Youcef Djenouri
Biographic Data
| ID | 9446362 |
|---|---|
| NAME | Youcef Djenouri |
| GIVEN NAMES | Youcef |
| FAMILY NAME | Djenouri |
| SIGNATURE | DJENOURI Y |
| AFFILIATIONS | University of South-Eastern Norway |
| ORCID | 0000-0003-0135-7450 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Grouping Interesting Patterns for Understanding Customer Behaviors
This article presents a highly efficient technique for pattern mining in the realm of customer behavior analysis, termed hybrid clustering patterns for customer behavior analysis (HCP-CBA). It leverages decomposition techniques to uncover relevant patterns by examining correlations among customer transactions within the dataset. Initially, the transaction dataset undergoes decomposition, grouping together transactions exhibiting high correlations…
Social Web in IoT
Many Internet of Things (IoT) applications can benefit from Social Web of Things (S-WoT) methods that enable knowledge discovery and help solving interoperability problems. The semantic modeling of S-WoT is the main emphasis of this work where we suggest a novel solution, evolutionary clustering for ontology matching (ECOM), to explore correlations between S-WoT data using clustering and evolutionary computation methodologies. The ECOM approach u…
Advanced Pattern-Mining System for Fake News Analysis
Decomposition MapReduce mining for fake news analysis (DMRM-FNA), a novel generic parallel pattern-mining framework, is developed in this article to solve difficulties in social network analysis using big data exploration. The first difficulty faced by existing techniques is the inability to retrieve actionable insights into the structure of fake news data. This can be solved by extracting patterns from fake news and matching them with real news …
Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis
This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitte…
Knowledge graph based trajectory outlier detection in sustainable smart cities
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Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis
This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitte…
Knowledge graph based trajectory outlier detection in sustainable smart cities
Advanced Pattern-Mining System for Fake News Analysis
Decomposition MapReduce mining for fake news analysis (DMRM-FNA), a novel generic parallel pattern-mining framework, is developed in this article to solve difficulties in social network analysis using big data exploration. The first difficulty faced by existing techniques is the inability to retrieve actionable insights into the structure of fake news data. This can be solved by extracting patterns from fake news and matching them with real news …
Social Web in IoT
Many Internet of Things (IoT) applications can benefit from Social Web of Things (S-WoT) methods that enable knowledge discovery and help solving interoperability problems. The semantic modeling of S-WoT is the main emphasis of this work where we suggest a novel solution, evolutionary clustering for ontology matching (ECOM), to explore correlations between S-WoT data using clustering and evolutionary computation methodologies. The ECOM approach u…
Grouping Interesting Patterns for Understanding Customer Behaviors
This article presents a highly efficient technique for pattern mining in the realm of customer behavior analysis, termed hybrid clustering patterns for customer behavior analysis (HCP-CBA). It leverages decomposition techniques to uncover relevant patterns by examining correlations among customer transactions within the dataset. Initially, the transaction dataset undergoes decomposition, grouping together transactions exhibiting high correlations…
Computer Science (5 works) · Artificial Intelligence (4 works) · Data mining (3 works) · Data science (3 works) · Information retrieval (3 works) · Theoretical Computer Science (3 works) · Caching and Content Delivery (2 works) · Digital Marketing and Social Media (2 works) · Graph (2 works) · Machine learning (2 works)