17809310. DYNAMIC RESOURCE ALLOCATION METHOD FOR SENSOR-BASED NEURAL NETWORKS USING SHARED CONFIDENCE INTERVALS simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
- 1 DYNAMIC RESOURCE ALLOCATION METHOD FOR SENSOR-BASED NEURAL NETWORKS USING SHARED CONFIDENCE INTERVALS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DYNAMIC RESOURCE ALLOCATION METHOD FOR SENSOR-BASED NEURAL NETWORKS USING SHARED CONFIDENCE INTERVALS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
DYNAMIC RESOURCE ALLOCATION METHOD FOR SENSOR-BASED NEURAL NETWORKS USING SHARED CONFIDENCE INTERVALS
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Paul Schardt of Rochester MN (US)
Rachel Mertz of Rochester MN (US)
LAURA J Mokrzycki of Zumbrota MN (US)
CHAD Albertson of Rochester MN (US)
DYNAMIC RESOURCE ALLOCATION METHOD FOR SENSOR-BASED NEURAL NETWORKS USING SHARED CONFIDENCE INTERVALS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17809310 titled 'DYNAMIC RESOURCE ALLOCATION METHOD FOR SENSOR-BASED NEURAL NETWORKS USING SHARED CONFIDENCE INTERVALS
Simplified Explanation
The abstract describes a method, computer program, and computer system for resource allocation in sensor-based neural networks. Here is a simplified explanation of the abstract:
- The invention provides a method, computer program, and computer system for allocating resources in sensor-based neural networks.
- The method involves identifying one or more nodes in an edge computing environment.
- Data from these nodes, which includes a classification dataset with reference classification and confidence value data, is received.
- A node is selected from the identified nodes based on having the highest confidence interval associated with the reference classification.
- The selected node is then assigned to process the classification dataset.
Potential Applications
This technology has potential applications in various fields, including:
- Internet of Things (IoT) systems
- Edge computing environments
- Sensor networks
- Artificial intelligence and machine learning systems
Problems Solved
The technology addresses the following problems:
- Efficient allocation of resources in sensor-based neural networks
- Optimizing the processing of classification datasets in edge computing environments
- Improving the accuracy and reliability of classification tasks in distributed systems
Benefits
The technology offers several benefits, including:
- Improved resource allocation and utilization in sensor-based neural networks
- Enhanced accuracy and efficiency in processing classification datasets
- Increased reliability and performance of edge computing environments
- Facilitates distributed processing and decision-making in IoT systems
Original Abstract Submitted
A method, computer program, and computer system are provided for resource allocation for sensor-based neural networks. One or more nodes associated with an edge computing environment are identified. Data corresponding to a classification dataset is received from the identified nodes. The dataset includes a reference classification and confidence value data. A node is selected from among the identified nodes based on the selected node having a greatest confidence interval associated with the reference classification within the confidence value data. The selected node is assigned to process the classification dataset.
