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Comparison of Performance Metrics Level of Restricted Boltzmann Machine and Backpropagation Algorithms in Detecting Diabetes Mellitus Disease
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Diabetes is a disease caused by high sugar levels. Currently, diabetes is one of the most common diseases in the number of people with diabetes worldwide. The increase in diabetes is caused by the delay in establishing the diagnosis of the disease. Therefore, an initial action is needed as a solution that requires the most appropriate and accurate data mining to manage diabetes mellitus. The algorithms used are artificial neural network algorithms, namely Restricted Boltzmann Machine and Backpropagation. This research aims to compare the two algorithms to find which algorithm can produce high accuracy, and determine which algorithm is more accurate in detecting diabetes mellitus. Several stages were involved in this research, including data collection, data pre-processing, data processing, and evaluation models. This research shows that the Restricted Boltzmann Machine algorithm achieved accuracy of 82.02% while the Backpropagation algorithm reached87.01% when using the normalization method. Thus, the diabetes mellitus dataset used can be said to have a better value for the backpropagation algorithm than the restricted Boltzmann machine algorithm.

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Publication Date
Thu Dec 31 2020
Journal Name
Iraqi Journal Of Market Research And Consumer Protection
STUDY THE INHIBITION ACTIVITY OF PURIFIED BACTERIOCIN FROM LOCAL ISOLATION Lactococcuslactis ssp. lactisagainst SOME PATHOGENIC BACTERIAL SPECIES ISOLATED FROM CLINICAL SAMPLES: دراسة الفعالية التثبيطية للبكتريوسين المنقى من العزلة المحلية Lactococcuslactis ssp. Lactis تجاه بعض البكتريا الممرضة المعزولة من عينات سريرية
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This study aimed to study the inhibition activity of purified bacteriocin produced from the local isolation Lactococcuslactis ssp. lactis against pathogenic bacteria species isolated from clinical samples in some hospitals Baghdad city. Screening of L. lactis ssp. Lactis and isolated from the intestines fish and raw milk was performed in well diffusion method. The results showed that L. lactis ssp. lactis (Lc4) was the most efficient isolate in producing the bacteriocin as well observed inhibitory activity the increased that companied with the concentration, the concentration of the twice filtrate was better in obtaining higher inhibition diameters compared to the one-fold concentration. The concentrate

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Publication Date
Tue Jul 20 2021
Journal Name
Materials Science Forum
Red Laser Irradiation Effect on the Structural Properties of MawsoniteCu<sub>6</sub>Fe<sub>2</sub>SnS<sub>8</sub> [CFTS] Thin Films Deposited via Semi-Computerized Spray Pyrolysis Technique
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The existing investigation explains the consequence of irradiation of violet laser on the structure properties of MawsoniteCu6Fe2SnS8 [CFTS] thin films. The film was equipped by the utilization of semi-computerized spray pyrolysis technique (SCSPT), it is the first time that this technique is used in the preparation and irradiation using a laser. when the received films were processed by continuous red laser (700 nm) with power (>1000mW) for different laser irradiation time using different number of times a laser scan (0, 6, 9, 12, 15 and 18 times) with total irradiation time (0,30,45,60,75,90 min) respectively at room temperature.. The XRD diffraction gave polycrysta

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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
Arabic Sentiment Analysis (ASA) Using Deep Learning Approach
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Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

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Publication Date
Sat Sep 30 2023
Journal Name
Iraqi Journal Of Science
Hybrid CNN-SMOTE-BGMM Deep Learning Framework for Network Intrusion Detection using Unbalanced Dataset
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This paper proposes a new methodology for improving network security by introducing an optimised hybrid intrusion detection system (IDS) framework solution as a middle layer between the end devices. It considers the difficulty of updating databases to uncover new threats that plague firewalls and detection systems, in addition to big data challenges. The proposed framework introduces a supervised network IDS based on a deep learning technique of convolutional neural networks (CNN) using the UNSW-NB15 dataset. It implements recursive feature elimination (RFE) with extreme gradient boosting (XGB) to reduce resource and time consumption. Additionally, it reduces bias toward

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Publication Date
Tue Jan 18 2022
Journal Name
Materials Science Forum
The Effect of Gamma Radiation on the Manufactured HgBa<sub>2</sub>Ca<sub>2</sub>Cu<sub>2.4</sub>Ag<sub>0.6</sub>O<sub>8+δ</sub> Compound
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In this article four samples of HgBa2Ca2Cu2.4Ag0.6O8+δ were prepared and irradiated with different doses of gamma radiation 6, 8 and 10 Mrad. The effects of gamma irradiation on structure of HgBa2Ca2Cu2.4Ag0.6O8+δ samples were characterized using X-ray diffraction. It was concluded that there effect on structure by gamma irradiation. Scherrer, crystallization, and Williamson equations were applied based on the X-ray diffraction diagram and for all gamma doses, to calculate crystal size, strain, and degree of crystallinity. I

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