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BLOG Classification Interval for Qeexo AutoML Inference Settings
Inference settings contain two important parameters; Instance length and Classification interval. In this blog, we will explain the Classification Interval and in conjunction with raw sensor signals, ODR, Instance length, latency, and performance of...
Sidharth Gulati and Dr. William Levine 12 August 2020 -
BLOG Anomaly Detection in Qeexo AutoML
Qeexo AutoML supports three one-class classification algorithms widely used for anomaly/outlier detection; Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine. These algorithms build models by learning from only one class of...
Dr. Karanpreet Singh and Dr. Rajen Bhatt 15 July 2020 -
BLOG Detecting Anomalies in Machine Data with Qeexo AutoML
In industrial environments, it is often important to be able to recognize when a machine needs to be serviced before the machine experiences a critical failure. This type of problem is often called predictive maintenance. One approach to solving...
Josh Stone 07 June 2020 -
BLOG ODR and FSR of Sensors
Qeexo’s AutoML enables Machine Learning and AI applications development for a range of sensors. A comprehensive list of sensors includes Accelerometer, Gyroscope, Magnetometer, Temperature, Pressure, Humidity, Microphone, Doppler Radar, Geophone,...
Dr. Rajen Bhatt and Josh Stone 28 May 2020 -
BLOG Detecting Air Gestures with Qeexo AutoML
We would like to build a machine learning model to distinguish between the following three classes: "X", "O", "No Gesture". This blog describes building the Air Gesture with Arduino Nano 33 BLE Sense. You can also build the same using any of the...
Josh Stone 26 May 2020 -
BLOG Feature Selection Approaches: Part – I
In machine learning, the quality of feature selection strongly affects the quality of the trained model. Feature selections approaches differ depending on the type of machine learning problem, e.g., supervised learning or unsupervised learning. For...
Qifan He and Dr. Rajen Bhatt