scholarly journals Proposing Algorithm Using YOLOV4 and VGG-16 for Smart-Education

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Phat Nguyen Huu ◽  
Khang Doan Xuan
Keyword(s):  
Deep Learning ◽  
High Order ◽  
Second Step ◽  
Systems Of Equations ◽  
Web Based ◽  
Solution Methods ◽  
Smart Education

In this paper, we propose an algorithm to identify and solve systems of high-order equations. We rely on traditional solution methods to build algorithms to solve automated equations based on deep learning. The proposal method includes two main steps. In the first step, we use YOLOV4 (Kumar et al. 2020; Canu, 2020) to recognize equations and letters associated with the VGG-16 network (Simonyan and Zisserman, 2015) to classify them. We then used the SymPy model to solve the equations in the second step. Data are images of systems of equations that are typed and designed by ourselves or handwritten from other sources. Besides, we also built a web-based application that helps users select an image from their devices. The results show that the proposed algorithm is set out with 95% accuracy for smart-education applications.

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

scholarly journals Diabetes classification application with efficient missing and outliers data handling algorithms

2021 ◽  
Author(s):  
Hanaa Torkey ◽  
Elhossiny Ibrahim ◽  
EZZ El-Din Hemdan ◽  
Ayman El-Sayed ◽  
Marwa A. Shouman
Keyword(s):  
Deep Learning ◽  
Healthcare System ◽  
Wearable Sensors ◽  
Model Performance ◽  
Analysis Model ◽  
Human Errors ◽  
Web Based ◽  
Machine Learning Model ◽  
Complete Dataset ◽  
Smart Wearable

AbstractCommunication between sensors spread everywhere in healthcare systems may cause some missing in the transferred features. Repairing the data problems of sensing devices by artificial intelligence technologies have facilitated the Medical Internet of Things (MIoT) and its emerging applications in Healthcare. MIoT has great potential to affect the patient's life. Data collected from smart wearable devices size dramatically increases with data collected from millions of patients who are suffering from diseases such as diabetes. However, sensors or human errors lead to missing some values of the data. The major challenge of this problem is how to predict this value to maintain the data analysis model performance within a good range. In this paper, a complete healthcare system for diabetics has been used, as well as two new algorithms are developed to handle the crucial problem of missed data from MIoT wearable sensors. The proposed work is based on the integration of Random Forest, mean, class' mean, interquartile range (IQR), and Deep Learning to produce a clean and complete dataset. Which can enhance any machine learning model performance. Moreover, the outliers repair technique is proposed based on dataset class detection, then repair it by Deep Learning (DL). The final model accuracy with the two steps of imputation and outliers repair is 97.41% and 99.71% Area Under Curve (AUC). The used healthcare system is a web-based diabetes classification application using flask to be used in hospitals and healthcare centers for the patient diagnosed with an effective fashion.

scholarly journals Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4045
Author(s):  
Alessandro Sassu ◽  
Jose Francisco Saenz-Cogollo ◽  
Maurizio Agelli
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Data ◽  
Video Analytics ◽  
Web Based ◽  
Real Time Analysis ◽  
Open Source Framework ◽  
Cluster Configuration ◽  
Time Requirements ◽  
High Level

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

scholarly journals Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 357
Author(s):  
Dae-Hyun Jung ◽  
Na Yeon Kim ◽  
Sang Ho Moon ◽  
Changho Jhin ◽  
Hak-Jin Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Model Development ◽  
Classification Model ◽  
Intelligence Analysis ◽  
Noise Filtering ◽  
Meat Industry ◽  
Mel Frequency Cepstral Coefficients ◽  
Web Based ◽  
The Status

The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.

scholarly journals effects of anemia education using web-based she smart to improve knowledge, attitudes, and practice in adolescent girls

2022 ◽  
Vol 5 (1) ◽  
pp. 44-49
Author(s):  
Ernawati Ernawati ◽  
Yusring Sanusi Baso ◽  
Healthy Hidayanty ◽  
Syafruddin Syarif ◽  
Aminuddin Aminuddin ◽  
...  
Keyword(s):  
Adolescent Girls ◽  
Statistical Tests ◽  
Sampling Technique ◽  
P Value ◽  
Growth Disorders ◽  
Normal Numbers ◽  
Web Based ◽  
Before And After ◽  
Smart Education ◽  
The Impact

Anemia is a state of hemoglobin levels in the bloodless than normal numbers according to the sex and age group. The impact of anemia in adolescents is a decrease in achievement and learning spirit and can cause symptoms such as paleness, lethargy, decreased appetite, and growth disorders. Anemia has an impact not only on the health of adolescent girls but can have a long impact on the health of the mother and fetus. You can see the influence of anemia education on knowledge, attitudes, and practice. Uses the Pre-experimental method with the design of one group pretest and posttest. Sampling technique using purposive sampling with the number of 47 adolescent girls. The research was conducted at Senior High School 12 Makassar in September-October 2021. Data analysis using the McNemar test. From the results of statistical tests showed that there was an influence on the use of web-based she smart education model on the use of adolescent girls about anemia with p-value = 0.000 (p<0.05), attitude p-value = 0.016 (p<0.05) and action p-value = 0.001 (p<0.05). Anemia education using web-based she smart can improve knowledge, attitudes, and practice before and after an intervention.

