Content-Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm.

Audio classification and retrieval has been recognized as a fascinating field of endeavor for as long as it has existed due to the topic of identifying and choosing the most useful audio attributes. The categorization of audio files is significant not only in the area of multimedia applications but also in the disciplines of medicine, sound analysis, intelligent homes and cities, urban informatics, entertainment, and surveillance. This study introduces a new algorithm called the modified bacterial foraging optimization algorithm (MBFOA), which is based on a method that retrieves and classifies audio data. The goal of this algorithm is to reduce the computational complexity of existing techniques. Along with the combination of the peak estimated signal, the enhanced mel-frequency cepstral coefficient (EMFCC) and the enhanced power normalized cepstral coefficients (EPNCC) are used. These are then optimized using the fitness function utilizing MBFOA. The probabilistic neural network is used to differentiate between a music signal and a spoken signal from an audio source (PNN). It is next necessary to extract and list the characteristics that correspond to the class that was arrived at as a consequence of the categorization. When compared to other approaches that are somewhat similar, MBFOA demonstrates superior levels of sensitivity, specificity, and accuracy.

The influencing factors of digital health passport adoption and acceptance during COVID-19 in Saudi Arabia.

Background: The COVID-19 outbreak has left a destructive trail around the world in terms of deaths, travel restrictions, trade deficits, and an economy that is collapsing, including job losses, real estate, health benefit loss, and a decrease in the quality of access to care and services in almost all sectors, as well as generally in the overall quality of life. The successful development of COVID-19 vaccines may hasten the acceleration of global post-pandemic recovery by vaccinating residents, with a particular focus on important groups, in order to decrease secondary transmission. This will facilitate the easing of enforced restrictions on global and local travel, the tourism industry, education sectors, and other aspects of social life. Vaccinating residents may also help reduce the risk of secondary transmission. The efforts that Saudi Arabia made to control the epidemic were outstanding on all fronts and in all spheres, including the health, education, commerce, and tourism industries, among others. Objective: The purpose of this research was to investigate the elements that influence a traveler's decision to acquire and use a digital health passport (DHP), which was introduced by the Tawakkalna application in Saudi Arabia at the COVID-19 conference. Methods: The technology acceptance model (TAM) and the information system success model (ISSM) were the primary theoretical frameworks that guided this investigation. The terms "perceived ease of use" (PEOU), "perceived usefulness" (PU), "information quality" (IQ), "service quality" (SQ), and "net benefit" (NB) were applied in order to investigate the user's acceptance and use of the DHP, as well as how it contributes to the facilitation of traveling and public perception toward using the DHP. Results: In order to assess the validity of the proposed model and its four assumptions, a survey was sent through social media platforms to get responses from nationals and residents of Saudi Arabia. The SPSS program was used to evaluate a total of 103 replies that were considered valid. Following the completion of the study, the findings revealed that PEOU, PU, IQ, SQ, and NB all had favorable impacts on the use of DHP. Conclusion: PEOU, PU, IQ, and SQ have a significant relationship with NB that affects the public's acceptance and use of DHP. This study has established validity and reliability while testing the relationship between the variables suggested in the research model.