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Article
Open AccessFederated Constrastive Learning and Visual Transformers for Personal Recommendation
This paper introduces a novel solution for personal recommendation in consumer electronic applications. It addresses, on the one hand, the data confidentiality during the training, by exploring federated learn...
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Article
Open AccessBuilding an open-source system test generation tool: lessons learned and empirical analyses with EvoMaster
Research in software testing often involves the development of software prototypes. Like any piece of software, there are challenges in the development, use and verification of such tools. However, some challe...
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Article
Open AccessWhen explainable AI meets IoT applications for supervised learning
This paper introduces a novel and complete framework for solving different Internet of Things (IoT) applications, which explores eXplainable AI (XAI), deep learning, and evolutionary computation. The IoT data ...
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Article
Group intrusion detection in the Internet of Things using a hybrid recurrent neural network
This paper introduces a novel framework for identifying a group of intrusions in the context of the Internet of Things (IoT). It combines both deep learning and decomposition. A set of data is first collected ...
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Article
SS-ITS: secure scalable intelligent transportation systems
This paper introduces a secure and scalable intelligent transportation and human behavior system to accurately discover knowledge from urban traffic data. The data are secured using blockchain learning technol...
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Article
Open AccessCluster-based information retrieval using pattern mining
This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fa...
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Article
Open AccessA recurrent neural network for urban long-term traffic flow forecasting
This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced...
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Article
Open AccessA general-purpose distributed pattern mining system
This paper explores five pattern mining problems and proposes a new distributed framework called DT-DPM: Decomposition Transaction for Distributed Pattern Mining. DT-DPM addresses the limitations of the existi...
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Chapter and Conference Paper
GBSO-RSS: GPU-Based BSO for Rules Space Summarization
In this paper, we present a novel GBSO-RSS algorithm to deal with exploration and mining of association rules in big data, with the big challenge of increasing computation time. The GBSO-RSS algorithm is based...
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Chapter
Metaheuristics for Frequent and High-Utility Itemset Mining
Metaheuristics are often used to solve combinatorial problems. They can be viewed as general purpose problem-solving approaches based on stochastic methods, which explore very large search spaces to find near-...
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Article
A new framework for metaheuristic-based frequent itemset mining
This paper proposes a novel framework for metaheuristic-based Frequent Itemset Mining (FIM), which considers intrinsic features of the FIM problem. The framework, called META-GD, can be used to steer any metah...
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Article
How to exploit high performance computing in population-based metaheuristics for solving association rule mining problem
The application of population-based metaheuristics approaches to the association rules mining problem is explored in this paper. The combination of GPU and cluster-based parallel computing techniques is invest...
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Chapter and Conference Paper
An Hybrid Multi-Core/GPU-Based Mimetic Algorithm for Big Association Rule Mining
This paper addresses the problem of big association rule mining using an evolutionary approach. The mimetic method has been successfully applied to small and medium size databases. However, when applied on lar...
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Chapter and Conference Paper
Discovering Strong Meta Association Rules Using Bees Swarm Optimization
For several applications, association rule mining produces an extremely large number of rules. Analyzing a large number of rules can be very time-consuming for users. Therefore, eliminating irrelevant associat...
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Chapter and Conference Paper
Reasoning with Multiple-Agent Possibilistic Logic
In multiple-agent logic, a formula is in the form of (a, A) where a is a propositional formula and A is a subset of agents. It states that at least all agents in A believe that a is true. This paper presents a me...