Least Squares Support Vector Machine Research Articles

NEURAL MECHANISM OF PHYSICAL EXERCISE IN PREVENTING AND TREATING CARDIOVASCULAR DISEASE BY DEEP LEARNING AND EDGE COMPUTING

This paper is to explore the improvement of clinical symptoms in patients with cardiovascular neurosis (CN) by physical exercise based on the deep learning architecture of edge computing, and to deeply explore the effect of physical exercise on autonomic function. Fifty-two patients with CN in this cardiovascular rehabilitation center were randomly divided into drug group and exercise group, with 26 cases in each group, and their electrocardiogram (ECG) was examined. Based on the deep learning architecture of edge computing, a four-layer stacked sparse auto encoder (SSAE) deep neural network was constructed, and the accuracy rates of least squares support vector machine (LSSVM), message passing neural network (MPNN), convolutional neural network (CNN), and SSAEs were measured to be 95.4%, 93.6%, 96.3%, and 99.5%, respectively. After physical exercise intervention, the total score of Symptom Checklist 90 (SCL-90) as well as each single item score were lower in the exercise group than in the drug group ([Formula: see text]). Heart rate recovery (HRR1) improved more significantly after 1[Formula: see text]min of exercise in patients in the exercise group ([Formula: see text]). The low-frequency (LF) power and normalized low-frequency (LFn) power of blood pressure variability (BPV) parameters in the exercise group were lower than those in the drug group ([Formula: see text]); the total power (TP), high-frequency (HF) power, and normalized high-frequency (HFn) power of heart rate variability (HRV) parameters in the exercise group were higher than those in the drug group ([Formula: see text]), LF/HF in the exercise group was lower than that in the drug group ([Formula: see text]); and the baroreflex sensitivity (BRS) in the exercise group was higher than that in the drug group ([Formula: see text]). A four-layer SSAEs was successfully constructed; the mechanism of exercise may be related to the regulation of cardiovascular autonomic nervous function, and it can effectively prevent and treat the clinical symptoms of patients with CN.

Classification of focal and non-focal EEG signals using optimal geometrical features derived from a second-order difference plot of FBSE-EWT rhythms

Background/introduction:Manual detection and localization of the brain’s epileptogenic areas using electroencephalogram (EEG) signals is time-intensive and error-prone. An automated detection system is, thus, highly desirable for support in clinical diagnosis. A set of relevant and significant non-linear features plays a major role in developing a reliable, automated focal detection system. Methods:A new feature extraction method is designed to classify focal EEG signals using eleven non-linear geometrical attributes derived from the Fourier–Bessel series expansion-based empirical wavelet transform (FBSE-EWT) segmented rhythm’s second-order difference plot (SODP). A total of 132 features (2 channels × 6 rhythms × 11 geometrical attributes) were computed. However, some of the obtained features might be non-significant and redundant features. Hence, to acquire an optimal set of relevant non-linear features, a new hybridization of ‘Kruskal–Wallis statistical test (KWS)’ with ‘VlseKriterijuska Optimizacija I Komoromisno Resenje’ termed as the KWS-VIKOR approach was adopted. The KWS-VIKOR has a two-fold operational feature. First, the significant features are selected using the KWS test with a p-value lesser than 0.05. Next, the multi-attribute decision-making (MADM) based VIKOR method ranks the selected features. Several classification methods further validate the efficacy of the features of the selected top n%. Results:The proposed framework has been evaluated using the Bern-Barcelona dataset. The highest classification accuracy of 98.7% was achieved using the top 35% ranked features in classifying the focal and non-focal EEG signals with the least-squares support vector machine (LS-SVM) classifier. Conclusions:The achieved results exceeded those reported through other methods. Hence, the proposed framework will more effectively assist the clinician in localizing the epileptogenic areas.

