Futuristic Technology in Architecture & Planning - Augmented and Virtual Reality: an Overview

Speed has become a way of life. We are asymptotically piling data. Speed can be achieved with new design processes, techniques, and Technology. Innovations AR and VR are just some of the many forms of technologies that will play a key role in shaping the Architecture and Planning of tomorrow, making it future-ready and ushering in a new age of innovation. AR and VR in Architecture & Planning were introduced as assisting tools and has helped generate multiple design options, expanded possibilities of visualization, and provided us with more enhanced, detailed, and specific experience in real-time; enabling us to see the resultsof work on hand well before the commencement of the project. These tools are further developed for city development decisions, helping citizens interact with local authorities, access public services, and plan their commute. After reviewing multiple research papers, it had been observed that each one is moving forward with the changes brought by it, without entirely understanding its role. This paper provides a summary of theappliance of AR & VR in architecture and planning.

SVM &Ga-clustering Based Feature Selection Approach for Breast Cancer Detection

Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.