Use of SecureSVM, Boosting, Bagging, Clustering, LSTM, CNN, GAN in Retail with BlockChain

Introduction

This blog highlights different ML algorithms used in blockchain transactions with a special emphasis on bitcoins in retail payments. This blog is structured as follows:

• Overview of the role of blockchain in the retail industry.
• Different traditional…

Anomaly Detection from Head and Abdominal Fetal ECG — A Case study of IOT anomaly detection using Generative Adversarial Networks

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal…

Earlier this week, I was speaking at an event on AI for Real Estate where I showed an example from a BBC clip which said that “central London is now a ghost town” (due to COVID 19)

A few months ago, this headline would have been laughable

In London, central London and the London underground are a key fabric of daily…

Time Series forecasting in PBI is based on the thumb technique of smoothening time series prediction called Exponential Smoothening (ES). ES of time series data assigns exponentially decreasing weights for newest to oldest observations. ES is also be used for time series with trend and seasonality. This model is usually used to make short term forecasts, as longer-term forecasts using this technique can be quite unreliable. Collectively, the methods are sometimes referred to as ETS models,…

Venn diagram showing overlap between "quantum" and "neural network"

The term "quantum neural networks" is being used with increasing frequency by the quantum computing community. Maybe as a dishonest, bait-and-switch advertising strategy, this makes sense. However, from a scientific standpoint, "quantum neural network" is a very poor name choice for what is being alluded to here.

Artificial Neural Networks, often called "Neural Nets" for short, are supposed to…

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…

(This article is now a chapter of my github proto-book Bayesuvius)

Simpson's paradox is a recurring nightmare for all statisticians overseeing a clinical trial for a medicine. It is possible that if they leave out a certain "confounding" variable from a study, the study's conclusion on whether a medicine is effective or not, might be, without measuring that confounding variable, the opposite of what it would have been had that variable been measured. Statisticians have to enlist expert…

The following graphic is based on Sam Priddy's excellent DSC/Tableau Webinar How to Accelerate and Scale Your Data Science Workflows. Sam covered many interesting points for organizing, analyzing and presenting data--including which graph is best suited for different data types. This graphic is an overview of some of Sam's points. For more…

In this article, a few applications of Fourier Series in solving differential equations will be described. All the problems are taken from the edx Course: MITx - 18.03Fx: Differential Equations Fourier Series and Partial Differential Equations. The article will be posted in two parts (two separate blongs)

First a basic introduction to the Fourier series will be given and then we shall see how to solve the following ODEs / PDEs using Fourier series:

1. Find…

What is a commodity?

Commodity are basic raw materials with certain standards that are used with other goods, commodity are often the basis of the production of various finished goods or services and then referred to as commodities (something made based on commodity). Some examples of commodity are seen from their types: metal (gold, silver, platinum, and copper); energy (crude oil and natural gas); livestock and agriculture (beef, mutton, rice, wheat, corn, soybeans,…

Over a period, large organizations have been transformed into disintegrated silos that has grown to be a major impediment to respond to changing market demands with agility. Organizational silos have led to innumerous disconnected policies, systems, processes, standards, teams. In order to sustain in this digital era, it’s imperative for organizations to take a top down enterprise value chain view to continually evaluate its ability to adapt (as well as compete) to changing market…