Synthetic Aperture Radar (SAR) and Optical sensors are very popular depending on their strengths and application needs. The ocean surface will be covered with clouds most of the time and optical sensors cannot capture...
Synthetic Aperture Radar (SAR) and Optical sensors are very popular depending on their strengths and application needs. The ocean surface will be covered with clouds most of the time and optical sensors cannot capture the information as they cannot operate in dust and cloudy conditions. So, SAR sensors come into existence to capture the atmospheric phenomena in those regions. There is a wide range of texture extraction techniques such as traditional-based wavelet analysis, statistical analysis, local pixel-based approaches, and dimensional reduction approaches. However, these approaches have limitations in boundary extraction, high-dimensional feature extraction, and uniformity representation. In recent times, deep learning (DL) approaches to extract features which varies based on the applications, image interpretation, and so on. At the same time, DL approaches are limited in transparency, computation requirements, and hyperparameter tuning. In contrast, the proposed S5ELBP i.e., Supervised SAR image classification using the integration of Support Vector Machine (SVM) and Scale Selective Extended Local Binary Pattern (SSELBP) extraction techniques shall overcome various challenges of existing traditional and DL approaches such as adaptive thresholding, less computational resources, and noise robustness. Hence, the objective of the proposed methodology is to extract the image features from the Scale Selective Extended Local Binary Pattern (SSELBP). Also, the SSELBP features are integrated with SVM to show the strength of the proposed approach in SAR image classification. Additionally, the performance of the present work is enhanced by comparing the extracted results with various classifiers. Finally, the work is concluded by recommending the scope of future work.
The spread of false news threatens news and information sources, especially in Indian politics. We present a machine learning-based strategy to identify false news by vectorizing and tokenizing news headlines using a ...
详细信息
ISBN:
(数字)9798350317060
ISBN:
(纸本)9798350317077
The spread of false news threatens news and information sources, especially in Indian politics. We present a machine learning-based strategy to identify false news by vectorizing and tokenizing news headlines using a pre-defined dataset of authentic and fraudulent news items. We want to create a model that can properly identify news stories by textual content, separating propaganda from real news. Our technique is tested on a benchmark dataset of news items. Our suggested technique outperforms various state-of-the-art false news detection algorithms in the literature. Our methodology can prevent false news and safeguard news and information sources in Indian politics. This technique is versatile and successful for identifying and reducing the impact of false news across several topics and languages.
Complex biological networks, comprising metabolic reactions, gene interactions, and protein interactions, often exhibit scale-free characteristics with power-law degree distributions. However, empirical studies have r...
详细信息
Like most diseases that affect a human's life, car-diovascular disease is dangerous and unwanted for anyone who has heard of it as it brings a high risk of death. In today's world, most deaths are caused by he...
详细信息
We propose a method to statistically analyze rates obtained from count data in spatio-temporal terms, allowing for regional and temporal comparisons. Generalized fused Lasso Poisson model is used to estimate the spati...
详细信息
This article conducts a comprehensive examination of brand-based equity closely linked to environmental sustainability in the Amazon Rainforest. The research is primarily dedicated to identifying the factors that infl...
详细信息
With the prevalence of Internet of Things(loT)devices,data collection has the potential to improve people's lives and create a significant ***,it also exposes sensitive information,which leads to privacy *** appro...
详细信息
With the prevalence of Internet of Things(loT)devices,data collection has the potential to improve people's lives and create a significant ***,it also exposes sensitive information,which leads to privacy *** approach called N-source anonymity has been used for privacy preservation in raw data collection,but most of the existing schemes do not have a balanced efficiency and *** this work,a lightweight and efficient raw data collection scheme is *** proposed scheme can not only collect data from the original users but also protect their ***,the proposed scheme can resist user poisoning attacks,and the use of the reward method can motivate users to actively provide *** and simulation indicate that the proposed scheme is safe against poison ***,the proposed scheme has better performance in terms of computation and communication overhead compared to existing *** efficiency and appropriate incentive mechanisms indicate that the scheme is practical for IoT systems.
Knowledge Graphs popularity has been rapidly growing in last years. All that knowledge is available for people to query it through the many online databases on the internet. Though, it would be a great achievement if ...
详细信息
Social media platforms are widely to exchange ideas and sharing news with each other. Consequently, any idea, post, or new posted on social media is likely to become viral in a short span of time. However, if a fake n...
详细信息
The mumps virus causes a very infectious sickness. The enlargement of the parotid salivary glands can be quite uncomfortable. Mumps treatment centers on symptom management. The illness must take its natural course. Wh...
详细信息
暂无评论