Chronic kidney disease (CKD) is a dangerous ailment that can last a person’s entire life and is caused by either kidney malignancy or decreased kidney functioning. It is feasible to halt or slow the ...progression of this chronic disease to an end-stage wherein dialysis or surgical intervention is the only method to preserve a patient’s life. Earlier detection and appropriate therapy can increase the likelihood of this happening. Throughout this research, the potential of several different machine learning approaches for providing an early diagnosis of CKD has been investigated. There has been a significant amount of research conducted on this topic. Nevertheless, we are bolstering our approach by making use of predictive modeling. Therefore, in our approach, we investigate the link that exists between data factors as well as the characteristics of the target class. We are capable of constructing a collection of prediction models with the help of machine learning and predictive analytics, thanks to the better measures of attributes that can be introduced using predictive modeling. This study starts with 25 variables in addition to the class property, but by the end, it has narrowed the list down to 30% of those parameters as the best subset to identify CKD. Twelve different machine learning-based classifiers have been tested in a supervised learning environment. Within the confines of a supervised learning environment, a total of 12 different machine learning-based classifiers have indeed been examined, with the greatest performance indicators being an accuracy of 0.983, a precision of 0.98, a recall of 0.98, and an F1-score of 0.98 for the XgBoost classifier. The way the research was done leads to the conclusion that recent improvements in machine learning, along with the help of predictive modeling, make for an interesting way to find new solutions that can then be used to test the accuracy of prediction in the field of kidney disease and beyond.
The real-time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early stage of coronavirus ...disease 2019 (COVID-19). Meanwhile, the manifestations of COVID-19 as seen through computed tomography (CT) imaging show individual characteristics that differ from those of other types of viral pneumonia such as influenza-A viral pneumonia (IAVP). This study aimed to establish an early screening model to distinguish COVID-19 from IAVP and healthy cases through pulmonary CT images using deep learning techniques. A total of 618 CT samples were collected: 219 samples from 110 patients with COVID-19 (mean age 50 years; 63 (57.3%) male patients); 224 samples from 224 patients with IAVP (mean age 61 years; 156 (69.6%) male patients); and 175 samples from 175 healthy cases (mean age 39 years; 97 (55.4%) male patients). All CT samples were contributed from three COVID-19-designated hospitals in Zhejiang Province, China. First, the candidate infection regions were segmented out from the pulmonary CT image set using a 3D deep learning model. These separated images were then categorized into the COVID-19, IAVP, and irrelevant to infection (ITI) groups, together with the corresponding confidence scores, using a location-attention classification model. Finally, the infection type and overall confidence score for each CT case were calculated using the Noisy-OR Bayesian function. The experimental result of the benchmark dataset showed that the overall accuracy rate was 86.7% in terms of all the CT cases taken together. The deep learning models established in this study were effective for the early screening of COVID-19 patients and were demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.
Stab-resistant clothing significantly contribute to personal protection. In the field of stab resistance, traditional methods typically use the known impact conditions to evaluate the protection ...performance and damage of stab-resistant materials. However, these methods are unable to backtrack impact information from known damage, which makes it difficult to determine impactor characteristics. This study introduces a novel puncture damage prediction model capable of predicting the impact kinetic energy, peak puncture force, and number of penetration layers of aramid stab-resistant fabrics solely from surface damage images under various puncture conditions. First, the different puncture damages images and their corresponding parameters are obtained through dynamic stabbing tests and image acquisition system. Second, the segmentation network (named SAN_SE model) developed in this study overcomes the complexity of the surface texture of fiber-reinforced composites and achieves precise segmentation of damage regions. The training loss is stable at 1.5 × 10−4. Then a classification model is constructed to establish a relationship between the images and puncture parameters, followed by the application of transfer learning to derive a regression model from the classification model. The error of this regression model is below 6 %. Finally, a real-time puncture damage prediction system is constructed, applying this puncture damage prediction model to actual damage scenarios. The system achieves an accuracy of 88.57 % in predicting the number of penetration layers and could execute single images within 0.025s. The puncture damage prediction model proposed in this study is applicable to real-time monitoring systems in medical and military fields, such as injury assessment and counter-surveillance.
