet transform, after rigorous experimentation, emerges as a reliable tool for capturing transient fault characteristics with an optimal balance between time and frequency resolutions.

Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone

The study focuses on developing an accurate prediction model for suspended sediment load (SSL) based on antecedent SSL and water discharge values. Two Artificial Intelligence (AI) models, Hybrid and Parallel, were employed to test on the Kelantan and Mississippi Rivers in different climate zones and river sizes. The parallel model showed better performance than the hybrid in most cases, with the best results based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) (432.06 and 782.15 respectively) for Kelantan and (31672.25 and 62356.60 respectively) for Mississippi. The multifunctional GA neural-network model results have proven its ability to predict SSL in tropical and semi-arid zones. In the Kelantan River, the 8-input combination set was the best prediction model, showing an improvement of more than 38% compared to traditional models. The proposed method has proven to be more accurate than traditional models, ensuring better water resource planning, agricultural management and reservoir operation.

Non-Destructive Classification of Maize Seeds Based on RGB and Hyperspectral Data with Improved Grey Wolf Optimization Algorithms

Ensuring the security of germplasm resources is of great significance for the sustainable development of agriculture and ecological balance. By combining the morphological characteristics of maize seeds with hyperspectral data, maize variety classification has been achieved using machine learning algorithms. Initially, the morphological data of seeds are obtained from images, followed by the selection of feature subsets using Recursive Feature Elimination (RFE) and Select From Model (SFM) methods, indicating that features selected by RFE exhibit better performance in maize seed classification. For hyperspectral data (350–2500 nm), Competitive Adaptive Re-weighted Sampling (CARS) and the Successive Projections Algorithm (SPA) are employed to extract feature wavelengths, with the SPA algorithm demonstrating superior performance in maize seed classification tasks. Subsequently, the two sets of data are merged, and a Random Forest (RF) classifier optimized by Grey Wolf Optimization (GWO) is utilized. Given the limitations of GWO, strategies such as logistic chaotic mapping for population initialization, random perturbation, and final replacement mechanisms are incorporated to enhance the algorithm’s search capabilities. The experimental results show that the proposed ZGWO-RF model achieves an accuracy of 95.9%, precision of 96.2%, and recall of 96.1% on the test set, outperforming the unimproved model. The constructed model exhibits improved identification effects on multi-source data, providing a new tool for non-destructive testing and the accurate classification of seeds in the future.

A universal ANN-based approach predicting PCHEs’ off-design performance across various operating conditions of sCO2 RCBCs

This paper proposes a universal approach that employs artificial neural networks (ANN) to predict the off-design performance of printed circuit heat exchangers (PCHEs) using supercritical carbon dioxide (sCO2) fluid. PCHEs are widely studied precoolers and recuperators of sCO2 power cycles, for which predicting their off-design heat transfer performance is challenging due to the fluid’s dramatic non-linear property variations. Due to their narrow valid range and constrained expressing ability, the existing empirical correlations might introduce significant out-of-scope errors that result in diverged simulation results, slow convergence and abnormal quitting. This work addresses the issue by proposing an ANN-based Nusselt number correlation, ANNu, to predict the off-design performance of PCHEs quickly and universally over the vast operating range of sCO2 cycles. Multiple techniques were adopted to promote ANNu’s prediction accuracy and generalization, such as consolidating 494 training data, data-driven input selection, and global optimization for the networks’ architecture and hyperparameters. This paper comprehensively evaluates ANNu’s performance by comparing its predictions with ten empirical correlations over four typical real-world scenarios, where ANNu features an overall MSE of 83.4222 and overall MAPE of 16.285 % that rank the second best and third best among eleven candidates (even overpassing two references), respectively. Moreover, ANNu exhibits good generalization and reliability, featuring MAPEs ranging from 9.89 % to 27.73 % when processing unmet experiment data from sCO2 PCHE applications.

A novel hybrid forecasting model with feature selection and deep learning for wind speed research

AbstractAccurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model that integrates data preprocessing, feature selection, and optimized forecasting for improved wind speed prediction. Specifically, a powerful preprocessing technique is utilized to reduce data noise disturbances, while an innovative two‐stage feature selection is designed to achieve the optimal input data format for forecasting purposes. Moreover, a hybrid forecasting module based on long‐short term memory, which is optimized by the Bayesian optimization algorithm, has been developed to enhance the efficiency and reliability of the model. The empirical study used 10‐min interval wind speed data of four seasons for presentation and evaluation results demonstrated its superior performance in effectively learning the volatility and irregularity features of wind speed series, which established a solid foundation for practical applications in wind power systems.

Machine learning aided uncertainty quantification for engineering structures involving material-geometric randomness and data imperfection

In real-world engineering, uncertainty is ubiquitous within material properties, structural geometry, load conditions, and the like. These uncertainties have substantial impacts on the estimation of structural performance. Furthermore, information or datasets in real life commonly contain imperfections, e.g., noise, outliers, or missing data. To quantify these impacts induced by uncertainties on structural behaviours and reduce the effects of data imperfections simultaneously, a machine learning-aided stochastic analysis framework is proposed. A novel supervised machine learning technique, namely the Capped Extended Support Vector Regression (CX-SVR) technique, is developed to effectively suppress the effects of outliers and noise in datasets. Its inherent convexity in optimization and capped strategy theoretically supports the accuracy of CX-SVR, especially in handling datasets with imperfections. Once the effective surrogate model is established, subsequent analyses, like sampling-based methods, can circumvent the cumbersome physical model, which is potentially the nest of computational burden and errors in engineering applications. The high robustness of the proposed approach can be summarized in four main aspects: unrestrictive selection of the system inputs and their statistical information, ‘perfect’ or ‘imperfect’ data, enough statistical information (including statistical moments, probability density functions, and cumulative distribution functions) of the system outputs, and physical problems from various engineering fields.

