Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Saravanan, V.
- Enhanced Frost Filter and Cosine Tanimoto Classsification based Natural Disaster Management with Satellite Images
Abstract Views :139 |
PDF Views:1
Authors
M. Nirmala
1,
V. Saravanan
2
Affiliations
1 Department of Computer Applications, Hindusthan College of Engineering and Technology, IN
2 Department of Information Technology, Hindusthan College of Arts and Science, IN
1 Department of Computer Applications, Hindusthan College of Engineering and Technology, IN
2 Department of Information Technology, Hindusthan College of Arts and Science, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2751-2758Abstract
Natural disasters are utmost incidents inside the earth's system that lead to sudden demise or bruise to humans, and destruction of precious materials, involving buildings, conveyance systems, farming land, forest and natural environment. Occurrences of economic losses due to natural disasters have resulted owing to the escalated susceptibility of the society globally and also due to weather-related disasters. Satellite image sensing remains the hypothetical instrument for disaster management as it provides information spanning wide-reaching areas and also at short time period. In this work we plan to develop a method called, Enhanced Frost Filter and Tanimoto Similarity Classification (EFF-TSC) for efficient disaster management using satellite images is proposed. The EFF-TSC method for disaster management is split into three steps. They are pre-processing, segmentation and classification. With the input image collected from satellite image database, first preprocessing is performed to preserve important features at the edges and remove the noisy pixel by means of an Enhanced Frost Filter Preprocessing model. Second, to the pre-processed satellite image, Threshold Pixel Segmentation is applied to partition into multiple segments. Finally, to the partitioned images, Tanimoto Similarity Classification is applied to classify the segmented image into two types, namely disastrous image and non-disastrous image. With this, an efficient disaster management is carried out with better accuracy and minimal time consumption. The application of the study is demonstrated using the Disaster image data set collected from Kaggle during the 2017. The results show the capability of the proposed EFF-TSC method for disaster management across time and space from different images with considerable accuracy by also reducing peak signal to noise ratio with considerable time. The findings also suggest that the potential for forensic analysis of disasters using pixel segmentation and classification based on collected images can be utilized to several locations affected by disasters.Keywords
Disaster Management, Frost Filter, Threshold Pixel Segmentation, Tanimoto Similarity Classification, Satellite Image.
References
- Saramsha Dotel, Avishekh Shrestha, Anish Bhusal, Ramesh Pathak, Aman Shakya and Sanjeeb Prasad Panday, “Disaster Assessment from Satellite Imagery by Analysing Topographical Features using Deep Learning”, ACM Digital Library, pp. 86-92, 2020.
- Chao Fan, Fangsheng Wu and Ali Mostafavi, “A Hybrid Machine Learning Pipeline for Automated Mapping of Events and Locations from Social Media in Disasters”, IEEE Access, Vol. 8, pp. 10478-10490, 2020.
- Lokabhiram Dwarakanath, Amirrudin Kamsin, Rasheed Abubukar Rasheed, Anitha Anandhan and Liyana Shuib, “Automated Machine Learning Approaches for Emergency Response and Coordination via Social Media in the Aftermath of a Disaster: A Review”, IEEE Access, Vol. 10, pp. 1-13, 2021.
- Abu Reza Md Towfiqul Islam, Swapan Talukdar, Susanta Mahato, Sonali Kundu, Kutub Uddin Eibek, Quoc Bao Pham, Alban Kuriqi and Nguyen Thi Thuy Linh, “Flood susceptibility modelling using advanced ensemble machine learning models”, Geoscience Frontiers, Vol. 87, pp. 1-12, 2020.
- Brett W. Robertson, Matthew Johnson, Dhiraj Murthy, William Roth Smith and Keri K. Stephens, “Using a Combination of Human Insights and ‘Deep Learning’ for Real-Time Disaster Communication”, Progress in Disaster Science, Vol. 65, No. 2, pp. 1-9, 2019.
- Wenjuan Sun, Paolo Bocchini and Brian D. Davison, “Applications of Artificial Intelligence for Disaster Management”, Natural Hazards, Vol. 103, pp. 2631-2689, 2020.
