Set Of Features Research Articles

Fast building detection using new feature sets derived from a very high-resolution image, digital elevation and surface model

ABSTRACT Detecting building rooftops with very high-resolution (VHR) images is an important issue in many fields, including disaster management, urban planning, and climate change research. Buildings with varying geometrical features are challenging to detect accurately from VHR image due to complicated image scenes containing spectrally similar objects, illumination, occlusions, viewing angles, and shadows. This study aims to detect building rooftops with high accuracy using a new framework that includes VHR image, visible band difference vegetation index, digital surface and elevation models, the terrain ruggedness and the topographic position index. Five distinct feature sets were generated in order of importance by exposing the ten related stacking features to a feature selection procedure using the maximum relevance minimum redundancy method. Then, Auto-Encoder, k-NN, decision tree, RUSBoost, and random forest machine learning algorithms were utilized for binary classification. Random forest yielded the highest accuracy (97.2% F-score, 98.72% accuracy) when all features (F10) were used, while decision tree was the least successful (59.16% F-score, 83.56% accuracy) for RGB feature set (FRGB). It was revealed that classification of F10 with random forest increased F-score by about 23% compared to classification with FRGB. Additionally, McNemar’s tests showed no statistically significant difference between random forest vs k-NN and decision tree vs RUSBoost.

Rolling bearing fault diagnosis based on improved whale-optimization- algorithm–variational-mode-decomposition method

Rolling bearings are a key component of rotating machinery and their health directly affects the safe operation of mechanical equipment. Therefore, fault diagnose for rolling bearings is very important. The fault diagnosis process of rolling bearings includes three stages: signal decomposition, feature extraction, and pattern recognition. Variational mode decomposition (VMD) can suppress end effects, but improper parameter settings will cause information losses or excessive decomposition. In this work, an improved whale optimization algorithm (IWOA) is applied to parameter settings of VMD. Correspondingly, an IWOA-VMD signal decomposition method is proposed. The decomposed signal is combined with a Laplace score method and classifier to remove the redundancy and noise in the feature set and obtain a low-dimensional sensitive feature subset. Then, aiming at the problem of the parameter settings of a least squares support vector machine (LSSVM) affecting the recognition performance and accuracy, a salp swarm algorithm (SSA) is used to globally optimize the penalty parameter and kernel width in the LSSVM to establish an SSA-LSSVM fault recognition model. This model is applied to the fault diagnosis of rolling bearings. In particular, rolling bearing fault samples at Case Western Reserve University are used to verify the method. The results indicate that the proposed method is effective and improves the speed and accuracy of fault diagnosis.

The reptile optimized deep learning model for land cover classification of the uppal earth region in telangana state using satellite image fusion

This study addresses challenges in land use and cover identification using remote sensing (RS) imagery, focusing on the Uppal region. By leveraging deep learning models, particularly an optimized ResNext-50 architecture, we aim to enhance efficiency and accuracy in classifying land features. Our approach integrates Landsat-8 and hyper-spectral satellite data, utilizing preprocessing techniques like dark subtraction, stacking, merging, and spectral enhancement. Principal Component Analysis (PCA) is applied to streamline high-dimensional feature sets obtained from pre-processed spectral data. We further employ hybrid NSCT-FDCT fusion for integrating Landsat-8 and hyperspectral images. The resulting fused image is fed into our classification process, utilizing the modified ResNext50 (Deep Learning Architecture) model with Reptile Search Optimization for weight link optimization. Notably, our proposed method achieves impressive outcomes: 97% accuracy, 96% sensitivity, 99% specificity, 3% error, 97% precision, and a 95% Matthew Correlation Coefficient. This demonstrates the efficacy of our approach in predicting diverse land covers within the Uppal region, showcasing the potential of Landsat-8 and Hyper-spectral data for accurate land use and cover identification.

