Breast cancer has affected many women worldwide. To perform detection and classification of breast cancer many computer-aided diagnosis (CAD) systems have been established because the inspection of the mammogram images by the radiologist is a difficult and time taken task. To early diagnose the disease and provide better treatment lot of CAD systems were established. There is still a need to improve existing CAD systems by incorporating new methods and technologies in order to provide more precise results. This paper aims to investigate ways to prevent the disease as well as to provide new methods of classification in order to reduce the risk of breast cancer in women’s lives. The best feature optimization is performed to classify the results accurately. The CAD system’s accuracy improved by reducing the false-positive rates.The Modified Entropy Whale Optimization Algorithm (MEWOA) is proposed based on fusion for deep feature extraction and perform the classification. In the proposed method, the fine-tuned MobilenetV2 and Nasnet Mobile are applied for simulation. The features are extracted, and optimization is performed. The optimized features are fused and optimized by using MEWOA. Finally, by using the optimized deep features, the machine learning classifiers are applied to classify the breast cancer images. To extract the features and perform the classification, three publicly available datasets are used: INbreast, MIAS, and CBIS-DDSM. The maximum accuracy achieved in INbreast dataset is 99.7%, MIAS dataset has 99.8% and CBIS-DDSM has 93.8%. Finally, a comparison with other existing methods is performed, demonstrating that the proposed algorithm outperforms the other approaches.

Localization is the process of determining the location of an object or device in an environment, and it can be achieved using radio frequency signals in wireless communication systems. Localization can be achieved using active or passive localization methods. Active localization is the most accurate but power-intensive, while passive localization is used in an environment where a targeted object does not have any devices. Consequently, this review paper is centred on the passive localization technique using radio tomography images, wherein object location is estimated using received signal strength measurements. The review paper presents passive localization as an improvement over active localization methods, problems, and limitations of tomography for Received Signal Strength Indicator (RSSI), recent contributions, and possible research directions. This review paper provides a broad and generic perspective of the ongoing research on passive localization, which might benefit researchers and professionals in wireless communications, localization, and signal processing.