Intelligent Request Grabber: Increases the Vehicle Traffic Prediction Rate Using Social and Taxi Requests Based on LSTM

The problem of forecasting self-similar traffic, which is characterized by a considerable number of ripples and the property of long-term dependence, is considered. It is proposed to use the method of spline extrapolation using linear and cubic splines. The results of self-similar traffic prediction were obtained, which will allow to predict the necessary size of the buffer devices of the network nodes in order to avoid congestion in the network and exceed the normative values of QoS quality characteristics. The solution of the problem of self-similar traffic forecasting obtained with the Simulink software package in Matlab environment is considered. A method of extrapolation based on spline functions is developed. The proposed method has several advantages over the known methods, first of all, it is sufficient ease of implementation, low resource intensity and accuracy of prediction, which can be enhanced by the use of quadratic or cubic interpolation spline functions. Using the method of spline extrapolation, the results of self-similar traffic prediction were obtained, which will allow to predict the required volume of buffer devices, thereby avoiding network congestion and exceeding the normative values of QoS quality characteristics. Given that self-similar traffic is characterized by the presence of "bursts" and a long-term dependence between the moments of receipt of applications in this study, given predetermined data to improve the prediction accuracy, it is possible to use extrapolation based on wavelet functions, the so-called wavelet-extrapolation method. Based on the results of traffic forecasting, taking into account the maximum values of network node traffic, you can give practical guidance on how traffic is redistributed across the network. This will balance the load of network objects and increase the efficiency of network equipment.

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Network traffic prediction based on wavelet FARIMA model

Journal of Computer Applications ◽

10.3724/sp.j.1087.2011.00901 ◽

2011 ◽

Vol 31 (4) ◽

pp. 901-903

Author(s):

Yong SUN ◽

Guang-wei BAI ◽

Lu ZHAO

Keyword(s):

Network Traffic ◽

Traffic Prediction

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A Novel Prediction of Quaternary Structural Type of Proteins with Gene Ontology

Protein and Peptide Letters ◽

10.2174/0929866526666191014144618 ◽

2020 ◽

Vol 27 (4) ◽

pp. 313-320 ◽

Cited By ~ 1

Author(s):

Xuan Xiao ◽

Wei-Jie Chen ◽

Wang-Ren Qiu

Keyword(s):

Gene Ontology ◽

Feature Extraction ◽

Extraction Method ◽

Quaternary Structure ◽

Structural Type ◽

Sequence Information ◽

Prediction System ◽

Data Set ◽

Feature Extraction Method ◽

Prediction Rate

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

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Identification of Anti-cancer Peptides Based on Multi-classifier System

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666191203141102 ◽

2020 ◽

Vol 22 (10) ◽

pp. 694-704 ◽

Cited By ~ 2

Author(s):

Wanben Zhong ◽

Bineng Zhong ◽

Hongbo Zhang ◽

Ziyi Chen ◽

Yan Chen

Keyword(s):

Machine Learning ◽

Side Effect ◽

Learning Models ◽

Normal Cells ◽

Classifier System ◽

Prediction Rate ◽

Anti Cancer ◽

Feature Information ◽

Machine Learning Models ◽

Better Than

Aim and Objective: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti Materials and Methods: In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting. Results and Conclusion: The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

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