MACHINE LEARNING FOR STOCK MARKET FORECASTING: A REVIEW OF MODELS AND ACCURACY

As financial markets become increasingly complex and dynamic, the application of machine learning (ML) techniques for stock market forecasting has garnered significant attention. This paper presents a comprehensive review of various ML models employed in the realm of stock market forecasting, focusing on their methodologies and the accuracy achieved in predicting market trends. The review begins by examining traditional time-series models such as autoregressive integrated moving average (ARIMA) and moving average convergence divergence (MACD) and their limitations in capturing the intricate patterns present in financial data. Subsequently, the discussion transitions to more advanced ML models, including support vector machines (SVM), artificial neural networks (ANN), and ensemble methods like random forests and gradient boosting. Each model's strengths and weaknesses are scrutinized in the context of stock market forecasting. The paper explores the pivotal role of feature selection and engineering in enhancing the predictive power of ML models. Feature sets encompassing financial indicators, macroeconomic variables, sentiment analysis from news articles, and social media data are analyzed for their impact on forecasting accuracy. Additionally, the incorporation of technical indicators and alternative data sources is explored as potential avenues to improve model robustness. A critical aspect of this review is the assessment of accuracy in predicting stock market movements. The evaluation is conducted through a comparative analysis of model performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and accuracy rates. The study also addresses the challenge of model overfitting and proposes strategies to mitigate this issue for more reliable predictions. This review provides a nuanced understanding of the landscape of ML models for stock market forecasting, highlighting the diverse approaches, challenges, and opportunities in the quest for improved accuracy. It contributes valuable insights for researchers, practitioners, and investors seeking to leverage the potential of ML in navigating the complexities of financial markets.
 Keywords:  Machine Learning, Stock Market, Forecasting, Models, Review.

Pre-trained models for detection and severity level classification of dysarthria from speech

Automatic detection and severity level classification of dysarthria from speech enables non-invasive and effective diagnosis that helps clinical decisions about medication and therapy of patients. In this work, three pre-trained models (wav2vec2-BASE, wav2vec2-LARGE, and HuBERT) are studied to extract features to build automatic detection and severity level classification systems for dysarthric speech. The experiments were conducted using two publicly available databases (UA-Speech and TORGO). One machine learning-based model (support vector machine, SVM) and one deep learning-based model (convolutional neural network, CNN) was used as the classifier. In order to compare the performance of the wav2vec2-BASE, wav2vec2-LARGE, and HuBERT features, three popular acoustic feature sets, namely, mel-frequency cepstral coefficients (MFCCs), openSMILE and extended Geneva minimalistic acoustic parameter set (eGeMAPS) were considered. Experimental results revealed that the features derived from the pre-trained models outperformed the three baseline features. It was also found that the HuBERT features performed better than the wav2vec2-BASE and wav2vec2-LARGE features. In particular, when compared to the best-performing baseline feature (openSMILE), the HuBERT features showed in the detection problem absolute accuracy improvements that varied between 1.33% (the SVM classifier, the TORGO database) and 2.86% (the SVM classifier, the UA-Speech database). In the severity level classification problem, the HuBERT features showed absolute accuracy improvements that varied between 6.54% (the SVM classifier, the TORGO database) and 10.46% (the SVM classifier, the UA-Speech database) compared to the best-performing baseline feature (eGeMAPS).

Hybrid intelligent technique for intrusion detection in cyber physical systems with improved feature set

Machine learning techniques commonly used for intrusion detection systems (IDSs face challenges due to inappropriate features and class imbalance. A novel IDS comprises four stages: Pre-processing, Feature Extraction, Feature Selection, and Detection. Initial pre-processing balances input data using an improved technique. Features (statistical, entropy, correlation, information gain) are extracted, and optimal ones selected using Improved chi-square. Intrusion detection is performed by a hybrid model combining Bi-GRU and CNN classifiers, with optimized weight parameters using SI-BMO. The outputs from both classifiers are averaged for the result. The SI-BMO-based IDS is compared with conventional techniques Blue Monkey Optimization (BMO), Grasshopper Optimization Algorithm (GOA), Deer Hunting Optimization (DHO), Poor Rich Optimization (PRO), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN) for performance evaluation.

Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework

ABSTRACT: Plant-allergenic proteins (PAPs) have the potential to induce allergic reactions in certain individuals. While these proteins are generally innocuous for the majority of people, they can elicit an immune response in those with particular sensitivities. Thus, screening and prioritizing the allergenic potential of plant proteins is indispensable for the development of diagnostic tools, therapeutic interventions or medications to treat allergic reactions. However, investigating the allergenic potential of plant proteins based on experimental methods is costly and labour-intensive. Therefore, we develop StackPAP, a three-layer stacking ensemble framework for accurate large-scale identification of PAPs. In StackPAP, at the first layer, we conducted a comprehensive analysis of an extensive set of feature descriptors. Subsequently, we selected and fused five potential sequence-based feature descriptors, including amphiphilic pseudo-amino acid composition, dipeptide deviation from expected mean, amino acid composition, pseudo amino acid composition and dipeptide composition. Additionally, we applied an efficient genetic algorithm (GA-SAR) to determine informative feature sets. In the second layer, 12 powerful machine learning (ML) methods, in combination with all the informative feature sets, were employed to construct a pool of base classifiers. Finally, 13 potential base classifiers were selected using the GA-SAR method and combined to develop the final meta-classifier. Our experimental results revealed the promising prediction performance of StackPAP, with an accuracy, Matthew’s correlation coefficient and AUC of 0.984, 0.969 and 0.993, respectively, as judged by the independent test dataset. In conclusion, both cross-validation and independent test results indicated the superior performance of StackPAP compared with several ML-based classifiers. To accelerate the identification of the allergenicity of plant proteins, we developed a user-friendly web server for StackPAP (https://pmlabqsar.pythonanywhere.com/StackPAP). We anticipate that StackPAP will be an efficient and useful tool for rapidly screening PAPs from a vast number of plant proteins. Communicated by Ramaswamy H. Sarma

