scholarly journals Al-Quran recitation verification for memorization test using Siamese LSTM network

2021 ◽  
Vol 6 (1) ◽  
pp. 35-40
Author(s):  
Rian Adam Rajagede ◽  
Rochana Prih Hastuti
Keyword(s):  
Binary Classification ◽  
Model Performance ◽  
Classification Approach ◽  
The Public ◽  
Spectral Coefficient ◽  
Public Dataset ◽  
Lstm Network ◽  
Mel Frequency Cepstral Coefficient ◽  
Existing Data

In the process of verifying Al-Quran memorization, a person is usually asked to recite a verse without looking at the text. This process is generally done together with a partner to verify the reading. This paper proposes a model using Siamese LSTM Network to help users check their Al-Quran memorization alone. Siamese LSTM network will verify the recitation by matching the input with existing data for a read verse. This study evaluates two Siamese LSTM architectures, the Manhattan LSTM and the Siamese-Classifier. The Manhattan LSTM outputs a single numerical value that represents the similarity, while the Siamese-Classifier uses a binary classification approach. In this study, we compare Mel-Frequency Cepstral Coefficient (MFCC), Mel-Frequency Spectral Coefficient (MFSC), and delta features against model performance. We use the public dataset from Every Ayah website and provide the usage information for future comparison. Our best model, using MFCC with delta and Manhattan LSTM, produces an F1-score of 77.35%

scholarly journals SISTEM PENDUKUNG KEPUTUSAN PENENTUAN INVESTASI KELAPA SAWIT DENGAN METODE INTERNAL RATE OF RETURN DALAM SATU BLOCK SAWIT DI LINGGA KUAMANG BUNGO JAMBI

Compiler ◽  
2015 ◽  
Vol 4 (2) ◽  
Author(s):  
Bambang Sudibyo ◽  
Joko Suismianto
Keyword(s):  
Decision Support ◽  
Data Processing ◽  
Decision Support Systems ◽  
Palm Oil ◽  
Support Systems ◽  
Rate Of Return ◽  
Internal Rate Of Return ◽  
The Public ◽  
Internal Rate ◽  
Existing Data

ABSTRACTTechnology developed in order to facilitate human work that would not be done manually, with the presence of the latest technologies that it is not a work of man may not be done quickly, accurately, and save time. One was for the manufacture of palm data processing applications that provide an overview to the public that it is feasible to plant palm oil or not to do, based on existing data and then recycled into Decision Support Systems. To process these data using the Internal Rate Of Return (IRR) as a method to make decisions based on data that have been processed. In this study processed data is the data of a group of Palm Jl. Salak Linga Kuamang Bungo Jambi. From the results of the data managed by the farmer group Palm block 22, Jalan Salak, Linga Kuamang, with interpolation of 30% per year, with a capital of USD 888,253,000.00 and generate income of Rp 2,063,387,050.00 in if using the Internal rate of Return produce interpolation (I) of 30, 113%. Since IRR> I, then it is worth to continue.

scholarly journals Improving tuberculosis surveillance by detecting international transmission using publicly available whole-genome sequencing data

2019 ◽  
Author(s):  
Andrea Sanchini ◽  
Christine Jandrasits ◽  
Julius Tembrockhaus ◽  
Thomas Andreas Kohl ◽  
Christian Utpatel ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Added Value ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
International Transmission ◽  
The Public ◽  
Public Dataset ◽  
Public Repositories

AbstractIntroductionImproving the surveillance of tuberculosis (TB) is especially important for multidrug-resistant (MDR) and extensively drug-resistant (XDR)-TB. The large amount of publicly available whole-genome sequencing (WGS) data for TB gives us the chance to re-use data and to perform additional analysis at a large scale.AimWe assessed the usefulness of raw WGS data of global MDR/XDR-TB isolates available from public repositories to improve TB surveillance.MethodsWe extracted raw WGS data and the related metadata of Mycobacterium tuberculosis isolates available from the Sequence Read Archive. We compared this public dataset with WGS data and metadata of 131 MDR- and XDR-TB isolates from Germany in 2012-2013.ResultsWe aggregated a dataset that includes 1,081 MDR and 250 XDR isolates among which we identified 133 molecular clusters. In 16 clusters, the isolates were from at least two different countries. For example, cluster2 included 56 MDR/XDR isolates from Moldova, Georgia, and Germany. By comparing the WGS data from Germany and the public dataset, we found that 11 clusters contained at least one isolate from Germany and at least one isolate from another country. We could, therefore, connect TB cases despite missing epidemiological information.ConclusionWe demonstrated the added value of using WGS raw data from public repositories to contribute to TB surveillance. By comparing the German and the public dataset, we identified potential international transmission events. Thus, using this approach might support the interpretation of national surveillance results in an international context.