An anomaly detection method for spacecraft solar arrays based on the ILS-SVM model

Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit missions. When a spacecraft is in orbit, because the solar array is exposed to the harsh space environment, with increasing working time, the performance of its internal electronic components gradually degrade until abnormal damage occurs. This damage makes solar array power generation unable to fully meet the energy demand of a spacecraft. Therefore, timely and accurate detection of solar array anomalies is of great significance for the on-orbit operation and maintenance management of spacecraft. In this paper, we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine (ILS-SVM) model: it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set, then gets n corresponding least squares support vector machine (LS-SVM) submodels by training on these training subsets, respectively; after that, the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on; finally, based on the obtained ILS-SVM model, a parameterfree and unsupervised anomaly determination method is proposed to detect the health status of solar arrays. We use the telemetry data set from a satellite in orbit to carry out experimental verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs, which reflects the applicability of the method.

Closure to “Improving Prediction Accuracy of Hydrologic Time Series by Least-Squares Support Vector Machine Using Decomposition Reconstruction and Swarm Intelligence”

Closure to “Improving Prediction Accuracy of Hydrologic Time Series by Least-Squares Support Vector Machine Using Decomposition Reconstruction and Swarm Intelligence”

Discussion of “Improving Prediction Accuracy of Hydrologic Time Series by Least-Squares Support Vector Machine Using Decomposition Reconstruction and Swarm Intelligence”

Discussion of “Improving Prediction Accuracy of Hydrologic Time Series by Least-Squares Support Vector Machine Using Decomposition Reconstruction and Swarm Intelligence”

Intelligent fault diagnosis method for rolling bearing using WMNRS and LSSVM

A weighted multi-neighborhood rough set (WMNRS) algorithm is designed to resolve the issue in which the neighborhood radius must be adjusted iteratively and cannot be automatically determined in the neighborhood rough set. This algorithm combined with the least squares support vector machines (LSSVM) is used for analyzing rolling bearing condition monitoring data; consequently, an intelligent fault diagnosis method is proposed. Specifically, the time-domain and frequency-domain features are extracted from the collected vibration signals to construct an original feature set. The WMNRS algorithm is then applied to screen the primary sensitive components from the constructed feature set. Finally, an optimized LSSVM is utilized to recognize the fault types. The developed method is validated on a public dataset and a measured rolling bearing dataset. The results demonstrate that the method achieves excellent diagnostic performance. Furthermore, the proposed method has some supremacy regarding running time.

Predicting Discharge Coefficient of Triangular Side Orifice Using LSSVM Optimized by Gravity Search Algorithm

Side orifices are commonly installed in the side of a main channel to spill or divert some of the flow from the source channel to lateral channels. The aim of the present study is the accurate estimation of the discharge coefficient for flow through triangular (Δ-shaped) side orifices by applying three data-driven models including support vector machine (SVM), least squares support vector machine (LSSVM) and least squares support vector machine improved by gravity search algorithm (LSSVM-GSA). The discharge coefficient was estimated by utilizing five dimensionless variables resulted from experimental data (570 runs). Five different scenarios were applied based on the input variables. The models were evaluated through several statistical indices and graphical charts. The results showed that all of the models could successfully estimate the discharge coefficient of Δ-shaped side orifices with adequate accuracy. However, the LSSVM-GSA produced the best performance for the input combination of all variables with the highest coefficients of determination (R2) and Nash–Sutcliffe efficiency (NSE), equal to 0.965 and 0.993, and the least root mean square error (RMSE) and mean absolute error (MAE), equal to 0.0099 and 0.0077, respectively. The LSSVM-GSA improved the RMSE of the SVM and LSSVM by 26% and 20% in estimating the discharge coefficient. Furthermore, the ratio of orifice crest height to orifice height (W/H) was identified as having the highest influence on the discharge coefficient of triangular side orifices among the various input variables.

