Rock mass quality classification based on deep learning:A feasibility study for stacked autoencoders

Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.

Analysis on the Application of Quality Classification of Cultivated Land Resources in Municipal Land Space Planning:A Case Study of Chongzuo City

Cultivated land is the most important strategic resource to ensure food security.The newly constructed quality classification system of cultivated land resources considers the cultivated land health index for the first time.How the new classification and grading index system and the quality classification results of cultivated land resources to effectively guide the preparation of municipal land space planning has become a key research direction.This paper expounds the overall design idea for quality classification of cultivated land resources and classification index system.Taking Chongzuo City as an example,through the analysis of the quality classification results of cultivated land resources in the study area,using GIS spatial analysis and factor pairwise comparison method,this paper explores the application ideas and methods of quality classification research results of cultivated land resources in the formulation of cultivated land retention target,the delineation of dominant areas of cultivated land protection,the delineation of three control lines,the comprehensive improvement of land,and ecological restoration zoning in the municipal land space planning.

Classification and rating of disintegrated dolomite strata for slope stability analysis

Although disintegrated dolomite,widely distributed across the globe,has conventionally been a focus of research in underground engineering,the issue of slope stability issues in disintegrated dolomite strata is gaining increasing prominence.This is primarily due to their unique properties,including low strength and loose structure.Current methods for evaluating slope stability,such as basic quality(BQ)and slope stability probability classification(SSPC),do not adequately account for the poor integrity and structural fragmentation characteristic of disintegrated dolomite.To address this challenge,an analysis of the applicability of the limit equilibrium method(LEM),BQ,and SSPC methods was conducted on eight disintegrated dolomite slopes located in Baoshan,Southwest China.However,conflicting results were obtained.Therefore,this paper introduces a novel method,SMRDDS,to provide rapid and accurate assessment of disintegrated dolomite slope stability.This method incorporates parameters such as disintegrated grade,joint state,groundwater conditions,and excavation methods.The findings reveal that six slopes exhibit stability,while two are considered partially unstable.Notably,the proposed method demonstrates a closer match with the actual conditions and is more time-efficient compared with the BQ and SSPC methods.However,due to the limited research on disintegrated dolomite slopes,the results of the SMRDDS method tend to be conservative as a safety precaution.In conclusion,the SMRDDS method can quickly evaluate the current situation of disintegrated dolomite slopes in the field.This contributes significantly to disaster risk reduction for disintegrated dolomite slopes.

A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction

A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes.There are significantly more samples for day-to-day classes,while rare events such as polluted classes are uncommon.Consequently,the limited availability of minority outcomes lowers the classifier’s overall reliability.This study assesses the capability of machine learning(ML)algorithms in tackling imbalanced water quality data based on the metrics of precision,recall,and F1 score.It intends to balance the misled accuracy towards the majority of data.Hence,10 ML algorithms of its performance are compared.The classifiers included are AdaBoost,SupportVector Machine,Linear Discriminant Analysis,k-Nearest Neighbors,Naive Bayes,Decision Trees,Random Forest,Extra Trees,Bagging,and the Multilayer Perceptron.This study also uses the Easy Ensemble Classifier,Balanced Bagging,andRUSBoost algorithm to evaluatemulti-class imbalanced learning methods.The comparison results revealed that a highaccuracy machine learning model is not always good in recall and sensitivity.This paper’s stacked ensemble deep learning(SE-DL)generalization model effectively classifies the water quality index(WQI)based on 23 input variables.The proposed algorithm achieved a remarkable average of 95.69%,94.96%,92.92%,and 93.88%for accuracy,precision,recall,and F1 score,respectively.In addition,the proposed model is compared against two state-of-the-art classifiers,the XGBoost(eXtreme Gradient Boosting)and Light Gradient Boosting Machine,where performance metrics of balanced accuracy and g-mean are included.The experimental setup concluded XGBoost with a higher balanced accuracy and G-mean.However,the SE-DL model has a better and more balanced performance in the F1 score.The SE-DL model aligns with the goal of this study to ensure the balance between accuracy and completeness for each water quality class.The proposed algorithm is also capable of higher efficiency at a lower computational time against using the standard SyntheticMinority Oversampling Technique(SMOTE)approach to imbalanced datasets.

Automated quality inspection of baby corn using image processing and deep learning

The food industry typically relies heavily on manual operations with high proficiency and skills.According to the quality inspection process,a baby corn with black marks or blemishes is considered a defect or unqualified class which should be discarded.Quality inspection and sorting of agricultural products like baby corn are labor-intensive and time-consuming.The main goal of this work is to develop an automated quality inspection framework to differentiate between‘pass’and‘fail’categories based on baby corn images.A traditional image processing method using a threshold principle is compared with relatively more advanced deep learning models.Particularly,Convolutional neural networks,specific sub-types of deep learning models,were implemented.Thorough experiments on choices of network architectures and their hyperparameters were conducted and compared.A Shapley additive explanations(SHAP)framework was further utilized for network interpretation purposes.The EfficientNetB5 networks with relatively larger input sizes yielded up to 99.06%accuracy as the best performance against 95.28%obtained from traditional image processing.Incorporating a region of interest identification,several model experiments,data application on baby corn images,and the SHAP framework are our main contributions.Our proposed quality inspection system to automatically differentiate baby corn images provides a potential pipeline to further support the agricultural production process.

Improving Software Quality Prediction by Noise Filtering Techniques

Accuracy of machine learners is affected by quality of the data the learners are induced on. In this paper, quality of the training dataset is improved by removing instances detected as noisy by the Partitioning Filter. The fit dataset is first split into subsets, and different base learners are induced on each of these splits. The predictions are combined in such a way that an instance is identified as noisy if it is misclassified by a certain number of base learners. Two versions of the Partitioning Filter are used： Multiple-Partitioning Filter and Iterative-Partitioning Filter. The number of instances removed by the filters is tuned by the voting scheme of the filter and the number of iterations. The primary aim of this study is to compare the predictive performances of the final models built on the filtered and the un-filtered training datasets. A case study of software measurement data of a high assurance software project is performed. It is shown that predictive performances of models built on the filtered fit datasets and evaluated on a noisy test dataset are generally better than those built on the noisy （un-filtered） fit dataset. However, predictive performance based on certain aggressive filters is affected by presence of noise in the evaluation dataset.

Application of biomonitoring and support vector machine in water quality assessment

The behavior of schools of zebrafish （Danio rerio） was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine （SVM） was de- veloped. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration （LC50） of a pollutant. The data were used to develop a method to evaluate water quality, so as 6＋ 2＋ to give an early indication of toxicity. Four kinds of metal ions （Cu2~, Hg2~, Cr , and Cd ） were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm （GA） was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality.

CSOG MFM Committee Guideline:Clinical Management Guidelines for Acute Fatty Liver of Pregnancy in China(2021)

Acute fatty liver of pregnancy(AFLP)is a rare but critical obstetric-specific disease with a high fatality rate,posing a serious threat to the safety of mothers and infants.These guidelines were specially formulated to standardize AFLP clinical pathways and to improve maternal and infant outcomes.Based on a two-round questionnaire survey,the guideline development team identified the following nine clinical issues that clinicians were most concerned about,and developed recommendations for each of them:prenatal outpatient screening for AFLP,diagnosis,preoperative risk assessment,delivery modes and timing,anesthesia methods,perinatal complications,selecting AFLP patients for artificial liver treatment,prognostic assessment,and monitoring during treatment.The guidelines cover the key issues related to AFLP diagnosis and treatment that concern clinicians.