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Myocardial infarction (MI) may precipitate severe health damage and lead to irreversible death of the heart muscle, the result of prolonged lack of oxygen if it is not treated in a timely manner. Lack of accurate and early detection techniques for this heart disease has reduced the efficiency of MI diagnosis. In this paper, the design, and implementation of an efficient deep learning algorithm called Adaptive Recurrent neural network (ARNN) is proposed for the MI detection. The main objective of the proposed work is the accurate identification of MI disease using ECG signals. ECG signal denoising has been performed using the Multi-Notch filter, which removes the specified noise frequency range. Discrete wavelet transform (DWT) is utilized for performing the feature extraction that decomposes the ECG signal into varied scales with waveletfiltering bank. After the extraction of specific QRS features, classification of the defected and normal ECG arrhythmic beat has been performed using the deep learning-based ARNN classifier. The MIT-BIH database has been used for testing and training data. The performance of the proposed algorithm is evaluated based on classification accuracy. Results that are attained include the classification accuracy of about 99.21%, 99% of sensitivity and 99.4% of specificity with PPV and NPV of about 99.4 and 99.01 values indicate the enhanced performance of our proposed work compared with the conventional LSTM-CAE and LSTM-CNN techniques.

In recent days Wireless Sensor Networks and Internet of Things have become a growing and challenging research area. Those are used in various hard and sophisticated real time environments. A lot of challenges have to be faced by the researchers in these areas to meet the features like the quality level of sensed data, nodes autonomy, less energy utilization, battery storage, cluster range with cluster head selection and size of nodes…etc. In this paper, We did an extensive analysis on their recent developments in various application areas such as intelligent buildings, smart homes, Smart city developments, healthcare and smart hospital, transport and traffic management, Horticulture, water resources and quality monitoring, smart grid, space research…etc. This analysis will be helpful for the fresh researchers for doing research in WSN and IoT. The researchers have to look in identifying better solutions to the above said challenges must meet.