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There is an unprecedented growth of internet and internet-based technologies in the recent times. We are now switching to 5G as the most recent wireless communication technology. The internet of things has become a blessing for Industry 4.0 by challenging all the existing technologies in its utility for contributing to the industrial growth. There are a lot of wireless communication technologies for IoT, and it becomes difficult choice to select one suitable for an application. Authors have presented multi-criteria decision-making techniques which are very instrumental in making a confirmed decision on the choice of appropriate technology. This choice is done based on a number of deciding parameter which are used to differentiate between all the available options. The authors have identified 11 wireless communication technologies and seven parameters to evaluate the performance of the WCT's. All the seven parameters are considered in ranking and rank matrix is obtained. This technique can be very helpful for application designers so as to choose the right platform for their applications.

The objective of this work is to present a comprehensive exploration of deep learning based wind forecasting model. The forecasting of speed of wind is called as the wind speed forecasting/prediction. It is basically done to achieve the better sustainability for power generation and production. The availability of wind energy in ample amount makes it quite comfortable to be utilized for various functionalities. In this research work the main aim is to forecast speed using LSTM including certain parameters and then comparative analysis is done using SVM. Both are machine learning approaches but have different functionalities in comparison to each other. This comparison is done to obtain the better technique which can be further applied on larger datasets to design a better, accurate, efficient forecasting model for speed of wind. The survey and implementation of both the techniques gave a clear idea about the utilisation of long short term memory for the better and enhanced wind speed forecasting. The forecasting is based on various atmospheric variables, and the data set is taken from the kaggle datsets which have numerous attributes but we have considered few of them only for the prediction purpose.

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Medical data analysis is being recognized as a field of enormous research possibilities due to the fact there is a huge amount of data available and prediction in initial stage may save patient lives with timely intervention. With machine learning, a particular algorithm may be created through which any disease may be predicted well in advance on the basis of its feature sets or its symptoms can be detected. With respect to this research work, heart disease will be predicted with support vector machine that falls under the category of supervised machine learning algorithm. The main idea of this study is to focus on the significance of parameter tuning to elevate the performance of classifier. The results achieved were then compared with normal classifier SVM before tuning the parameters. Results depict that the hyperparameters tuning enhances the performance of the model. Finally, results were calculated by using various validation metrics.