Qeexo

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...

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...

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...

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,...

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...

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...