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Journal articles on the topic 'Auto-regressive neural network architecture'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Anand, C. "Comparison of Stock Price Prediction Models using Pre-trained Neural Networks." March 2021 3, no. 2 (2021): 122–34. http://dx.doi.org/10.36548/jucct.2021.2.005.

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Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto-Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. The deep learning architectures

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Czubenko, Michał, and Zdzisław Kowalczuk. "A Simple Neural Network for Collision Detection of Collaborative Robots." Sensors 21, no. 12 (2021): 4235. http://dx.doi.org/10.3390/s21124235.

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Due to the epidemic threat, more and more companies decide to automate their production lines. Given the lack of adequate security or space, in most cases, such companies cannot use classic production robots. The solution to this problem is the use of collaborative robots (cobots). However, the required equipment (force sensors) or alternative methods of detecting a threat to humans are usually quite expensive. The article presents the practical aspect of collision detection with the use of a simple neural architecture. A virtual force and torque sensor, implemented as a neural network, may be

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Zhang, Lei. "Chaotic System Design Based on Recurrent Artificial Neural Network for the Simulation of EEG Time Series." International Journal of Cognitive Informatics and Natural Intelligence 13, no. 1 (2019): 25–35. http://dx.doi.org/10.4018/ijcini.2019010103.

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Electroencephalogram (EEG) signals captured from brain activities demonstrate chaotic features, and can be simulated by nonlinear dynamic time series outputs of chaotic systems. This article presents the research work of chaotic system generator design based on artificial neural network (ANN), for studying the chaotic features of human brain dynamics. The ANN training performances of Nonlinear Auto-Regressive (NAR) model are evaluated for the generation and prediction of chaotic system time series outputs, based on varying the ANN architecture and the precision of the generated training data.

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Omkar, S. N., Dheevatsa Mudigere, J. Senthilnath, and M. Vijaya Kumar. "Identification of Helicopter Dynamics based on Flight Data using Nature Inspired Techniques." International Journal of Applied Metaheuristic Computing 6, no. 3 (2015): 38–52. http://dx.doi.org/10.4018/ijamc.2015070102.

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The complexity of helicopter flight dynamics makes modeling and helicopter system identification a very difficult task. Most of the traditional techniques require a model structure to be defined a priori and in case of helicopter dynamics, this is difficult due to its complexity and the interplay between various subsystems. To overcome this difficulty, non-parametric approaches are commonly adopted for helicopter system identification. Artificial Neural Network are a widely used class of algorithms for non-parametric system identification, among them, the Nonlinear Auto Regressive eXogeneous i

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Louzazni, Mohamed, Heba Mosalam, and Daniel Tudor Cotfas. "Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis." Electronics 10, no. 16 (2021): 1953. http://dx.doi.org/10.3390/electronics10161953.

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In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the nonlinear system based on neural network and time series analysis is proposed to deal with the one-month forecast of the produced power from photovoltaic modules (PVM). The PVM is a monocrystalline cell with a rated production of 175 watts that is placed at Heliopolis University, Bilbéis city, Egypt. The NARX model is considered powerful enough to emulate the nonlinear dynamic state-space model. It is extensively performed to resolve a variety of problems and is mainly important in complex process contr

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Singh, Navneet, Asheesh Singh, and Manoj Tripathy. "Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting." Journal of Electrical Engineering 63, no. 3 (2012): 153–61. http://dx.doi.org/10.2478/v10187-012-0023-9.

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Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load ForecastingFor power industries electricity load forecast plays an important role for real-time control, security, optimal unit commitment, economic scheduling, maintenance, energy management, and plant structure planningetc. A new technique for long term load forecasting (LTLF) using optimized feed forward artificial neural network (FFNN) architecture is presented in this paper, which selects optimal number of neurons in the hidden layer as well as the best training method for the case study.

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Rahman, Rashedur M., Ruppa K. Thulasiram, and Parimala Thulasiraman. "Performance Analysis of Sequential and Parallel Neural Network Algorithm for Stock Price Forecasting." International Journal of Grid and High Performance Computing 3, no. 1 (2011): 45–68. http://dx.doi.org/10.4018/jghpc.2011010103.

