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Heartbeat is visualized as a series of electrical waves on the display. ECG signal conveys information about structure and functions of heart. To check the heart's electrical activity and rhythm a test called Electrocardiogram (ECG) is used An ECG helps to analyze symptoms of heart problems such as chest pain, Arrhythmias, shortness of breath, Coronary Artery Diseases, Heart Attacks etc. Artifacts and noises may get added to the signals and leads to a wrong diagnosis. Therefore, it is necessary to remove these noises to get proper diagnosis. ECG signals are low frequency signals in the range of 0.5Hz-100Hz. This paper discusses various ECG denoising techniques and also compares existing signal processing Algorithms for Denoising of ECG Signal Using Deep Learning. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine potential machine learning techniques that could have been applied to predict spinal abnormalities. Univariate extraction of features as just a filtration system for selecting the features and principal component analysis (PCA) as just a tool for features extracted are one of the data preprocessing stages. A variety of machine learning methodologies, SVM classifier (SVM), regression models (LR), and bagging classification techniques, are now being examined as well for the diagnosis of spinal abnormalities. The Support Vector Machine, Logistic Regression, bagging Svm Classifier, bagging Logistic Multiple regressions are used on a data of 300 sample images inside the Kaggle source. The effectiveness of identification of aberrant and healthy spinal patients is assessed using several parameters, such traas is n and test correct True Negatives, recall, and miss rates. The categorization model was analyzed to use the receiver- operating characteristic (ROC) and highly accurate curves, and the kernel parameters. The obtained training prediction accuracy for Svm Classifier, Logistic Regression, bagged Svm Classifier, and bag Logistic Regression are 86 %, 86 %, and 85 percent, correspondingly, if % of input could be characterized for training. For testing data, the prediction accuracy for Svm Classifier, Logistic Regression, bagging Support Vector Machine, and bagging Regression Analysis all are 87%. [ABSTRACT FROM AUTHOR]

Water conservation is the world's most spoken and written topic in recent times, coupled with global environmental phenomena like Global warming and climate change. There has been increased efforts to curtail unnecessary use of water for irrigation purposes. Drip irrigation solves only a portion of this problem. The real necessity today revolves around creating a smart system that is independent of human interference and human judgment. This work aims to establish a consensus between both by calculating a parameter known as Evapotranspiration (ETo). The standard Food and Agricultural Organization of the United Nations (FAO)-56 Penman-Monteith (PM) equation is taken as a reference for calculating the ETo value. This equation requires several meteorological inputs for estimating reference evapotranspiration (ETo). This work aims to define the model of evapotranspiration estimation that best adapts to the region of Coimbatore, Tamil Nadu, India. Here atmospherically parameters were fed into the Penman-Monteith equation and the ET predicted by the empirical model is considered as target and this was used to train the ANN model. The best results are tabulated through this work and their wavelet family is recommended for the Coimbatore region alone. [ABSTRACT FROM AUTHOR]