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bsj-9029
An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction
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Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-cancerous cells to find the best combination of parameters in CNN to predict lung cancer accurately.  The proposed system recorded the highest accuracy of 92.79%. In addition to that, the paper addresses 192 observations made using the CNN model. 

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Publication Date
Thu Jul 31 2025
Journal Name
Lecture Notes In Networks And Systems
Integration Between Network Analysis and Business Process Reengineering and Their Role in Reduce Project Costs/Applied Research in the Oil Projects Company
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Publication Date
Mon Oct 01 2018
Journal Name
2018 Ieee/acs 15th International Conference On Computer Systems And Applications (aiccsa)
Utilizing Hopfield Neural Network for Pseudo-Random Number Generator
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Publication Date
Fri Aug 27 2021
Journal Name
Human Interaction, Emerging Technologies And Future Systems V: Proceedings Of The 5th International Virtual Conference On Human Interaction And Emerging Technologies, Ihiet 2021, August 27-29, 2021 And The 6th Ihiet: Future Systems (ihiet-fs 2021), October 28-30, 2021, France
Electricity Consumption Forecasting in Iraq with Artificial Neural Network
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Publication Date
Sun Jan 14 2018
Journal Name
Journal Of Engineering
A Nonlinear MIMO-PID Neural Controller Design for Vehicle Lateral Dynamics model based on Modified Elman Neural Network
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This paper presents a new design of a nonlinear multi-input multi-output PID neural controller of the active brake steering force and the active front steering angle for a 2-DOF vehicle model based on modified Elman recurrent neural. The goal of this work is to achieve the stability and to improve the vehicle dynamic’s performance through achieving the desired yaw rate and reducing the lateral velocity of the vehicle in a minimum time period for preventing the vehicle from slipping out the road curvature by using two active control actions: the front steering angle and the brake steering force. Bacterial forging optimization algorithm is used to adjust the parameters weights of the proposed controller. Simulation resul

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Publication Date
Fri Apr 01 2022
Journal Name
Journal Of Engineering
Prediction of Shear Strength Parameters of Gypseous Soil using Artificial Neural Networks
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The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Engineering
Intelligent Congestion Control of 5G Traffic in SDN using Dual-Spike Neural Network
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Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification

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Publication Date
Fri Jun 16 2023
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Study The Lung-Protective Effects of Riboflavin and Cyanocobalamin Against Lung Toxicity-Induced by Cyclophosphamide in Rats
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Cyclophosphamide (CP) is a cytotoxic alkylating agent it's used associated with different side effects including lung toxicity. Vitamin B2 and vitamin B12 have lung-protective effects. This study was designed to evaluate lung-protective effects of both vitamins against lung toxicity induced by cyclophosphamide. seventy healthy adult albino male and female rats divided into seven groups each group containing ten rats were used in the present study and treated for seven days. On day eight rats were sacrificed and serum was obtained for glutathione and total antioxidant capacity measurement and lung extracted for immunohistochemical study; both vitamins significantly (P<0.05) increased glutathione and total antioxidant capacity in compar

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Publication Date
Fri May 30 2025
Journal Name
Journal Of Internet Services And Information Security
Enhancing Lung Cancer Classification using CT Images using Processing Techniques Employing U-Net Architecture
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Publication Date
Thu Dec 01 2022
Journal Name
Advances In Cancer Biology - Metastasis
CX3CL1 as potential immunotherapeutic tool for bone metastases in lung cancer: A preclinical study
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Publication Date
Thu Dec 01 2011
Journal Name
Journal Of Economics And Administrative Sciences
Measuring and Analysis of Demand Function on Oil derivatives of A Selected countries the Period 1985- 2008
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The subject of demand on oil derivative has occupied an important position at present time in the daily life context. The fuel of benzene and gas oil and kerosene is one of basic elements of that concern, and on local , regional and international levels. The oil derivatives have played a leading role in determining the course and nature of development since early 1970 to the present time whether in the productive Arab countries or the importing. The researcher set out from the hypothesis that the increase of the local consumer demand on some of the oil derivatives is because of the internal and external factors accompanied by the inability of the productive capability and local production to confront this increase, and the resort

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