Anomaly detection has gained considerable attention in the past couple of years. Emerging technologies, such as the Internet of Things (IoT), are known to be among the most critical sources of data streams that produce massive amounts of data continuously from numerous applications. Examining these collected data to detect suspicious events can reduce functional threats and avoid unseen issues that cause downtime in the applications. Due to the dynamic nature of the data stream characteristics, many unresolved problems persist. In the existing literature, methods have been designed and developed to evaluate certain anomalous behaviors in IoT data stream sources. However, there is a lack of comprehensive studies that discuss all the aspects of IoT data processing. Thus, this paper attempts to fill this gap by providing a complete image of various state-of-the-art techniques on the major problems and core challenges in IoT data. The nature of data, anomaly types, learning mode, window model, datasets, and evaluation criteria are also presented. Research challenges related to data evolving, feature-evolving, windowing, ensemble approaches, nature of input data, data complexity and noise, parameters selection, data visualizations, heterogeneity of data, accuracy, and large-scale and high-dimensional data are investigated. Finally, the challenges that require substantial research efforts and future directions are summarized.
Social media is known as detectors platform that are used to measure the activities of the users in the real world. However, the huge and unfiltered feed of messages posted on social media trigger social warnings, particularly when these messages contain hate speech towards specific individual or community. The negative effect of these messages on individuals or the society at large is of great concern to governments and non-governmental organizations. Word clouds provide a simple and efficient means of visually transferring the most common words from text documents. This research aims to develop a word cloud model based on hateful words on online social media environment such as Google News. Several steps are involved including data acquisition and pre-processing, feature extraction, model development, visualization and viewing of word cloud model result. The results present an image in a series of text describing the top words. This model can be considered as a simple way to exchange high-level information without overloading the user's details.
To provide a sustainable fiber-to-the-home (FTTH), several multiplexing techniques have been developed for this purpose. The correlation features are the main obstacle behind the network performance limitation, which imposes to a high level of multiple access interference. However, the development of multiplexing techniques helps to overcome these limitations, such as optical-code division multiple access (Optical-CDMA). Optical-CDMA is considered as one of the most powerful solutions for FTTH. This paper aims to enhance FTTH network performance by applying Zero cross-correlation code (ZCC) with Optical-CDMA with maximum link single-mode fiber. In the simulation, the system performance is demonstrated in terms of bit error rate, Q-Factor and eye diagram measurements.
The optical vortex has recently attracted scholars to implement it in optical tweezers, microscopy, optical communications, quantum information processing, optical trapping, and laser machining. Optical vortex beam applied classically that can be transferred to the transverse amplitude of a heralded single-photon, and optical vortex possesses a helical wavefront and carries orbital angular momentum. In this study, Optical vortex is applied in optical-CDMA (optical code-division multiple-access) in conjunction with WDM (wavelength division multiplexing). This mechanism aims to increase the capacity and security in optical communication significantly. The implementation of Laguerre-Gaussian (LG) modes with optical vortex based on one dimension zero cross-correlation (ZCC) code shows that mode coupling reduces effectively. Consequently, a positive increase in channel performance and response. Accordingly, the LG modes based on the 1D-ZCC code are investigated and propagated over multi-mode fiber (MMF) based on an optical vortex, which also substantially reduces channel effects. Consequently, all these attributes combined will result in a hybrid WDM-Optical-CDMA with an optical vortex system over MMF.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.