scholarly journals On Fast Converging Data-Selective Adaptive Filtering

Algorithms ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 4 ◽  
Author(s):  
Marcele O. K. Mendonça ◽  
Jonathas O. Ferreira ◽  
Christos G. Tsinos ◽  
Paulo S R Diniz ◽  
Tadeu N. Ferreira
Keyword(s):  
Adaptive Filtering ◽  
Learning Algorithm ◽  
Real Data ◽  
Data Selection ◽  
Estimation Accuracy ◽  
Selection Strategy ◽  
Selection Strategies ◽  
The World ◽  
Characteristic Features ◽  
Complexity Cost

The amount of information currently generated in the world has been increasing exponentially, raising the question of whether all acquired data is relevant for the learning algorithm process. If a subset of the data does not bring enough innovation, data-selection strategies can be employed to reduce the computational complexity cost and, in many cases, improve the estimation accuracy. In this paper, we explore some adaptive filtering algorithms whose characteristic features are their fast convergence and data selection. These algorithms incorporate a prescribed data-selection strategy and are compared in distinct applications environments. The simulation results include both synthetic and real data.

Estimating Ability and Item-Selection Strategy in Self-Adapted Testing: A Latent Class Approach

2004 ◽  
Vol 29 (4) ◽  
pp. 379-396 ◽  
Author(s):  
Javier Revuelta
Keyword(s):  
Em Algorithm ◽  
Latent Class ◽  
Real Data ◽  
Information Criterion ◽  
Missing At Random ◽  
Model Fit ◽  
Item Selection ◽  
Selection Strategy ◽  
Missing Responses ◽  
Selection Strategies

This article presents a psychometric model for estimating ability and item-selection strategies in self-adapted testing. In contrast to computer adaptive testing, in self-adapted testing the examinees are allowed to select the difficulty of the items. The item-selection strategy is defined as the distribution of difficulty conditional on the responses given to previous items. The article shows that missing responses in self-adapted testing are missing at random and can be ignored in the estimation of ability. However, the item-selection strategy cannot always be ignored in such an estimation. An EM algorithm is presented to estimate an examinee’s ability and strategies, and a model fit is evaluated using Akaike’s information criterion. The article includes an application with real data to illustrate how the model can be used in practice for evaluating hypotheses, estimating ability, and identifying strategies. In the example, four strategies were identified and related to examinees’ ability. It was shown that individual examinees tended not to follow a consistent strategy throughout the test.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

scholarly journals AnANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

2021 ◽  
Vol 11 (11) ◽  
pp. 4757
Author(s):  
Aleksandra Bączkiewicz ◽  
Jarosław Wątróbski ◽  
Wojciech Sałabun ◽  
Joanna Kołodziejczyk
Keyword(s):  
Atmospheric Pressure ◽  
Real Life ◽  
Real Data ◽  
Weather Conditions ◽  
Maximum Temperature ◽  
Air Masses ◽  
Weather Forecasts ◽  
Life Problems ◽  
The World ◽  
The City

Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature, especially weather indicators, has been widely discussed. However, the various areas of the world differ in terms of their difficulty and ability in preparing accurate weather forecasts. Poland lies in a zone with a moderate transition climate, which is characterized by seasonality and the inflow of many types of air masses from different directions, which, combined with the compound terrain, causes climate variability and makes it difficult to accurately predict the weather. For this reason, it is necessary to adapt the model to the prediction of weather conditions and verify its effectiveness on real data. The principal aim of this study is to present the use of a regressive model based on a unidirectional multilayer neural network, also called a Multilayer Perceptron (MLP), to predict selected weather indicators for the city of Szczecin in Poland. The forecast of the model we implemented was effective in determining the daily parameters at 96% compliance with the actual measurements for the prediction of the minimum and maximum temperature for the next day and 83.27% for the prediction of atmospheric pressure.