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The neural network is popular and used in many areas within the financial field, such as credit authorization screenings, regularities in security price movements, simulations of market behaviour, and so forth. In this research, the authors use a neural network technique for stock price forecasting of Great West Life, an insurance company based in Winnipeg, Canada. The Backpropagation algorithm is a popular algorithm to train a neural network. However, one drawback of traditional Backpropagation algorithm is that it takes a substantial amount of training time. To expedite the training process,

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Gijón, Carolina, Matías Toril, Salvador Luna-Ramírez, María Luisa Marí-Altozano, and José María Ruiz-Avilés. "Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series." Electronics 10, no. 10 (2021): 1151. http://dx.doi.org/10.3390/electronics10101151.

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Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medi

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Clifford, G., L. Tarassenko, and N. Townsend. "One-pass training of optimal architecture auto-associative neural network for detecting ectopic beats." Electronics Letters 37, no. 18 (2001): 1126. http://dx.doi.org/10.1049/el:20010762.

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Lin, Szu-Yin, Chi-Chun Chiang, Jung-Bin Li, Zih-Siang Hung, and Kuo-Ming Chao. "Dynamic fine-tuning stacked auto-encoder neural network for weather forecast." Future Generation Computer Systems 89 (December 2018): 446–54. http://dx.doi.org/10.1016/j.future.2018.06.052.

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An, Jie, Haoyi Xiong, Jun Huan, and Jiebo Luo. "Ultrafast Photorealistic Style Transfer via Neural Architecture Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10443–50. http://dx.doi.org/10.1609/aaai.v34i07.6614.

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The key challenge in photorealistic style transfer is that an algorithm should faithfully transfer the style of a reference photo to a content photo while the generated image should look like one captured by a camera. Although several photorealistic style transfer algorithms have been proposed, they need to rely on post- and/or pre-processing to make the generated images look photorealistic. If we disable the additional processing, these algorithms would fail to produce plausible photorealistic stylization in terms of detail preservation and photorealism. In this work, we propose an effective

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Wu, Dao Xi, Wei Pan, Li Dong Xie, and Chao Xi Huang. "An Adaptive Stacked Denoising Auto-Encoder Architecture for Human Action Recognition." Applied Mechanics and Materials 631-632 (September 2014): 403–9. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.403.

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In this paper, a stacked denoising auto-encoder architecture method with adaptive learning rate for action recognition based on skeleton features of human is presented. Firstly a Kinect is used for capturing the skeleton images and extracting skeleton features. Then an adaptive stacked denoising auto-encoder with three hidden layers is constructed for unsupervised pre-training. So the trained weights are achieved. Finally, a neural network is constructed for action recognition, in which the trained weights are used as the initial value, covering the random value. Based on the experimental resu

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Chamorro, Harold R., Alvaro D. Orjuela-Cañón, David Ganger, et al. "Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models." Electronics 10, no. 2 (2021): 151. http://dx.doi.org/10.3390/electronics10020151.

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Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for predicting it by using machine learning techniques like Nonlinear Auto-regressive (NAR) Neural Networks (NN) and Long Short Term Memory (LSTM) networks from simulated and measured Phasor Measurement Unit (PMU) data. The proposed method uses a horizon-window that reconstructs the frequency input time

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Subramaniam, Sudha, K. B. Jayanthi, C. Rajasekaran, and C. Sunder. "Measurement of Intima-Media Thickness Depending on Intima Media Complex Segmentation by Deep Neural Networks." Journal of Medical Imaging and Health Informatics 11, no. 10 (2021): 2546–57. http://dx.doi.org/10.1166/jmihi.2021.3841.

