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ObjectiveTo evaluate the value of the malignant subregion-based texture analysis in predicting Ki-67 status in breast cancer.Materials and methodsThe dynamic contrast-enhanced magnetic resonance imaging data of 119 histopathologically confirmed breast cancer patients (81 patients with high Ki-67 expression status) from January 2018 to February 2023 in our hospital were retrospectively collected. According to the enhancement curve of each voxel within the tumor, three subregions were divided: washout subregion, plateau subregion, and persistent subregion. The washout subregion and the plateau subregion were merged as the malignant subregion. The texture features of the malignant subregion were extracted using Pyradiomics software for texture analysis. The differences in texture features were compared between the low and high Ki-67 expression cohorts and then the receiver operating characteristic (ROC) curve analysis to evaluate the predictive performance of texture features on Ki-67 expression. Finally, a support vector machine (SVM) classifier was constructed based on differential features to predict the expression level of Ki-67, the performance of the classifier was evaluated using ROC analysis and confirmed using 10-fold cross-validation.ResultsThrough comparative analysis, 51 features exhibited significant differences between the low and high Ki-67 expression cohorts. Following feature reduction, 5 features were selected to build the SVM classifier, which achieved an area under the ROC curve (AUC) of 0.77 (0.68–0.87) for predicting the Ki-67 expression status. The accuracy, sensitivity, and specificity were 0.76, 0.80, and 0.68, respectively. The average AUC from the 10-fold cross-validation was 0.72 ± 0.14.ConclusionThe texture features of the malignant subregion in breast cancer were potential biomarkers for predicting Ki-67 expression level in breast cancer, which might be used to precisely diagnose and guide the treatment of breast cancer.

The article provides an overview of the accuracy of various yield forecasting algorithms and offers a detailed explanation of the models and machine learning algorithms that are required for crop yield forecasting. A unified crop yield forecasting methodology is developed, which can be adjusted by adding new indicators and extensions. The proposed methodology is based on remote sensing data taken from free sources. Experiments were carried out on crops of cereals, legumes, oilseeds and forage crops in eastern Kazakhstan. Data on agricultural lands of the experimental farms were obtained using processed images from Sentinel-2 and Landsat-8 satellites (EO Browser) for the period of 2017–2022. In total, a dataset of 1600 indicators was collected with NDVI and MSAVI indices recorded at a frequency of once a week. Based on the results of this work, it is found that yields can be predicted from NDVI vegetation index data and meteorological data on average temperature, surface soil moisture and wind speed. A machine learning programming language can calculate the relationship between these indicators and build a neural network that predicts yield. The neural network produces predictions based on the constructed data weights, which are corrected using activation function algorithms. As a result of the research, the functions with the highest prediction accuracy during vegetative development for all crops presented in this paper are multi-layer perceptron, with a prediction accuracy of 66% to 99% (85% on average), and polynomial regression, with a prediction accuracy of 63% to 98% (82% on average). Thus, it is shown that the use of machine learning and neural networks for crop yield prediction has advantages over other mathematical modelling techniques. The use of machine learning (neural network) technologies makes it possible to predict crop yields on the basis of relevant data. The individual approach of machine learning to each crop allows for the determination of the optimal learning algorithms to obtain accurate predictions. [ABSTRACT FROM AUTHOR]

Dynamic and accurate identification of pilot intention is an important prerequisite for more accurate identification of control behavior, automatic flight early warning, and human–aircraft shared autonomy. Meanwhile, it is also the basic requirement of microscopic research on flight safety. In response to these demands, the airfield traffic pattern flight simulation experiment was carried out to obtain the pilot's physiological data, such as electrocardiogram, respiration, and skin electricity, under different intentions. The extended symbol aggregation approximation theory (ESAX) and the intelligent icon method were utilized to analyze and extract the characteristics of the pilot's intention. Furthermore, combined with the crow search algorithm (CSA) and extreme learning machine (ELM), a CSA-ELM pilot intention identification model was constructed and it is applied to climb, descend, level flight, and other situations in airfield traffic pattern missions to effectively identify whether the pilot has an intention. The rationality and validity of the identification model were verified through experiments with interactive computer simulations. In addition, compared with the traditional machine learning method, the accuracy of the identification method proposed in this paper is improved by about 10%. The above shows that the research results in this paper can provide support for improving the flight safety early-warning system and the pilot's micro-behavior evaluation system. [ABSTRACT FROM AUTHOR]

It is of great energy-saving significance to provide accurate occupancy schedules for building energy simulation. Inaccurate occupancy information can lead to biased results. However, the reference occupancy schedules provided by the standard documents are fixed and differ significantly from the actual occupancy fluctuations. Moreover, the large amount of accurate occupancy information is difficult and expensive to obtain, limiting the development of occupancy fluctuation pattern exploration and occupancy prediction. In this paper, a framework is proposed to provide accurate and flexible occupancy schedules based on occupancy-related variables and occupancy information extracted from surveillance videos through Convolutional Neural Network-based density estimation methods. Machine learning algorithms are used in the framework to predict occupancy owing to their powerful predictive capabilities. A case study based on educational buildings is conducted to examine the performance of the framework and to compare its predicted occupancy schedules with those provided by national standard documents. The average recognition accuracy of the proposed framework for different crowd densities is as high as 95.67%, and the mean absolute percentage error for the prediction of the occupancy by the proposed framework is only 16.88%. The average deviation degree between the predicted occupancy rate schedules and the actual occupancy rate schedules is reduced from 676.08% to 23.19%. The average deviation degree of building energy simulation results caused by the occupancy rate schedule is reduced from 42.56% to 0.93%. These results provide conclusive evidence that the proposed framework can provide realistic and flexible occupancy schedules to improve the accuracy of building energy simulation. • A framework for crowd counting and prediction in public buildings with high-density scenes. • Occupancy information obtained from images by CNN-based density estimation methods. • Average recognition accuracy of 95.67% for different crowd densities. • Average all-day prediction accuracy of 83.12% for occupancy of the target building. [ABSTRACT FROM AUTHOR]