scholarly journals Remote Network Monitoring Technology Based on Computer Technology

2022 ◽  
Vol 2146 (1) ◽  
pp. 012040
Author(s):  
Huaben Wang
Keyword(s):  
Rapid Development ◽  
Image Data ◽  
Image Features ◽  
Color Histogram ◽  
Internet Technology ◽  
Image Search ◽  
Chi Square ◽  
Query Image ◽  
Data Set ◽  
Color Distribution

Abstract With the rapid development of Internet technology, using images to express the characteristics of things more direct, compared with text, audio, image expression content is more ambiguous, which makes the rapid increase of digital images on the Internet. Nowadays one of the hot directions of computer vision research is how to accurately and quickly retrieve the target image from a large amount of image data. This paper summarizes the development of image retrieval technology at home and abroad, and proposes an image search method based on color histogram and Chi-square distance. This paper discusses how to construct an image search system, which can search the image quickly, describe the color distribution of the photo with color histogram, divide the image into five regions, extract image features from the color histogram of each region, and then get the data set of multi-dimensional image features. Then the chi-square distance is used to calculate the similarity of color histogram, and the closest image is selected as the first similar image, which realizes the necessary logic of receiving query image and returning related results.

scholarly journals PENGARUH DURASI BERMAIN TERHADAP ADIKSI GAME ONLINE PADA REMAJA Di Warung Internet XGC Kecamatan Dolopo Kabupaten Madiun

2018 ◽  
Vol 2 (2) ◽  
pp. 72
Author(s):  
Nindar Oktavian ◽  
Saiful Nurhidayat ◽  
Ririn Nasriati
Keyword(s):  
Rapid Development ◽  
Online Games ◽  
Self Control ◽  
Internet Technology ◽  
P Value ◽  
Online Game ◽  
Chi Square ◽  
Cross Sectional ◽  
Purposive Sampling ◽  
Online Addiction

Abstract Background: As the rapid development of Internet technology, online games are also experiencing development. Online Game presents a more varied challenge compared to offline games, this makes the players feel at home and play long enough. Online game is seductive which means it can cause addiction, Addictive online game is marked by how far someone play excessive game so that can interfere its life everyday.Method: Design of research using Cross Sectional with a population of 78, a sample of 45 respondents using purposive sampling technique. Data collection using questionnaires with data processing using Chi Square test.Result: From the result of research from total 45 respondent 24 (53,3%) respondent playing game excessively, and 21 (47,7%) not excessive. And 24 (53.3%) respondents experienced addiction. Statistical analysis showed significant result with p-value 0,002 <0,05. Then H1 accepted which means there is influence between the duration of playing online games towards online game addiction.Conclusion: Playing games with a long duration is one of the factors causing online game addiction among teenagers. So there needs to be self-control of online game play behavior.Keywords:Game Online, Addiction, Teenagers Abstrak Latar Belakang: Seiring pesatnya perkembangan teknologi internet, game online juga mengalami perkembangan. Game online. Game Online menyajikan tantangan yang lebih bervariasi dibandingkan dengan game offline, hal ini membuat para pemain betah dan bermain cukup lama. Game online bersifat seduktif yang berarti dapat menyebabkan kecanduan, Kecanduan game online ditandai oleh sejauhmana seseorang bermain game secara berlebihan sehingga dapat mengganggu kehidupannya sehari-hari.Metode:Desain penelitian menggunakan Cross Sectional dengan jumlah populasi 78, sampel 45 responden dengan menggunakan teknik purposive sampling. Pengumpulan data menggunakan kuesioner dengan pengolahan data menggunakan uji chi square.           Hasil: Dari hasil penelitian didapatkan dari total 45 responden 24 (53,3%) responden bermain game secara berlebihan, dan 21 (47,7%) tidak berlebihan. Dan 24 (53,3%) responden mengalami adiksi. Analisis statistika menunjukkan hasil yang signifikan dengan p-value 0,002 < 0,05. Maka H1 diterima yang artinya ada pengaruh antara durasi bermain game online terhadap adiksi game online.Kesimpulan: Bermain game dengan durasi waktu yang cukup lama menjadi salah satu faktor penyebab adiksi game online di kalangan remaja. Sehingga perlu adanya kontrol diri terhadap perilaku bermain game online.Kata kunci: Game Online, Adiksi, Remaja

scholarly journals Transfer learning with chest X-rays for ER patient classification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jonathan Stubblefield ◽  
Mitchell Hervert ◽  
Jason L. Causey ◽  
Jake A. Qualls ◽  
Wei Dong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Current Model ◽  
Image Data ◽  
Learning Model ◽  
Image Features ◽  
Clinical Feature ◽  
Patient Classification ◽  
X Rays ◽  
Data Set ◽  
Deep Learning Model

AbstractOne of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We conducted a retrospective study with the collected data of 171 ER patients. ER patient classification for cardiac and infection causes was evaluated with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. An analysis of clinical feature importance was performed to identify the most important clinical features for ER patient classification. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/.

