Before earlier, the primary reason why academics were interested in human observations from books was so they could quickly identify human cognition. Conversely, these signals from linguistic processing can also help ...
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Accurate identification of gemstones is crucial in various fields, including gemology research, jewelry appraisal, and consumer protection, where authenticity verification is paramount to prevent fraud and ensure trus...
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This study addresses the problem of wild animal attacks and disappearance in areas such as farms or villages near forest and mountains, proposing a solution through deep learning algorithms. Using deep learning archit...
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Online shopping is rapidly increasing nowadays. As a reason when we are going to purchase any kind of item through an e-commerce site, we are always concerned about the reviews and ratings of the product that we are g...
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Multilingual speaker identification and verification is a challenging task, especially for languages with diverse acoustic and linguistic features such as Indo-Aryan and Dravidian languages. Previous models have strug...
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作者：
Murthy, AnanthaPrathwiniKulkarni, SanjeevSavitha, G.Nitte
Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India
Department of Master of Computer Applications Karkala India Srinivas University
Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology
Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India
Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challe...
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Currently, most mobile devices are equipped with vibration functions that notify users or simulate the feel of a touchscreen keyboard. However, most of these use a single motor for vibration, and the information about...
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In today's dynamic software development environment, effective collaboration between development and operations teams is essential for success. DevOps, derived from Development and Operations, encompasses a range ...
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Zero-shot Learning (ZSL) involves classifying samples from unseen classes using semantic similarities with seen classes. However, ZSL models are often subject to bias toward seen classes, especially in a generalized s...
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Wireless Sensor Networks (WSNs) have advanced quickly due to the fast expansion of wireless networks. Yet, because of their ease of use and versatility, security concerns have grown. This means that conducting researc...
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ISBN:
(纸本)9798350348460
Wireless Sensor Networks (WSNs) have advanced quickly due to the fast expansion of wireless networks. Yet, because of their ease of use and versatility, security concerns have grown. This means that conducting research on intrusion protection in WSNs is now essential. Denial of Service (DoS) assaults are among the most common types of network attacks. They are dangerous because they take down the target network in order to accomplish their goal. Within WSNs, where devices function with limited resources, a denial-of-service attack has the potential to be disastrous. This research suggests a novel solution for WSNs, which are susceptible to assaults because to their devices' little storage capacity. To find abnormalities in DoS traffic within WSNs, the technique combines a Deep Convolutional Neural Network (DCNN) with Principal Component Analysis (PCA). By detecting and reducing the effects of DoS assaults, and by utilising the complementary capabilities of PCA and DCNN in this particular situation, the goal is to improve the security of WSNs. Compared with other traditional DL architectures, the proposed model has a more simplified structure and better feature extraction capabilities. This special combination gives it the power to quickly identify anomalous network activity in WSNs devices, especially those with limited storage. Because of its lightweight design, the suggested model addresses the inherent resource limits and guarantees optimal performance in the context of WSNs. A variety of assessment measures, such as confusion matrices, different classification metrics, and Receiver Operating Characteristic (ROC) curves, are used to verify the effectiveness of the suggested model. These metrics are used to evaluate the model's categorization performance in a rigorous manner. Extensive experimental comparisons reveal that the small size of the proposed model outperforms other popular models for anomalous traffic detection with regards to classification performance
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