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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
Wed Nov 07 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Assessment of students' knowledge regarding breast self-examination in Erbil City-Iraq
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Breast cancer is the most common cause of death among women worldwide (1)
. Breast self-exam (BSE) is considered
an important public health procedure; primary prevention should be given the highest priority in the fight against
cancer.
Cancer is considered the second leading cause of death in developed countries there was some 6.2 million cancer
related deaths, accounƟng for 12% of all deaths globally (5).Patients perception toward this disease and preference
concerning the types and aims of their treatment are vary they may loss hopes and become devastated and crippled
or even dies earlier, if told about the diagnosis (13). The study aimed to assess knowledge of female students regarding
BSE, and to find out rel

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Publication Date
Fri Mar 31 2017
Journal Name
Al-khwarizmi Engineering Journal
Design of Nonlinear PID Neural Controller for the Speed Control of a Permanent Magnet DC Motor Model based on Optimization Algorithm
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In this paper, the speed control of the real DC motor is experimentally investigated using nonlinear PID neural network controller. As a simple and fast tuning algorithm, two optimization techniques are used; trial and error method and particle swarm optimization PSO algorithm in order to tune the nonlinear PID neural controller's parameters and to find best speed response of the DC motor. To save time in the real system, a Matlab simulation package is used to carry out these algorithms to tune and find the best values of the nonlinear PID parameters. Then these parameters are used in the designed real time nonlinear PID controller system based on LabVIEW package. Simulation and experimental results are compared with each other and showe

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Publication Date
Sat Oct 01 2022
Journal Name
Colloids And Surfaces A: Physicochemical And Engineering Aspects
Application of natural deep eutectic solvents in bulk liquid membrane system for removal of free glycerol from crude fatty acid methyl ester
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Fatty Acid Methyl Ester (FAME) produced from biomass offers several advantages such as renewability and sustainability. The typical production process of FAME is accompanied by various impurities such as alcohol, soap, glycerol, and the spent catalyst. Therefore, the most challenging part of the FAME production is the purification process. In this work, a novel application of bulk liquid membrane (BLM) developed from conventional solvent extraction methods was investigated for the removal of glycerol from FAME. The extraction and stripping processes are combined into a single system, allowing for simultaneous solvent recovery whereby low-cost quaternary ammonium salt-glycerol-based deep eutectic solvent (DES) is used as the membrane phase.

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Publication Date
Wed Mar 01 2023
Journal Name
Ecti Transactions On Computer And Information Technology
Diagnosis of COVID-19 Infection via Association Rules of Cough Encoding
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Article information: COVID-19 has roused the scientic community, prompting calls for immediate solutions to avoid the infection or at least reduce the virus's spread. Despite the availability of several licensed vaccinations to boost human immunity against the disease, various mutated strains of the virus continue to emerge, posing a danger to the vaccine's ecacy against new mutations. As a result, the importance of the early detection of COVID-19 infection becomes evident. Cough is a prevalent symptom in all COVID-19 mutations. Unfortunately, coughing can be a symptom of various of diseases, including pneumonia and inuenza. Thus, identifying the coughing behavior might help clinicians diagnose the COVID-19 infection earlier and distinguish

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Publication Date
Tue May 02 2023
Journal Name
Journal Of Physical Education
Designing compound exercises with hardware technology and its impact on some physical and skill abilities of the Airways Club players under (19) years old.
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ER Abbas, AA Jasim, Journal of Physical Education, 2023 - Cited by 1

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Publication Date
Thu Jan 01 2015
Journal Name
International Journal Of Science And Research (ijsr
Interaction of Alpha Particles with Breast Tissue
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As modern radiotherapy technology advances, radiation dose and dose distribution have improved significantly. As part of a natural evolution, there has recently been renewed interest in therapy, particularly in the use of heavy charged particles, because these types of radiation serve theoretical advantages in all biological and physical aspects. The interactions of alpha particle with matter were studied and the stopping powers of alpha particle with Breast Tissue were calculated by using Beth-Bloch equation, Zeigler's formula and SRIM software, also the Range and Liner Energy Transfer (LET) and Breast Thickness As well as Dose and Dose equivalent for this particle were calculated by using Mat lab language for (0.01-200) MeV alpha ene

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Publication Date
Mon Jun 17 2019
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Dynamic Channel Assignment Using Neural Networks
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This paper presents a proposed neural network algorithm to solve the shortest path problem (SPP) for communication routing. The solution extends the traditional recurrent Hopfield architecture introducing the optimal routing for any request by choosing single and multi link path node-to-node traffic to minimize the loss. This suggested neural network algorithm implemented by using 20-nodes network example. The result shows that a clear convergence can be achieved by 95% valid convergence (about 361 optimal routes from 380-pairs). Additionally computation performance is also mentioned at the expense of slightly worse results.

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Publication Date
Wed Nov 01 2023
Journal Name
Iop Conference Series: Earth And Environmental Science
Requirements for Developing the Technical Capabilities of Agricultural Extension Service Providers to Face the Effects of Climate Change in Baghdad Province
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Abstract<p>The aim of the research is to determine the requirements for developing the technical capabilities of the agricultural extension service providers to face the effects of climatic changes in Baghdad Governorate, to achieve the goal of the research and in order to obtain the respondents’ approval of the requirements (28) requirements were identified in the light of the literature and studies related to the subject and the opinions of specialists to develop the technical capabilities of the agricultural extension service providers distributed on two axes (the ability to know the effects of climate changes, the ability to know the practices to reduce the effects of climate changes). The</p> ... Show More
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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Engineering
Modeling and Control of Fuel Cell Using Artificial Neural Networks
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This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
The Effect Of Optimizers On The Generalizability Additive Neural Attention For Customer Support Twitter Dataset In Chatbot Application
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When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat

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