AIMC Topic: Machine Learning

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Named Entity Aware Transfer Learning for Biomedical Factoid Question Answering.

IEEE/ACM transactions on computational biology and bioinformatics
Biomedical factoid question answering is an important task in biomedical question answering applications. It has attracted much attention because of its reliability. In question answering systems, better representation of words is of great importance...

Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques.

Sensors (Basel, Switzerland)
Remotely monitoring people's healthcare is still among the most important research topics for researchers from both industry and academia. In addition, with the Wireless Body Networks (WBANs) emergence, it becomes possible to supervise patients throu...

Explaining Aha! moments in artificial agents through IKE-XAI: Implicit Knowledge Extraction for eXplainable AI.

Neural networks : the official journal of the International Neural Network Society
During the learning process, a child develops a mental representation of the task he or she is learning. A Machine Learning algorithm develops also a latent representation of the task it learns. We investigate the development of the knowledge constru...

Cyber-Threat Detection System Using a Hybrid Approach of Transfer Learning and Multi-Model Image Representation.

Sensors (Basel, Switzerland)
Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper...

Improved prediction of gene expression through integrating cell signalling models with machine learning.

BMC bioinformatics
BACKGROUND: A key problem in bioinformatics is that of predicting gene expression levels. There are two broad approaches: use of mechanistic models that aim to directly simulate the underlying biology, and use of machine learning (ML) to empirically ...

ppdx: Automated modeling of protein-protein interaction descriptors for use with machine learning.

Journal of computational chemistry
This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein-protein interactions. The descriptors can be ...

Mapping Simulated Two-Dimensional Spectra to Molecular Models Using Machine Learning.

The journal of physical chemistry letters
Two-dimensional (2D) spectroscopy encodes molecular properties and dynamics into expansive spectral data sets. Translating these data into meaningful chemical insights is challenging because of the many ways chemical properties can influence the spec...

Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring.

Sensors (Basel, Switzerland)
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Many publications...

Crop Mapping Using the Historical Crop Data Layer and Deep Neural Networks: A Case Study in Jilin Province, China.

Sensors (Basel, Switzerland)
Machine learning combined with satellite image time series can quickly, and reliably be implemented to map crop distribution and growth monitoring necessary for food security. However, obtaining a large number of field survey samples for classifier t...

Explainable Artificial Intelligence Helps in Understanding the Effect of Fibronectin on Survival of Sepsis.

Cells
Fibronectin (FN) plays an essential role in the host's response to infection. In previous studies, a significant decrease in the FN level was observed in sepsis; however, it has not been clearly elucidated how this parameter affects the patient's sur...