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Journal articles on the topic 'Dati neurali'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

AVeselý. "Neural networks in data mining." Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (2012): 427–31. http://dx.doi.org/10.17221/5427-agricecon.

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To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining.

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Trenz, O., J. Šťastný, and V. Konečný. "Agricultural data prediction by means of neural network." Agricultural Economics (Zemědělská ekonomika) 57, No. 7 (2011): 356–61. http://dx.doi.org/10.17221/108/2011-agricecon.

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The contribution deals with the prediction of crop yield levels, using an artificial intelligence approach, namely a multi-layer neural network model. Subsequently, we are contrasting this approach with several non-linear regression models, the usefulness of which has been tested and published several times in the specialized periodicals. The main stress is placed on judging the accuracy of the individual methods and of the implementation. A neural network simulation device is that which enables the user to set an adequate configuration of the neural network vis &aacute; vis the required t

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Harliman, Rheza, and Kaoru Uchida. "Data- and Algorithm-Hybrid Approach for Imbalanced Data Problems in Deep Neural Network." International Journal of Machine Learning and Computing 8, no. 3 (2018): 208–13. http://dx.doi.org/10.18178/ijmlc.2018.8.3.689.

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4

Teh, Chee Siong, Ming Leong Yii, and Chwen Jen Chen. "Dimensional Reduction and Data Visualization Using Hybrid Artificial Neural Networks." International Journal of Machine Learning and Computing 5, no. 5 (2015): 420–25. http://dx.doi.org/10.7763/ijmlc.2015.v5.545.

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Liu, Bin, Lirong He, Yingming Li, Shandian Zhe, and Zenglin Xu. "NeuralCP: Bayesian Multiway Data Analysis with Neural Tensor Decomposition." Cognitive Computation 10, no. 6 (2018): 1051–61. http://dx.doi.org/10.1007/s12559-018-9587-4.

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6

IATAN, Iuliana. "DEALING THE NONLINEARITY ASSOCIATED WITH THE DATA USING ARTIFICIAL NEURAL NETWORKS." Review of the Air Force Academy 15, no. 2 (2017): 15–22. http://dx.doi.org/10.19062/1842-9238.2017.15.2.2.

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Ito, Toshio. "Supervised Learning Methods of Bilinear Neural Network Systems Using Discrete Data." International Journal of Machine Learning and Computing 6, no. 5 (2016): 235–40. http://dx.doi.org/10.18178/ijmlc.2016.6.5.604.

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Vogt, Siegfried, and Daniel Sacher. "A neural network method for wind estimation using wind profiler data." Meteorologische Zeitschrift 10, no. 6 (2001): 479–87. http://dx.doi.org/10.1127/0941-2948/2001/0010-0479.

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Ouadfeul, Sid-Ali, and Leila Aliouane. "Noise Attenuation from GPR Data Using Wavelet Transform and Artificial Neural Network." International Journal of Applied Physics and Mathematics 4, no. 6 (2014): 426–33. http://dx.doi.org/10.17706/ijapm.2014.4.6.426-433.

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Yu, Youhao, and Richard M. Dansereau. "Fast Reconstruction of 1D Compressive Sensing Data Using a Deep Neural Network." International Journal of Signal Processing Systems 8, no. 1 (2020): 26–31. http://dx.doi.org/10.18178/ijsps.8.1.26-31.

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11

Liang, Tao, Gaofeng Xie, Dabin Mi, Wen Jiang, and Guilin Xu. "PM2.5 Concentration Forecasting Based on Data Preprocessing Strategy and LSTM Neural Network." International Journal of Machine Learning and Computing 10, no. 6 (2020): 729–34. http://dx.doi.org/10.18178/ijmlc.2020.10.6.997.

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Suriani, Nor Surayahani, and Fadilla ‘Atyka Nor Rashid. "Smartphone Sensor Accelerometer Data for Human Activity Recognition Using Spiking Neural Network." International Journal of Machine Learning and Computing 11, no. 4 (2021): 298–303. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1051.

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Recognizing human actions is a challenging task and actively research in computer vision community. The task of human activity recognition has been widely used in various application such as human monitoring in a hospital or public spaces. This work applied open dataset of smartphones accelerometer data for various type of activities. The analogue input data is encoded into the spike trains using some form of a rate-based method. Spiking neural network is a simplified form of dynamic artificial network. Therefore, this network is expected to model and generate action potential from the leaky i

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Ito, Shin-ichi, Momoyo Ito, Shoichiro Fujisawa, and Minoru Fukumi. "Electroencephalogram Data for Classifying Answers to Questions with Neural Networks and Support Vector Machine." International Journal of Signal Processing Systems 7, no. 4 (2019): 118–22. http://dx.doi.org/10.18178/ijsps.7.4.118-122.

