Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

Carbohydrate antigen 19-9 (CA 19-9) levels were measured in sera and tissues of 40 patients with breast cancer (01), 8 patients with prostate cancer (G2)and 12 patients with thyroid cancer (G3), by the enzyme­ linked immunosorbent assay (ELISA) technique.

The patients were admitted to Medical City Hospitals (Baghdad Teaching Hospital and Nursing Home Hospital). The sera were taken just before surgery, where the specimens were taken immediately after surgery and kept in saline solution at -20°C until the time of homogenizing process.

The results of CA 19-9 levels in sera were (16.309±7.143; 31.281±0.766;

11.5±0.707 U/ml respectively compared with serum CA 19-9 level of control group G4 which was 7.74

Herein, a cost-effective bio approach using extract derived from desert truffles (Tirmania nivea) is utilized to synthesize gold nanoparticles (AuNPs). AuNPs were thoroughly investigated using UV–vis, XRD, SEM, and TEM analyses. It was shown that nanoparticles had an fcc structure with a smooth spherical surface, an average diameter of 9.44 ± 0.26 nm, and an SPR band observed at 548 nm. Investigations were conducted on AuNPs' antibacterial and anti-cancer properties of prostate cancer cells. The findings suggest that AuNPs showed better antibacterial effects against S. aureus compared to E. coli, P. aeruginosa, and K. pneumoniae. AuNPs’ combination with antibiotics demonstrated a synergistic effect with significant antibacterial activi

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

Background: breast cancer is commonest cancer globally and the 1st cancer in Iraq among females, its management and prognosis depend on early diagnosis, the traditional method was excisional biopsy which is expensive and invasive leading to delayed diagnosis, FNAB is cheap nom invasive more acceptable to women, Aim of the study: to test the reliability of FNAB in preoperative diagnosis of breast lump.

Methodology: This is a retrospective study of 204 cases, 102 breast cancer cases and 102 benign breast lesions, taken between Jan. 2017 – Nov. 2017. The sample taken from the breast cancer early detection center in  Al-Alwiyaa maternity teaching hospital, during the year 2017