Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolutional Neural Network (CNN) technology to detect areas impacted by Invasive Ductal Carcinoma (IDC) in whole slide pictures. The trials used a dataset of 162 individuals with IDC, split into training (113 photos) and testing (49 images) groups. Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. The results highlight the effectiveness of the created model in properly detecting IDC-affected tissue areas, showing great promise for improving breast cancer diagnosis and treatment planning. We surpassing other models as such, CNN, VGG19, ResNet50.
Keys for 22 species representing ten genera Thripidae collection carried out during 1999-2001 in different localities in the middle of Iraq. Of them four species are described as new to science, Frankliniella megacephala sp. nov; Retithrips bagdadensis sp. nov; Chirothrips imperatus sp. nov; Taeniothrips tigridis sp. nov; Another thirteen species are recorded for the first time in Iraq; Thrips meridionalis (Pri.); Microcephalothrips abdominils (Crawford); Scolothrips pallidus (Beach); Scritothrips mangiferae Pri.; Frankliniella tritici Bagnall; Frankliniella schultzie Trybom; Frankliniella unicolor Morgan; Retithrips aegypticus Mar
... Show MoreThis research deals with the use of a number of statistical methods, such as the kernel method, watershed, histogram and cubic spline, to improve the contrast of digital images. The results obtained according to the RSME and NCC standards have proven that the spline method is the most accurate in the results compared to other statistical methods
Steganography art is a technique for hiding information where the unsuspicious cover signal carrying the secret information. Good steganography technique must be includes the important criterions robustness, security, imperceptibility and capacity. The improving each one of these criterions is affects on the others, because of these criterions are overlapped each other. In this work, a good high capacity audio steganography safely method has been proposed based on LSB random replacing of encrypted cover with encrypted message bits at random positions. The research also included a capacity studying for the audio file, speech or music, by safely manner to carrying secret images, so it is difficult for unauthorized persons to suspect
... Show MoreThis research deals with the use of a number of statistical methods, such as the kernel method, watershed, histogram, and cubic spline, to improve the contrast of digital images. The results obtained according to the RSME and NCC standards have proven that the spline method is the most accurate in the results compared to other statistical methods.
Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c
... Show MoreObjective: The goal of this research is to load Doxorubicin (DOX) on silver nanoparticles coupled with folic acid and test their anticancer properties against breast cancer. Methods: Chitosan-Capped silver nanoparticles (CS-AgNPs) were manufactured and loaded with folic acid as well as an anticancer drug, Doxorubicin, to form CS-AgNPs-DOX-FA conjugate. AFM, FTIR, and SEM techniques were used to characterize the samples. The produced multifunctional nano-formulation served as an intrinsic drug delivery system, allowing for effective loading and targeting of chemotherapeutics on the Breast cancer (AMJ 13) cell line. Flowcytometry was used to assess therapy efficacy by measuring apoptotic induction. Results: DOX and CS-Ag
... Show MoreKE Sharquie, MM Al-Waiz, AA Al-Nuaimy, Saudi medical journal, 2005 - Cited by 8
The objective of this study is to enable the role of modern and advanced computerized information systems. The model or mechanism should be developed by collecting the necessary information about the taxpayers and the sources of the taxpayers' income, on the basis of which the accuracy of the inventory process will be adopted. In addition to studies related to computerized information systems and showing their importance to the tax institution. To achieve the objectives of the study and to answer its questions, the researcher relied on collecting data and information on the subject on the literature and previous studies The secondary sources, which also formed the theoretical framework of the study, were obtained either as a practical fr
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