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The authors propose a new tool to extensively analyze and plan the radio resources based on machine learning technique using available data analytics methods like clustering, correlation and regression. This will be done through; Classifying the cells according to their Priority and required quality of service, detecting resources utilization that cause throughput limitation, efficient dimensioning...
A large number of aviation equipment maintenance data exhibit seasonal behavior, such as aircraft failure rate. Consequently, seasonal forecasting problems are of considerable importance in aviation maintenance support. Aircraft failure rate is an important parameter of aviation equipment RMS (Reliability-Maintainability-Supportability). It is indispensable to scientifically predict the aircraft failure...
Organizations face the issue of how to best allocate their security resources. Thus, they need an accurate method for assessing how many new vulnerabilities will be reported for the operating systems (OSs) they use in a given time period. Our approach consists of clustering vulnerabilities by leveraging the text information within vulnerability records, and then simulating the mean value function...
We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time. To deal with the potential discrepancy between the source and target distributions, both in features and labels, we exploit a copula-based regression framework. The benefits of this approach are two-fold: (a) it allows us to model a broader range of conditional predictive...
One of the first steps towards the effective Technical Debt (TD) management is the quantification and continuous monitoring of the TD principal. In the current state-ofresearch and practice the most common ways to assess TD principal are the use of: (a) structural proxies—i.e., most commonly through quality metrics; and (b) monetized proxies—i.e., most commonly through the use of the SQALE (Software...
The allocation of resources to challenge city centre violent crime traditionally relies on historical data to identify hot-spots. The usefulness of such data-driven approaches is limited when historical data is scarce or unavailable (e.g. planning of a new city) or insufficiently representative (e.g. does not account for novel events, such as Olympic Games). In some cities, crime data is not systematically...
There are some uncertainties associated with the influencing factors in the stock price forecasting model. Main influencing factors of stock price are selected by gray relational analysis, and the main influencing factor was used as an exogenous variable to establish the ARMAX model to forecast the stock price. Taking PetroChina as an example to carry out case analysis, the result shows that the fitting...
In this paper, we propose a method for optimizing the game live service. Especially, we focus on improving user retention. Firstly, we define player churn in the game and extract features that contain the properties of the player churn from the game logs. And then we evaluate the importance of features using random forest in classification. Finally, we build association matrix between features and...
Financial variables are of primary importance in financial modeling, fraud detection, financial distress management, price modeling, credit and risk evaluations and in evaluating the return on assets and portfolios. There usually exist a large number of financial variables, where their exhaustive integration in a model increases its dimensionality and the associated computational time. We extensively...
The aims of this study were to forecast change of Chinese social donation in recent years by grey Verhulst model to find out characteristics of social donation and grey correlation analysis which was proposed by Professor Deng Julong is used to detect of an interpersonal relationship between the number of social donations from oversea and the number of foundations. This paper provides a quantitative...
In this paper, by using grey system theory, grey relation analysis and trend prediction was carried out for the influencing factors of agricultural mechanization development levels in major granary provinces in China. At present, the total power of agricultural mechanization, total income from agricultural mechanization, fuel consumption for agricultural production have great influence on the agricultural...
Aiming at the problem of fault detection for satellite communication system, a prediction method based on Gaussian mixture model is proposed. Firstly, the observation sequence is collected by modem as well as frequency conversion equipment. Then feature parameters are extracted after pre-processing. The expectation maximum algorithm is applied to train the Gaussian mixture model. The posterior probabilities...
5G communication will bring a surge traffic in cellular network. The traffic in cellular network not only has strong variability by time, but also has strong spatio-temporal correlation, which brings large difficulty to predict. In order to make reasonable use of communication network resources, it is important to describe and predict the spatio-temporal information of traffic in cellular network...
Stock market analysis is a very popular area of research. Achieving good prediction in forecasting the stock markets is a very challenging task. The prediction of the future stock markets is done using cascading statistical models. This paper investigates the MCX commodity (Gold) on which the model is applied. Objective: To predict the trend of the gold commodity will remove the uncertainty in the...
The CNN-RNN design pattern is increasingly widely applied in a variety of image annotation tasks including multi-label classification and captioning. Existing models use the weakly semantic CNN hidden layer or its transform as the image embedding that provides the interface between the CNN and RNN. This leaves the RNN overstretched with two jobs: predicting the visual concepts and modelling their...
So as to improve the detectability of small targets on sea clutter, the method based on variable forgetting factor linear prediction is proposed. First, the recursive least square method is used to dynamically adjust the parameters of the prediction model. Secondly, the value of forgetting factor is given by the adjustable formula. Finally, the prediction error is calculated and the target results...
Sedentary behaviors such as sitting and watching TV are ubiquitous in modern societies. Increases in sedentary time have been linked with an increased risk of obesity, diabetes, cardiovascular disease, and all-cause mortality. While smartphones and wearables can now detect sedentary user behaviors, few computational models exist for predicting when they will occur in future. In this paper, we propose...
Personalized recommendation aims to use the historical behavior of users to recommend new items that are likely to be of interest to them. Due to a tiny improvement of it can lead to a huge profits, lots of giant e-commerce companies, such as Amazon, Alibaba and eBay, have put their great effort on this field. In this paper, we formulate the problem of personalized recommendation as a tree based regression...
This paper presents a case study concerning the forecasting of monthly retail time series recorded by the US Census Bureau from 1992 to 2016. The modeling problem is tackled in two steps. First, original time series are de-trended by using a moving windows averaging approach. Subsequently, the residual time series are modeled by Non-linear Auto-Regressive (NAR) models, by using both Neuro-Fuzzy and...
This paper investigates expert behaviour in Q&A communities in order to understand their influence in online discussions. Our evaluation shows that experts are more likely to provide help than non-experts, and when they participate in a discussion, the quality and length of the discussions tend to increase. In addition, we propose the usage of two models (Artificial Neural Network and Stochastic...
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