The purpose of effective electromagnetic interference (EMI) shielding is to prevent EMI from smartphone, wireless, and utilization of other electronic devices. The electrical conductivity of materials strongly influences on the EMI shielding properties. In this work, mainly focus to predict the EMI shielding effectiveness on the ultralight weight fibrous materials by artificial neural network (ANN). Prior to the ANN modelling, the ultra-lightweight fibrous materials were electroplated with different concentration of Ni/Cu and then coated with different silanes. This work utilizes the algorithm to provide accurate quantitative values of EMI shielding effectiveness (EM SE). To compare its performance, the experimental and the predicted EM SE values were validated by root-mean-square error (RMSE), mean absolute percentage error (MAPE) values and correlation coefficient ‘r’. The proposed ANN results accurately predict the experimental data with correlation coefficients of 0.991 and 0.997. Further due to its simplicity, reliability as well as its efficient computational capability the proposed ANN model permits relatively fast, cost effective and objective estimates to be made of serving in this industry.

Microplastic particles are a burgeoning population crisis in the marine environment. This work is to predict the releasing of microplastic fibers from the jeans made from polyester during domestic washing by using of adaptive neuro-fuzzy inference system (ANFIS) model.The advantage of the ANFIS model is to predict the variations in between the randomly chosen parameters. This prediction model can be cost-effective, slowed down to study behavior more closely. The consequence of washing duration, temperature, spin-speed, detergent types, and conditioner usage was investigated against the microplastic fibers releases. The washing temperature, washing duration, spin-speed, detergent types, and addition of conditioner are the main factors for this research work. The forecast presentations have been exposed by having a considerably lowered RMSE of 3.23 value than the variant of the experiment as exposed by its standard deviation for the ANFIS version. This ANFIS model will be able to provide a theoretical understanding to enhance and inhibit microplastic fibers releases from jeans.

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