Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algor

Metallic alloys for a given application are usually designed to achieve the desired properties by devising experimentsbased on experience, thermodynamic and kinetic principles, and various modeling and simulation exercises.However, the influence of process parameters and material properties is often non-linear and non-colligative. Inrecent years, machine learning (ML) has emerged as a promising tool to dealwith the complex interrelation betweencomposition, properties, and process parameters to facilitate accelerated discovery and development of new alloysand functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles,to design novel copper alloys for achieving seemingly contradictory targets of high strength and high electricalconductivity. Initially, we establish a correlation between the alloy composition (binary to multi-component) andthe target properties, namely, electrical conductivity and mechanical strength. Catboost, an ML model coupledwith GA, was used for this task. The accuracy of the model was above 93.5%. Next, for obtaining the optimizedcompositions the outputs fromthe initial model were refined by combining the concepts of data augmentation andPareto front. Finally, the ultimate objective of predicting the target composition that would deliver the desired rangeof properties was achieved by developing an advancedMLmodel through data segregation and data augmentation.To examine the reliability of this model, results were rigorously compared and verified using several independentdata reported in the literature. This comparison substantiates that the results predicted by our model regarding thevariation of conductivity and evolution ofmicrostructure and mechanical properties with composition are in goodagreement with the reports published in the literature.

An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet Model

COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest Model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively.

Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve

The rapid expansion of Internet of Things(IoT)devices deploys various sensors in different applications like homes,cities and offices.IoT applications depend upon the accuracy of sensor data.So,it is necessary to predict faults in the sensor and isolate their cause.A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults.This technique identifies the faulty sensor and determines the correct working of the sensor.Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form.Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described.There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique.So,some solutions are provided to overcome the limitations of the fall curve technique.In this paper,a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years.Its novelty is to predict a fault before its occurrence by looking at the fall curve.The sensing of current flow in devices is important to prevent a major loss.So,the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices.The analysis result proved that if any of the current sensors gets faulty,then the fall curve will differ and the value will immediately drop to zero.Various evaluation metrics for fault prediction are also described in this paper.At last,this paper also addresses some possible open research issues which are important to deal with false IoT sensor data.

Hybrid Image Compression-Encryption Scheme Based on Multilayer Stacked Autoencoder and Logistic Map

Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic map.The proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio(PSNR), mean square error(MSE), structural similarity index metrics(SSIM) and statistical,differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission..

An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease.In addition to forecasting the occurrences of conjunctivitis incidences,the proposed model also improves performance by using the ensemble model.Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019.Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets.A stacked generalization ensemble model based on Auto-ARIMA(Autoregressive Integrated Moving Average),NNAR(Neural Network Autoregression),ETS(Exponential Smoothing),HW(Holt Winter)is proposed and applied on the dataset.Predictive analysis is conducted on the collected dataset of conjunctivitis disease,and further compared for different performance measures.The result shows that the RMSE(Root Mean Square Error),MAE(Mean Absolute Error),MAPE(Mean Absolute Percentage Error),ACF1(Auto Correlation Function)of the proposed ensemble is decreased significantly.Considering the RMSE,for instance,error values are reduced by 39.23%,9.13%,20.42%,and 17.13%in comparison to Auto-ARIMA,NAR,ETS,and HW model respectively.This research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA,NAR,ETS,and HW model applied discretely.