The paper presents an innovative methodology for designing frequency selective surface (FSS) based radar absorbing materials using machine learning (ML) technique. In conventional electromagnetic design, unit cell dimensions of FSS are used as input and absorption coefficient is then predicted for a given design. In this paper, absorption coefficient is considered as input to ML model and image of FSS unit cell is predicted. Later, this image is used for generating the FSS unit cell parameters. Eleven different ML models are studied over a wide frequency band of 1GHz to 30GHz. Out of which six ML models (i.e. (a) Random Forest classification, (b) K- Neighbors Classification, (c) Grid search regression, (d) Random Forest regression, (e) Decision tree classification, and (f) Decision tree regression) show training accuracy more than 90%. The absorption coefficients with varying frequencies of these predicted images are subsequently evaluated using commercial electromagnetic solver. The performance of these ML models is encouraging, and it can be used for accelerating design and optimization of high performance FSS based radar absorbing material for advanced electromagnetic applications in future.

Decision Tree Classification: This type divides a dataset into segments based on particular feature variables. The divisions' threshold values are typically the mean or mode of the feature variable in question if they happen to be numerical. K-Nearest Neighbors: This Classification type identifies the K nearest neighbors to a given observation point. It then uses K points to evaluate the proportions of each type of target variable and predicts the target variable that has the highest ratio. Logistic Regression: This classification type isn't complex so it can be easily adopted with minimal training. It predicts the probability of Y being associated with the X input variable.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. This is going to be part 4 of the Explaining Machine Learning Algorithms To A 10-Year Old series.

This paper addresses the interesting problem of processing and analyzing data in geographic information systems (GIS) to achieve a clear perspective on urban sprawl. The term urban sprawl refers to overgrowth and expansion of low-density areas with issues such as car dependency and segregation between residential versus commercial use. Sprawl has impacts on the environment and public health. In our work, spatiotemporal features related to real GIS data on urban sprawl such as population growth and demographics are mined to discover knowledge for decision support. We adapt data mining algorithms, Apriori for association rule mining and J4.8 for decision tree classification to geospatial analysis, deploying the ArcGIS tool for mapping. Knowledge discovered by mining this spatiotemporal data is used to implement a prototype spatial decision support system (SDSS). This SDSS predicts whether urban sprawl is likely to occur. Further, it estimates the values of pertinent variables to understand how the variables impact each other. The SDSS can help decision-makers identify problems and create solutions for avoiding future sprawl occurrence and conducting urban planning where sprawl already occurs, thus aiding sustainable development. This work falls in the broad realm of geospatial intelligence and sets the stage for designing a large scale SDSS to process big data in complex environments, which constitutes part of our future work.

This is my first data analysis related video. In this video, I have solved the Fake news detection problem using four machine learning classification algorithms. Hi everyone, This is my first data analysis related video. In this video, I have solved the Fake news detection problem using four machine learning classification algorithms. From this video, you will learn how you can apply Linear regression, Decision Tree classification, Gradient boost classification, and random forest classification model.

Parent and Child Node - The node which get divided into several sub-node is parent node and the sub-node formed is called child node. Parent and Child Node - The node which get divided into several sub-node is parent node and the sub-node formed is called child node. Subtree /Branch - If a subnode again split into further subnodes that entire part is called subtree (one Parent - Child part).It is a part of entire tree. Subtree /Branch - If a subnode again split into further subnodes that entire part is called subtree (one Parent - Child part).It is a part of entire tree. Decision Node - If a subnode split into further subnodes Then that splitted subnode is called decision node.

Quality is determined by Accuracy and completeness. Companies use machine learning models to make practical business decisions, and more accurate model outcomes result in better decisions. The cost of errors can be huge, but optimizing model accuracy mitigates that cost. Machine Learning model accuracy is a measurement used to determine which model is best at identifying relationships and patterns between variables in a dataset based on the input, or training data. The better a model can generalize to'unseen' data, the better predictions and insights it can produce, which in turn deliver more business value. The dataset which I have chosen is the Breast Cancer Prediction dataset.

Gini Impurity is named after the Italian statistician Corrado Gini. Gini impurity can be understood as a criterion to minimize the probability of misclassification. To understand the definition (as shown in the figure) and exactly how we can build up a decision tree, let's get started with a very simple data-set, where depending on various weather conditions, we decide whether to play an outdoor game or not. From the definition, a data-set containing only one class will have 0 Gini Impurity. In building up the decision tree our idea is to choose the feature with least Gini Impurity as root node and so on... Let's get started with the simple data-set -- Here we see that depending on 4 features (Outlook, Temperature, Humidity, Wind), decision is made on whether to play tennis or not.