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2022 ◽  
Vol 16 (1) ◽  
pp. 0-0

To avoid information systems malfunction, their integrity disruption, availability violation as well as data confidentiality, it is necessary to detect anomalies in information system operation as quickly as possible. The anomalies are usually caused by malicious activity – information systems attacks. However, the current approaches to detect anomalies in information systems functioning have never been perfect. In particular, statistical and signature-based techniques do not allow detection of anomalies based on modifications of well-known attacks, dynamic approaches based on machine learning techniques result in false responses and frequent anomaly miss-outs. Therefore, various hybrid solutions are being frequently offered on the basis of those two approaches. The paper suggests a hybrid approach to detect anomalies by combining computationally efficient classifiers of machine learning with accuracy increase due to weighted voting. Pilot evaluation of the developed approach proved its feasibility for anomaly detection systems.


2021 ◽  
Author(s):  
Sloan Swieso ◽  
Powen Yao ◽  
Mark Miller ◽  
Adityan Jothi ◽  
Andrew Zhao ◽  
...  
Keyword(s):  

2021 ◽  
Author(s):  
Adityan Jothi ◽  
Powen Yao ◽  
Andrew Zhao ◽  
Mark Miller ◽  
Sloan Swieso ◽  
...  
Keyword(s):  

2021 ◽  
Author(s):  
Mark Miller ◽  
Powen Yao ◽  
Adityan Jothi ◽  
Andrew Zhao ◽  
Sloan Swieso ◽  
...  

Author(s):  
Tharun Palla

Abstract: With rapid growth in personal luxury and increasing jobs, People are comfortable using their personal vehicles rather than public transport to fulfill their transportation needs. This is because of ease of access and feasibility to use the vehicles at their own will at any point of time. It is leading to heavy traffic congestions and long waiting periods at traffic signals which is becoming a heavy burden in all major cities and will be affecting environment because of pollution caused by so many vehicles and also will disturb the individual’s time schedule. This paper proposes a system using data analytics, machine learning algorithms, Internet of things to predict the traffic flow, generate precise data about real time traffic congestions at that instant and rerouting the vehicles using navigation through a less congested path ultimately developing an Intelligent Traffic Management system. The architecture of the system is based on image analysis of vehicles using cameras at signals, using GPS in mobiles to monitor traffic in particular route. The combination of these two can be used to generate useful data about traffic congestions. Next part is calculating the efficient path to reach the destination with the generated data to minimize traffic and reach destination short period of time. The generated efficient route and traffic intensity is updated to the user with the help of maps application. Keywords: data analytics, machine learning, GPS, image analysis, intelligent traffic management system, Internet of things


Author(s):  
T Heena Fayaz

Abstract: The way politicians communicate with the electorateand run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SN). Due to inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial, and many times challenged. In this context, this work aims to investigate and summarize how research on predicting elections based on SM data has evolved since its beginning, to outline the state of both the art and the practice,and to identify research opportunities within this field. In termsof method, we performed a systematic literature review analyzingthe quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges, and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances on process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-art machine learning approaches. Index Terms: Elections, Social Media, Social Networks, Machine Learning, Systematic Review


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