Machine Learning for Internet of Things

The growth and expansion of Artificial Intelligence and IoT have changed the way organizations regulate their workflow and the way customers respond to the market. With the rapid use of IoT and artificial intelligence by industries and customer-oriented companies, recent surveys and forecasts predict that AI and IoT will reshape and refine business processes. Thanks to this trend that companies are scrambling to hire skilled machine learning professionals. Deplorably, it has not proved to be easy. While the need for machine learning engineers has skyrocketed, the number of machine learning professionals have not grown at all. In a bid to equip themselves, companies are now enrolling their employees in machine Learning  courses, so they can adapt to the future.

Now, customers have begun to use smart home devices such as Amazon’s Alexa and Google Home, along with smart appliances and heating systems. The recent smart home and smart business installations are more than gateway hubs. These digital installations provide feedback that benefits utility companies maintain customer relations and set new benchmarks in a market.

Integrating Chatbots

The time had gone when chatbots were not taken seriously. As machine learning has come to view, chatbots get refined. Companies can automate solutions to customers’ queries using chatbots thus fastening customer service resolution. How are chatbots able to understand human emotions? They consume customers’ data and history that helps them understand the context to questions. This streamlines the work of customer service officers as chatbots feed them customer data that helps them to get a full history of each account quickly.

User interaction with a chatbot app / Image: Pexels, by John Jackson

User interaction with a chatbot app / Image: Pexels, by John Jackson

Chatbots works as an interface to make sense of all the data in an IoT landscape. There are resources available over internet which allow you to integrate or design your own voice or text based chatbot based on implementing natural language understanding and automatic speech recognition.

Fraud Prevention

Detecting fraud cases is a major challenge for the financial industry. The machine learning based algorithm used in payment processing system to distinguish between the authorised and fake transactions. The use of machine learning algorithms helps a payment system know that a transaction is genuine and authorized. Keeping a check overuse behaviour helps machine learning algorithms track any fraudulent actions.

 Image: CC0Salvatore P in Pexels

Image: CC0 by Salvatore P in Pexels

Kount is a live example of fraud detection and prevention platform. It collects data from billions of transactions and analyse those transaction with the help of AI to fend off malicious activities.

Fraud is a pain point for insurance companies as they need to avoid improper payouts. Advanced AI-powered solutions, for example, SAS Fraud Framework, use in-depth analytics to check abuse.

Product Recommendations

Unsupervised learning plays a vital role in building product-based recommendation systems. Most of the e-commerce websites like Amazon, eBay use machine learning for recommending products. ML algorithms use customers’ buying history and sync it with the large product inventory to unveil hidden patterns and show similar products together. Amazon allows users to use bulk order pad with the help of Dash Buttons. This shows that Amazon has a clear understanding of the internet of things and how it can ve used in different ways.

Automating Repetitive Tasks

Most marketers perform similar tasks like social media and email marketing and lead management that take a lot of time. Machine learning algorithms automate email and social media marketing that saves time to do repetitive tasks. There are multiple companies available in the market offering automation tools for marketing like marketo. These tools help you to attract and possessing customers, start campaigns by performance analysis.

Email Spam and Malware Filtering

Spam filtering approaches have widened today. For continuous updating of spam filters, organizations have started relying on machine learning. Rule-based spam filtering fails to evaluate tricks adopted by spammers. ML empowered spam filtering techniques are Multi-Layer Perceptron, C 4.5 Decision Tree Induction.
Around 325,000 malware are detected everyday and their code are almost similar in pattern. The system security programs based on machine learning can detect the same coding pattern. In this way, they track new malware with 2–10% variation and protect us from them.

Image: CC0

Better sales forecasting and accurate prediction user behaviour are what ML commits. /Image: CC0

Moreover, ISPs role is critical in solidifying IoT security. This begins by blocking malicious traffic run by malware in already known patterns. Today, some ISPs use BCP38 to minimize spoofing, the technique used by hackers to inculcate network packets with fake sender addresses. Secondly, ISPs could inform customers whether a device is sending or receiving malicious traffic on their network or not.

Machine learning is a vital component of an IoT system designed to deliver effective predictive analytics, but surely it does perform not any magic. Being part of an IoT system, machine learning offers more significant insights. However, organizations must integrate these insights into the real-time operating landscapes to experience their business value entirely. Machine learning results with SME involvement need to be connected to analytics and translated into a rules-based monitoring system to find when unusual events can happen.

Danish Wadhwa