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Found 10 papers, 0 papers with code
Zero-shot learning approach to adaptive Cybersecurity using Explainable AI
These explanations generated based on game-theory are used to allocate credit to specific features based on their influence on a specific prediction.
Explaining Network Intrusion Detection System Using Explainable AI Framework
With deep learning models, detection rates of network intrusion detection system are improved.
Contextual Bandits for adapting to changing User preferences over time
We explore details on contextual bandits, an extension to the traditional reinforcement learning (RL) problem and build a novel algorithm to solve this problem using an array of action-based learners.
Healthcare NER Models Using Language Model Pretraining
Our solution uses a combination of Natural Language Processing (NLP) techniques and a web-based annotation tool to optimize the performance of a custom Named Entity Recognition (NER) [1] model trained on a limited amount of EHR training data.
Leveraging human Domain Knowledge to model an empirical Reward function for a Reinforcement Learning problem
This modeling of empirical human domain rules into a reward function for RL is the unique aspect of this paper.
Regional Priority Based Anomaly Detection using Autoencoders
In the recent times, autoencoders, besides being used for compression, have been proven quite useful even for regenerating similar images or help in image denoising.
Face recognition for monitoring operator shift in railways
If they exceed 8 hours of shift the railroads may be penalized for over-tiring their drivers.
Data Augmentation of Railway Images for Track Inspection
Training images are simulated for sun kink and vegetation overgrowth.
Railway Track Specific Traffic Signal Selection Using Deep Learning
Using advanced image analysis and deep learning techniques, signals are detected in these camera images and a database of their locations is created.
Vision Based Railway Track Monitoring using Deep Learning
Computer vision based methods have been explored in the past for detection of railway track defects, but full automation has always been a challenge because both traditional image processing methods and deep learning classifiers trained from scratch fail to generalize that well to infinite novel scenarios seen in the real world, given limited amount of labeled data.