scholarly journals Web Atlas como Herramienta para la Gestión Integrada Costera: de los Datos al Conocimiento Práctico

2021 ◽  
Vol 6 (Vol Esp. 2) ◽  
pp. 427-454
Author(s):  
Sílvia Maria Sartor ◽  
Marcos Reis Rosa ◽  
Juliana Tristão Pires ◽  
Claudio Augusto Oller Nascimento
Keyword(s):  
Collaborative Work ◽  
Decision Makers ◽  
Scientific Language ◽  
The Public ◽  
Paulo State ◽  
Impact Indicators ◽  
Coastal Area Management ◽  
The One ◽  
Multiple Impact ◽  
Existing Data

Despite the importance of coastal areas to sustainable development, they are poorly known by the public or even by decision-makers. This undermines consistent action towards their protection. Existing data and information, published in very complex language, tend to be restricted to academic use. The Coastal Web Atlas as the one developed here is a tool that makes this information more accessible to managers, by preserving, integrating, comparing, and sharing data as smart maps. The spatial analysis based on multiple impact indicators facilitates the correlation of causes and effects. The Coastal Web Atlas is available to a broad audience and it could be a strong instrument for spatial planning and oversight. The authors propose to improve coastal area management by using colors on maps to decode scientific language to friendly language and to publish it on a geoportal. This technology promotes the use of collected data and enables collaborative work. A pilot experiment is being developed in the Santos Port Region, at the São Paulo state coast, Brazil: http://santoswebatlas.com.br/

scholarly journals An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models

2012 ◽  
Vol 4 (1) ◽  
pp. 13-21 ◽  
Author(s):  
S. Morin ◽  
Y. Lejeune ◽  
B. Lesaffre ◽  
J.-M. Panel ◽  
D. Poncet ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Model Performance ◽  
Short Wave ◽  
Wave Radiation ◽  
Density Profiles ◽  
The Public ◽  
Snow Water ◽  
Snowpack Properties

Abstract. A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France). Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September), when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN), in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo) and hourly (snow depth, albedo, runoff, surface temperature, soil temperature) time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles) and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://dx.doi.org/10.1594/PANGAEA.774249) as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

Analisis Faktor yang Mempengaruhi Minat Masyarakat terhadap Jual Beli Online dengan Menggunakan Technology Acceptance Model

2021 ◽  
Vol 3 (2) ◽  
pp. 1-15
Author(s):  
Miki Wijana
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Perceived Usefulness ◽  
Male And Female ◽  
The Public ◽  
The Difference ◽  
Information Work ◽  
Acceptance Model ◽  
Online Buying ◽  
Existing Data

The use of information technology aims to improve the performance of public services to be more effective and efficient. But problems arise in the Public, for example in e-commerce users or the Olx.co.id website. Problems that arise include the lack of public desire to use technology and (Perceived Usefulness), the technology cannot be fully used by the Public. This study aims to analyze the factors that affect the level of acceptance of the Olx.co.id online buying and selling website by using the Technology Acceptance Model (TAM) framework as a conceptual model. At the beginning of the study, information work was carried out by collecting data from the questionnaire results from 500 respondents as samples and processing them with AMOS 22 software. Then after being processed as a whole, the writer tries to re-process the existing data, but the difference is the separation of the data based on gender respondents (male and female). Thus, a significant relationship will be obtained and can be used as recommendations for improvement for e-commerce websites.

A novel binary classification approach based on geometric semantic genetic programming

2021 ◽  
pp. 101028
Author(s):  
I. Bakurov ◽  
M. Castelli ◽  
A. Scotto di Freca ◽  
L. Vanneschi ◽  
F. Fontanella
Keyword(s):  
Genetic Programming ◽  
Binary Classification ◽  
Classification Approach

A vision transformer for emphysema classification using CT images

2021 ◽  
Author(s):  
Yanan Wu ◽  
Shouliang Qi ◽  
Yu Sun ◽  
Shuyue Xia ◽  
Yudong Yao ◽  
...  
Keyword(s):  
Lung Parenchyma ◽  
Ct Images ◽  
Normal Lung ◽  
Massive Data ◽  
Large Patch ◽  
The Public ◽  
Evaluation Approach ◽  
Average Accuracy ◽  
Public Dataset ◽  
Aided Diagnosis

