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Talib Mohammed Albayati,Ghanim Magbol Alwan,Omar Sabah Mahdy 한국화학공학회 2017 Korean Journal of Chemical Engineering Vol.34 No.1
The Magnetic nanoporous material Fe/MCM-41 was prepared, and its physical characterization studied, to determine the effect of its properties on separation efficiency of methyl orange (MO) from wastewater by adsorption process. The experimental results were analyzed for both adsorbent mesoporous material samples, MCM-41 and magnetic Fe/MCM-41, in order to select the best operating conditions for the different studied parameters, which are: constant temperature (20 oC), pH: (2) adsorbent dosage (0.03 gm), contact time (10minute) and concentrations (30mg/L). The results demonstrate that the adsorption processes can be well fitted by the Langmuir isotherm model for pure MCM-41, with a correlation coefficient of (0.999), and fitted by the Freundlich isotherm model for magnetic Fe/ MCM-41, with a correlation coefficient of (0.994). The adsorption kinetics of MO on to MCM-41 and Fe/MCM-41 are well described by a pseudo-second-order kinetic model.
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Predicting Brain Tumor Using Transfer Learning
Mustafa Abdul Salam,Sanaa Taha,Sameh Alahmady,Alwan Mohamed International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.5
Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.
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