作者:
Shobanadevi, A.Kottu, SreekanthKumar, K. R. SenthilAmudha, K.Praveena, K.Venkatesh, R.School of Computing
Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Chennai600026 India Mallareddy University
Department of Computer Science & Engineering Telangana Hyderabad500043 India R.M.K. Engineering College
Department of Mechanical Engineering Tamil Nadu Kavaraipettai601206 India
Department of Science And Humanities-Physics Tamil Nadu Kavaraipettai601206 India Mohan Babu University
Erstwhile SreeVidyanikethan Engineering College Department of Electronics And Communication Engineering Andhra Pradesh 517102 India
Department of Physics Tamil Nadu Dindigul624622 India
This exploration paper explores the operation of convolutional neural networks(CNNs) in automating the discovery of blights in electronic factors. With the rapid-fire advancement of technology, the demand for high- qu...
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With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. W...
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ISBN:
(数字)9798350372120
ISBN:
(纸本)9798350372137
With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score.
Fault localization and diagnosis of Integrated Circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing data Augmentation, ...
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Uncertainty quantification plays an important role in achieving trustworthy and reliable learning-based computational imaging. Recent advances in generative modeling and Bayesian neural networks have enabled the devel...
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This paper presents a comprehensive framework for assessing the efficacy of Distributed Ledger Technology (DLT) in IoT retail applications. The framework integrates five key algorithms: data Validation Algorithm (DVA)...
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This paper explores the promising interplay between spiking neural networks (SNNs) and event-based cameras for privacy-preserving human action recognition (HAR). The unique feature of event cameras in capturing only t...
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Standard machine-learning approaches involve the centralization of training data in a data center,where centralized machine-learning algorithms can be applied for data analysis and ***,due to privacy restrictions and ...
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Standard machine-learning approaches involve the centralization of training data in a data center,where centralized machine-learning algorithms can be applied for data analysis and ***,due to privacy restrictions and limited communication resources in wireless networks,it is often undesirable or impractical for the devices to transmit data to parameter *** approach to mitigate these problems is federated learning(FL),which enables the devices to train a common machine learning model without data sharing and *** paper provides a comprehensive overview of FL applications for envisioned sixth generation(6G)wireless *** particular,the essential requirements for applying FL to wireless communications are first *** potential FL applications in wireless communications are *** main problems and challenges associated with such applications are ***,a comprehensive FL implementation for wireless communications is described.
In this work, we extend the concept of the p-mean welfare objective from social choice theory (Moulin 2004) to study p-mean regret in stochastic multi-armed bandit problems. The p-mean regret, defined as the differenc...
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Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by social communication difficulties, re- stricted interests, and repetitive behaviors. Early and accurate diagnosis is critical for time...
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Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by social communication difficulties, re- stricted interests, and repetitive behaviors. Early and accurate diagnosis is critical for timely intervention and improved outcomes. Traditional diagnostic methods, which rely on clinical observations and assessments, can be subjective and time-consuming. This study introduces a novel approach to predicting ASD using a machine learning technique, specifically a Bayesian-optimized Ran- dom Forest classification algorithm. The proposed method uses a comprehensive dataset containing various behavioral, cognitive, and demographic features associated with ASD. The Optimized Random Forest with Bayesian approach is used to categorize indi- viduals into two groups: those who have ASD and those who do not. To develop this ensemble learning technique, multiple decision trees are used, and to achieve higher performance as well as better capability to generalize, the hyperparameters are optimized with Bayesian optimization. Preliminary analysis and comparison with existing databases substantiated the findings and revealed the effectiveness of the proposed approach. The Bayesian-optimized Random Forest classifier achieved an accuracy value of 0.9890. Such performance is highly promising and could enable increased use of machine learning methods in timely and accurate diagno- sis of ASD. The proposed approach provides recommendations for a verified diagnostic technique when implemented and creates a strong foundation for future investigations into the use of machine learning for identifying and supporting children with ASD.
Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require ca...
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Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).
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