A comparative study for COVID-19 cases forecasting with loss function as AIC and MSE in RNN family and ARIMA

Forecasting COVID-19 incidents is a trending research study in today's world. Since Machine learning models have been occupied in forecasting recently, this study focus on comparing statical and machine learning models such as ARIMA, RNN, LSTM, Seq2Seq, and Stacked LSTM. The performances were evaluated using two loss functions, namely, AIC and RMSE. The results showed that RNN performs with the lowest RMSE with-49.5% compared with the ARIMA. Seq2Seq scored the highest correlation of determination (R2) with 0.92. © 2022 IEEE.

Two-layer fabrics for moisture management

Purpose: Woven fabrics have been popularised in use owing to their superior properties and functionality. Today, weavers strive to add value to their product to be competitive and to secure profit in performance fabrics such as technical fabrics, smart fabrics and sportswear fabrics. Over the years, fabrics with special properties such as moisture management have gained higher demand. In this context, multi-layer fabrics provide a reasonable solution to the demand. Design/methodology/approach: An attempt was made to develop two-layer fabrics with different compositions and properties. A two-layer woven fabric was produced using handloom weaving, with a hydrophobic inner layer and hydrophilic outer layer, the two layers being attached together using different stitching methods. Different fabric structures and yarn counts were used to achieve the objectives. Findings: Experiments carried out verified the suitability of the developed fabric for effective moisture management. It was found that a fabric with a 100% cotton outer layer and 100% polyester inner layer, both layers of 2 × 2 matt weave, showed the best properties. Practical implications: In the present COVID-19 pandemic situation, the use of masks in public has become mandatory in many countries. This research will help handloom manufacturers meet the need using simple methods. Originality/value: This research uses handloom fabric. As such it provides an opportunity for small and medium enterprises to use available low-cost technology to develop fabric with superior properties. © 2020, Emerald Publishing Limited.