Application of Artificial Intelligence in predicting earthquakes: state-of-the-art and future challenges

Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field

DEVELOPMENT OF BASIC CONCEPT OF ICT PLATFORMS DEPLOYMENT STRATEGY FOR SOCIAL MEDIA MARKETING CONSIDERING TECTONIC THEORY

This paper presents authors analytical view on social impacts as targeted advertisement into the network environment using Omori tectonic theory for description the processes of audience response evolution. This could be extremely important and useful in the modern world to realize desirable e-Gov informational policy in the circumstances of hybrid treats emergence that is especially relevant for the informational space and reaching a cyber-supremacy. Some mathematical and algorithmic basics were contributed for narrative description of information and communications technologies (ICT) architectural deployment could be used for outer regulation of audience response character by Social Media Marketing (SMM) principles. That could be performed by controlled distribution of specified digital content that contains respective key phrases, for example social advertisements and analyzing respective feed-backs. Some results of the empiric study of live audience response dependence on controlled impacts are discussed. Election processes data and recent media recordings for preliminary proof of the contributed concept feasibility have been analyzed. There were shown using gathered empiric data sets, that the extent of impacts to targeted audience response intensity could be the subject of outer regulation. The index has been contributed for assessment the efficiency of the impact’s propagation inside the audience by calculation of row correlation of keyword occurrence and audience response intensity. The approaches suggested in the article can be useful both for building effective interactive systems of state-society interaction and for detecting manipulative traits when influencing a specific audienc

DEVELOPMENT OF BASIC CONCEPT OF ICT PLATFORMS DEPLOYMENT STRATEGY FOR SOCIAL MEDIA MARKETING CONSIDERING TECTONIC THEORY

This paper presents authors analytical view on social impacts as targeted advertisement into the network environment using Omori tectonic theory for description the processes of audience response evolution. This could be extremely important and useful in the modern world to realize desirable e-Gov informational policy in the circumstances of hybrid treats emergence that is especially relevant for the informational space and reaching a cyber-supremacy. Some mathematical and algorithmic basics were contributed for narrative description of information and communications technologies (ICT) architectural deployment could be used for outer regulation of audience response character by Social Media Marketing (SMM) principles. That could be performed by controlled distribution of specified digital content that contains respective key phrases, for example social advertisements and analyzing respective feed-backs. Some results of the empiric study of live audience response dependence on controlled impacts are discussed. Election processes data and recent media recordings for preliminary proof of the contributed concept feasibility have been analyzed. There were shown using gathered empiric data sets, that the extent of impacts to targeted audience response intensity could be the subject of outer regulation. The index has been contributed for assessment the efficiency of the impact’s propagation inside the audience by calculation of row correlation of keyword occurrence and audience response intensity. The approaches suggested in the article can be useful both for building effective interactive systems of state-society interaction and for detecting manipulative traits when influencing a specific audienc

Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of architectural design and visualization; material design and optimization; structural design and analysis; offsite manufacturing and automation; construction management, progress monitoring, and safety; smart operation, building management and health monitoring; and durability, life cycle analysis, and circular economy. This paper presents a unique perspective on applications of AI/DL/ML in these domains for the complete building lifecycle, from conceptual stage, design stage, construction stage, operational and maintenance stage until the end of life. Furthermore, data collection strategies using smart vision and sensors, data cleaning methods (post-processing), data storage for developing these models are discussed, and the challenges in model development and strategies to overcome these challenges are elaborated. Future trends in these domains and possible research avenues are also presented