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Intima Media Thickness (IMT) of the carotid artery is an important marker indicating the sign of cardiovascular disease. Automated measurement of IMT requires segmentation of intima media complex (IMC).Traditional methods which use shape, color and texture for classification have poor generalization capability. This paper proposes two models- the pipeline model and the end-to-end model using Convolutional Neural Networks (CNN) and auto encoder–decoder network respectively. CNN architecture is implemented and tested by varying the number of convolutional layer, size of the kernel as well as the

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Vaškevičius, Mantas, Jurgita Kapočiūtė-Dzikienė, and Liudas Šlepikas. "Prediction of Chromatography Conditions for Purification in Organic Synthesis Using Deep Learning." Molecules 26, no. 9 (2021): 2474. http://dx.doi.org/10.3390/molecules26092474.

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In this research, a process for developing normal-phase liquid chromatography solvent systems has been proposed. In contrast to the development of conditions via thin-layer chromatography (TLC), this process is based on the architecture of two hierarchically connected neural network-based components. Using a large database of reaction procedures allows those two components to perform an essential role in the machine-learning-based prediction of chromatographic purification conditions, i.e., solvents and the ratio between solvents. In our paper, we build two datasets and test various molecular

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Tang, Ta-Wei, Wei-Han Kuo, Jauh-Hsiang Lan, Chien-Fang Ding, Hakiem Hsu, and Hong-Tsu Young. "Anomaly Detection Neural Network with Dual Auto-Encoders GAN and Its Industrial Inspection Applications." Sensors 20, no. 12 (2020): 3336. http://dx.doi.org/10.3390/s20123336.

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Recently, researchers have been studying methods to introduce deep learning into automated optical inspection (AOI) systems to reduce labor costs. However, the integration of deep learning in the industry may encounter major challenges such as sample imbalance (defective products that only account for a small proportion). Therefore, in this study, an anomaly detection neural network, dual auto-encoder generative adversarial network (DAGAN), was developed to solve the problem of sample imbalance. With skip-connection and dual auto-encoder architecture, the proposed method exhibited excellent im

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Zhong, Yuan hong, Shun Zhang, Rongbu He, et al. "A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue." Applied Sciences 9, no. 12 (2019): 2518. http://dx.doi.org/10.3390/app9122518.

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Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are “hand-crafted”, which are difficult in features selection and poor in stability. The lack of automatic, efficient and accurate features extraction methods has limited the application and development of electronic tongue systems. In this work, a convolutional neural network-based auto features extraction strategy (CNN-AFE) in an electronic tongue (e-tongue) system for tea classification was proposed. First, the sensor response of the e-tongue was converted to time-frequen

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Huang, Yuyao, Yizhou Li, Yuan Liu, Runyu Jing, and Menglong Li. "A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix Decomposition." Symmetry 13, no. 8 (2021): 1467. http://dx.doi.org/10.3390/sym13081467.

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Single-cell ATAC-seq (scATAC-seq), as the updating of ATAC-seq, provides a novel method for probing open chromatin sites. Currently, research of scATAC-seq is faced with the problem of high dimensionality and the inherent sparsity of the generated data. Recently, several works proposed the use of an autoencoder–decoder, a symmetry neural network architecture, and non-negative matrix factorization methods to characterize the high-dimensional data. To evaluate the performance of multiple methods, in this work, we performed a multiple comparison for characterizing scATAC-seq based on four kinds o

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Li, Dongping. "AUTOMATIC DETECTION OF CARDIOVASCULAR DISEASE USING DEEP KERNEL EXTREME LEARNING MACHINE." Biomedical Engineering: Applications, Basis and Communications 30, no. 06 (2018): 1850038. http://dx.doi.org/10.4015/s1016237218500382.

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The electrocardiogram (ECG) is a principal signal employed to automatically diagnose cardiovascular disease in shallow and deep learning models. However, ECG feature extraction is required and this may reduce diagnosis accuracy in traditional shallow learning models, while backward propagation (BP) algorithm used by the traditional deep learning models has the disadvantages of local minimization and slow convergence rate. To solve these problems, a new deep learning algorithm called deep kernel extreme learning machine (DKELM) is proposed by combining the extreme learning machine auto-encoder

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Kulikajevas, Audrius, Rytis Maskeliūnas, Robertas Damaševičius, and Marta Wlodarczyk-Sielicka. "Auto-Refining Reconstruction Algorithm for Recreation of Limited Angle Humanoid Depth Data." Sensors 21, no. 11 (2021): 3702. http://dx.doi.org/10.3390/s21113702.

