International Journal of Geoinformatics最新文献

{"title":"MRI Brain Images Mapping for Tumour Detection Using CNN","authors":"","doi":"10.52939/ijg.v19i7.2747","DOIUrl":"https://doi.org/10.52939/ijg.v19i7.2747","url":null,"abstract":"Brain tumor is a serious life-threatening disease which occurs due to peculiar growth of cells or tissues present in brain. In recent times it is becoming a considerable cause of death of many people. The seriousness of this tumor growing in brain is very huge when compared to all other varieties of cancers and tumors. Hence, to save the affected people detection of the tumor and proper treatment should be done instantaneously without any delay. In this new age of technology, Machine Learning (ML) and Deep Learning (DL) models can be utilized to identify the tumor at early stages more precisely so that proper medication can be given to the affected person which will help in curing them. This paper proposes two different machine learning models to identify the brain tumor by analysing the Magnetic Resonance Image (MRI) scans of the brain. Both unsupervised and supervised learning models were implemented to detect the tumors in brain. Fuzzy C means is used as a part of unsupervised learning model, it is a data clustering algorithm in which entire data set is grouped into predefined number of clusters with every data point belonging to every cluster to a specific degree of membership value. In this approach tumor region is treated as one cluster and healthy brain is another cluster. Moving forward, as a part of supervised learning, transfer learning approach is implemented for classifying whether the given input MRI scan consists of tumor or not. Visual Geometric Group (VGG-19) model was used which is a 19-layer deep pre-trained neural network architecture for better accuracy and results. All the models were developed using python in jupyter notebook.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42487017","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}

{"title":"Geostatistical Exploratory Analysis on Child Malnutrition and its Determinants in India","authors":"","doi":"10.52939/ijg.v19i6.2699","DOIUrl":"https://doi.org/10.52939/ijg.v19i6.2699","url":null,"abstract":"","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43613843","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}

{"title":"Assessment of Urban Flood Vulnerability Using Integrated Multi-parametric AHP and GIS","authors":"","doi":"10.52939/ijg.v19i6.2689","DOIUrl":"https://doi.org/10.52939/ijg.v19i6.2689","url":null,"abstract":"","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47015687","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}

{"title":"Spatial Analysis and Modelling of Malaria Trend in Si Sa Ket Province, Thailand","authors":"","doi":"10.52939/ijg.v19i6.2695","DOIUrl":"https://doi.org/10.52939/ijg.v19i6.2695","url":null,"abstract":"","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44583014","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}

{"title":"Green Tourism Planning for Coastal Development in Gunungsewu Geopark, Indonesia","authors":"","doi":"10.52939/ijg.v19i6.2701","DOIUrl":"https://doi.org/10.52939/ijg.v19i6.2701","url":null,"abstract":"","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45653362","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}

{"title":"Flood Event Detection and Assessment using Sentinel-1 SAR-C Time Series and Machine Learning Classifiers Impacted on Agricultural Area, Northeastern, Thailand","authors":"","doi":"10.52939/ijg.v19i6.2691","DOIUrl":"https://doi.org/10.52939/ijg.v19i6.2691","url":null,"abstract":"","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47656563","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}

{"title":"Suitability Evaluation of Land Use/ Land Cover (LULC) Towards Landslide Prone Areas in Structural and Volcano Landform","authors":"","doi":"10.52939/ijg.v19i6.2697","DOIUrl":"https://doi.org/10.52939/ijg.v19i6.2697","url":null,"abstract":"","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43683940","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}

{"title":"Geospatial Mapping of Inland Flood Susceptibility Based on Multi-Criteria Analysis – A Case Study in the Final Flow of Busu River Basin, Papua New Guinea","authors":"","doi":"10.52939/ijg.v19i6.2693","DOIUrl":"https://doi.org/10.52939/ijg.v19i6.2693","url":null,"abstract":"","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44560690","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}

