At the end of December 2019, the COVID-19 virus was the initial report case in China Wuhan City. On March 11, 2020. The Department of Health (WHO) announced COVID-19, a global pandemic. The COVID-19 spread rapidly out all over the world within a few weeks. We will propose to develop a forecasting model of COV-19 positive case predict outbreak in Pakistan using Deep Learning (DL) models. We assessed the main features to forecast patterns and indicated The new COVID-19 disease pattern in Pakistan and other countries of the world. This research will use the deep learning model to measure several COVID-19 positive case reports in Pakistan. LSTM cell to process time-series data forecasts is very efficient. Recurrent neural network processes to handle time-dependent and involve hidden layers are confirmed and predict positive cases and weekly cases reported in the future. Bidirectional LSTM (Bi-LSTM) processes data and information in one direction to predict and analyze the weekly 6-9 days readily forecast the number of positive cases of COVID-19
The advancement in mobile technology has increased the demand for high data rates and quality of services in a wireless communication system. To fulfill the requirements for the 6G network, Reconfigurable Intelligent Surface (RIS) has been recommended as an efficient solution for increasing and improving the energy efficiency of wireless systems. RIS consists of different programmable passive components which are used to tune the wireless channel. We design the optimization problem concerning resource allocation, in which the target is to enhance and maximize the overall energy efficiency of the network. The constructed problem is an MINLP (Mixed Integer non-linear programable problem). In order to achieve results for the formulated problem, Outer Approximation Algorithm (OAA) is suggested in this article. The ν optimal solution is obtained using OAA. NOMAD is also put forward in our work for comparing the results obtained by using the proposed algorithm (OAA).
In this work, we present an approach for building a low voltage, fast throughput multiplier utilizing a full adder with the fewest transistors possible (6-T). Multiplier circuits are widely utilized in Integrated Circuits (ICs). As a result, high-speed functioning of the subunits is required. A 4-bit x 4-bit multiplier is constructed in this paper using only fundamental combinational circuits with minimum number of transistors, and its functionality is evaluated and contrasted to related multipliers assembled utilizing peer combinational architectures that were accessible in the literary works. The proposed technique results in less silicon area, increased performance and low voltage multiplier architecture. Simulation findings show that the suggested approach outperforms the standard CMOS multiplier.
Coronavirus (COVID-19) is a catastrophic illness that has already infected several million individuals and caused thousands of fatalities globally. Any technical technique that enables quick testing of the COVID-19 with high accuracy might be essential for healthcare providers. X-ray imaging is an easily available technique that might be a great option for its quick detection. This research was conducted to examine the usefulness of artificial intelligence (AI) to detect COVID-19 quickly and accurately from chest X-ray scans. The objective of this study is to provide a solid technical method for the automatic identification of COVID-19, Pneumonia, Lung opacity, and Normal digital X-ray scans using pretrained, deep learning algorithms while optimizing detection accuracy. Inception v3 with an additional added dense layer is used with image augmentation to train and validate the selected dataset. The obtained accuracy of 99.72% promises speedy detection of COVID-19.
Energy plays an important role in the economic growth of any nation, but unfortunately, Pakistan is facing a severe power crisis that has caused economic losses in the past two decades. One reason is the lack of proper planning, and planning is made with the help of accurate forecasting. Usually, energy consumption prediction is based on the univariate models neglecting the other influencing factors like economic, social and environmental. And even when we consider these external factors, it is very hard to select the appropriate factors. We have taken into consideration nine different external factors and also the lag values of dependent variables (Electricity, gas and oil). Upon making different combinations, we derived over 4000 regression models and our algorithms selected the best models based on the value of mean absolute percentage error and root-mean-square error. Our method provides a comprehensive approach for variable selection and best model selection for prediction.