- Yuko Murayama, Hans Jochen Scholl and Dimiter Velev, “Information Technology in Disaster Risk Reduction”, Information Systems Frontiers, Vol. 98, pp. 1-17, 2021.
- Amna Asif, Shaheen Khatoon, Md Maruf Hasan, Majed A. Alshamari, Sherif Abdou, Khaled Mostafa Elsayed and Mohsen Rashwan, “Automatic Analysis of Social Media Images to Identify Disaster Type and Infer Appropriate Emergency Response”, Journal of Big Data, Vol. 83, pp. 1-14, 2021.
- Clemens Havas and Bernd Resch, “Portability of Semantic and Spatial-Temporal Machine Learning Methods to Analyse Social Media for Near-Real-Time Disaster Monitoring”, Natural Hazards, Vol. 108, pp. 2939-2969, 2021.
- Ruo Qian Wang, Yingjie Hu, Zikai Zhou and Kevin Yang, “Tracking Flooding Phase Transitions and Establishing a Passive Hotline With AI-Enabled Social Media Data”, IEEE Access, Vol. 8, pp. 103395-103404, 2020.
- Samira Pouyanfar, Yudong Tao, Haiman Tian, Shu-Ching Chen and Mei-Ling Shyu, “Multimodal Deep Learning based on Multiple Correspondence Analysis for Disaster Management”, World Wide Web, Vol. 89, pp. 1-13, 2018.
- Wei Pan, Ying Guo and Shujie Liao, “Risk-Averse Evolutionary Game Model of Aviation Joint Emergency Response”, Discrete Dynamics in Nature and Society, Vol. 2016, pp. 1-13, 2016.
- M. Ankush Kumar and A. Jaya Laxmi, “Machine Learning Based Intentional Islanding Algorithm for DERs in Disaster Management”, IEEE Access, Vol. 9, pp. 85300-85309, 2021.
- Nilani Algiriyage, Raj Prasanna, Kristin Stock, Emma E.H. Doyle and David Johnston, “Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review”, Computer Science, Vol. 78, pp. 1-15, 2021.
- M. Ponnusamy, P. Bedi and R. Manikandan, “Design and Analysis of Text Document Clustering using Salp Swarm Algorithm”, The Journal of Supercomputing, Vol. 89, pp. 1-17, 2022.
- Ling Tan, Ji Guo, Selvarajah Mohanarajah and Kun Zhou, “Can We Detect Trends in Natural Disaster Management with Artificial Intelligence? A Review of Modeling Practices”, Natural Hazards, Vol. 107, pp. 2389-2417, 2020.
- Zhengjing Ma, Gang Mei and Francesco Piccialli, “Machine Learning for Landslides Prevention: A Survey”, Neural Computing and Applications, 2020.
- Ines Robles Mendo, Gonçalo Marques, Isabel de la Torre Diez, Miguel Lopez-Coronado and Francisco Martin-Rodriguez, “Machine Learning in Medical Emergencies: a Systematic Review and Analysis”, Journal of Medical Systems, Vol. 88, pp. 1-16, 2021.
- Hafiz Suliman Munawar, Ahmed W.A. Hammad and S. Travis Waller, “A Review on Flood Management Technologies related to Image Processing and Machine Learning”, Automation in Construction, Vol. 132, pp. 1-19, 2021.
- Pouria Babvey, Gabriela Gongora-Svartzman, Carlo Lipizzi and Jose E. Ramirez-Marquez, “Content-based user Classifier to Uncover Information Exchange in Disaster-Motivated Networks”, PLOS One, Vol. 74, pp. 1-13, 2021.
