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Comparing Kurtosis Score to Traditional Statistical Metrics for Characterizing the Structure in Neural Ensemble Activity
[chapter]
2008
Lecture Notes in Computer Science
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We therefore propose that kurtosis is a useful addition to the statistical toolbox for identifying interesting structure in neuron ensemble activity. ...
This study investigates the range of behaviors possible in ensembles of spiking neurons and the effect of their connectivity on ensemble dynamics utilizing a novel application of statistical measures and ...
The authors thank Markus Diesmann and Tom Tetzlaff for insightful discussions. This work was supported by an ARC Thinking Systems grant and a COSNet Overseas Travel grant (COSNet Proposal number 92). ...
doi:10.1007/978-3-540-88853-6_9
fatcat:rilau7hlenas7j3vdnrbxcy4ca
An Advanced CNN-LSTM Model for Cryptocurrency Forecasting
2021
Electronics
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In this research, we propose a multiple-input deep neural network model for the prediction of cryptocurrency price and movement. ...
with traditional fully-connected deep neural networks. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/electronics10030287
fatcat:hx53erpc4fg55ppslorz5kl4ji
A Spatio-Temporal Ensemble Deep Learning Architecture for Real-Time Defect Detection during Laser Welding on Low Power Embedded Computing Boards
2021
Sensors
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Ultimately, the ensemble deep neural network is implemented and optimized to operate on low-power embedded computing devices with low latency (1.1 ms), demonstrating sufficient performance for real-time ...
In addition, the ability to recognize the current state of product quality in real-time is an important prerequisite for autonomous and self-improving manufacturing systems. ...
The ensemble deep learning architecture is also compared to state-of-the-art CNNs for image processing. ...
doi:10.3390/s21124205
fatcat:ro2gpcb5p5gxnl57yzhibw6pzu
Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning
2021
Entropy
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While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is ...
The spleen is one of the most frequently injured organs in blunt abdominal trauma. ...
Fractal features have been widely applied in texture and shape analyses of images, including medical images [9, 21] to characterize the irregularity of physical structures. ...
doi:10.3390/e23040382
pmid:33804831
fatcat:lol7wa6m4jhplp5lccdsiz72zm
Fault Detection and Identification Methodology Under an Incremental Learning Framework Applied to Industrial Machinery
2018
IEEE Access
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This work is also supported in part by the Spanish Ministry of Economy and Competitiveness under the TRA2016-80472-R Research Project. ...
The authors would like to thank the support and the access to the friction test machine database provided by MAPRO Sistemas de ensayo S.A. especially to Álvaro Istúriz and Alberto Saéz. ...
First, a set of statistical time-based features are estimated in order to characterize the available physical magnitudes. ...
doi:10.1109/access.2018.2868430
fatcat:lj3drth4ardfnj6irvnra3nxdq
Bankruptcy Prediction: The Case of the Greek Market
2020
Forecasting
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The research on bankruptcy prediction is of the utmost importance as it aims to build statistical models that can distinguish healthy firms from financially distressed ones. ...
A comparison of linear discriminant analysis, logit, decision trees and neural networks is performed. The results show that discriminant analysis is slightly superior to the other methods. ...
All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/forecast2040027
fatcat:we5op3ofpba3hnsuntqduw3t3a
A Machine Learning Approach for Micro-Credit Scoring
2021
Risks
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This presents inexpensive and reliable means to micro-lending institutions around the developing world with which to assess creditworthiness in the absence of credit history or central credit databases ...
This research compares various machine learning algorithms on real micro-lending data to test their efficacy at classifying borrowers into various credit categories. ...
The tree flow display is comparable to a progress diagram, with a tree structure in which cases are arranged based on their feature values (Szczerbicki 2001) . ...
doi:10.3390/risks9030050
fatcat:osqzqypywjhlbdv7xfrz6ewfum
A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management
2019
Energies
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Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments ...
To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding ...
In [25] , the authors made use of Convolutional Neural Networks (CNNs) for detecting abnormal behavior related to dementia, the results were compared with methods such as Naïve Bayes (NB), Hidden Markov ...
doi:10.3390/en12244745
fatcat:eix222pupjcmddolute2qh4wia
HIFA: Promising Heterogeneous Solar Irradiance Forecasting Approach Based on Kernel Mapping
2021
IEEE Access
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The results reveal that HIFA substantially improves the accuracy of solar irradiance forecasting when compared to ensemble-based approaches, thanks to the generalization capability of the proposed aggregation ...
