9 Result(s)
within
Manar Amayri
Computer Science
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Article
A scaled dirichlet-based predictive model for occupancy estimation in smart buildings
In this study, we introduce a predictive model leveraging the scaled Dirichlet mixture model (SDMM). This data-driven approach offers enhanced accuracy in predictions, especially with a limited training datase...
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Article
Cross-collection latent Beta-Liouville allocation model training with privacy protection and applications
Cross-collection topic models extend previous single-collection topic models, such as Latent Dirichlet Allocation (LDA), to multiple collections. The purpose of cross-collection topic modeling is to model docu...
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Chapter and Conference Paper
Deep Learning Based Solution for Appliance Operational State Detection and Power Estimation in Non-intrusive Load Monitoring
This paper introduces a novel NILM algorithm that utilizes deep learning Temporal Convolutional Networks (TCN) for the regression and classification NILM tasks. The deep TCN layers in the proposed architecture...
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Chapter and Conference Paper
A Selective Supervised Latent Beta-Liouville Allocation for Document Classification
We propose a novel model, selective supervised Latent Beta-Liouville (ssLBLA), that improves the performance and generative process of supervised probabilistic topic models with a more flexible prior and simpl...
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Chapter and Conference Paper
Enhanced Energy Characterization and Feature Selection Using Explainable Non-parametric AGGMM
In this paper, we propose an asymmetric generalized Gaussian mixture model (AGGMM) with simultaneous feature selection for efficient and interpretable energy characterization in the context of demand response ...
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Chapter and Conference Paper
Novel Topic Models for Parallel Topics Extraction from Multilingual Text
In this work, we propose novel topic models to extract topics from multilingual documents. We add more flexibility to conventional LDA by relaxing some constraints in its prior. We apply other alternative prio...
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Chapter and Conference Paper
Stochastic Expectation Propagation Learning for Unsupervised Feature Selection
We introduce a statistical procedure for the simultaneous clustering and feature selection of positive vectors. The proposed method is based on well-principled infinite generalized inverted Dirichlet (GID) mix...
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Chapter and Conference Paper
A Generalized Inverted Dirichlet Predictive Model for Activity Recognition Using Small Training Data
In this paper, we develop the predictive distribution of the generalized inverted Dirichlet (GID) mixture model using local variational inference. The main goal is to be able to tackle classification problems ...
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Chapter and Conference Paper
Interactive Generalized Dirichlet Mixture Allocation Model
A lot of efforts have been put in recent times for research in the field of natural language processing. Extracting topics is undoubtedly one of the most important tasks in this area of research. Latent Dirich...
