Feature-Based Risk Analysis With Real-Time Alert Triggering
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Solution Overview
Problem
Existing systems lack effective methods for automatically generating and displaying alerts based on user inputs using machine-learning algorithms, particularly for identifying and addressing potential risks in real-time or pre-emptive scenarios.
Innovation Solution
A system and method that utilizes a machine-learning algorithm to generate input-based features from electrical signals, evaluate these features, and trigger alerts when a risk prediction exceeds a threshold, incorporating features like Hurst coefficient and average scores, using algorithms such as Random Forest, AdaBoost, and Support Vector Machine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated feature-based risk analysis using machine-learning algorithms is implemented, then risk prediction accuracy and real-time alerting capability are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the risk analysis process into distinct modules: electrical signal reception, feature generation (including Hurst coefficient calculation), machine-learning algorithm execution, and alert generation. This modular segmentation allows each component to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The system performs preliminary feature extraction and transformation before risk prediction, pre-calculating features like Hurst coefficient, percent correct on first try, and average scores from electrical signals. This preliminary action reduces computational complexity during real-time prediction by preparing data in advance.
2Speed
If real-time risk prediction and alerting are implemented, then responsiveness to risks is improved, but processing time and computational resources increase
Solution Approach 1:
The system pre-calculates and stores feature transformations (Hurst coefficient, percent correct on first try, average scores) from electrical signals before prediction is needed. This preliminary feature extraction enables rapid real-time prediction by eliminating complex calculations during the actual alerting moment.
Solution Approach 2:
The machine-learning algorithm automatically generates risk predictions and alerts based on input features without requiring manual intervention or complex post-processing. The system serves itself by autonomously completing the entire risk analysis pipeline from electrical signal to alert generation.
3Reliability
If multiple machine-learning algorithms are used for risk prediction, then prediction reliability is improved, but computational complexity and processing overhead increase
Solution Approach 1:
The system merges multiple machine-learning algorithms (Random Forest, AdaBoost, Support Vector Machine, Naïve Bayes, Gradient Boosting) into a unified prediction framework. These algorithms collectively process the same set of features (Hurst coefficient, percent correct on first try, average scores, number of attempted parts) to generate complementary predictions, improving overall reliability through ensemble methodology.
Data Source
AI summary
Systems and methods for feature-based alert triggering are disclosed herein. The system can include memory including a model database containing a machine-learning algorithm. The system can include a user device that can receive inputs from a user; and at least one server. The at least one server can: receive electrical signals from the user device, the electrical signals corresponding to a plurality of user inputs provided to the user device; automatically generate input-based features from the received electrical signals; input the input-based features into the machine-learning algorithm; automatically and directly generate a risk prediction with the machine-learning algorithm from the input-based features; and generate and display an alert when the risk prediction exceeds a threshold value.