637 research outputs found
Data mining and classification for traffic systems using genetic network programming
制度:新 ; 報告番号:甲3271号 ; 学位の種類:博士(工学) ; 授与年月日:2011/3/15 ; 早大学位記番号:新557
Evaluation of machine learning algorithms for anomaly detection
Malicious attack detection is one of the critical cyber-security challenges in the peer-to-peer smart grid platforms due to the fact that attackers' behaviours change continuously over time. In this paper, we evaluate twelve Machine Learning (ML) algorithms in terms of their ability to detect anomalous behaviours over the networking practice. The evaluation is performed on three publicly available datasets: CICIDS-2017, UNSW-NB15 and the Industrial Control System (ICS) cyber-attack datasets. The experimental work is performed through the ALICE high-performance computing facility at the University of Leicester. Based on these experiments, a comprehensive analysis of the ML algorithms is presented. The evaluation results verify that the Random Forest (RF) algorithm achieves the best performance in terms of accuracy, precision, Recall, F1-Score and Receiver Operating Characteristic (ROC) curves on all these datasets. It is worth pointing out that other algorithms perform closely to RF and that the decision regarding which ML algorithm to select depends on the data produced by the application system
Efficient Object Detection in Optical Remote Sensing Imagery via Attention-based Feature Distillation
Efficient object detection methods have recently received great attention in
remote sensing. Although deep convolutional networks often have excellent
detection accuracy, their deployment on resource-limited edge devices is
difficult. Knowledge distillation (KD) is a strategy for addressing this issue
since it makes models lightweight while maintaining accuracy. However, existing
KD methods for object detection have encountered two constraints. First, they
discard potentially important background information and only distill nearby
foreground regions. Second, they only rely on the global context, which limits
the student detector's ability to acquire local information from the teacher
detector. To address the aforementioned challenges, we propose Attention-based
Feature Distillation (AFD), a new KD approach that distills both local and
global information from the teacher detector. To enhance local distillation, we
introduce a multi-instance attention mechanism that effectively distinguishes
between background and foreground elements. This approach prompts the student
detector to focus on the pertinent channels and pixels, as identified by the
teacher detector. Local distillation lacks global information, thus attention
global distillation is proposed to reconstruct the relationship between various
pixels and pass it from teacher to student detector. The performance of AFD is
evaluated on two public aerial image benchmarks, and the evaluation results
demonstrate that AFD in object detection can attain the performance of other
state-of-the-art models while being efficient
Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network
Skin lesion detection in dermoscopic images is essential in the accurate and
early diagnosis of skin cancer by a computerized apparatus. Current skin lesion
segmentation approaches show poor performance in challenging circumstances such
as indistinct lesion boundaries, low contrast between the lesion and the
surrounding area, or heterogeneous background that causes over/under
segmentation of the skin lesion. To accurately recognize the lesion from the
neighboring regions, we propose a dilated scale-wise feature fusion network
based on convolution factorization. Our network is designed to simultaneously
extract features at different scales which are systematically fused for better
detection. The proposed model has satisfactory accuracy and efficiency. Various
experiments for lesion segmentation are performed along with comparisons with
the state-of-the-art models. Our proposed model consistently showcases
state-of-the-art results
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