Abstract The concept of quantum transition is based on the completion of a succession of time dependent (TD) perturbation theories in Quantum mechanics (QM). The kinetics of "quantum" transition, which are dictated by the coupled motions of a lightweight electrons and very massive nuclei, are inherent by nature in chemical and molecular physics, and the sequence of TD perturbation theory become unique. The first way involved adding an additional assumption into molecule quantum theory in the shape of the Franck-Condon rule, which use the isothermal approach. The author developed the second strategy, which involved injecting chaos to dampen the unique dynamically of the bonding movement of electrons and nuclei in the intermediary state of

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

Background: Dental implants act as infrastructure for fixed restoration to look like as a natural tooth. Osseointegration is a biological events and considered as a base for success of dental implant. The aim of this study is to evaluate the bond strength between bone and Ti implant coated with mixture of nano hydroxyapatite-chitosan-collagen compared with Ti implants coated with nano hydroxyapatite implanted in rabbit tibia, after different period of implantation time (two and six weeks) by torque removal test. Material and methods: 36 screws of commercially pure titanium; 8mm in length and 3mm diameter , 18 screws coated with mixture of nano hydroxyapatite-chitosan-collagen and18 screws coated with nano hydroxyapatite by dip coating. St

The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o

With the continuous progress of image retrieval technology, the speed of searching for the required image from a large amount of image data has become an important issue. Convolutional neural networks (CNNs) have been used in image retrieval. However, many image retrieval systems based on CNNs have poor ability to express image features. Content-based Image Retrieval (CBIR) is a method of finding desired images from image databases. However, CBIR suffers from lower accuracy in retrieving images from large-scale image databases. In this paper, the proposed system is an improvement of the convolutional neural network for greater accuracy and a machine learning tool that can be used for automatic image retrieval. It includes two phases