While social media are a key source of data for computational social science, their ease of manipulation by malicious actors threatens the integrity of online information exchanges and their analysis. In this Chapter,...
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Abstract: Laser-induced acoustic cavitation microbubble dynamics have exhibited significant development in biological systems such as lithotripsy. Investigating the mechanically laser-induced acoustic cavitation micro...
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This paper proposes a novel framework to realize self-stabilizing Byzantine tolerant swarms. In this framework, non-Byzantine robots execute tasks while satisfying location functions, that is, the robots use a policy ...
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In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise spars...
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Since the COVID-19 pandemic, several research studies have proposed Deep Learning (DL)-based automated COVID-19 detection, reporting high cross-validation accuracy when classifying COVID-19 patients from normal or oth...
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Since the COVID-19 pandemic, several research studies have proposed Deep Learning (DL)-based automated COVID-19 detection, reporting high cross-validation accuracy when classifying COVID-19 patients from normal or other common Pneumonia. Although the reported outcomes are very high in most cases, these results were obtained without an independent test set from a separate data source(s). DL models are likely to overfit training data distribution when independent test sets are not utilized or are prone to learn dataset-specific artifacts rather than the actual disease characteristics and underlying pathology. This study aims to assess the promise of such DL methods and datasets by investigating the key challenges and issues by examining the compositions of the available public image datasets and designing different experimental setups. A convolutional neural network-based network, called CVR-Net (COVID-19 Recognition Network), has been proposed for conducting comprehensive experiments to validate our hypothesis. The presented end-to-end CVR-Net is a multi-scale-multi-encoder ensemble model that aggregates the outputs from two different encoders and their different scales to convey the final prediction probability. Three different classification tasks, such as 2-, 3-, 4-classes, are designed where the train–test datasets are from the single, multiple, and independent sources. The obtained binary classification accuracy is 99.8% for a single train–test data source, where the accuracies fall to 98.4% and 88.7% when multiple and independent train–test data sources are utilized. Similar outcomes are noticed in multi-class categorization tasks for single, multiple, and independent data sources, highlighting the challenges in developing DL models with the existing public datasets without an independent test set from a separate dataset. Such a result concludes a requirement for a better-designed dataset for developing DL tools applicable in actual clinical settings. The datas
Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the Sports, Yoga, and Dance (SYD) postures for maintaining health conditions. The SYD pos...
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The Internet of Vehicles (10V) brings significant economic benefits to countries. However, large-scale smart vehicle production planning remains challenging in the 10V. Currently, heuristic algorithms and solvers comm...
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ISBN:
(数字)9798350387414
ISBN:
(纸本)9798350387421
The Internet of Vehicles (10V) brings significant economic benefits to countries. However, large-scale smart vehicle production planning remains challenging in the 10V. Currently, heuristic algorithms and solvers commonly used for these problems often lack scalability and fall into local optima. Moreover, security concerns about wireless data transfer arising from multi-factory manufacturing processes are garnering attention. To address these issues, this paper introduces an algorithm, TRL, which is a Transformer-based Reinforcement Learning for vehicle production planning problems. Furthermore, we propose a Transformer-based Federated Reinforcement Learning algorithm, named TFRL, tailored for large-scale manufacturing and secure wireless communication. Experimental results showcase the high performance and security of TFRL. It schedules 1000 orders in about 14 seconds and avoids exchanging plaintext during the interaction. Compared to Non-dominated Sorting Genetic Algorithm II(NSGA-II), the TFRL enhances computational speed by 95.12% and reduces constraint violation scores by 93.18%.
Electronic commerce, or people call it e-commerce, it refers to buying and selling products or services on the Internet and transferring money and data to carry out these transactions. More consumers are shifting from...
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With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined commun...
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ISBN:
(数字)9798350369106
ISBN:
(纸本)9798350369113
With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined communication protocol. Other software programs can be linked to operate together using Message Queue Telemetry Transport Protocol (MQTT) or MQSeries components. Business integration software and middleware are common terms for this category of applications. With its suggested lightweight, easy-to-implement, and lowbandwidth system for billions of smart items, the MQTT protocol offers a wide range of potential applications, including home automation. In order to enhance the power savings in MQservice routing, we presented an intelligent assistant for optimizing routing model. For Internet of Things (IoT) purposes, it introduces an optimal routing model based on optimized hybrid machine learning-based anomaly detection (OHMLA). In order to extend the reach of a network, the offered approach allows Internet of Things devices to make efficient use of energy. To further enhance Intrusion Detection identification and get optimum network selection routes, the moth Salp Swarm technique (SSA) technique is used for the optimized hyperparameters. The model is capable of accurately detecting attacks that result in network congestion, causing delays in connection response and data transfer in IoT network.
Soliton generation in AlGaAs microresonators at room temperation is reported for the first time. The destabilizing thermo-optic effect is shown to instead provide stability for high soliton repetition rates. The optic...
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ISBN:
(纸本)9781957171258
Soliton generation in AlGaAs microresonators at room temperation is reported for the first time. The destabilizing thermo-optic effect is shown to instead provide stability for high soliton repetition rates. The optical pump power is sub-milliWatt.
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