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Musri, Nabilla,Christie, Brenda,Ichwan, Solachuddin Jauhari Arief,Cahyanto, Arief Korean Academy of Oral and Maxillofacial Radiology 2021 Imaging Science in Dentistry Vol.51 No.3
Purpose: The aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords(deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed. Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects. Conclusion: Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.
Aziman, Eli Syafiqah,Ismail, Aznan Fazli,Muttalib, Nabilla Abdul,Hanifah, Muhammad Syafiq Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.9
Rare-earth (RE) industries generate a massive amount of radioactive residue containing high thorium concentrations. Due to the fact that thorium is considered a non-economic element, large volume of these RE processed residues are commonly disposed of without treatment. It is essential to study an appropriate treatment that could reduce the volume of waste for final disposition. To this end, this research investigates the applicability of carbon-based adsorbent in separating thorium from aqueous phase sulphate is obtained from the cracking and leaching process of solid rare-earth by-product residue. Adsorption of thorium from the aqueous phase sulphate by carbon-based electrodes was investigated through electrosorption experiments conducted at a duration of 180 minutes with a positive potential variable range of +0.2V to +0.6V (vs. Ag/AgCl). Through this research, the specific capacity obtained was equivalent to 1.0 to 5.14 mg-Th/g-Carbon. Furthermore, electrosorption of thorium ions from aqueous phase sulphate is found to be most favorable at a higher positive potential of +0.6V (vs. Ag/AgCl). This study's findings elucidate the removal of thorium from the rare-earth residue by carbon-based electrodes and simultaneously its potential to reduce disposal waste of untreated residue.
Saputra, Oka Danil,Murti, Fahri Wisnu,Irfan, Mohammad,Putri, Nadea Nabilla,Shin, Soo Young The Korea Institute of Information and Commucation 2018 Journal of information and communication convergen Vol.16 No.2
Wireless capsule endoscopy (WCE) is considered as recent technology for the detection cancer cells in the human digestive system. WCE sends the captured information from inside the body to a sensor on the skin surface through a wireless medium. In WCE, the design of low-power consumption devices is a challenging topic. In the Shannon-Nyquist sampling theorem, the number of samples should be at least twice the highest transmission frequency to reconstruct precise signals. The number of samples is proportional to the power consumption in wireless communication. This paper proposes compressive sensing as a method to reduce power consumption in WCE, by means of a trade-off between samples and reconstruction accuracy. The proposed scheme is validated under channel constraints, expressed as the realistic human body path loss. The results show that the proposed scheme achieves a significant reduction in WCE power consumption and achieves a faster computation time with low signal error reconstruction.