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Found 16 papers, 0 papers with code
Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing
Purpose: Our study explored the use of artificial intelligence (AI) to diagnose autism spectrum disorder (ASD).
Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19
COVID-19 disease has affected almost every country in the world.
Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques
Objective: The study aims to evaluate the most influential variables in PUI using machine learning, focusing on intrinsic, extrinsic, and combined variable groups.
Implementing local-explainability in Gradient Boosting Trees: Feature Contribution
The proposed method takes advantage of the GBDT architecture to calculate the contribution of each feature using the residue of each node.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Feasibility of Social-Network-Based eHealth Intervention on the Improvement of Healthy Habits among Children
The intervention group had access to the web through a user account and a password.
Clustering Techniques Selection for a Hybrid Regression Model: A Case Study Based on a Solar Thermal System
This work addresses the performance comparison between four clustering techniques with the objective of achieving strong hybrid models in supervised learning tasks.
Determining the severity of Parkinson's disease in patients using a multi task neural network
Parkinson's disease is easy to diagnose when it is advanced, but it is very difficult to diagnose in its early stages.
Heart disease risk prediction using deep learning techniques with feature augmentation
Cardiovascular diseases state as one of the greatest risks of death for the general population.
Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study
Background: Eating disorders are increasingly prevalent, and social networks offer valuable information.
Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts
We tested our approach on a dataset of 2, 000 tweets about eating disorders, finding that merging word embeddings with knowledge graph information enhances the predictive models' reliability.
Survival and grade of the glioma prediction using transfer learning
In conclusion, this study presents a promising method for predicting the survival and grade of glioblastoma.
Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques
Aim: To study the existence of subgroups by exploring the similarities between the attributes of the nodes of the groups, in relation to diet and gender and, to analyse the connectivity between groups based on aspects of similarities between them through SNA and artificial intelligence techniques.
A generalized decision tree ensemble based on the NeuralNetworks architecture: Distributed Gradient Boosting Forest (DGBF)
Tree ensemble algorithms as RandomForest and GradientBoosting are currently the dominant methods for modeling discrete or tabular data, however, they are unable to perform a hierarchical representation learning from raw data as NeuralNetworks does thanks to its multi-layered structure, which is a key feature for DeepLearning problems and modeling unstructured data.
Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data
This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals.
Diabetes detection using deep learning techniques with oversampling and feature augmentation
Conclusions: Using a full deep learning pipeline for data preprocessing and classification has demonstrate to be very promising in the diabetes detection field outperforming the state-of-the-art proposals.
Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network
In this work, a sentiment analysis method that is capable of accepting audio of any length, without being fixed a priori, is proposed.