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With the majority of research, in relation to 3D object reconstruction, focusing on single static synthetic object reconstruction, there is a need for a method capable of reconstructing morphing objects in dynamic scenes without external influence. However, such research requires a time-consuming creation of real world object ground truths. To solve this, we propose a novel three-staged deep adversarial neural network architecture capable of denoising and refining real-world depth sensor input for full human body posture reconstruction. The proposed network has achieved Earth Mover and Chamfer

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Elnour, Mariam, Nader Meskin, and Mohammed Al-Naemi. "Sensor data validation and fault diagnosis using Auto-Associative Neural Network for HVAC systems." Journal of Building Engineering 27 (January 2020): 100935. http://dx.doi.org/10.1016/j.jobe.2019.100935.

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Karim, Ahmad M., Hilal Kaya, Mehmet Serdar Güzel, Mehmet R. Tolun, Fatih V. Çelebi, and Alok Mishra. "A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification." Sensors 20, no. 21 (2020): 6378. http://dx.doi.org/10.3390/s20216378.

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This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation a

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García González, Gastón, Pedro Casas, Alicia Fernández, and Gabriel Gómez. "On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series." ACM SIGMETRICS Performance Evaluation Review 48, no. 4 (2021): 49–52. http://dx.doi.org/10.1145/3466826.3466843.

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Despite the many attempts and approaches for anomaly de- tection explored over the years, the automatic detection of rare events in data communication networks remains a com- plex problem. In this paper we introduce Net-GAN, a novel approach to network anomaly detection in time-series, us- ing recurrent neural networks (RNNs) and generative ad- versarial networks (GAN). Different from the state of the art, which traditionally focuses on univariate measurements, Net-GAN detects anomalies in multivariate time-series, ex- ploiting temporal dependencies through RNNs. Net-GAN discovers the underlyi

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Lai, Jui-Lin, and Chung-Yu Wu. "Design Ratio-Memory Cellular Neural Network (RMCNN) in CMOS Circuit Used in Association-Memory Applications for 0.25 mm Silicon Technology." Open Materials Science Journal 10, no. 1 (2016): 54–69. http://dx.doi.org/10.2174/1874088x01610010054.

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The paper is proposed the Ratio-Memory Cellular Neural Network (RMCNN) that structure with the self-feedback and the modified Hebbian learning algorithm. The learnable RMCNN architecture was designed and realized in CMOS technology for associative memory neural network applications. The exemplar patterns can be learned and correctly recognized the output patterns for the proposed system. Only self-output pixel value in A template and B template weights are updated by the nearest neighboring five elements for all test input exemplar patterns. The learned ratio weights of the B template are gene

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Can, Recep, Sultan Kocaman, and Candan Gokceoglu. "A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality." ISPRS International Journal of Geo-Information 8, no. 7 (2019): 300. http://dx.doi.org/10.3390/ijgi8070300.

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Several scientific processes benefit from Citizen Science (CitSci) and VGI (Volunteered Geographical Information) with the help of mobile and geospatial technologies. Studies on landslides can also take advantage of these approaches to a great extent. However, the quality of the collected data by both approaches is often questionable, and automated procedures to check the quality are needed for this purpose. In the present study, a convolutional neural network (CNN) architecture is proposed to validate landslide photos collected by citizens or nonexperts and integrated into a mobile- and web-b

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Ye, Qing, Shaohu Liu, and Changhua Liu. "A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals." Sensors 20, no. 15 (2020): 4300. http://dx.doi.org/10.3390/s20154300.

