2020 IEEE International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET)最新文献
Pub Date : 2020-12-10DOI: 10.1109/TEMSMET51618.2020.9557557
Narender Singh Bhati, B. Arya
Covid-19 and its terrible effect has shaken the globe at its core. Therefore, majority of the governments across the globe have shut down the all educational institutions for an unanticipated period to control the spread of COVID-19 pandemic. As a result, to this, the pandemic has triggered the experts to have a relook at the traditional method of education and learning. Therefore, integration of online education is likely to take place in conventional education system. India, to an extent, has been able to include modern information communication technology (ICT) into the education sector. Therefore, the elements affecting learners’ Usage Intention towards e-learning becomes vital to be studied. This study empirically examines the impact of factors affecting e-learning on undergraduate students’ Usage Intention on digital platform, using Technology Acceptance Model (TAM). The data was collected of 300 undergraduate and post graduate students at private universities in Rajasthan state. which was further analysed using Structured Equation Modelling (SEM). The findings of the study reveal that Subjective Norms (SN), Experience (EXP), Computer Anxiety (CA), and Enjoyment (ENJ) have positive significant effect on students’ e-learning ease of use. Further, Enjoyment and Computer Anxiety showed a positive significant influence on users’ perceived usefulness. Additionally, a positive significant association was found between Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) and both had a strong and positive influence on students’ e learning Usage Intention (UI). The outcomes of the study play a significant role in policy decision making for designing a new e-learning environment in university education systems
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Pub Date : 2020-12-10DOI: 10.1109/TEMSMET51618.2020.9557441
Maheshvar Chandrasekar, Mukkesh Ganesh, B. Saleena, P. Balasubramanian
Breast cancer is the most common type of cancer affecting women. The formation of lumps in the breast is one of the first signs of the presence of this disease. These tumors can either be cancerous or benign and hence a breast tissue biopsy is conducted to determine their nature. Advancements in the field of vision-based Deep Learning have facilitated the wide adoption of automated diagnostic systems in hospitals, for tasks such as cancer and COVID detection from lung X-ray scans, diabetic retinopathy detection from retinal fundus images, brain MRI segmentation, etc. Moving forward, reduction in training, validation and development times, and efficient usage of training resources for these models will be more in focus. The EfficientNet architecture proposed by Google has recently outperformed prior state-of-the-art architectures such as DenseNet and ResNet on the ImageNet classification task while using fewer parameters and epochs to converge faster. In this paper, we compare the performance of the EfficientNetB3 architecture with the above-mentioned architectures for the tasks of binary and multinomial tumor classification on the benchmark BreakHis dataset, which consists of around 8000 breast histopathology images of varying magnification. Our results show that under similar training conditions, the EfficientNetB3 can converge faster and outperform the previous benchmark models by a significant margin. Our best models achieved 100% sensitivity and accuracy on certain binary classification tasks and a sensitivity of 95.45% and precision of 95.15% on 8-ary classification tasks.
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