Web Usage Mining by Neural Hybrid Prediction with Markov Chain Components

2021 ◽  
Author(s):  
Arpad Gellert
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Web Browser ◽  
Web Prefetching ◽  
Web Resources ◽  
Web Usage ◽  
The Neural Network ◽  
Prediction Technique ◽  
Web Access

This paper presents and evaluates a two-level web usage prediction technique, consisting of a neural network in the first level and contextual component predictors in the second level. We used Markov chains of different orders as contextual predictors to anticipate the next web access based on specific web access history. The role of the neural network is to decide, based on previous behaviour, whose predictor’s output to use. The predicted web resources are then prefetched into the cache of the browser. In this way, we considerably increase the hit rate of the web browser, which shortens the load times. We have determined the optimal configuration of the proposed hybrid predictor on a real dataset and compared it with other existing web prefetching techniques in terms of prediction accuracy. The best configuration of the proposed neural hybrid method provides an average web access prediction accuracy of 86.95%.

scholarly journals Hard-threshold neural network-based prediction of organic synthetic outcomes

2020 ◽  
Author(s):  
Haoyang Hu ◽  
Zhihong Yuan
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Organic Molecules ◽  
Backpropagation Algorithm ◽  
Forward Reaction ◽  
Drug Discovery And Development ◽  
Reaction Prediction ◽  
The Neural Network ◽  
Reaction Data ◽  
Retrosynthetic Analysis

Abstract Retrosynthetic analysis is a canonical technique for planning the synthesis route of organic molecules in drug discovery and development. In this technique, the screening of synthetic tree branches requires accurate forward reaction prediction, but existing software is far from completing this step independently. Previous studies attempted to apply a neural network to forward reaction prediction, but the accuracy was not satisfying. Through using the Edit Vector-based description and extended-connectivity fingerprints to transform the reaction into a vector, this study focuses on the update of the neural network to improve the template-based forward reaction prediction. Hard-threshold activation and the target propagation algorithm are implemented by introducing mixed convex-combinatorial optimization. Comparative tests were conducted to explore the optimal hyperparameter set. Using 15,000 experimental reaction data extracted from granted United States patents, the proposed hard-threshold neural network was systematically trained and tested. The results demonstrated that a higher prediction accuracy was obtained than that for the traditional neural network with backpropagation algorithm. Some successfully predicted reaction examples are also briefly illustrated.

scholarly journals Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1832
Author(s):  
Wojciech Sitek ◽  
Jacek Trzaska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vital Role ◽  
Materials Engineering ◽  
The Neural Network ◽  
Critical Issues ◽  
Artificial Neural ◽  
The Individual

Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented.

scholarly journals Pattern Recognition and Neural Network-Driven Roller Track Analysis via 5G Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yuliang Guo
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Data Storage ◽  
Prediction Accuracy ◽  
Video Monitor ◽  
Advanced Technologies ◽  
The Neural Network ◽  
5G Network ◽  
Processing Module ◽  
Track Analysis

Roller skating is an important and international physical exercise, which has beautiful body movements to be watched. However, the falling of roller athletes also happens frequently. Upon the roller athletes’ fall, it means that the whole competition is over and even the roller athletes are perhaps injured. In order to stave off the tragedy, the roller track can be analyzed and be notified the roller athlete to terminate the competition. With such consideration, this paper analyzes the roller track by using two advanced technologies, i.e., pattern recognition and neural network, in which each roller athlete is equipped with an automatic movement identifier (AMI). Meanwhile, AMI is connected with the remote video monitor referee via the transmission of 5G network. In terms of AMI, its function is realized by pattern recognition, including data collection module, data processing module, and data storage module. Among them, the data storage module considers the data classification based on roller track. In addition, the neural network is used to train the roller tracks stored at AMI and give the further analysis results for the remote video monitor referee. Based on NS3, the devised AMI is simulated and the experimental results reveal that the prediction accuracy can reach 100% and the analyzed results can be used for the falling prevention timely.

BCWB

2017 ◽  
Vol 13 (2) ◽  
pp. 55-73 ◽  
Author(s):  
Sofien Gannouni ◽  
Nourah Alangari ◽  
Hassan Mathkour ◽  
Hatim Aboalsamh ◽  
Kais Belwafi
Keyword(s):  
Assistive Technologies ◽  
Web Browser ◽  
Motor Disabilities ◽  
Web Resources ◽  
Human Computer Interfaces ◽  
Universal Accessibility ◽  
Government Education ◽  
The Disabled ◽  
Severe Motor ◽  
Web Access

Web access and web resources open many horizons, their usage increases in all life aspects including government, education, commerce and entertainment, where the key to such resources lies in Web browsers. Acknowledging the importance of universal accessibility to web resources, the W3C has developed a series of guidelines into a Web Accessibility Initiative (WAI), with the goal of providing access to web resources for people with disabilities. In order to bridge the gap in the digital divide between the disabled and the non-disabled people, the authors believe that the development of novel assistive technologies using new human-computer interfaces will go a long way towards achieving this lofty goal. In this paper, they present a P300 Electroencephalography Brain-controlled Web browser to enhance the accessibility of people with severe motor disabilities to Web resources. It enhances their interaction with the Web taking their needs into account. The proposed Web browser satisfies the Mankoff's requirements of a system that would “allow true web access.”

scholarly journals Modelling of Deformation Resistance with Big Data and Its Application in the Prediction of Rolling Force of Thick Plate

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shun Hu Zhang ◽  
Li Zhi Che ◽  
Xin Ying Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Rolling Force ◽  
Deformation Resistance ◽  
Force Model ◽  
The Neural Network ◽  
Bp Neural Network Model