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•Developing more suitable small deep learning model for ore image classification.•Evaluating the gas-coal image classification performance of small deep learning models with different ...depths.•Optimizing the convergence speed and classification accuracy of small deep learning classification models by adding BN layer.•Evaluating the gas-coal image classification performance of small deep learning models under different dataset sizes.
The ore image classification technology based on deep learning is an effective way to improve the image sensor-based ore sorting classification capability. However, in practice, the image sensor-based ore sorting technique often has the problem of insufficient data, and has not systematically considered the impact of model structure and dataset size on the modeling efficiency and classification performance of deep learning. Therefore, this paper attempts to explore a more suitable small deep learning model for ore image classification by considering the model depth, model structure, and dataset size. Six Convolutional Neural Networks (CNNs) models are established with different depths based on Alex Net and VGG Net and the model structure is optimized by adding BN layer. Taking the gas-coal image dataset as case study, we systematically explore the influence of model depth, model structure, dataset size on the training process efficiency and classification accuracy. Meanwhile, the operational process of coal image classifiers is analyzed visually through the ways of Channel Visualization maps, Heatmaps, Grad-CAM map, and Guided Backpropagation maps.
Training a Machine Learning (ML) model in the industrial field faces special challenges, such as data privacy, lack of data, data imbalance, and unlabeled data. Therefore, it is not realistic to ...gather production data directly from various companies and use them to train a machine learning model. In this paper, we proposed a novel framework named Bi-level Federated Learning (BFL) to tackle the above challenges. In the first level, a Weakly Supervised Anomaly Detection method named Pairwise Relation prediction-based Ordinal regression Network (PRO) is utilized for training a Deep Anomaly Detection (DAD) model under Federated Learning (FL) mechanism. According to the DAD model, anomalies are identified and labeled with ‘anomaly’ tags. In order to train a multi-classification model that can identify different types of defects not just anomalies, the anomalies are manually labeled according to their respective defect types. In the second level, labeled anomaly and normal data are applied to train a multi-classification model under the FL mechanism. A framework is proposed to classify defects in pre-baked carbon anodes. Its performance is verified through a real case study. The results show that the DAD model achieved an acceptable performance with a 0.988 recall and 0.866 F1 score. The multi-classification model has shown good performance on 0.942 accuracy, 0.913 precision, 0.903 recall, and 0.906 F1. The results of the BFL exhibit improved performance compared to our previous work and classical ML methods.
Bots are frequently used in Github repositories to automate repetitive activities that are part of the distributed software development process. They communicate with human actors through comments. ...While detecting their presence is important for many reasons, no large and representative ground-truth dataset is available, nor are classification models to detect and validate bots on the basis of such a dataset. This paper proposes a ground-truth dataset, based on a manual analysis with high interrater agreement, of pull request and issue comments in 5,000 distinct Github accounts of which 527 have been identified as bots. Using this dataset we propose an automated classification model to detect bots, taking as main features the number of empty and non-empty comments of each account, the number of comment patterns, and the inequality between comments within comment patterns. We obtained a very high weighted average precision, recall and F1-score of 0.98 on a test set containing 40% of the data. We integrated the classification model into an open source command-line tool to allow practitioners to detect which accounts in a given Github repository actually correspond to bots.
•Bots can be detected based on pull request and issue comments in GitHub repositories.•We proposed a ground-truth dataset of 5,000 GitHub accounts including 527 bots.•We developed a classification model that detects bots based on their comments.•We implemented an open source tool to detect bots in GitHub repositories.
•The effects of high temperature and cyclic load on red sandstone were investigated.•The failure process of the specimen was analyzed in depth by AE and DIC technologies.•A crack classification model ...based on K-medoids and SVM algorithms was proposed.•The failure precursor characteristics were discussed by critical slowing down theory.
