5th International Symposium on Data Mining Applications (eBook, PDF)

Redaktion: Alenezi, Mamdouh; Qureshi, Basit

• Format: PDF

Produktbeschreibung

The 5th Symposium on Data Mining Applications (SDMA 2018) provides valuable opportunities for technical collaboration among data mining and machine learning researchers in Saudi Arabia, Gulf Cooperation Council (GCC) countries and the Middle East region. This book gathers the proceedings of the SDMA 2018. All papers were peer-reviewed based on a strict policy concerning the originality, significance to the area, scientific vigor and quality of the contribution, and address the following research areas.

. Applications: Applications of data mining in domains including databases, social networks, web, bioinformatics, finance, healthcare, and security.

. Algorithms: Data mining and machine learning foundations, algorithms, models, and theory.

. Text Mining: Semantic analysis and mining text in Arabic, semi-structured, streaming, multimedia data.

. Framework: Data mining frameworks, platforms and systems implementation.

. Visualizations: Data visualization and modeling.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 248
• Erscheinungstermin: 28. März 2018
• Englisch
• ISBN-13: 9783319787534
• Artikelnr.: 52938030

Inhaltsangabe

1. Use of Machine Learning for Rate Adaptation in MPEG-DASH for Quality of Experience Improvement.- 2. An Analysis of Traffic Accident in KSA,Riyadh by Using Clustering Techniques.- 3. The Effect of Vitamin B12 Deficiency on Blood Count Using Data Mining.- 4.Support of Existing Chatbot Development Framework for Arabic Language: A Brief Survey.- 5. Pattern Orientation in Arabic Corpus Annotation and a Proposed Pattern Ontology.- 6. A Benchmark Collection for Program Objectives Mapping to ABET Outcomes: Accreditation.- 7. Bug Reports Evolution in Open Source System.- 8. Analysis of Call Detail Records for Understanding Users Behavior and Anomaly Detection Using Neo4j.- 9.Empirical Analysis of Static Code Metrics for Predicting Risk Scores in Android Applications.- 10.Toward Stream Analysis of Software Debugging Data.

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