scholarly journals Wealth Rheology

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 842
Author(s):  
Zdzislaw Burda ◽  
Malgorzata J. Krawczyk ◽  
Krzysztof Malarz ◽  
Malgorzata Snarska
Keyword(s):  
Exchange Rate ◽  
Simple Model ◽  
Real Data ◽  
Ranking List ◽  
Kendall’S Τ ◽  
The World ◽  
The Usa ◽  
Overlap Ratio ◽  
Growth Volatility ◽  
Wealth Rank

We study wealth rank correlations in a simple model of macroeconomy. To quantify rank correlations between wealth rankings at different times, we use Kendall’s τ and Spearman’s ρ, Goodman–Kruskal’s γ, and the lists’ overlap ratio. We show that the dynamics of wealth flow and the speed of reshuffling in the ranking list depend on parameters of the model controlling the wealth exchange rate and the wealth growth volatility. As an example of the rheology of wealth in real data, we analyze the lists of the richest people in Poland, Germany, the USA and the world.

Application of a Rough Set-Based Inductive Learning System

1993 ◽  
Vol 18 (2-4) ◽  
pp. 209-220
Author(s):  
Michael Hadjimichael ◽  
Anita Wasilewska
Keyword(s):  
Machine Learning ◽  
Rough Set ◽  
Presidential Election ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Inductive Learning ◽  
Real Data ◽  
Semantic Content ◽  
Learning System ◽  
Voter Preferences

We present here an application of Rough Set formalism to Machine Learning. The resulting Inductive Learning algorithm is described, and its application to a set of real data is examined. The data consists of a survey of voter preferences taken during the 1988 presidential election in the U.S.A. Results include an analysis of the predictive accuracy of the generated rules, and an analysis of the semantic content of the rules.

Toward Islam Through Political Parties, Ideology, and Democracy: A Discourse Analysis on Turkey’s AK Party, Tunisian Ennahda, and Bangladesh Jamaat-e-Islami

2021 ◽  
Vol 25 (1) ◽  
pp. 26-51
Author(s):  
Md. Nazmul Islam ◽  
Yılmaz Bingöl ◽  
Israel Nyaburi Nyadera ◽  
Gershon Dagba
Keyword(s):  
Political Parties ◽  
Political Development ◽  
Political Ideology ◽  
Role Model ◽  
Theoretical Perspective ◽  
Political Movement ◽  
The World ◽  
Characteristic Features ◽  
Democratic Principles ◽  
Islamic Political Parties

This article aims to examine the legacy and policy of AK Party in Turkey, Ennahda’s political movement in Tunisia, and Jamaat-e-Islami (BJI) in Bangladesh, which is ostensibly identified with Islamic political ideology and acquainted with the world as a ‘moderate-conservative political Islam party.’ The study interrogates the nature, processes, and the characteristic features of the three countries’ administrative system, comparatively from three regions of the world, particularly from the Middle East and Europe region, Africa and Arab region, and the South Asian region. This study also highlights these political parties’ history, political ideology differences, and their practices reflective of democratic principles from a theoretical perspective on politics, policy, and philosophy. It also acknowledges whether the political development of Turkey from 2002 onward is feasible for Bangladeshi and Tunisian Islamic political parties to accept as a role model in their political arena.

scholarly journals Accelerating assimilation development for new observing systems using EFSO

2018 ◽  
Vol 25 (1) ◽  
pp. 129-143 ◽  
Author(s):  
Guo-Yuan Lien ◽  
Daisuke Hotta ◽  
Eugenia Kalnay ◽  
Takemasa Miyoshi ◽  
Tse-Chun Chen
Keyword(s):  
Selection Criteria ◽  
Ensemble Forecast ◽  
Data Selection ◽  
Development Work ◽  
Observation Data ◽  
Precipitation Data ◽  
Trial And Error ◽  
Satellite Precipitation ◽  
Selection Strategies ◽  
Observing Systems

