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RISS 인기검색어
- 검색키워드
- 저자 : Faizan Ullah (검색결과 3 건)
- 무료
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Faizan Ullah,Muhammad Nadeem,Mohammad Abrar 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.1
Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.
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Ahmad, Faizan,Lau, K.K.,Lock, S.S.M.,Rafiq, Sikander,Khan, Asad Ullah,Lee, Moonyong Elsevier 2015 Journal of industrial and engineering chemistry Vol.21 No.-
<P><B>Abstract</B></P> <P>Conceptual process simulations and optimization are essential in the design, operation and troubleshooting stages of a membrane-based gas separation system. Despite this, there are few mathematical models/tools associated with a hollow fiber membrane module available in a commercial process simulator. A mathematical model dealing with the hollow fiber module characteristics that can be included within a commercial process simulator is needed to examine the performance and economics of a gas separation system. In this study, a hollow fiber membrane model was incorporated in Aspen HYSYS as a user defined unit operation for the study of carbon dioxide separation from methane. The hollow fiber membrane model was validated experimentally. The study of a double stage membrane module with a permeate recycle, which was proposed to be the optimal configuration in previous studies, was extended to consider the effects of the module characteristics (such as the fiber length, radius of the fiber bundle, diameter of the fibers, and porosity) on the process performance and economics. The gas processing cost (GPC) increased with increasing fiber length and bundle radius, and decreased with increasing outer diameter of the fibers and porosity. At the same time, the separation efficiency (product quality) was also dependent on these module parameters. Therefore, the tradeoff for the hollow fiber membrane module characteristics needs to be determined based on the minimum GPC with respect to the desired product purity.</P>
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Arshad Hussain Wazir,Qudratullah Khan,Nisar Ahmad,Faizan Ullah,Imdadullah Quereshi,Hazrat Ali 한국재료학회 2022 한국재료학회지 Vol.32 No.2
Copper nanoparticles (CuNPs) are considered of great importance due to their high catalytic and antimicrobial activities. This study focuses on the preparation and characterization of CuNPs, and on their antibacterial/antifungal activities. A copper salt (copper sulfate pentahydrate) as precursor, starch as stabilizing agent, and ascorbic acid as reducing agent were used to fabricate CuNPs. The resulting product was characterized via different techniques such as X-ray diffractrometry (XRD), Fourier Transform Infrared (FTIR) spectroscopy, and Scanning electron microscopy (SEM) to confirm its characteristic properties. Employing the Scherrer formula, the mean crystallite sizes of copper (Cu) and cuprous oxide (Cu2O) nanocrystals were found to be 29.21 and 25.33 nm, respectively, as measured from the main X-ray diffraction peaks. The functional groups present in the resulting CuNPs were confirmed by FTIR. In addition, the engineered CuNPs showed antibacterial and antifungal activity against tested pathogenic bacterial and fungal strains.
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