Automatic Classification Method Research Articles

Automatic soil classification method from CPTU data based on convolutional neural networks

Automatic soil classification method from CPTU data based on convolutional neural networks

Research on the classification of complex noise-mixed microseismic events based on machine vision

Event classification is important for accurately monitoring and warning against rockburst hazards using microseismic technology. Here, we propose an automatic classification method for microseismic events based on machine vision. The method uses Histogram of Oriented Gradient (HOG) integrated with Support Vector Machine (SVM) as the core model (HOG-SVM, HSVM) to classify microseismic events. First, the method uses as input spectrograms generated from microseismic event signals recorded in the field. Next, the HOG method is used to accurately extract the spectral feature information of the useful signals of microseismic events under the interference of noisy signal. Finally, the extracted feature data is used to train SVM, after the training is completed, the SVM is used to classify the microseismic events. The performance of the method for categorizing microseismic events was tested using multiple independent test sets built from data monitored in the field of a mine in Shandong Province. The results show that the method can effectively extract the spectral feature information of useful signals of microseismic events contaminated with noise, with good classification accuracy and robustness to noise. It classifies microseismic events with high accuracy and efficiency compared to well-performing classification methods based on seismic source parameters and typical depth models. The method can provide technical support for the effective classification of microseismic events in complex construction sites, especially in noisy deep underground construction environments.

Deep learning based identification and interpretability research of traditional village heritage value elements: a case study in Hubei Province

The preservation and transmission of traditional villages is crucial to the prosperity and development of ethnic cultures. However, current traditional village surveys usually require a large number of experts and scholars to conduct field research, which is an expensive and time-consuming method, especially for large-scale tasks. Therefore, this study proposes an automatic classification method based on deep learning (DL) for the identification of traditional village heritage value elements (TVHVE). The study evaluates four selected convolutional neural network (CNN) frames using traditional villages in Hubei Province as a sample dataset. The results show that Residual Network152 (ResNet152) is the most suitable CNN frame for identifying TVHVE in Hubei. The stability and consistency of various TVHVE present in the ResNet152 model were evaluated using Area Under Curve (AUC) and Precision Recall Curve (PRC), which indicated satisfactory prediction performance for most elements, except for specific elements such as tombstones and stone carvings, which showed lower accuracy. In addition, the study sheds light on the areas of concern of the model with respect to different TVHVE images and elucidates the reasons behind the confusion between elements through semantic clustering based on image classification and interpretability analysis using the Gradient-Weighted Class Activation Mapping (Grad-CAM) heat map. By using an automated classification method based on DL, this study significantly reduces the cost and effort associated with traditional surveys. At the same time, insight into areas of concern and confusion in the model improves guidance for conservation efforts and provides valuable references for subsequent research.

DSRL: A low-resolution stellar spectral of LAMOST automatic classification method based on discrete wavelet transform and deep learning methods

DSRL: A low-resolution stellar spectral of LAMOST automatic classification method based on discrete wavelet transform and deep learning methods

Convolutional neural network with parallel convolution scale attention module and ResCBAM for breast histology image classification

Breast cancer is the most common cause of female morbidity and death worldwide. Compared with other cancers, early detection of breast cancer is more helpful to improve the prognosis of patients. In order to achieve early diagnosis and treatment, clinical treatment requires rapid and accurate diagnosis. Therefore, the development of an automatic detection system for breast cancer suitable for patient imaging is of great significance for assisting clinical treatment. Accurate classification of pathological images plays a key role in computer-aided medical diagnosis and prognosis. However, in the automatic recognition and classification methods of breast cancer pathological images, the scale information, the loss of image information caused by insufficient feature fusion, and the enormous structure of the model may lead to inaccurate or inefficient classification. To minimize the impact, we proposed a lightweight PCSAM-ResCBAM model based on two-stage convolutional neural network. The model included a Parallel Convolution Scale Attention Module network (PCSAM-Net) and a Residual Convolutional Block Attention Module network (ResCBAM-Net). The first-level convolutional network was built through a 4-layer PCSAM module to achieve prediction and classification of patches extracted from images. To optimize the network's ability to represent global features of images, we proposed a tiled feature fusion method to fuse patch features from the same image, and proposed a residual convolutional attention module. Based on the above, the second-level convolutional network was constructed to achieve predictive classification of images. We evaluated the performance of our proposed model on the ICIAR2018 dataset and the BreakHis dataset, respectively. Furthermore, through model ablation studies, we found that scale attention and dilated convolution play an important role in improving model performance. Our proposed model outperforms the existing state-of-the-art models on 200 × and 400 × magnification datasets with a maximum accuracy of 98.74 %.

