NSTL国家科技图书文献中心

1. Segmentation of breast lesion using fuzzy thresholding and deep learning NSTL国家科技图书文献中心

Sahaya Pushpa Sarmil... | Inbamalar T.M.... - 《Computers in Biology and Medicine》 - 2025,184 - 109406～109406 - 共12页

摘要： segment the lesions are: i) Fuzzy* C-mean Thresholding* | image, and the fuzzy* thresholded image. In this study* | © 2024 Elsevier LtdBreast cancer is a major | cause of morbidity and mortality in women. In breast | cancer screening, Dynamic Contrast Enhanced Magnetic

关键词： Anisotropic diffusion filter | Breast lesion | DeepLabV3+ | Fuzzy C-Mean cluster | SegNet | Thresholding

2. Reservoir Permeability Prediction Method Based on Fuzzy Clustering and Machine Learning NSTL国家科技图书文献中心

Fu, Jianwei | Chen, Mengling ... - 《Chemistry and Technology of Fuels and Oils》 - 2025,60(6) - 1518～1527 - 共10页

摘要： model combined with actual conditions. A Fuzzy* C-means* | algorithm based on fuzzy* logic is used to cluster the data* | indicators (such as mean* square error, determination* | 0.73, respectively. It is proved that fuzzy* logic* | With the in-depth development of oil and gas

关键词： permeability prediction | fuzzy logic | machine learning | support vector machine | LSTM neural network

3. Predicting lane change risk for intelligent driving vehicles considering driving style NSTL国家科技图书文献中心

Lingyi Meng | Fuhao Li ... - 《Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024)》 - International Conference on Intelligent Traffic Systems and Smart City - 2025, - 134221S.1～134221S.6 - 共6页

摘要： c-mean* (FCM). Finally, the vehicle lane-changing* | highD dataset is selected as a sample to cluster | distance index, and the risk level is classified by fuzzy | Lane change driving is a core behavior for | adjusting speed and enhancing driving experience, in which

关键词： Driving style | FCM clustering | Risk prediction for lane change | LSTM model

4. Fuzzy c-mean (FCM) integration of geophysical data from an iron-oxide copper gold (IOCG) deposit under thick cover NSTL国家科技图书文献中心

Simon Carter | Graham Heinson ... - 《Exploration geophysics》 - 2024,55(6) - 678～689 - 共12页

摘要： depositgeometry, and drilling is very expensive. Fuzzy c-mean | and electrical resistivity model data. Fuzzy c-mean | cluster analyses are undertaken in 2D forgravity data | Geophysical methods depend on a range of | physical and chemical mechanisms, and eachmethod has

关键词： Fuzzy c-mean clustering | FCM | magnetotellurics | ambient noise tomography | gravity | IOCG

5. A Fuzzy Portfolio Model With Cardinality Constraints Based on Differential Evolution Algorithms NSTL国家科技图书文献中心

He, JianDong - 《International Journal of Data Warehousing and Mining》 - 2024,20(1) - ARTN 341268～ - 共14页

摘要：, using fuzzy* c -means clustering algorithm, 12 stocks* | numerical calculation examples. At the same time, fuzzy* c* | liquidity are considered as trapezoidal fuzzy* numbers. The* | possibility mean* and mean absolute deviation of expected* | assets, while the possibility mean* of expected turnover*

关键词： the efficient frontier of the fuzzy portfolio model | Fuzzy c-means clustering algorithm | liquidity | transaction costs | trapezoidal fuzzy numbers | OPTIMIZATION

6. A Multi-Stream Deep Neural Network to Predict the Energy Consumption of Smart Home Appliances NSTL国家科技图书文献中心

Mollashahi, Mozhdeh | Jafari, Pouria ... - 《International Journal of Computational Intelligence and Applications》 - 2024,23(2) - 2450003～ - 共16页

摘要： combines the Fuzzy* C-Means method with a multi-stream* | the fuzzy-weighted sum of these cluster-specific | subsets. Each subset is used to train a cluster-specific | methods in terms of root mean square error and mean | speed and model performance. Furthermore, the fuzzy