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Priya, R., and Dr R. Mallika. "Ground Water Quality Modelling Using Data Mining Techniques and Artificial Neural Network Based Approach." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 1001–7. http://dx.doi.org/10.5373/jardcs/v11sp10/20192897.

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15

Hermansah, Hermansah, Dedi Rosadi, Abdurakhman Abdurakhman, and Herni Utami. "SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING." MEDIA STATISTIKA 13, no. 2 (2020): 116–24. http://dx.doi.org/10.14710/medstat.13.2.116-124.

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NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best

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Kundu, Sourav, and Rajshekhar Singhania. "Forecasting the United States Unemployment Rate by Using Recurrent Neural Networks with Google Trends Data." International Journal of Trade, Economics and Finance 11, no. 6 (2020): 135–40. http://dx.doi.org/10.18178/ijtef.2020.11.6.679.

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We study the problem of obtaining an accurate forecast of the unemployment claims using online search data. The motivation for this study arises from the fact that there is a need for nowcasting or providing a reliable short-term estimate of the unemployment rate. The data regarding initial jobless claims are published by the US Department of labor weekly. To tackle the problem of getting an accurate forecast, we propose the use of the novel Long Short-Term Memory (LSTM) architecture of Recurrent Neural Networks, to predict the unemployment claims (initial jobless claims) using the Google Tren

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17

Marar, João Fernando, and Aron Bordin. "Multidimensional wavelet neural networks Based on polynomial powers of sigmoid." DAT Journal 1, no. 2 (2016): 106–23. http://dx.doi.org/10.29147/2526-1789.dat.2016v1i2p106-123.

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Wavelet functions have been used as the activation function in feed forward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical back propagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of po

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18

Taha, Lamyaa Gamal EL-Deen, A. I. Ramzi, A. Syarawi, and A. Bekheet. "Urban Feature Extraction from Merged Airborne LiDAR Data and Digital Camera Data." Geoplanning: Journal of Geomatics and Planning 7, no. 2 (2021): 57–74. http://dx.doi.org/10.14710/geoplanning.7.2.57-74.

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Until recently, the most highly accurate digital surface models were obtained from airborne lidar. With the development of a new generation of large format digital photogrammetric aerial camera, a fully digital photogrammetric workflow became possible. Digital airborne images are sources for elevation extraction and orthophoto generation. This research concerned with the generation of digital surface models and orthophotos as applications from high-resolution images. In this research, the following steps were performed. A Benchmark data of LIDAR and digital aerial camera have been used. Firstl

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19

B., Prabadevi. "Hybrid Brain Storm Optimization based Feature Selection and Optimal Deep Neural Network for Medical Data Classification." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 62–72. http://dx.doi.org/10.5373/jardcs/v12sp4/20201467.

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20

Budiarto, Hary. "Segitiga Fuzzy-Neural Network untuk Mengenali Pola dari Model Input Data yang Berdistribusi." Limits: Journal of Mathematics and Its Applications 4, no. 1 (2007): 9. http://dx.doi.org/10.12962/j1829605x.v4i1.1405.

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21

Srimanchari, Dr P., and Dr G. Anandharaj. "Real-Time Data Representation Control in Convolution Neural Networks Based Indoor Wi-Fi Localization for Internet of Things." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (2017): 1043–50. http://dx.doi.org/10.31142/ijtsrd4694.

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22

Maharani, Dian, Hendri Murfi, and Yudi Satria. "Performance of Deep Neural Network for Tabular Data — A Case Study of Loss Cost Prediction in Fire Insurance." International Journal of Machine Learning and Computing 9, no. 6 (2019): 734–42. http://dx.doi.org/10.18178/ijmlc.2019.9.6.866.

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23

D., Kavitha. "Modified Step Size based Glowworm Swarm Optimization with Improved Regularization based Convolutional Neural Network for Imbalanced Data Classification." Journal of Advanced Research in Dynamical and Control Systems 24, no. 4 (2020): 390–400. http://dx.doi.org/10.5373/jardcs/v12i4/20201453.