Classification of the quality of canine and feline ventrodorsal and dorsoventral thoracic radiographs through machine learning.

Thoracic radiographs are an essential diagnostic tool in companion animal medicine and are frequently used as a part of routine workups in patients presenting for coughing, respiratory distress, cardiovascular diseases, and for staging of neoplasia. Quality control is a critical aspect of radiology practice in preventing misdiagnosis and ensuring consistent, accurate, and reliable diagnostic imaging. Implementing an effective quality control procedure in radiology can impact patient outcomes, facilitate clinical decision-making, and decrease healthcare costs. In this study, a machine learning-based quality classification model is suggested for canine and feline thoracic radiographs captured in both ventrodorsal and dorsoventral positions. The problem of quality classification was divided into collimation, positioning, and exposure, and then an automatic classification method was proposed for each based on deep learning and machine learning. We utilized a dataset of 899 radiographs of dogs and cats. Evaluations using fivefold cross-validation resulted in an F1 score and AUC score of 91.33 (95% CI: 88.37-94.29) and 91.10 (95% CI: 88.16-94.03), respectively. Results indicated that the proposed automatic quality classification has the potential to be implemented in radiology clinics to improve radiograph quality and reduce nondiagnostic images.

Automatic Classification of All-Sky Nighttime Cloud Images Based on Machine Learning

Cloud-induced atmospheric extinction and occlusion significantly affect the effectiveness and quality of telescope observations. Real-time cloud-cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. Visual inspection is currently the primary approach for analyzing cloud distribution at ground-based astronomical sites. However, the main disadvantages of manual observation methods are human subjectivity, heavy workloads, and poor real-time performance. Therefore, a real-time automatic cloud image classification method is desperately needed. This paper presents a novel cloud identification method named the PSO+XGBoost model, which combines eXtreme Gradient Boosting (XGBoost) with particle-swarm optimization (PSO). The entire cloud image is divided into 37 sub-regions to identify the distribution of the clouds more precisely. Nineteen features, including the sky background, star density, lighting conditions, and subregion grayscale values, are extracted. The experimental results have shown that the overall classification accuracy is 96.91%, and our model can outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

Partial label learning for automated classification of single-cell transcriptomic profiles.

Single-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell types and identify the lineage relationship for individual cells. Because of the fast accumulation of datasets and the high dimensionality of the data, it has become challenging to explore and annotate single-cell transcriptomic profiles by hand. To overcome this challenge, automated classification methods are needed. Classical approaches rely on supervised training datasets. However, due to the difficulty of obtaining data annotated at single-cell resolution, we propose instead to take advantage of partial annotations. The partial label learning framework assumes that we can obtain a set of candidate labels containing the correct one for each data point, a simpler setting than requiring a fully supervised training dataset. We study and extend when needed state-of-the-art multi-class classification methods, such as SVM, kNN, prototype-based, logistic regression and ensemble methods, to the partial label learning framework. Moreover, we study the effect of incorporating the structure of the label set into the methods. We focus particularly on the hierarchical structure of the labels, as commonly observed in developmental processes. We show, on simulated and real datasets, that these extensions enable to learn from partially labeled data, and perform predictions with high accuracy, particularly with a nonlinear prototype-based method. We demonstrate that the performances of our methods trained with partially annotated data reach the same performance as fully supervised data. Finally, we study the level of uncertainty present in the partially annotated data, and derive some prescriptive results on the effect of this uncertainty on the accuracy of the partial label learning methods. Overall our findings show how hierarchical and non-hierarchical partial label learning strategies can help solve the problem of automated classification of single-cell transcriptomic profiles, interestingly these methods rely on a much less stringent type of annotated datasets compared to fully supervised learning methods.

Automatic classification of immune-mediated glomerular diseases based on multi-modal multi-instance learning

To develop a multi-modal deep learning method for automatic classification of immune-mediated glomerular diseases based on images of optical microscopy (OM), immunofluorescence microscopy (IM), and transmission electron microscopy (TEM). We retrospectively collected the pathological images from 273 patients and constructed a multi-modal multi- instance model for classification of 3 immune-mediated glomerular diseases, namely immunoglobulin A nephropathy (IgAN), membranous nephropathy (MN), and lupus nephritis (LN). This model adopts an instance-level multi-instance learning (I-MIL) method to select the TEM images for multi-modal feature fusion with the OM images and IM images of the same patient. By comparing this model with unimodal and bimodal models, we explored different combinations of the 3 modalities and the optimal methods for modal feature fusion. The multi-modal multi-instance model combining OM, IM, and TEM images had a disease classification accuracy of (88.34±2.12)%, superior to that of the optimal unimodal model [(87.08±4.25)%] and that of the optimal bimodal model [(87.92±3.06)%]. This multi- modal multi- instance model based on OM, IM, and TEM images can achieve automatic classification of immune-mediated glomerular diseases with a good classification accuracy.

