To solve the problems of inaccurate evaluation results and insufficient data mining in the conventional teaching effect evaluation algorithm, this paper proposes a task-based online entrepreneurship teaching effect ev...
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To solve the problems of inaccurate evaluation results and insufficient data mining in the conventional teaching effect evaluation algorithm, this paper proposes a task-based online entrepreneurship teaching effect evaluation algorithm based on decision tree classification algorithm. First, the decisiontree structure is analyzed, the total amount of task-based online entrepreneurship teaching data samples is set, and the data classification mining model is constructed by calculating conditional entropy and information gain;then, it constructs the index system of task-based online entrepreneurship teaching effect evaluation, calculates the weight of the evaluation index, and constructs the teaching effect evaluation algorithm after consistency test. The proposed algorithm has mining accuracy and high weight calculation accuracy.
With the financial informatization, the total amount of financial data is increasing rapidly. Relying solely on people's data processing and decision-making cannot meet the requirements of enterprise finance, nor ...
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With the rapid development of modern computer network technology and image storage technology, the number of image data on the Internet has increased dramatically, and there is a phenomenon that image data is extremel...
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Prediction algorithms and classificationalgorithms, as important algorithms in the field of computer science, can automatically extract patterns and patterns from data to predict future events or classify unknown dat...
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The decision tree classification algorithm is becoming increasingly important in machine learning (ML) technology. It is being used in a variety of fields to solve extremely complicated issues. DTCA is also utilised i...
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The decision tree classification algorithm is becoming increasingly important in machine learning (ML) technology. It is being used in a variety of fields to solve extremely complicated issues. DTCA is also utilised in medical health data to identify chronic kidney disorders such as cancer and diabetes utilising computer-aided diagnosis. Deep learning is an intelligent area of machine learning in which neural networks are used to learn unsupervised from unstructured or unlabeled data. For CKD, the DL employed the deep stacked auto-encoder and soft-max classifier techniques. Kidney illness is another condition that can lead to a variety of health problems. Random forest, SVM, C5.0, decision tree classification algorithm, C4.5, ANN, neuro-fuzzy systems, classification and clustering, DSAE, DNN, FNC, MLP are used in this study to predict and identify an early diagnosis of CKD patients using various machine and deep learning algorithms using R Studio and Python Colab software. The many stages of chronic kidney disease are identified in this paper.
PurposeScreening programs use mammography as a diagnostic tool for the early detection of breast cancer. Mammogram enhancement is used to increase the local contrast of the mammogram so that the lesions are more visib...
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PurposeScreening programs use mammography as a diagnostic tool for the early detection of breast cancer. Mammogram enhancement is used to increase the local contrast of the mammogram so that the lesions are more visible in the advanced image. For accurate diagnosis in the early stage of breast cancer, the appearance of masses and microcalcification on the mammographic image are two important indicators. The objective of this study was to evaluate the feasibility of the automatic separation of images of breast tissue microcalcifications and also to evaluate its *** research was carried out by using two techniques of image enhancement and highlighting of breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor filtering method. After determining the clusters of breast tissue microcalcifications, the clusters are classified using the decision tree classification algorithm. Then, for segmentation, samples suspected of microcalcification are highlighted and masked, and in the last stage, tissue characteristics are extracted. Subsequently, with the help of an artificial neural network (ANN), determining the benign and malignant types of segmented ROI clusters was accomplished. The proposed system is trained with a Digital Database for Screening Mammography (DDSM) developed by the University of South Florida, USA, and the simulations are performed under MATLAB software and the results are compared with previous *** results of this training performed under this work show an accuracy of 93% and an improvement of sensitivity above 95%.ConclusionThe result indicates that the proposed approach can be applied to ensure breast cancer diagnosis.
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