No abstract

Summary Medical imaging systems have broadly used in the diagnosis and identification of breast cancer. It is essential to recognize breast tumor as soon as possible. Mammography is a widely utilized method for the identification of breast cancer. The identification of cancer is trailed by the segmentation of the cancer area in an image of the mammogram. Numerous researches have been made on the diagnosing and identification of breast cancer utilizing different classification and image processing methods. In this work, we proposed the Convolutional Neural Network (CNN) classifier for diagnosing breast cancer utilizing MIAS (Mammographic Image Analysis Society)‐dataset. CNN established as an efficient class of methods for image recognition problems. CNN is a deep learning system that extricates the feature of an image and utilizes those features for classification of the image. Because deep learning methods are utilized for high task objective Computer Vision, Medical Diagnosis, Image processing, and so on. Wiener filter is utilized to expel the noise and background of the image and the K‐means clustering technique was utilized for the segmentation. After segmentation, the features are extracted and classified utilizing CNN classifier. The performance of this proposed method was analyzed and compared with the conventional techniques based on accuracy, sensitivity, and specificity result parameters. From the comparison result, it is seen that the CNN classifier performed better compared with different techniques with 0.5‐4% additional accuracy and 3‐13% specificity.

No abstract

Analytical performance evaluation is crucial in designing mobile multihop networks under different operational conditions. It provides the best insight into the effects of various network parameters and their interactions. The route and link lifetime in mobile multihop networks under a random mobility environment have significant effects on network performance. Many analytical studies have been introduced to the body of work concerned with the link and route lifetime in mobile multihop networks. However, there is no closed-form analytical study for link and route stability in MANETs under any random mobility model. In addition, to simplify the analysis, one or more network characteristics were not considered in most of related studies. This work presents a closed-form analytical model for the link and route lifetime in mobile multihop networks, where nodes move according to the random waypoint mobility model. The proposed model can capture the effects of most network characteristics including the number of mobile nodes, the number of hops, the network area size, the transmission range, and the speed of nodes. In addition, we used the proposed model to derive expressions for the bounds of the link and route lifetime under different network characteristics. The proposed model can help characterize the network performance, analyze the network as to meet a certain level of quality-of-service, and design better routing protocols to cope with link breakage caused by node mobility. To verify the proposed model, extensive simulations were performed under different network characteristics.INDEX TERMS Link lifetime, mobile ad hoc networks, multihop networks, random waypoint mobility, route lifetime, vehicle ad hoc networks.

No abstract