Hepatitis is an inflammation of the liver, most commonly caused by a viral infection. Supervised data mining techniques have been successful in hepatitis disease diagnosis through a set of datasets. Many methods have been developed by the aids of data mining techniques for hepatitis disease diagnosis. The majority of these methods are developed by single learning techniques. In addition, these methods do not support the ensemble learning of the data. Combining the outputs of several predictors can result in improved accuracy in classification problems. This study aims to propose an accurate method for the hepatitis disease diagnosis by taking the advantages of ensemble learning. We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies. The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine. The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare.

Carbon Nanotubes (CNTs) are generally nano-scale tubes comprising a network of carbon atoms in a cylindrical setting that compared with silicon counterparts present outstanding characteristics such as high mechanical strength, high sensing capability and large surface-to-volume ratio. These characteristics, in addition to the fact that CNTs experience changes in their electrical conductance when exposed to different gases, make them appropriate candidates for use in sensing/measuring applications such as gas detection devices. In this research, a model for a Field Effect Transistor (FET)-based structure has been developed as a platform for a gas detection sensor in which the CNT conductance change resulting from the chemical reaction between NH₃ and CNT has been employed to model the sensing mechanism with proposed sensing parameters. The research implements the same FET-based structure as in the work of Peng et al. on nanotube-based NH₃ gas detection. With respect to this conductance change, the I–V characteristic of the CNT is investigated. Finally, a comparative study shows satisfactory agreement between the proposed model and the experimental data from the mentioned research.

Silicene, the Si analogue of graphene, has recently extended the short list of existing two-dimensional (2D) atomic crystals. There are many remarkable electrical properties as well as unique thermal conductivities associated with graphene and silicene making them perfect materials that possess great potential to replace and provide an even better performance than silicon in future generation semiconductor devices. It is expected that novel devices developed with these will be much faster and smaller in size than their contemporary counterparts. Although graphene and silicene display different electrical conductivity behavior, their carrier concentration has similar behavior. The current–voltage characteristics of silicene/graphene field effect transistors (FETs) have been demonstrated at different operating temperatures under the flow of different NH₃ gas concentrations. It was found that in similar conditions, the suggested model for a gas sensor based on graphene shows higher electrical conductivity compared to silicene.

Graphene is an allotrope of carbon with two‐dimensional (2D) monolayer honeycombs. A larger detection area and higher sensitivity can be provided by graphene‐based nanosenor because of its 2D structure. In addition, owing to its special characteristics, including electrical, optical and physical properties, graphene is known as a more suitable candidate compared to other materials used in the sensor application. A novel model employing a field‐effect transistor structure using graphene is proposed and the current–voltage ( I – V ) characteristics of graphene are employed to model the sensing mechanism. This biosensor can detect Escherichia coli ( E. coli ) bacteria, providing high levels of sensitivity. It is observed that the graphene device experiences a drastic increase in conductance when exposed to E. coli bacteria at 0–10 ⁵ cfu/ml concentration. The simple, fast response and high sensitivity of this nanoelectronic biosensor make it a suitable device in screening and functional studies of antibacterial drugs and an ideal high‐throughput platform which can detect any pathogenic bacteria. Artificial neural network and support vector regression algorithms have also been used to provide other models for the I – V characteristic. A satisfactory agreement has been presented by comparison between the proposed models with the experimental data.