IPSI BgD Transactions on Internet Research最新文献

A Meta-model for Key Performance Indicators in Higher Education 高等教育关键绩效指标的元模型

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IPSI BgD Transactions on Internet Research Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.09

G. Savić, M. Segedinac, Milan Čeliković, I. Luković

引用次数: 0

An analysis of using binary JSON versus native JSON on the example of Oracle DBMS 以Oracle DBMS为例，分析使用二进制JSON与本机JSON的对比

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IPSI BgD Transactions on Internet Research Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.10

Srđa Bjeladinović, Milica Škembarević, Olga Jejic, Marko Asanović,

引用次数: 0

LVRF: A Latent Variable Based Approach for Exploring Geographic Datasets LVRF:一种基于潜变量的地理数据集挖掘方法

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IPSI BgD Transactions on Internet Research Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.02

Liangdong Deng, Arpan Mahara, N. Rishe, Malek Adjouadi

{"title":"LVRF: A Latent Variable Based Approach for Exploring Geographic Datasets","authors":"Liangdong Deng, Arpan Mahara, N. Rishe, Malek Adjouadi","doi":"10.58245/ipsi.tir.2302.02","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2302.02","url":null,"abstract":"Geographic datasets are usually accompanied by spatial non-stationarity – a phenomenon that the relationship between features varies across space. Naturally, nonstationarity can be interpreted as the underlying rule that decides how data are generated and alters over space. Therefore, traditional machine learning algorithms are not suitable for handling non-stationary geographic datasets, as they only render a single global model. To solve this problem, researchers often adopt the multiple-local-model approach, which uses different models to account for different sub-regions of space. This approach has been proven efficient but not optimal, as it is inherently difficult to decide the size of subregions. Additionally, the fact that local models are only trained on a subset of data also limits their potential. This paper proposes an entirely different strategy that interprets nonstationarity as a lack of data and addresses it by introducing latent variables to the original dataset. Backpropagation is then used to find the best values for these latent variables. Experiments show that this method is at least as efficient as multiple-local-model-based approaches and has even greater potential.","PeriodicalId":41192,"journal":{"name":"IPSI BgD Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76594173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Diameter-2-critical graphs with at most 13 nodes 直径为2的临界图，最多有13个节点

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IPSI BgD Transactions on Internet Research Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.11

Jovan G. Radosavljević

引用次数: 0

A Decision Support System for Internal Migration Policy-Making 国内移民决策的决策支持系统

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IPSI BgD Transactions on Internet Research Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.07

Boris Delibasic, S. Radovanović, S. Vukanovic

引用次数: 0

Spatiotemporal Model of Real Estate Valuation Trend 房地产估价趋势的时空模型

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IPSI BgD Transactions on Internet Research Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.05

N. Rishe, Dan Tamir, Malek Adjouadi

引用次数: 0

An Organizational Perspective of Human Resource Modeling 人力资源建模的组织视角

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IPSI BgD Transactions on Internet Research Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.08

Dajana Antanasijević, Marko Vještica, Vladimir Dimitrieski, L. Grubić-Nešić, S. Ristić, M. Pisarić

{"title":"An Organizational Perspective of Human Resource Modeling","authors":"Dajana Antanasijević, Marko Vještica, Vladimir Dimitrieski, L. Grubić-Nešić, S. Ristić, M. Pisarić","doi":"10.58245/ipsi.tir.2302.08","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2302.08","url":null,"abstract":"Although Industry 4.0 improved humanmachine relationship in technical aspects, it failed to put human needs at the focus of the production process. Industry 5.0 is complementing the Industry 4.0 focusing on the workers’ skills, knowledge, and abilities to cooperate with machines and robots. In our previous research, we proposed a framework for the formal description and automatic execution of production processes within Industry 4.0. As a result, a Domain-Specific Modeling Language (DSML) named Multi-Level Production Process Modeling Language (MultiProLan) was created aimed at modeling production processes at different levels of abstraction. The importance given to the workers within Industry 5.0 motivated us to investigate two different roles of a human worker: as an employee within an organization and as a human production resource. We propose a DSML named HResModLan aimed at human resource modeling from two different perspectives: organizational and production. The part of HResModLan language representing the organizational perspective is presented in this paper. The main goal of its creation is to enable the easier and more effective requiring, selection, hiring and development of employees within an organization. The paper presents an analysis of the human resource domain, abstract and concrete syntaxes of the HResModLan language, and a model of a furniture factory and its employees expressed using theconcepts of the HResModLan language.","PeriodicalId":41192,"journal":{"name":"IPSI BgD Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82797619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

