다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining Practical Machine Learning Tools and Techniques

National Institute of Standards and Technology における超伝導研究及び生活

Matrix Factorization Techniques for Recommender Systems

http://wpage.unina.it/valerio.persico/pubs/CloudIoT_FGCS.pdf

Integration of Cloud computing and Internet of Things

Utilizing cloud services in running large-scale video surveillance systems is not uncommon. However, special attention should be given to data security and privacy because, typically, data travels over insecure public networks. In this work, we propose an end-to-end security framework for a cloud-based video surveillance system that supports a large number of cameras. Our framework provides mutual authentication, session key management, data confidentiality, and data integrity. Consequently, encrypted video frames can only be sourced from authenticated cameras and only destined to authenticated cloud devices where the integrity of such frames can also be verified against potential change. As video streaming is a very delay-sensitive application, we study different variations of the proposed framework to find security options that achieve the best trade-off between the added delay and the security of the system.

A security framework for cloud-based video surveillance system

Enabling efficient and secure energy cloud using edge computing and 5G

This paper discusses the problem of collusion attacks in Internet of Things (IoT) environments and how mobility of IoT devices increases the difficulty of detecting such types of attacks. It demonstrates how approaches used in detecting collusion attacks in WSNs are not applicable in IoT environments. To this end, the paper introduces a model based on the Fog Computing infrastructure to keep track of IoT devices and detect collusion attackers. The model uses fog computing layer for real-time monitoring and detection of collusion attacks in IoT environments. Moreover, the model uses a software defined system layer to add a degree of flexibility for configuring Fog nodes in order to enable them to detect various types of collusion attacks. Furthermore, the paper highlights the possible overhead on Fog nodes and network when applying the proposed model, and claims that the Fog layer infrastructure can provide the required resources for the scalability of the model.

Collusion attacks in Internet of Things: Detection and mitigation using a fog based model

Multi-label classification of textual data is an important problem with the growing size of available data and the increasing difficulties in assigning a single label to each piece of text. Examples range from news articles to emails. Most of the existing works consider English text. This work focuses on multi-label classification of Arabic articles. After dataset collection, three multi-label classifiers are considered (DT, RF and KNN). The results show a superiority of DT over the other two classifiers.

A supervised approach for multi-label classification of Arabic news articles

Opinion mining is an important step towards facilitating information in health data. Several studies have demonstrated the possibility of tracking diseases using public tweets. However, most studies were applied to English language tweets. Influenza is currently one of the world’s greatest infectious disease challenges. In this study, a new approach is proposed in order to detect Influenza using machine learning techniques from Arabic tweets in Arab countries. This paper is the first study of epidemic diseases based on Arabic language tweets. In this work, we have collected, labeled, filtered and analyzed the influenza-related tweets written in the Arabic language. Several classifiers were used to measure the quality and the performance of the approach, which are: Naive Bayes, Support Vector Machines, Decision Trees, and K-Nearest Neighbor. The classifiers which achieved the best accuracy results for the three experiments were: Näıve Bayes with 89.06%, and K-Nearest Neighbor with 86.43%, respectively.

/pdf/detecting-epidemic-diseases-using-sentiment-analysis-of-2b5opglm0j.pdf

Yaser Jararweh

Papers

A security framework for cloud-based video surveillance system

Enabling efficient and secure energy cloud using edge computing and 5G

Collusion attacks in Internet of Things: Detection and mitigation using a fog based model

A supervised approach for multi-label classification of Arabic news articles

Detecting Epidemic Diseases Using Sentiment Analysis of Arabic Tweets