http://localhost:8080/xmlui/handle/123456789/45 2026-01-28T11:23:50Z http://localhost:8080/xmlui/handle/123456789/2070 Title: Enhancing the energy efficiency for prolonging the network life time in multi-conditional multi-sensor based wireless sensor network Authors: Kocherla R.; Chandra Sekhar M.; Vatambeti R. Abstract: A wireless sensor network is one of the networks that is highly demanding by various real-time networking applications nowadays. A huge amount of sensor nodes is deployed in the network randomly and distributed. Most of the applications using wireless sensor network (WSN) are surveillance monitoring applications like a forest, home, healthcare, environment, and remote monitoring systems. Based on the application usage, the type of sensor, a number of sensor nodes are deployed in such a manner where the sensors can be used effectively. But the sensor nodes are restricted in the battery and sensing region. Thus, the battery of the sensor nodes is decreased based on the nodes’ function. The energy level of the sensor nodes highly affects the network lifetime. Improving the energy efficiency in WSN is one of the most important challenging tasks. Most of the earlier research works have proposed various methods, techniques, and routing protocols, but they are application dependent and as a common method. So, this paper is motivated to propose a Multi-Conditional Network Analysis (MCNA) framework for saving the energy level of the sensor nodes by reducing energy consumption. The MCNA framework involves two different clustering processes with cluster head selection, choosing the best nodes based on the signal strength, and the best route for data transmission. The data transmission is done by cluster based on source-destination based. The simulation results proved that the proposed MCNA framework outperforms the other existing methods. © 2022 Northeastern University, China. 2022-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/2067 Title: Induction of model trees for predicting BOD in river water: A data mining perspective Authors: Alamelu Mangai J.; Gulyani B.B. Abstract: Water is a primary natural resource and its quality is negatively affected by various anthropogenic activities. Deterioration of water bodies has triggered serious management efforts by many countries. BOD is an important water quality parameter as it measures the amount of biodegradable organic matter in water. Testing for BOD is a time-consuming task as it takes 5 days from data collection to analyzing with lengthy incubation of samples. Also, interpolations of BOD results and their implications are mired in uncertainties. So, there is a need for suitable secondary (indirect) method for predicting BOD. A model tree for predicting BOD in river water from a data mining perspective is proposed in this paper. The proposed model is also compared with two other tree based predictive methods namely decision stump and regression trees. The predictive accuracy of the models is evaluated using two metrics namely correlation coefficient and RMSE. Results show that the model tree has a correlation coefficient of 0.9397 which is higher than the other two methods. It also has the least RMSE of 0.5339 among these models. © Springer International Publishing Switzerland 2016. 2016-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/2068 Title: Analysis of Entropy Generation and Energy Transport of Cu-Water Nanoliquid in a Tilted Vertical Porous Annulus Authors: Swamy H.A.K.; Sankar M.; Reddy N.K. Abstract: The physical structure in several industrial applications which includes cooling of electronic equipment, heat exchangers, nuclear reactors, and solar collectors, could aptly represent the cylindrical annular porous geometry. The prior knowledge of buoyant flow and thermal transport rates in this geometry provides the vital information to the design engineers. In this article, we analyze the convective nanoliquid flow and associated thermal dissipation as well as entropy generation rates in an inclined annular enclosure filled with nanoliquid saturated porous medium. The vertical surfaces of inner and outer cylinders are maintained at uniform, but different temperatures and horizontal boundaries are kept insulated. The momentum equations are modeled utilizing the Darcy law, the coupled partial differential equations are numerically solved adopting the time splitting and line over relaxation techniques. For the numerical simulations, a vast range of parameters such as the Darcy Rayleigh number (10 ≤ RaD ≤ 103), annulus inclination angle (0° ≤ γ ≤ 60°), aspect ratio (0.5 ≤ Ar ≤ 2) and nanoparticle volume fraction (0 ≤ ϕ ≤ 0.05) are considered. The contributions of heat transfer and fluid friction entropies to global entropy production in the geometry are determined through the Bejan number. The numerical results reveal that the convective flow, heat transfer and entropy generation rates could be controlled with the aid of cavity inclination angle. It is found that the shallow annular enclosure gives better thermal performance with minimum entropy generation regardless of RaD, γ and ϕ. Further, the results are in excellent agreement with standard benchmark simulations. The predicted results could provide some vital information to enhance the system efficiency. © 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited. 2022-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/2072 Title: Biserial targeted feature projection based radial kernel regressive deep belief neural learning for covid-19 prediction Authors: Subash Chandra Bose S.; Vinoth Kumar A.; Premkumar A.; Deepika M.; Gokilavani M. Abstract: Coronavirus disease 2019 (COVID-19) is a highly infectious viral disease caused by the novel SARS-CoV-2 virus. Different prediction techniques have been developed to predict the coronavirus disease’s existence in patients. However, the accurate prediction was not improved and time consumption was not minimized. In order to address these existing problems, a novel technique called Biserial Targeted Feature Projection-based Radial Kernel Regressive Deep Belief Neural Learning (BTFP-RKRDBNL) is introduced to perform accurate disease prediction with lesser time consumption. The BTFP-RKRDBNL techniques perform disease prediction with the help of different layers such as two visible layers namely input and layer and two hidden layers. Initially, the features and data are collected from the dataset and transmitted to the input layer. The Point Biserial Correlative Target feature projection is used to select relevant features and other irrelevant features are removed with minimizing the disease prediction time. Then the relevant features are sent to the hidden layer 2. Next, Radial Kernel Regression is applied to analyze the training features and testing disease features to identify the disease with higher accuracy and a lesser false positive rate. Experimental analysis is planned to measure the prediction accuracy, sensitivity, and specificity, and prediction time for different numbers of patients. The result illustrates that the method increases the prediction accuracy, sensitivity, and specificity by 10, 6, and 21% and reduces the prediction time by 10% as compared to state-of-the-art works. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. 2022-01-01T00:00:00Z