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Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic m

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Dai, Xiaoai, Xuwei He, Shouheng Guo, Senhao Liu, Fujiang Ji, and Huihua Ruan. "Research on hyper-spectral remote sensing image classification by applying stacked de-noising auto-encoders neural network." Multimedia Tools and Applications 80, no. 14 (2021): 21219–39. http://dx.doi.org/10.1007/s11042-021-10735-0.

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Huang, Yan, Yi Zhong, Qiang Wu, Eryk Dutkiewicz, and Ting Jiang. "Cost-Effective Foliage Penetration Human Detection Under Severe Weather Conditions Based on Auto-Encoder/Decoder Neural Network." IEEE Internet of Things Journal 6, no. 4 (2019): 6190–200. http://dx.doi.org/10.1109/jiot.2018.2878880.

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Wang, Shuai, Yiping Yao, Feng Zhu, Wenjie Tang, and Yuhao Xiao. "A Probabilistic Prediction Approach for Memory Resource of Complex System Simulation in Cloud Computing Environment." Symmetry 12, no. 11 (2020): 1826. http://dx.doi.org/10.3390/sym12111826.

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Accurate memory resource prediction can achieve optimal performance for complex system simulation (CSS) using optimistic parallel execution in the cloud computing environment. However, because of the varying memory resource demands of CSS applications caused by the simulation entity scale and frequent optimistic synchronization, the existing approaches are unable to predict the memory resource required by a CSS application accurately, which cannot take full advantage of the elasticity and symmetry of cloud computing. In this paper, a probabilistic prediction approach based on ensemble learning

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Ahn, Jun Hyong, Heung Cheol Kim, Jong Kook Rhim, et al. "Multi-View Convolutional Neural Networks in Rupture Risk Assessment of Small, Unruptured Intracranial Aneurysms." Journal of Personalized Medicine 11, no. 4 (2021): 239. http://dx.doi.org/10.3390/jpm11040239.

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Auto-detection of cerebral aneurysms via convolutional neural network (CNN) is being increasingly reported. However, few studies to date have accurately predicted the risk, but not the diagnosis itself. We developed a multi-view CNN for the prediction of rupture risk involving small unruptured intracranial aneurysms (UIAs) based on three-dimensional (3D) digital subtraction angiography (DSA). The performance of a multi-view CNN-ResNet50 in accurately predicting the rupture risk (high vs. non-high) of UIAs in the anterior circulation measuring less than 7 mm in size was compared with various CN

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Sun, Jingzhou, and Yongbin Wang. "An Improved Approach to Audio Segmentation and Classification in Broadcasting Industries." Journal of Database Management 30, no. 2 (2019): 44–66. http://dx.doi.org/10.4018/jdm.2019040103.

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Audio segmentation and classification are the basis of audio processing in broadcasting industries. A Dual-CNN (Dual-Convolutional Neural Network) method is proposed in this article in which it is possible to pre-train a CNN with unlabeled audio data so as to deal with the scarcity of labeled data. Auto-encoders (including an encoder and a decoder) are utilized, thus the name “Dual.” In the first place, audio sampling points and the derived STFT (Short-Time Fourier Transform) spectrograms pass through their own CNNs. Fusion of the extracted features is then performed. Finally, the merged featu

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Wang, Jihong, Hao Wang, Xiaodan Wang, and Huiyou Chang. "Predicting Drug-target Interactions via FM-DNN Learning." Current Bioinformatics 15, no. 1 (2020): 68–76. http://dx.doi.org/10.2174/1574893614666190227160538.

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Background: Identifying Drug-Target Interactions (DTIs) is a major challenge for current drug discovery and drug repositioning. Compared to traditional experimental approaches, in silico methods are fast and inexpensive. With the increase in open-access experimental data, numerous computational methods have been applied to predict DTIs. Methods: In this study, we propose an end-to-end learning model of Factorization Machine and Deep Neural Network (FM-DNN), which emphasizes both low-order (first or second order) and high-order (higher than second order) feature interactions without any feature

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Abed, Ali, Abduladhem Ali, Nauman Aslam, and Ali Marhoon. "Fuzzy-Neural Petri Net Distributed Control System Using Hybrid Wireless Sensor Network and CAN Fieldbus." Iraqi Journal for Electrical and Electronic Engineering 12, no. 1 (2016): 54–70. http://dx.doi.org/10.37917/ijeee.12.1.6.