Abstract Objective: Emphysema is characterized by the destruction and permanent enlargement of the alveoli in the lung. According to visual CT appearance, emphysema can be divided into three subtypes: centrilobular emphysema (CLE), panlobular emphysema (PLE), and paraseptal emphysema (PSE). Automating emphysema classification can help precisely determine the patterns of lung destruction and provide a quantitative evaluation. Approach: We propose a vision transformer (ViT) model to classify the emphysema subtypes via CT images. First, large patches (61×61) are cropped from CT images which contain the area of normal lung parenchyma (NLP), CLE, PLE, and PSE. After resizing, the large patch is divided into small patches and these small patches are converted to a sequence of patch embeddings by flattening and linear embedding. A class embedding is concatenated to the patch embedding, and the positional embedding is added to the resulting embeddings described above. Then, the obtained embedding is fed into the transformer encoder blocks to generate the final representation. Finally, the learnable class embedding is fed to a softmax layer to classify the emphysema. Main results: To overcome the lack of massive data, the transformer encoder blocks (pre-trained on ImageNet) are transferred and fine-tuned in our ViT model. The average accuracy of the pre-trained ViT model achieves 95.95% in our lab’s own dataset which is higher than that of AlexNet, Inception-V3, MobileNet-V2, ResNet34, and ResNet50. Meanwhile, the pre-trained ViT model outperforms the ViT model without the pre-training. The accuracy of our pre-trained ViT model is higher than or comparable to that by available methods for the public dataset. Significance: The results demonstrated that the proposed ViT model can accurately classify the subtypes of emphysema using CT images. The ViT model can help make an effective computer-aided diagnosis of emphysema, and the ViT method can be extended to other medical applications.

Women and Media

2021 ◽  
pp. 8-23
Author(s):  
Zeljka Lekic-Subasic
Keyword(s):  
Decision Making ◽  
Public Sphere ◽  
Gender Equality ◽  
Public Service ◽  
General Public ◽  
The Public ◽  
The Media ◽  
Existing Data ◽  
National Borders

Difficulties that women face in the media professions and discrimination against women's access to decision-making posts within the media is a problem that transcends national borders. Becoming a greater part of this particular workforce would help to expand both the amount and quality of visibility for women – in news, television, and public sphere in general. Public service media (PSM), as broadcasting, made, financed, and controlled by the public and for the public, with the output designed to reach everyone and reflect all voices, should treat gender equality with the utmost importance. The existing data indicate however that, although some progress have been made, there is a lot to be done: while women among European PSMs represent 44% of the workforce, the number falls to less than 25% at the higher and executive positions. This chapter analyses the efforts made by the European Broadcasting Union's members and the measures they recommend.

scholarly journals Multi-Person Pose Estimation using an Orientation and Occlusion Aware Deep Learning Network

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1593 ◽  
Author(s):  
Yanlei Gu ◽  
Huiyang Zhang ◽  
Shunsuke Kamijo
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Body Orientation ◽  
Learning Approaches ◽  
The Public ◽  
Learning Network ◽  
Proposed Model ◽  
Body Boundary ◽  
Public Dataset ◽  
Deep Learning Network

Image based human behavior and activity understanding has been a hot topic in the field of computer vision and multimedia. As an important part, skeleton estimation, which is also called pose estimation, has attracted lots of interests. For pose estimation, most of the deep learning approaches mainly focus on the joint feature. However, the joint feature is not sufficient, especially when the image includes multi-person and the pose is occluded or not fully visible. This paper proposes a novel multi-task framework for the multi-person pose estimation. The proposed framework is developed based on Mask Region-based Convolutional Neural Networks (R-CNN) and extended to integrate the joint feature, body boundary, body orientation and occlusion condition together. In order to further improve the performance of the multi-person pose estimation, this paper proposes to organize the different information in serial multi-task models instead of the widely used parallel multi-task network. The proposed models are trained on the public dataset Common Objects in Context (COCO), which is further augmented by ground truths of body orientation and mutual-occlusion mask. Experiments demonstrate the performance of the proposed method for multi-person pose estimation and body orientation estimation. The proposed method can detect 84.6% of the Percentage of Correct Keypoints (PCK) and has an 83.7% Correct Detection Rate (CDR). Comparisons further illustrate the proposed model can reduce the over-detection compared with other methods.

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