Search results for "Machine learning"
showing 10 items of 1464 documents
On Assessing Vulnerabilities of the 5G Networks to Adversarial Examples
2022
The use of artificial intelligence and machine learning is recognized as the key enabler for 5G mobile networks which would allow service providers to tackle the network complexity and ensure security, reliability and allocation of the necessary resources to their customers in a dynamic, robust and trustworthy way. Dependability of the future generation networks on accurate and timely performance of its artificial intelligence components means that disturbance in the functionality of these components may have negative impact on the entire network. As a result, there is an increasing concern about the vulnerability of intelligent machine learning driven frameworks to adversarial effects. In …
Kernels and Graphs on M25 + H
2023
Codes related to article "Graphs and Kernelized Learning Applied to Interactions of Hydrogen with Doped Gold Nanoparticle Electrocatalysts". There are two main types of codes: codes to transform a catalytic system of protected gold nanoparticle and a single hydrogen atom into a graph-based representation, and codes to run kernel-based machine learning methods to predict interaction energies between the nanoparticle and the hydrogen atom. This is a snapshot of the code dataset that has been taken on 06.06.2023. A more detailed description of the data and the address to the GitLab repository for the latest version of the code can be found from the parent dataset of this data publication.
Kernels and Graphs on M25 + H (parent repository)
2023
The repository contains codes related to article "Graphs and Kernelized Learning Applied to Interactions of Hydrogen with Doped Gold Nanoparticle Electrocatalysts". There are two main types of codes: codes to transform a catalytic system of protected gold nanoparticle and a single hydrogen atom into a graph-based representation, and codes to run kernel-based machine learning methods to predict interaction energies between the nanoparticle and the hydrogen atom. This is the metadata for the parent repository of the codes. Updates and possible corrections are documented in the GitLab project, where the material saved and shared. The GitLab project can be found and downloaded from the followin…
Supplementary data for the article "Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relev…
2022
The data set contains the supplementary data of the article "Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions" published in J. Phys. Chem. Lett., https://doi.org/10.1021/acs.jpclett.2c02612. The data includes: - A machine learning (EMLM) model for predicting chemical potentials of individual conformers of multifunctional organic compounds calculated by the COSMOtherm program - COSMO-files used for training and testing the EMLM model - Descriptors and chemical potentials used for the training and testing the model Artikkelin "Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds i…
Assessment of microalgae species, biomass and distribution from spectral images using a convolution neural network
2021
Artikkeliin "Assessment of microalgae species, biomass and distribution from spectral images using a convolution neural network" liittyvä aineisto koostuu seuraavista osista: 1.Transmittanssi-hyperspektrikuvat levänäytteistä kuvattuina 24-kuoppalevyllä 2.Biomassamääritykset elektronisella solulaskurilla 3.Opetus- ja validointiaineisto konvoluutioneuroverkolle 4.Testiaineisto konvoluutioneuroverkolle 5.Opetus-, validointi- ja testiaineiston käsittelyyn käytetty Python koodi 6.Seitsemään eri malliin käytetty Python koodi ja mallit itsessään The data and code related to the article "Assessment of microalgae species, biomass and distribution from spectral images using a convolution neural netwo…
Anomaly detection in wireless sensor networks
2016
Wireless Sensor Network can be defined as a network of integrated sensors responsible for environmental sensing, data processing and communication with other sensors and the base station while consuming low power. Today, WSNs are being used in almost every part of life. The cost effective nature of WSNs is beneficial for environmental monitoring, production facilities and security monitoring. At the same time WSNs are vulnerable to security breaches, attacks and information leakage. Anomaly detection techniques are used to detect such activities over the network that do not conform to the normal behavior of the network communication. Supervised Machine learning approach is one way to detect…
ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING
2020
Understanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimation of bio-optical proxies of POC (i.e. particulate backscattering coefficient, bbp) in the surface layer of the ocean quasi-synoptically. In parallel, the Argo program distributes vertical profiles of the physical properties with a global coverage and a high spatio-temporal resolution. Merging satellite ocean color and Argo data using a neural networkbased method has already shown strong potentia…
Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs
2019
Abstract The T cell repertoire is composed of T cell receptors (TCR) selected by their cognate MHC-peptides and naive TCR that do not bind known peptides. While the task of distinguishing a peptide-binding TCR from a naive TCR unlikely to bind any peptide can be performed using sequence motifs, distinguishing between TCRs binding different peptides requires more advanced methods. Such a prediction is the key for using TCR repertoires as disease-specific biomarkers. We here used large scale TCR-peptide dictionaries with state-of-the-art natural language processing (NLP) methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific classifier to predict which TCR binds to which…
Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space
2019
Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunotherapies. However, the underlying principles of this "convergence" of adaptive immunity among different individuals remain poorly understood. To quantitatively describe epitope immunogenicity, here we propose a supervised machine learning framework generating probabilistic estimates of immunogenicity, termed "immunogenicity scores," based on the numerical features computed through sequence-based simulation approximating the molecular scanning process of peptides presented onto major histocompatibility complex (MHC) by the human T cell receptor (T…
Do Diacritical Marks Play a Role at the Early Stages of Word Recognition in Arabic?
2016
Published: 22 August 2016 A crucial question in the domain of visual word recognition is whether letter similarity plays a role in the early stages of visual word processing. Here we focused on Arabic because in this language there are various groups of letters that share the same basic shape and only differ in the number/location of diacritical points. We conducted a masked priming lexical decision experiment in which a target word was preceded by: (i) an identity prime; (ii) a prime in which the critical letter was replaced by a letter with the same shape that differed in the number of diacritics (e.g., ); or (iii) a prime in which the critical letter was replaced by a letter with differe…