Structural, chemical and magnetic features of gold nanoshapes integrated-Er2O3-doped tellurite glass system prepared by a conventional melt-quenching technique

Salim, A.A.; Mahraz, Zahra Ashur Said; Anigrahawati, Puzi; Jan, N. A. M.; Ghoshal, Sib Krishna; Sahar, Md. Rahim; Noor, Fatimah Mohd.; Samah, K.A.; Yaacob, Syariffah Nurathirah Syed; Zain, S.K.Md.; Aziz, M. S.; Alias, Siti Salwa; Ahmad, Nurul Huda; Nurhafizah, H.; Harun, Aizul Nahar; Bakhtiar, Hazri; Sazali, E.S.

doi:10.1007/s00339-021-04841-x

Cited by 8 publications

(2 citation statements)

References 64 publications

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“…Their deep convolutional neural network is based on the VGG16 architecture. Using the ISBI of 2016 challenge dataset included 173 malignant and 727 benign pictures [29][30][31] updated the LightNet architecture to consist of seventeen layers, including five blocks, and achieved 81.6% 2-class classification accuracy. Convolution, ReLU, pooling, and a dropout layer are present in each and every block.…”

Section: Related Workmentioning

confidence: 99%

Melanoma Skin Cancer Classification based on CNN Deep Learning Algorithms

Waheed

Saadi

Rahim

et al. 2023

Mal. J. Fund. Appl. Sci.

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Melanoma, the deadliest form of skin cancer, is on the rise. The goal of this study is to present a deep learning system implementation for the detection of melanoma lesions on a server equipped with a graphics processing unit (GPU). When applied by a dermatologist, the recommended method might aid in the early detection of this kind of skin cancer. Evidence shows that deep learning may be used in a variety of settings to successfully extract patterns from data such as signals and images. This research presents a convolution neural network–based strategy for identifying early-stage melanoma skin cancer. Images are input into a deep learning model known as a convolutional neural network (CNN) that has already been pre-trained. The CNN classifier, which is trained with large amounts of data, can discriminate between malignant and nonmalignant melanoma. The method's success in the lab bodes well for its potential to aid dermatologists in the early detection of melanoma. However, the experimental results show that the proposed technique excels beyond the state-of-the-art methods in terms of diagnostic accuracy.

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Section: Related Workmentioning

confidence: 99%

Melanoma Skin Cancer Classification based on CNN Deep Learning Algorithms

Waheed

Saadi

Rahim

et al. 2023

Mal. J. Fund. Appl. Sci.

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“…10.11113/mjfas.v19n3.2902 317 Fakhruldeen et al | Malaysian Journal of Fundamental and Applied Sciences, Vol. 19 (2023) 313-322never found, a program restart will restore everything to normal[29][30][31][32]. During the elimination process, no new defects or vulnerabilities are introduced.…”

mentioning

confidence: 99%

Models and Methods of Information and Control System Cyber–security for Smart Buildings

Fakhruldeen,

A. Kadhim,

Abdulridha Shyaa

et al. 2023

Mal. J. Fund. Appl. Sci.

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Smart building management systems today employ the architectural multi-level creation of a system of unified components. Such a market is dynamic and always expanding, and sometimes the developers of such components stop maintaining their products or even collapse and vanish. It's not always possible to use professional-level components from proven developers when a building automation system (BAS) project budget is limited. The construction and analysis of models that consider the dependability and cyber security aspects of maintenance as well as ways of validating the selection of components, strategies, and service parameters are both necessary steps in finding compromises between the BAS architecture's value and quality. Use of information technology to determine maintenance parameters based on a model and model development approach.

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