scholarly journals Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4613
Author(s):  
Yi-Cheng Mao ◽  
Tsung-Yi Chen ◽  
He-Sheng Chou ◽  
Szu-Yin Lin ◽  
Sheng-Yu Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Rapid Development ◽  
Radiographic Examination ◽  
Analysis Model ◽  
Common Procedure ◽  
Medical Treatments ◽  
Pass Filter ◽  
Single Tooth ◽  
Imaging Research

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

Implementasi Manajemen Mutu Terpadu Dalam Memberikan Layanan Untuk Meningkatkan Prestasi Peserta Didik di SLB YPAC Pangkalpinang

2019 ◽  
Vol 2 (2) ◽  
pp. 265-278
Author(s):  
Diah Rina Miftakhi ◽  
Nurjanah Nurjanah
Keyword(s):  
Quality Management ◽  
Data Collection ◽  
Human Resources ◽  
Learning Process ◽  
School Environment ◽  
Quality Of Services ◽  
Teaching Staff ◽  
Learning Material ◽  
Analysis Model

describe the implementation of an integrated quality management component consisting of the quality of services provided by the school, human resources in teaching, the school environment, and learning process  in SLB YPAC Pangkalpinang.               The method used in this study, namely by using a naturalistic qualitative approach. Data collection is done through observation, interviews, and documentation. The subjects of this study include the principal, teachers, employees, and students. The validity of the data is done by triangulation, and deeper observation. Analysis of the data used is the interactive analysis model of Miles and Huberman through data collection, data reduction, data presentation, and conclusion drawing.              The results showed that: (a) the quality of services to students in SLB YPAC Pangkalpinang had met good service standards. This can be seen from the services in the form of facilities and infrastructure which are quite complete in schools; (b) the quality of human resources in the education process shows good teacher resources. This can be seen from the teacher data which shows that the teaching staff at SLB YPAC Pangkalpinang 95% of educators with S1 qualifications in the field of education; (c) the quality of the environment in SLB YPAC Pangkalpinang is already good. This can be seen from the very strategic location of the school because the location of the school is in the middle of the city so that it is easily accessible by the community; (d) the quality of the learning process carried out by teachers at Pangkal Pinang YPAC SLB is good. This can be seen from the realization of the form of activities through learning planning by preparing lesson plans for each subject, then implementing learning, which includes strategies and methods used by teachers in delivering learning material, and evaluation of learning. Keywords: Integrated quality management, student achievement     ABSTRAK Tujuan dalam melaksanakan penelitian ini  adalah untuk melihat pelaksanaan serta mendeskripsikan implementasi  komponen Manajemen Mutu Terpadu yang terdiri dari kualitas layanan yang diberikan sekolah, sumber daya manusia dalam mengajar, lingkungan sekolah, dan proses pembelajaran di SLB YPAC Pangkalpinang. Metode yang digunakan dalam penelitian ini, yaitu dengan menggunakan pendekatan kualitatif naturalistik. Pengumpulan data dilakukan melalui observasi, wawancara, dan dokumentasi. Subyek penelitian ini antara lain kepala sekolah, guru, pegawai, dan peserta didik. keabsahan data dilakukan dengan triangulasi, dan pengamatan yang lebih mendalam. Analisis data yang digunakan adalah model analisis interaktif Miles dan Huberman melalui kegiatan pengumpulan data, reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa: (a) mutu layanan terhadap peserta didik di SLB YPAC Pangkalpinang sudah memenuhi standar layanan yang baik. Hal ini dilihat dari layanan yang berupa fasilitas sarana dan prasarana yang sudah cukup lengkap di sekolah; (b) mutu sumber daya manusia dalam proses pendidikan menunjukkan sumber daya guru yang baik. Hal ini dapat dilihat dari data guru yang menunjukkan bahwa tenaga pengajar di SLB YPAC Pangkalpinang 95% pendidik berkualifikasi S1 bidang kependidikan; (c) mutu lingkungan yang ada di SLB YPAC Pangkalpinang sudah baik. Hal ini terlihat dari letak sekolah yang sangat strategis karena lokasi sekolah yang berada di tengah kota sehingga mudah dijangkau oleh masyarakat; (d) mutu proses pembelajaran yang dilakukan oleh guru di SLB YPAC Pangkalpinang sudah baik. Hal ini dapat dilihat dari realisasi bentuk kegiatan melalui perencanaan pembelajaran dengan menyusun RPP setiap mata pelajaran, kemudian pelaksanaan pembelajaran, yang meliputi strategi dan metode yang digunakan guru dalam menyampaikan materi pembelajaran, dan evaluasi pembelajaran.

