The use of an automatic histopathological image identification system is essential for expediting diagnoses and lowering mistake rates. Although it is of enormous clinical importance, computerized breast cancer multiclassification using histological pictures has rarely been investigated. A deep learning-based classification strategy is suggested to solve the challenge of automated categorization of breast cancer pathology pictures. The attention model that acts on the feature channel is the channel refinement model. The learned channel weight may be used to reduce superfluous features when implementing the feature channel. To increase classification accuracy, calibration is necessary. To increase the accuracy of channel recalibration findings, a multiscale channel recalibration model is provided, and the msSE-ResNet convolutional neural network is built. The multiscale properties flow through the network’s highest pooling layer. The channel weights obtained at different scales are delivered into line fusion and used as input to the next channel recalibration model, which may improve the results of channel recalibration. The experimental findings reveal that the spatial recalibration model fares poorly on the job of classifying breast cancer pathology pictures when applied to the semantic segmentation of brain MRI images. The public BreakHis dataset is used to conduct the experiment. The network performs benign/malignant breast pathology picture classification collected at various magnifications with a classification accuracy of 88.87 percent, according to experimental data. The diseased images are also more resilient. Experiments on pathological pictures at various magnifications show that msSE-ResNet34 is capable of performing well when used to classify pathological images at various magnifications.

The diagnosis and treatment of patients in the healthcare industry are greatly aided by data analytics. Massive amounts of data should be handled using machine learning approaches to provide tools for prediction and categorization to support practitioner decision-making. Based on the kind of tumor, disorders like breast cancer can be categorized. The difficulties associated with evaluating vast amounts of data should be overcome by discovering an efficient method for categorization. Based on the Bayesian method, we analyzed the influence of clinic pathological indicators on the prognosis and survival rate of breast cancer patients and compared the local resection value directly using the lymph node ratio (LNR) and the overall value using the LNR differences in effect between estimates. Logistic regression was used to estimate the overall LNR of patients. After that, a probabilistic Bayesian classifier-based dynamic regression model for prognosis analysis is built to capture the dynamic effect of multiple clinic pathological markers on patient prognosis. The dynamic regression model employing the total estimated value of LNR had the best fitting impact on the data, according to the simulation findings. In comparison to other models, this model has the greatest overall survival forecast accuracy. These prognostic techniques shed light on the nodal survival and status particular to the patient. Additionally, the framework is flexible and may be used with various cancer types and datasets.

Mixed application of organic and inorganic fertilizers in mixture improves soil fertility and crop productivity. However, the identification of combined application level is important. Therefore, a field experiment was conducted in 2020 in the Guto Gida district to assess the effect of maize cob biochar levels and inorganic NPS fertilizer rates on the growth and yield of maize. The study was conducted in factorial combinations of five rates of maize cob biochar and three rates of inorganic NPS fertilizer using a randomized complete block design with three replications. The main effect of the biochar level and NPS rate significantly affected crop phenology and biomass yield, whereas the number of kernels ear−1 was affected by the main effect of NPS rate. The combined application of biochar and NPS fertilizer significantly influenced plant height, leaf area index, ear weight, thousand kernel weight, grain yield, and percentage of grain yield. The interaction of biochar at 8 t·ha−1 with 100 kg·ha−1 NPS resulted in highest leaf area index (5.56), grain yield (7.03 t·ha−1), and yield increment (18.11%) followed by 8 t·ha−1  × 50 kg·ha−1 and all biochar levels with 100 kg·ha−1 NPS. In addition, the highest values of ear weight (276 g) and thousand kernel weight (47.81 g) were recorded in plots treated with combined application of biochar and NPS fertilizer at rates of 8 t·ha−1 × 50 kg·ha−1 and 4 t·ha−1 × 100 kg·ha−1, respectively, whereas plots not treated with both biochar and NPS resulted in lowest yield followed by 0 t·ha−1 × 50 kg·ha−1. In conclusion, integrated application of maize cob biochar at 8 t·ha−1 with NPS fertilizer at 50 kg·ha−1 improved the yield of maize by about 16.85% with net benefit of 61700.50 ETB ha−1 and marginal rate of return 733.68%, and therefore, the application of biochar at this rate with mineral NPS fertilizer at 50 kg·ha−1 is considered as suitable for the study area.

The random forest algorithm under the MapReduce framework has too many redundant and irrelevant features, low training feature information, and low parallelization efficiency when dealing with multihoming big data network problems, so parallelism is based on information theory, and norms is proposed for random forest algorithm (PRFITN). In this paper, the technique used first builds a hybrid dimensional reduction approach (DRIGFN) focused on information gain and the Frobenius norm, successfully reducing the number of redundant and irrelevant features; then, an information theory feature is offered. This results in the dimensionality-reduced dataset. Finally, a technique is suggested in the Reduce stage. The features are grouped in the FGSIT strategy, and the stratified sampling approach is employed to assure the information quantity of the training features in the building of the decision tree in the random forest. When datasets are provided as key/value pairs, it is common to want to aggregate statistics across all objects with the same key. To acquire global classification results and achieve a rapid and equal distribution of key-value pairs, a key-value pair redistribution method (RSKP) is used, which improves the cluster’s parallel efficiency. The approach provides a superior classification impact in multihoming large data networks, particularly for datasets with numerous characteristics, according to the experimental findings. We can utilize feature selection and feature extraction together. In addition to minimizing overfitting and redundancy, lowering dimensionality contributes to improved human interpretation and cheaper computing costs through model simplicity.