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Summary Demand of cognitive radio technology and wireless sensor network is increasing in various applications. Currently, the combined cognitive wireless sensor networks have gained attraction by research community due to their extensive applications and advantages. The wireless sensor networks operate in ISM bands where managing the available spectrum is considered as a crucial task. Moreover, the sensor networks are deployed in harsh environment and equipped with limited power supply; hence, replacement of power source is not possible. Hence, efficient spectrum sensing and lifetime management are the challenging task in Cognitive Radio Sensor Networks (CRSNs). In this work, we present a combined approach to enhance the network enactment, i.e., network lifetime, energy depletion, and packet delivery with a novel spectrum sensing approach. In order to handle the issue of energy utilization, we introduce inter‐ and intra‐cluster communication model along with a clustering algorithm. Further, we present a posterior transition probability‐based model for spectrum sensing. We present an experimental study where we measure the network enactment in context of alive node, dead node, enduring energy, and packet to the base station. The experimental study shows that average spectrum sensing performance is obtained as 0.9030, 0.9188, 0.9213, 0.9355, and 0.9628 by using DE, FMODE, NSGA, ODE, and proposed approach, respectively. Experimental analysis shows that proposed approach archives better performance when compared with advanced methods.

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Percentage mammographic breast density (MBD) is one of the most notable biomarkers. It is assessed visually with the support of radiologists with the four qualitative Breast Imaging Reporting and Data System (BIRADS) categories. It is demanding for radiologists to differentiate between the two variably allocated BIRADS classes, namely, “BIRADS C and BIRADS D.” Recently, convolution neural networks have been found superior in classification tasks due to their ability to extract local features with shared weight architecture and space invariance characteristics. The proposed study intends to examine an artificial intelligence (AI)-based MBD classifier toward developing a latent computer-assisted tool for radiologists to distinguish the BIRADS class in modern clinical progress. This article proposes a multichannel DenseNet architecture for MBD classification. The proposed architecture consists of four-channel DenseNet transfer learning architecture to extract significant features from a single patient's two a mediolateral oblique (MLO) and two craniocaudal (CC) views of digital mammograms. The performance of the proposed classifier is evaluated using 200 cases consisting of 800 digital mammograms of the different BIRADS density classes with validated density ground truth. The classifier's performance is assessed with quantitative metrics such as precision, responsiveness, specificity, and the area under the curve (AUC). The concluding preliminary outcomes reveal that this intended multichannel model has delivered good performance with an accuracy of 96.67% during training and 90.06% during testing and an average AUC of 0.9625. Obtained results are also validated qualitatively with the help of a radiologist expert in the field of MBD. Proposed architecture achieved state-of-the-art results with a fewer number of images and with less computation power.