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RISS 인기검색어
- 검색키워드
- 저자 : Amin Muhammad Nadeem (검색결과 2 건)
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Optimization analysis of the absorption-stabilization process for fluid catalytic cracking unit
Hussain Muhammad Saddam,Ahmed Ashfaq,Yibin Liu,Amin Muhammad Nadeem,Zahoor Tahir,Saleem Muhammad Afnan,Roh Kosan,Hussain Murid,Abu Bakar Muhammad Saifullah,박영권 한국화학공학회 2023 Korean Journal of Chemical Engineering Vol.40 No.7
The absorption-stabilization process (ASP), an important part of petroleum refinery used in the end-use products of petroleum (such as stable gasoline, liquid petroleum gas, and dry gas), is energy-intensive and has low product quality. Aspen Plus process simulator was used for the development of the ASP process model. The developed process model was validated with the actual plant data. The validated model was used to optimize to minimize the cost of the ASP. This work shows that the optimization analysis of the ASP can further improve the product quality and reduce thermal energy consumption. In the new process, changing feeding parameters of supplementary absorption oil, stripping tower intermediate reboiler, and feeding position of stabilization tower reduced the C3 contents of dry gas considerably and lowered the C2 and lighter contents of LPG. Additionally, the new process saved 1.32 MW of thermal energy consumption compared with the existing process. The operating cost has been reduced from 10.921 million USD annually to 9.830 million USD per year. Furthermore, the cost-saving effect of this optimization is about 9.99% (1.091 million USD per year).
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Malware Classification using Dynamic Analysis with Deep Learning
Asad Amin,Muhammad Nauman Durrani,Nadeem Kafi,Fahad Samad,Abdul Aziz International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.8
There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.
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