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.
{"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}
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.
{"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}
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. Article Details How to Cite Halim, 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
本文强调了使用探地雷达提供三维（深度）信息的可能性，以促进马来西亚的滑坡搜索和恢复（SAR）任务。该研究基于2022年巴塘卡利父亲有机农场发生山体滑坡悲剧期间的一个实际使用案例。2022年12月18日和19日，两组带屏蔽天线的MALA RAMAC X3M（250Mhz和500Mhz）被用于在B区30m x 20m和8m x 6m网格区域测量1m x 1m的剖面间隔。250Mhz天线检测到的网格线轮廓2211-A、2212-A和2213-A显示出可疑的反射图案。该模式与土壤背景的振幅对比以及与马来西亚成年人平均身高的一致性被认为是滑坡受害者最有可能的位置。利用Reflex3DScan ReflexW模块中3D处理的时间片y切，可以更准确、更清晰地查看反射的位置。2022年12月21日，搜救队在B区的可疑探地雷达线路剖面附近找到了一名受害者和他的两只狗。根据当局公布的特别搜救受害者位置图，可疑探地卫星信号反射与发现受害者的地点相证实。由于挖掘后进入归零地受到限制，因此无法对可疑剖面进行现场验证。尽管如此，由于双曲线的频率较低，最大深度约为8米，因此本文得出结论，使用地面探地雷达作为掩埋滑坡受害者的搜索和恢复替代方案仍然可行。挑战将是让一名熟练的操作员在时间关键的场景中检测双曲线或异常。该研究还得出结论，陆地GPR至少可以通过缩小搜救潜在位置、挖掘深度和减少搜索和恢复受害者的时间，为急救人员提供态势感知，巴塘卡利搜救团队对此表示赞同。文章详细介绍如何引用哈利姆，N.，阿卜杜拉，N.，加扎利，M.和哈桑，H.（2023）。利用地面探地雷达技术为滑坡受害者的搜索和恢复任务提供三维信息的可能性。国际地理信息学杂志，19（5）。https://doi.org/10.52939/ijg.v19i5.2669
{"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}
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