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24

Thorne, James, Majid Yazdani, Marzieh Saeidi, Fabrizio Silvestri, Sebastian Riedel, and Alon Halevy. "From natural language processing to neural databases." Proceedings of the VLDB Endowment 14, no. 6 (2021): 1033–39. http://dx.doi.org/10.14778/3447689.3447706.

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In recent years, neural networks have shown impressive performance gains on long-standing AI problems, such as answering queries from text and machine translation. These advances raise the question of whether neural nets can be used at the core of query processing to derive answers from facts, even when the facts are expressed in natural language. If so, it is conceivable that we could relax the fundamental assumption of database management, namely, that our data is represented as fields of a pre-defined schema. Furthermore, such technology would enable combining information from text, images,

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25

Polimeni, Jonathan, and Eric Schwartz. "Neural representation of sensory data." Behavioral and Brain Sciences 25, no. 2 (2002): 207–8. http://dx.doi.org/10.1017/s0140525x02470045.

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In the target article Pylyshyn revives the spectre of the “little green man,” arguing for a largely symbolic representation of visual imagery. To clarify this problem, we provide precise definitions of the key term “picture,” present some examples of our definition, and outline an information-theoretic analysis suggesting that the problem of addressing data in the brain requires a partially analogue and partially symbolic solution. This is made concrete in the ventral stream of object recognition, from V1 to IT cortex.

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26

Singh Gill, Ripundeep. "Neural Networks in Data Mining." IOSR Journal of Engineering 4, no. 3 (2014): 01–06. http://dx.doi.org/10.9790/3021-04360106.

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27

Wang, F., J. Litva, T. Lo, and É Bossé. "Performance of neural data associator." IEE Proceedings - Radar, Sonar and Navigation 143, no. 2 (1996): 71. http://dx.doi.org/10.1049/ip-rsn:19960249.

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28

Murphey, Yi L., Hong Guo, and Lee A. Feldkamp. "Neural Learning from Unbalanced Data." Applied Intelligence 21, no. 2 (2004): 117–28. http://dx.doi.org/10.1023/b:apin.0000033632.42843.17.

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29

Blank, T. B., and S. D. Brown. "Data processing using neural networks." Analytica Chimica Acta 277, no. 2 (1993): 273–87. http://dx.doi.org/10.1016/0003-2670(93)80440-v.

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30

Cottrell, Marie, Madalina Olteanu, Fabrice Rossi, Joseph Rynkiewicz, and Nathalie Villa-Vialaneix. "Neural Networks for Complex Data." KI - Künstliche Intelligenz 26, no. 4 (2012): 373–80. http://dx.doi.org/10.1007/s13218-012-0207-2.

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31

Jung, Se-Hoon, and Jong-Chan Kim. "CkLR Algorithm for Improvement of Data Prediction and Accuracy Based on Clustering Data." International Journal of Software Engineering and Knowledge Engineering 29, no. 05 (2019): 631–52. http://dx.doi.org/10.1142/s0218194019400011.

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In this paper, we proposed the CkLR algorithm to build a model to analyze the response variables of clustered outcomes with a K-means algorithm to categorize and analyze unstructured data such as SNS and assess the prediction rate of clustering and its accuracy rate after the entry of new data. CkLR performs a neural network algorithm based on the clustering outcomes of the data classified in the previous stage. The CkLR model applies a neural network to reflect the entire form of data learning methods rather than stepwise data learning based on clustering data, and conducts research to select

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32

Klem, K., M. Váňová, J. Hajšlová, K. Lancová, and M. Sehnalová. "A neural network model for prediction of deoxynivalenol content in wheat grain based on weather data and preceding crop." Plant, Soil and Environment 53, No. 10 (2008): 421–29. http://dx.doi.org/10.17221/2200-pse.

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Deoxynivalenol (DON) is the most prevalent Fusarium toxin in Czech wheat samples and therefore forecasting this mycotoxin is a potentially useful tool to prevent it from entering into food chain. The data about DON content in wheat grain, weather conditions during the growing season and cultivation practices from two field experiments conducted in 2002–2005 were used for the development of neural network model designed for DON content prediction. The winning neural network is based on five input variables: a categorial variable – preceding crop, and continuous variables – average April tempera

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33

Li, Yun Jie, and Hui Song. "Applying Data Mining Techniques on Continuous Sensed Data for Daily Living Activity Recognition." Applied Mechanics and Materials 738-739 (March 2015): 191–96. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.191.