Quantum mayfly optimization based feature subset selection with hybrid CNN for biomedical Parkinson’s disease diagnosis

Parkinson's disease (PD) arises from brain cell damage and necessitates early detection for effective treatment and symptom management. While various methods such as voice, speech, and written exams have been explored, utilizing automated tools is crucial to enhance accuracy. Recent advancements in artificial intelligence (AI) and deep learning (DL) provide an opportunity for precise early-stage PD identification. This study introduces a novel approach known as Quantum Mayfly Optimization-based feature subset selection with hybrid convolutional neural network (QMFOFS-HCNN) to improve PD detection and classification. QMFOFS-HCNN is designed to identify optimal feature subsets and overcome the dimensionality challenge. It combines a quantum mayfly optimization approach for feature selection with a convolutional neural network with attention-based long short-term memory for PD detection and classification. Additionally, hyperparameter selection is optimized using the Nadam optimizer. Experimental validation using benchmark datasets yielded compelling results. The QMFOFS-HCNN technique achieved accuracy rates: 96.35% for HandPD Spiral, 96.7% for HandPD Meander, 98.5% for Speech PD, and a perfect 100% for Voice PD datasets. These quantitative findings underscore the potential of AI and DL to enhance early PD detection accuracy significantly. These results offer promising prospects for improving healthcare outcomes in managing PD and related neurological disorders.

Monthly Streamflow Prediction of the Source Region of the Yellow River Based on Long Short-Term Memory Considering Different Lagged Months

Accurate and reliable monthly streamflow prediction plays a crucial role in the scientific allocation and efficient utilization of water resources. In this paper, we proposed a prediction framework that integrates the input variable selection method and Long Short-Term Memory (LSTM). The input selection methods, including autocorrelation function (ACF), partial autocorrelation function (PACF), and time lag cross-correlation (TLCC), were used to analyze the lagged time between variables. Then, the performance of the LSTM model was compared with three other traditional methods. The framework was used to predict monthly streamflow at the Jimai, Maqu, and Tangnaihai stations in the source area of the Yellow River. The results indicated that grid search and cross-validation can improve the efficiency of determining model parameters. The models incorporating ACF, PACF, and TLCC with lagged time are evidently superior to the models using the current variable as the model inputs. Furthermore, the LSTM model, which considers the lagged time, demonstrated better performance in predicting monthly streamflow. The coefficient of determination (R2) improved by an average of 17.46%, 33.94%, and 15.29% for each station, respectively. The integrated framework shows promise in enhancing the accuracy of monthly streamflow prediction, thereby aiding in strategic decision-making for water resources management.

Active Learning for Discrete Latent Variable Models.

Active learning seeks to reduce the amount of data required to fit the parameters of a model, thus forming an important class of techniques in modern machine learning. However, past work on active learning has largely overlooked latent variable models, which play a vital role in neuroscience, psychology, and a variety of other engineering and scientific disciplines. Here we address this gap by proposing a novel framework for maximum-mutual-information input selection for discrete latent variable regression models. We first apply our method to a class of models known as mixtures of linear regressions (MLR). While it is well known that active learning confers no advantage for linear-gaussian regression models, we use Fisher information to show analytically that active learning can nevertheless achieve large gains for mixtures of such models, and we validate this improvement using both simulations and real-world data. We then consider a powerful class of temporally structured latent variable models given by a hidden Markov model (HMM) with generalized linear model (GLM) observations, which has recently been used to identify discrete states from animal decision-making data. We show that our method substantially reduces the amount of data needed to fit GLM-HMMs and outperforms a variety of approximate methods based on variational and amortized inference. Infomax learning for latent variable models thus offers a powerful approach for characterizing temporally structured latent states, with a wide variety of applications in neuroscience and beyond.

Runoff projection in the Tibetan Plateau using a long short-term memory network-based framework under various climate scenarios

Runoff projections in the Tibetan Plateau (TP) can provide a valuable basis for making decisions regarding water resource management and facilitating the assessment of potential risks to water security. This study estimated the runoff of the Tibetan Plateau (TP) at a grid resolution of 0.5°for the near-term period (2022–2051) and the long-term period (2052–2082) under various climate scenarios. A framework based on long short-term memory (LSTM) was used to project the impact of climate change on runoff by integrating input selection, LSTM modelling and runoff projection application. This novel framework was used to determine the variables that contribute to runoff occurrence in different grid cells, analyze the relationship between these variables and runoff, and predict how runoff may change in the future due to precipitation, temperature, and antecedent runoff under various climate scenarios. The projected results showed that the runoff would be approximately 450 mm/a for the period from 2022 to 2051 and approximately 465 mm/a during the period from 2052 to 2082, representing an increase of about 46 % compared to the period from 1982 to 2012. Future runoff will increase in a decreasing pattern from the southwest to the northeast across the TP region. These findings regarding the projected increase in runoff will assist in the development of proactive adaptation strategies and support long-term economic growth through effective water resource management plans.