Flexible variable selection in the presence of missing data.

In many applications, it is of interest to identify a parsimonious set of features, or panel, from multiple candidates that achieves a desired level of performance in predicting a response. This task is often complicated in practice by missing data arising from the sampling design or other random mechanisms. Most recent work on variable selection in missing data contexts relies in some part on a finite-dimensional statistical model, e.g., a generalized or penalized linear model. In cases where this model is misspecified, the selected variables may not all be truly scientifically relevant and can result in panels with suboptimal classification performance. To address this limitation, we propose a nonparametric variable selection algorithm combined with multiple imputation to develop flexible panels in the presence of missing-at-random data. We outline strategies based on the proposed algorithm that achieve control of commonly used error rates. Through simulations, we show that our proposal has good operating characteristics and results in panels with higher classification and variable selection performance compared to several existing penalized regression approaches in cases where a generalized linear model is misspecified. Finally, we use the proposed method to develop biomarker panels for separating pancreatic cysts with differing malignancy potential in a setting where complicated missingness in the biomarkers arose due to limited specimen volumes.

A learning-based framework for spatial join processing: estimation, optimization and tuning

AbstractThe importance and complexity of spatial join operation resulted in the availability of many join algorithms, some of which are tailored for big-data platforms like Hadoop and Spark. The choice among them is not trivial and depends on different factors. This paper proposes the first machine-learning-based framework for spatial join query optimization which can accommodate both the characteristics of spatial datasets and the complexity of the different algorithms. The main challenge is how to develop portable cost models that once trained can be applied to any pair of input datasets, because they are able to extract the important input characteristics, such as data distribution and spatial partitioning, the logic of spatial join algorithms, and the relationship between the two input datasets. The proposed system defines a set of features that can be computed efficiently for the data to catch the intricate aspects of spatial join. Then, it uses these features to train five machine learning models that are used to identify the best spatial join algorithm. The first two are regression models that estimate two important measures of the spatial join performance and they act as the cost model. The third model chooses the best partitioning strategy to use with spatial join. The fourth and fifth models further tune two important parameters, number of partitions and plane-sweep direction, to get the best performance. Experiments on large-scale synthetic and real data show the efficiency of the proposed models over baseline methods.

Detection of Brain Tumour Via Reversing Hexagonal Feature Pattern for Classifying Double-Modal Brain Images

The diagnosis of brain tumours (BT) is time-consuming and heavily dependent on the radiologists' abilities. Multiple algorithms have been developed for detecting and classifying BT that are both accurate and fast. Recent years have seen an increase in the popularity of deep learning, especially when it comes to developing automated systems that can diagnose and segment BT more accurately and with less time. In this paper, a novel Brain Hexagonal Pattern Network (BHPN) has been proposed to classify the MEG and PET images into normal, benign and malignant tumours. For pre-processing, a bilateral filter is employed to remove noise artifacts from the collected MEG and PET images. To remove the outer cortical and skull region, skull stripping is used, to be implemented to raise the volume of the training datasets. The pre-processed images are segmented using the Otsu threshold algorithm to segment the BT. These segmented tumours are taken as input to the Reversing Hexagonal algorithm to generate the hexagonal feature sets with and without a segmentation mask. In order to categorize tumours into normal, benign and malignant cases, a Spiking Dilated Convolutional Neural Network (SDCNN) classifier system is implemented. The classification accuracy of the Proposed BHPN approach is 99.54%. The Proposed BHPN approach improves the overall accuracy by 1.49%, 2.52%, and 3.93% better than hybrid deep autoencoder (DAE) and Bayesian Fuzzy Clustering (BFC), Deep CNN, and Neutrosophy and Convolutional Neural Network (NS-CNN) respectively.

