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- Ahmadian, Zahra, et al.
(författare)
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New Attacks on UMTS Network Access
- 2009
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Konferensbidrag (refereegranskat)abstract
- In this paper we propose two new attacks on UMTS network. Both attacks exploit the UMTS-GSM interworking and are possible in the GSM access area of UMTS network. The first attack allows the attacker to eavesdrop on the entire traffic of the victim UMTS subscriber in the GERAN coverage of the UMTS network. The second attack is an impersonation attack i.e. the attacker impersonates a genuine UMTS subscriber to a UMTS network and fools the network to provide services at the expense of the victim subscriber in its GERAN coverage.
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- Amini, Mehdi, et al.
(författare)
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Interpretable PET/CT Radiomic Based Prognosis Modeling of NSCLC Recurrent Following Complete Resection
- 2022
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Ingår i: 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665488723
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Konferensbidrag (refereegranskat)abstract
- This study aimed to develop an interpretable prognostic model with a nomogram for Non-Small Cell Lung Cancer (NSCLC) recurrence prediction following complete resection, using multi-modality PET/CT fusion radiomics and patients' clinical features. Retrospectively, 181 NSCLC patients who had undergone18F-FDG PET/CT scan were enrolled and split into training (2/3) and testing (1/3) partitions. Before image fusion, PET and CT images were registered, resized to equal isotropic voxel size, and clipped and normalized. Guided Filtering Fusion GFF algorithm was used for image fusion. Two hundred eighteen radiomic features were extracted from each PET, CT, and fused image, including morphological, first-order statistical, and texture features. Clinical features included age, sex, smoking status, weight, radiation, chemotherapy, pathological stage, etc. Feature selection and univariate and multivariate modeling were performed using the CoxBoost algorithm. Harrell's Concordance index (C-index) was used to evaluate the performance of the models, and compare C test was used to statistically compare the performance of the models (p-values < 0.05 were considered significant). Clinical, Clinical+PET, Clinical+CT, and Clinical+GFF resulted in c-indices (confidence interval) of 0.701 (0.589-0.812), 0.757 (0.647-0.867), 0.706 (0.607, 0.807), and 0.824 (0.751-0.896), respectively. Statistical comparison of the performance of different models with the Clinical model revealed that while PET and GFF features can significantly increase the performance (p-values 0.009 and 0.001, respectively), CT features did not significantly improve the performance of the Clinical model (p-value 0.279). Therefore, the nomogram was developed based on the Clinical+GFF model (with the best performance). Radiomic features extracted from PET and PET/CT fusion images can improve the recurrence prognosis in NSCLC patients compared to the conventional clinical factors alone.
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- Amirhossein, Berenji, et al.
(författare)
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curr2vib : Modality Embedding Translation for Broken-Rotor Bar Detection
- 2023
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Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer Nature. - 9783031236334 ; , s. 423-437
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Konferensbidrag (refereegranskat)abstract
- Recently and due to the advances in sensor technology and Internet-of-Things, the operation of machinery can be monitored, using a higher number of sources and modalities. In this study, we demonstrate that Multi-Modal Translation is capable of transferring knowledge from a modality with higher level of applicability (more usefulness to solve an specific task) but lower level of accessibility (how easy and affordable it is to collect information from this modality) to another one with higher level of accessibility but lower level of applicability. Unlike the fusion of multiple modalities which requires all of the modalities to be available during the deployment stage, our proposed method depends only on the more accessible one; which results in the reduction of the costs regarding instrumentation equipment. The presented case study demonstrates that by the employment of the proposed method we are capable of replacing five acceleration sensors with three current sensors, while the classification accuracy is also increased by more than 1%.
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- Arani, Zahra Moteshaker, et al.
(författare)
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Non-uniform Sampling Methods for Large Itemset Mining
- 2023
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Ingår i: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. ; , s. 5714-5722
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Konferensbidrag (refereegranskat)abstract
- A well-studied problem in data mining is large itemset mining. To address this problem over very large datasets, several approximate algorithms have been introduced, where an important class of such methods relies on sampling. However in the literature, only methods that are based on uniform sampling are investigated. In this paper, first we discuss how different sampling methods can be described using a generic sampling algorithm and study a property desirable for sampling methods. Then we use this property to argue that some non-uniform sampling methods may work better. We accordingly propose methods that sample each transaction proportional to its number of items or proportional to its number of frequent items. Finally, by conducting extensive experiments over real-world datasets, we show that non-uniform sampling methods usually outperform the uniform method.
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- Bagheri, Zahra, et al.
(författare)
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Performance assessment of an insect-inspired target tracking model in background clutter
- 2014
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Ingår i: 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. - 9781479951994 ; , s. 822-826
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Konferensbidrag (refereegranskat)abstract
- Biological visual systems provide excellent examples of robust target detection and tracking mechanisms capable of performing in a wide range of environments. Consequently, they have been sources of inspiration for many artificial vision algorithms. However, testing the robustness of target detection and tracking algorithms is a challenging task due to the diversity of environments for applications of these algorithms. Correlation between image quality metrics and model performance is one way to deal with this problem. Previously we developed a target detection model inspired by physiology of insects and implemented it in a closed loop target tracking algorithm. In the current paper we vary the kinetics of a salience-enhancing element of our algorithm and test its effect on the robustness of our model against different natural images to find the relationship between model performance and background clutter.
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