HIFA utilizes efficient deep recurrent neural networks, which can exploit long-term information from previous computations to model the fluctuated solar irradiance, for building the IFMs. ...
To demonstrate such diversity of datasets, we compare solar irradiance at the three sites in terms of statistical metrics, namely the mean (µ), standard deviation (σ 2 ), kurtosis, and skewness. ...
doi:10.1109/access.2021.3122826
fatcat:jpxxqowgtrhidnijjxrd3sfyiq
An ensemble of deep learning-based multi-model for ECG heartbeats arrhythmia classification
2021
IEEE Access
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RNN varies in the learning structure from feedforward neural networks by connecting the outputs of the current time step to the inputs of the next time step. ...
traditional machine learning methods or deep neural networks. ...
doi:10.1109/access.2021.3098986
fatcat:fusxrz66qfhcrdduj2sedtsmn4
Smoothing and stationarity enforcement framework for deep learning time-series forecasting
2021
Neural computing & applications (Print)
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These transformations are performed in two successive stages: The first stage is based on the smoothing technique for the development of a new de-noised version of the original series in which every value ...
The second stage of transformations is performed on the smoothed series and it is based on differencing the series in order to be stationary and be considerably easier fitted and analyzed by a deep learning ...
accuracy and AUC scores, in case trained with Smoothed FD series compared to that trained with the traditional first-differenced series. ...
doi:10.1007/s00521-021-06043-1
pmid:33967398
pmcid:PMC8096631
fatcat:pbyemgutlbcr5ar7kpxzazmtxq
A New Distributed Anomaly Detection Approach for Log IDS Management Based on Deep Learning
2021
Turkish Journal of Electrical Engineering and Computer Sciences
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The results obtained in these experiments appear to provide a 16 promising gain in performance evaluation metrics compared to the other available methods. 17 ...
We conducted 14 comparative experiments with the approach we propose to detect cyberattack anomalies in log data management with 15 the classification methods used in machine learning. ...
In the next stage after 7 balancing the data, we use a deep neural network structure to detect outliers. ...
doi:10.3906/elk-2102-89
fatcat:uuvpnnwclvfpzmppfda5a3iqse
Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics
2018
International Journal of Biomedical Imaging
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The MRI data containing 220 high-grade gliomas and 54 low-grade gliomas are used to evaluate our system. A multiscale 3D convolutional neural network is trained to segment whole tumor regions. ...
Our CAD system is highly effective for the grading of gliomas with an accuracy of 91.27%, a weighted macroprecision of 91.27%, a weighted macrorecall of 91.27%, and a weighted macro-F1 score of 90.64%. ...
Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper. ...
doi:10.1155/2018/2512037
pmid:29853828
pmcid:PMC5964423
fatcat:fsrvakaddzh6fdwghip3h5aqqe
Perceptual image quality assessment: a survey
2020
Science China Information Sciences
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Third, the performances of the state-of-the-art quality measures for visual signals are compared with an introduction of the evaluation protocols. ...
Perceptual quality assessment plays a vital role in the visual communication systems owing to the existence of quality degradations introduced in various stages of visual signal acquisition, compression ...
Then quality score prediction model is built for each feature, and an ensemble method is utilized to combine all quality scores. Freitas et al. ...
doi:10.1007/s11432-019-2757-1
fatcat:kizmju2lbbbcxjb42y6stct5sq
Fault state detection and remaining useful life prediction in AC powered solenoid operated valves based on traditional machine learning and deep neural networks
2020
Nuclear Engineering and Technology
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Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. ...
Jung, Fault state detection and remaining useful life prediction in AC powered solenoid operated valves based on traditional machine learning and deep neural networks, Nuclear Engineering and Technology ...
Acknowledgement This research was supported by the 2019 Research Fund of the KEPCO international Nuclear Graduate School (KINGS), the Republic of Korea. ...
doi:10.1016/j.net.2020.02.001
fatcat:ceepsl7ypjhe5i4lj5ybvucig4
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