Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

Recently, the stock market prediction has become one of the essential application areas of time-series forecasting research. The successful prediction of the stock market can be better guided to the investors to maximize their profit and to minimize the risk of investment. The stock market data are very much complex, non-linear and dynamic. Due to this reason, still, it is a challenging task. In recent time, deep learning method has become one of the most popular machine learning methods for time-series forecasting due to their temporal feature extraction capabilities. In this paper, we have proposed a novel Deep Learning-based Integrated Stacked Model (DISM) that integrates both the 1D Convolution neural network and LSTM recurrent neural network to find the spatial and temporal features from the stock market data. Our proposed DISM is applied to forecast the stock market. Here, we have also compared our proposed DISM with the single structured stacked LSTM, and 1D Convolution neural network models, and some other statistical models. We have observed that our proposed DISM produces better results in terms of accuracy and stability.

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Classification of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-3-201-2006 ◽

2006 ◽

Vol 3 (1) ◽

pp. 201-227 ◽

Cited By ~ 2

Author(s):

N. Lauzon ◽

F. Anctil ◽

C. W. Baxter

Keyword(s):

Neural Network ◽

Neural Networks ◽

Multilayer Perceptron ◽

Network Models ◽

Rainfall Runoff ◽

Neural Network Models ◽

Kohonen Neural Network ◽

Streamflow Forecasts ◽

Rainfall Runoff Model

Abstract. This work addresses the issue of better considering the heterogeneity of precipitation fields within lumped rainfall-runoff models where only areal mean precipitation is usually used as an input. A method using a Kohonen neural network is proposed for the classification of precipitation fields. The evaluation and improvement of the performance of a lumped rainfall-runoff model for one-day ahead predictions is then established based on this classification. Multilayer perceptron neural networks are employed as lumped rainfall-runoff models. The Bas-en-Basset watershed in France, which is equipped with 23 rain gauges with data for a 21-year period, is employed as the application case. The results demonstrate the relevance of the proposed classification method, which produces groups of precipitation fields that are in agreement with the global climatological features affecting the region, as well as with the topographic constraints of the watershed (i.e., orography). The strengths and weaknesses of the rainfall-runoff models are highlighted by the analysis of their performance vis-à-vis the classification of precipitation fields. The results also show the capability of multilayer perceptron neural networks to account for the heterogeneity of precipitation, even when built as lumped rainfall-runoff models.

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Object Recognition in Image Sequence using Heuristic Convolution Neural Network

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a9644.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 520-525

Keyword(s):

Neural Network ◽

Object Recognition ◽

Heuristic Method ◽

Recognition Task ◽

Network Models ◽

Poor Performance ◽

Image Sequence ◽

Convolution Neural Network ◽

Neural Network Models ◽

Self Learning

The human visual system can make a distinction of tiger from cat very easily without taking any efforts. But in case of a computer system, it is a very complicated job. Identifying and differentiating task has to deal with many challenges but the human brain makes it effortless. Self learning or heuristic techniques are most relevant in this area. The recognition task is to search for the particular object of same shape, color and texture and so on, of the trained objects and match with input. The geometrical distinction such as zoom in, zoom out, rotation etc result in poor performance. This paper uses convolution neural network models Alexnet and VGGNet on object recognition problems which are added with novel heuristic method. We have used CIFAR-10 dataset. The performance and computation speeds are found efficient.

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Detection of Fake Doctored Images used to Circulate Fake News

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g5747.059720 ◽

2020 ◽

Vol 9 (7) ◽

pp. 1031-1036

Keyword(s):

Neural Network ◽

Network Models ◽

Machine Learning Techniques ◽

Fake News ◽

Neural Network Models ◽

The Public ◽

Learning Techniques ◽

The World ◽

Classification Of Images

Over the few years the world has seen a surge in fake news and some people are even calling it an epidemic. Misleading false articles are sold as news items over social media, whatsapp etc where no proper barrier is set to check the authenticity of posts. And not only articles but news items also contain images which are doctored to mislead the public or cause sabotage. Hence a proper barrier to check for authenticity of images related to news items is absolutely necessary. And hence classification of images(related to news items) on the basis of authenticity is imminent. This paper discusses the possibilities of identifying fake images using machine learning techniques. This is an introduction into fake news detection using the latest evolving neural network models

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