scholarly journals DESAIN WEB BAGI PEMULA MENGGUNAKAN FRAMEWORK BOOTSTRAP PADA SMK TARUNA BANGSA BEKASI

2021 ◽  
Vol 3 (1) ◽  
pp. 134-148
Author(s):  
Mardi Yudhi Putra ◽  
Nadya Safitri ◽  
Nofia Filda Fauziah ◽  
Ahmad Safei ◽  
Rayhan Wahyudin Ratu Lolly
Keyword(s):  
Information And Communication Technology ◽  
Community Service ◽  
Communication Technology ◽  
Web Based ◽  
A Value ◽  
Front End ◽  
Information And Communication ◽  
Smart Education ◽  
The City ◽  
Positive Results

Penguasaan terhadap Teknologi Informasi dan Komunikasi perlu diajarkan pada semua tingkatan agar suatu proses dan kegiatan dapat dilakukan dengan lebih cepat, mudah dan efisien. Para siswa kelas XII SMK Taruna Bangsa dituntut untuk memiliki kompetensi yang dapat dikuasai sebelum lulus sekolah. Salah satunya kompetensi adalah dapat membuat website atau aplikasi berbasis web. Disamping itu, kepala program jurusan RPL SMK Taruna Bangsa menyampaikan perlu adanya pendalaman dan pengetahuan lebih dalam pembuatan website selain yang dibekali di sekolah seperti mendesain website front end. Oleh karena itu perlu dilakukan pelatihan mendesain website menggunakan framework Bootstrap. Pelaksanaan Pengabdian kepada Masyarakat (PkM) ini dijalankan sesuai dengan wujud implementasi MoU yang telah dilakukan antara SMK Taruna Bangsa dengan Universitas Bina Insani. Pelaksanaan kegiatan pada masa pandemi Covid-19 dilakukan secara online melalui media konferensi www.zoom.us dan www.youtube.com. Kegiatan ini diikuti oleh 78 peserta dengan hasil akhir memberikan hasil yang positif, ditunjukkan pada umpan balik peserta terhadap materi yaitu pada nilai 4 (Baik) sebesar 32,1% dan nilai 5 (Sangat Baik) sebesar 60.7%. Berdasarkan paparan tersebut kegiatan PkM ini telah memberikan kemampuan dan penguasaan terhadap siswa dalam mendesain website dan memiliki bekal dalam mengimplementasikan pada bidang Teknologi informasi yang pada akhirnya mendorong smart education kota Bekasi. Kata kunci—bootstrap, pengabdian kepada masyarakat, website Mastery of Information and Communication Technology needs to be taught at all levels so that processes and activities can be carried out more quickly, easily and efficiently. Class XII students of SMK Taruna Bangsa are required to have competencies that can be mastered before graduating from school. One of the competencies is being able to create a website or web-based application. In addition, the head of the RPL department program at SMK Taruna Bangsa said that there is a need for more in-depth and knowledge in making websites other than those provided in schools such as designing front end websites. Therefore, training in designing websites using the Bootstrap framework is necessary. The implementation of Community Service (PkM) is carried out in accordance with the implementation of the MoU that has been carried out between SMK Taruna Bangsa and Bina Insani University. The implementation of activities during the Covid-19 pandemic was carried out online through the media conference www.zoom.us and www.youtube.com. This activity was attended by 78 participants with the final result giving positive results, shown in the participants' feedback on the material, namely a value of 4 (Good) of 32.1% and a value of 5 (Very Good) of 60.7%. Based on the explanation, this PkM activity has given students the ability and mastery in designing websites and has provisions in implementing it in the field of information technology which ultimately encourages smart education in the city of Bekasi. Keywords— bootstrap, community service, website,

scholarly journals 3DLEB-Net: Label-Efficient Deep Learning-Based Semantic Segmentation of Building Point Clouds at LoD3 Level

2021 ◽  
Vol 11 (19) ◽  
pp. 8996
Author(s):  
Yuwei Cao ◽  
Marco Scaioni
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Training Data ◽  
Second Step ◽  
Point Cloud Data ◽  
Dynamic Graph ◽  
Cloud Data ◽  
Supervised Methods ◽  
Global And Local

In current research, fully supervised Deep Learning (DL) techniques are employed to train a segmentation network to be applied to point clouds of buildings. However, training such networks requires large amounts of fine-labeled buildings’ point-cloud data, presenting a major challenge in practice because they are difficult to obtain. Consequently, the application of fully supervised DL for semantic segmentation of buildings’ point clouds at LoD3 level is severely limited. In order to reduce the number of required annotated labels, we proposed a novel label-efficient DL network that obtains per-point semantic labels of LoD3 buildings’ point clouds with limited supervision, named 3DLEB-Net. In general, it consists of two steps. The first step (Autoencoder, AE) is composed of a Dynamic Graph Convolutional Neural Network (DGCNN) encoder and a folding-based decoder. It is designed to extract discriminative global and local features from input point clouds by faithfully reconstructing them without any label. The second step is the semantic segmentation network. By supplying a small amount of task-specific supervision, a segmentation network is proposed for semantically segmenting the encoded features acquired from the pre-trained AE. Experimentally, we evaluated our approach based on the Architectural Cultural Heritage (ArCH) dataset. Compared to the fully supervised DL methods, we found that our model achieved state-of-the-art results on the unseen scenes, with only 10% of labeled training data from fully supervised methods as input. Moreover, we conducted a series of ablation studies to show the effectiveness of the design choices of our model.

HIGH-ORDER SCHEME IMPLEMENTATION USING NEWTON-KRYLOV SOLUTION METHODS

1997 ◽  
Vol 31 (3) ◽  
pp. 295-312 ◽  
Author(s):  
Richard W. Johnson ◽  
Paul R. McHugh ◽  
Dana A. Knoll
Keyword(s):  
High Order ◽  
High Order Scheme ◽  
Solution Methods ◽  
Order Scheme
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