{"title":"Flood Mapping and Damage Analysis Using Multispectral Sentinel-2 Satellite Imagery and Machine Learning Techniques","authors":"Rashmi Saini, Shivam Rawat, Suraj Singh, Prabhakar Semwal","doi":"10.2174/0126662558309143240529104953","DOIUrl":"https://doi.org/10.2174/0126662558309143240529104953","url":null,"abstract":"\u0000\u0000Floods are among the deadliest natural calamities, devastating ecosystems and human lives worldwide. In India, Bihar is a state grappling with economic hardships\u0000and faces severe agricultural devastation due to recurring floods, destroying crops and natural\u0000resources, which significantly impacts local farmers. This research addresses the critical need\u0000to deeply understand the flood dynamics of selected study areas.\u0000\u0000\u0000\u0000This research presents a case study that focuses on leveraging Remote Sensing tools\u0000and Machine Learning techniques for comprehensive flood mapping and damage analysis in\u0000Gopalganj District, Bihar, India, using remote sensing data. More specifically, this research\u0000presents three major objectives: (i) Flood damage mapping and change analysis before and after the flood using the Sentinel-2 satellite dataset, (ii) Evaluation of the impact of integrating\u0000spectral indices on the accuracy of classification, (iii) Identification of most robust predictor\u0000spectral indices for the classification.\u0000\u0000\u0000\u0000The Sentinel-2 satellite dataset encompasses 13 bands with resolutions of 10m, 20m,\u0000and 60m. Here, four spectral bands (NIR, Red, Green, and Blue) with the finest resolution of\u000010m have been selected for this study. These bands are integrated with four spectral indices,\u0000namely Normalized Difference Water Index (NDWI), MNDWI (Modified NDWI), Normalized\u0000Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI). Two ML\u0000classifiers, namely Support Vector Machine (SVM) and Random Forest (RF) have been employed for pixel-based supervised classification.\u0000\u0000\u0000\u0000Results have shown that RF outperformed and worked well in extracting water bodies\u0000and flood-damaged areas effectively. The results demonstrated that RF obtained (Overall Accuracy (OA)= 89.54% and kappa value (ka) = 0.872) and SVM reported (OA= 87.69%, ka=\u00000.849) for pre-crisis data, whereas, for post-crisis, RF reported (OA=91.54%, ka = 0.897),\u0000SVM reported (OA= 89.77%, ka= 0.875).\u0000\u0000\u0000\u0000It was reported that the integration of spectral indices improved the OA by\u0000+3.41% and +2.86% using RF and SVM, respectively. The results of this study demonstrated\u0000that the waterbody area increased from 12.72 to 88.23 km2,\u0000as shown by the RF classifier. The\u0000variable importance computation results indicated that MNDWI is the most important predictor\u0000variable, followed by NDWI. This study recommends the use of these two predictor variables\u0000for flood mapping.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141833376","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":"Efficacy of Keystroke Dynamics-Based User Authentication in the Face of Language Complexity","authors":"Sandip Dutta, Utpal Roy, Soumen Roy","doi":"10.2174/0126662558309578240705101526","DOIUrl":"https://doi.org/10.2174/0126662558309578240705101526","url":null,"abstract":"\u0000\u0000This study investigates the impact of language complexity on Keystroke\u0000Dynamics (KD) and its implications for accurate KD-based user authentication system\u0000performance in smartphones.\u0000\u0000\u0000\u0000This research meticulously analyzes keystroke patterns using 160 volunteers, including\u0000both frequently typed and infrequently typed texts. Our analysis of 12 anomaly detection\u0000algorithms reveals that a simple text-based KD system consistently outperforms its complex\u0000counterpart with superior Equal Error Rates (EERs).\u0000\u0000\u0000\u0000As a result, the Scaled Manhattan anomaly detector achieves an EER of 2.48% for\u0000simple text and an improvement over 2.98% for complex text. The incorporation of soft biometrics\u0000further enhances algorithmic performance, emphasizing strategies to build resilience\u0000into KD-based user authentication systems.\u0000\u0000\u0000\u0000Throughout this study, the importance of text complexity is emphasized, and innovative\u0000pathways are introduced to strengthen KD-based user authentication paradigms.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"55 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653423","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":"Innovation in Knowledge Economy: A Case Study of 3D Printing's Rise in\u0000Global Markets and India","authors":"Aman Semalty, R. Agrawal","doi":"10.2174/0126662558304420240705114015","DOIUrl":"https://doi.org/10.2174/0126662558304420240705114015","url":null,"abstract":"\u0000\u00003D printing is a rapidly growing technology with features of enhanced\u0000customizability, reduced errors, zero material waste, reduced costs, and quick turnaround\u0000times. In this work, the data were collected from the Derwent Innovation and Web of Science\u0000databases for patent and publication search, respectively.\u0000\u0000\u0000\u0000The results were critically\u0000analysed and correlated with the global and Indian market growth. USA (with 5 out of the top\u0000ten patent contributing companies), China, Germany, France, and Taiwan were determined to\u0000be the top countries with the maximum number of patents on 3D printing technology. Both patents and publications exhibited consistent growth until 2011. From 2012 onwards, the rate of\u0000patent filings began to surpass that of academic publications, indicating a shift in the dynamics.\u0000\u0000\u0000\u0000This trend has continued over the years, leading to a notable difference between the\u0000number of patents (19,322) and publications (10,571) in the year 2022. India has been found to\u0000rank 8th in 3D printing innovation and research, globally.\u0000\u0000\u0000\u0000In this study, the global\u0000and Indian market growth has been observed and the opportunities and challenges for the Indian market have been critically studied.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"73 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141658372","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}