scholarly journals Forecasting Models for Coronavirus (COVID-19): A Survey of the State-of-the-Art

2020 ◽  
Author(s):  
Parikshit Mahalle ◽  
Asmita B. Kalamkar ◽  
Nilanjan Dey ◽  
Jyotismita Chaki ◽  
Aboul ella Hassanien ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
State Of The Art ◽  
The State ◽  
Forecasting Models ◽  
Data Source

When new virus and its respective disease cause more infections, it is very important to decide the strategies to control the spread and determine its impact. Considering the recent example of Coronavirus initially identified in Wuhan China has now targeted Italy badly, It is very important to study different forecasting models to control this pandemic. In the view of this, this study present the comparative analysis of various forecasting models, their classification and the techniques used. The detail analysis of forecasting models with respect to the parameters like data source, techniques, algorithms, mathematical parameters is also presented in this study. In the sequel, this study also presents useful recommendations to help government and healthcare community in designing better strategies and in taking productive decisions to control this outbreak.<div><br></div>

scholarly journals Forecasting Models for Coronavirus (COVID-19): A Survey of the State-of-the-Art

2020 ◽  
Author(s):  
Parikshit Mahalle ◽  
Asmita B. Kalamkar ◽  
Nilanjan Dey ◽  
Jyotismita Chaki ◽  
Aboul ella Hassanien ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
State Of The Art ◽  
The State ◽  
Forecasting Models ◽  
Data Source

When new virus and its respective disease cause more infections, it is very important to decide the strategies to control the spread and determine its impact. Considering the recent example of Coronavirus initially identified in Wuhan China has now targeted Italy badly, It is very important to study different forecasting models to control this pandemic. In the view of this, this study present the comparative analysis of various forecasting models, their classification and the techniques used. The detail analysis of forecasting models with respect to the parameters like data source, techniques, algorithms, mathematical parameters is also presented in this study. In the sequel, this study also presents useful recommendations to help government and healthcare community in designing better strategies and in taking productive decisions to control this outbreak.<div><br></div>

scholarly journals Symbolic Melodic Similarity: State of the Art and Future Challenges

2016 ◽  
Vol 40 (2) ◽  
pp. 70-83 ◽  
Author(s):  
Valerio Velardo ◽  
Mauro Vallati ◽  
Steven Jan
Keyword(s):  
Information Retrieval ◽  
Comparative Analysis ◽  
State Of The Art ◽  
Music Information Retrieval ◽  
The State ◽  
Information Retrieval Evaluation ◽  
Future Challenges ◽  
Music Information

Fostered by the introduction of the Music Information Retrieval Evaluation Exchange (MIREX) competition, the number of systems that calculate symbolic melodic similarity has recently increased considerably. To understand the state of the art, we provide a comparative analysis of existing algorithms. The analysis is based on eight criteria that help to characterize the systems, highlighting strengths and weaknesses. We also propose a taxonomy that classifies algorithms based on their approach. Both taxonomy and criteria are fruitfully exploited to provide input for new, forthcoming research in the area.

scholarly journals Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art

2020 ◽  
Vol 1 (4) ◽  
Author(s):  
Gitanjali R. Shinde ◽  
Asmita B. Kalamkar ◽  
Parikshit N. Mahalle ◽  
Nilanjan Dey ◽  
Jyotismita Chaki ◽  
...  
Keyword(s):  
State Of The Art ◽  
The State ◽  
Forecasting Models

scholarly journals Process of change: states, transitions, and determinants

2021 ◽  
Vol Volume 9, Number 1, Special... (Special Issue...) ◽  
Author(s):  
Alessandro Fenicio ◽  
Yann Laurillau ◽  
Gaëlle Calvary
Keyword(s):  
Comparative Analysis ◽  
Scientific Literature ◽  
State Of The Art ◽  
Research Field ◽  
The State ◽  
Process Of Change ◽  
Example Analyse

The goal of persuasion is to change the behaviour or the attitude of a person without using any form of coercion (Oinas et al. 2010). In the last ten years, several models, approaches and theories have been developed in the research field of persuasion, producing a copious scientific literature. Different reviews of the state-of-the-art focusing on specific aspects have been proposed. Pindel et al. (Pinder et al. 2018) for example analyse the state of the art under the perspective of the mechanisms that lead to the habit forming. In this work, we report a state-of-the-art review on the key elements of the process of change using the concepts of states, transitions, and determinants to propose a common generic paradigm. We conclude with a discussion about the operationalization of persuasive processes and with a comparative analysis on the reviewed theories. Si le but et les garde-fous éthiques de la persuasion sont aujourd’hui bien compris, la littérature est abondante et les analyses critiques multiples, sans qu’un langage de comparaison n’ait été défini. Cet article propose les concepts d’états, de transitions et de déterminants pour caractériser les travaux de la littérature et ainsi en faciliter la compréhension et la comparaison. L’opérationnalisation de ce cadre ouvre des perspectives intéressantes au domaine.

EXPLOITING INTELLIGENT CONTENT VIA AXMEDIS/MPEG-21 FOR MODELLING AND DISTRIBUTING NEWS

2011 ◽  
Vol 21 (01) ◽  
pp. 3-32 ◽  
Author(s):  
PIERFRANCESCO BELLINI ◽  
IVAN BRUNO ◽  
PAOLO NESI
Keyword(s):  
Comparative Analysis ◽  
State Of The Art ◽  
The State ◽  
News Production ◽  
Content Model ◽  
Production And Distribution ◽  
Semantic Computing ◽  
Container Production

The content technology needs to attain forms with more intelligence, flexibility and complete features than those being currently on the market or proposed by standards. In this paper, an analysis of the state of the art about intelligent and complex content models is presented. The analysis allowed identifying a number of topics and features which models and formats should evolve according to. The work has been used to extend AXMEDIS content model and format which in turn is grounded on MPEG-21, SMIL, HTML, and other standards. The Extended AXMEDIS format presents a set of new features among them: semantic descriptors, extended annotations, intelligent behavioral and semantic computing capabilities. The newly obtained format has been compared against NewsML which is one of the most widespread formats for news production and distribution. The management of news has some peculiarities such as container, production tools and players, that may take advantage of the intelligent content features and applications. Moreover, news have to be massively processed for ingestion and repurposing, and present relevant requirements on right control. Also these features may be satisfied by AXMEDIS tools. To this end, a comparative analysis of processing and modeling NewsML with AXMEDIS tools and format has been performed and reported to verify the usage. In addition, AXMEDIS format can be profitably used for a range of innovative applications of intelligent content.

scholarly journals Review of the state of the art: discovering and associating semantics to tags in folksonomies

2012 ◽  
Vol 27 (1) ◽  
pp. 57-85 ◽  
Author(s):  
Andrés García-Silva ◽  
Oscar Corcho ◽  
Harith Alani ◽  
Asunción Gómez-Pérez
Keyword(s):  
State Of The Art ◽  
The State ◽  
Semantic Association ◽  
Unified View ◽  
Data Source

AbstractThis paper describes and compares the most relevant approaches for associating tags with semantics in order to make explicit the meaning of those tags. We identify a common set of steps that are usually considered across all these approaches and frame our descriptions according to them, providing a unified view of how each approach tackles the different problems that appear during the semantic association process. Furthermore, we provide some recommendations on (a) how and when to use each of the approaches according to the characteristics of the data source, and (b) how to improve results by leveraging the strengths of the different approaches.

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