Location Identification of the Individual based on Image Metadata

P, Ramesh Kumar; Srikanth, Ch.; Sailaja, K. L.

doi:10.1016/j.procs.2016.05.191

Cited by 13 publications

(5 citation statements)

References 4 publications

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“…Kafee et al [24] has showed location-based services (LBS) refer to services that account for the realtime, geographic positions of a person or mobile object, as determined by GNSS. Kumar et al [25] proposed…”

Section: Related Workmentioning

confidence: 99%

Android mobile application for wildfire reporting and monitoring

et al. 2021

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Peat fires cause major environmental problems in Central Kalimantan Province, Indonesia and threaten human health and effect the social-economic sector. The lack of peat fire detection systems is one factor that causing these reoccurring fires. Therefore, in this study, we develop an Android mobile platform application and a web-based application to support the citizen-volunteers who want to contribute wildfires reports, and the decision-makers who wish to collect, visualize, and evaluate these wildfires reports. In this paper, the global navigation satellite system (GNSS) and a global position system (GPS) sensor from a smartphone’s camera, is a useful tool to show the potential fire and smoke’s close-range location. The exchangeable image (EXIF) file image and GPS metadata captured by a mobile phone can store and supply raw observation to our devices and sent it to the data center through global internet communication. This work’s results are the proposed application easy-to-use to monitoring potential peat fire by location and data activity. This paper focuses on developing an application for the mobile platform for peat fire reporting and a web-based application to collect peat fire location for decision-makers. Our main objective is to detect the potential and spread of fire in peatlands as early as possible by utilizing community reports using smartphones.

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Section: Related Workmentioning

confidence: 99%

Android mobile application for wildfire reporting and monitoring

et al. 2021

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“…Algoritmo duomenų bazės lentelės schema Šaltinis: sudarytas autoriaus Paveiksle matoma, kad algoritmui bus reikalinga tik viena lentelė "layout_image_data", kuri saugos visą reikalingą papildomą informaciją apie optimizuotus skaitmeninius vaizdus. Duomenų bazėje jau esančioje lentelėje "media", kurioje taip pat jau yra saugomi neoptimizuoti skaitmeniniai vaizdai, bus saugojami ir optimizuoti skaitmeniniai vaizdai, bei jų automatiškai sugeneruoti meta duomenys, kurie gali suteikti informaciją apie įrenginio ID, naudojamą programinę įrangą, sukūrimo datą ir laiką, vaizdo plotį ir aukštį, GPS funkcijas, ir t. t. (Kumar, Srikanth ir Sailaja, 2016). Lentelėje "layout_image_data" yra saugomas ryšys su prekinių ženklų informacijos valdymo sistemos lentelėmis "brands" ir "media".…”

Section: Duomenų Bazės Projektavimasunclassified

Untitled

2021

vtb

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Eleidinio redkolegijaŽurnale publikuojami konferencijos "VERSLAS, TECHNOLOGIJOS, BIOMEDICINA: INOVACIJŲ ĮŽVALGOS 2021" pranešimų pagrindu parengti straipsniai.Žurnale "Verslas, technologijos, biomedicina: inovacijų įžvalgos" publikuojami straipsniai yra recenzuojami redakcinės kolegijos narių arba jų paskirtų ekspertų.Šis darbas licencijuojamas pagal Creative Commons Attribution 4.0 International License. Atskiri straipsniai yra skelbiami "Open Access" pagal "Creative Commons" licenciją CC-BY 4.0, leidžiančią neribotai naudoti, platinti ir atgaminti bet kokioje laikmenoje, su sąlyga, kad nurodytas originalus autorius ir šaltinis. Autoriai išlaiko autorių teises į savo straipsnius, tačiau suteikia Klaipėdos valstybinei kolegijai pirmojo leidinio teisę.Už straipsnių tekstų turinį atsako straipsnių autoriai ir jų vadovai.

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“…As shown by the picture, filter sliding over on the whole picture. With this technique, it will take the maximum value as a characteristic of the pixel and written on (1,1) in the output layer [18]. When the stride is worth 1, this means the filter will move one pixel to the right and perform the same operation as described previously.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Image forgery detection using error level analysis and deep learning

et al. 2019

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Many images are spread in the virtual world of social media. With the many editing software that allows so there is no doubt that many forgery images. By forensic the image using Error Level Analysis to find out the compression ratio between the original image and the fake image, because the original image compression and fake images are different. In addition to knowing whether the image is genuine or fake can analyze the metadata of the image, but the possibility of metadata can be changed. In this case the authors apply Deep Learning to recognize images of manipulations through the dataset of a fake image and original images via Error Level Analysis on each image and supporting parameters for error rate analysis. The result of our experiment is that we get the best accuracy of training 92.2% and 88.46% validation by going through 100 epoch.

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Location Identification of the Individual based on Image Metadata

Cited by 13 publications

References 4 publications

Android mobile application for wildfire reporting and monitoring

Android mobile application for wildfire reporting and monitoring

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Image forgery detection using error level analysis and deep learning

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