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In this paper, several data mining techniques were discussed and analyzed in order to achieve the objective of human daily activities recognition based on a continuous sensing data set. The data mining techniques of decision tree, Naïve Bayes and Neural Network were successfully applied to the data set. The paper also proposed an idea of combining the Neural Network with the Decision Tree, the result shows that it works much better than the typical Neural Network and the typical Decision Tree model.

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34

Jeong, Dongkyo, Dongeon Kim, Seunghyeon Lee, and Jang-Myung Lee. "Optimal Gripping Point Extraction Algorithm for Gripper Using Point Cloud Data Based Neural Network." Journal of Institute of Control, Robotics and Systems 27, no. 1 (2021): 44–53. http://dx.doi.org/10.5302/j.icros.2021.20.0099.

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35

Gao, Xiang, Guanghui Li, Rong Tan, and Leijiang Yao. "Research on Predicting the Bending Strength of Ceramic Matrix Composites with Process of Incomplete Data." International Journal of Machine Learning and Computing 11, no. 3 (2021): 224–29. http://dx.doi.org/10.18178/ijmlc.2021.11.3.1039.

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With the rapid development of machine learning, it is possible to use neural networks to build models to predict performance of Ceramic Matrix Composites (CMCs) with raw materials and environments. In the traditional material science engineering, it always took a long time to develop a new CMC. Furthermore, there is still no theoretical basis providing references to design experiments to develop CMCs with ideal performances. This work proposed a model to predict the bending strength of CMCs with a Convolution Neural Network (CNN) using 8 factors considered to affect the bending strength of CMC

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36

Behrns, Kevin E., Michael G. Sarr, Russell B. Hanson, and Alan R. Zinsmeister. "Canine small bowel motor patterns and contractions are not neurally regulated during enteric nutrient infusion." American Journal of Physiology-Gastrointestinal and Liver Physiology 274, no. 5 (1998): G912—G922. http://dx.doi.org/10.1152/ajpgi.1998.274.5.g912.

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The aims of this study were to determine the effects of duodenal and jejunoileal nutrient infusions on small intestinal motor patterns and intestinal contractions in neurally intact and neurally isolated small bowel. Fifteen dogs were prepared with duodenal and jejunal infusion and manometry catheters and a diverting jejunal cannula. Ten of the dogs underwent in situ neural isolation of the jejunoileum. A mixed nutrient meal (0.5 kcal/ml) was infused into the duodenum or jejunum at 3 ml/min for 5 h. Control experiments involved infusion of a balanced salt solution. Manometric data collected on

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37

Comia, Jophet, Lou Sushmita Mae Bernardino, Christian Joseph Fandiño, Kevin Drexler Gregorio, Ranil Montaril, and Benilda Eleonor Comendador. "Teacher SCARLET: An Application of Artificial Neural Networks in Off-Line Blackboard-Handwritten Character Recognition for Biology Lesson Data Extraction." Journal of Advances in Computer Networks 2, no. 3 (2014): 193–97. http://dx.doi.org/10.7763/jacn.2014.v2.110.

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38

Kovacheva, Zlatinka Svetoslavova. "Application of Neural Networks to Data Mining." Sultan Qaboos University Journal for Science [SQUJS] 12, no. 2 (2007): 121. http://dx.doi.org/10.24200/squjs.vol12iss2pp121-141.

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Data Mining accomplishes nontrivial extraction of implicit, previously unknown, and potentially useful information of data. The aim of data mining is to discover knowledge out of data and present it in a form that is easily comprehensible to humans. Neural Networks are analytic techniques capable of predicting new observations from other observations after executing a process of so-called learning from existing data. Neural Network techniques can also be used as a component of analyses designed to build explanatory models. Now there is neural network software that uses sophisticated algorithms

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39

P., Tymoshchuk. "SIMPLIFIED PARALLEL SORTING DISCRETE-TIME NEURAL NETWORK MODEL." Computer systems and network 2, no. 1 (2017): 94–101. http://dx.doi.org/10.23939/csn2020.01.094.

