Tag: machine learning

A pedagogical approach in interleaving software quality concerns at an artificial intelligence course (2022)

The software engineering industry is an everchanging domain requiring professionals to have a good knowledge base and adaptability skills.Artificial Intelligence (AI) has achieved substantial success in enhancing program analysis techniques and applications, including bug prediction. It is a promising direction by applying advanced Machine Learning techniques into suitable software engineering tasks. The main goal…

SERS liquid biopsy in breast cancer. What can we learn from SERS on serum and urine? (2022)

SERS analysis of biofluids, coupled with classification algorithms, has recently emerged as a candidate for point-of-care medical diagnosis. Nonetheless, despite the impressive results reported in the literature, there are still gaps in our knowledge of the biochemical information provided by the SERS analysis of biofluids. Therefore, by a critical assignment of the SERS bands,…

On the use of multi–objective evolutionary classifiers for breast cancer detection (2022)

Purpose Breast cancer is one of the most common tumours in women, nevertheless, it is also one of the cancers that is most usually treated. As a result, early detection is critical, which can be accomplished by routine mammograms. This paper aims to describe, analyze, compare and evaluate three image descriptors involved in classifying…

Comparing Automatic Approaches for Mvc Architecture Detection in Ios Codebases (2022)

Model View Controller (MVC) is one of the most widespread and used software architecture in client-side software. We are interested in automatically inferring and analyzing MVC architectures from mobile application codebases that could help to identify the architectural problems earlier in the development process, offering insightful knowledge for both software developers, architects, and the…

Applying Deep Q-learning for Multi-agent Cooperative-Competitive Environments (2022)

Cooperative-competitive social group dynamics may be modelled with multi-agent environments with a large number of agents from a few distinct agent-types. Even the simplest games modelling social interactions are suitable to analyze emerging group dynamics. In many cases, the underlying computational problem is NP-complex, thus various machine learning techniques are implemented to accelerate the…

The Impact of Convolutional Neural Network Parameters in the Binary Classification of Mammograms (2022)

Breast cancer is the most commonly diagnosed type of cancer. It is essential to classify patients as quickly as possible into groups with a high or low risk of cancer, to provide adequate treatment. This paper aims to address the impact of the parameters of convolutional neural networks in the binary classification of mammograms.…

An Unsupervised Threshold-based GrowCut Algorithm for Mammography Lesion Detection (2022)

Breast cancer causes numerous deaths worldwide; yet the numbers have decreased in the past years as a result of computer-aided diagnosis and proper treatment. The current paper is addressed to the base of such diagnosis system: pre-processing and segmentation. After a robust pre-processing, an unsupervised version of GrowCut is applied to define the location…