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First-episode psychosis (FEP) typically marks the onset of severe psychiatric disorders and represents a critical period in the field of mental health. The early diagnosis of this condition is essential for timely intervention and improved clinical outcomes. In this study, the classification of FEP was investigated using the analysis of electroencephalography (EEG) signals and circulant spectrum analysis (ciSSA) sub-band signals. FEP poses a significant diagnostic challenge in the realm of mental health, and it is aimed at introducing a novel and effective approach for early diagnosis. To achieve this, the LASSO method was utilized to select the most significant features derived from entropy, frequency, and statistical-based characteristics obtained from ciSSA sub-band signals, as well as their hybrid combinations. Subsequently, a high-performance classification model has been developed using machine learning techniques, including ensemble, support vector machine (SVM), and artificial neural network (ANN) methods. The results of this study demonstrated that the hybrid features extracted from EEG signals' ciSSA sub-bands, in combination with the SVM method, achieved a high level of performance, with an area under curve (AUC) of 0.9893, an accuracy of 96.23%, a sensitivity of 0.966, a specificity of 0.956, a precision of 0.9667, and an F1 score of 0.9666. This has revealed the effectiveness of the ciSSA-based method for classifying FEP from EEG signals. [ABSTRACT FROM AUTHOR]

Attention refers to the human psychological ability to focus on doing an activity. The attention assessment plays an important role in diagnosing attention deficit hyperactivity disorder (ADHD). In this paper, the attention assessment is performed via a classification approach. First, the single-channel electroencephalograms (EEGs) are acquired from various participants when they perform various activities. Then, fast Fourier transform (FFT) is applied to the acquired EEGs, and the high-frequency components are discarded for performing denoising. Next, empirical mode decomposition (EMD) is applied to remove the underlying trend of the signals. In order to extract more features, singular spectrum analysis (SSA) is employed to increase the total number of the components. Finally, some typical models such as the random forest-based classifier, the support vector machine (SVM)-based classifier, and the back-propagation (BP) neural network-based classifier are used for performing the classifications. Here, the percentages of the classification accuracies are employed as the attention scores. The computer numerical simulation results show that our proposed method yields a higher classification performance compared to the traditional methods without performing the EMD and SSA. [ABSTRACT FROM AUTHOR]

Due to the relative insufficiencies of conventional time-domain waveform and spectrum analysis in fault diagnosis research, a diesel engine fault diagnosis method based on the Stacked Sparse Autoencoder and the Support Vector Machine is proposed in this study. The method consists of two main steps. The first step is to utilize the Stacked Sparse Autoencoder (SSAE) to reduce the feature dimension of the multi-sensor vibration information; when compared with other dimension reduction methods, this approach can better capture nonlinear features, so as to better cope with dimension reduction. The second step consists of diagnosing faults, implementing the grid search, and K-fold cross-validation to optimize the hyperparameters of the SVM method, which effectively improves the fault classification effect. By conducting a preset failure experiment for the diesel engine, the proposed method achieves an accuracy rate of more than 98%, better engineering application, and promising outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Survival of ovarian cancer patients largely relies on the surgical removal of all cancer cells. To achieve this, good vision is crucial. In this study, we evaluate the ability of hyperspectral imaging to detect ovarian cancer. Images of surgically removed tissue samples of 11 patients were taken and compared to histopathology in order to train machine learning software. For training purposes, only healthy tissues and tissues with high tumor cell content (>50%) were included. In total, 26 tissue samples and 26,446 data points of 10 patients were included. Tissue classification as either tumorous or healthy was evaluated by leave-one-out cross-validation. This resulted in a power of 0.83, a sensitivity of 0.81, a specificity of 0.70 and a Matthew's correlation coefficient of 0.41. To conclude, this study shows that hyperspectral imaging can be used to recognize ovarian cancer. In the future, the technique may enable real-time image guidance during surgery. The most important prognostic factor for the survival of advanced-stage epithelial ovarian cancer (EOC) is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumors would be of great value. The aim of this pilot study is to assess the feasibility of near-infrared hyperspectral imaging (HSI) for EOC detection in ex vivo tissue samples. Images were collected during CRS in 11 patients in the wavelength range of 665–975 nm, and processed by calibration, normalization, and noise filtering. A linear support vector machine (SVM) was employed to classify healthy and tumorous tissue (defined as >50% tumor cells). Classifier performance was evaluated using leave-one-out cross-validation. Images of 26 tissue samples from 10 patients were included, containing 26,446 data points that were matched to histopathology. Tumorous tissue could be classified with an area under the curve of 0.83, a sensitivity of 0.81, a specificity of 0.70, and Matthew's correlation coefficient of 0.41. This study paves the way to in vivo and intraoperative use of HSI during CRS. Hyperspectral imaging can scan a whole tissue surface in a fast and non-contact way. Our pilot study demonstrates that HSI and SVM learning can be used to discriminate EOC from surrounding tissue. [ABSTRACT FROM AUTHOR]