Cropland abandonment between 1986 and 2018 across the United States: spatiotemporal patterns and current land uses

Knowing where and when croplands have been abandoned or otherwise removed from cultivation is fundamental to evaluating future uses of these areas, e.g. as sites for ecological restoration, recultivation, bioenergy production, or other uses. However, large uncertainties remain about the location and time of cropland abandonment and how this process and the availability of associated lands vary spatially and temporally across the United States. Here, we present a nationwide, 30 m resolution map of croplands abandoned throughout the period of 1986–2018 for the conterminous United States (CONUS). We mapped the location and time of abandonment from annual cropland layers we created in Google Earth Engine from 30 m resolution Landsat imagery using an automated classification method and training data from the U.S. Department of Agriculture Cropland Data Layer. Our abandonment map has overall accuracies of 0.91 and 0.65 for the location and time of abandonment, respectively. From 1986 to 2018, 12.3 (±2.87) million hectares (Mha) of croplands were abandoned across CONUS, with areas of greatest change over the Ogallala Aquifer, the southern Mississippi Alluvial Plain, the Atlantic Coast, North Dakota, northern Montana, and eastern Washington state. The average annual nationwide abandoned area across our study period was 0.51 Mha per year. Annual abandonment peaked between 1997 and 1999 at a rate of 0.63 Mha year−1, followed by a continuous decrease to 0.41 Mha year−1 in 2009–2011. Among the abandoned croplands, 53% (6.5 Mha) changed to grassland and pasture, 18.6% (2.28 Mha) to shrubland and forest, 8.4% (1.03 Mha) to wetlands, and 4.6% (0.56 Mha) to non-vegetated lands. Of the areas that we mapped as abandoned, 19.6% (2.41 Mha) were enrolled in the Conservation Reserve Program as of 2020. Our new map highlights the long-term dynamic nature of agricultural land use and its relation to various competitive pressures and land use policies in the United States.

ACE-DNV: Automatic classification of gaze events in dynamic natural viewing

Eye movements offer valuable insights for clinical interventions, diagnostics, and understanding visual perception. The process usually involves recording a participant’s eye movements and analyzing them in terms of various gaze events. Manual identification of these events is extremely time-consuming. Although the field has seen the development of automatic event detection and classification methods, these methods have primarily focused on distinguishing events when participants remain stationary. With increasing interest in studying gaze behavior in freely moving participants, such as during daily activities like walking, new methods are required to automatically classify events in data collected under unrestricted conditions. Existing methods often rely on additional information from depth cameras or inertial measurement units (IMUs), which are not typically integrated into mobile eye trackers. To address this challenge, we present a framework for classifying gaze events based solely on eye-movement signals and scene video footage. Our approach, the Automatic Classification of gaze Events in Dynamic and Natural Viewing (ACE-DNV), analyzes eye movements in terms of velocity and direction and leverages visual odometry to capture head and body motion. Additionally, ACE-DNV assesses changes in image content surrounding the point of gaze. We evaluate the performance of ACE-DNV using a publicly available dataset and showcased its ability to discriminate between gaze fixation, gaze pursuit, gaze following, and gaze shifting (saccade) events. ACE-DNV exhibited comparable performance to previous methods, while eliminating the necessity for additional devices such as IMUs and depth cameras. In summary, ACE-DNV simplifies the automatic classification of gaze events in natural and dynamic environments. The source code is accessible at https://github.com/arnejad/ACE-DNV.

