Social mining for terroristic behavior detection through Arabic tweets characterization

2021 ◽  
Vol 116 ◽  
pp. 132-144
Author(s):  
Wadee Alhalabi ◽  
Jari Jussila ◽  
Kamal Jambi ◽  
Anna Visvizi ◽  
Hafsa Qureshi ◽  
...  
2008 ◽  
Vol 128 (11) ◽  
pp. 1649-1656 ◽  
Author(s):  
Hironobu Satoh ◽  
Fumiaki Takeda ◽  
Yuhki Shiraishi ◽  
Rie Ikeda

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1635
Author(s):  
Neeraj Chugh ◽  
Geetam Singh Tomar ◽  
Robin Singh Bhadoria ◽  
Neetesh Saxena

To sustain the security services in a Mobile Ad Hoc Networks (MANET), applications in terms of confidentially, authentication, integrity, authorization, key management, and abnormal behavior detection/anomaly detection are significant. The implementation of a sophisticated security mechanism requires a large number of network resources that degrade network performance. In addition, routing protocols designed for MANETs should be energy efficient in order to maximize network performance. In line with this view, this work proposes a new hybrid method called the data-driven zone-based routing protocol (DD-ZRP) for resource-constrained MANETs that incorporate anomaly detection schemes for security and energy awareness using Network Simulator 3. Most of the existing schemes use constant threshold values, which leads to false positive issues in the network. DD-ZRP uses a dynamic threshold to detect anomalies in MANETs. The simulation results show an improved detection ratio and performance for DD-ZRP over existing schemes; the method is substantially better than the prevailing protocols with respect to anomaly detection for security enhancement, energy efficiency, and optimization of available resources.


2016 ◽  
Vol 59 (3) ◽  
pp. 99-106 ◽  
Author(s):  
Xi Wang ◽  
Nickolai Zeldovich ◽  
M. Frans Kaashoek ◽  
Armando Solar-Lezama

Author(s):  
Hajra Binte Naeem ◽  
Muhammad Haroon Yousaf ◽  
Farhan Hassan Khan ◽  
Amanullah Yasin

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alina Trifan ◽  
José Luis Oliveira

Abstract With the continuous increase in the use of social networks, social mining is steadily becoming a powerful component of digital phenotyping. In this paper we explore social mining for the classification of self-diagnosed depressed users of Reddit as social network. We conduct a cross evaluation study based on two public datasets in order to understand the impact of transfer learning when the data source is virtually the same. We further complement these results with an experiment of transfer learning in post-partum depression classification, using a corpus we have collected for the matter. Our findings show that transfer learning in social mining might still be at an early stage in computational research and we thoroughly discuss its implications.


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