The precision of traditional deformation resistance model is limited, which leads to the inaccuracy of the existing rolling force model. In this paper, the back propagation (BP) neural network model was established according to the industrial big data to accurately predict the deformation resistance. Then, a new rolling force model was established by using the BP neural network model. During the establishment of the neural network model, the data set of deformation resistance was established, which was calculated back from the actual rolling force data. Based on the data set after normalization, the BP neural network model of deformation resistance was established through the optimization of algorithm and network structure. It is shown that both the prediction accuracy of the neural network model on the training set and the test set are high, indicating that the generalization ability of the model is strong. The neural network model of the deformation resistance is compared with the theoretical one, and the maximum error is only 3.96%. Furthermore, by comparison with the traditional rolling force model, it is found that the prediction accuracy of the rolling force model imbedding with the present neural network model is improved obviously. The maximum error of the present rolling force model is just 3.86%. The research in this paper provides a new way to improve the prediction accuracy of rolling force model.

scholarly journals Heat Emitting Damage in Skin: A Thermal Pathway for Mechanical Algesia

2021 ◽  
Vol 15 ◽  
Author(s):  
Tom Vincent-Dospital ◽  
Renaud Toussaint ◽  
Knut Jørgen Måløy
Keyword(s):  
Neural Network ◽  
Infrared Camera ◽  
Biological Tissues ◽  
Thermal Dissipation ◽  
Comprehensive Understanding ◽  
Porcine Skin ◽  
Thermal Pain ◽  
The Neural Network ◽  
Lower Temperature

Mechanical pain (or mechanical algesia) can both be a vital mechanism warning us for dangers or an undesired medical symptom important to mitigate. Thus, a comprehensive understanding of the different mechanisms responsible for this type of pain is paramount. In this work, we study the tearing of porcine skin in front of an infrared camera, and show that mechanical injuries in biological tissues can generate enough heat to stimulate the neural network. In particular, we report local temperature elevations of up to 24°C around fast cutaneous ruptures, which shall exceed the threshold of the neural nociceptors usually involved in thermal pain. Slower fractures exhibit lower temperature elevations, and we characterise such dependency to the damaging rate. Overall, we bring experimental evidence of a novel—thermal—pathway for direct mechanical algesia. In addition, the implications of this pathway are discussed for mechanical hyperalgesia, in which a role of the cutaneous thermal sensors has priorly been suspected. We also show that thermal dissipation shall actually account for a significant portion of the total skin's fracture energy, making temperature monitoring an efficient way to detect biological damages.

Neural Syntheses

2021 ◽  
pp. 556-578
Author(s):  
You Nakai
Keyword(s):  
Neural Network ◽  
The Neural Network ◽  
Actual Mechanism ◽  
Custom Made ◽  
Universal Instrument ◽  
Network Instrument

One of Tudor’s last projects used an instrument custom-made for him using the neural network chip that had just been developed. The Neural Synthesizer began as an attempt to build a universal instrument that would synthesize the proliferation of his modular devices. But the actual mechanism of the analog chip, which happened to be an extensive array of amplifiers, shifted the nature of the endeavor, causing a return to the no-input works from the 1970s. In this way, the neural network instrument, used against its usual purpose of extracting patterns from past examples, nonetheless found a strange connection with reminiscences of Tudor’s own past. The analyses of Neural Syntheses and Neural Network Plus, two series of works Tudor made using his new synthesizer, further brings up the issue of memory concerning the performance of his music, which is different every time yet open to revivals, something he tried to capture by setting a number to each performance. This also connects to the problem of how Tudor thought of passing his music on to others so that they could be performed in his absence, a natural concern in the last years of his life, but also something that reflected his lifelong interest in the role of memory and reminiscence in music.

APPLICATION OF ARTIFICIAL NEURAL NETWORK TO THE IDENTIFICATION OF QUARK AND GLUON JETS

2005 ◽  
Vol 20 (32) ◽  
pp. 7603-7611 ◽  
Author(s):  
MEILING YU ◽  
KUNSHI ZHANG ◽  
LIANSHOU LIU
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Architectures ◽  
The Neural Network ◽  
Gluon Jets ◽  
Input Parameters ◽  
Order Of Magnitude ◽  
Two Parameters

The Back-Propagation neural network method is used to identify quark and gluon jets generated by Monte Carlo method. The effects of some factors, such as the architecture of neural network, the input parameters, the training precision and the acceptance cut, on the performance of the neural network are studied in detail. The efficiency and purity of identified quark and gluon jets are calculated for different network architectures. It is found that in order to keep the role of all the input parameters balance, they have to be scaled to the same order of magnitude. Through the study on how the efficiency and purity of the identified quark- and gluon-jets vary with the training precision and acceptance cut, a guidance for how to choose these two parameters is given.

Study on Displacement Prediction Model of Foundation Pit

2013 ◽  
Vol 834-836 ◽  
pp. 679-682
Author(s):  
Qiang Song ◽  
Jun Jian Zhang ◽  
Yun Sheng Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Prediction Model ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Foundation Pit ◽  
Displacement Prediction ◽  
Fitness Evaluation ◽  
The Neural Network

The prediction model is proposed in this paper to predict the displacement of foundation pit. In the model, genetic algorithms is applied to optimize the node function of the neural network (15 node function coefficients are optimized simultaneously). Next, do the further optimization to the model, and GA-transFcn3 Model is established whose fitness evaluation takes into account the multi-step prediction error. Finally, it is verified that the GA-transFcn3 Model created in this article has the desirable prediction accuracy through engineering examples. The establishment of GA-transFcn3 Model can provide researchers and engineers with ideas and methods for the displacement prediction of foundation pit, and can be popularized and applied in practical projects.

Sign in / Sign up

Export Citation Format

Share Document