关键词： Deep neural network | energy consumption | fuzzy clustering | smart homes | MODELS

7. Fuzzy cluster-based multi-node charging strategy in mobile sensor networks NSTL国家科技图书文献中心

Zhu F. | Lv X.... - 《Ad hoc networks》 - 2024,156(Apr.) - 1.1～1.11 - 共11页

摘要： improved multi-factor fuzzy* C-mean clustering algorithm* | fuzzy clustering-based multi-node charging strategy | (MFCM++) is designed to cluster* nodes with similar* | the optimal charging position for each cluster* based* | , thereby reducing the cluster* charging waiting time*

关键词： Cuckoo search multi-node model | Energy replenishment | MSN | Multi-factor

8. ML Based Hybrid Computational Intelligence Protocol to Improve Energy Efficiency and Security in Opportunistic Networks (Oppnets) NSTL国家科技图书文献中心

Sachdeva, Rahul | Dev, Amita - 《Wireless personal communications》 - 2024,139(2) - 1203～1223 - 共21页

摘要： Secure Fuzzy* Trust-Based C-Mean Clustering-based* | using the fuzzy* c-means clustering technique. The* | the best cluster* head selection and security are* | route the cluster* head using the secure identify path* | detection. When selecting the cluster* head, the most*

关键词： Opportunistic network | Clustering | Trustiness | Key distribution | Tracing and strategy | Malicious node

9. Fuzzy C-Means Clustering and Improved Arithmetic Optimization Algorithm-Based Layering Cooperative Routing Protocol for UASNs NSTL国家科技图书文献中心

Duoliang Han | Xiujuan Du ... - 《IEEE sensors journal》 - 2024,24(15) - 24810～24824 - 共15页

摘要： introduces a fuzzy* C-mean (FCM) algorithm to cluster the* | protocols, a fuzzy* C-means clustering and improved* | short network lifetime in existing cluster* routing* | cluster head forwarding node, which accelerates the | In underwater acoustic sensor networks (UASNs

关键词： Clustering | cooperative routing | fuzzy C-means (FCM) | improved arithmetic optimization algorithm (AOA) | network lifetime | underwater acoustic sensor networks (UASNs) | Energy consumption | Sensors | Routing protocols | Clustering algorithms...

10. FCM-CSMOTE: Fuzzy C-Means Center-SMOTE NSTL国家科技图书文献中心

Roudani Mohammed | El Moutaouakil Karim - 《Expert Systems with Application》 - 2024,248(Aug.) - 123406.1～123406.25 - 共25页

摘要： oversampling, called Fuzzy* C-Means Center-SMOTE (FCM-CSMOTE* | ), which generates synthetic samples in each cluster | , including Geometric Mean* (GM), F-Measure (FM), Area Under* | Imbalanced class distributions in machine | learning, where the minority class is often under

摘要： segment the lesions are: i) Fuzzy* C-mean Thresholding* | image, and the fuzzy* thresholded image. In this study* | © 2024 Elsevier LtdBreast cancer is a major | cause of morbidity and mortality in women. In breast | cancer screening, Dynamic Contrast Enhanced Magnetic

关键词： Anisotropic diffusion filter | Breast lesion | DeepLabV3+ | Fuzzy C-Mean cluster | SegNet | Thresholding

摘要： model combined with actual conditions. A Fuzzy* C-means* | algorithm based on fuzzy* logic is used to cluster the data* | indicators (such as mean* square error, determination* | 0.73, respectively. It is proved that fuzzy* logic* | With the in-depth development of oil and gas

关键词： permeability prediction | fuzzy logic | machine learning | support vector machine | LSTM neural network

摘要： c-mean* (FCM). Finally, the vehicle lane-changing* | highD dataset is selected as a sample to cluster | distance index, and the risk level is classified by fuzzy | Lane change driving is a core behavior for | adjusting speed and enhancing driving experience, in which

关键词： Driving style | FCM clustering | Risk prediction for lane change | LSTM model

摘要： depositgeometry, and drilling is very expensive. Fuzzy c-mean | and electrical resistivity model data. Fuzzy c-mean | cluster analyses are undertaken in 2D forgravity data | Geophysical methods depend on a range of | physical and chemical mechanisms, and eachmethod has