An assembly tightness recognition method for bolted connection states with singular-value entropy and GA least-squares support vector machine

The bolted connections are widely used in high-end equipment such as aerospace and energy fields. However, when the equipment is assembled, the assembly tightness of bolted connection structure is easily affected by friction and manual experience, resulting in inconsistent preload. It will affect the dynamic performance of equipment, resulting in abnormal vibration or failure of equipment. Currently, the existing assembly tightness testing methods mainly focus on whether the bolts are loose, ignoring the impact of assembly tightness on the vibration of the whole machine. This paper proposes an assembly tightness recognition method for bolted connection states based on modified ensemble empirical mode decomposition (MEEMD) and genetic algorithm least squares support vector machine (GALSSVM). Firstly, an ultrasonic pre-tightening force test system is used for multi-bolt pre-tightening force calibration and precise control, and subsequently, accurate vibration signal samples are collected. The feature extraction of multi-bolt connection states is carried out based on MEEMD. Secondly, a quantitative evaluation index of assembly tightness, singular-value entropy, is proposed. Finally, experimental verification is performed. Multiple sets of different preloads case experiments are designed to verify the identification accuracy of the proposed algorithm. The results show that with the bolted connection structure from loose to tight, the singular-value entropy of the quantitative index decreases monotonically. It provides some theoretical references for improving the reliability of high-end equipment and the evaluation of assembly performance.

Combined kernel with parameter optimization for least squares support vector machine postdistorter in visible light communications

The nonlinearity and memory effects induced by light-emitting diode distort the transmitted signal, which significantly limits the performance of visible light communication (VLC) system. With the characteristics of low computational complexity, fast convergence speed, and strong generalization ability, least squares support vector machine (LSSVM) is investigated in this paper and employed to alleviate the nonlinear effect. Since the performance of LSSVM is largely determined by the type of the adopted kernel functions, we utilize Gaussian and Laplace functions and propose a new combined kernel, which can increase the reproducing kernel Hilbert space and thus enhance the global expressive power compared with conventionally used Gaussian kernel. To further improve the performance, a hybrid parameter optimization algorithm is exploited to optimize the proposed kernel, based on genetic algorithm and particle swarm optimization. The property of the proposed postdistortion method is verified by numerical simulations, and better performance is obtained in VLC systems compared with the Gaussian-kernel LSSVM and conventional memory polynomial method.

Optimal feature selection for COVID-19 detection with CT images enabled by metaheuristic optimization and artificial intelligence.

There is a broad range of novel Coronaviruses (CoV) such as the common cold, cough, and severe lung infections. The mutation of this virus, which originally started as COVID-19 in Wuhan, China, has continued the rapid spread globally. As the mutated form of this virus spreads across the world, testing and screening procedures of patients have become tedious for healthcare departments in largely populated countries such as India. To diagnose COVID-19 pneumonia by radiological methods, high-resolution computed tomography (CT) of the chest has been considered the most precise method of examination. The use of modern artificial intelligence (AI) techniques on chest high-resolution computed tomography (HRCT) images can help to detect the disease, especially in remote areas with a lack of specialized physicians. This article presents a novel metaheuristic algorithm for automatic COVID-19 detection using a least square support vector machine (LSSVM) classifier for three classes namely normal, COVID, and pneumonia. The proposed model results in a classification accuracy of 87.2% and an F1-score of 86.3% for multiclass classifications from simulations. The analysis of information transfer rate (ITR) revealed that the modified quantum-based marine predators algorithm (Mq-MPA) feature selection algorithm reduces the classification time of LSSVM by 23% when compared to the deep learning models.

Online modeling and prediction of maritime autonomous surface ship maneuvering motion under ocean waves

The development of maritime autonomous surface ships (MASS) has attracted much attention at present. As a necessary technology of MASS, ship motion prediction still has some problems, such as low efficiency, poor real-time performance, and easy to be affected by sea conditions. Aiming at the above problems, based on the mathematical model of ship maneuvering motion, this paper uses the least squares support vector machine (LS-SVM) identification algorithm, to establish an offline black-box identification model of MASS motion considering rudder angle and propeller revolution rates, and effectively predicts the motion of ships. On this basis, a sliding window is designed, and an error-based online modeling method of ship motion is proposed to realize adaptive prediction of MASS motion under the effect of ocean waves. A large MASS was selected for modeling and simulation experiments. The results show that compared with the traditional LS-SVM modeling algorithm, the model built by this method has a strong adaptive ability and can maintain high accuracy for a long time, meeting the needs of real-time prediction of MASS motion in practical applications.

Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020–2022

Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving average-least-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving average-support vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency.

Hybrid modified weighted water cycle algorithm and Deep Analytic Network for forecasting and trend detection of forex market indices

This paper presents forecasting and trend analysis of foreign currency exchange rate in financial market using a hybrid Deep Analytic Network (DAN) technique optimized by a modified water cycle algorithm called Weighted WCA (WWCA) with better generalization capability than the traditional WCA.DAN comprises several stacked KRR (Kernel Ridge Regression) Auto encoders in a multilayer nonlinear regression architecture approach that provides better generalization and accuracy using regularized least squares technique. Further DAN using wavelet kernel function is particularly attractive for its strong data fitting and generalization ability along with its simplified execution procedure, high speed, and better performance achievements in comparison to LSSVM (least squares support vector machine). The output from the DAN is fed to a weighted KRR module to reject noise or the outliers in the noisy data and to make DAN a more robust predictor of the Forex markets, To obtain optimal values of wavelet kernel parameters, a modified metaheuristic water cycle algorithm i.e. the proposed WWCA is utilized. Applications of this new approach to predict forex rate along with trend analysis on three stock markets provide successful results and validate its superiority over some well known approaches like ANN, SVM, Naïve-Bayes, ELM.

A Gas Emission Prediction Model Based on Feature Selection and Improved Machine Learning

This paper proposed a gas emission prediction method based on feature selection and improved machine learning, as traditional gas emission prediction models are neither accurate nor universally applicable. Through analysis, this paper identified 12 factors that affected gas emissions. A total of 30 groups of typical data for gas outflow were standardized, after which a full subset regression feature selection method was used to categorize 12 influencing factors into different regular patterns and select 18 feature parameter sets. Meanwhile, based on nuclear principal component analysis (KPCA), an optimized gas emission prediction model was constructed where the dimensionality of the original data was reduced. An optimized algorithm set was constructed based on the hybrid kernel extreme learning machine (HKELM) and the least squares support vector machine (LSSVM). The performance of feature parameters adopted in the prediction algorithm was evaluated according to certain metrics. By comparing the results of different sets, the final prediction sequence could be obtained, and a model that was composed of the optimal feature parameters was applied to the optimal machine learning algorithm. The results showed that the HKELM outperformed LSSVM in prediction accuracy, running speed, and stability. The root meant square error (RMSE) for the final prediction sequence was 0.22865, the determination coefficient (R2) was 0.99395, the mean absolute error (MAE) was 0.20306, and the mean absolute percentage error (MAPE) was 1.0595%. Every index of accuracy evaluation performed well and the constructed prediction model had high-prediction accuracy and a wide application.

Water Quality Prediction of the Yamuna River in India Using Hybrid Neuro-Fuzzy Models

The potential of four different neuro-fuzzy embedded meta-heuristic algorithms, particle swarm optimization, genetic algorithm, harmony search, and teaching–learning-based optimization algorithm, was investigated in this study in estimating the water quality of the Yamuna River in Delhi, India. A cross-validation approach was employed by splitting data into three equal parts, where the models were evaluated using each part. The main aim of this study was to find an accurate prediction model for estimating the water quality of the Yamuna River. It is worth noting that the hybrid neuro-fuzzy and LSSVM methods have not been previously compared for this issue. Monthly water quality parameters, total kjeldahl nitrogen, free ammonia, total coliform, water temperature, potential of hydrogen, and fecal coliform were considered as inputs to model chemical oxygen demand (COD). The performance of hybrid neuro-fuzzy models in predicting COD was compared with classical neuro-fuzzy and least square support vector machine (LSSVM) methods. The results showed higher accuracy in COD prediction when free ammonia, total kjeldahl nitrogen, and water temperature were used as inputs. Hybrid neuro-fuzzy models improved the root mean square error of the classical neuro-fuzzy model and LSSVM by 12% and 4%, respectively. The neuro-fuzzy models optimized with harmony search provided the best accuracy with the lowest root mean square error (13.659) and mean absolute error (11.272), while the particle swarm optimization and teaching–learning-based optimization showed the highest computational speed (21 and 24 min) compared to the other models.

