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Abdullah Alamri
Researcher at Information Technology University
Publications - 21
Citations - 1116
Abdullah Alamri is an academic researcher from Information Technology University. The author has contributed to research in topics: Semantic computing & Semantic analytics. The author has an hindex of 7, co-authored 21 publications receiving 889 citations. Previous affiliations of Abdullah Alamri include RMIT University.
Papers
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Journal ArticleDOI
A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis
Adil Fahad,Najlaa Alshatri,Zahir Tari,Abdullah Alamri,Ibrahim Khalil,Albert Y. Zomaya,Sebti Foufou,Abdelaziz Bouras +7 more
TL;DR: Concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as a comparison, both from a theoretical and an empirical perspective are introduced.
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An Efficient Data-Driven Clustering Technique to Detect Attacks in SCADA Systems
TL;DR: An innovative intrusion detection approach to detect SCADA tailored attacks is presented based on a data-driven clustering technique of process parameters, which automatically identifies the normal and critical states of a given system and extracts proximity-based detection rules from the identified states for monitoring purposes.
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Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources
Urooj Asgher,Muhammad Babar Rasheed,Ameena Saad Al-Sumaiti,Atiq Ur-Rahman,Ihsan Ali,Amer Alzaidi,Abdullah Alamri +6 more
TL;DR: Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort and a multi-objective optimization problem has been formulated.
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A Multi-Layer Dual Attention Deep Learning Model With Refined Word Embeddings for Aspect-Based Sentiment Analysis
Syeda Rida-E-Fatima,Ali Javed,Ameen Banjar,Aun Irtaza,Hassan Dawood,Hussain Dawood,Abdullah Alamri +6 more
TL;DR: A deep learning-based multilayer dual-attention model is proposed to exploit the indirect relation between the aspect and opinion terms and word embeddings are refined by providing distinct vector representations to dissimilar sentiments, unlike the Word2Vec model.
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Ontology Middleware for Integration of IoT Healthcare Information Systems in EHR Systems
TL;DR: This research proposes a semantic middleware that exploits ontology to support the semantic integration and functional collaborations between IoT healthcare Information Systems and EHR systems.