关键词： Fuzzy c-mean clustering | FCM | magnetotellurics | ambient noise tomography | gravity | IOCG

关键词： the efficient frontier of the fuzzy portfolio model | Fuzzy c-means clustering algorithm | liquidity | transaction costs | trapezoidal fuzzy numbers | OPTIMIZATION

摘要： combines the Fuzzy* C-Means method with a multi-stream* | the fuzzy-weighted sum of these cluster-specific | subsets. Each subset is used to train a cluster-specific | methods in terms of root mean square error and mean | speed and model performance. Furthermore, the fuzzy

关键词： Deep neural network | energy consumption | fuzzy clustering | smart homes | MODELS

摘要： improved multi-factor fuzzy* C-mean clustering algorithm* | fuzzy clustering-based multi-node charging strategy | (MFCM++) is designed to cluster* nodes with similar* | the optimal charging position for each cluster* based* | , thereby reducing the cluster* charging waiting time*

关键词： Cuckoo search multi-node model | Energy replenishment | MSN | Multi-factor

摘要： Secure Fuzzy* Trust-Based C-Mean Clustering-based* | using the fuzzy* c-means clustering technique. The* | the best cluster* head selection and security are* | route the cluster* head using the secure identify path* | detection. When selecting the cluster* head, the most*

关键词： Opportunistic network | Clustering | Trustiness | Key distribution | Tracing and strategy | Malicious node

摘要： introduces a fuzzy* C-mean (FCM) algorithm to cluster the* | protocols, a fuzzy* C-means clustering and improved* | short network lifetime in existing cluster* routing* | cluster head forwarding node, which accelerates the | In underwater acoustic sensor networks (UASNs

关键词： Clustering | cooperative routing | fuzzy C-means (FCM) | improved arithmetic optimization algorithm (AOA) | network lifetime | underwater acoustic sensor networks (UASNs) | Energy consumption | Sensors | Routing protocols | Clustering algorithms...

摘要： oversampling, called Fuzzy* C-Means Center-SMOTE (FCM-CSMOTE* | ), which generates synthetic samples in each cluster | , including Geometric Mean* (GM), F-Measure (FM), Area Under* | Imbalanced class distributions in machine | learning, where the minority class is often under

关键词： Clustering | SMOTE | Unbalanced Data | Oversampling | Big Data | Classification

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： segment the lesions are: i) Fuzzy C-mean Thresholding | image, and the fuzzy thresholded image. In this study | © 2024 Elsevier LtdBreast cancer is a major | cause of morbidity and mortality in women. In breast | cancer screening, Dynamic Contrast Enhanced Magnetic

关键词： Anisotropic diffusion filter | Breast lesion | DeepLabV3+ | Fuzzy C-Mean cluster | SegNet | Thresholding

摘要： model combined with actual conditions. A Fuzzy C-means | algorithm based on fuzzy logic is used to cluster the data | indicators (such as mean square error, determination | 0.73, respectively. It is proved that fuzzy logic | With the in-depth development of oil and gas

关键词： permeability prediction | fuzzy logic | machine learning | support vector machine | LSTM neural network

摘要： c-mean (FCM). Finally, the vehicle lane-changing | highD dataset is selected as a sample to cluster | distance index, and the risk level is classified by fuzzy | Lane change driving is a core behavior for | adjusting speed and enhancing driving experience, in which

关键词： Driving style | FCM clustering | Risk prediction for lane change | LSTM model

摘要： depositgeometry, and drilling is very expensive. Fuzzy c-mean | and electrical resistivity model data. Fuzzy c-mean | cluster analyses are undertaken in 2D forgravity data | Geophysical methods depend on a range of | physical and chemical mechanisms, and eachmethod has

关键词： Fuzzy c-mean clustering | FCM | magnetotellurics | ambient noise tomography | gravity | IOCG

关键词： the efficient frontier of the fuzzy portfolio model | Fuzzy c-means clustering algorithm | liquidity | transaction costs | trapezoidal fuzzy numbers | OPTIMIZATION

