An Agent-Based Architecture Using Deep Reinforcement Learning for the Intelligent Internet of Things Applications

Noureddine, Dhouha Ben; Krichen, Moez; Mechti, Seifeddine; Nahhal, Tarik; Adoni, Wilfried Yves Hamilton

doi:10.1007/978-981-15-6048-4_24

Cited by 3 publications

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“…The first step is automatic. It uses the Wordnet-similarity tool 4 for the detection of classes of high frequency terms. As for the second step, it involves a manual verification carried out by three different linguists, of the classes obtained automatically.…”

Section: Feature Extractionmentioning

confidence: 99%

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A decision system for computational authors profiling: From machine learning to deep learning

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Concurrency and Computation

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In this study, we tackle the problem of author profiling. The aim of the proposed approach is to determine the author's age and gender. Once the user connects to the company website, this company collects the available data about him (which is usually very limited). Then, the user receives a service recommendation according to his gender and age. Thus, a context-specific decision-making system based on these limited data is required to produce an efficient classification. Such a decision system allows companies to promote their marketing. To obtain the best categorization, machine learning (ML) and deep learning (DL) techniques have been applied in the literature. In this article, we apply both classical ML techniques and recently developed DL techniques. More precisely, we adopt the gated recurrent unit model. Our experiments show that our findings are positively comparable with the best state-of-the-art methods.

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Section: Feature Extractionmentioning

confidence: 99%

“…Many works concentrated on the categorization of texts and more exactly on the recognition of the gender, the age and the native language. 2 To detect the author profile machine learning (ML) and deep learning (DL) 3,4 techniques are used. For this purpose, it is interesting to compare these two techniques.…”

Section: Introductionmentioning

confidence: 99%

A decision system for computational authors profiling: From machine learning to deep learning

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et al. 2020

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Mining Fleet Management System in Real-Time “State of Art”

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The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

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An Agent-Based Architecture Using Deep Reinforcement Learning for the Intelligent Internet of Things Applications

Cited by 3 publications

References 12 publications

A decision system for computational authors profiling: From machine learning to deep learning

A decision system for computational authors profiling: From machine learning to deep learning

Mining Fleet Management System in Real-Time “State of Art”

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

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