Binary classification of day-ahead deregulated electricity market prices using neural networks

AbstractA desired objective in radar target detection is to satisfy two very contradictory requirements: offer a high probability of detection with a low false alarm rate. In this paper, we propose the utilization of artificial neural networks for binary classification of targets detected by a depreciated detection process. It is shown that trained neural networks are capable of identifying false detections with considerable accuracy and can to this extent utilize information present in guard cells and Doppler profiles. This allows for a reduction in the false alarm rate with only moderate loss in the probability of detection. With an appropriately designed neural network, an overall improved system performance can be achieved when compared against traditional constant false alarm rate detectors for the specific trained scenarios.

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Explainable Deep Learning Reproduces a ‘Professional Eye’ on the Diagnosis of Internal Disorders in Persimmon Fruit

Plant and Cell Physiology ◽

10.1093/pcp/pcaa111 ◽

2020 ◽

Vol 61 (11) ◽

pp. 1967-1973

Author(s):

Takashi Akagi ◽

Masanori Onishi ◽

Kanae Masuda ◽

Ryohei Kuroki ◽

Kohei Baba ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Binary Classification ◽

Network Models ◽

Plant Biology ◽

Neural Network Models ◽

Persimmon Fruit

Abstract Recent rapid progress in deep neural network techniques has allowed recognition and classification of various objects, often exceeding the performance of the human eye. In plant biology and crop sciences, some deep neural network frameworks have been applied mainly for effective and rapid phenotyping. In this study, beyond simple optimizations of phenotyping, we propose an application of deep neural networks to make an image-based internal disorder diagnosis that is hard even for experts, and to visualize the reasons behind each diagnosis to provide biological interpretations. Here, we exemplified classification of calyx-end cracking in persimmon fruit by using five convolutional neural network models with various layer structures and examined potential analytical options involved in the diagnostic qualities. With 3,173 visible RGB images from the fruit apex side, the neural networks successfully made the binary classification of each degree of disorder, with up to 90% accuracy. Furthermore, feature visualizations, such as Grad-CAM and LRP, visualize the regions of the image that contribute to the diagnosis. They suggest that specific patterns of color unevenness, such as in the fruit peripheral area, can be indexes of calyx-end cracking. These results not only provided novel insights into indexes of fruit internal disorders but also proposed the potential applicability of deep neural networks in plant biology.

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Artificial Intelligence in Electricity Market Operations and Management

Intelligent Information Technologies ◽

10.4018/978-1-59904-941-0.ch105 ◽

2011 ◽

pp. 1821-1840

Author(s):

Zhao Yang Dong ◽

Tapan Kumar Saha ◽

Kit Po Wong

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Neural Networks ◽

Time Series ◽

Electricity Market ◽

Support Vector ◽

Market Demand ◽

Deregulated Electricity Market ◽

Vector Machines ◽

Spike Analysis

This chapter introduces advanced techniques such as artificial neural networks, wavelet decomposition, support vector machines, and data-mining techniques in electricity market demand and price forecasts. It argues that various techniques can offer different advantages in providing satisfactory demand and price signal forecast results for a deregulated electricity market, depending on the specific needs in forecasting. Furthermore, the authors hope that an understanding of these techniques and their application will help the reader to form a comprehensive view of electricity market data analysis needs, not only for the traditional time-series based forecast, but also the new correlation-based, price spike analysis.

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Neural networks application for phasechronometric measurement information processing

Izmeritel`naya Tekhnika ◽

10.32446/0368-1025it.2020-9-31-35 ◽

2020 ◽

pp. 31-35

Author(s):

D. D. Boldasov ◽

J. V. Drozdova ◽

A. S. Komshin ◽

A. B. Syritskii

Keyword(s):

Neural Networks ◽

Information Processing ◽

Binary Classification ◽

Processing Technique ◽

Measurement Information ◽

Perceptron Algorithm ◽

Classification Feature ◽

Operation Modes ◽

The Neural Networks

This article describes the processing technique of measuring phasechronometric information based on the neural networks use. The novelty of the proposed approach lies in the choice of a classification feature and the perceptron algorithm use as an algorithm for binary classification performing. In this article, to assess the concept operability, the simplest binary classification of the lathe operation modes is made: idle or cutting.

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A neural network approach to day-ahead deregulated electricity market prices classification

Electric Power Systems Research ◽

10.1016/j.epsr.2011.12.011 ◽

2012 ◽

Vol 86 ◽

pp. 140-150 ◽

Cited By ~ 23

Author(s):

S. Anbazhagan ◽

N. Kumarappan

Keyword(s):

Neural Network ◽

Electricity Market ◽

Network Approach ◽

Neural Network Approach ◽

Market Prices ◽

Deregulated Electricity Market

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Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2020-0106 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Patrick Beyersdorffer ◽

Wolfgang Kunert ◽

Kai Jansen ◽

Johanna Miller ◽

Peter Wilhelm ◽

...

Keyword(s):

Neural Networks ◽

Adverse Events ◽

Convolutional Neural Networks ◽

Surgical Training ◽

Binary Classification ◽

Image Data ◽

Training Image ◽

Training Environment ◽

Data Set

Abstract Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train the CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labeled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.

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