Unveiling the potential of machine learning approaches in predicting the emergence of stroke at its onset: a predicting framework.

A stroke is a dangerous, life-threatening disease that mostly affects people over 65, but an unhealthy diet is also contributing to the development of strokes at younger ages. Strokes can be treated successfully if they are identified early enough, and suitable therapies are available. The purpose of this study is to develop a stroke prediction model that will improve stroke prediction effectiveness as well as accuracy.

Performance Evaluation of Multilayer Clustering Network Using Distributed Energy Efficient Clustering with Enhanced Threshold Protocol.

In this research, pure deterministic system has been established by a new Distributed Energy Efficient Clustering Protocol with Enhanced Threshold (DEECET) by clustering sensor nodes to originate the wireless sensor network. The DEECET is very dynamic, highly distributive, self-confessed and much energy efficient as compared to most of the other existing protocols. The MATLAB simulation provides aim proved result by means of energy dissipation being emulated in the networks lifespan for homogeneous as well as heterogeneous sensor network, which when contrasted for other traditional protocols.

Enhancement of biogas production from microalgal biomass through cellulolytic bacterial pretreatment.

Generation of bioenergy from microalgal biomass has been a focus of interest in recent years. The recalcitrant nature of microalgal biomass owing to its high cellulose content limits methane generation. Thus, the present study investigates the effect of bacterial-based biological pretreatment on liquefaction of the microalga Chlorella vulgaris prior to anaerobic biodegradation to gain insights into energy efficient biomethanation.