Arrhythmias are anomalies in the heartbeat rhythm that occur occasionally in people’s lives. These arrhythmias can lead to potentially deadly consequences, putting your life in jeopardy. As a result, arrhythmia identification and classification are an important aspect of cardiac diagnostics. An electrocardiogram (ECG), a recording collecting the heart’s pumping activity, is regarded the guideline for catching these abnormal episodes. Nevertheless, because the ECG contains so much data, extracting the crucial data from imagery evaluation becomes extremely difficult. As a result, it is vital to create an effective system for analyzing ECG’s massive amount of data. The ECG image from ECG signal is processed by some image processing techniques. To optimize the identification and categorization process, this research presents a hybrid deep learning-based technique. This paper contributes in two ways. Automating noise reduction and extraction of features, 1D ECG data are first converted into 2D pictures. Then, based on experimental evidence, a hybrid model called CNNLSTM is presented, which combines CNN and LSTM models. We conducted a comprehensive research using the broadly used MIT_BIH arrhythmia dataset to assess the efficacy of the proposed CNN-LSTM technique. The results reveal that the proposed method has a 99.10 percent accuracy rate. Furthermore, the proposed model has an average sensitivity of 98.35 percent and a specificity of 98.38 percent. These outcomes are superior to those produced using other procedures, and they will significantly reduce the amount of involvement necessary by physicians.
Objective: To propose Multi-parametric Deep Neural Network (MDNN) for modeling the impact of climate changes, multiple parameters related to the weather and soil for accurate crop yield prediction. Methods: In MDNN, a measure called Growing-Degree Day (GDD) is introduced for measuring the overall effect of weather conditions related to the crop yield. One of the key elements in MDNN is the neuron's layer-wise activation function. In order to enhance the crop yield predictive performance, a leaky rectified linear unit is used in the activation units of MDNN. For the analysis of performance of DNN and MDNN, data about weather, crop and soil are collected from http://www.cc afs-climate.org/climatewizard/, https://data.world/thatzprem/agriculture-india and https://data.gov.in/search/site?query=soil respectively. From the collected data, 60000 records are used for training and 40,000 records are used for testing. Findings: By considering multiple parameters of climate and the effect of weather on crop yield, the accuracy of MDNN is improved for predicting the crop yield. The effectiveness of MDNN is tested and compared with DNN for different types of crops. The MDNN achieves 91.84% of mean accuracy for five different crops compared to the DNN classification. Novelty: This proposed work tries to predict the crop yield more accurately by analyzing the climate, weather and soil parameters. The MDNN considerably improves statistical efficiency over typical DNN by using previous knowledge about important phenomena and functional forms relating them to the crop yield.
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