No abstract

No abstract

Voice Recognition is a fascinating field spanning several areas of computer science and mathematics. Reliable speech recognition is a hard problem, requiring a combination of many complex techniques; however modern methods have been able to achieve an impressive degree of accuracy. On the other hand, today, most of the companies or institutes are conducting their examinations online to be a part of this best ever growing world. In this system user can give any available examination at any accessible center as per his/her choice and authority also can condense manpower and process delay overhead. This paper offers one way to conduct online examination for physically challenged people who can use their voice only to register and attend the examination. In addition, in the course of this paper it has been tried to authenticate one registered user and to make the authentication process persistent throughout the examination interlude.

Sentiment analysis is a circumstantial analysis of text, identifying the social sentiment to better understand the source material. The article addresses sentiment analysis of an English-Hindi and English-Bengali code-mixed textual corpus collected from social media. Code-mixing is an amalgamation of multiple languages, which previously mainly was associated with spoken language. However, social media users also deploy it to communicate in ways that tend to be somewhat casual. The coarse nature of social media text poses challenges for many language processing applications. Here, the focus is on the low predictive nature of traditional machine learners when compared to Deep Learning counterparts, including the contextual language representation model BERT (Bidirectional Encoder Representations from Transformers), on the task of extracting user sentiment from code-mixed texts. Three deep learners (a BiLSTM CNN, a Double BiLSTM and an Attention-based model) attained accuracy 20–60% greater than traditional approaches on code-mixed data, and were for comparison also tested on monolingual English data.

Software defined networking (SDN) is a modern and upcoming standard in today's network as it centralizes the network intelligence by separating the control plane from the forwarding plane. Placing a controller in an appropriate location and minimizing the switch to controller (SC) latency are important factors in SDN. In this article, we proposed a mathematical algorithm to form the clusters and placed one controller in each cluster to shorten the worst-case SC latency. We have proposed a technique called the "$-method" by using the distance matrix of the network nodes and placed a "$" whenever we have found the matrix element is greater than a given maximum distance. Initially, the maximum distance is calculated using the center of mass method that we have developed for minimizing the average SC latency. Our method guarantees that it will generate the minimum worst-case SC latency efficiently that one could ever achieve for a different number of controller placement. Simulation has been done under some real-world network topologies from the dataset of Internet Topology Zoo. Our result shows that the "$-method" performs better compared with other existing algorithms in terms of worst-case SC latency minimization with less number of controllers. Further, we have analyzed our method for the failure mode of a controller by assigning the switches of that failed controller to its nearest controllers which shows that our method also performs better in terms of network fault tolerance and improves network resilience.