This paper investigated red sandstone’s acoustic emission (AE) and fracture characteristics under high temperature and cyclic load. The specimen’s behavior throughout the process from stabilization to failure was systematically analyzed in terms of the distribution of AE activities, rupture scale, crack type, crack evolution, and failure mode. A crack classification model based on K-medoids and support vector machine (SVM) algorithms was proposed, and the optimal dividing line for tensile-shear cracks at different temperatures was determined as AF=0.335×RA+76.890. The specimen’s failure precursor information was recognized based on the critical slowing down theory.
•A machine vision system was set up for image acquisition and feature extraction of Xuesaitong dropping pills.•Different classification models were established to detect the appearance quality ...defects of Xuesaitong dropping pills.•The Random Forest outperformed all the explored models in the defects detection of Xuesaitong dropping pills.
Defect detection is a critical issue for the quality control of dropping pills, which is a special dosage form of traditional Chinese Medicine. Machine vision is a non-destructing testing technology and cost-effective with high accuracy that can be used to predict the detects of both interior and exterior of the sample by employing the camera. In this research, a machine vision system for inspecting quality of the Xuesaitong dropping pills (XDPs) that include non-spherical, abnormal sizes and colors was developed to evaluate the appearance quality of XDPs rapidly and accurately. Firstly, 270 images of XDPs containing qualified and three different types of defects were collected. Subsequently, the processing of the XDPs images were carried out. Finally, Three defecting categories classification models were developed and compared based on contour and color features. The experimental results showed that the Random Forest outperformed all the explored models and the classification accuracy for non-spherical, abnormal sizes and colors reached 98.52%, 100.00% and 100.00%, respectively. In summary, the method established in this research is scientific, reliable, fast and accurate, which has great application potential and can provide technical support for the automatic defect detection of dropping pills.
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•To characterize irradiated baijius using ATR-FTIR spectroscopy.•The heat map analysis visualizes the dose–effect relationship of irradiation.•The Venn analysis helps select a subset ...of features with high robustness.•Spectral pre-processing improves the recognition performance of the model.
Although some methods have been proposed for the identification of irradiated baijius, they are often costly, time-consuming, and destructive. It is also unclear what instrumentation can be used to fully characterize the quality changes in irradiated baijius. To address this issue, this study pioneers the use of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy in combination with chemometrics to open up new avenues for characterizing irradiated baijius and their quality control. Principal component analysis, five spectral pre-processing methods (Savitzky-Golay smoothing (S-G), second-order derivative (SD), multiple scattering correction (MSC), S-G + SD and S-G + MSC), five wavelength selection methods (random forest variable importance (RFVI), two-dimensional correlation spectroscopy (2D-COS), variable importance in projection (VIP), ReliefF, and Venn), and three classification models (partial least squares-discriminant analysis (PLS-DA), random forest (RF), and grasshopper optimization algorithm-based support vector machine (GOA-SVM)) were integrated into the analytical framework of ATR-FTIR spectroscopy, aiming to accurately identify baijiu samples according to different irradiation doses and to search for irradiation-induced spectral difference characteristics (spectral markers). The results showed that SD was the best spectral pre-processing method, and RF models constructed using the 20 most competitive and discriminative spectral markers (selected by Venn) could achieve accurate identification of baijiu samples based on irradiation dose (0, 4, 6, and 8 kGy). After Pearson correlation analysis, the five significantly (P＜0.05) changed spectral markers (1596, 2025, 2309, 2329, and 2380 cm−1) were attributed to changes in the content of total acids, alcohols, and aromatic compounds. These findings demonstrate for the first time the potential of ATR-FTIR spectroscopy as a fast, low-cost, and non-destructive tool for the characterization and identification of irradiated baijiu samples. This approach may also offer a promising solution for labeling management of irradiated foods, vintage identification of baijius, and brand protection.
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