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 the cancer disease detection. Developing a proposed data mining model is useful to diagnose the cancer disease once the cancer detection is accomplished using data mining for the examination and classification of machine learning supervised algorithms.
A numerical method is developed for calculation of the wake geometry and aerodynamic forces on two-dimensional airfoil under going an arbitrary unsteady motion in an inviscid incompressible flow (panel method). The method is applied to sudden change in airfoil incidence angle and airfoil oscillations at high reduced frequency. The effect of non-linear wake on the unsteady aerodynamic properties and oscillatory amplitude on wake rollup and aerodynamic forces has been studied. The results of the present method shows good accuracy as compared with flat plate and for unsteady motion with heaving and pitching oscillation the present method also shows good trend with the experimental results taken from published data. The method shows good result
... Show MoreBackground Cold atmospheric plasma (CAP) is widely used in the cancer therapy field. This type of plasma is very close to room temperature. This paper illustrates the effects of CAP on breast cancer tissues both in vivo and in vitro. Methods The mouse mammary adenocarcinoma cell line AN3 was used for the in vivo study, and the MCF7, AMJ13, AMN3, and HBL cell lines were used for the in vitro study. A floating electrode-dielectric barrier discharge (FE-DBD) system was used. The cold plasma produced by the device was tested against breast cancer cells. Results The induced cytotoxicity percentages were 61.7%, 68% and 58.07% for the MCF7, AMN3, and AMJ13 cell lines, respectively, whereas the normal breast tissue HBL cell line exhibited very li
... Show MoreClinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision b
In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa
... Show MorePharmaceutical-instigated pollution is a major concern, especially in relation to aquatic environments and drugs such as meropenem antibiotics. Adsorbents, such as multi-walled carbon nanotubes, offer potential as means of removing polluting meropenem antibiotics and other similar compounds from water. In order to evaluate the effectiveness of multi-walled carbon nanotubes in this capacity, various experimental parameters, including contact time, initial concentration, pH, temperature and the dose of adsorbent have been investigated. The Langmuir and the Freundlich isotherm models have been used. The data obtained using a modified Langmuir model have been consistent with the experimental ones; the best pH value has been obtained to have the
... Show MoreDue to its various resistance mechanisms, Pseudomonas aeruginosa is the most prevalent opportunistic infection that kills hospitalized patients. Thus, therapeutic options become limited. Objective: The study aimed to estimate the antibiofilm effectiveness of Conocarpus erectus leaf extracts against MDR P. aeruginosa isolates and examines pelA and algD gene expression. Subjects and Methods: One hundred-fifty clinical samples were collected from five Baghdad hospitals between September 2021 and January 2022. Samples were grown on different mediums. Despite cetrimide agar's ability to detect P. aeruginosa, only 83 isolates developed at 42°C. VITEK 2 compact system identification followed. This study examined 83 of P. aeruginosa isolates for r
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