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The reluctance of industry to allow wireless paths to be incorporated in process control loops has limited the potential applications and benefits of wireless systems. The challenge is to maintain the performance of a control loop, which is degraded by slow data rates and delays in a wireless path. To overcome these challenges, this paper presents an application–level design for a wireless sensor/actuator network (WSAN) based on the “automated architecture”. The resulting WSAN system is used in the developing of a wireless distributed control system (WDCS). The implementation of our wireless s

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Chen, Pei-Fu, Ssu-Ming Wang, Wei-Chih Liao, et al. "Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning." JMIR Medical Informatics 9, no. 8 (2021): e23230. http://dx.doi.org/10.2196/23230.

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Background The International Classification of Diseases (ICD) code is widely used as the reference in medical system and billing purposes. However, classifying diseases into ICD codes still mainly relies on humans reading a large amount of written material as the basis for coding. Coding is both laborious and time-consuming. Since the conversion of ICD-9 to ICD-10, the coding task became much more complicated, and deep learning– and natural language processing–related approaches have been studied to assist disease coders. Objective This paper aims at constructing a deep learning model for ICD-

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Wethington, Niles W., and Matthew J. Pranter. "Stratigraphic architecture of the Mississippian limestone through integrated electrofacies classification, Hardtner field area, Kansas and Oklahoma." Interpretation 6, no. 4 (2018): T1095—T1115. http://dx.doi.org/10.1190/int-2018-0042.1.

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The Mississippian Limestone formed through complex structural, stratigraphic, and diagenetic processes involving subsidence, tectonic uplift leading to periodic subaerial exposure, changes in ocean chemistry, variability inherent with carbonate cyclicity, as well as postdepositional alteration. These geologic complexities led to significant heterogeneity and compartmentalization within Mississippian mid-continent reservoirs, obscuring stratigraphic relationships. A novel log-based approach, called derivative trend analysis (DTA), is used to identify and correlate depositional cycles associated

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Tavarageri, Sanket, Alexander Heinecke, Sasikanth Avancha, Bharat Kaul, Gagandeep Goyal, and Ramakrishna Upadrasta. "PolyDL." ACM Transactions on Architecture and Code Optimization 18, no. 1 (2021): 1–27. http://dx.doi.org/10.1145/3433103.

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Deep Neural Networks (DNNs) have revolutionized many aspects of our lives. The use of DNNs is becoming ubiquitous, including in software for image recognition, speech recognition, speech synthesis, language translation, to name a few. The training of DNN architectures, however, is computationally expensive. Once the model is created, its use in the intended application—the inference task, is computationally heavy too and the inference needs to be fast for real time use. For obtaining high performance today, the code of Deep Learning (DL) primitives optimized for specific architectures by exper

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Shamseldin, A. Y., and K. M. O’Connor. "A non-linear neural network technique for updating of river flow forecasts." Hydrology and Earth System Sciences 5, no. 4 (2001): 577–98. http://dx.doi.org/10.5194/hess-5-577-2001.

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Abstract. A non-linear Auto-Regressive Exogenous-input model (NARXM) river flow forecasting output-updating procedure is presented. This updating procedure is based on the structure of a multi-layer neural network. The NARXM-neural network updating procedure is tested using the daily discharge forecasts of the soil moisture accounting and routing (SMAR) conceptual model operating on five catchments having different climatic conditions. The performance of the NARXM-neural network updating procedure is compared with that of the linear Auto-Regressive Exogenous-input (ARXM) model updating procedu

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Kadupitiya, JCS, Geoffrey C. Fox, and Vikram Jadhao. "Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles." International Journal of High Performance Computing Applications 34, no. 3 (2020): 357–74. http://dx.doi.org/10.1177/1094342019899457.

