Wireless sensor networks (WSNs) are generally a group of spatially scattered and devoted sensors to record and monitor the physical environmental condition, and the collected data is grouped at a... Show More

Wireless sensor networks (WSNs) are generally a group of spatially scattered and devoted sensors to record and monitor the physical environmental condition, and the collected data is grouped at a central location. In fact, the environmental conditions such as sound, humidity, temperature, wind, pollution levels, etc., can be clearly determined by WSNs. The principal objective of WSNs is to organize the whole sensor nodes in their related positions, thereby developing an effective network. In WSNs, target COVerage (TCOV) and Network CONnectivity (NCON) are the main concern of the sensor deployment problem. Many research works aspire the evolvement of smart context awareness algorithm for sensor deployment issues in WSN. Here the TCOV and NCON process are deployed as the minimization problem. This article makes an analysis of different GA variations in attaining the objective. The GA variations are as follows: self-adaptive genetic algorithm (SAGA), deterministic-adaptive genetic algorithm (DAGA), Individual- Adaptive Genetic Algorithm (IAGA). Finally, the methods are compared to one another in terms of connectivity and coverage performance.

This article addresses the issue regarding the exploitation of conventional fuel diesel. To overcome this issue, the Tamanu oil-diesel oil blend is introduced, where a new neural model is proposed... Show More

This article addresses the issue regarding the exploitation of conventional fuel diesel. To overcome this issue, the Tamanu oil-diesel oil blend is introduced, where a new neural model is proposed, which is trained by renowned firefly algorithm, termed as FF-NM. In addition, different compression ratios such as 15, 16, 17, 17.5 and blend ratios like 5:95, 6:94, 7:93, 8:92, and 9:91and 10:90 is exploited. The emission analysis and the combustion characteristics of the TO-diesel oil blend are evaluated as well as the MSE analysis is carried out for the proposed FF-NM method. For all the predicted parameters, the MSE of the proposed method is low for varying blend as well as the compression ratios. Moreover, the emission characteristics of the HC, CO2, NOx, CO, as well as O2 at different CR concerning the actual, and FF-NM is computed with the chosen blend ratios. From analysis, it is recognized that the estimation errors are less for the FF-NM approach. Hence, the simulation outcomes demonstrate the better performance of the proposed FF-NM approach under various compression ratios of 15, 16, 17 and 17.5, respectively.

Tooth caries or cavities diagnosing are concerned as the most significant research work, as this is the common oral disease suffered by humans. Many approaches have been proposed under the topics... Show More

Tooth caries or cavities diagnosing are concerned as the most significant research work, as this is the common oral disease suffered by humans. Many approaches have been proposed under the topics including demineralization and decaying as well. However, the imaging modalities often suffer from various critical or complex aspects that struggles the methods to attain accurate diagnosis. This article turns to introduce a new cavity diagnosis model with three phases: (i) pre-processing (ii) feature extraction (iii) classification. In the first phase, a new bi-histogram equalization with adaptive sigmoid functions (BEASF) is introduced to enhance the image quality followed by other enhancements models like grey thresholding and active contour. Then, the features are extracted using multilinear principal component analysis (MPCA). Further, the classification is done via neural network (NN) classifier. After the implementation, the proposed model compares its performance over other conventional methods like principal component analysis (PCA), linear discriminant analysis (LDA) and independent component analysis (ICA) and the performance of the approach is analyzed in terms of measures such as accuracy, sensitivity, specificity, precision, false positive rate (FPR), false negative rate (FNR), negative predictive value (NPV), false discovery rate (FDR), F1Score and Mathews correlation coefficient (MCC), and proves the superiority of proposed work.

One of the infrastructureless networks built by various independent mobile nodes is mobile ad hoc network (MANET), which is an emerging technology, requiring a secure routing protocol for data... Show More

One of the infrastructureless networks built by various independent mobile nodes is mobile ad hoc network (MANET), which is an emerging technology, requiring a secure routing protocol for data transmission. Accordingly, literature presents various secure routing protocols for MANETs by utilizing trust and data encryption. In this article, a whale optimization algorithm (WOA) is utilized for selecting the optimal secured routing path in the MANET. The WOA algorithm utilizes the trust factor and the distance between the nodes for computing the fitness for the routing path. Overall, the steps involved in the proposed routing algorithm are as follows: i) Measuring the trust and the distance-based metrics for every node; ii) Discovering k-disjoint path; and iii) Determining the optimal path based on the trust and the distance-based metrics. The performance of the trust-based WOA (T-Whale) is analyzed using the metrics, energy, throughput, and packet delivery rate. From the simulation results, it is evident that the T-Whale algorithm has the improved energy, throughput, and PDR values of 27.4520, 0.4, and 0.4, at the simulation time of 10 sec over the conventional trust random search algorithm when the node is under attack.