Predicting the sequential behavior of mobile Internet users based on MSM model

Examining annual transitions in healthcare spending among U.S. medicare beneficiaries using multistate Markov models: Analysis of medicare current beneficiary survey data, 2003–2019

Finding prevalent mobile user patterns in large amount of data has been one of the major problems in the area of mobile data mining. Particularly, the algorithms of discovering frequent user's behavior patterns in the mobile agent system have been studied extensively in recent years. The key feature in most of these algorithms is that they use a location log dataset with userpsilas requested services. In this case, some problems occur because they do not consider that mobile user's behavior patterns are dynamically variable as time passes. In this paper, we propose a novel data mining method based on temporal mobile access patterns that can efficiently discover mobile user's behavior patterns. An advantage of our method compactly stores user's behavior patterns according to location log dataset and requested services in memory. Thus, even the information datasets require large shared memory when they store, our approach still provides faster access and consume less memory than existing techniques.

Preexisting frailty at ICU admission has been well investigated and linked to adverse outcomes, the continuum and determinants of new onset of frailty remain poorly characterized. This study aimed to estimate transition probabilities and intensities across non-frailty, frailty, and death after critical illness, and explore a set of potential variables associated with these transitions. We conducted a multicenter prospective cohort study of ICU survivors, followed from ICU discharge to 6 months. Frailty was assessed with the Clinical Frailty Scale (CFS), with scores ≥5 defined as frail and <4 as non-frail. Multistate Markov models were used to estimate transition probabilities and intensities among three states: frailty, non-frailty, and death. Univariate and multivariable Markov models were used to identify predictors of state transitions. At follow-up, 66.1% of frail patients reversed to non-frail, 25.7% remained frail, and 8.2% died. Among those non-frails at baseline, 72.9% remained stable, 21.2% developed frailty, and 5.9% died. Recovery from frailty occurred more than three times as often as progression from non-frailty to frailty (transition intensity ratio, 3.12; 95% CI, 2.22-4.38). The transition from frailty to death was over twice as frequent as from non-frailty to death (ratio, 2.52; 95% CI, 0.39-16.37). Sepsis-related complications significantly increased the risk of frailty onset and mortality, whereas higher body mass index, muscle mass, handgrip strength, and phase angle consistently promoted recovery and survival. Older age, female sex, comorbidities, malnutrition, and prolonged ICU or hospital stay impaired recovery. Frailty after critical illness is a dynamic and potentially reversible condition, with transitions shaped by sepsis-related complications, nutritional and muscular status, and patient vulnerability factors. Early post-ICU frailty assessment and targeted interventions addressing infection, nutrition, and physical function may facilitate recovery and improve survival in ICU survivors.

With the ubiquitous Internet applications brought by widespread popularity of smart device, an exhaustive understanding of user behavior is becoming essential for Internet Service Providers (ISPs) to implement network management and resource optimization. Existing researches on mobile user behavior principally focus on studying people's application interests and mobility properties, especially characterizing relationship between the two perspectives. In this paper, distinct from prior work, we propose a new idea to model and predict mobile user behavior after extracting the behavior with strong correlation between browsing interests and location. Initially, improved Apriori algorithm is applied to find the association rule and estimate the strength of correlation between user's location and application. On that basis, behavior pattern with close correlation between two features is extracted. Subsequently, we use HMM (Hidden Markov Model) to model aforementioned behavior and predict applications used in given location. The effectiveness and accuracy of our method are verified by real data traffic collected from mobile Internet covering 4.51 million people in a large metropolitan area of China over a week.

Aiming at the data security problem of the system in the network environment, this paper uses the hidden Markov model to analyze the user's access behavior in the network system and identify the malicious user behavior. First, the behavior characteristics of network users are analyzed, and then according to the related characteristics of hidden Markov, the observation order symbol and hidden state in the model are determined, and the user's access behavior is mapped into a hidden Markov chain. We give detailed experiments to prove the feasibility of applying the hidden Markov model to network user behavior authentication, and compare with other methods to get the accuracy of the model.