- Classification of Social Media Content and Improved Community Detection (C&CD) Using VGGNet Learning and Analytics
Abstract Views :76 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Telecommunications Engineering, MIT Academy of Engineering, IN
2 Department of Computer Science, Johns Hopkins University, US
3 Department of Artificial Intelligence and Machine Learning, Excel Engineering College, IN
4 Department of Electronics and Communication Engineering, Roever Engineering College, IN
1 Department of Electronics and Telecommunications Engineering, MIT Academy of Engineering, IN
2 Department of Computer Science, Johns Hopkins University, US
3 Department of Artificial Intelligence and Machine Learning, Excel Engineering College, IN
4 Department of Electronics and Communication Engineering, Roever Engineering College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 3 (2024), Pagination: 3181-3186Abstract
Social media platforms generate vast amounts of data, necessitating efficient content classification and community detection methods. This study addresses this challenge through the utilization of VGGNet analytics, a powerful deep learning architecture. We employed a two-step approach, beginning with VGGNet-based content classification to categorize social media posts. Subsequently, a community detection algorithm was applied to identify distinct user groups based on their interactions and content preferences. This research contributes an novel framework that seamlessly integrates VGGNet for content analysis and community detection, enhancing the understanding of user behavior in social media platforms. The proposed method aims to provide more accurate and insightful results compared to traditional approaches. Our experiments on diverse social media datasets demonstrate the effectiveness of the VGGNet-based approach. The content classification accurately assigns posts to relevant categories, while the community detection algorithm identifies cohesive user groups. The results highlight the potential for improved content recommendation systems and targeted marketing strategies.Keywords
Community Detection, Content Classification, Social Media, VGGNet Analytics, Deep Learning.References
- D. Rogers and I. Spasic, “Real-Time Text Classification of User-Generated Content on Social Media: Systematic Review”, IEEE Transactions on Computational Social Systems, Vol. 9, No. 4, pp. 1154-1166, 2021.
- C. Southerton and R. Cover, “Restricted Modes: Social Media, Content Classification and LGBTQ Sexual Citizenship”, New Media and Society, Vol. 23, No. 5, pp. 920-938, 2021.
- A. Kumar, Y.K. Dwivedi and N.P. Rana, “A Deep Multi-Modal Neural Network for Informative Twitter Content Classification during Emergencies”, Annals of Operations Research, Vol. 76, pp. 1-32, 2020.
- R. Rivas, Y. Guo and V. Hristidis, “Classification of Health-Related Social Media Posts: Evaluation of Post Content-Classifier Models and Analysis of User Demographics”, JMIR Public Health and Surveillance, Vol. 6, No. 2, pp. 1-13, 2020.
- Z. Shahbazi and D.C. Lee, “Toward Representing Automatic Knowledge Discovery from Social Media Contents based on Document Classification”, International Journal on Advance Science and Technology, Vol. 29, pp. 14089-14096, 2020.
- A. Bhardwaj, “Sentiment Analysis and Text Classification for Social Media Contents using Machine Learning Techniques”, Proceedings of International Conference on IoT, Social, Mobile, Analytics and Cloud in Computational Vision and Bio Engineering, pp. 1-12, 2020.
- I.H. Ting and C.S. Yen, “Towards Automatic Generated Content Website based on Content Classification and Auto-Article Generation”, Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 436-438, 2021.
- M.A. Al Garadi and A. Sarker, “Text Classification Models for the Automatic Detection of Nonmedical Prescription Medication use from Social Media”, BMC Medical Informatics and Decision Making, Vol. 21, No. 1, pp. 1-13, 2021.
- T. Xiang and N. Goharian, “ToxCCIn: Toxic Content Classification with Interpretability”, Proceedings of International Conference on Artificial Intelligence, pp. 1-8, 2021.
- A.S. Raamkumar and H.L. Wee, “Use of Health Belief Model-Based Deep Learning Classifiers for Covid-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study”, JMIR Public Health and Surveillance, Vol. 6, No. 3, pp. 1-12, 2020.
- D. Kumar and M. Bailey, “Designing Toxic Content Classification for a Diversity of Perspectives”, Proceedings of 17th Symposium on Usable Privacy and Security, pp. 299-318, 2021.
- S.D. Rane, “Social Media Content Analysis and Classification using Data Mining and ML”, International Journal of Data Analytics, Vol. 2, No. 2, pp. 75-84, 2021.