Hybrid model for rainfall prediction with statistical and technical indicator feature set

As excessive rain may cause numerous disasters, rainfall prediction is very crucial and the prediction should be realistic since it encourages individuals to take preventative steps. This work proposes a new rainfall prediction model by following three major phases: Preprocessing, Extraction of Features and Prediction. The Improved Data Normalization technique is carried out for preprocessing the data. Then, it extracts the technical Indicator features like Average Directional Movement (ADX), Moving Average Convergence Divergence (MACD), RSI, Welles Wilder's Smoothing Average (WWS), statistical & higher order statistical features. According to these features, prediction takes place by a hybrid model that includes the Improved DBN & LSTM methods. By fine-tuning the best weights, the training will be carried out optimally to increase the prediction model's efficacy. For this, the Brownian Motion-based Pelican Optimization Algorithm (BMPOA) is implemented. Finally, the proposed technique has contrasted over existing models concerning different metrics.

Providing a Feature Selection Method for Lung Cancer Prediction Using Neural Network

Introduction: The remarkable growth of lung cancer and its associated impacts and consequences, along with the substantial costs it imposes on society, has driven the medical community to pursue programs aimed at further examination, prevention, early detection, and diagnosis. In medicine science, timely discovery and diagnosis of diseases can prevent many life-threatening conditions and save people's lives.Material and Methods: This study aims to predict lung cancer using a novel feature selection method integrated with a classifier. Our approach entails a comprehensive four-stage method. Initially, we calculate feature similarities within a lung cancer dataset using the absolute value of the Pearson correlation coefficient, followed by the clustering of initial features using the community detection algorithm called Louvain. Next, we employ techniques to determine the optimal subset of features using the concept of node centrality. Ultimately, lung cancer diagnosis is executed using the selected features, leveraging a classifier.Results: Comparative analysis reveals that our proposed method outperforms existing techniques in terms of reduced execution time and improved prediction accuracy. When compared with established methods, our approach demonstrates superior outcomes in terms of the number of selected features and classification accuracy. Our method reduced 12600 features to 118 features and its accuracy was 95.28, 95.49, 95.23 and 95.32 for Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB) and K-Nearest Neighbor (KNN) classifier. The comparison of runtime shows that the proposed method is significantly improved with a runtime of 2.146 seconds compared to other methods.Conclusion: The proposed feature selection method successfully reduced the initial feature set and significantly decreased computational time. Moreover, the achieved prediction accuracies underscore the reliability of our approach. This significant reduction in feature space while maintaining consistently high prediction accuracies serves as a strong validation of the potency and practical applicability of our methodology in the domain of lung cancer prediction. These compelling results strongly advocate for the potential real-world impact of our approach.

Research on electric vehicle load forecasting considering regional special event characteristics

With the rise of electric vehicles and fast charging technology, electric vehicle load forecasting has become a concern for electric vehicle charging station planners and operators. Due to the non-stationary nature of traffic flow and the instability of the charging process, it is difficult to accurately predict the charging load of electric vehicles, especially in sudden major events. In this article, We proposes a high-precision EV charging load forecasting model based on mRMR and IPSO-LSTM, which can quickly respond to the epidemic (or similar emergencies). Firstly, the missing data in the original EV charging load data are supplemented, and the abnormal data are corrected. Based on this, a factor set is established, which included five epidemic factors, including new confirmed cases, the number of moderate risk areas, the number of high risk areas, epidemic situation and epidemic prevention policies of the city, and other factors such as temperature. Secondly, the processed load data and other data in the influencing factor set are normalized, and the typical characteristic curve is established for personalized processing of the relevant data of epidemic factors, so as to improve the sensitivity of load response to epidemic changes and the ability to capture special data (peak and valley values and turning points of load). Then the maximum relevant minimum redundancy (mRMR) is used to select the optimal feature set from the set of influencing factors. Then, the processed load data and its corresponding optimal selection are input into the IPSO-LSTM model to obtain the final prediction result. Finally, taking the relevant data of EV charging load in a city in China from November 2021 to April 2022 (the city experienced two local epidemics in December 2021 and March 2022 respectively) as an example, the model is evaluated and compared with other models under the forecast period of 1 h. Meanwhile, the performance of the model under different foresight periods (2 h, 4 h, 6 h) is compared and analyzed. The results show that the model has good stability and representativeness, and can be used for EV charging load prediction under the COVID-19 pandemic.

