An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

Densely connected convolutional networks-based COVID-19 screening model

The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.

     

Hierarchical clustering and routing protocol to ensure scalability and reliability in large-scale wireless sensor networks

The Journal of Supercomputing - Cluster-based routing protocols have been proven efficient in prolonging the life cycle of wireless sensor networks (WSNs). Periodic and multi-hop clustering are the...     

Predator–prey optimization based clustering algorithm for wireless sensor networks

Neural Computing and Applications - Grouping the sensor nodes into clusters is an effective way to organize wireless sensor networks and to prolong the networks’ lifetime. This paper presents...     

Investigation of rectangular solid-core photonic crystal fiber as temperature sensor

Microsystem Technologies - In this article, a rectangular solid-core photonic crystal fiber (PCF) is proposed as temperature sensor. The air-holes of the PCF have been filled with Ethyl alcohol...     

Estimation of elastic constant of rocks using an ANFIS approach

The engineering properties of the rocks have the most vital role in planning of rock excavation and construction for optimum utilization of earth resources with greater safety and least damage to surroundings. The design and construction of structure is influenced by physico-mechanical properties of rock mass. Young's modulus provides insight about the magnitude and characteristic of the rock mass deformation due to change in stress field. The determination of the Young's modulus in laboratory is very time consuming and costly. Therefore, basic rock properties like point load, density and water absorption have been used to predict the Young's modulus. Point load, density and water absorption can be easily determined in field as well as laboratory and are pertinent properties to characterize a rock mass. The artificial neural network (ANN), fuzzy inference system (FIS) and neuro fuzzy are promising techniques which have proven to be very reliable in recent years. In, present study, neuro fuzzy system is applied to predict the rock Young's modulus to overcome the limitation of ANN and fuzzy logic. Total 85 dataset were used for training the network and 10 dataset for testing and validation of network rules. The network performance indices correlation coefficient, mean absolute percentage error (MAPE), root mean square error (RMSE), and variance account for (VAF) are found to be 0.6643, 7.583, 6.799, and 91.95 respectively, which endow with high performance of predictive neuro-fuzzy system to make use for prediction of complex rock parameter.     

Phase transformations in Mn-Al permanent magnet alloys

Phase transformation studies have been made of the Mn-Al alloys with compositions near the equiatomic range with or without small amounts of carbon, copper and nickel, using differential thermal analysis, X-ray diffraction and optical and electron microscopy. The high temperature hexagonal phase obtained by quenching, transforms to the ferromagnetic phase between 500 and 550° C and on further heating transforms back to the hexagonal phase between 750 and 950° C. Also, on controlled cooling of the phase from about 900° C, the ferromagnetic phase is formed between 800 and 670° C. TEM studies have shown the presence of the B19 ordered phase, ferromagnetic phase and Mn₅Al₈ precipitates even in quenched alloys.     

Effect of silicides on tensile properties and fracture of alloy Ti-6AI-5Zr-0.5Mo-0.25Si from 300 to 823 K

The tensile properties and fracture behaviour of alloy Ti-6AI-5Zr-0.5Mo-0.25Si (wt%) have been investigated over a wide range of temperature from 300 to 823 K, in the as-water-quenched (WQ) and different aged (473 to 1073 K for 24 h)conditions following-solution-treatment (1323 K for 0.5 h). There is only a limited increase in strength but a drastic reduction in the ductility, at 300 K, due to ageing at 923 K. There is strong dynamic strain-ageing (DSA) in the unaged (WQ) state from 623 to 823 K and it is essentially due to silicon in the solid solution. The degree of DSA decreases with the ageing temperature and DSA does not occur in specimens aged at 973 and 1073 K. In general, the ductility of the WQ as well as the aged material increases with test temperature, except in the range of DSA, where the ductility of WQ material is reduced. The mode of fracture of the WQ specimens remains ductile in the lower and higher ranges of test temperature, but changes to quasi-cleavage at intermediate test temperatures. The minimum in the ductility and quasi-cleavage mode of fracture at 773 K, in the WQ material, is due to strong DSA. Three different modes of fracture, namely faceted, ductile, and mixed intergranular and ductile in the lower, intermediate and higher range of test temperature, respectively, are observed also in the aged conditions (at and above 923 K) of the material. The tensile properties and fracture characteristics in the aged conditions are controlled by the silicides.     

High-temperature properties of the Bi-Sr-Ca-Cu-O system: Simultaneous occurrence of melting, oxygen desorption, and a metal-insulator transition

We have investigated the temperature dependence of the electrical transport and the thermogravimetric properties, from -200°C to +1000, of the Bi-Sr-Ca-Cu-O high-temperature superconductor. We conclude that this system has a cooperative, simultaneous-melting/oxygen desorption/metal-insulator transition that occurs near 900°C. We speculate on its nature and on its relationship to phenomena found in other high-T _c superconductors.