With 298 heterogeneous soil samples from Yixing (Jiangsu Province), Zhongxiang and Honghu (Hubei Province), this study aimed to combine a successive projections algorithm (SPA) with a support vector machine regression (SVMR) model (SPA-SVMR model) to improve the estimation accuracy of soil organic carbon (SOC) contents using the laboratory-based visible and near-infrared (VIS/NIR, 350-2500 nm) spectroscopy of soils. The effects of eight spectra pre-processing methods, i.e., Log (1/R), Log (1/R) coupled with Savitzky-Golay (SG) smoothing (Log (1/R) + SG), first derivative with SG smoothing (FD), second derivative with SG smoothing (SD), SG, standard normal variate (SNV), mean center (MC) and multiplicative scatter correction (MSC), on SPA-based informative wavelength selection were explored. The SVMR model (i.e., SVMR without SPA) and SPA-PLSR model (i.e., SPA combined with partial least squares regression (PLSR)) were developed and compared with the SPA-SVMR model in order to evaluate the performance of SPA-SVMR. The results indicated that the variables selected by SPA and their distributions were strongly affected by different pre-processing methods, and SG was the optimal pre-processing method for SPA-SVMR model development; the SPA-SVMR model using SG pre-processing and 28 SPA-selected wavelengths obtained a better result (R2 V = 0.73, RMSEV = 2.78 g·kg-1 and RPDV = 1.89) and outperformed the SVMR model (R2 V = 0.72, RMSEV = 2.83 g·kg-1 and RPDV = 1.86) and the SPA-PLSR model (R2 V = 0.62, RMSEV = 3.23 g·kg-1 and RPDV = 1.63). Most of the spectral bands used by the SPA-SVMR model over the near-infrared region were important wavelengths for SOC content estimation. This study demonstrated that the combination of SPA and SVMR is feasible and reliable for estimating SOC content from the VIS/NIR spectra of soils in regions with multiple soil and land-use types. [ABSTRACT FROM AUTHOR]

The granite processing sector of the northwest of Spain handles many varieties of granite with specific technical and aesthetic properties that command different prices in the natural stone market. Hence, correct granite identification and classification from the outset of processing to the end-product stage optimizes the management and control of stocks of granite slabs and tiles and facilitates the operation of traceability systems. We describe a methodology for automatically identifying granite varieties by processing spectral information captured by a spectrophotometer at various stages of processing using functional machine learning techniques. [ABSTRACT FROM AUTHOR]

With the rapid development of aeronautic and deep space exploration technologies, a large number of high-resolution asteroid spectral data have been gathered, which can provide diagnostic information for identifying different categories of asteroids as well as their surface composition and mineralogical properties. However, owing to the noise of observation systems and the ever-changing external observation environments, the observed asteroid spectral data always contain noise and outliers exhibiting indivisible pattern characteristics, which will bring great challenges to the precise classification of asteroids. In order to alleviate the problem and to improve the separability and classification accuracy for different kinds of asteroids, this paper presents a novel Neighboring Discriminant Component Analysis (NDCA) model for asteroid spectrum feature learning. The key motivation is to transform the asteroid spectral data from the observation space into a feature subspace wherein the negative effects of outliers and noise will be minimized while the key category-related valuable knowledge in asteroid spectral data can be well explored. The effectiveness of the proposed NDCA model is verified on real-world asteroid reflectance spectra measured over the wavelength range from 0.45 to 2.45 μm, and promising classification performance has been achieved by the NDCA model in combination with different classifier models, such as the nearest neighbor (NN), support vector machine (SVM) and extreme learning machine (ELM). [ABSTRACT FROM AUTHOR]

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives. [ABSTRACT FROM AUTHOR]