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A model of parallel sorting neural network of discrete-time has been proposed. The model is described by system of difference equations and by step functions. The model is based on simplified neural circuit of discrete-time that identifies maximal/minimal values of input data and is described by difference equation and by step functions. A bound from above on a number of iterations required for reaching convergence of search process to steady state is determined. The model does not need a knowledge of change range of input data. In order to use the model a minimal difference between values of

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40

Li, Juntao, Lisong Qiu, Bo Tang, Dongmin Chen, Dongyan Zhao, and Rui Yan. "Insufficient Data Can Also Rock! Learning to Converse Using Smaller Data with Augmentation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6698–705. http://dx.doi.org/10.1609/aaai.v33i01.33016698.

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Recent successes of open-domain dialogue generation mainly rely on the advances of deep neural networks. The effectiveness of deep neural network models depends on the amount of training data. As it is laboursome and expensive to acquire a huge amount of data in most scenarios, how to effectively utilize existing data is the crux of this issue. In this paper, we use data augmentation techniques to improve the performance of neural dialogue models on the condition of insufficient data. Specifically, we propose a novel generative model to augment existing data, where the conditional variational

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41

Kamrunnahar, Mst, and Mirna Urquidi-Macdonald. "Data Mining of Experimental Corrosion Data Using Neural Network." ECS Transactions 1, no. 4 (2019): 71–79. http://dx.doi.org/10.1149/1.2215491.

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42

Majoros, Tamás, Balázs Ujvári, and Stefan Oniga. "EEG data processing with neural network." Carpathian Journal of Electronic and Computer Engineering 12, no. 2 (2019): 33–36. http://dx.doi.org/10.2478/cjece-2019-0014.

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Abstract Machine-learning techniques allow to extract information from electroencephalographic (EEG) recordings of brain activity. By processing the measurement results of a publicly available EEG dataset, we were able to obtain information that could be used to train a feedforward neural network to classify two types of volunteer activities with high efficiency.

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Calia, Alberto, Eugenio Denti, Roberto Galatolo, and Francesco Schettini. "Air Data Computation Using Neural Networks." Journal of Aircraft 45, no. 6 (2008): 2078–83. http://dx.doi.org/10.2514/1.37334.

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Freitag, Steffen, Wolfgang Graf, and Michael Kaliske. "Recurrent neural networks for fuzzy data." Integrated Computer-Aided Engineering 18, no. 3 (2011): 265–80. http://dx.doi.org/10.3233/ica-2011-0373.

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Drakopoulos, John A., and Ahmad Abdulkader. "Training neural networks with heterogeneous data." Neural Networks 18, no. 5-6 (2005): 595–601. http://dx.doi.org/10.1016/j.neunet.2005.06.011.

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46

Craven, Mark W., and Jude W. Shavlik. "Using neural networks for data mining." Future Generation Computer Systems 13, no. 2-3 (1997): 211–29. http://dx.doi.org/10.1016/s0167-739x(97)00022-8.

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47

Gatts, C., and A. Mariano. "Data Categorization and Neural Pattern Recognition." Microscopy and Microanalysis 3, S2 (1997): 933–34. http://dx.doi.org/10.1017/s1431927600011557.

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The natural ability of Artificial Neural Networks to perform pattern recognition tasks makes them a valuable tool in Electron Microscopy, especially when large data sets are involved. The application of Neural Pattern Recognition to HREM, although incipient, has already produced interesting results both for one dimensional spectra and 2D images.In the case of ID spectra, e.g. a set of EELS spectra acquired during a line scan, given a “vigilance parameter” (which sets the threshold for the correlation between two spectra to be high enough to consider them as similar) an ART-like network can dis

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48

Yilmaz, Anil, and Ihsan Sabuncuoglu. "Input Data Analysis Using Neural Networks." SIMULATION 74, no. 3 (2000): 128–37. http://dx.doi.org/10.1177/003754970007400301.

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Mandic, Danilo P. "Data-Reusing Recurrent Neural Adaptive Filters." Neural Computation 14, no. 11 (2002): 2693–707. http://dx.doi.org/10.1162/089976602760408026.

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A class of data-reusing learning algorithms for real-time recurrent neural networks (RNNs) is analyzed. The analysis is undertaken for a general sigmoid nonlinear activation function of a neuron for the real time recurrent learning training algorithm. Error bounds and convergence conditions for such data-reusing algorithms are provided for both contractive and expansive activation functions. The analysis is undertaken for various configurations that are generalizations of a linear structure infinite impulse response adaptive filter.

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Takalo, Jouni, and Jussi Timonen. "Neural network prediction of AE data." Geophysical Research Letters 24, no. 19 (1997): 2403–6. http://dx.doi.org/10.1029/97gl02457.

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