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.
The development of Web 2.0 has improved people's ability to share their opinions. These opinions serve as an important piece of knowledge for other reviewers. To figure out what the opinions is all about, an automatic system of analysis is needed. Aspect-based sentiment analysis is the most important research topic conducted to extract reviewers-opinions about certain attribute, for instance opinion-target (aspect). In aspect-based tasks, the identification of the implicit aspect such as aspects implicitly implied in a review, is the most challenging task to accomplish. However, this paper strives to identify the implicit aspects based on hierarchical algorithm incorporated with common-sense knowledge by means of dimensionality reduction.
Five serological methods for detection of Brucella were compaired in this study, Four of the methods are commonely used in the detections:- 1-Rose-Bengal: as primary screening test which depends on detecting antibodies in the blood serum. 2-IFAT: which detects IgG and IgM antibodies in the serum. 3-ELISA test: which detects IgG antibodies in the serum. 4-2ME test: which detects IgG antibodies The fifth methods. It was developed by a reasercher in one of the health centers in Baghdad. It was given the name of spot Immune Assay (SIA). Results declares that among (100) samples of patients blood, 76, 49, 49, 37, and 28. samples were positive to Rose Bengal, ELISA, SIA, 2ME and IFAT tests, respectively. When efficiency, sensitivity and specific
... Show MoreThe study included the investigation of fungi ringed and inventory and Aflatoxins in rice and recorded average temperatures and humidity 22.75 degree Celsius and 13.2% respectively were obtained 1356 isolation innate possible diagnosis 15 species inherent in rice imported back to 8 races represented races b Fusarium , Cladosporium, Aspergillus and Alternaria
A composite section is made up of a concrete slab attached to a steel beam by means of shear connectors. Under positive and negative bending moment, part of the slab will act as a flange of the beam, resisting the longitudinal compression or tension force. When the spacing between girders becomes large, it is evident that the simple beam theory does not strictly apply because the longitudinal stress in the flange will vary with distance from the girder web, the flange being more highly stressed over the web than in the extremities. This phenomenon is termed "shear lag". In this paper, a nonlinear three-dimensional finite element analysis is employed to evaluate and determine the actual effective slab width of the composite steel-concrete
... Show MoreThe formula is effective in Surat Nisa
(A study of gramophone)
in the name of o Allah the Merciful
Praise be to Allah, Lord of the Worlds, and prayers and peace be upon the Seal of the Prophets and Messengers. The envoy is a mercy to the worlds Muhammad Sadiq Al - Amin and to the pure and good companions of the Tayyibites.
The formula of the morphological formulas, which have diverged from other meanings, whether lexicon or contextual and what this formula contains many meanings (effective source) and (effective in the sense of effective) and (effective sense reactor) and (effective sense) and ( (F) in the sense of a similar character) and ((the name of the) (collect). This was dealt with in the formula in Surat al-Nisaa
Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy
... Show MoreTraffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c
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