Noureen Fatima¹, Rashid Jahangir², Ghulam Mujtaba¹, Adnan Akhunzada^3,*, Zahid Hussain Shaikh⁴, Faiza Qureshi¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4357-4374, 2022, DOI:10.32604/cmc.2022.023830

Abstract The Coronavirus Disease 2019 (COVID-19) pandemic poses the worldwide challenges surpassing the boundaries of country, religion, race, and economy. The current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) testing. Nevertheless, this testing method is accurate enough for the diagnosis of COVID-19. However, it is time-consuming, expensive, expert-dependent, and violates social distancing. In this paper, this research proposed an effective multi-modality-based and feature fusion-based (MMFF) COVID-19 detection technique through deep neural networks. In multi-modality, we have utilized the cough samples, breathe samples and sound samples of healthy as well as COVID-19 patients from… More >

Rakesh Kumar¹, Meenu Gupta¹, Shakeel Ahmed^2,*, Abdulaziz Alhumam², Tushar Aggarwal¹

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 693-706, 2022, DOI:10.32604/iasc.2022.019811

Abstract Hearing a species in a tropical rainforest is much easier than seeing them. If someone is in the forest, he might not be able to look around and see every type of bird and frog that are there but they can be heard. A forest ranger might know what to do in these situations and he/she might be an expert in recognizing the different type of insects and dangerous species that are out there in the forest but if a common person travels to a rain forest for an adventure, he might not even know how to recognize these species,… More >