S
Samir Brahim Belhaouari
Researcher at Khalifa University
Publications - 103
Citations - 687
Samir Brahim Belhaouari is an academic researcher from Khalifa University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 11, co-authored 64 publications receiving 309 citations. Previous affiliations of Samir Brahim Belhaouari include Alfaisal University & École Polytechnique.
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Journal ArticleDOI
Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice
TL;DR: This paper proposes a multiclass-pathologic voice classification using a novel multileveled textural feature extraction with iterative feature selector and shows that the fused features are more suitable for describing voice-based disease detection.
Journal ArticleDOI
Machine learning approach for the classification of corn seed using hybrid features
Aqib Ali,Salman Qadri,Wali Khan Mashwani,Samir Brahim Belhaouari,Samreen Naeem,Sidra Rafique,Farrukh Jamal,Christophe Chesneau,Sania Anam +8 more
TL;DR: The purpose of this study was to examine the feasibility of a machine learning (ML) approach for classifying different types of corn seeds through a digital camera in a natural environment without a complicated laboratory system.
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Convergence Results and Sharp Estimates for the Voter Model Interfaces
TL;DR: In this article, the authors studied the evolution of the interface for the one-dimensional voter model and showed that if the random walk kernel associated with the voter model has finite ε-th moment for some ε > 3, then the evolution convergence weakly converges to a Brownian motion under diffusive scaling.
Proceedings ArticleDOI
Intelligent methods for weather forecasting: A review
TL;DR: In this paper, some hybrid methods are discussed with their merits and demerits for weather forecasting with further accuracy is expectable by constructing a consortium of statistical and artificial intelligent methods.
Journal ArticleDOI
Unsupervised outlier detection in multidimensional data
TL;DR: The working of classification-based methods mostly relies on a confidence score, which is calculated by the classifier while making a prediction for the test observation, and some clusteringbased methods identify the outliers by not forcing every observation to belong to a label.