Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.

Smart technology has potential in tracking the key challenges based on population based on the sustainable future. In today’s cultures, a smart approach enables for the integration of information needed to address crucial concerns. The critical challenge is to limit the effects of global warming while maintaining a balanced economic growth. The impact of global warming mitigates the fundamental problem while ensuring the balance economic development. Intense research efforts should be directed toward balanced resource utilization, renewable energy system integration, efficient energy conversion technologies, effective process integration, effective techniques to enable a circular economy framework, and other issues that are important to the population. This paper finds the latest technology in the field of smart grid technologies which focused on the effective enhancement and efficient utilization of resource. The issues and challenges in using sustainable future are discussed and bring new ideas towards the sustainable future base on the application of smart technologies.

In a mobile ad hoc network, packets are lost by interference occurrence in the communication path because there is no backup information for the previous routing process. The communication failure is not efficiently identified. Node protection rate is reduced by the interference that occurs during communication time. So, the proposed reliability antecedent packet forwarding (RAF) technique is applied to approve the reliable routing from the source node to the destination node. The flooding nodes are avoided by this method; the previous routing information is backed up; this backup information is retrieved if any interference occurred in the communication period. To monitor the packet flow rate of every node, the straddling path recovery algorithm is designed to provide an interference free-routing path. This path has more number of nodes to proceed with communication. These nodes have a higher resource level and also used to back up the forwarded data; since sometimes routing breakdowns occurred, data are lost, which is overcome by using a backup process. It improves the network lifetime and reduces the packet loss rate.

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