These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about t

Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of

Silver/polyvinyl alcohol (Ag/PVA) nanocomposite films were synthesized via solution casting with varying concentrations of Ag nanoparticles (1–5 wt%). A comprehensive investigation was conducted to understand the influence of Ag content on the structural, optical, mechanical, thermal, electrical, and antibacterial properties of the composites. UV-Vis spectroscopy revealed a red shift in absorption peaks and a reduction in the optical band gap, which decreased from 3.78 eV for pure PVA to 3.37 eV for the 5 wt% Ag composite. FTIR and SEM analyses confirmed successful nanoparticle incorporation and morphological changes. The nanocomposites exhibited enhanced tensile strength, elongation at break, Young’s modulus, and hardness due t

The gas sensing properties of Co3O4and Co3O4:Y nano structures were investigated. The films were synthesized using the hydrothermal method on a seeded layer. The XRD, SEM analysis and gas sensing properties were investigated for Co3O4and Co3O4:Y thin films. XRD analysis shows that all films are polycrystalline in nature, having a cubic structure, and the crystallite size is (11.7)nm for cobalt oxide and (9.3)nm for the Co3O4:10%Y. The SEM analysis of thin films obviously indicates that Co3O4possesses a nanosphere-like structure and a flower-like structure for Co3O4:Y.The sensitivity, response time and recovery time to a H2S reducing gas were tested at different operating

This work presents a completely new develop an analyzer, named NAG-5SX1-1D-SSP, that is simple, accurate, reproducible, and affordable for the determination of cefotaxime sodium (CFS) in both pure and pharmaceutical drugs. The analyzer was designed according to flow injection analysis, and conducted to turbidimetric measurements. Ammonium cerium nitrate was utilized as a precipitating agent. After optimizing the conditions, the analysis system exhibited a linear range of 0.008-27 mmol. L-1 (n=29), with a limit of detection of 439.3 ng/sample, a limit of quantification of 0.4805 mg/sample, and a correlation coefficient of 0.9988. The repeatability of the responses was assessed by performing six successive injections of CFS at concentra