Breast cancer is the most common cancer-related death in women, accounting for 16% of all cancer-related fatalities globally. Breast cancer is fatal in just half of all cases. Radiologists may misread worrisome lesion...
详细信息
Breast cancer is the most common cancer-related death in women, accounting for 16% of all cancer-related fatalities globally. Breast cancer is fatal in just half of all cases. Radiologists may misread worrisome lesions due to imaging quality concerns and diverse breast densities, which raises the false-(positive and negative) ratio, as the primary explanation for the problem. Early intervention is critical in building a current prognosis process that can successfully limit disease consequences and increase recovery. Patients are being referred back for biopsies to dispel suspicions when the inconsistent feature-extraction approach is used for manual screening of breast abnormalities in traditional schemes. With the Multi-kernel Support Vector Machine (MKSVM), we develop a new modality for the prediction of breast cancer and train its properties using the supervised machine learning approaches. Furthermore, the IDA-MKSVM technique achieved maximum average accuracy is 95.75%, average sensitivity is 94.29%, average specificity is 95.16% and average F-score is 95.39% for different training datasets. For selecting ideal features, the improved dragonfly algorithm (IDA) is also used. A 10-fold cross validation procedure is used in the system under consideration to ensure accuracy. The UCI machine learning repository holds the breast cancer diagnosis data referred to as the Wisconsin breast cancer diagnosis data set.
It is hard to predict wind power with high-precision due to its non-stationary and stochastic nature. The wind power has developed rapidly around the world as a promising renewable energy industry. The uncertainty of ...
详细信息
It is hard to predict wind power with high-precision due to its non-stationary and stochastic nature. The wind power has developed rapidly around the world as a promising renewable energy industry. The uncertainty of wind power brings difficult challenges to the operation of the power system with the integration of wind farms into power grid. Accurate wind power prediction is increasingly important for the stable operation of wind farms and the power grid. This study is combined support vector machine and improved dragonfly algorithm to forecast short-term wind power for a hybrid prediction model. The adaptive learning factor and differential evolution strategy are introduced to improve the performance of traditional dragonflyalgorithm. The improved dragonfly algorithm is used to choose the optimal parameters of support vector machine. The effectiveness of the proposed model has been confirmed on the real datasets derived from La Haute Borne wind farm in France. The proposed model has shown better prediction performance compared with the other models such as back propagation neural network and Gaussian process regression. The proposed model is suitable for short-term wind power prediction. (C) 2019 Elsevier Ltd. All rights reserved.
In recent years, the Face recognition task has been an active research area in computer vision and biometrics. Many feature extraction and classification algorithms are proposed to perform face recognition. However, t...
详细信息
In recent years, the Face recognition task has been an active research area in computer vision and biometrics. Many feature extraction and classification algorithms are proposed to perform face recognition. However, the former usually suffer from the wide variations in face images, while the latter usually discard the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the Key points Local Binary/Tetra Pattern (KP-LTrP) and improved Hough Transform (IHT) with the improved dragonfly algorithm-Kernel Ensemble Learning Machine (IDFA-KELM) is proposed to address the face recognition problem in unconstrained conditions. Initially, the face images are collected from the publicly available dataset. Then noises in the input image are removed by performing preprocessing using Adaptive Kuwahara filter (AKF). After preprocessing, the face from the preprocessed image is detected using the Tree-Structured Part Model (TSPM) structure. Then, features, such as KP-LTrP, and IHT are extracted from the detected face and the extracted feature is reduced using the Information gain based Kernel Principal Component Analysis (IG-KPCA) algorithm. Then, finally, these reduced features are inputted to IDFA-KELM for performing FR. The outcomes of the proposed method are examined and contrasted with the other existing techniques to confirm that the proposed IDFA-KELM detects human faces efficiently from the input images.
In order to improve integrity management evaluation level of pipeline, the fuzzy surfacelet neural network optimized by improved dragonfly algorithm. Firstly, the domestic and foreign related research progresses of pi...
详细信息
In order to improve integrity management evaluation level of pipeline, the fuzzy surfacelet neural network optimized by improved dragonfly algorithm. Firstly, the domestic and foreign related research progresses of pipeline integrity management are summarized. Secondly, the pipeline integrity management evaluation index system is constructed according to management and technology characteristics of pipeline integrity management. Thirdly, the fuzzy surfacelet neural network with five layers is established by combining Surfacelet transfer, wavelet neural network and fuzzy theory. The improved dragonfly algorithm is established by improved population initialization strategy and inertia weight updating strategy. Finally, simulation analysis of pipeline integrity management is carried out based on fuzzy B-spline wavelet neural network optimized by improved particle swarm algorithm (BWNN-IPSA), fuzzy surfacelet neural network optimized by traditional dragonflyalgorithm (FSNN-TDA) and fuzzy surface neural network optimized by improved dragonfly algorithm (FSNN-IDA), simulation results show that the fuzzy Surfacelet neural network optimized by improved dragonfly algorithm can achieve convergence after 500 times, it has less convergence times than other evaluation models. The mean square error of the proposed evaluation model ranges from 0.79 to 1.02%, it has less error than other evaluation models. Therefore the proposed fuzzy surface neural network optimized by improved dragonfly algorithm has higher computing precision and efficiency. The proposed evaluation model of pipeline integrity management can improve intelligent level of pipeline management, and ensure the safety and reliability of pipeline system. (C) 2021 Elsevier B.V. All rights reserved.
IoT (Internet of Things) is a sophisticated analytics and automation system that utilizes networks, big data, artificial intelligence, and sensing technology, and is controlled by an embedded module. It allows one to ...
详细信息
IoT (Internet of Things) is a sophisticated analytics and automation system that utilizes networks, big data, artificial intelligence, and sensing technology, and is controlled by an embedded module. It allows one to use affordable wireless technology and transmits the data into the cloud at a component level. It also provides a place to save the data - however, the significant challenges in IoT relay on security restrictions related with device cost. Moreover, the increasing amount of devices further generate opportunities for attacks. Hence, to overcome this issue, this paper intends to develop a promising methodology associated with data privacy preservation for handling the IoT network. It is obvious that the IoT devices often generate time series data, where the range of respective time series data can be vast. Under such circumstances, proper information extraction through effective analysis and relevant privacy preservation of sensitive data from IoT is challenging. In this paper, the problem that occurred in the data preservation is formulated as a non-linear objective model. To solve this objective model, an improved, optimized dragonflyalgorithm (DA) is adopted, which is termed the improved DA (IDA) algorithm. Here, the proposed model focused on preserving the physical activity of human monitoring data in the IoT sector. Moreover, the proposed IDA algorithm is compared with conventional schemes such as Genetic algorithm (GA), Particle Swarm Optimization (PSO), Ant Bee Colony (ABC), Firefly (FF) and DA and the outcomes prove that the suggested scheme is highly used for preserving the sensitive information uploaded in IoT.
暂无评论