[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

International Journal of Computer and Applications

There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to monitor people wearing masks in public places. Most recent and advanced face mask detection approaches are designed using deep learning. In this article, two state-of-the-art object detection models, namely, YOLOv3 and faster R-CNN are used to achieve this task. The authors have trained both the models on a dataset that consists of images of people of two categories that are with and without face masks. This work proposes a technique that will draw bounding boxes (red or green) around the faces of people, based on whether a person is wearing a mask or not, and keeps the record of the ratio of people wearing face masks on the daily basis. The authors have also compared the performance of both the models i.e., their precision rate and inference time.

/pdf/face-mask-detection-using-yolov3-and-faster-r-cnn-models-40yvurabts.pdf

Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment.

The Internet connects hundreds of millions of computers across the world running on multiple hardware and software platforms providing communication and commercial services. However, this interconnectivity among computers also enables malicious users to misuse resources and mount Internet attacks. The continuously growing Internet attacks pose severe challenges to develop a flexible, adaptive security oriented methods. Intrusion detection system (IDS) is one of most important component being used to detect the Internet attacks. In literature, different techniques from various disciplines have been utilized to develop efficient IDS. Artificial intelligence (AI) based techniques plays prominent role in development of IDS and has many benefits over other techniques. However, there is no comprehensive review of AI based techniques to examine and understand the current status of these techniques to solve the intrusion detection problems. In this paper, various AI based techniques have been reviewed focusing on development of IDS. Related studies have been compared by their source of audit data, processing criteria, technique used, dataset, classifier design, feature reduction technique employed and other experimental environment setup. Benefits and limitations of AI based techniques have been discussed. The paper will help the better understanding of different directions in which research has been done in the field of IDS. The findings of this paper provide useful insights into literature and are beneficial for those who are interested in applications of AI based techniques to IDS and related fields. The review also provides the future directions of the research in this area.

The use of artificial intelligence based techniques for intrusion detection: a review

D-FACE: An anomaly based distributed approach for early detection of DDoS attacks and flash events

Detection of DDoS attacks and flash events using novel information theory metrics

DDoS attacks are best detected near the victim's site as maximum attack traffic converges at this point. In most of the current solutions, monitoring and analysis of traffic for DDoS detection have been carried at a single link which connects victim to ISP. However the mammoth volume generated by DDoS attacks pose the biggest challenge in terms of memory and computational overheads. These overheads make DDoS solution itself vulnerable against DDoS attacks. We propose to distribute these overheads amongst all POPs of the ISP using an ISP level traffic feature distribution based approach. An ISP level topology and well known attack tools are used for simulations in ns-2. The comparison with volume based approach clearly indicates the supremacy of the proposed methodology

Krishan Kumar

Papers

Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment.

The use of artificial intelligence based techniques for intrusion detection: a review

D-FACE: An anomaly based distributed approach for early detection of DDoS attacks and flash events

Detection of DDoS attacks and flash events using novel information theory metrics

A Distributed Approach using Entropy to Detect DDoS Attacks in ISP Domain