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Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained models is extremely challenging due to the need to solve the Poisson equation at every simulation timestep. Recently, a molecular dynamics (MD) method based on a dynamical optimization framework bypassed this obstacle by representing the polarization charge density as virtual dynamic variables and evolving them in parallel with the physical dynamics of ions. We highlight the computational gains accessible with the integration of machine learning (ML) methods for parameter prediction in MD simulations by d

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Nurmaini, Siti, Radiyati Umi Partan, Wahyu Caesarendra, et al. "An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique." Applied Sciences 9, no. 14 (2019): 2921. http://dx.doi.org/10.3390/app9142921.

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An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feature learning and Deep Neural Networks (DNNs) as a classifier. The objective of this study is to improve on the previous machine learning technique that consists of several data processing steps such as feature extraction and feature selection or feature reduction. It is also noticed that the previously used machine learning technique required h

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Buscema, Massimo, Silvana Penco, and Enzo Grossi. "A Novel Mathematical Approach to Define the Genes/SNPs Conferring Risk or Protection in Sporadic Amyotrophic Lateral Sclerosis Based on Auto Contractive Map Neural Networks and Graph Theory." Neurology Research International 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/478560.

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Background. Complex diseases like amyotrophic lateral sclerosis (ALS) implicate phenotypic and genetic heterogeneity. Therefore, multiple genetic traits may show differential association with the disease. The Auto Contractive Map (AutoCM), belonging to the Artificial Neural Network (ANN) architecture, “spatializes” the correlation among variables by constructing a suitable embedding space where a visually transparent and cognitively natural notion such as “closeness” among variables reflects accurately their associations.Results. In this pilot case-control study single nucleotide polymorphism

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Sabuhi, Mikael, Nima Mahmoudi, and Hamzeh Khazaei. "Optimizing the Performance of Containerized Cloud Software Systems Using Adaptive PID Controllers." ACM Transactions on Autonomous and Adaptive Systems 15, no. 3 (2020): 1–27. http://dx.doi.org/10.1145/3465630.

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Control theory has proven to be a practical approach for the design and implementation of controllers, which does not inherit the problems of non-control theoretic controllers due to its strong mathematical background. State-of-the-art auto-scaling controllers suffer from one or more of the following limitations: (1) lack of a reliable performance model, (2) using a performance model with low scalability, tractability, or fidelity, (3) being application- or architecture-specific leading to low extendability, and (4) no guarantee on their efficiency. Consequently, in this article, we strive to

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Lu, Xing Han, Aihua Liu, Shih-Chieh Fuh, et al. "Recurrent disease progression networks for modelling risk trajectory of heart failure." PLOS ONE 16, no. 1 (2021): e0245177. http://dx.doi.org/10.1371/journal.pone.0245177.

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Motivation Recurrent neural networks (RNN) are powerful frameworks to model medical time series records. Recent studies showed improved accuracy of predicting future medical events (e.g., readmission, mortality) by leveraging large amount of high-dimensional data. However, very few studies have explored the ability of RNN in predicting long-term trajectories of recurrent events, which is more informative than predicting one single event in directing medical intervention. Methods In this study, we focus on heart failure (HF) which is the leading cause of death among cardiovascular diseases. We

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Orlando, Gabriele, Daniele Raimondi, Luciano Porto Kagami, and Wim F. Vranken. "ShiftCrypt: a web server to understand and biophysically align proteins through their NMR chemical shift values." Nucleic Acids Research 48, W1 (2020): W36—W40. http://dx.doi.org/10.1093/nar/gkaa391.

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Abstract Nuclear magnetic resonance (NMR) spectroscopy data provides valuable information on the behaviour of proteins in solution. The primary data to determine when studying proteins are the per-atom NMR chemical shifts, which reflect the local environment of atoms and provide insights into amino acid residue dynamics and conformation. Within an amino acid residue, chemical shifts present multi-dimensional and complexly cross-correlated information, making them difficult to analyse. The ShiftCrypt method, based on neural network auto-encoder architecture, compresses the per-amino acid chemic

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Dang-Quang, Nhat-Minh, and Myungsik Yoo. "Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes." Applied Sciences 11, no. 9 (2021): 3835. http://dx.doi.org/10.3390/app11093835.

