Inteligencia Artificial

A CP-based approach for mining sequential patterns with quantities

Amina Kemmar — 2023-03-13

This paper addresses the problem of mining sequential patterns (SPM) from data represented as a set of
sequences. In this work, we are interested in sequences of items in which each item is associated with its quantity.
To the best of our knowledge, existing approaches don’t allow to handle this kind of sequences under constraints.
In the other hand, several proposals show the efficiency of constraint programming (CP) to solve SPM problem
dealing with several kind of constraints. However, in this paper, we propose the global constraint QSPM which
is an extension of the two CP-based approaches proposed in [5] and [7]. Experiments on real-life datasets show
the efficiency of our approach allowing to specify many constraints like size, membership and regular expression
constraints.

A Novel Approach for Diagnosing Neuro-Developmental Disorders using Artificial Intelligence

Priyanka Vashisht — 2023-03-13

Artificial Intelligence (AI) has been rapidly advancing especially in the field of medicine. One of the highly considerable medical fields in the world today is that of neurodevelopment and diagnosing any disorders pertaining to the same can be overwhelming. Considering the fact that neurodevelopment plays a significant role in the growth and nourishment of a child, the former sentence is an irony as parents wouldn’t wish for their children to possess reduced capabilities in comparison to other children of the same age. In fact, testing the metal growth of a child is a tedious task which involves visiting the doctor each time and spending a lot of time. The proposition of this paper overcomes the above--mentioned hassles by utilizing computer aided techniques for identifying neurodevelopmental disorder. The proposed framework has its foundation over mathematical and Deep Learning (DL) models which helps in the diagnosis of four varied neurodevelopmental disorders which often tend to occur in the early phases of a child’s life. The application put forward here would suggest suitable remedies and strategies to parents and teachers which they can adopt to help their child recover from the illness.

Person Re-Identification by Siamese Network

Newlin Shebiah Russel — 2023-03-13

Re-Identification of person aims at retrieval of person across multiple non overlapping camera. There was a huge gain in the computer vision community with the advancement of deep learning features and also the number of surveillance in videos increased. The challenges faced by person re-identification is low resolution images, pose variation etc., and convolutional neural networks are supported by a number of state-of-the-art algorithms for person re-identification. In this paper, Siamese network is used to predict the similarity or dissimilarity of a person across two cameras. It's a neural architecture that takes as input a pair of images or videos and the output as the prediction of similar and dissimilar persons along with their prediction scores. The experimentation is done by using datasets iLIDS-VID, PRID 2011 and obtained a recognition accuracy of 79.52% and 85.82% respectively.

Identifying Acoustic Features to Distinguish Highly and Moderately Altered Soundscapes in Colombia

Fernando Martínez-Tabares — 2023-03-24

Numerous acoustic features have been proposed as useful measures to characterize natural soundscapes, which can be employed to examine the impact of land transformation on the audible properties of a location. The extensive collection of available features demands an examination to identify the most informative and discriminative ones for a given problem. In this study, we conduct an empirical investigation into the selection of acoustic features for discriminating between highly and moderately transformed versions of four Colombian soundscapes: Moorlands, coffee plantations, dry tropical forests, and pastures. We employ classical supervised feature selection techniques along with exploratory tools such as correlation matrices and scatter plots. Our results indicate that a few acoustic features are sufficient to differentiate between the classes. Specifically, those features that estimate acoustic complexity via intrinsic variability of sound intensities or biodiversity through species richness or abundance in specific frequency bands are the most discriminative ones. These findings suggest that the selection of acoustic features can assist in analyzing and distinguishing between different soundscapes.