Bright Kanisha

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus diseases (COVID-19) in late 2019 in China created a devastating economical loss and loss of human lives. To date, 11 variants have been identified with minimum to maximum severity of infection and surges in cases. Bacterial co-infection/secondary infection is identified during viral respiratory infection, which is a vital reason for morbidity and mortality. The occurrence of secondary infections is an additional burden to the healthcare system; therefore, the quick diagnosis of both COVID-19 and secondary infections will reduce work pressure on healthcare workers. Therefore, well-established support from Artificial Intelligence (AI) could reduce the stress in healthcare and even help in creating novel products to defend against the coronavirus. AI is one of the rapidly growing fields with numerous applications for the healthcare sector. The present review aims to access the recent literature on the role of AI and how its subfamily machine learning (ML) and deep learning (DL) are used to curb the pandemic’s effects. We discuss the role of AI in COVID-19 infections, the detection of secondary infections, technology-assisted protection from COVID-19, global laws and regulations on AI, and the impact of the pandemic on public life.

Sequential pattern mining is one of the important functionalities of data mining. It is used for analyzing sequential database and discovers sequential patterns. It is focused for extracting interesting subsequences from a set of sequences. Various factors such as rate of occurrence, length, and profit are used to define the interestingness of subsequence derived from the sequence database. Sequential pattern mining has abundant real-life applications since sequential data is logically programmed as sequences of cipher in many fields such as bioinformatics, e-learning, market basket analysis, texts, and webpage click-stream analysis. A large diversity of competent algorithms such as Prefixspan, GSP and Freespan have been proposed during the past few years. In this paper we propose a data model for organizing the sequential database, which consists of a directed graph DGS (cycles and several edges are allowed) and an organization of directed paths in DGS to represent a sequential data for discovering sequential pattern3 from a sequence database. Competent algorithms for constructing the digraph model (DGS) for extracting all sequential patterns and mining association rules are proposed. A number of theoretical parameters of digraph model are also introduced, which lead to more understanding of the problem.

Speech recognition process applications are emerging as ever-zooming and efficient mechanisms in the hi-tech universe. There is a host of diverse interactive speech-aware applications in the market. With the rocketing requirement for upcoming embedded platforms and with the incredible increase in the demand for embedded computing, it is highly indispensable that the speech recognition systems (SRS) are put in place at the right time and in the proper form so that it is easily possible to perform multimedia tasks on these mechanisms. In this work, primarily through preprocessing the speech signal is processed where for the recognition of the particular signal, the noise is detached and then it enters into feature extraction in that peak signal frequency and it is compared with the standard signal and recognized. The signal is processed and noise free signal is produced by processing the signal to Mel frequency cepstral coefficients (MFCC), Tri-spectral feature, and discrete wave transform (DWT). To the input of the multi-class Support vector machine, the output of the above mentioned features is given. The processed signal is converted in to text by multi SVM. It is proved that our proposed technique is better than the existing technique by comparing the existing technique (FFBN) feed forward back propagation with the proposed technique. The proposed technique is implemented in the working platform of MATLAB.