Method and system for minimizing impact of faulty nodes associated with an artificial neural network

A system for minimizing impact of a faulty node associated with an artificial neural network, the system comprising:

• a processor;
• and
• a memory communicatively coupled to the processor, wherein the memory stores processor executable instructions, which on execution causes the processor to:
• detect the faulty node associated with the artificial neural network based on testing the artificial neural network periodically with a training dataset, wherein the faulty node causes a faulty path in the artificial neural network;
• identify a plurality of alternate paths to reroute the faulty path, wherein the plurality of alternate paths is identified based on at least one network connection between one or more upstream nodes of the faulty node and one or more downstream nodes of the faulty node;
• and
• reroute the faulty path by assigning one or more weights associated with the faulty node to one or more nodes associated with the plurality of alternate paths, wherein the faulty path is rerouted if the faulty path is associated with one or more priority nodes, wherein the one or more priority nodes are identified based on a relevance heat map associated with the training dataset, wherein the relevance heat map is generated through at least one of Layer-wise Relevance Propagation (LRP) and Sensitivity Analysis (SA).

A non-transitory computer-readable medium for minimizing impact of a faulty node associated with an artificial neural network, wherein upon execution of instructions by one or more processors, the one or more processors perform one or more operations comprising:

• detecting the faulty node associated with the artificial neural network based on testing the artificial neural network periodically with a training dataset, wherein the faulty node causes a faulty path in the artificial neural network;
• identifying a plurality of alternate paths to reroute the faulty path, wherein the plurality of alternate paths is identified based on at least one network connection between one or more upstream nodes of the faulty node and one or more downstream nodes of the faulty node;
• and
• rerouting the faulty path by assigning one or more weights associated with the faulty node to one or more nodes associated with the plurality of alternate paths, wherein the faulty path is rerouted if the faulty path is associated with one or more priority nodes, wherein the one or more priority nodes are identified based on a relevance heat map associated with the training dataset, wherein the relevance heat map is generated through at least one of Layer-wise Relevance Propagation (LRP) and Sensitivity Analysis (SA).