This paper proposes a two dimensional quaternion valued singular spectrum analysis based method for enhancing the hyperspectral image. Here, the enhancement is for performing the object recognition, but neither for improving the visual quality nor suppressing the artifacts. In particular, the two dimensional quaternion valued singular spectrum analysis components are selected in such a way that the ratio of the interclass separation to the intraclass separation of the pixel vectors is maximized. Next, the support vector machine is employed for performing the object recognition. Compared to the conventional two dimensional real valued singular spectrum analysis based method where only the pixels in a color plane is exploited, the two dimensional quaternion valued singular spectrum analysis based method fuses four color planes together for performing the enhancement. Hence, both the spatial information among the pixels in the same color plane and the spectral information among various color planes are exploited. The computer numerical simulation results show that the overall classification accuracy based on our proposed method is higher than the two dimensional real valued singular spectrum analysis based method, the three dimensional singular spectrum analysis based method, the multivariate two dimensional singular spectrum analysis based method, the median filtering based method, the principal component analysis based method, the Tucker decomposition based method and the hybrid spectral convolutional neural network (hybrid SN) based method. [ABSTRACT FROM AUTHOR]

A machine learning approach is applied to Raman spectra of cells from the MIA PaCa-2 human pancreatic cancer cell line to distinguish between tumor repopulating cells (TRCs) and parental control cells, and to aid in the identification of molecular signatures. Fifty-one Raman spectra from the two types of cells are analyzed to determine the best combination of data type, dimension size, and classification technique to differentiate the cell types. An accuracy of 0.98 is obtained from support vector machine (SVM) and k-nearest neighbor (kNN) classifiers with various dimension reduction and feature selection tools. We also identify some possible biomolecules that cause the spectral peaks that led to the best results. [ABSTRACT FROM AUTHOR]

In this paper we report a method to determine the soluble solids content (SSC) of 'Rocha' pear (Pyrus communis L. cv. Rocha) based on their short-wave NIR reflectance spectra (500–1100 nm) measured in conditions similar to those found in packinghouse fruit sorting facilities. We obtained 3300 reflectance spectra from pears acquired from different lots, producers and with diverse storage times and ripening stages. The macroscopic properties of the pears, such as size, temperature and SSC were measured under controlled laboratory conditions. For the spectral analysis, we implemented a computational pipeline that incorporates multiple pre-processing techniques including a feature selection procedure, various multivariate regression models and three different validation strategies. This benchmark allowed us to find the best model/preproccesing procedure for SSC prediction from our data. From the several calibration models tested, we have found that Support Vector Machines provides the best predictions metrics with an RMSEP of around 0.82 ∘ Brix and 1.09 ∘ Brix for internal and external validation strategies respectively. The latter validation was implemented to assess the prediction accuracy of this calibration method under more 'real world-like' conditions. We also show that incorporating information about the fruit temperature and size to the calibration models improves SSC predictability. Our results indicate that the methodology presented here could be implemented in existing packinghouse facilities for single fruit SSC characterization. [ABSTRACT FROM AUTHOR]

Landslides of Taiwan occur frequently in high mountain areas. Soil disturbance causes by the earthquake and heavy rainfall of the typhoon seasons often produced the earth and rock to landslide in the upper reaches of the catchment area. Therefore, the landslide near the hillside has an influence on the catchment area. The hyperspectral images are effectively used to monitor the landslide area with the spectral analysis. However, it is rarely studied how to interpret it in the image of the landslide. If there are no elevation data on the slope disaster, it is quite difficult to identify the landslide zone and the bareland area. More specifically, this study used a series of spectrum analysis to identify the difference between them. Therefore, this study conducted a spectrum analysis for the classification of the landslide, bareland, and vegetation area in the mountain area of NanXi District, Tainan City. On the other hand, this study used the following parallel study on Support Vector Machine (SVM) for error matrix and thematic map for comparison. The study simultaneously compared the differences between them. The spectral similarity analysis reaches 85% for testing data, and the SVM approach has 98.3%. [ABSTRACT FROM AUTHOR]

In this study, we presented the concept and implementation of a fully functional system for the recognition of bi-heterocyclic compounds. We have conducted research into the application of machine learning methods to correctly recognize compounds based on THz spectra, and we have described the process of selecting optimal parameters for the kernel support vector machine (KSVM) with an additional 'unknown' class. The chemical compounds used in the study contain a target molecule, used in pharmacy to combat inflammatory states formed in living organisms. Ready-made medical products with similar properties are commonly referred to as non-steroidal anti-inflammatory drugs (NSAIDs) once authorised on the pharmaceutical market. It was crucial to clearly determine whether the tested sample is a chemical compound known to researchers or is a completely new structure which should be additionally tested using other spectrometric methods. Our approach allows us to achieve 100% accuracy of the classification of the tested chemical compounds in the time of several milliseconds counted for 30 samples of the test set. It fits perfectly into the concept of rapid recognition of bi-heterocyclic compounds without the need to analyse the percentage composition of compound components, assuming that the sample is classified in a known group. The method allows us to minimize testing costs and significant reduction of the time of analysis. [ABSTRACT FROM AUTHOR]

