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    Browsing by Author "Amira, Abbes"

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      • 3D point cloud enhancement using unsupervised anomaly detection 

        Regaya, Yousra; Fadli, Fodil; Amira, Abbes ( Institute of Electrical and Electronics Engineers Inc. (IEEE) , 2019 , Conference)
        3D point cloud is increasingly getting attention for perceiving 3D environment which is needed in many emerging applications. This data structure is challenging due to its characteristics and the limitation of the acquisition ...
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        Automated Segmentation of Cerebral Aneurysm Using a Novel Statistical Multiresolution Approach 

        Regaya, Yousra Mounir (2018 , Master Thesis)
        Cerebral Aneurysm (CA) is a vascular disease that threatens the lives of many adults. It a ects almost 1:5 - 5% of the general population. Sub- Arachnoid Hemorrhage (SAH), resulted by a ruptured CA, has high rates ...
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        Deep transfer learning for automatic speech recognition: Towards better generalization 

        Hamza, Kheddar; Himeur, Yassine; Al-Maadeed, Somaya; Amira, Abbes; Bensaali, Faycal ( Elsevier B.V. , 2023 , Article)
        Automatic speech recognition (ASR) has recently become an important challenge when using deep learning (DL). It requires large-scale training datasets and high computational and storage resources. Moreover, DL techniques ...
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        Differential Architecture Search in Deep Learning for DNA Splice Site Classification 

        Moosa, Shabir (2019 , Master Thesis)
        The data explosion caused by unprecedented advancements in the field of genomics is constantly challenging the conventional methods used in the interpretation of the human genome. The demand for robust algorithms over the ...
      • Exploring 2D Representation and Transfer Learning Techniques for People Identification in Indoor Localization 

        Kerdjidj, Oussama; Himeur, Yassine; Atalla, Shadi; Copiac, Abigail; Sohail, Shahab Saquib; Fadli, Fodil; Amira, Abbes; Mansoor, Wathiq; Gawanmeh, Amjad... more authors ... less authors ( Institute of Electrical and Electronics Engineers Inc. (IEEE) , 2023 , Conference)
        Indoor localization is a crucial aspect of various disciplines in our daily lives. It enables efficient administration tasks and improves safety by identifying the position of items or people inside spaces, making it useful ...
      • Exploring Deep Time-Series Imaging for Anomaly Detection of Building Energy Consumption 

        Copiaco, Abigail; Himeur, Yassine; Amira, Abbes; Mansoor, Wathiq; Fadli, Fodil; Atalla, Shadi... more authors ... less authors ( Institute of Electrical and Electronics Engineers Inc. (IEEE) , 2022 , Conference)
        Although deep anomaly detection (DAD) is crucial to optimize energy management in smart buildings, there is a lack of efficient research investigating DAD of energy consumption time-series. Besides, analyzing one-dimensional ...
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        Features Ranking Techniques for Single Nucleotide Polymorphism Data 

        Abounada, Mohanad Feisal M H (2017 , Master Thesis)
        Identifying biomarkers like single nucleotide polymorphisms (SNPs) is an important topic in biomedical applications. Such SNPs can be associated with an individual’s metabolism of drugs, which make these SNPs targets for ...
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        An innovative deep anomaly detection of building energy consumption using energy time-series images 

        Abigail, Copiaco; Himeur, Yassine; Amira, Abbes; Mansoor, Wathiq; Fadli, Fodil; Atalla, Shadi; Sohail, Shahab Saquib... more authors ... less authors ( Elsevier , 2023 , Article)
        Deep anomaly detection (DAD) is essential in optimizing building energy management. Nonetheless, most existing works concerning this field consider unsupervised learning and involve the analysis of sensor readings through ...
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        Latent Semantic Indexing (LSI) Based Distributed System and Search On Encrypted Data 

        Shouman, Abdelrahman Mossad (2018 , Master Thesis)
        Latent semantic indexing (LSI) was initially introduced to overcome the issues of synonymy and polysemy of the traditional vector space model (VSM). LSI, however, has challenges of its own, mainly scalability. Despite being ...
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        Pedestrian Detection Using Motion Saliency Aided Convoltional Neural Network 

        Farhat, Ali Abdul Hafiz (2018 , Master Thesis)
        Pedestrian and crowd analysis is one of the oldest problems in the area of computer vision and image processing. Researchers have proposed several algorithms that analyze crowds for different purposes aiming to improve ...
      • Point-Denoise: Unsupervised outlier detection for 3D point clouds enhancement 

        Regaya, Yousra; Fadli, Fodil; Amira, Abbes ( Springer Nature , 2021 , Article)
        3D point cloud denoising is an increasingly demanding field as such type of data structure is getting more attention in perceiving the 3D environment for diverse applications. Despite their novelty, recently proposed ...
      • Recommendation System Towards Residential Energy Saving Based on Anomaly Detection 

        Atalla, Shadi; Himeur, Yassine; Mansoor, Wathiq; Amira, Abbes; Fadli, Fodil; Copiaco, Abigail; Sohail, Shahab Saquib... more authors ... less authors ( Institute of Electrical and Electronics Engineers Inc. (IEEE) , 2022 , Conference)
        This paper presents a recommender system to promote energy consumption reduction behaviors in residential buildings. The system exploits data stream processing methods jointly with machine learning algorithms on real-time ...
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        Safety Aware Vehicle Routing Algorithm, A Weighted Sum Approach 

        Nizam, Radwan (2017 , Master Thesis)
        Driving is an essential part of work life for many people. Although driving can be enjoyable and pleasant, it can also be stressful and dangerous. Many people around the world are killed or seriously injured while driving. ...
      • A Two-Stage Energy Anomaly Detection for Edge-based Building Internet of Things (BIoT) Applications 

        Himeur, Yassine; Fadli, Fodil; Amira, Abbes ( Institute of Electrical and Electronics Engineers Inc. (IEEE) , 2022 , Conference)
        The Building Internet of Energy (BIoE) is quite promising for curtailing energy consumption, reducing costs, and promoting building transformation. Integrating Artificial Intelligence into the BIoE is essential for big ...

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