Explanation of the influence of geomorphometric variables on the landform classification based on selected areas in Poland

In recent years, automatic image classification methods have significantly progressed, notably black box algorithms such as machine learning and deep learning. Unfortunately, such efforts only focused on improving performance, rather than attempting to explain and interpret how classification models actually operate. This article compares three state-of-the-art algorithms incorporating random forests, gradient boosting and convolutional neural networks for geomorphological mapping. It also attempts to explain how the most effective classifier makes decisions by evaluating which of the geomorphometric variables are most important for automatic mapping and how they affect the classification results using one of the explainable artificial intelligence techniques, namely accumulated local effects (ALE). This method allows us to understand the relationship between predictors and the model’s outcome. For these purposes, eight sheets of the digital geomorphological map of Poland on the scale of 1:100,000 were used as the reference material. The classification results were validated using the holdout method and cross-validation for individual sheets representing different morphogenetic zones. The terrain elevation entropy, absolute elevation, aggregated median elevation and standard deviation of elevation had the greatest impact on the classification results among the 15 geomorphometric variables considered. The ALE analysis was conducted for the XGBoost classifier, which achieved the highest accuracy of 92.8%, ahead of Random Forests at 84% and LightGBM at 73.7% and U-Net at 59.8%. We conclude that automatic classification can support geomorphological mapping only if the geomorphological characteristics in the predicted area are similar to those in the training dataset. The ALE plots allow us to analyze the relationship between geomorphometric variables and landform membership, which helps clarify their role in the classification process.

An Automatic Method for Detection and Naive Classification of the Martian Ionospheric Irregularities

AbstractThe abundant observations and research established a detailed category of the terrestrial ionospheric irregularities, which significantly advanced our understanding of how the Earth system's complicated physical and chemical process generates the intermediate‐scale structures of the charged particles. Motivated by a future attempt at categorizing the Martian ionospheric irregularity, this study designs a method for naive classification of the plasma density depletion, enhancement, and oscillation based on the in situ measurements of the Martian ionosphere. The technique consists of several procedures: trend estimation, detrending and candidate extraction, and parameterization. The classification is achieved through a machine‐learning‐like process using some testing artificial density profiles. A preliminary credence test shows a good performance in separating the terrestrial low‐latitude Equatorial Plasma bubble (depletion) and mid‐latitude Median‐scale Traveling Ionospheric Disturbance (oscillation). Another detection experiment of the Martian plasma depletion events (collected by Basuvaraj et al. (2022a, https://doi.org/10.1029/2022je007302)) showed a recall rate (i.e., true positive) of 38% but with a high precision of 67.8%. Therefore, we believe the proposed method could convincingly extract different Martian ionospheric irregularities and help uncover the climatological characteristics in the future.

An Improved k-Nearest Neighbor Algorithm for Recognition and Classification of Thyroid Nodules.

To complete the task of automatic recognition and classification of thyroid nodules and solve the problem of high classification error rates when the samples are imbalanced. An improved k-nearest neighbor (KNN) algorithm is proposed and a method for automatic thyroid nodule classification based on the improved KNN algorithm is established. In the improved KNN algorithm, we consider not only the number of class labels for various classes of data in KNNs, but also the corresponding weights. And we use the Minkowski distance measure instead of the Euclidean distance measure. A total of 508 ultrasound images of thyroid nodules, including 415 benign nodules and 93 malignant nodules, were used in the paper. Experimental results show the improved KNN has 0.872549 accuracy, 0.867347 precision, 1 recall, and 0.928962 F1-score. At the same time, we also considered the influence of different distance weights, the value of k, different distance measures on the classification results. A comparison result shows that our method has a better performance than the traditional KNN and other classical machine learning methods.

A novel Nine-SequenceNet with attention for liver histopathological classification

Liver cancer is a prevalent cancer worldwide and is also the leading cause of cancer-related deaths. Histopathological image diagnosis is taken as the standard to identify liver cancer. However, manually histopathological examination of liver cancer WSIs (Whole Slide Image) is time consuming. To address this issue, researchers have applied the automatic classification methods to recognize cancerous tissues in histopathological images, which can effectively reduce the examination time and improving the recognition efficiency. In recent years, deep learning based methods have been widely used in histopathological image analysis because of their impressive performance level. But only a little research work based on transfer learning has been done on liver cancer histopathological images because the sample dataset is difficult to collected and the classification results lack of effectiveness and pertinence. One issue with fine-grained classification is that using a single patch for classification results in the loss of edge information, degrading classification performance. To solve the problem, this paper proposes a novel deep learning model based on sequence prediction with attention, which is used to identify and classify liver cancer histopathological images. The proposed method uses the surrounding 8 patches to supplement information to the current patch, resulting in better results than other methods. The experimental results show that this method achieves the best F1-score of 95.23% which illustrates that the proposed method is efficient in liver cancer histopathological image classification.