Supervised learning for automatic emotion recognition in Parkinson’s disease through smartwatch signals

People with Parkinson’s Disease (PwPD) usually experience several neuropsychiatric signs such as anxiety, depression, and negative emotions that contribute to disability and worsening of quality of life. Notwithstanding, the assessment of these symptoms are largely underrated, subjective and difficult due to a large overlapping with other PD symptoms, like hypomimia and bradikinesia. The aim and novelty of the current work is to study and validate a method for automatic emotion recognition in PwPD during daily living through autonomic signals acquired by acceptable and low-cost consumer technology. The best shallow learning algorithm and the best minimal feature set are individuated. 11 PwPD and 8 subjects with no history of neurological injury or illness were enrolled in the study. Participants were asked to watch video clips purposely selected to arouse emotions, and annotate arousal and valence of emotions triggered by video clips, while their heart rate, skin conductance, and temperature were recorded by a smartwatch. Smartwatch data was used for features extraction, while participants’ reported arousal and valence were used as gold-standard to train machine learning algorithms for emotion classification (low/high arousal, positive/negative valence). Different feature sets and different algorithms (i.e. decision tree (DT), random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP)) were evaluated to find the best solution for each group of participant. In each group of participants, it was possible to find a combination of feature set and algorithm to reach a classification accuracy greater than 90%. The Random Forest reached the best performance in both groups and for both valence and arousal. For each classification task (valence or arousal, PwPD or controls), the best model was selected and the minimal feature set was found by performing a recursive feature elimination based on the Shapley value. A lower accuracy of appraisal emerged for arousal compared to valence. Obtained results showed the feasibility of automatic emotion recognition in PwPDs through autonomic signals. Autonomic dysfunction in PwPDs may explain the lower arousal accuracy. The findings warrant confirmation from trials on larger samples and there are open issues to be deepened in future work.

White blood cells classification using multi-fold pre-processing and optimized CNN model

White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.

Quantitative blade damage detection based on multisource domain and multistage joint transfer

For blade damage detection under variable rotational speeds of centrifugal fans, transfer learning under working conditions appears to be more effective than deep learning. However, damage detection models with small samples and single-source domain transfer are still characterized by insufficiencies, such as negative transfer and poor accuracy. Therefore, in this paper, a quantitative blade damage detection method based on multisource domain and multistage joint transfer is proposed. First, an anchor adapter is constructed using linear discriminant analysis projection combined with a feature similarity index metric to acquire the weight matrix of multisource domain-target domain data. Second, a multisource domain feature extractor based on the fusion of vibroacoustic information is established, obtaining the feature set of the target domain data. Then, the feature set is filtered through information gain and max-relevance and min-redundancy to remove the negative transfer features and is combined with an improved supervised locally linear embedding to build a subspace structure preservation model for the alignment between the source and target domain feature distributions. Finally, the classifier is fine-tuned with small sample data for quantitative damage detection of blades with variable rotational speeds. The proposed method is verified using experimental data from two centrifugal fans. The results show that the detection accuracy of the proposed model is significantly higher than that of any comparison model with a single source domain or single stage. Compared with other transfer models, the proposed method is characterized by higher detection accuracy and generalization performance.

A Comprehensive Survey on Feature Selection with Grasshopper Optimization Algorithm

Recent growth in data dimensions presents challenges to data mining and machine learning. A high-dimensional dataset consists of several features. Data may include irrelevant or additional features. By removing these redundant and unwanted features, the dimensions of the data can be reduced. The feature selection process eliminates a small set of relevant and important features from a large data set, reducing the size of the dataset. Multiple optimization problems can be solved using metaheuristic algorithms. Recently, the Grasshopper Optimization Algorithm (GOA) has attracted the attention of researchers as a swarm intelligence algorithm based on metaheuristics. An extensive review of papers on GOA-based feature selection algorithms in the years 2018–2023 is presented based on extensive research in the area of feature selection and GOA. A comparison of GOA-based feature selection methods is presented, along with evaluation strategies and simulation environments in this paper. Furthermore, this study summarizes and classifies GOA in several areas. Although many researchers have introduced their novelty in the feature selection problem, many open challenges and enhancements remain. The survey concludes with a discussion about some open research challenges and problems that require further attention.

