scholarly journals Classificação de Espécies de Plantas Usando Extreme Learning Machine

2019 ◽  
Author(s):  
Larissa Britto ◽  
Luciano Pacífico
Keyword(s):  
Extreme Learning Machine ◽  
Plant Species ◽  
Hypothesis Test ◽  
Species Recognition ◽  
General Purpose ◽  
Statistical Hypothesis ◽  
Data Sets ◽  
Real World Data ◽  
Statistical Hypothesis Test ◽  
Learning Machine

Plants play an important role in nature, but correct plant species identification is still a challenging task for non-specialized people. Many works have been proposed towards the development of automatic plant species recognition systems through Machine Learning methods, but most of them lack the proper experimental analysis. In this work, we evaluate the performance of a general-purpose Artificial Neural Network to perform plant classification task: the Extreme Learning Machine (ELM).We compare ELM with several classifiers from plant recognition literature by means of three real-world data sets obtained from different image processing and feature extraction processes. A statistical hypothesis test is employed to perform proper experimental evaluation.

scholarly journals A Novel Multiple Instance Learning Method Based on Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Jie Wang ◽  
Liangjian Cai ◽  
Jinzhu Peng ◽  
Yuheng Jia
Keyword(s):  
Extreme Learning Machine ◽  
Multiple Instance Learning ◽  
Data Sets ◽  
Feedforward Network ◽  
Real World Data ◽  
Benchmark Data ◽  
Input And Output ◽  
Learning Machine ◽  
Hidden Layer

Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the classification of bags comprising unlabeled instances. In this paper, a novel efficient method based on extreme learning machine (ELM) is proposed to address MIL problem. First, the most qualified instance is selected in each bag through a single hidden layer feedforward network (SLFN) whose input and output weights are both initialed randomly, and the single selected instance is used to represent every bag. Second, the modified ELM model is trained by using the selected instances to update the output weights. Experiments on several benchmark data sets and multiple instance regression data sets show that the ELM-MIL achieves good performance; moreover, it runs several times or even hundreds of times faster than other similar MIL algorithms.

Parameter Estimation for Industrial Servomotor by Using Statistical Hypothesis Test

2019 ◽  
Vol 19 (2) ◽  
pp. 134-140
Author(s):  
Baek-Ju Sung ◽  
Sung-kyu Lee ◽  
Mu-Seong Chang ◽  
Do-Sik Kim
Keyword(s):  
Parameter Estimation ◽  
Hypothesis Test ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Test

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach

2021 ◽  
Author(s):  
Gediminas Adomavicius ◽  
Yaqiong Wang
Keyword(s):  
Machine Learning ◽  
General Purpose ◽  
Reliability Estimation ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Real World Data ◽  
Learning Techniques ◽  
Reliability Indicator ◽  
Machine Learning Approach ◽  
Prediction Reliability

Numerical predictive modeling is widely used in different application domains. Although many modeling techniques have been proposed, and a number of different aggregate accuracy metrics exist for evaluating the overall performance of predictive models, other important aspects, such as the reliability (or confidence and uncertainty) of individual predictions, have been underexplored. We propose to use estimated absolute prediction error as the indicator of individual prediction reliability, which has the benefits of being intuitive and providing highly interpretable information to decision makers, as well as allowing for more precise evaluation of reliability estimation quality. As importantly, the proposed reliability indicator allows the reframing of reliability estimation itself as a canonical numeric prediction problem, which makes the proposed approach general-purpose (i.e., it can work in conjunction with any outcome prediction model), alleviates the need for distributional assumptions, and enables the use of advanced, state-of-the-art machine learning techniques to learn individual prediction reliability patterns directly from data. Extensive experimental results on multiple real-world data sets show that the proposed machine learning-based approach can significantly improve individual prediction reliability estimation as compared with a number of baselines from prior work, especially in more complex predictive scenarios.

Item-specific overlap between hallucinatory experiences and cognition in the general population: A three-step multivariate analysis of international multi-site data

2021 ◽  
Author(s):  
Abhijit Mahesh Chinchani ◽  
Mahesh Menon ◽  
Meighen Roes ◽  
Heungsun Hwang ◽  
Paul Allen ◽  
...  
Keyword(s):  
Signal Detection ◽  
Source Monitoring ◽  
Hypothesis Test ◽  
The Other ◽  
Statistical Hypothesis ◽  
Auditory Signal ◽  
Attentional Processes ◽  
Hearing Voices ◽  
Statistical Hypothesis Test ◽  
Using Data

Cognitive mechanisms hypothesized to underlie hallucinatory experiences (HEs) include dysfunctional source monitoring, heightened signal detection, or impaired attentional processes. HEs can be very pronounced in psychosis, but similar experiences also occur in nonclinical populations. Using data from an international multisite study on nonclinical subjects (N = 419), we described the overlap between two sets of variables - one measuring cognition and the other HEs - at the level of individual items, allowing extraction of item-specific signal which might considered off-limits when summary scores are analyzed. This involved using a statistical hypothesis test at the multivariate level, and variance constraints, dimension reduction, and split-half reliability checks at the level of individual items. The results showed that (1) modality-general HEs involving sensory distortions (hearing voices/sounds, troubled by voices, everyday things look abnormal, sensations of presence/movement) were associated with more liberal auditory signal detection, and (2) HEs involving experiences of sensory overload and vivid images/imagery (viz., HEs for faces and intense daydreams) were associated with other-ear distraction and reduced laterality in dichotic listening. Based on these results, it is concluded that the overlap between HEs and cognition variables can be conceptualized as modality-general and bi-dimensional: one involving distortions, and the other involving overload or intensity.

scholarly journals Particulate Matter and COVID-19 Disease Diffusion in Emilia-Romagna (Italy). Already a Cold Case?

Computation ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 59 ◽  
Author(s):  
Giovanni Delnevo ◽  
Silvia Mirri ◽  
Marco Roccetti
Keyword(s):  
Granger Causality ◽  
Air Pollutants ◽  
Climatic Factors ◽  
Virus Disease ◽  
Hypothesis Test ◽  
Statistical Hypothesis ◽  
Clear Correlation ◽  
The Real ◽  
Statistical Hypothesis Test ◽  
Before And After

As we prepare to emerge from an extensive and unprecedented lockdown period, due to the COVID-19 virus infection that hit the Northern regions of Italy with the Europe’s highest death toll, it becomes clear that what has gone wrong rests upon a combination of demographic, healthcare, political, business, organizational, and climatic factors that are out of our scientific scope. Nonetheless, looking at this problem from a patient’s perspective, it is indisputable that risk factors, considered as associated with the development of the virus disease, include older age, history of smoking, hypertension and heart disease. While several studies have already shown that many of these diseases can also be favored by a protracted exposure to air pollution, there has been recently an insurgence of negative commentary against authors who have correlated the fatal consequences of COVID-19 (also) to the exposition of specific air pollutants. Well aware that understanding the real connection between the spread of this fatal virus and air pollutants would require many other investigations at a level appropriate to the scale of this phenomenon (e.g., biological, chemical, and physical), we propose the results of a study, where a series of the measures of the daily values of PM2.5, PM10, and NO2 were considered over time, while the Granger causality statistical hypothesis test was used for determining the presence of a possible correlation with the series of the new daily COVID19 infections, in the period February–April 2020, in Emilia-Romagna. Results taken both before and after the governmental lockdown decisions show a clear correlation, although strictly seen from a Granger causality perspective. Moving beyond the relevance of our results towards the real extent of such a correlation, our scientific efforts aim at reinvigorating the debate on a relevant case, that should not remain unsolved or no longer investigated.

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