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Presently, the cloud computing environment attracts many application developers to deploy their web applications on cloud data centers. Kubernetes, a well-known container orchestration for deploying web applications on cloud systems, offers an automatic scaling feature to meet clients’ ever-changing demands with the reactive approach. This paper proposes a system architecture based on Kubernetes with a proactive custom autoscaler using a deep neural network model to handle the workload during run time dynamically. The proposed system architecture is designed based on the Monitor–Analyze–Plan–E

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Fraz, Tayyab Raza, and Samreen Fatima. "Are Neural Network Models Truly Effective at Forecasting? An Evaluation of Forecast Performance of Traditional Models with Neural Network Model for the Macroeconomic Data of G-7 Countries." International Journal of Economic and Environmental Geology 11, no. 3 (2020): 49–52. http://dx.doi.org/10.46660/ijeeg.vol11.iss3.2020.475.

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Forecasting macroeconomic and financial data are always difficult task to the researchers. Various statisticaland econometrics techniques have been used to forecast these variables more accurately. Furthermore, in the presenceof structural break, linear models are failed to model and forecast. Therefore, this study examines the forecastingperformance of economic variables of G7 countries: France, Italy, Canada, Germany, Japan, United Kingdom andUnited States of America using non-linear autoregressive neural network (ARNN) model, linear auto regressive (AR)and Auto regressive integrated moving

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Ribeiro, David Augusto, Juan Casavílca Silva, Renata Lopes Rosa, et al. "Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems." Electronics 10, no. 10 (2021): 1136. http://dx.doi.org/10.3390/electronics10101136.

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Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this wo

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D’Antoni, Federico, Mario Merone, Vincenzo Piemonte, Giulio Iannello, and Paolo Soda. "Auto-Regressive Time Delayed jump neural network for blood glucose levels forecasting." Knowledge-Based Systems 203 (September 2020): 106134. http://dx.doi.org/10.1016/j.knosys.2020.106134.

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Patil, S. L., H. J. Tantau, and V. M. Salokhe. "Modelling of tropical greenhouse temperature by auto regressive and neural network models." Biosystems Engineering 99, no. 3 (2008): 423–31. http://dx.doi.org/10.1016/j.biosystemseng.2007.11.009.

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Park, Han G., and Michail Zak. "Gray-Box Approach for Fault Detection of Dynamical Systems." Journal of Dynamic Systems, Measurement, and Control 125, no. 3 (2003): 451–54. http://dx.doi.org/10.1115/1.1589032.

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We present a fault detection method called the gray-box. The term “gray-box” refers to the approach wherein a deterministic model of system, i.e., “white box,” is used to filter the data and generate a residual, while a stochastic model, i.e., “black-box” is used to describe the residual. The residual is described by a three-tier stochastic model. An auto-regressive process, and a time-delay feed-forward neural network describe the linear and nonlinear components of the residual, respectively. The last component, the noise, is characterized by its moments. Faults are detected by monitoring the

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Gao, Cheng, Jiao Ying Huang, and Wei Guo. "Prothesis Movements Pattern Recognition Based on Auto-Regressive Model and Wavelet Neural Network." Applied Mechanics and Materials 121-126 (October 2011): 2156–61. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.2156.

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Wavelet neural networks (WNN) combine the functions of time–frequency localization from the wavelet transform and of self-studying from the neural network, which make them particularly suitable for the classification of complex patterns. Based on auto-regressive (AR) model and WNN, pattern recognition of prothesis movements was studied in this paper. Firstly, an AR model was used to analysis the surface myoelectric signals (SMES) which recorded on the ulnar flexor carpi and extensor carpi region of the right hand in resting position. Four types of prosthesis movements are recognized by extract

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