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| 1 | Primary Sjogren's syndrome with central nervous system involvement 显示文摘 | Alhomoud IA Bohlega SA Alkawi MZ | 2009 | Saudi Med J2009，30，8： | 1 |
| 2 | Primary Sjogren's syn- drome with central nervous system involvement显示文摘 | Alhomoud IA Bohlega SA Alkawi MZ | 2009 | Saudi Med J2009，30，8： | 1 |
| 3 | Primary Sjogren's syndrome with central nervous system involvement显示文摘 | Alhomoud IA Bohlega SA Alkawi MZ | | 0，，： | 1 |
| 4 | A classification system for the assessment of slope stability of terrains along highway routes in Jordan显示文摘 | ALHOMOUD A S MASANAT Y | 1998 | Environ- mental Geology1998，734，1： | 1 |
| 5 | Risk factors for abdominal incision infection after colorectal surgery in a saudi arabian population:the method of surveillance matters显示文摘 | Hibbert D Abduljabbar AS Alhomoud SJ | 2015 | Surg Infect(Larchmt)2015，16，3： | 1 |
| 6 | Towards Securing Machine Learning Models Against Membership Inference Attacks显示文摘From fraud detection to speech recognition,including price prediction,Machine Learning(ML)applications are manifold and can significantly improve different areas.Nevertheless,machine learning models are vulnerable and are exposed to different security and privacy attacks.Hence,these issues should be addressed while using ML models to preserve the security and privacy of the data used.There is a need to secure ML models,especially in the training phase to preserve the privacy of the training datasets and to minimise the information leakage.In this paper,we present an overview of ML threats and vulnerabilities,and we highlight current progress in the research works proposing defence techniques againstML security and privacy attacks.The relevant background for the different attacks occurring in both the training and testing/inferring phases is introduced before presenting a detailed overview of Membership Inference Attacks(MIA)and the related countermeasures.In this paper,we introduce a countermeasure against membership inference attacks(MIA)on Conventional Neural Networks(CNN)based on dropout and L2 regularization.Through experimental analysis,we demonstrate that this defence technique can mitigate the risks of MIA attacks while ensuring an acceptable accuracy of the model.Indeed,using CNN model training on two datasets CIFAR-10 and CIFAR-100,we empirically verify the ability of our defence strategy to decrease the impact of MIA on our model and we compare results of five different classifiers.Moreover,we present a solution to achieve a trade-off between the performance of themodel and the mitigation of MIA attack. | Sana Ben Hamida Hichem Mrabet Sana Belguith Adeeb Alhomoud Abderrazak Jemai | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 7 | Vehicle Detection in Challenging Scenes Using CenterNet Based Approach显示文摘Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System(ITS)and reduces road accidents.The major obstacles in automatic detection of tiny vehicles are due to occlusion,environmental conditions,illumination,view angles and variation in size of objects.This research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed area.In this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks(DNN)namely CenterNet.Substantially DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in crowded area.Primarily there are two categories of deep learning models single-step and two-step.A single forward pass model is the one in which detection is performed directly to possible location over dense sampling,wherein two-step models incorporated by Region proposals followed by object detection.We in this research scrutinize one-step State of the art(SOTA)model CenteNet as proposed recently with three different feature extractor ResNet-50,HourGlass-104 and ResNet-101 one by one.We train our model on challenging KITTI dataset which outperforms in comparison with SOTA single-step technique MSSD300∗which depicts performance improvement by 20.2%mAPandSMOKEby with 13.2%mAP respectively.Effectiveness of CenterNet can be justified through the huge improved performance.The performance of our model is evaluated on KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute)benchmark dataset with different backbones such as ResNet-50 gives 62.3%mAP ResNet-10182.5%mAP,last but not the least HourGlass-104 outperforms with 98.2%mAP CenterNet-HourGlass-104 achieved high mAP among above mentioned feature extractors.We also compare our model with other SOTA techniques. | Ayesha Muhammad Javed Iqbal Iftikhar Ahmad Madini OAlassafi Ahmed SAlfakeeh Ahmed Alhomoud | 2023 | Computers, Materials & Continua2023，，2： | 0 |
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