Leveraging Quantum Computing for the Ising Model to Simulate Two Real Systems: Magnetic Materials and Biological Neural Networks (BNNs)

Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.

Kinetic model of vibration screening for granular materials based on biological neural network

The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration excitation.The material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs,respectively,and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen surface.The dynamic process of material vibration screening was simulated using discrete element method(DEM).By comparing the similarity between the material distribution established using biological neural network(BNN)and that obtained using DEM simulation,the optimum coefficients of BNN model under a certain screening parameter were determined,that is,one relationship between the BNN model coefficients and the screening operation parameters was established.Different screening parameters were randomly selected,and the corresponding relationships were established as a database.Then,with straw/grain ratio,aperture diameter,inclination angle,vibration strength in normal and tangential directions as inputs,five independent adaptive neuro-fuzzy inference systems(ANFIS)were established to predict the optimum BNN model coefficients,respectively.The training results indicated that ANFIS models had good stability and accuracy.The flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.

Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques that have been employed for cancer diagnosis.Exposure to air pollution has been related to various adverse health effects.This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer.We have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge discovery.Initially,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common source.From that source,we will designate the information relevant to our investigation to be regained from that assortment.Following that,we will convert the designated data into a suitable mining process.The patterns are abstracted by utilizing a relational suggestion rule mining process.These patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classification method(AANN).The proposed method is compared with the existing method in various factors.In conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.