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Green ICT framework to reduce carbon footprints in universities
Uddin, Mueen,Okai, Safiya,Saba, Tanzila Techno-Press 2017 Advances in energy research Vol.5 No.1
The world today has reached a certain level where it is impossible to get the quality education at the tertiary level without the use of Information and Communication Technology (ICT). ICT has made life better, communication easier and faster, teaching and learning more practical through computers and other technology based learning tools. However, despite these benefits ICT has equally contributed immensely to environmental problems. Therefore there is the need to use ICT resources efficiently in universities for environmental sustainability so as to save both the university environment and the world at large from the effects of global warming. This paper evaluates the carbon footprints from the use of ICT devices and comes up with a proposed green ICT framework to reduce the carbon footprints in universities. The framework contains techniques and approaches to achieve greenness in the data center, personal computers (PCs) and monitors, and printing in order to make ICT more environmentally friendly, cheaper, safer and ultimately more efficient. Concerned experts in their respective departments at Asia Pacific University of Technology and Innovation (APU) Malaysia evaluated the proposed framework. It was found to be effective for achieving efficiency, reducing energy consumption and carbon emissions.
Computer-assisted brain tumor type discrimination using magnetic resonance imaging features
Sajid Iqbal,M. Usman Ghani Khan,Tanzila Saba,Amjad Rehman 대한의용생체공학회 2018 Biomedical Engineering Letters (BMEL) Vol.8 No.1
Medical imaging plays an integral role in theidentification, segmentation, and classification of braintumors. The invention of MRI has opened new horizons forbrain-related research. Recently, researchers have shiftedtheir focus towards applying digital image processingtechniques to extract, analyze and categorize brain tumorsfrom MRI. Categorization of brain tumors is defined in ahierarchical way moving from major to minor ones. Aplethora of work could be seen in literature related to theclassification of brain tumors in categories such as benignand malignant. However, there are only a few worksreported on the multiclass classification of brain imageswhere each part of the image containing tumor is taggedwith major and minor categories. The precise classificationis difficult to achieve due to ambiguities in images andoverlapping characteristics of different type of tumors. Inthe current study, a comprehensive review of recentresearch on brain tumors multiclass classification usingMRI is provided. These multiclass classification studies arecategorized into two major groups: XX and YY and eachgroup are further divided into three sub-groups. A set ofcommon parameters from the reviewed works is extractedand compared to highlight the merits and demerits ofindividual works. Based on our analysis, we provide a setof recommendations for researchers and professionalsworking in the area of brain tumors classification.