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Social big data: Recent achievements and new challenges
TLDR
This paper presents a revision of the new methodologies that are designed to allow for efficient data mining and information fusion from social media and of thenew applications and frameworks that are currently appearing under the “umbrella” of the social networks, socialMedia and big data paradigms.
Intelligent Travel Planning: A MultiAgent Planning System to Solve Web Problems in the e-Tourism Domain
TLDR
Intelligent Travel Planning (ITP), a multiagent planning system to solve Web electronic problems in the Web, whose main goal is to search for useful solutions in the electronic-Tourism domain to system users.
Measuring the Radicalisation Risk in Social Networks
TLDR
A set of radicalization indicators and a model to assess them are presented using a data set of tweets published by several Islamic State of Iraq and Sham sympathizers and there is a strong correlation between the values assigned by the model to the indicators.
ADROIT: Android malware detection using meta-information
TLDR
A novel method called ADROIT is proposed for analysing and detecting malicious Android applications by employing meta-information available on the app store website and also in the Android Manifest, based on a text mining process used to extract significant information from meta-data that later is used to build efficient and highly accurate classifiers.
CANDYMAN: Classifying Android malware families by modelling dynamic traces with Markov chains
TLDR
This paper presents CANDYMAN, a tool that classifies Android malware families by combining dynamic analysis and Markov chains, and indicates a precision performance of 81.8% over this dataset.
Adaptive k-Means Algorithm for Overlapped Graph Clustering
TLDR
A soft clustering approach based on a genetic algorithm where a new encoding is designed to achieve two main goals: first, the automatic adaptation of the number of communities that can be detected and second, the definition of several fitness functions that guide the searching process using some measures extracted from graph theory.
Bio-inspired computation: Where we stand and what's next
TLDR
The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.
A Genetic Graph-Based Approach for Partitional Clustering
TLDR
This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution, named genetic graph-based clustering (GGC), which takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph.
Android malware detection through hybrid features fusion and ensemble classifiers: The AndroPyTool framework and the OmniDroid dataset
TLDR
OmniDroid is presented, a large and comprehensive dataset of features extracted from 22,000 real malware and goodware samples aiming to help anti-malware tools creators and researchers when improving, or developing, new mechanisms and tools for Android malware detection.
Cellulat: an agent-based intracellular signalling model.
TLDR
An intracellular signalling model obtained by integrating several computational techniques into an agent-based paradigm is presented and the goal of a virtual laboratory based on this model and presently under development is discussed.
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