Perilaku Birokrat Dalam Pelayanan Publik Di Distrik Asologaima Kabupaten Jayawijaya

2020 ◽  
Vol 12 (2) ◽  
pp. 93-100
Author(s):  
Yoel Tabuni
Keyword(s):  
Consumer Satisfaction ◽  
Rapid Development ◽  
Main Task ◽  
Government Administration ◽  
Following Instructions ◽  
District Government ◽  
The Times ◽  
Rule Governance ◽  
Descriptive Method

In line with the rapid development of the times and the increasingly complex problems faced by the state, there has also been a development in government administration which has been marked by a shift in the paradigm of governance from Rule Governance. This situation makes the bureaucracy rigid, in an environment that is only limited to flowing the instructions or following instructions. The district government in an Asologaima District has the main task of carrying out part of the authority delegated by the district head in the fields of government, economy, and development, society, peace, and order as well as coordination.The method is sed is descriptive method. Bureaucrats as providers of public services must be able to provide quality services, the quality of service of bureaucrats to society is closely related to customer satisfaction or consumer satisfaction as the recipient of the service itself.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

scholarly journals Transformer-based artificial neural networks for the conversion between chemical notations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lev Krasnov ◽  
Ivan Khokhlov ◽  
Maxim V. Fedorov ◽  
Sergey Sosnin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rapid Development ◽  
Performance Level ◽  
Rule Based ◽  
Development Costs ◽  
Overall Performance ◽  
Artificial Neural

AbstractWe developed a Transformer-based artificial neural approach to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct. The overall performance level of our model is comparable to the rule-based solutions. We proved that the accuracy and speed of computations as well as the robustness of the model allow to use it in production. Our showcase demonstrates that a neural-based solution can facilitate rapid development keeping the required level of accuracy. We believe that our findings will inspire other developers to reduce development costs by replacing complex rule-based solutions with neural-based ones.

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

scholarly journals Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3279
Author(s):  
Maria Habib ◽  
Mohammad Faris ◽  
Raneem Qaddoura ◽  
Manal Alomari ◽  
Alaa Alomari ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Quality Assessment ◽  
Transcript Level ◽  
Assessment Process ◽  
Classification Model ◽  
Systematic Evaluation ◽  
Transcript Levels ◽  
Perceptual Evaluation

Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often handled based on a human auditory-perceptual evaluation. Typically, trained experts are needed for such tasks, as they follow systematic evaluation criteria. However, the daily rapid increase of consultations makes the evaluation process inefficient and impractical. This paper investigates the automation of the quality assessment process of patient–doctor voice-based conversations in a telehealth service using a deep-learning-based classification model. For this, the data consist of audio recordings obtained from Altibbi. Altibbi is a digital health platform that provides telemedicine and telehealth services in the Middle East and North Africa (MENA). The objective is to assist Altibbi’s operations team in the evaluation of the provided consultations in an automated manner. The proposed model is developed using three sets of features: features extracted from the signal level, the transcript level, and the signal and transcript levels. At the signal level, various statistical and spectral information is calculated to characterize the spectral envelope of the speech recordings. At the transcript level, a pre-trained embedding model is utilized to encompass the semantic and contextual features of the textual information. Additionally, the hybrid of the signal and transcript levels is explored and analyzed. The designed classification model relies on stacked layers of deep neural networks and convolutional neural networks. Evaluation results show that the model achieved a higher level of precision when compared with the manual evaluation approach followed by Altibbi’s operations team.

scholarly journals Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 894-918
Author(s):  
Luís Rosa ◽  
Fábio Silva ◽  
Cesar Analide
Keyword(s):  
Internet Of Things ◽  
Mobile Networks ◽  
Smart Cities ◽  
Human Mobility ◽  
Rapid Development ◽  
Disruptive Technology ◽  
New Paradigm ◽  
Design And Optimization ◽  
High Level

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.

scholarly journals Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1263
Author(s):  
Zhaojun Wang ◽  
Jiangning Wang ◽  
Congtian Lin ◽  
Yan Han ◽  
Zhaosheng Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Digital Technology ◽  
Rapid Development ◽  
Image Data ◽  
Image Database ◽  
Test Accuracy ◽  
Practical Application ◽  
Accuracy Rate ◽  
Deep Convolutional Neural Networks

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

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