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Found 8 papers, 0 papers with code
An Approach for Improving Automatic Mouth Emotion Recognition
The study proposes and tests a technique for automated emotion recognition through mouth detection via Convolutional Neural Networks (CNN), meant to be applied for supporting people with health disorders with communication skills issues (e. g. muscle wasting, stroke, autism, or, more simply, pain) in order to recognize emotions and generate real-time feedback, or data feeding supporting systems.
Sharing Linkable Learning Objects with the use of Metadata and a Taxonomy Assistant for Categorization
In this work, a re-design of the Moodledata module functionalities is presented to share learning objects between e-learning content platforms, e. g., Moodle and G-Lorep, in a linkable object format.
Towards a learning-based performance modeling for accelerating Deep Neural Networks
Emerging applications such as Deep Learning are often data-driven, thus traditional approaches based on auto-tuners are not performance effective across the wide range of inputs used in practice.
An example of use of Variational Methods in Quantum Machine Learning
This paper introduces a deep learning system based on a quantum neural network for the binary classification of points of a specific geometric pattern (Two-Moons Classification problem) on a plane.
Skin Cancer Classification using Inception Network and Transfer Learning
Medical data classification is typically a challenging task due to imbalance between classes.
IoT to monitor people flow in areas of public interest
The unexpected historical period we are living has abruptly pushed us to loosen any sort of interaction between individuals, gradually forcing us to deal with new ways to allow compliance with safety distances; indeed the present situation has demonstrated more than ever how critical it is to be able to properly organize our travel plans, put people in safe conditions, and avoid harmful circumstances.
A new method for binary classification of proteins with Machine Learning
In this work we set out to find a method to classify protein structures using a Deep Learning methodology.
Binary classification of proteins by a Machine Learning approach
The aim of the work is to design a prototypical Deep Learning machinery for the collection and management of a huge amount of data and to validate it through its application to the classification of a sequences of amino acids.