Machine learning approach for estimating the human-related VOC emissions in a university classroom.

Indoor air quality becomes increasingly important, partly because the COVID-19 pandemic increases the time people spend indoors. Research into the prediction of indoor volatile organic compounds (VOCs) is traditionally confined to building materials and furniture. Relatively little research focuses on estimation of human-related VOCs, which have been shown to contribute significantly to indoor air quality, especially in densely-occupied environments. This study applies a machine learning approach to accurately estimate the human-related VOC emissions in a university classroom. The time-resolved concentrations of two typical human-related (ozone-related) VOCs in the classroom over a five-day period were analyzed, i.e., 6-methyl-5-hepten-2-one (6-MHO), 4-oxopentanal (4-OPA). By comparing the results for 6-MHO concentration predicted via five machine learning approaches including the random forest regression (RFR), adaptive boosting (Adaboost), gradient boosting regression tree (GBRT), extreme gradient boosting (XGboost), and least squares support vector machine (LSSVM), we find that the LSSVM approach achieves the best performance, by using multi-feature parameters (number of occupants, ozone concentration, temperature, relative humidity) as the input. The LSSVM approach is then used to predict the 4-OPA concentration, with mean absolute percentage error (MAPE) less than 5%, indicating high accuracy. By combining the LSSVM with a kernel density estimation (KDE) method, we further establish an interval prediction model, which can provide uncertainty information and viable option for decision-makers. The machine learning approach in this study can easily incorporate the impact of various factors on VOC emission behaviors, making it especially suitable for concentration prediction and exposure assessment in realistic indoor settings.

Research on the flow characteristics identification of steam turbine valve based on FCM-LSSVM

Due to aging and deformation of the through-flow path and system modifications, the flow characteristics of the turbine inlet valve often deviate from the design value, which affects the unit load control accuracy and operational stability. In order to obtain the actual valve flow characteristics of the turbine and thus improve the FM performance, an FCMLSSVM model is proposed in this paper to identify the valve flow characteristics. First, FCM clustering is proposed to classify the historical operating data of the plant and obtain a wide range of variable operating conditions. Then, using least squares support vector machine (LSSVM), the relationship between turbine input and output variables was modeled in each condition cluster, with integrated valve position command, speed, and real power generated as input variables and actual steam inlet flow as output variables. Using a 330 MW turbine unit as an application example, the established FCM-LSSVM model was validated for the valve flow characteristics of the turbine. The results show that the model can obtain accurate valve flow characteristics without conducting tests on the turbine. The method can save a lot of labor and material resources in doing the characteristic test, and after comparison, the proposed method can identify the flow characteristics more accurately among the existing neural network identification methods, which can provide technical support to improve the unit frequency regulation characteristics and improve the accuracy of valve operation.

Study on finishing inner wall of Co[sbnd]Cr alloy cardiovascular stent tube via novel atomized CBN/metal spherical magnetic abrasive powders

In this study, in order to solve the problem that the inner wall of the cardiovascular stent is difficult to polish, the magnetic abrasive finishing (MAF) method is used to finish the inner wall of the CoCr alloy tube that is used for the manufacture of the cardiovascular stent. The surface defects such as wrinkles, cracks and pits generated in the production process are removed, and the internal surface roughness is decreased to obtain better surface quality. Firstly, aiming at the problem of poor performance of traditional magnetic abrasive powders (MAPs), “gas-solid two-phase double-stage atomization and rapid solidification” is proposed and a novel atomized CBN/metal spherical MAPs with different sizes are successfully prepared. This MAP is used for MAF of the inner wall of CoCr alloy cardiovascular stent. Secondly, the slotted magnetic pole is designed according to the numerical simulation, and the MAF setup for the inner wall of ultra-fine and ultra-long tubes is developed. Finally, the orthogonal experiment is carried out with surface roughness as the evaluation index. Through the analysis of range theory, the order of the apparent degree of each process parameter on surface roughness is as follows: tube rotational speed > feed speed of magnetic pole > MAPs filling quantity > MAP particle size. According to the result, a model of surface roughness prediction based on whale optimization algorithm (WOA) and least squares support vector machine (LSSVM) is constructed. The interaction between tube rotational speed and other process parameters is analyzed based on the model fitting results, and the process parameters are optimized. The result show that MAF technology can effectively remove the defects of the inner wall of the CoCr alloy cardiovascular stent tube and accurately predict the surface roughness. After MAF under the optimized process parameters, the surface roughness Ra decreases from 0.485 μm to 0.092 μm.

Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites

Ongoing anthropogenic carbon dioxide (CO2) emissions to the atmosphere cause severe air pollution that leads to complex changes in the climate, which pose threats to human life and ecosystems more generally. Geological CO2 storage (GCS) offers a promising solution to overcome this critical environmental issue by removing some of the CO2 emissions. The performance of GCS projects depends directly on the solubility and residual trapping efficiency of CO2 in a saline aquifer. This study models the solubility trapping index (STI) and residual trapping index (RTI) of CO2 in saline aquifers by applying four robust machine learning (ML) and deep learning (DL) algorithms. Extreme learning machine (ELM), least square support vector machine (LSSVM), general regression neural network (GRNN), and convolutional neural network (CNN) are applied to 6811 compiled simulation records from published studies to provide accurate STI and RTI predictions. Employing different statistical error metrics coupled with supplementary evaluations, involving score and robustness analyses, the prediction accuracy of the models proposed is comparatively assessed. The findings of the study revealed that the LSSVM model delivers the lowest RMSE values: 0.0043 (STI) and 0.0105 (RTI) with few outlying predictions. Presenting the highest STI and RTI prediction scores the LSSVM is distinguished as the most credible model among all the four models studied. The models consider eight input variables, of which the time elapsed and injection rate displays the strongest correlations with STI and RTI, respectively. The results suggest that the proposed LSSVM model is best suited for monitoring CO2 sequestration efficiency from the data variables considered. Applying such models avoids time-consuming complex simulations and offers the potential to generate fast and reliable assessments of GCS project feasibility. Accurate modeling of CO2 storage trapping indexes guarantees successful geological CO2 storage operation, which is, in fact, the cornerstone of properly controlling and managing environmentally polluting gases.

Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology

Detection of skin defects in Cerasus humilis fruit is a critical process to guarantee its quality and price. This study presents a valid method for the detection of defective features in Cerasus humilis fruits based on hyperspectral imaging. A total of 420 sample images were acquired that included three types of natural defects and undamaged samples. After acquiring hyperspectral images of Cerasus humilis fruits, the spectral data were extracted from the region of interest (ROI). Five spectral preprocessing methods were used to preprocess the original spectral data, including Savitsky–Golay (S-G), standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction (BC), and de-trending (De-T). Regression coefficient (RC), successive projections algorithm (SPA), and competitive adaptive reweighed sampling (CARS) were conducted to select optimal sensitive wavelengths (SWs); as a result, 11 SWs, 17 SWs, and 13 SWs were selected, respectively. Then, the least squares-support vector machine (LS-SVM) discrimination model was established using the selected SWs. The results showed that the discriminate accuracy of the CARS-LS-SVM method was 91.43%. Based on the characteristics of image information, images corresponding to eight sensitive wavebands (950, 994, 1071, 1263, 1336, 1457, 1542, and 1628 nm) selected by CARS were subjected to principal component analysis (PCA). Then, an effective approach for detecting the defective features was exploited based on the imfill function, canny operator, region growing algorithm, bwareaopen function, and the images of PCA. The location and area of defect feature of 105 Cerasus humilis fruits could be recognized; the detect precision was 88.57%. This investigation demonstrated that hyperspectral imaging combined with an image processing technique could achieve the rapid identification of undamaged samples and natural defects in Cerasus humilis fruit. This provides a theoretical basis for the development of Cerasus humilis fruit grading and sorting equipment.