An emerging technology Li-Fi, or light fidelity is a bidirectional and fully networked wireless communications medium which uses light from light-emitting diodes (LEDs) and provides transmission of data through illumination by sending data through a LED light bulb that varies in intensity faster than the human eye can follow. It can provide connectivity within a very large area with more security and with higher data rates and high speed than data that can be transmitted through Wi-Fi. It uses visible light communication or infra-red and near ultraviolet spectrum which works by switching bulbs on and off within nanoseconds. By using Li-Fi technology highly reliable vehicle to vehicle communication is possible by transmitting and receiving data through LED head-lights and tail-lights. In this paper we propose a system that uses Li-Fi enabled LED head-light, tail-light and traffic signal light that can be used for traffic management and road safety by using vehicle to vehicle data transmission The aim of designing this system is to reduce road accidents and managing traffic more accurately.
Motivation
Protein backbone angle prediction has achieved significant accuracy improvement with the development of deep learning methods. Usually the same deep learning model is used in making prediction for all residues regardless of the categories of secondary structures they belong to. In this paper, we propose to train separate deep learning models for each category of secondary structures. Machine learning methods strive to achieve generality over the training examples and consequently loose accuracy. In this work, we explicitly exploit classification knowledge to restrict generalisation within the specific class of training examples. This is to compensate the loss of generalisation by exploiting specialisation knowledge in an informed way.
Results
The new method named SAP4SS obtains mean absolute error (MAE) values of 15.59, 18.87, 6.03, and 21.71 respectively for four types of backbone angles $$\phi$$
ϕ
, $$\psi$$
ψ
, $$\theta$$
θ
, and $$\tau$$
τ
. Consequently, SAP4SS significantly outperforms existing state-of-the-art methods SAP, OPUS-TASS, and SPOT-1D: the differences in MAE for all four types of angles are from 1.5 to 4.1% compared to the best known results.
Availability
SAP4SS along with its data is available from https://gitlab.com/mahnewton/sap4ss.
Protein structure prediction (PSP) is essential for drug discovery. PSP involves minimising an unknown scoring function over an astronomical search space. PSP has achieved significant progress recently via end-to-end deep learning models that require enormous computational resources and almost all known proteins as training data. In this paper, we develop conformational search methods for PSP based on scoring functions involving geometric constraints learnt by deep learning models. When machine learning models achieve generality and thus obviously loose accuracy, conformational search methods could perform protein-specific fine tuning of the predicted conformations. However, effective conformational sampling in PSP remains a key challenge. Existing conformational search algorithms adopt random selection approaches for neighbor generation and thus greatly depend on luck. We propose a new approach to analyse geometric constraint-based scores, to identify the regions of the current conformations causing inferior scores, and to alter the identified regions to generate neighbour conformations. Our approach prefers informed decisions to random selections from an artificial intelligence perspective. The proposed method also provides promising search guidance as it obtains significant improvements from given initial conformations. Our approach significantly outperforms state-of-the-art PSP search algorithms that use random sampling with a similar scoring function on a set of benchmark proteins of varying types and sizes. Our sample generation approach could be used in other bioinformatics research areas requiring search.
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