This article introduces a non-invasive pressure therapy method using monaural beats (MB) sonic resonance to promote relaxation and reduce stress. Unlike binaural beats (BB), MB requires no headphones, enhancing access...
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The prediction of paint film thickness plays a crucial role in the spraying process. With the advent of the big data era, machine learning methods have found widespread applications in the field of spraying. However, ...
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Federated learning is a new distributed machine learning method. Decentralized clients can train data locally, and multi-party machine learning can be implemented efficiently by aggregating clients to update models th...
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With the exponential growth of big data and advancements in large-scale foundation model techniques, the field of machine learning has embarked on an unprecedented golden era. This period is characterized by significa...
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With the exponential growth of big data and advancements in large-scale foundation model techniques, the field of machine learning has embarked on an unprecedented golden era. This period is characterized by significant innovations across various aspects of machine learning, including data exploitation, network architecture development, loss function settings and algorithmic innovation.
Chatbots use artificial intelligence (AI) and natural language processing (NLP) algorithms to construct a clever system. By copying human connections in the most helpful way possi-ble, chatbots emulate individuals and...
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The primary concern of modern technology is cyber attacks targeting the Internet of *** it is one of the most widely used networks today and vulnerable to ***-time threats pose with modern cyber attacks that pose a gr...
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The primary concern of modern technology is cyber attacks targeting the Internet of *** it is one of the most widely used networks today and vulnerable to ***-time threats pose with modern cyber attacks that pose a great danger to the Internet of Things(IoT)networks,as devices can be monitored or service isolated from them and affect users in one way or *** Internet of Things networks is an important matter,as it requires the use of modern technologies and methods,and real and up-to-date data to design and train systems to keep pace with the modernity that attackers use to confront these *** of the most common types of attacks against IoT devices is Distributed Denial-of-Service(DDoS)*** paper makes a unique contribution that differs from existing studies,in that we use recent data that contains real traffic and real attacks on IoT *** a hybrid method for selecting relevant features,And also how to choose highly efficient *** gives the model a high ability to detect distributed denial-of-service *** model proposed is based on a two-stage process:selecting essential features and constructing a detection model using the K-neighbors algorithm with two classifier algorithms logistic regression and Stochastic Gradient Descent classifier(SGD),combining these classifiers through ensemble machine learning(stacking),and optimizing parameters through Grid Search-CV to enhance system *** were conducted to evaluate the effectiveness of the proposed model using the CIC-IoT2023 and CIC-DDoS2019 *** evaluation demonstrated the potential of our model in robust intrusion detection in IoT networks,achieving an accuracy of 99.965%and a detection time of 0.20 s for the CIC-IoT2023 dataset,and 99.968%accuracy with a detection time of 0.23 s for the CIC-DDoS 2019 ***,a comparative analysis with recent related works highlighted the superiority of our methodology in in
Data clustering refers to the process of grouping similar data points based on patterns or characteristics. It finds applications in image analysis, pattern recognition, and data mining. The k-means algorithm is commo...
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Forensic audio analysis is a foundation stone of many crime investigations. In forensic evidence;the audio file of the human voice is analyzed to extract much information in addition to the content of the speech, such...
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Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud Computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-...
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Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplor...
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Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.
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