"Detecting and Removing Clouds Affected Regions from Satellite Images Using Deep Learning" “利用深度学习从卫星图像中检测和去除云影响区域”

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IPSI BgD Transactions on Internet Research Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.03

Lawrence Egharevba, Sanjoy Kumar, N. Rishe, Hadi Amini, Malek Adjouadi

引用次数: 0

"Towards Real-time House Detection in Aerial Imagery Using Faster Region-based Convolutional Neural Network" 利用更快的基于区域的卷积神经网络实现航拍图像中的实时房屋检测

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IPSI BgD Transactions on Internet Research Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.06

Khandaker Mamun Ahmed, Farid Ghareh Mohammadi, M. Matus, Farzan Shenavarmasouleh, Luiz Manella Pereira, Zisis Ioannis, M. Amini

{"title":"\"Towards Real-time House Detection in Aerial Imagery Using Faster Region-based Convolutional Neural Network\"","authors":"Khandaker Mamun Ahmed, Farid Ghareh Mohammadi, M. Matus, Farzan Shenavarmasouleh, Luiz Manella Pereira, Zisis Ioannis, M. Amini","doi":"10.58245/ipsi.tir.2302.06","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2302.06","url":null,"abstract":"In the past few years, automatic building detection in aerial images has become an emerging field in computer vision. Detecting the specific types of houses will provide information in urbanization, change detection, and urban monitoring that play increasingly important roles in modern city planning and natural hazard preparedness. In this paper, we demonstrate the effectiveness of detecting various types of houses in aerial imagery using Faster Region-based Convolutional Neural Network (Faster-RCNN). After formulating the dataset and extracting bounding-box information, pre-trained ResNet50 is used to get the feature maps. The fully convolutional Region Proposal Network (RPN) first predicts the bounds and objectness score of objects (in this case house) from the feature maps. Then, the Region of Interest (RoI) pooling layer extracts interested regions to detect objects that are present in the images. To the best of our knowledge, this is the first attempt at detecting houses using Faster R-CNN that has achieved satisfactory results. This experiment opens a new path to conduct and extent the works not only for civil and environmental domain but also other applied science disciplines.","PeriodicalId":41192,"journal":{"name":"IPSI BgD Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86378569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

"Integrating Location Information as Geohash Codes in Convolutional Neural Network-Based Satellite Image Classification" 基于卷积神经网络的卫星图像分类中位置信息Geohash码的集成

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IPSI BgD Transactions on Internet Research Pub Date : 2023-07-01 DOI: 10.58245/ipsi.tir.2302.04

Arpan Mahara, N. Rishe

{"title":"\"Integrating Location Information as Geohash Codes in Convolutional Neural Network-Based Satellite Image Classification\"","authors":"Arpan Mahara, N. Rishe","doi":"10.58245/ipsi.tir.2302.04","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2302.04","url":null,"abstract":"In the past few years, there have been many research studies conducted in the field of Satellite Image Classification. The purposes of these studies included flood identification, forest fire monitoring, greenery land identification, and land-usage identification. In this field, finding suitable data is often considered problematic, and some research has also been done to identify and extract suitable datasets for classification. Although satellite data can be challenging to deal with, Convolutional Neural Networks (CNNs), which consist of multiple interconnected neurons, have shown promising results when applied to satellite imagery data. In the present work, first we have manually downloaded satellite images of four different classes in Florida locations using the TerraFly Mapping System, developed and managed by the High Performance Database Research Center at Florida International University. We then develop a CNN architecture suitable for extracting features and capable of multi-class classification in our dataset. We discuss the shortcomings in the classification due to the limited size of the dataset. To address this issue, we first employ data augmentation and then utilize transfer learning methodology for feature extraction with VGG16 and ResNet50 pretrained models. We use these features to classify satellite imagery of Florida. We analyze the misclassification in our model and, to address this issue, we introduce a location-based CNN model. We convert coordinates to geohash codes, use these codes as an additional feature vector and feed them into the CNN model. We believe that the new CNN model combined with geohash codes as location features provides a better accuracy for our dataset.","PeriodicalId":41192,"journal":{"name":"IPSI BgD Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91160878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1