摘要： combines the Fuzzy C-Means method with a multi-stream | the fuzzy-weighted sum of these cluster-specific | subsets. Each subset is used to train a cluster-specific | methods in terms of root mean square error and mean | speed and model performance. Furthermore, the fuzzy

关键词： Deep neural network | energy consumption | fuzzy clustering | smart homes | MODELS

摘要： improved multi-factor fuzzy C-mean clustering algorithm | fuzzy clustering-based multi-node charging strategy | (MFCM++) is designed to cluster nodes with similar | the optimal charging position for each cluster based | , thereby reducing the cluster charging waiting time

关键词： Cuckoo search multi-node model | Energy replenishment | MSN | Multi-factor

摘要： Secure Fuzzy Trust-Based C-Mean Clustering-based | using the fuzzy c-means clustering technique. The | the best cluster head selection and security are | route the cluster head using the secure identify path | detection. When selecting the cluster head, the most

关键词： Opportunistic network | Clustering | Trustiness | Key distribution | Tracing and strategy | Malicious node

摘要： introduces a fuzzy C-mean (FCM) algorithm to cluster the | protocols, a fuzzy C-means clustering and improved | short network lifetime in existing cluster routing | cluster head forwarding node, which accelerates the | In underwater acoustic sensor networks (UASNs

关键词： Clustering | cooperative routing | fuzzy C-means (FCM) | improved arithmetic optimization algorithm (AOA) | network lifetime | underwater acoustic sensor networks (UASNs) | Energy consumption | Sensors | Routing protocols | Clustering algorithms...

摘要： oversampling, called Fuzzy C-Means Center-SMOTE (FCM-CSMOTE | ), which generates synthetic samples in each cluster | , including Geometric Mean (GM), F-Measure (FM), Area Under | Imbalanced class distributions in machine | learning, where the minority class is often under

关键词： Clustering | SMOTE | Unbalanced Data | Oversampling | Big Data | Classification

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： segment the lesions are: i) Fuzzy* C-mean Thresholding* | image, and the fuzzy* thresholded image. In this study* | © 2024 Elsevier LtdBreast cancer is a major | cause of morbidity and mortality in women. In breast | cancer screening, Dynamic Contrast Enhanced Magnetic

摘要： model combined with actual conditions. A Fuzzy* C-means* | algorithm based on fuzzy* logic is used to cluster the data* | indicators (such as mean* square error, determination* | 0.73, respectively. It is proved that fuzzy* logic* | With the in-depth development of oil and gas

摘要： c-mean* (FCM). Finally, the vehicle lane-changing* | highD dataset is selected as a sample to cluster | distance index, and the risk level is classified by fuzzy | Lane change driving is a core behavior for | adjusting speed and enhancing driving experience, in which

摘要： combines the Fuzzy* C-Means method with a multi-stream* | the fuzzy-weighted sum of these cluster-specific | subsets. Each subset is used to train a cluster-specific | methods in terms of root mean square error and mean | speed and model performance. Furthermore, the fuzzy

摘要： improved multi-factor fuzzy* C-mean clustering algorithm* | fuzzy clustering-based multi-node charging strategy | (MFCM++) is designed to cluster* nodes with similar* | the optimal charging position for each cluster* based* | , thereby reducing the cluster* charging waiting time*

摘要： Secure Fuzzy* Trust-Based C-Mean Clustering-based* | using the fuzzy* c-means clustering technique. The* | the best cluster* head selection and security are* | route the cluster* head using the secure identify path* | detection. When selecting the cluster* head, the most*

摘要： introduces a fuzzy* C-mean (FCM) algorithm to cluster the* | protocols, a fuzzy* C-means clustering and improved* | short network lifetime in existing cluster* routing* | cluster head forwarding node, which accelerates the | In underwater acoustic sensor networks (UASNs

摘要： oversampling, called Fuzzy* C-Means Center-SMOTE (FCM-CSMOTE* | ), which generates synthetic samples in each cluster | , including Geometric Mean* (GM), F-Measure (FM), Area Under* | Imbalanced class distributions in machine | learning, where the minority class is often under