With the constant development of socialist modernization construction in our country, our country's computer information technology has made effective progress, and makes the world into an information age. People's production and life style experience a series of change. However, with the emergence of information diversity and multi-user mode, traditional computer information technology already can't satisfy people's needs, and the development and application of cloud computing technology come to the stage. This article focuses on analyzing the big data user behavior engine design under cloud computing technology, covering big data flow management, multi-user system design, and other aspects to explore the effect of the system test. In recent years, China's mobile Internet technology has made full development, which makes the Internet operators in our country face a new development opportunity and began from traffic management to flow management. The law of user behavior is analyzed to explore the real demand of market and the users. In order to fully meet the changing needs of users, operators must constantly develop and launch new products and strengthen the function of computer technology. Cloud computing technology is such kind of engine system which can satisfy the analysis and processing of huge amounts of data. RESEARCH ON CLOUD COMPUTING SYSTEM OVERALL DESIGN The overall architecture of cloud computing system This study mainly uses cloud computing technology’s huge amounts of data computing to set up complete mobile Internet data mining analysis system. Realize the analysis for Internet user behavior engine, and according to the user's preferences online habits and behavior, provide users with targeted personalized service and form a unified organic whole of data collection, analysis and service type and marketing strategy to improve enterprise's marketing efficiency. In addition, cloud computing system mainly realizes data collection with the help of FTP server, and makes distributed computing and data batch processing at the system interface. Large data shall be deposited in Hbase database. The system can not only realize mass data storage, but those unstructured data storage. Then through Hive integration layer and summary EIL processing, use MapReduce data analysis model to transmit the processing result into the database. The system’s overall structure is shown in Figure 1: 3rd International Conference on Materials Engineering, Manufacturing Technology and Control (ICMEMTC 2016) © 2016. The authors Published by Atlantis Press 1351 Figure 1 The overall architecture of cloud computing system System topology and function distribution System topology mainly refers that a server is taken as Hapdoop platform’s master node server, and the others are Hapdoop platform’s node servers. In general, node server can be dynamically extended according to the actual need, and the master node server not only assigns tasks and flow to node server, but also monitors the work execution of the node server. More than one node server participates to complete the task, and it can improve data processing efficiency. The master node server’s software running status is shown in Figure 2: Figure 2 Master node server function structure

With the rapid development of the mobile Internet, mobile Internet users grow rapidly and the preference is more diverse, and the user's behavior is also showing a new feature. In this study, we use qualitative research and quantitative research, combined with empirical data and simulation analysis and the combination of traditional statistical analysis and modern data mining methods, to study the behavior preference of mobile Internet. Besides, we use the discrete choice model to build the binaryLogit model using SAS tools, and analyze the selected variables through data processing. Based on the significant influence factors, the behavior preference of mobile Internet users is classified by using support vector machine. The C-SVM binary classification machine is used as kernel function, and the parameters of the model and kernel function are optimized by cross validation. In this research, we introduce the support vector machine theory into the research field of the mobile Internet user behavior effectively, solve the problem of small and medium-sized samples effectively, and provide new ideas and methods for the research of mobile Internet user behavior.

Web prediction is a classification problem in which we attempt to predict the next set of Web pages that a user may visit based on the knowledge of the previously visited pages. Predicting user's behavior while serving the Internet can be applied effectively in various critical applications. Such application has traditional tradeoffs between modeling complexity and prediction accuracy. In this paper, we analyze and study Markov model and all- Kth Markov model in Web prediction. We propose a new modified Markov model to alleviate the issue of scalability in the number of paths. In addition, we present a new two-tier prediction framework that creates an example classifier EC, based on the training examples and the generated classifiers. We show that such framework can improve the prediction time without compromising prediction accuracy. We have used standard benchmark data sets to analyze, compare, and demonstrate the effectiveness of our techniques using variations of Markov models and association rule mining. Our experiments show the effectiveness of our modified Markov model in reducing the number of paths without compromising accuracy. Additionally, the results support our analysis conclusions that accuracy improves with higher orders of all- Kth model.

Functions for fitting continuous-time Markov and hidden Markov multi-state models to longitudinal data. Designed for processes observed at arbitrary times in continuous time (panel data) but some other observation schemes are supported. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates, which may be constant or piecewise-constant in time.

This paper explains how to use Markov models for evaluating the availability and reliability of a system when the transition rates of each component depend on the state of the system. To this purpose, the structural properties of the system transition-rate matrix, obtained by the Kronecker sum of transition rate matrices of the components, are analyzed. As a result, an efficient use of the Markov model is possible by mapping the transition rates of the single components into the elements of the system transition-rate matrix. The potential of parallel computers can facilitate this approach, giving renewed interest to the Markov method for complex systems.

