Breast cancer is the first female cancer responsible for high mortality worldwide. Despite the progress that has made it possible to better understand the mechanisms of cancer development, the causes of breast cancer ...
详细信息
ISBN:
(纸本)9781538673287
Breast cancer is the first female cancer responsible for high mortality worldwide. Despite the progress that has made it possible to better understand the mechanisms of cancer development, the causes of breast cancer are currently unknown. Nevertheless, studies have identified some risk factors that promote breast cancer and a healthy lifestyle can reduce risk. In Morocco, breast cancer is the first cancer in women. It represents 34.3% of all female cancers. In this work, the fastcorrelationbased Feature selection (FCBF) method is used to filter irrelevant and redundant characteristics in order to improve the quality of cancer classification, and we will provide an overview of the evolution of key data in the health system and apply five learning algorithms to a breast cancer data set. The purpose of this research work is to predict breast cancer, using several machine learning algorithms that are Random Forest, Naive Bayes, Support Vector Machines SVM, K-Nearest Neighbors K-NN, and Multilayer Perception MLP, in order to select the most effective algorithm with and without FCBF. The experimental results show that SVM gives the highest accuracy of 97.9% without FCBF but if we apply this method we find that the SVM and MLP show the best results in comparison with other algorithms. The results will help to choose the best learning algorithm machine classification for breast cancer prediction.
Monitoring and evaluating the skin temperature value are considerably important for neonates. A system detecting diseases without any harmful radiation in early stages could be developed thanks to thermography. This s...
详细信息
Monitoring and evaluating the skin temperature value are considerably important for neonates. A system detecting diseases without any harmful radiation in early stages could be developed thanks to thermography. This study is aimed at detecting healthy/unhealthy neonates in neonatal intensive care unit (NICU). We used 40 different thermograms belonging 20 healthy and 20 unhealthy neonates. Thermograms were exported to thermal maps, and subsequently, the thermal maps were converted to a segmented thermal map. Local binary pattern and fast correlation-based filter (FCBF) were applied to extract salient features from thermal maps and to select significant features, respectively. Finally, the obtained features are classified as healthy and unhealthy with decision tree, artificial neural networks (ANN), logistic regression, and random forest algorithms. The best result was obtained as 92.5% accuracy (100% sensitivity and 85% specificity). This study proposes fast and reliable intelligent system for the detection of healthy/unhealthy neonates in NICU.
Multiple myeloma (Kahler's disease) is a complex blood cancer that affects the plasma cells, which produce the antibodies to protect the human body. The diagnosis of multiple myeloma is difficult in the early stag...
详细信息
ISBN:
(纸本)9781728122205
Multiple myeloma (Kahler's disease) is a complex blood cancer that affects the plasma cells, which produce the antibodies to protect the human body. The diagnosis of multiple myeloma is difficult in the early stage and depends on several medical exams and tests in advanced stages, thus its process is very long and can discourage patients. In this study, the fast correlation-based filter (FCBF) method has been proposed to predict the clinical and paraclinical factors for multiple myeloma diagnosis. The obtained results by the proposed method are compared to the other filter-based feature selection algorithms, where the proposed FCBF method achieved the best performance.
High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting m...
详细信息
High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models.
A total transfer capability (TTC) calculation model based on stacked denoising autoencoder (SDAE) is proposed in this paper, considering static security, static voltage stability and transient stability constraints. T...
详细信息
ISBN:
(纸本)9781538664612
A total transfer capability (TTC) calculation model based on stacked denoising autoencoder (SDAE) is proposed in this paper, considering static security, static voltage stability and transient stability constraints. The TTC calculation model consists of feature pre-screening, SDAE and the regression layer. fast correlation-based filter (FCBF) is used to eliminate irrelevant and redundant features to improve the training efficiency of SDAE. SDAE takes advantage of the deep structure to extract high-order features relevant to TTC from original features. The regression layer is utilized to create the mapping between high-order features and the TTC value. Experiment results of a real power system demonstrate that the proposed TTC calculation model has higher computational accuracy than shallow machine learning models and feature pre-screening decreases the training time of the TTC calculation model obviously.
暂无评论