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Home / Blog / Machine Learning / Machine Learning with IoT: Giving A New Vision & Making World Smarter
Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of AiSPRY and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
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Machine learning makes use of past data to find patterns, create models, and make better predictions about the future. Machine learning focuses on a system's accuracy and efficacy in order to reduce human error, produce insights from data, and enable IoT to be used to its full potential. With the Internet of Things (IoT), millions of networked devices are producing enormous amounts of data. Machine learning leverages this data to identify future trends, detect abnormalities, and replace human operations with automation in crucial activities.
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By the year 2025 more than 60 billion IoT devices will be available to draw more insights and perform analytics. Industrial IoT platforms like Google Cloud IoT Edge, Microsoft Azure IoT, Amazon AWS IoT are now integrating Machine learning for its predictive capabilities Companies today to improve efficiency and cut costs are installing more and more sensors hoping it would transform sensor recordings into actionable insights but, without proper analytical strategies and data management in place, these technologies are simply occupying more servers. Is there a way to use data collected from these sensors and turn them into valuable insights for industrial use? Yes, and the answer lies in Machine Learning.
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Due to the enormous quantity of data involved and the various potential for automation, Machine Learning and Predictive Analysis are viable solutions for the IoT domain, respectively. A moving vehicle equipped with sensors that gather data from thousands of data points and alert the driver to possible dangers is an example of automation. Automation is the only solution to this problem because a human cannot do such a task for every automobile. Machine learning analyses this data and alerts the vehicle's main computer to activate safety systems when it detects a risky circumstance, such as excessive speed or friction.
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Machine learning analyses this data and alerts the vehicle's main computer to activate safety systems when it detects a risky circumstance, such as excessive speed or friction.
Moving on to the predictive capacity of ML, it aids IoT in recognising both broader patterns and hazard predictions in addition to forecasting threats. ML approaches would advise the driver appropriately in these challenging situations, for instance if the driver had trouble parking the automobile in reverse. The ability of ML for IoT to raise the required red flags when it discovers outliers and unusual activities is its most crucial feature. By accurately identifying the causes of an event, adjusting the inputs, and then regulating the output, machine learning also develops models that aid in event prediction.
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Agriculture is one of the most elementary human activities and the integration of IoT and ML in agriculture has made communication between farmers and agricultural processes more data-driven. The analytical tools along with Machine Learning are in perfect sync to scale the automation in the agricultural sector. The data collected through IoT sensors are being extensively used for research purposes to help increase the production of the crops to meet the demand of the growing population. Leveraging these technologies has helped in better crop quality, seed selection, weather analysis, soil analysis, storage management, logistics, etc.
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Chronic patients require special care, therefore nanosensors are affixed to the skin of the patient's body to assist assess their blood pressure, pulse rate, and other vital signs. Then, this data is sent to a highly secure cloud platform, where doctors may view the raw data, past prescriptions, and propose the best medications to patients in remote locations so they can avoid unexpected heart attacks, paralysis, etc. by applying cutting-edge ML algorithms. IoT and machine learning approaches have been widely applied to provide real-world solutions for issues in a variety of fields.
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IoT generates enormous volumes of raw data, which machine learning analyses to produce priceless insights that have the potential to fundamentally alter business models as well as our daily lives. IoT components can be found in computers, mobile phones, household appliances, traffic control towers, parking lots, and many other places. A machine can learn from the massive amount of data gathered by millions of IoT devices, and occasionally this data is sent to multiple servers or gateways to create better and more intelligent machine learning models that can perform more efficiently and overcome their weaknesses. With the use of IoT and machine learning (ML) technology, we now experience automation in equipment such as central heating, remotely turning on air conditioning, opening doors, and turning off kitchen appliances. In this era of communication and connection, machine learning and IoT will inevitably result in improved technology, which will guarantee a rise in efficiency and production across all key industries.
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