Changing the point of view of a character in a story alters the reading experience by reshaping the reader's involvement and identification with the character's thoughts and feelings. An effective NLP solution...
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Learning models that have been known to multi-task are gaining too much attention in various fields. One such area of work where Multi-tasking models are extracting hopeful outcomes is Autonomous Driving. Panoptic Dri...
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Learning models that have been known to multi-task are gaining too much attention in various fields. One such area of work where Multi-tasking models are extracting hopeful outcomes is Autonomous Driving. Panoptic Driving Perception has become an inevitable aspect of autonomous driving systems which demands a very good accuracy and speed. Over a decade, various multi-tasking models have been proposed to simultaneously carry out traffic object detection, drivable area segmentation, and lane detection. Among all of them, YOLOP, YOLOPv, and HybridNets are the 3 models that have accomplished the above tasks using the BDD100K Dataset with high excellent accuracy and efficiency, thus achieving the new State-Of-The-Art (SOTA) performance. This paper focuses mainly on comparing the performance of the above-mentioned models in various weather conditions based on accuracy, robustness, and computational speed.
Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection *** machine learning approaches to phishing detection have relied...
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Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection *** machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 *** WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation *** results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.
With the introduction of Mobile Cloud Computing (MCC), the notion of leveraging cloud-hosted components to get around mobile devices' resource constraints became more widely accepted. However, as smartphones and t...
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Traditional rigid robots face limitations in exploration during learning processes in dynamic and unknown environments where contact and impacts are common. Soft robots that leverage compliant materials, for example a...
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As Internet of Things (IoT) ecosystems grow more complex, ensuring real-time security has become a major challenge. Traditional security approaches are insufficient for handling dynamic and interconnected IoT networks...
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ISBN:
(纸本)9798350352931
As Internet of Things (IoT) ecosystems grow more complex, ensuring real-time security has become a major challenge. Traditional security approaches are insufficient for handling dynamic and interconnected IoT networks, which are increasingly targeted by sophisticated cyber-attacks. To address these issues, new methodologies that combine real-time monitoring and adaptive security mechanisms are needed. Cyber Twin technology, an innovative extension of digital twin technology, presents a promising solution by creating AI-driven digital replicas of IoT devices and software systems for continuous security monitoring and management. This paper introduces a Cyber Twin Technology Framework for AI-driven real-time software security in IoT ecosystems. The framework employs advanced AI models, including Convolutional Neural Networks (CNNs) for anomaly detection and Generative Adversarial Networks (GANs) for synthetic data generation to simulate potential attack scenarios. A dynamic reinforcement learning module is integrated to optimize threat response strategies based on evolving threat patterns. By creating real-time digital replicas of IoT components, the Cyber Twin framework continuously monitors device behaviors, identifies anomalies, and autonomously initiates mitigation actions. The system is evaluated in a simulated IoT environment with over 500 interconnected devices. Experimental results demonstrate that the Cyber Twin framework achieved a 99.2% detection accuracy in identifying cyber threats, with a false positive rate of 1.3%. The dynamic response module reduced incident response time by 35% compared to traditional methods, enhancing the system's ability to neutralize potential threats in real-time. The use of GAN-based synthetic data also enabled proactive defense strategies, reducing attack success rates by 40% during testing. The Cyber Twin Technology Framework provides a robust solution for real-time software security in complex IoT ecosystems. By leveraging A
This study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. The research integrates Graph Neural Networks (GNNs) and Convolutional Long and...
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Temperature and power dependent time-resolved photoluminescence measurements are performed on a novel ultra-stable red-emitting InGaN/GaN heterostructure. PL dynamics at two distinct wavelengths indicate a lateral car...
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This work addresses the critical question of why and when diffusion models, despite being designed for generative tasks, can excel at learning high-quality representations in a self-supervised manner. To address this,...
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This paper presents a novel reflectarray with independent beam scanning at dual frequencies. Loaded by a single p-i-n diode, two separate radiating structures in a unit cell are used to flexibly fulfill ideal reflecti...
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