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Overcoming an Imbalanced Dataset using Oversampling.


How oversampling yielded great results for classifying cases of Sexual Harassment. When it comes to data science, sexual harassment is an imbalanced data problem, meaning there are few (known) instances of harassment in the entire dataset. An imbalanced problem is defined as a dataset which has disproportional class counts. Oversampling is one way to combat this by creating synthetic minority samples. SMOTE -- Synthetic Minority Over-sampling Technique -- is a common oversampling method widely used in machine learning with imbalanced high-dimensional datasets using Oversampling.

Model Selection: Adjusted Coefficient of Determination-Variance Tradeoff


In my previous article, we analyzed the COVID-19 data of Turkey and selected the cubic model for predicting the spread of disease. In this article, we will show in detail why we selected the cubic model for prediction and see whether our decision was right or not. When we analyze the regression trend models we should consider overfitting and underfitting situations; underfitting indicates high bias and low variance while overfitting indicates low bias and high variance. The adjusted coefficient of determination is used in the different degrees of polynomial trend regression models comparing. When we examine the above formulas, we can notice the similarity between SSE and bias.

Traditional vs Deep Learning Algorithms used in BlockChain in Retail Industry


This blog highlights different ML algorithms used in blockchain transactions with a special emphasis on bitcoins in retail payments. The potential of blockchain to solve the retail supply chain manifests in three areas. Provenance: Both the retailer and the customer can track the entire product life cycle along the supply chain. Smart contracts: Transactions among disparate partners that are prone to lag can be automated for more efficiency. IoT backbone: Supports low powered mesh networks for IoT devices reducing the needs for a central server and enhancing the reliability of sensor data.

New predictive capabilities in Google Analytics


Google Analytics helps you measure the actions people take across your app and website. By applying Google's machine learning models, Analytics can analyze your data and predict future actions people may take. Today we are introducing two new predictive metrics to App Web properties. The first is Purchase Probability, which predicts the likelihood that users who have visited your app or site will purchase in the next seven days. And the second, Churn Probability, predicts how likely it is that recently active users will not visit your app or site in the next seven days.

The Fundamental Theorem of Algebra


According to the Fundamental Theorem of Algebra, every polynomial has a root (it equals zero) for some point in its domain. Though the theorem was already stated in the early 1700s (by the three mathematicians, Peter Roth, Albert Girard, and René Descartes), the first (non-rigorous) proof was published in 1746 by the French polymath Jean Le Rond d'Alembert in his book "Recherches Sur le Calcul Integral." The author of the first rigorous proof of the theorem was Carl Friedrich Gauss, one of history's most prominent mathematicians. Let us first discuss some relevant concepts that will be used in the proof. The renowned 16th-century Italian mathematician Gerolamo Cardano (he was also a physician, biologist, physicist, chemist, philosopher, among other things) introduced complex numbers in his studies of the roots of cubic equations.

Is AI Contributing to Climate Change and Delaying People Coming out of Poverty? - ReadWrite


AI has become the buzzword of the world, and an integral part of almost every company's digital transformation agenda. AI users have become producers of AI tools and services. Corporate leaders and even the White House have come with forward with a directive on promotion, promulgation, and advancement of artificial intelligence. On February 11, 2019, President Trump signed Executive Order 13859 announcing the American AI Initiative. Executive Order 13859 is the United States' national strategy on artificial intelligence.

New AI Enables Rapid Detection of Harmful Bacteria


Testing for pathogens is a critical component of maintaining public health and safety. Having a method to rapidly and reliably test for harmful germs is essential for diagnosing diseases, maintaining clean drinking water, regulating food safety, conducting scientific research, and other important functions of modern society. In recent research, scientists from University of California, Los Angeles (UCLA), have demonstrated that artificial intelligence (AI) can detect harmful bacteria from a water sample up to 12 hours faster than the current gold-standard Environmental Protection Agency (EPA) methods. In a new study published yesterday in Light: Science and Applications, the researchers created a time-lapse imaging platform that uses two separate deep neural networks (DNNs) for the detection and classification of bacteria. The team tested the high-throughput bacterial colony growth detection and classification system using water suspensions with added coliform bacteria of E. coli (including chlorine-stressed E. coli), K. pneumoniae and K. aerogenes, grown on chromogenic agar as the culture medium.

Coronavirus: Compliance with social distancing during early stages linked to working memory, study finds

FOX News

Dr. Tom Inglesby, director of the Center for Health Security at Johns Hopkins University, joins Chris Wallace on'Fox News Sunday.' A new study published in the Proceedings of the National Academy of Sciences claims compliance in America with social distancing during the early stages of the coronavirus pandemic is linked to working memory. The study, "Working memory capacity predicts individual differences in social-distancing compliance during the COVID-19 pandemic in the United States," assessed the working memory, personality, mood and fluid intelligence of test subjects; the researchers surveyed 850 U.S. residents between March 13 and March 25. The study found a link between working memory and social distancing, and subjects -- noting more benefits than costs -- with higher levels of fluid intelligence, fairness and agreeableness followed the new rules of social distancing compliance, the study found. "The decision of whether or not to follow social distancing guidelines is a difficult one, especially when there is a conflict between the societal benefits (e.g., prevent straining public health resources) and personal costs (e.g., loss in social connection and financial challenges). This decision critically relies on our mental capacity in retaining multiple pieces of potentially conflicting information in our head, which is referred to as working memory capacity," study author Weizhen Xie (Zane) told PsyPost.

CSRIO's Data61 alumni Emesent is flying drones beyond line-of-sight


Brisbane-based drone company Emesent has launched what it has dubbed as the "first plug-and-play payload" that enables industrial drones to fly beyond communications range and into unmapped areas. Built on Emesent's Hovermap simultaneous localisation and mapping (SLAM) autonomous flight system, the autonomy level 2 (AL2) technology was designed to enable companies to map, navigate, and collect data in challenging environments, such as mines, civil construction works, telecommunications infrastructure, and areas hit by natural disasters. "With the intelligence to navigate environments without a prior map, customers can use the system to carry out complex missions, secure the safety of personnel, and drive greater efficiency in their operations," Emesent co-founder and CEO Stefan Hrabar said. Emesent added that using AL2 would mean the drone processes data on-board in real-time to stream a 3D map of the environment back to the operator's tablet. It also touted that the ability for a drone to fly beyond line of sight allows workers to avoid hazardous environments while also enhancing visibility.