Abstract. To successfully assimilate data from a new observing system, it is necessary to develop appropriate data selection strategies, assimilating only the generally useful data. This development work is usually done by trial and error using observing system experiments (OSEs), which are very time and resource consuming. This study proposes a new, efficient methodology to accelerate the development using ensemble forecast sensitivity to observations (EFSO). First, non-cycled assimilation of the new observation data is conducted to compute EFSO diagnostics for each observation within a large sample. Second, the average EFSO conditionally sampled in terms of various factors is computed. Third, potential data selection criteria are designed based on the non-cycled EFSO statistics, and tested in cycled OSEs to verify the actual assimilation impact. The usefulness of this method is demonstrated with the assimilation of satellite precipitation data. It is shown that the EFSO-based method can efficiently suggest data selection criteria that significantly improve the assimilation results.

syris: a flexible and efficient framework for X-ray imaging experiments simulation

2017 ◽  
Vol 24 (6) ◽  
pp. 1283-1295 ◽  
Author(s):  
Tomáš Faragó ◽  
Petr Mikulík ◽  
Alexey Ershov ◽  
Matthias Vogelgesang ◽  
Daniel Hänschke ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Data Processing ◽  
Graphics Processing Units ◽  
High Speed ◽  
Real Data ◽  
Estimation Accuracy ◽  
Processing Parameter ◽  
X Ray ◽  
X Ray Imaging ◽  
Imaging Conditions

An open-source framework for conducting a broad range of virtual X-ray imaging experiments,syris, is presented. The simulated wavefield created by a source propagates through an arbitrary number of objects until it reaches a detector. The objects in the light path and the source are time-dependent, which enables simulations of dynamic experiments,e.g.four-dimensional time-resolved tomography and laminography. The high-level interface ofsyrisis written in Python and its modularity makes the framework very flexible. The computationally demanding parts behind this interface are implemented in OpenCL, which enables fast calculations on modern graphics processing units. The combination of flexibility and speed opens new possibilities for studying novel imaging methods and systematic search of optimal combinations of measurement conditions and data processing parameters. This can help to increase the success rates and efficiency of valuable synchrotron beam time. To demonstrate the capabilities of the framework, various experiments have been simulated and compared with real data. To show the use case of measurement and data processing parameter optimization based on simulation, a virtual counterpart of a high-speed radiography experiment was created and the simulated data were used to select a suitable motion estimation algorithm; one of its parameters was optimized in order to achieve the best motion estimation accuracy when applied on the real data.syriswas also used to simulate tomographic data sets under various imaging conditions which impact the tomographic reconstruction accuracy, and it is shown how the accuracy may guide the selection of imaging conditions for particular use cases.

Feasting Ritually: An Ethnography on the Implications of Feasts in Religious Rituals

2021 ◽  
pp. 0972558X2110571
Author(s):  
Anil Gopi
Keyword(s):  
Social Life ◽  
The Other ◽  
Social Solidarity ◽  
Cultural Significance ◽  
Religious Rituals ◽  
The People ◽  
Religious Festivals ◽  
The World ◽  
Characteristic Features ◽  
And Gender

Food and feast are integral and key components of human cultures across the world. Feasts associated with religious rituals have special social and cultural significance when compared to those in any other festivities or celebrations in people’s life. In this study, an approach is made to comparatively analyze the feasts at religious festivals of two distinctive groups of people, one with a characteristic of simple society and the other of a complex society. The annual feast happening at the hamlets of the Anchunadu Vellalar community in the last days of the calendar year is an occasion that portrays the egalitarian nature of the people. While this feast is restricted within a single community of particular caste affiliation and geographical limitations, the feast associated with the kaliyattam ritual of village goddess in North Malabar is much wider in scope and participation. The enormous feast brings the people in a larger area and exhibits a solidarity that cuts across boundaries of religion, caste and community. Beyond the factors of social solidarity and togetherness, these events also illustrate its divisive characters mainly in terms of social hierarchy and gender. A comparative study of both the two feasts of two different contexts reveals the characteristic features of religious feasts and the value of food and feast in social life and solidarity and also how it acts as a survival of their past and as a tradition.

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