High-Dimensional Image Data Sets Retrieval: Improving Accuracy Using a Weighted Relevance Feedback

2015 ◽  
Vol 09 (02) ◽  
pp. 239-259
Author(s):  
Abir Gallas ◽  
Walid Barhoumi ◽  
Ezzeddine Zagrouba
Keyword(s):  
Relevance Feedback ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Image Data ◽  
High Dimensional ◽  
General Context ◽  
Query Image ◽  
Data Set ◽  
Dimensional Image ◽  
Minimum Number

The user's interaction with the retrieval engines, while seeking a particular image (or set of images) in large-scale databases, defines better his request. This interaction is essentially provided by a relevance feedback step. In fact, the semantic gap is increasing in a remarkable way due to the application of approximate nearest neighbor (ANN) algorithms aiming at resolving the curse of dimensionality. Therefore, an additional step of relevance feedback is necessary in order to get closer to the user's expectations in the next few retrieval iterations. In this context, this paper details a classification of the different relevance feedback techniques related to region-based image retrieval applications. Moreover, a technique of relevance feedback based on re-weighting regions of the query-image by selecting a set of negative examples is elaborated. Furthermore, the general context to carry out this technique which is the large-scale heterogeneous image collections indexing and retrieval is presented. In fact, the main contribution of the proposed work is affording efficient results with the minimum number of relevance feedback iterations for high dimensional image databases. Experiments and assessments are carried out within an RBIR system for "Wang" data set in order to prove the effectiveness of the proposed approaches.

Textile Image Retrieval Using Composite Feature Vectors of Color and Wavelet Transformed Textural Property

2013 ◽  
Vol 333-335 ◽  
pp. 822-827 ◽  
Author(s):  
Jun Chul Chun ◽  
Wong Gi Kim
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Spatial Information ◽  
Image Data ◽  
Texture Features ◽  
Color Histogram ◽  
Large Set ◽  
Data Set ◽  
Feature Vectors ◽  
Composite Feature

It is known that wavelet transform provides very useful feature values in analyzing various types of images. This paper presents a novel approach for content-based textile image retrieval which uses composite feature vectors of low-level color feature from spatial domain and second-order statistic features from wavelet-transformed sub-band coefficients. Even though color histogram itself is efficient and most used signature for CBIR, it is unable to carry local spatial information of pixel and generate inaccurate retrieval results especially in large image data set. In this paper, we extract texture features such as contrast, homogeneity, ASM(angular-second momentum) and entropy from decomposed sub-band images by wavelet transform and utilize these multiple feature vector to retrieve textile images combining with color histogram. From the experimental results it is proven that the proposed approach is efficiently retrieve the desired images from a large set of textile image database.

scholarly journals Combining Low-Level Image Features with Features from A Simple Convolutional Neural Network

2019 ◽  
pp. 136-139
Author(s):  
Ozge Oztimur Karadag ◽  
Ozlem Erdas
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Image Data ◽  
Image Features ◽  
Data Set ◽  
Low Level ◽  
Learning Techniques ◽  
Processing Techniques ◽  
Layered Architectures ◽  
Traditional Image

In the traditional image processing approaches, first low-level image features are extracted and then they are sent to a classifier or a recognizer for further processing. While the traditional image processing techniques employ this step-by-step approach, majority of the recent studies prefer layered architectures which both extract features and do the classification or recognition tasks. These architectures are referred as deep learning techniques and they are applicable if sufficient amount of labeled data is available and the minimum system requirements are met. Nevertheless, most of the time either the data is insufficient or the system sources are not enough. In this study, we experimented how it is still possible to obtain an effective visual representation by combining low-level visual features with features from a simple deep learning model. As a result, combinational features gave rise to 0.80 accuracy on the image data set while the performance of low-level features and deep learning features were 0.70 and 0.74 respectively.

Semantic Tags and Neutral Network Based Personalized Advertisement Recommendation System for Movies

2012 ◽  
Vol 488-489 ◽  
pp. 1727-1731
Author(s):  
Wei Du ◽  
Jun Liang Chen
Keyword(s):  
Recommendation System ◽  
Rapid Development ◽  
Internet Technology ◽  
Neutral Network ◽  
User Preference ◽  
Simulation Experiments ◽  
Data Set ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Personalized Advertisement

With the rapid development of information especially internet technology, people have to choose the most suitable goods without any experience, so the recommendation system is seriously required. Yet no research on advertisement recommendation system for movie play is presented. Regarding this problem, the paper introduces the theory of semantic computing and annotates the semantic tags from the movie slices and the candidate advertisements, the potential preferences on them are predicted with neutral network model trained by some data set predefined. The user preference model and the predicting workflow are described in detail. Finally, the MovieLens dataset is employed to validate the validity of the system designed. The results of simulation experiments prove that the technology proposed can not only satisfy the requirement of matched advertisement recommendation but also outperform the traditional collaborative filtering algorithm.

