“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian *** dynamic nature of FANETs,wit...
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“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian *** dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol *** the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high *** paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and *** research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant *** research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in *** compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance *** protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive *** comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in ***,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.
The discount factor for reinforcement learning has been constrained to the range [0, 1] to avoid overestimation of state values during training. Although theoretical studies have indicated that discount factors greate...
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In recent years, how to achieve stable localization and construct high-quality dense maps in large-scale scenes has become a research highlight. In large-scale scenes, for the consideration of the mapping accuracy and...
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In recent years, how to achieve stable localization and construct high-quality dense maps in large-scale scenes has become a research highlight. In large-scale scenes, for the consideration of the mapping accuracy and efficiency, multi-agent systems rather than single-agent ones are usually employed. Currently, as far as we know, collaborative VI-SLAM (Visual Inertial Simultaneous Localization And Mapping) systems applicable to multi-agent systems are still sporadic, and systems those can achieve a good balance among the localization accuracy, the mapping density, and the transmission efficiency are temporarily lacking. In this paper, we propose a novel centralized collaborative VI-SLAM framework, namely TES-CVIDS (Transmission Efficient Sub-map based Collaborative Visual-Inertial Dense SLAM). In TES-CVIDS, instead of the original RGBD images, the compact sub-maps are transmitted, effectively reducing the transmission data redundancy. After that, the server completes key-frame processing, hierarchical pose-graph optimization, and global dense map construction in three separate threads. Besides, thanks to our depth search mechanism, the geometry information of all key-frames can be recovered on the server-end. Thus, sub-maps can be regenerated after the global pose-graph optimization to maintain the consistency between the localization and the mapping. Both the qualitative and the quantitative experimental results corroborate the superior performance of our TES-CVIDS. To make our results reproducible, the source code has been released at https://***/TES-CVIDS-MainPage/. IEEE
Machine learning models are the backbone of smart grid optimization, but their effectiveness hinges on access to vast amounts of training data. However, smart grids face critical communication bottlenecks due to the e...
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VR gloves can greatly enhance the realism of the VR experience by allowing users to not only see and hear the virtual environment, but also touch it without having to press buttons. This could make VR more appealing t...
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In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit...
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In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)*** models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio *** assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM *** results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models ***,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters *** is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering *** this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.
Using large language model to generate vehicle type recognition algorithm can reduce the burden of developers and realize the rapid development of projects. In this paper, LangChain large model interface provided by B...
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Information-flow tracking is useful for preventing malicious code execution and sensitive information leakage. Unfortunately, the performance penalty of the currently available solutions is too high for real-world app...
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Signature verification plays an important role in document authentication. The efficient and robust system is required for forgery detection in documents in the presence of distortion such as rotation, scaling and noi...
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Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving ...
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Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack *** approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time *** for easy integration with existing web security systems,our framework supports adaptable Application programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.
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