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The study intends to employ Pre-deterministic Key Distribution (PKD) approach to choose a route that comprises of high verification nodes. The research executes to improve Load Balancing (LB) upon numerous participating nodes in network through the usage of Dynamic Energy-aware Routing (DER). Finally, the study aims to extend lifetime of WSN using proposed novel Fuzzy based Grey Wolf Optimization (FUZZY-GWO) which is an appropriate re-clustering method that resolves the hotspot issue. Subsequently, key distribution is implemented on the basis of Fraudulent Traffic Rate (FTR). Subsequently, sensor selection strategy is done. In the course of time, re-clustering is executed by using proposed novel FUZZY-GWO. The proposed system is evaluated by correlating it with various well-established methods with respect to metrics like average residual energy, energy consumption, average delay, Packet Delivery Ratio (PDR), dead nodes and number of alive nodes. The investigational outcomes of study reveals the efficacy of proposed novel FUZZY-GWO. [ABSTRACT FROM AUTHOR]

Population increase has resulted in an increase in the worldwide demand for alternative fuels due to depleting resources. There is a periodic increase in concern about the engine performance, pollutant emissions, and their predictions, from an engine using biodiesels. The use of intelligent algorithms in modeling and forecasting alternative fuels characteristics and their performance in engines are critically reviewed in this study. The paper aims at demonstrating with artificial intelligence methodologies the main conclusions of the recent research done for the above topic from 2012 to 2020. This article attempted to demonstrate an exploratory examination of the adaptive neuro-fuzzy inference system (ANFIS) soft computing technique used for the exact measurement and analysis of engine performance, emissions of exhaust engines when biodiesel is used as an alternative fuel. Additionally, the yield of biodiesel and their different characteristics predicted using ANFIS are also reviewed. Integration of particle swarm optimization (PSO), genetic algorithm (GA), and response surface methodology (RSM), either for comparison or optimization with ANFIS is presented. The summary of all studies is provided in tabular form. For the demonstration purpose, the ANFIS studies predicting different biodiesel and engine characters are provided with illustrative figures. The ANFIS prediction related to biodiesel used engine and biodiesel self-characteristics is found to be excellent. The ANFIS accuracy reported is better than the artificial neural network (ANN) accuracy. A minimum of 0.9 R2 value is generally obtained which is around 5% greater than the ANN modeling results reported. However, the ANFIS predictions are much more fitter than the RSM predictions. The integration of ANFISPSO and ANFIS-GA provided much more optimized results. [ABSTRACT FROM AUTHOR]

Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model. [ABSTRACT FROM AUTHOR]