Using natural language processing for automated classification of disease and to identify misclassified ICD codes in cardiac disease

Abstract Aims ICD codes are used for classification of hospitalizations. The codes are used for administrative, financial, and research purposes. It is known, however, that errors occur. Natural language processing (NLP) offers promising solutions for optimizing the process. To investigate methods for automatic classification of disease in unstructured medical records using NLP and to compare these to conventional ICD coding. Methods and results Two datasets were used: the open-source Medical Information Mart for Intensive Care (MIMIC)-III dataset (n = 55.177) and a dataset from a hospital in Belgium (n = 12.706). Automated searches using NLP algorithms were performed for the diagnoses ‘atrial fibrillation (AF)’ and ‘heart failure (HF)’. Four methods were used: rule-based search, logistic regression, term frequency-inverse document frequency (TF-IDF), Extreme Gradient Boosting (XGBoost), and Bio-Bidirectional Encoder Representations from Transformers (BioBERT). All algorithms were developed on the MIMIC-III dataset. The best performing algorithm was then deployed on the Belgian dataset. After preprocessing a total of 1438 reports was retained in the Belgian dataset. XGBoost on TF-IDF matrix resulted in an accuracy of 0.94 and 0.92 for AF and HF, respectively. There were 211 mismatches between algorithm and ICD codes. One hundred and three were due to a difference in data availability or differing definitions. In the remaining 108 mismatches, 70% were due to incorrect labelling by the algorithm and 30% were due to erroneous ICD coding (2% of total hospitalizations). Conclusion A newly developed NLP algorithm attained a high accuracy for classifying disease in medical records. XGBoost outperformed the deep learning technique BioBERT. NLP algorithms could be used to identify ICD-coding errors and optimize and support the ICD-coding process.

GAF-MAE: A Self-Supervised Automatic Modulation Classification Method Based on Gramian Angular Field and Masked Autoencoder

GAF-MAE: A Self-Supervised Automatic Modulation Classification Method Based on Gramian Angular Field and Masked Autoencoder

Retracted: Research on Automatic Classification Method of Ethnic Music Emotion Based on Machine Learning

Retracted: Research on Automatic Classification Method of Ethnic Music Emotion Based on Machine Learning

Automatic classification of circuit topologies of appliances based on higher order statistic

Electronic devices have a non-linear characteristic and are sources of harmonic emission. Their massive use pollutes the network and consequently it is needed to measure and characterize those devices. Harmonic current emitted by electronic devices is closely linked to their circuit topology and the distortion of the supply voltage. Different circuit topologies have also different current waveforms. This paper proposes an automatic classification method of steady-state appliances based on current waveform characterization in the higher order statistics space. The translation from the time domain to a statistical space enables the automatic identification of individual devices. The algorithm has been applied to the current waveforms of a large set of household appliances measured under sinusoidal conditions. The classification analysis proves that clusters of circuit topologies can be clearly identified. In addition, authors show that kurtosis and variance of an individual cycle provide enough information about the distribution of a waveform shape with respect to its average value, while the skewness inform about the half cycle bias. The method can be a useful tool to identify prevailing circuit topologies in the market. It can also improve automatic load identification, e.g. part of the future intelligent measurement systems such as smart meters.

Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

Timely and accurate rice spatial distribution maps play a vital role in food security and social stability. Early-season rice mapping is of great significance for yield estimation, crop insurance, and national food policymaking. Taking Tongjiang City in Heilongjiang Province with strong spatial heterogeneity as study area, a hierarchical K-Means binary automatic rice classification method based on phenological feature optimization (PFO-HKMAR) is proposed, using Google Earth Engine platform and Sentinel-1/2, and Landsat 7/8 data. First, a SAR backscattering intensity time series is reconstructed and used to construct and optimize polarization characteristics. A new SAR index named VH-sum is built, which is defined as the summation of VH backscattering intensity for specific time periods based on the temporal changes in VH polarization characteristics of different land cover types. Then comes feature selection, optimization, and reconstruction of optical data. Finally, the PFO-HKMAR classification method is established based on Simple Non-Iterative Clustering. PFO-HKMAR can achieve early-season rice mapping one month before harvest, with overall accuracy, Kappa, and F1 score reaching 0.9114, 0.8240 and 0.9120, respectively (F1 score is greater than 0.9). Compared with the two crop distribution datasets in Northeast China and ARM-SARFS, overall accuracy, Kappa, and F1 scores of PFO-HKMAR are improved by 0.0507–0.1957, 0.1029–0.3945, and 0.0611–0.1791, respectively. The results show that PFO-HKMAR can be promoted in Northeast China to enable early-season rice mapping, and provide valuable and timely information to different stakeholders and decision makers.