Artificial neural network based ECG feature extraction using wavelet transform

In this research, Automatic techniques to detect diseases have been developed be-cause of requirements of continuous attention to patient having heart diseases. This research deals with implementation of Artificial neural network methods for analyz-ing ECG (Electrocardiogram) signals with a focus on early and accurate detection. Feature extraction of ECG signal plays vital role in cardiovascular diseases. ECG signal is decomposed using wavelet transform and then feature extracted of decom-posed ECG signal are given as input to Neural Network. The wavelets used for de-composition are Daubechies and Symmetric. The selection of detail coefficient d4 had been done based on the following important parameters i.e. Energy, Frequency and Correlation. The overall of detection using db6 and sym11 were 96.65% and 84.37%. In this work, study of the classification of ECG signal has been done in de-tail by using computational methods effectively for early cardiovascular diagnosis. Coefficients of discrete wavelet transforms are used for analyzing ECG signals in conjunction with the Artificial Neural network (ANN). Three different types of ECG data have been used normal sinus rhythm, supra ventricular arrhythmia and atrial fibrillation. Decomposition and Classification of ECG signals using discrete wavelet transform and Artificial Neural Network have been successfully designed. The meth-od has been implemented on 18 subjects. The results show that proposed method is effective for classification of normal and cardiac arrhythmia with an overall accura-cy of 97.5%

AI Based Prediction Algorithms for Enhancing the Waste Management System: A Comparative Analysis

Waste management has become an increasingly pressing issue due to urbanization, population growth, and economic development. According to World Bank projections, waste production will reach 3.4 billion tonnes by 2050. The paper is focused on detailed analysis of waste management techniques that has to be improved and resources to be maximized, to be able to deal with various types of waste, including agricultural waste, industrial waste, municipal solid waste (MSW), and electronic waste (e-waste). The advancement in the artificial intelligence in various fields has drawn the attention towards utilizing its benefits in achieving optimized management of different types of wastes also. The paper is focused on description of on-recyclable waste materials which can be transformed into energy by using waste-to-energy (WTE) technologies. The different types of wastes generated in different sectors are being studied with details on their quantity and challenges in handling the wastes. The literature highlights the performance analysis of various methodologies of waste handling in terms of their efficiency, economic impacts and ecological implications. The prediction models and their performance was discussed with respect to the R2 value and mean absolute error (MAE) root mean square error (RMSE) to find the most suitable algorithm. The conclusion suggested that these AI based optimization methods can bring about enhancement in the various waste to energy conversion process making the management of waste materials more sustainable and reliable

Examining the Relationship between Biotech Crop Cultivation and Global Food Security Sustainable Index: A Comparative Analysis from 2012 to 2018

This study examines the dynamics between the cultivation of biotech plants and food protection on a global scale from 2012 to 2018 which will ensure sustainability in food. The use of facts from the worldwide food security Index (GFSI) and biotech crop cultivation regions, we analyze modifications in food security metrics alongside developments in biotech crop adoption across various international locations. Our findings reveal intriguing patterns, including extensive increases in biotech crop cultivation in Brazil and the United States, coinciding with terrific enhancements in GFSI scores in nations like Chile, Uruguay, and Argentina. Conversely, a few countries, such as Burkina Faso and Myanmar, exhibited high-quality shifts in GFSI despite stagnant biotech crop cultivation. Furthermore, simultaneous will increase or decreases in each biotech crop cultivation and GFSI rankings were observed in positive international locations, underscoring the complicated interaction between biotech crop adoption and food security effects. Moreover, we discuss the importance of considering food security at each national and household stages, highlighting the need for nuanced analyses of biotech crop contributions to general food security