• Imaging Science in Dentistry
• /
• v.53 no.4
• /
• pp.271-281
• /
• 2023

Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Materials and Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion: CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.

• Journal of the Ergonomics Society of Korea
• /
• v.34 no.5
• /
• pp.455-467
• /
• 2015

Objective: The present study suggested practical measures for successful patent registration based on a review of success and failure cases of patent application filed based on inventions obtained from physical ergonomics research. Background: The protection of intellectual property (IP) contributes to economic growth and competitiveness and facilitates innovation and creativity. IP rights are pursued on research findings for effective technology transfer and commercialization; however, a patent application can be rejected if patentability requirements such as patent eligible subject matter, utility for industrial application, novelty, or non-obviousness are not satisfied. Method: Three successful and three failed cases of patent applications based on physical ergonomics research were reviewed, critical reasons for their successes and failures were examined, and measures were proposed to avoid failures in patent registration. Results: The following measures were identified based on the patent application case review. First, abstract ideas including logical procedures and/or mathematical formulas need to include use of tangible apparatus and methods in idea realization. Second, the provision of grace period inventor disclosure exception needs to be properly followed in case an invention is disclosed before filing of patent application. Lastly, a comprehensive analysis of prior art published or publicly known anywhere in the world and a claim preparation of distinguished, non-trivial features compared to prior art solutions are needed to avoid possible violation of novelty and non-obviousness. Application: The proposed measures can help to prepare a patent application with patent eligibility.

• Journal of Korean Neurosurgical Society
• /
• v.46 no.1
• /
• pp.1-4
• /
• 2009

Objective: To report an unsuspected adaptive plasticity of single upper motor neurons and of primary motor cortex found after microsurgical connection of the spinal cord with peripheral nerve via grafts in paraplegics and focussed discussion of the reviewed literature. Methods: The research aimed at making paraplegics walk again, after 20 years of experimental surgery in animals. Amongst other things, animal experiments demonstrated the alteration of the motor endplates receptors from cholinergic to glutamatergic induced by connection with upper motor neurons. The same paradigm was successfully performed in paraplegic humans. The nerve grafts were put into the ventral-lateral spinal tract randomly, with out possibility of choosing the axons coming from different areas of the motor cortex. Results: The patient became able to selectively activate the re-innervated muscles she wanted without concurrent activities of other muscles connected with the same cortical areas. Conclusion: Authors believe that unlike in nerve or tendon transfers, where the whole cortical area corresponding to the transfer changes its function a phenomenon that we call "brain plasticity by areas". in our paradigm due to the direct connection of upper motor neurons with different peripheral nerves and muscles via nerve grafts motor learning occurs based on adaptive neuronal plasticity so that simultaneous contractions of other muscles are prevented. We propose to call it adaptive functional "plasticity by single neurons". We speculate that this phenomenon is due to the simultaneous activation of neurons spread in different cortical areas for a given specific movement, whilst the other neurons of the same areas connected with peripheral nerves of different muscles are not activated at the same time. Why different neurons of the same area fire at different times according to different voluntary demands remains to be discovered. We are committed to solve this enigma hereafter.

• Analytical Science and Technology
• /
• v.28 no.3
• /
• pp.187-195
• /
• 2015

The heat of sublimation (HOS) is an essential parameter used to resolve environmental problems in the transfer of organic contaminants to the atmosphere and to assess the risk of toxic chemicals. The experimental measurement of the heat of sublimation is time-consuming, expensive, and complicated. In this study, quantitative structural property relationships (QSPR) were used to develop a simple and predictive model for measuring the heat of sublimation of organic compounds. The population-based forward selection method was applied to select an informative subset of descriptors of learning algorithms, such as by using multiple linear regression (MLR) and the support vector machine (SVM) method. Each individual model and consensus model was evaluated by internal validation using the bootstrap method and y-randomization. The predictions of the performance of the external test set were improved by considering their applicability to the domain. Based on the results of the MLR model, we showed that the heat of sublimation was related to dispersion, H-bond, electrostatic forces, and the dipole-dipole interaction between inter-molecules.

