The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The exponential growth of audiovisual and textual data is aggressively leading to the creation of tools and systems capable of selecting and recognizing given patterns of information. Such urge is at the basis of the development of machine learning techniques, techniques that are today employed to analyze all kinds of information. Computer gaming is, with no doubt, one of the fields where such techniques...
Link prediction is an important issue in Social Network Analysis area. Most of the existing link prediction methods aim to find the missing links or to predict the future links mainly based on a static network, ignoring the evolution of the network over time. This paper proposes a link prediction method that can learn from network dynamics. Using machine learning techniques, the method models the...
Given the on-demand nature of cloud computing, managing cloud-based services requires accurate modeling for the correlation between their Quality of Service (QoS) and cloud configurations/resources. The resulted models need to cope with the dynamic fluctuation of QoS sensitivity and interference. However, existing QoS modeling in the cloud are limited in terms of both accuracy and applicability due...
Twitter user profile information is very useful for various fields such as marketing, HRD, advertising, and personalization. Since user profile provided by Twitter is very limited, some latent attributes such as gender, age, work, or interest should be predicted. In this paper, we aim to predict those four latent attributes using her/his tweet and bio data by employing machine learning techniques...
Sound source localization algorithms commonly include assessment of inter-sensor (generalized) correlation functions to obtain direction-of-arrival estimates. Here, we present a classification-based method for source localization that uses discriminative support vector machine-learning of correlation patterns that are indicative of source presence or absence. Subsequent probabilistic modeling generates...
Hyper-heuristics is a very active field that is developing all the time. This area of bio-inspired intelligent systems covers a wide range of algorithms selection techniques. This type of self-organising mechanism uses heuristics to optimise heuristics. Many discussions focus on the quality of solutions of the problems obtained from the hyper-heuristics, very little discussion concentrates on the...
Today, gesture analysis lacks of global models able to characterize motion expressivity and its communicational character. In this paper, we propose a set of new gesture descriptors inspired from Laban Movement Analysis (LMA) and based on 3D body trajectories. We test our descriptors ability to characterize human actions in a machine learning framework (with SVM and different random forest techniques)...
The overwhelming growth and popularity of online social networks is also facing the issues of spamming, which mainly leads to uncontrolled dissemination of malware/viruses, promotional ads, phishing, and scams. It also consumes large amounts of network bandwidth leading to less revenue and significant financial losses to organizations. In literature, various machine learning techniques have been extensively...
Existing methods for transportation mode detection (TMD) using mobile sensing make it generally possible to distinguish between walking, cycling, and motorized transport. However, our means of transport evolve and we develop radically new ways of transporting ourselves, thus new TMD sub-classification methods are needed to distinguish these new transport forms. As we transition from fossil-fueled...
Big data analytics is the process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information. Its revolutionary potential is now universally recognized. Data complexity, heterogeneity, scale, and timeliness make data analysis a clear bottleneck in many biomedical applications, due to the complexity of the patterns...
Online social networks, and especially the popular microblogging service Twitter have taken to be the epicenter of massive social movements, where users often openly express political tendencies — a trend which has led to making the classification of political tendencies from social shares a research question of interest. In this research, we collect and hand label a small subset of political messages...
Short Message Service is one of the most using services in mobile phones. In daily life, several spam messages, which could disturb mobile phone users, are received frequently. Unwanted messages could be delivered for advertisement, annoucement of promotional events and/or only for disturbing people. In this study, 3-tier hybrid message filtering architecture has been introduced to protect the mobile...
Nowadays, one of the most important security threats are new, unseen malicious executables. Current anti-virus systems have been fairly successful against known malicious softwares whose signatures are known. However they are very ineffective against new, unseen malicious softwares. In this paper, we aim to detect new, unseen malicious executables using machine learning techniques. We extract distinguishing...
Social media has become an important information source with the expanding internet and ideas shared by the people. The social media raw data which is quite disordered and messy can not be processed as it is and obtain adequate results. In this study, sentiment analysis has been performed by collecting data from the Twitter. To perform this analysis an intelligent system has been created by using...
Lack of (semi)automatic mechanisms for service classification in the Universal Description Discovery and Integration repositories and non utilization of explicit or implicit semantic information of a service during its publishing are the two major challenges in the area of web service discovery and selection. We propose a semantic model of human-machine collaboration for the classification, discovery...
Subscriber churn is a concern of customer care management for most of the mobile and wireless service providers and operators due to its associated costs. This paper explains our work on subscriber churn analysis and prediction for such services. We work on data mining techniques to accurately and efficiently predict subscribers who will change-and-turn (churn) to another provider for the same or...
In SON enabled LTE networks many different SON functions may operate simultaneously in order to optimize the network performance. With the increasing number of SON functions the probability of conflicts and dependencies between them increases and become more challenging to handle. In this paper an integrated approach for two SON functions, namely handover optimization and load balancing combined with...
'Hubness' is a recently discovered general problem of machine learning in high dimensional data spaces. Hub objects have a small distance to an exceptionally large number of data points, and anti-hubs are far from all other data points. It is related to the concentration of distances which impairs the contrast of distances in high dimensional spaces. Computation of secondary distances inspired by...
Previous work within health communication has been concerned with how to tailor intervention content in a way that is most effective in supporting the individual in changing health behaviors such as smoking, physical activity or diet. This kind of tailoring is based on data gathered from the user through questionnaires with textual feedback adapted by algorithms pre-specified according to behavioral...
Non line of sight (NLOS) error identification and mitigation is of great importance in ultra wideband (UWB) ranging and localization. Based on the features extracted from the received waveform in practical experiments, a machine learning method is proposed for UWB NLOS identification in this paper. Corresponding NLOS error mitigation method is also given based on the identification results. Compared...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.