Testing Advanced Driver Assistance Systems using Multi-objective Search and Neural Networks

Recent years have seen a proliferation of complex Advanced Driver Assistance Systems (ADAS), in particular, for use in autonomous cars. These systems consist of sensors and cameras as well as image processing and decision support software components. They are meant to help drivers by providing proper warnings or by preventing dangerous situations. In this paper, we focus on the problem of design time testing of ADAS in a simulated environment. We provide a testing approach for ADAS by combining multi- objective search with surrogate models developed based on neural networks. We use multi-objective search to guide testing towards the most critical behaviors of ADAS. Surrogate modeling enables our testing approach to explore a larger part of the input search space within limited computational resources. We characterize the condition under which the multi-objective search algorithm behaves the same with and without surrogate modeling, thus showing the accuracy of our approach. We evaluate our approach by applying it to an industrial ADAS system. Our experiment shows that our approach automatically identifies test cases indicating critical ADAS behaviors. Further, we show that combining our search algorithm with surrogate modeling improves the quality of the generated test cases, especially under tight and realistic computational resources

Gene Reduction for Cancer Classification using Cascaded Neural Network with Gene Masking

This paper presents an approach to cancer classification from gene expression profiling using cascaded neural network classifier. The method used aims to reduce the genes required to successfully classify the small round blue cell tumours of childhood (SRBCT) into four categories. The system designed to do this consists of a feedforward neural network and is trained with genetic algorithm. A concept of ‘gene masking’ is introduced to the system which significantly reduces the number of genes required for producing very high accuracy classification

Predicting recurring telecommunications customer support problems using deep learning

In search of a better quality of experience and more revenue, telecommunication companies are searching for proactive ways of dealing with unsatisfactory user experiences and predicting customer’s behavior. Customer Support (CS) is one of the key areas of customer satisfaction. A good CS enables customers to have a smooth interaction with the company and the services provided when there are doubts or malfunction. Frequently, the problems reported by customers are not resolved in the first interaction, which leads to greater dissatisfaction with the service provider and possibly to future churn. If the company knows in advance of a possible recurrence, it can respond and try to fix the problem without customers noticing or being affected. In this article, a data set of customer data, CS data, and historical service are used to create a deep learning-based model for predicting customer recurrence. Deep neural networks are well-known for their capability to model complex problems when compared to classical machine learning algorithms. The obtained model, with a decision threshold most appropriated for the business needs, presented an F1-score of 60% and AUC-ROC of 61%, with a Recall and Precision of the recurrent class of 29% and 21%, respectively.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020