The role of Artificial Intelligence in the IoT

Problems The Internet of Things (IoT) is the network of devices, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these things to connect and exchange streams of data. This big data can be analyzed from the business value perspective, which will create better human-machine interface that is useful for the users. Solution Artificial Intelligence will play an important role to take useful actions from a large amount of digital data. An AI technology brings the ability to automatically identify patterns and detect variation in the data that smart sensors and devices generate. Automobile industries are incorporating artificial intelligence—in particular, machine learning—into their Internet of Things applications and seeing capabilities grow, including improving operational efficiency and helping avoid unplanned downtime. Artificial intelligence and Internet of Things Artificial intelligence is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. It is a software module to perform complicated jobs efficiently with zero human interference. AI will observe, learn, plan, manage, and control objects using natural processing languages and this technology will enable machines to learn based on experience and produce reliable, concurrent solutions similar to a human being. Internet of Things (IoT) is something where a group of devices, systems, and machines are connected through an internet ecosystem. This could be a LAN or a WAN or a Cloud-based setup. Patterns7 IoT platform helps enterprises to monitor device performance in real-time. With expanded capabilities of smart, connected devices and the data they generate is creating a new era of opportunities. Industrial IoT platform helps you to connect, manage, analyze and create an experience for the connected world. Patterns7tech is leading IoT solution...

Customer segmentation using Machine Learning K-Means Clustering

Most of platforms build in Information Technologies are generating huge amount of data. This data is called as Big Data and it carries lots of business intelligence. This data is crossing boundaries to meet different goals and opportunities. There is opportunity to apply Machine Learning to create value for clients. Problems We have big data based platforms in Accounting and IoT domain that keep on generating customer behavior and device monitoring data. Identifying targeted customer base or deriving patterns based on different dimensions is key and really provide an edge to the platforms. Idea Imagine you got 1000’s of customers using your platform and vast amount of big data that’s keep on generating, any insight on this is really going to value add. As part of Machine Learning initiatives and innovative things that Patterns7 team keep on trying, we experimented on K-Means Clustering and value it brings to our Clients is awesome. Solution Clustering is the process of partitioning a group of data points into a small number of clusters. In this part, you will understand and learn how to implement the K-Means Clustering. K-Means Clustering K-means clustering is a method commonly used to automatically partition a data set into k groups. It is unsupervised learning algorithm. K-Means Objective The objective of k-means is to minimize the total sum of the squared distance of every point to its corresponding cluster centroid. Given a set of observations (x1, x2, …, xn), where each observation is a d-dimensional real vector, k-means clustering aims to partition the n observations into k (≤ n) sets S = {S1, S2, …, Sk} so as to...