scholarly journals Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 167
Author(s):  
Ivan Kholod ◽  
Evgeny Yanaki ◽  
Dmitry Fomichev ◽  
Evgeniy Shalugin ◽  
Evgenia Novikova ◽  
...  
Keyword(s):  
Open Source ◽  
Rapid Development ◽  
Image Data ◽  
Ease Of Use ◽  
Security And Privacy ◽  
Data Sets ◽  
Data Set ◽  
Limited Bandwidth ◽  
Comparative Review ◽  
Iot Devices

The rapid development of Internet of Things (IoT) systems has led to the problem of managing and analyzing the large volumes of data that they generate. Traditional approaches that involve collection of data from IoT devices into one centralized repository for further analysis are not always applicable due to the large amount of collected data, the use of communication channels with limited bandwidth, security and privacy requirements, etc. Federated learning (FL) is an emerging approach that allows one to analyze data directly on data sources and to federate the results of each analysis to yield a result as traditional centralized data processing. FL is being actively developed, and currently, there are several open-source frameworks that implement it. This article presents a comparative review and analysis of the existing open-source FL frameworks, including their applicability in IoT systems. The authors evaluated the following features of the frameworks: ease of use and deployment, development, analysis capabilities, accuracy, and performance. Three different data sets were used in the experiments—two signal data sets of different volumes and one image data set. To model low-power IoT devices, computing nodes with small resources were defined in the testbed. The research results revealed FL frameworks that could be applied in the IoT systems now, but with certain restrictions on their use.

Indoor and outdoor image classification

2019 ◽  
Vol 7 (4) ◽  
pp. 150-161
Author(s):  
Rajasekar Velswamy ◽  
Sorna Chandra Devadass ◽  
Karunakaran Velswamy ◽  
Jeyakrishnan Venugopal
Keyword(s):  
Object Recognition ◽  
Discrete Cosine Transform ◽  
Image Data ◽  
Image Database ◽  
Color Histogram ◽  
Data Sets ◽  
Data Set ◽  
Content Type ◽  
Transform Coefficients ◽  
The Given

Purpose The purpose of this paper is to classify the given image as indoor or outdoor with higher success rate by mixing various features like brightness, number of straight lines, number of Euclidean shapes and recursive shapes. Design/methodology/approach For annotating an image, it is very easy, if the image is categorized as indoor or outdoor. Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object. Findings This work is carried out on the standard image data sets. The data sets are Microsoft Research Cambridge (MRC) object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly. Originality/value Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object. This work is carried out on the standard image data sets. The data sets are MRC object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly.

scholarly journals Multilayered autoencoders in problems of hyperspectral image analysis and processing

2021 ◽  
pp. 1-21
Author(s):  
Margarita Georgievna Kuzmina
Keyword(s):  
Image Analysis ◽  
Hyperspectral Image ◽  
Spectral Curve ◽  
Image Data ◽  
Image Features ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Data Set ◽  
Hyperspectral Image Analysis ◽  
Deep Image

A model of five-layered autoencoder (stacked autoencoder, SAE) is suggested for deep image features extraction and deriving compressed hyperspectral data set specifying the image. Spectral cost function, dependent on spectral curve forms of hyperspectral image, has been used for the autoencoder tuning. At the first step the autoencoder capabilities will be tested based on using pure spectral information contained in image data. The images from well known and widely used hyperspectral databases (Indian Pines, Pavia University и KSC) are planned to be used for the model testing.

scholarly journals Application of content based image retrieval in digital image search system

2021 ◽  
Vol 10 (2) ◽  
pp. 1122-1128
Author(s):  
Syamsul Yakin ◽  
Tasrif Hasanuddin ◽  
Nia Kurniati
Keyword(s):  
Image Retrieval ◽  
Digital Image ◽  
Image Data ◽  
Multimedia Data ◽  
Content Based Image Retrieval ◽  
Discrete Wavelet ◽  
Image Search ◽  
Image Description ◽  
Search System ◽  
Query Image

Multimedia data is growing rapidly in the current digital era, one of which is digital image data. The increasing need for a large number of digital image datasets makes the constraints faced eventually drain a lot of time and cause the process of image description to be inconsistent. Therefore, a method is needed in processing the data, especially in searching digital image data in large image dataset to find image data that are relevant to the query image. One of the proposed methods for searching information based on image content is content based image retrieval (CBIR). The main advantage of the CBIR method is automatic retrieval process, compared to traditional keyword. This research was conducted on a combination of the HSV color histogram methods and the discrete wavelet transform to extract color features and textures features, while the chi-square distance technique was used to compare the test images with images into a database. The results have showed that the digital image search system with color and texture features have a precision value of 37.5% - 100%, with an average precision value of 80.71%, while the percentage accuracy is 93.7% - 100% with an average accuracy is 98.03%.

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