Network connection logs have long been recognized as integral to proper network security, maintenance, and performance management. This paper provides a development of distributed systems and write optimized databases: However, even a somewhat sizable network will generate large amounts of logs at very high rates. This paper explains why many storage methods are insufficient for providing real-time analysis on sizable datasets and examines database techniques attempt to address this challenge. We argue that sufficient methods include distributing storage, computation, and write optimized datastructures (WOD). Diventi, a project developed by Sandia National Laboratories, is here used to evaluate the potential of WODs to manage large datasets of network connection logs. It can ingest billions of connection logs at rates over 100,000 events per second while allowing most queries to complete in under one second. Storage and computation distribution are then evaluated using Elastic-search, an open-source distributed search and analytics engine. Then, to provide an example application of these databases, we develop a simple analytic which collects statistical information and classifies IP addresses based upon behavior. Finally, we examine the results of running the proposed analytic in real-time upon broconn (now Zeek) flow data collected by Diventi at IEEE/ACM Supercomputing 2019.

Light Fidelity or commonly known as Li-fi, is an emerging technology that works on the principle of using light as a medium for transferring signals. It is bidirectional and is fully networked wireless communication that focuses on the use of light from light-emitting diodes (LEDs). The system is affluent and can provide connectivity within a larger area with more security, higher data rates and high-speed as compared to Wi-Fi. Apart from that, it relies on the use of visible light communication or infra-red and near-ultraviolet spectrum majorly working on the idea of switching bulbs on and off within nanoseconds. The paper consists of vivid insights about the use of Li-fi as a solution to the excessive traffic congestion in roadways. The paper is further divided into prominent sections that deal in providing an accurate idea about the working and implication of Li-fi technology in the current scenario. Moreover, the paper also proposes various technical standards and modulation that are and will be followed by Li-fi technologies, thereby focusing on its future implication. Apart from that, the main aim of designing the system is to limit traffic congestion and thus, reduce the increasing number of road accident in the present times.

Local Binary Patterns (LBP) is a non-parametric descriptor whose purpose is to effectively summarize local image configurations. It has generated increasing interest in many aspects including facial image analysis, vision detection, facial expression analysis, demographic classification, etc. in recent years and has proven useful in various applications. This paper presents a local binary pattern based face recognition (LBP) technology using a Vector Support Machine (SVM). Combine the local characteristics of LBP with universal characteristics so that the general picture characteristics are more robust. To reduce dimension and maximize discrimination, super vector machines (SVM) are used. Screened and Evaluated (FAR), FARR and Accuracy Score (Acc), not only on the Yale Face database but also on the expanded Yale Face Database B datasets, the test results indicate that the approach is accurate and practical, and gives a recognition rate of 98 %.

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