{"title":"Identification of e-Scooter Shared (ESS) Stations by using a GIS-based MCDM Approach","authors":"","doi":"10.52939/ijg.v19i5.2663","DOIUrl":"https://doi.org/10.52939/ijg.v19i5.2663","url":null,"abstract":"The popularity of micromobility-shared systems has been rising in cities all over the world due to a number of advantages. Cities are increasingly looking for more environmentally friendly ways of transportation due to both traffic congestion and environmental concerns. The positioning of rental stations in respect to prospective congruent criteria is a crucial element in the effectiveness of micromobility-shared networks. Thus, it is crucial to use quantitative methodologies while conducting a site appropriateness analysis for micromobility-shared stations. With a focus on e-scooter shared (ESS) services, this study was conducted to assist the local authorities in identifying the factors and suitability of the ESS operating area. The area selected for this study was Shah Alam as the city council still allows this ESS to operate in some specific areas, especially in the city centre. This can indirectly help in the identification of the characteristics of the existing ESS operating area. The results of the study found a total of 35 existing ESS station locations in Shah Alam. Most of these ESSs are in recreation/park and tourism areas. Accordingly, some characteristics have been adopted from the study of Kabak et al., (2018) according to suitability in Malaysia. A total of eight criteria have been identified and used, namely: proximity to sports centers/recreation/tourist/green area, proximity to shopping malls/business centers, proximity to educational institutions, proximity to residential, proximity to industries, proximity to bike lane/pathway, proximity to bus stop/bus station/train station; and population density. Besides. expert opinion has also been used in this study to obtain weighting information for each criterion. Results have recommended 9 new ESS locations for consideration by the local council.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71121766","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}

{"title":"The Possibility of Using Terrestrial-Based Ground Penetrating Radar (GPR) Technology for Supplying 3rd Dimension Information for A Search and Recovery Mission for Landslide Victims","authors":"","doi":"10.52939/ijg.v19i5.2669","DOIUrl":"https://doi.org/10.52939/ijg.v19i5.2669","url":null,"abstract":"This paper highlights the possibility of using GPR for providing third-dimension (depth) information to facilitate a landslide search and recovery (SAR) mission in Malaysia. The study was based on an actual use case during the 2022 landslide tragedy that occurred at the Father’s Organic Farm, Batang Kali. Two sets of MALA RAMAC X3M with shielded antennas (250Mhz and 500 Mhz) were used to survey a 1m x 1m profile interval at a 30m x 20m and 8m x 6m grid areas in Sector B on the 18th and 19th December 2022. Grid line profiles 2211-A, 2212-A, and 2213-A detected by the 250Mhz antenna showed suspicious reflection patterns. The pattern's amplitude contrast in relation to the soil background and the consistency with the average Malaysian adult stature were considered as the most likely locations of landslide victims. The location of the reflection was viewed with greater accuracy and clarity utilising time slice y-cut on 3D processing in the Reflex3DScan ReflexW module. On 21st December 2022, a victim and his two dogs were recovered by the SAR team near the suspected GPR line profiles at sector B. The suspected GPR signal reflection corroborated with the proximity where the victim was found according to the special SAR victim location map published by authorities. Since access to ground zero post excavation was restricted, on-site validation of the suspected profiles was not possible. Nonetheless, because hyperbolas were detectable at lower frequency with the maximum depth of around 8m, this paper concludes that using terrestrial-based GPR as a search and recovery alternative for buried landslide victims is still feasible. The challenge would be having a skilled operator to detect a hyperbola or abnormality in a time-critical scenario. The study also concluded that terrestrial-based GPR would, at the very least, provide first responders with situational awareness by narrowing down the SAR potential locations, excavation depths and reducing time for searching and recovering victims, as concurred by the Batang Kali SAR team.\u0000\u0000Article Details\u0000How to Cite\u0000Halim, N., Abdullah, N., Ghazali, M., & Hassan, H. (2023). The Possibility of Using Terrestrial-Based Ground Penetrating Radar (GPR) Technology for Supplying 3rd Dimension Information for A Search and Recovery Mission for Landslide Victims. International Journal of Geoinformatics, 19(5). https://doi.org/10.52939/ijg.v19i5.2669","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47788614","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}