• The korean journal of orthodontics
• /
• v.41 no.2
• /
• pp.138-146
• /
• 2011

The purpose of this article is to introduce a new virtual orthodontic treatment (VOT) system, which can be used to construct three-dimensional (3D) virtual models, establish a 3D virtual setup, enable the placement of the virtual brackets at the predetermined position, and fabricate the transfer jig with a customized bracket base for indirect bonding (IDB) using the stereolithographic technique. A 26-year-old woman presented with anterior openbite, crowding in the upper and lower arches, and narrow and tapered upper arch, despite having an acceptable profile and balanced facial proportion. The treatment plan was rapid palatal expansion (RPE) without extraction. After 10 days of RPE, sufficient space was obtained for decrowding. After a 10-week retention period, accurate pretreatment plaster models were obtained using silicone rubber impression. IDB was performed according to the protocol of the VOT system. Crowding of the upper and lower arches was effectively resolved, and anterior openbite was corrected to normal overbite. Superimposition of the 3D virtual setup models (3D-VSM) and post-treatment 3D virtual models showed that the latter deviated only slightly from the former. Thus, the use of the VOT system helped obtain an acceptable outcome in this case of mild crowding treated without extraction. More cases should be treated using this system, and the pre- and post-treatment virtual models should be compared to obtain feedback regarding the procedure; this will support doctors and dental laboratory technicians during the learning curve.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.353-359
• /
• 2021

Information technology and the need for timely and effective communication during the Covid-19 have made most governments adopt technological approaches to provide their services. E-government services have been adopted by most governments especially in developed countries to quickly and effectively share information. This study discusses the reasons why governments in the Gulf region should develop a new model for information technology knowledge management practices. To achieve this, the author identified possible benefits of adopting information technology knowledge management practices and why most governments in the Gulf find it hard to adopt them. Knowledge management allows for learning, transfer as well as sharing of information between government organizations and citizens and with the development of technology, the effectiveness of electronic services can easily be achieved. Also, effective adoption of information technology can improve knowledge management with the help of techniques that enhance capture, storage, retrieval as well as sharing of information. The author used systematic literature review to select 28 journals and articles published post 2019. IEEE, Google Scholar and Science Direct were used to select potential studies from which 722 journals and articles were selected. Through screening and eligibility assessment, 21 articles were retained while the back and forward search had 7 more articles which were also included in the study. Using information gathered from these articles and journals a new conceptual model was developed to help improve information technology knowledge management for governments in the Gulf region to effectively deliver e-services during Covid-19. This model was developed based on the process of KM, Theory of Planned Behavior and Unified Theory of Acceptance and Use of Technology. Based on the developed model. From UTAUT model, performance expectancy, effort expectancy as well as social influence had a great impact.

• International Journal of Computer Science & Network Security
• /
• v.22 no.9
• /
• pp.195-207
• /
• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.2
• /
• pp.311-326
• /
• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.6
• /
• pp.55-61
• /
• 2014

This paper related with the ECMA (Enchanced CMA) algorithm performance which is possible to simultaneously compensation of the amplitude and phase by appling the minimum disturbance techniques in the CMA adatpve equalizer. The ECMA can improving the gradient noise amplification problem, stability and roburstness performance by the minimum disturbance technique that is the minimization of the equalizer tap weight variation in the point of squared euclidiean norm and the decision directed mode, and then the now cost function were proposed in order to simultaneouly compensation of amplitude and phase of the received signal with the minimum increment of computational operations. The performance of ECMA algorithm was compared to present MCMA by the computer simulation. For proving the performance, the recovered signal constellation that is the output of equalizer output signal and the residual isi and Maximum Distortion charateristic and MSE learning curve that are presents the convergence performance in the equalizer and the overall frequency transfer function of channel and equalizer were used. As a result of computer simulation, the ECMA has more better compensation capability of amplitude and phase in the recovered constellation, and the convergence time of adaptive equalization has improved compared to the MCMA.

• Journal of Korean Elementary Science Education
• /
• v.25 no.1
• /
• pp.70-88
• /
• 2006

The purpose of this study is to investigate the effect of a multiple cognitive conflict strategy at remedying student's misconceptions. Elementary students have many misconceptions about the brightness of the electric bulb in simple dual circuits. Most of the misconceptions can be summed up as 'the more batteries or the fewer bulbs, the brighter is the output.' The students have learned about the brightness of the electric bulb while connected to a battery in Grade 4 and the brightness of multiple electric bulbs in Grade 5. However, about $50%$ of the students remain with the firm misconception that the brightness of the bulb is related to the number of source batteries. This strong misconception may not lead to a conceptual change in the case of only one cognitive conflict. This study used a multiple conflict strategy while tackling the cognitive conflicts in the students as they solved the problems many times. It involved 160 grade 5 students. The result was they often changed their misconceptions and used more scientific thinking than the same grade students of other schools. It remains to be seen if this success will transfer to other schools and students and we intend on studying further the differences in students regarding this learning process.