Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle-and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.
Free space optics (FSO) plays key role to establish a link in targeted remote and uncovered area of skip-zones where wireline (optical fiber cable) is not easy to deploy. FSO communication has been opted as a promising solution for reliable, low latency, cost effective and unlicensed spectrum wireless backhaul connectivity for 5G and beyond wireless communication system. The foremost impairments of FSO communication are atmospheric turbulence and line-of-sight (LoS) requirement between Source to Destination. To relax the LoS requirement of FSO communication, a novel scheme called intelligent reflecting surface (IRS)-assisted FSO communication is a very reliable and effective alternative solution like first-last mile access connectivity. In this work, an IRS-based FSO and radio frequency (RF) links are modeled. IRS-assisted FSO link achieves average symbol error-rate (ASER) of 10− 6 for SNR of 30 dB while FSO link with Gamma-Gamma and Malaga (M) distribution gives ASER value of 10− 3 and 10− 2 respectively. In Optical-multiple IRS-assisted FSO links, increase in number of reflecting elements improves ASER upto 10− 8 at SNR of 20dB.
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