In today's generation, the demand for data rates has also increased due to the rapid surge in the number of users. With this increasing growth, there is a need to develop the next fifth generation network keeping in mind the need to replace the current 4G cellular network. The fifth generation (5G) design in mobile communication technology has been developed keeping in mind all the communication needs of the users. Heterogeneous Cloud Radio Access Network (H-CRAN) has emerged as a capable architecture for the newly emerging network infrastructure for energy efficient networks and high data rate enablement. It is considered as the main technology. Better service quality has been achieved by developing small cells into macro cells through this type of network. In addition, the reuse of radio resources is much better than that of homogeneous networks. In the present paper, we propose the H-CRAN energy-efficient methods. This energy-efficient algorithm incorporates an energy efficient resource allocation management design to deal to heterogeneous cloud radio access networks in 5G. System throughput fulfillment is elevating by incorporating an efficient resource allocation design by the energy consumption model. The simulation results have been demonstrated by comparing the efficiency of the introduced design with the existing related design.

Weather Monitoring, surveillance of enemy vehicles, sensed data delivery are few of the applications of Wireless Sensor Networks. All the applications want the nodes to spend their energy in the critical activities. Lifetime depends on the residual energy of the nodes in the network. In this work we modify the Global Energy Balance [1] algorithms to have better network lifetime by making use of fixed relay nodes at various positions in the network. The selection of relay node is based on the distance and residual energy of the relay node all through the route discovery practice. The FRNS scheme is compared with existing algorithms for diverse parameters like End to End Delay, Overall Hops Count, Overall Alive nodes and Dead nodes, Residual energy, Lifetime ratio, Energy Consumption, Throughput and Routing Overhead.

The stock market prices of the company vary in a daily fashion. The social media pattern usage of the company can be determined to find the sentiment score values. The dependency factor between the social media tweet platform and the performance of an organization can have how much effect on the stock prices is determined. The historical data from the Yahoo Finance APIs are taken for the unique company ID and then the probability of stock being good or bad is determined. Also, the tweets related to the company are scanned and analyzed to find the positive and negative scores. The concentration value connected to growth, the intensity of capital expenditure, and the volume of promotion were among the factors utilized in the stock’s modeling. This paper also takes the yearly finances of the end-user based on LIC payments, medical insurance payments, and average rent and then performs a classification of the user. Based on the user classification, companies are recommended to the end-user based on descending order of stock value. The average volume, average price, average market index, average daily turnover, and sentiment discrepancy index are based on the tweets of a company and the predicted value of its performance. For the classification of the user, we make use of the support vector machine algorithm. For the sentiment analysis of the tweets, the naïve Bayes algorithm is made use of, and then stock classification is done based on mathematical modeling, which includes the sentiment analysis index.

The nodes available in the market are now of miniaturized nature, also have the characteristics of low cost and power values. Wireless Sensor Network (WSN) will be a sparse network with independent points acting as energy sources. The application in WSN includes temperature sensing, sound sensing, and pressure session. The data is sent from one point to other using multi intermediate nodes. The selection of intermediate nodes will be done based on computation of trust. As the number of hops increases energy consumption will become high and this can be improved with the help of relay node which can store the data and deliver the data once destination is in its range. The selection of relay node can be done in multiple ways like random, based on meeting probability and in the proposed two hop relay battery energy aware algorithm makes use of multiple factors namely residual energy, virtual currency based data, meeting probability, security computation so that the communication can be optimized. The proposed method is also compared with several existing methods with respect to delay, energy consumption, alive nodes, dead nodes, lifetime ratio and residual energy.

In this research, we aim to determine the water potability using three machine learning classification algorithms: decision tree, gradient boosting and bagging classifier. These algorithms were trained and tested on a dataset of water quality measurements. The outcomes of the experiment showed that the gradient boosting algorithm achieved the highest F1-score of 0.78 among all the algorithms. This indicates that the gradient boosting algorithm was most effective in correctly identifying both the safe and contaminated water samples. The results of this study demonstrate that gradient boosting is a promising approach for determining water potability and can be used as a reliable method for water quality assessment.