Though the key technology for 5G communication is the massive Multiple input and multiple output (MIMO), it has a major drawback when the user generated inference signals are to be handled by several other users. When these interference signals are used in various users, issues concerning system ability, power management issues and QoS related issues arises with the involvement of MIMO channel. Though internet of things (IoT) making use of MIMO is still under development, the requirements of the IoT is quite different when compared to several other connections. Thus, taking into account the demands, the solutions were framed with the help of the proposed system. One such solution is the Partition Square and Cross-Processing method that can possess several antennas at the given destination. Based on this solution obtained the data rate can be drastically increased correspondingly reducing the power consumed thus producing the most desirable result with high signal to noise ratio (SNR). Best victory is another such solution that solves the issues relating to power reduction thereby providing the best solution with minimum SNR. These solutions produced by the proposed system still has some disadvantages with respect to the following case: when the number of destination antennas is less than the number of source antennas. This case is considered as most common in the model proposed. For the above-mentioned case, near optimal solution is produced by both the algorithms. In this article, the transmitters are represented by the source antenna and the receiver is represented by the destination antenna. Thus, with the help of IoT connectivity, major benefits can be bought to the massive MIMO channel.

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Urban living in large modern cities exerts considerable adverse effects on health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanized countries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples is becoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions. The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, the iterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated using ensemble nonlinear support vector machines and random forests. Thus, instead of using the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vector machines, where the product rule is used to combine probability estimates of different classifiers. Performance is evaluated in terms of the prediction accuracy and interpretability of the model and the results.