Graph Neural Network and BERT Model for Antimalarial Drug Predictions Using Plasmodium Potential Targets

Malaria continues to pose a significant global health burden despite concerted efforts to combat it. In 2020, nearly half of the world’s population faced the risk of malaria, underscoring the urgency of innovative strategies to tackle this pervasive threat. One of the major challenges lies in the emergence of the resistance of parasites to existing antimalarial drugs. This challenge necessitates the discovery of new, effective treatments capable of combating the Plasmodium parasite at various stages of its life cycle. Advanced computational approaches have been utilized to accelerate drug development, playing a crucial role in every stage of the drug discovery and development process. We have witnessed impressive and groundbreaking achievements, with GNNs applied to graph data and BERT from transformers across diverse NLP text analysis tasks. In this study, to facilitate a more efficient and effective approach, we proposed the integration of an NLP based model for SMILES (i.e., BERT) and a GNN model (i.e., RGCN) to predict the effect of antimalarial drugs against Plasmodium. The GNN model was trained using designed antimalarial drug and potential target (i.e., PfAcAS, F/GGPPS, and PfMAGL) graph-structured data with nodes representing antimalarial drugs and potential targets, and edges representing relationships between them. The performance of BERT-RGCN was further compared with that of Mordred-RGCN to evaluate its effectiveness. The BERT-RGCN and Mordred-RGCN models performed consistently well across different feature combinations, showcasing high accuracy, sensitivity, specificity, MCC, AUROC, and AUPRC values. These results suggest the effectiveness of the models in predicting antimalarial drugs against Plasmodium falciparum in various scenarios based on different sets of features of drugs and potential antimalarial targets.

An ecological approach to the lexical evolution of Chinese English

Aims and objectives: Rooted in a contact-oriented stance, this study aims to approach the dynamic evolution of Chinese English lexis from the perspective of language ecology by seeking an ecological explanation of diachronic lexical change. Specifically, it attempts to investigate the division of labor between external and internal ecological factors motivating the evolutionary patterns of Chinese English lexis in the past 60 years. Methodology: A mixed-methods approach was adopted by combining meticulous coding of each lexical item and numerical calculation of the distribution of lexical manifestations on significant linguistic dimensions. Data collection and analysis: In terms of data collection, the Chinese English lexis was sourced coherently and longitudinally from Beijing Review (1958–2018). In terms of data analysis, a systematic analytical framework of five parameters (frequency, form, grammar, word formation, and semantics) was created before qualitatively charting the features and quantitatively calculating the statistics for the value of each parameter. Findings: Externally, ecological factors bear on lexical trends of all linguistic parameters except lexical forms; internally, system–internal interactions among lexical variants project congruent patterns of alignment among various sets of features of all parameters except grammar. Originality: Few studies have approached the Chinese English lexis from the diachronic perspective through the lens of language evolution based on coherently sourced data and systematic analytical procedures. Significance: The study is enlightening in providing basic statistics to permit comparisons of lexical evolution in the five linguistic parameters across Expanding Circle ecologies and in extending the evolutionary approach to language change to the lexical realm in the Chinese English context.

Bearing Fault Diagnosis Method Based on Multi-Domain Feature Selection and the Fuzzy Broad Learning System

In recent years, the Broad Learning System (BLS) has been acknowledged for its potential to revolutionize traditional artificial intelligence methods due to its short training time, strong interpretability, and simple structure. In the evolution of BLS, Prof. C. L. Philip Chen’s team introduced the Fuzzy Broad Learning System (FBLS) by replacing the feature nodes of BLS with fuzzy subsystems, thereby further reducing the training time. However, the traditional FBLS, with its straightforward structure, falls short in achieving higher fault diagnosis accuracy when handling raw vibration signals. This paper presents a bearing fault diagnosis approach employing multi-domain feature selection and the fuzzy broad learning system (MS-FBLS), aiming to enhance the diagnostic accuracy of FBLS through multi-domain feature selection. Primarily, a set of 49 features spanning time domain, frequency domain, time-frequency domain, and entropy values is extracted from the original vibrational signals. This combination builds a 49-dimensional multidomain feature set that exploits the information behind the input data as much as possible, thus compensating for the lack of feature extraction capability in FBLS. Afterward, the Random Forest algorithm assesses the significance of all features, leading to a reordering of the multidomain feature set based on their respective importance levels. Ultimately, the reorganized multidomain feature set is then fed into the FBLS, enabling the identification of various failure states within the bearing. The experimental validation conducted on the rolling bearing fault simulation test bed showcased that, in comparison to the traditional FBLS, the MS-FBLS method not only elevates diagnostic accuracy by 23.46%, but also substantially enhances diagnostic speed. These results serve as comprehensive evidence affirming the effectiveness of the method.