Health status trajectories using Markov modeling in patients with heart failure with preserved ejection fraction.

Secure operation of protective intelligent electronic devices (IEDs) has been recognized as a crucial issue for power grids. By gaining access to substation IEDs, intruders can severely disrupt the operation of protection systems. This paper develops an analytical reliability assessment framework for quantifying the impacts of the hypothesized integrity attacks against protection systems. Petri net models are used to simulate possible intrusion scenarios into substation networks. The cyber network model is constructed from firewall, intrusion prevention system (IPS), and password models, which are three types of defense mechanisms for protecting substation networks. In this paper, two main types of attacker actions on protective devices are recognized: 1) sending breaker opening commands through IED connections; and 2) changing IED settings. In this regard, a multistate Markov model is proposed for identifying possible consequences of cyber attacks on the protective devices. In addition, this model captures the essential remedial actions that are taken after a successful cyber attack against target equipment. Subsequently, an analytical approach based on the introduced Markov model is proposed for assessing power system reliability. The proposed framework is implemented on the modified IEEE reliability test system, and its effectiveness is demonstrated through extensive case studies.

Введение. На современном этапе развития цифрового пространства, особенности виртуальной сети обеспечивают определенный уровень анонимности пользователей, что расширяет возможности для формирования и развития личностных смыслов по отношению к деятельности в виртуальном пространстве. Несмотря на существование достаточного количества исследований особенностей смысловой сферы и смысловой регуляции поведения личности в реальном пространстве, на сегодняшний день практически нет исследований, изучающих особенности смысловой регуляции поведения пользователей Сети. Целью работы выступает исследование особенностей смысловой регуляции поведения интернет-активных пользователей, включающее в себя рассмотрение ценностных и смысловых образований, формирующихся в результате взаимодействия личности с интернет-пространством. Материалы и методы. Исследование проводилось на базе Академии предпрофессионального образования Крымского инженерно-педагогического университета имени Февзи Якубова. В нем приняли участие подростки в возрасте 14-15 лет (438 человек). В качестве эмпирических методов исследования использовались: анкетирование, психодиагностический набор методик для изучения особенностей смысловой регуляции поведения интернет-активных пользователей. В результате выделены типы поведения интернет-активных пользователей в соответствии со спецификой их смысловой сферы и направленности в интернет-пространстве. Выделенные типы были разделены на группы в зависимости от нормативности поведения в интернет-пространстве. Эмпирически выявлены различия в сформированности нравственных ориентаций у интернет-активных пользователей. Вывод. Интернет-пространство накладывает отпечаток на формирование смысловой сферы личности и определяет особенности регуляции поведения с учетом индивидуальных смысловых особенностей личности.

With the rapid development of the Internet and information technology, recommendation systems are playing an increasingly important role in various applications. Traditional recommendation algorithms, such as content-based recommendations and collaborative filtering, have achieved success to some extent. However, they show limitations when dealing with issues like data sparsity and the complexity of user behavior. This paper proposes a prediction recommendation system based on an improved Markov model to address these issues. By introducing the Hidden Markov Model (HMM) and an improved state transition mechanism, the model's predictive capability in handling user behavior sequences is enhanced. This paper first introduces the background and theoretical foundation of recommendation systems and Markov models, then details the design and implementation of the improved Markov model. Experiments on public datasets demonstrate that the recommendation system based on the improved Markov model outperforms traditional methods in terms of recommendation accuracy and user satisfaction. Finally, the paper summarizes the main contributions and suggests potential directions for future research.

The incompleteness and uncertainty about the state of the world and about the consequences of actions are unavoidable. If we want to predict the performance of multiuser computing systems, we have the uncertainty of what the users are going to do, and how that affects system performance. Intelligent interface agent development is one way to mitigate the uncertainty about user behaviors by predicting what users will do based on learned users' behaviors, preferences, and intentions. This work focuses on developing user models that can analyze and predict user behavior in multi-agent systems. We have developed a formal theory of user behavior prediction based on hidden Markov models. This work learns the user model through a time-series action analysis and abstraction by taking users' preferences and intentions into account in order to formally define user modeling.