In women, breast cancer is deadly disease which is increased the death rate of women. By exploiting the mammogram images, a precise and early recognition of breast cancer is a complex task. Therefore, a new breast cancer recognition technique was proposed that considered five important stages: segmentation, preprocessing, feature extraction, feature selection as well as classification. Initially, by exploiting the median filtering as well as Contrast Limited Adaptive Histogram Equalization (CLAHE), input mammogram images are preprocessed. Subsequently, through the region growing method, the preprocessed images are fed to segmentation. Then, from the segmented image, texture, geometric and gradient features are extracted. The feature vector length is higher, it is important to choose optimal features. Moreover, the optimal features chosen are performed using the proposed optimization method. After completing the selection of the optimal features, they are fed to the classification procedure including the Neural Network (NN) classifier. As an innovation, to improve the precision of diagnosis (benign as well as malignant), the NN weight is chosen optimally. The NN weight optimization and the optimal feature selection are attained using the Hybrid Wolf Pack Algorithm (WPA) and Particle Swarm Optimization (PSO) Algorithm called the Hybrid WPA-PSO algorithm. At last, the performance analysis is performed between the proposed and conventional techniques.
In Wireless Sensor Network (WSN), the development is augmented and an increased enormous concentration in computer vision. A huge amount of sensors are used to carry out dispersed sensing of the target field in WSN. The existing techniques exploited wireless chargers to provide energy to Sensor Networks (SNs), other than supplied energy is not adequate to control the sensor nodes. Hence, this work presents a method to reduce the energy utilization per node adopting effectual scheduling of sleep/awake of nodes. The technique experiences two stages for the sensor activation such as the initialization stage, and the activation stage. The initialization stage is proposed by a network beginning that is performed to express the network parameters to nodes or sensors. Subsequently, in every slot proposed optimization method is used to activate sensors in the activation stage. The proposed improved cuckoo search and squirrel search optimization approach named (Improved CS-SSA). Hence, the proposed technique produces control in terms of the turn-ON or OFF of sensors that represent active sensors and employ itself in sensing and monitoring distributed environment. The proposed technique performance is superior to other existing techniques through throughput, maximum energy, and alive nodes, correspondingly.
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