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Reduced hardware requirements of deep neural network for breast cancer diagnosis
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Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration on each subsystem to futher reduce the hardware requirements. The DNN was designed using a system generator and implemented using very hardware description language (VHDL). The system achievments outcomes the superior’s accuracy rate of approximately 99.6 percent in distinguishing bengin from malignant tissue. Also, the hardware resources were reduced by 30 percent from works of literature with an error rate of 7e-4 when using the Kintex-7 xc7k325t-3fbg676 board.

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Publication Date
Fri Dec 23 2022
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Impact and Association of Anaemia Severity and Its Treatment With Quality of Life of Breast Cancer Patients in Malaysia
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Anaemia is a crucial issue among cancer patients and need to be treated properly. High incidence of anaemia in patients with cancer have been associated with several physiological manifestations, leading to decreased quality of life (QOL).

The current study aimed to assess the severity of anaemia, evaluate the current treatment guideline of anaemia, and to determine the association between the level of anaemia and its treatment on quality of life of breast cancer patients in Malaysia. This prospective study conducted among breast cancer patients in multicancer centers in Malaysia including three follow ups after receiving their chemotherapy. Clinical data were collected from their medical records and at each follow up, they asked

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Publication Date
Sun Dec 04 2011
Journal Name
Baghdad Science Journal
Modifying Hebbian Network for Text Cipher
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The objective of this work is to design and implement a cryptography system that enables the sender to send message through any channel (even if this channel is insecure) and the receiver to decrypt the received message without allowing any intruder to break the system and extracting the secret information. This work modernize the feedforward neural network, so the secret message will be encrypted by unsupervised neural network method to get the cipher text that can be decrypted using the same network to get the original text. The security of any cipher system depends on the security of the related keys (that are used by the encryption and the decryption processes) and their corresponding lengths. In this work, the key is the final weights

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Publication Date
Wed Jan 01 2020
Journal Name
Desalination And Water Treatment
Combination of the artificial neural network and advection-dispersion equation for modeling of methylene blue dye removal from aqueous solution using olive stones as reactive bed
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Publication Date
Wed Jun 26 2024
Journal Name
Opera Medica Et Physiologica
The Impact of Global DNA Methylation and Hypoxia-Inducible Factor 1 Alpha Levels in the Progression of Breast Cancer
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Publication Date
Wed Feb 01 2023
Journal Name
Asian Pacific Journal Of Cancer Prevention
Cytotoxic Activity of the Ethyl Acetate Extract of Iraqi Carica papaya Leaves in Breast and Lung Cancer Cell Lines
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Publication Date
Sun Jan 01 2023
Journal Name
The Egyptian Journal Of Hospital Medicine
High Tumor Levels of Ki-67, VEGF and Endostatin Are Associated with Progression of Breast Cancer in Iraqi Women
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Publication Date
Mon Jan 01 2024
Journal Name
Lecture Notes On Data Engineering And Communications Technologies
Utilizing Deep Learning Technique for Arabic Image Captioning
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Publication Date
Tue Dec 21 2021
Journal Name
Mendel
Hybrid Deep Learning Model for Singing Voice Separation
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Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

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Publication Date
Thu Apr 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Requirements for the Application of IFRS 17 in the Iraqi Environment
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This research aims to address the most recent international standard in the field of insurance contracts, the International Financial Reporting Standard (IFRS17) and the theoretical framework of the standard in addition to the most important characteristics of the standard (IFRS17), as well as to identify the paragraphs of the modern standard, with the challenges its application in general and the use of the approach (inputs - operations - outputs) to present the challenges of its application in the Iraqi environment and specifically in the environment of Iraqi insurance sector companies (government), the research is based on the main premise that the identification of the requirements for the application of the International Fin

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Publication Date
Wed Oct 05 2011
Journal Name
Iraqi Journal Of Physics
Study of linear shrinkage for siliceous materials to meet insulating requirements
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