A new protection scheme based on applying a combination of wavelet multi-resolution singular spectrum entropy and support vector machine is proposed to identify different types of grid faults in a three-phase grid-tied photovoltaic system. In this technique, discrete wavelet transform with multi-resolution singular spectrum entropy is utilized to extract the unique features of three-phase voltage signals at the point of common coupling. The three-phase voltage signals are decomposed to provide detail and approximation coefficients of wavelet transform. Then, various features between different types of grid faults can be extracted by a combination of multi resolution analysis and spectrum analysis with entropy as the output. The constructed features vector is utilized as input data of a support vector machine classifier to identify and classify various types of faults. The results illustrate that the proposed intelligent technique not only recognizes different types of grid faults correctly, but also performs quickly in identifying grid faults in a grid-connected photovoltaic system. Apart from this, a graphical investigation is executed to observe the effects of different types of grid faults in photovoltaic (PV) operation which highlight the necessity of intelligent protection methods to protect PV systems. [ABSTRACT FROM AUTHOR]

Rotor is a widely used and easily defected mechanical component. Thus, it is significant to develop effective techniques for rotor fault diagnosis. Fault signature extraction and state classification of the extracted signatures are two key steps for diagnosing rotor faults. To complete the accurate recognition of rotor states, a novel evaluation index named characteristic frequency band energy entropy (CFBEE) was proposed to extract the defective features of rotors, and support vector machine (SVM) was employed to automatically identify the rotor fault types. Specifically, the raw vibration signal of rotor was first analyzed by a joint time–frequency method based on improved singular spectrum decomposition (ISSD) and Hilbert transform (HT) to derive its time–frequency spectrum (TFS), which is named ISSD-HT TFS in this paper. Then, the CFBEE of the ISSD-HT TFS was calculated as the fault feature vector. Finally, SVM was used to complete the automatic identification of rotor faults. Simulated processing results indicate that ISSD improves the end effects of singular spectrum decomposition (SSD) and is superior to empirical mode decomposition (EMD) in extracting the sub-components of rotor vibration signal. The ISSD-HT TFS can more accurately reflect the time–frequency information compared to the EMD-HT TFS. Experimental verification demonstrates that the proposed method can accurately identify rotor defect types and outperform some other methods. [ABSTRACT FROM AUTHOR]

Currently, the detection of blueberry internal bruising focuses mostly on single hyperspectral imaging (HSI) systems. Attempts to fuse different HSI systems with complementary spectral ranges are still lacking. A push broom based HSI system and a liquid crystal tunable filter (LCTF) based HSI system with different sensing ranges and detectors were investigated to jointly detect blueberry internal bruising in the lab. The mean reflectance spectrum of each berry sample was extracted from the data obtained by two HSI systems respectively. The spectral data from the two spectroscopic techniques were analyzed separately using feature selection method, partial least squares-discriminant analysis (PLS-DA), and support vector machine (SVM), and then fused with three data fusion strategies at the data level, feature level, and decision level. The three data fusion strategies achieved better classification results than using each HSI system alone. The decision level fusion integrating classification results from the two instruments with selected relevant features achieved more promising results, suggesting that the two HSI systems with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve blueberry internal bruising detection. This study was the first step in demonstrating the feasibility of the fusion of two HSI systems with complementary spectral ranges for detecting blueberry bruising, which could lead to a multispectral imaging system with a few selected wavelengths and an appropriate detector for bruising detection on the packing line. [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate. [ABSTRACT FROM AUTHOR]

Bearings are critical parts of rotating machines, making bearing fault diagnosis based on signals a research hotspot through the ages. In real application scenarios, bearing signals are normally non-linear and unstable, and thus difficult to analyze in the time or frequency domain only. Meanwhile, fault feature vectors extracted conventionally with fixed dimensions may cause insufficiency or redundancy of diagnostic information and result in poor diagnostic performance. In this paper, Self-adaptive Spectrum Analysis (SSA) and a SSA-based diagnosis framework are proposed to solve these problems. Firstly, signals are decomposed into components with better analyzability. Then, SSA is developed to extract fault features adaptively and construct non-fixed dimension feature vectors. Finally, Support Vector Machine (SVM) is applied to classify different fault features. Data collected under different working conditions are selected for experiments. Results show that the diagnosis method based on the proposed diagnostic framework has better performance. In conclusion, combined with signal decomposition methods, the SSA method proposed in this paper achieves higher reliability and robustness than other tested feature extraction methods. Simultaneously, the diagnosis methods based on SSA achieve higher accuracy and stability under different working conditions with different sample division schemes. [ABSTRACT FROM AUTHOR]