Tripathi, Manish Kumar Nath, Abhigyan Singh, Tej P Ethayathulla, A S Kaur, Punit
Published in
Molecular diversity
The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug discovery processes such as virtu...
Coelho, Gabriel Pereira, Pedro Matos, Luis Ribeiro, Alexandrine Ferreira, André Cortez, Paulo Pilastri, André
Recently, there have been advances in using unsupervised learning methods for Acoustic Anomaly Detection (AAD). In this paper, we propose an improved version of two deep AutoEncoders (AE) for unsupervised AAD for six types of working machines, namely Dense and Convolutional AEs. A large set of computational experiments was held, showing that the tw...
houska, vojtěch
Tato práce shrnuje nejmodernější metody využívané v hlubokém učení. Probírá použití autoenkodérů a metody předzpracování v oblasti rozpoznávání zvuku. Jako zdroj slabě anotovaných dat pro učení těchto modelů byla použita platforma YouTube. Práce porovnala vlastnosti latentních prostorů navrhovaných autoenkoderů, které byly testovány pomocí shluková...
Tangherloni, Andrea Ricciuti, Federico Besozzi, Daniela Liò, Pietro Cvejic, Ana
Published in
BMC Bioinformatics
BackgroundSingle-cell RNA sequencing (scRNA-Seq) experiments are gaining ground to study the molecular processes that drive normal development as well as the onset of different pathologies. Finding an effective and efficient low-dimensional representation of the data is one of the most important steps in the downstream analysis of scRNA-Seq data, a...
Tangherloni, Andrea Ricciuti, Federico Besozzi, Daniela Liò, Pietro Cvejic, Ana
BACKGROUND: Single-cell RNA sequencing (scRNA-Seq) experiments are gaining ground to study the molecular processes that drive normal development as well as the onset of different pathologies. Finding an effective and efficient low-dimensional representation of the data is one of the most important steps in the downstream analysis of scRNA-Seq data,...
Barot, Dvij Sharma, Honey Yadav, Mahika Kamat, Pooja
The Remaining Useful Life of a machine is very useful statistical information for the operator and manufacturer. It provides a very clear perspective to the user how long the machine can be operated and if any faults are detected how can they be prevented and ultimately increase the Remaining Useful Life. If the operators are aware of the forthcomi...
Sinha, Adwitiya Rathi, Megha
Published in
Applied intelligence (Dordrecht, Netherlands)
The severe spread of the COVID-19 pandemic has created a situation of public health emergency and global awareness. In our research, we analyzed the demographical factors affecting the global pandemic spread along with the features that lead to death due to the infection. Modeling results stipulate that the mortality rate increase as the age increa...
Peralta, Maxime Jannin, Pierre Haegelen, Claire Baxter, John S H
Published in
Artificial intelligence in medicine
Medical questionnaires are a valuable source of information but are often difficult to analyse due to both their size and the high possibility of them having missing values. This is a problematic issue in biomedical data science as it may complicate how individual questionnaire data is represented for statistical or machine learning analysis. In th...
Tangherloni, Andrea Ricciuti, Federico Besozzi, Daniela Liò, Pietro Cvejic, Ana
Abstract: Background: Single-cell RNA sequencing (scRNA-Seq) experiments are gaining ground to study the molecular processes that drive normal development as well as the onset of different pathologies. Finding an effective and efficient low-dimensional representation of the data is one of the most important steps in the downstream analysis of scRNA...
Villalba Pérez, Luis
[ES] El trabajo consiste en el desarrollo de una solución software para la detección de anomalías en el tráfico de una red industrial utilizando técnicas de aprendizaje automático. La solución incorporará en primer lugar una capa de compresión con el objetivo de reducir el costo computacional. Seguidamente se aplicarán técnicas de aprendizaje profu...
