Deepfake detection aims to mitigate the threat of manipulated content by identifying and exposing forgeries. However, previous methods primarily tend to perform poorly when confronted with cross-dataset scenarios. To ...
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Enforcing admired machine learning approaches to huge data enhanced novel issues for researchers. Conventional libraries could not suitably fulfil the requirement of complex model with wide variety of data and system ...
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Video interpretation systems are widely used for assisting people with visual impairments. The main goal of a video interpreter system is to help people with visual impairments. By leveraging technologies such as Text...
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A graph invariant is a number that can be easily and uniquely calculated through a ***,part of mathematical graph invariants has been portrayed and utilized for relationship ***,no reliable appraisal has been embraced...
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A graph invariant is a number that can be easily and uniquely calculated through a ***,part of mathematical graph invariants has been portrayed and utilized for relationship ***,no reliable appraisal has been embraced to pick,how much these invariants are associated with a network graph in interconnection networks of various fields of computerscience,physics,and *** this paper,the study talks about sudoku networks will be networks of fractal nature having some applications in computerscience like sudoku puzzle game,intelligent systems,Local area network(LAN)development and parallel processors interconnections,music composition creation,physics like power generation interconnections,Photovoltaic(PV)cells and chemistry,synthesis of chemical *** networks are generally utilized in disorder,fractals,recursive groupings,and complex *** outcomes are the normal speculations of currently accessible outcomes for specific classes of such kinds of networks of two unmistakable sorts with two invariants K-banhatti sombor(KBSO)invariants,Irregularity sombor(ISO)index,Contraharmonic-quadratic invariants(CQIs)and dharwad invariants with their reduced *** study solved the Sudoku network used in mentioned systems to improve the performance and find irregularities present in *** calculated outcomes can be utilized for the modeling,scalability,introduction of new architectures of sudoku puzzle games,intelligent systems,PV cells,interconnection networks,chemical compounds,and extremely huge scope in very large-scale integrated circuits(VLSI)of processors.
Inaccurate information that is purposefully spread for a certain goal known as fake news related to COVID-19 tweets posing threats to public health, harm to the society. But creating a reliable method to identify news...
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Driver’s mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in losses of humans, vehicles, and infrastructure. Likewise, persistent mental stress could develop me...
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Driver’s mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in losses of humans, vehicles, and infrastructure. Likewise, persistent mental stress could develop mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning (ML) approaches. These approaches recognize different stress levels based on handcrafted features extracted from various modalities including physiological, physical, and contextual data. Acquiring the good quality features from these modalities using feature engineering is often a difficult job. The recent developments in the form of deep learning (DL) algorithms have relieved feature engineering by automatically extracting and learning resilient features. Conventional DL models, however, frequently over-fit due to large number of parameters. Thus, large networks face gradient vanishing issues causing an increase in learning failure and generalization errors. Furthermore, it is often hard to acquire a large dataset for training a deep learning model from scratch. To overcome these problems for driver’s stress recognition domain, this paper proposes fast and computationally efficient deep transfer learning models based on Xception pre-trained neural networks. These models classify the driver’s Low, Medium, and High stress levels through electrocardiogram (ECG), heart rate (HR), galvanic skin response (GSR), electromyogram (EMG), and respiration (RESP) signals. Continuous Wavelet Transform (CWT) acquires the scalograms for ECG, HR, GSR, EMG, and RESP signals separately. Then unimodal Xception models are trained based on these scalograms to classify the three stress levels. The proposed Xception models have achieved 97.2%, 86.4%, 82.7%, 71.9%, and 68.9% average validation accuracies based on ECG, RESP, HR, GSR, and EMG signals, respectively. The fuzzy EDAS (evaluation based on distance from average solutio
Open Radio Access Networks (O-RANs) are transforming the landscape of telecommunications to better performance and higher cost-efficiency by enabling network operators to integrate diverse vendor components. Neverthel...
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Personalized federated learning (PFL) has emerged as a promising technique for addressing the challenge of data heterogeneity. While recent studies have made notable progress in mitigating heterogeneity associated wit...
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Reinforcement learning often needs to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces (often known as the curse of dimensionality). In this work, we add...
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In this paper, we study collision-free graph exploration in an anonymous network. The network is modeled as a graph G = (V, E) where the nodes of the graph are unlabeled, and each edge incident to a node v has a uniqu...
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ISBN:
(纸本)9798400710629
In this paper, we study collision-free graph exploration in an anonymous network. The network is modeled as a graph G = (V, E) where the nodes of the graph are unlabeled, and each edge incident to a node v has a unique label, called the port number, in {0, 1, ⋯, d - 1}, where d is the degree of the node v. Two identical mobile agents, starting from different nodes in G have to explore the nodes of G in such a way that for every node v in G, at least one mobile agent visits v and no two agents are in the same node in any round and stop. The time of exploration is the minimum round number by which both agents have terminated. The agents know the size of the graph but do not know its topology. If an agent arrives in the one-hop neighborhood of the other agent, both agents can detect the presence of the other agent but have no idea at which neighboring node the other agent resides. The agents may wake up in different rounds, but once awake, they execute a deterministic algorithm in synchronous rounds. An agent, after waking up, has no knowledge about the wake-up time of the other agent. The task of collision-free exploration is impossible to solve even for a line of length 2 where the agents are placed at the end nodes of the line and even if both agents wake up at the same time. We study the problem of collision-free exploration where some pebbles are placed by an Oracle at the nodes of the graph to assist the agents in achieving collision-free exploration. The Oracle knows the graph, the starting positions of the agents, and their wake-up schedule, and it places some pebbles that may be of different colors, at most one at each node. The number of different colors of the pebbles placed by the Oracle is called the color index of the corresponding pebble placement algorithm. The central question we study in this paper is: "What is the minimum number z such that there exists a collision-free exploration of a given graph with pebble placement of color index z?". For genera
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