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A. Ayadi

Researcher at University of Gabès

Publications -  20
Citations -  253

A. Ayadi is an academic researcher from University of Gabès. The author has contributed to research in topics: Wireless sensor network & Anomaly detection. The author has an hindex of 7, co-authored 20 publications receiving 173 citations.

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Outlier detection approaches for wireless sensor networks

TL;DR: This survey describes a comprehensive overview of existing outlier detection techniques specifically used for the wireless sensor networks and presents a comparative table used as a guideline to select which technique is adequate for the application in terms of characteristics such as detection mode, architectural structure and correlation extraction.
Journal Article

Association of severe autosomal recessive osteopetrosis and Dandy-Walker syndrome with agenesis of the corpus callosum.

TL;DR: A severe form of autosomal recessive osteopetrosis associated with Dandy-Walker syndrome and agenesis of the corpus callosum is reported in a full-term boy born to consanguineous parents, who died at the age of 2 months.
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Aspects cliniques et évolutifs des méningites bactériennes néonatales

TL;DR: Cette etude souligne la gravite des meningites bacteriennes neonatales avec des taux de mortalite et de sequelles neurosensorielles eleves surtout chez les prematures et les faibles poids de naissance.
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Spatio-temporal correlations for damages identification and localization in water pipeline systems based on WSNs

TL;DR: This work has presented a distributed one class classification technique for outliers detection based on WSNs, and investigated the notion of the relationship existing between closed neighboring nodes and the existing correlation between historical observations to identify damages sources.
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A framework of monitoring water pipeline techniques based on sensors technologies

TL;DR: After the evaluation of all the existing pipeline monitoring methods, it is much more evident that techniques based on wireless sensors networks have variety and are the best selection for water pipeline monitoring purposes.