PMC

(A) Forecasting of COVID-19 infections in China using automated artificial intelligence-enabled system selected Holt’s linear trend (HLT) and state-of-the-art Bidirectional long short-term Memory (Bi-LSTM) models. (B) Forecasting of COVID-19 infections in France using Automated Artificial Intelligence-enabled system selected HLT and state-of-the-art Autoregressive Integrated Moving Average (ARIMA) models. (C) Forecasting of COVID-19 infections in Germany using Automated Artificial Intelligence-enabled system selected HLT and state-of-the-art Bi-LSTM models. (D) Forecasting of COVID-19 infections in Italy using Automated Artificial Intelligence-enabled system selected HLT and state-of-the-art ARIMA and Bi-LSTM models. (E) Forecasting of COVID-19 infections in Malaysia using Automated Artificial Intelligence-enabled system selected HLT and state-of-the-art ARIMA models. (F) Forecasting of COVID-19 infections in Australia using Automated Artificial Intelligence-enabled system selected QT and state-of-the-art LSTM models. (G) Forecasting of COVID-19 infections in Iran using Automated Artificial Intelligence-enabled system selected QT and state-of-the-art Bi-GRU models. (H) Forecasting of COVID-19 infections in Russia using Automated Artificial Intelligence- enabled system selected Quadratic Trend (QT) and state-of-the-art Bi-LSTM models. (I) Forecasting of COVID-19 infections in Spain using Automated Artificial Intelligence-enabled system selected QT and state-of-the-art ARIMA, SutteARIMA, and Bi-LSTM models. (J) Forecasting of COVID-19 infections in the United Kingdom using Automated Artificial Intelligence-enabled system selected QT and state-of-the-art Bi-LSTM models. (K) Forecasting of COVID-19 infections in the United States using Automated Artificial Intelligence-enabled system selected Linear Trend (LT) and state-of-the-art ARIMA and Bi-LSTM models. (L) Forecasting of COVID-19 infections in Israel using Automated Artificial Intelligence-enabled system selected LT and state-of-the-art Bi-LSTM models. (M) Forecasting of COVID-19 infections in Brazil using Automated Artificial Intelligence-enabled system selected Damped Trend (DT) and state-of-the-art Bi-LSTM models. (N) Forecasting of COVID-19 infections in India using Automated Artificial Intelligence-enabled system selected DT and state-of-the-art Bi-LSTM models, and (O) forecasting of COVID-19 infections in Saudi Arabia using Automated Artificial Intelligence-enabled system selected DT and state-of-the-art ARIMA models.

Our Flan-PaLM 540B model exceeds the previous state-of-the-art performance (SOTA) on MedQA (four options), MedMCQA and PubMedQA datasets. The previous state-of-the-art results are from Galactica (MedMCQA), PubMedGPT (MedQA) and BioGPT (PubMedQA). The percentage accuracy is shown above each column.

(a) Average Results of Processing Time, PSNR, and End-to-end delay Cellular Network Standard for state of the art [] and EVQDMBRM in Wi-Fi network; (b) Average Results of Processing Time, PSNR, and End-to-end delay in Wi-Fi Network Standard state of the art [] and EVQDMBRM in Wi-Fi network; (c) Result of Processing Time of state of the art [] and EVQDMBRM in Wi-Fi network in a cellular network; (d) Result of Peak Signal to Noise Ratio (PSNR) of state of the art [] and EVQDMBRM in Wi-Fi network in Wi-Fi network; and (e) Result of End-to-end Delay of state of the art [] and EVQDMBRM in Wi-Fi network

Mutation Effect on State-of-the-art Session Key