Explanatory systems ("explainers") make the behavior of blackbox machinelearning models more transparent. However, the results of different explainers ("explanations") are often inconsistent with ...
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Endometrial cancer(EC) is the most common and rapidly increasing female cancer globally. Atypical endometrial hyperplasia (AEH) is a precancerous condition of EC. Although hysteroscopy serves as the primary modality f...
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Intelligent platforms are mainly service platforms composed of machinelearning integrations that rely on continuous reasoning and learning. this technology has been widely used in today's the Internet Age, and it...
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this paper examines the behavior of reinforcement learning systems in personalization environments and details the differences in policy entropy associated withthe type of learning algorithm utilized. We observe that...
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In general, natural data are long-tail distributed over semantic classes. Existing recognition methods tackle the imbalanced classification problem by designing architectures that focus more on the tail classes withou...
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the proceedings contain 200 papers. the topics discussed include: heat-aware graph data placement strategy for NVM;parameters estimation of photovoltaic models via an improved differential evolution algorithm;study on...
ISBN:
(纸本)9798400708831
the proceedings contain 200 papers. the topics discussed include: heat-aware graph data placement strategy for NVM;parameters estimation of photovoltaic models via an improved differential evolution algorithm;study on the extraction of law enforcement relationships in administrative law enforcement instrument data;online algorithm for exploring a grid polygon with two robots;research on gesture recognition method by improving dung beetle algorithm to optimize BP neural network;an improved algorithm for frequent sequence pattern mining based on PrefixSpan-ComplexPrefixSpan;machinelearning-based research on reserve prediction of natural-gas-hydrates;enhancing coal mine safety monitoring algorithm using graph computing techniques;reverse distillation support vector data description for unsupervised anomaly detection;and few-shot object counting model based on self-support matching and attention mechanism.
Due to the similarity in mushroom features and the difficulty in distinguishing between poisonous and nonpoisonous varieties, mushrooms pose a threat to human health. To address the challenge of mushroom classificatio...
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the field of Natural Language Processing (NLP) is witnessing a growing recognition of the significance of investigating stress-related phenomena. this research study proposes a new framework to address the diagnostic ...
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Due to their severity and prevalence, the prediction of chronic diseases (CDs) has become an important area of research, particularly with advances in deep learning. In this paper, we propose a new automated technique...
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ISBN:
(纸本)9783031821523;9783031821530
Due to their severity and prevalence, the prediction of chronic diseases (CDs) has become an important area of research, particularly with advances in deep learning. In this paper, we propose a new automated technique for detecting these diseases. A deep network architecture using a parallel unidimensional convolutional neural network (1D-PCNN) is employed to extract deep features. Subsequently, the Support Vector machine (SVM) technique is applied for CD classification. the uniqueness of our framework lies in the design of the 1D-PCNN, which can learn the deep features of the input layer through parallel convolutional layers. As a result, the deep features of each parallel branch are simultaneously extracted before being combined in the fusion layer. Furthermore, in order to improve the efficiency of the proposed model, the Synthetic Minority Oversampling Technique (SMOTE) is used. this strategy manages class imbalance in CD databases. the suggested model is analysed against standard 1D-CNN, 1D-CNN model combined with conventional machinelearning methods and other existing state-of-the-art models. the effectiveness of the suggested method was tested using two known databases the Pima Indian Diabetes database (PIDD) and the Cleveland Heart Disease database (CHDD). Results, from these databases indicate that the proposed approach yielded an accuracy rate of 83% and 88% an F score of 73% and 90% and an AUC of 80% and 87% correspondingly. Finally, after applying the SMOTE method, accuracy was improved to 86% and 92%, Fscore to 86% and 93%, and AUC to 86% and 92%, respectively, and outperforms other methods.
In the realm of flower-rich Bangladesh, the presence of these blossoms enriches our everyday experiences, whether encountered during leisurely strolls, along railway tracks, or within our gardens. However, the beauty ...
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