the usage of metaheuristic algorithms for the diagnosis and classification of chronic kidney disease (CKD) is investigated in this work. To improve feature selection and classification accuracy, this study proposes se...
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the traditional machine learning model can be formulated as an empirical risk minimization problem, which is typically optimized via stochastic gradient descent (SGD). Withthe emergence of big data, distributed optim...
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ISBN:
(纸本)9798400701030
the traditional machine learning model can be formulated as an empirical risk minimization problem, which is typically optimized via stochastic gradient descent (SGD). Withthe emergence of big data, distributed optimization, e.g., distributed SGD, has been attracting increasing attention to facilitate machine learning models for big data analytics. However, existing distributed optimization mainly focuses on the standard empirical risk minimization problem, failing to deal withthe emerging machine learning models that are beyond that category. thus, of particular interest of this tutorial includes the stochastic minimax optimization, stochastic bilevel optimization, and stochastic compositional optimization, which covers a wide range of emerging machine learning models, e.g., model-agnostic meta-learning models, adversarially robust machine learning models, imbalanced data classification models, etc. Since these models have been widely used in big data analytics, it is necessary to provide a comprehensive introduction about the new distributed optimizationalgorithms designed for these models. therefore, the goal of this tutorial is to present the state-of-the-art and recent advances in distributed minimax optimization, distributed bilevel optimization, and distributed compositional optimization. In particular, we will introduce the typical applications in each category and discuss the corresponding distributed optimizationalgorithms in both centralized and decentralized settings. through this tutorial, the researchers will be exposed to the fundamental algorithmic design and basic convergence theories, and the practitioners will be able to benefit from this tutorial to apply these algorithms to real-world data mining applications.
Exploring opinions from written or orally expressed data in sentiment classification has become an important decision-making tool that helps companies improve their profits using various machine-learning methods. In t...
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ISBN:
(纸本)9783031686528;9783031686535
Exploring opinions from written or orally expressed data in sentiment classification has become an important decision-making tool that helps companies improve their profits using various machine-learning methods. In this article, we introduce a new method, termed the combined approach, for performing sentiment analysis on consumer opinions it uses a hybrid Artificial Neuron Network ANN algorithm which combines PSO swarm particle optimization and PB back propagation. the results obtained by the Artificial Neural Network model developed by the PSO-PBB algorithm show very good prediction, better performance, and convergence.
In response to the task of anti-drone protection for power transmission lines, a game-theoretic decision-making intelligent learning method is proposed targeting the protection device. Firstly, a target-attack-defende...
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Lip Reading has evolved and from where it began to help deaf people has slowly turned into a service where in the Digital Entertainment industry has started utilizing it. Withthe recent rise of AI, automated technolo...
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this study focuses on enhancing the sustainability and efficiency of hospital data centers through the deployment of machine learningalgorithms. Support Vector Machines (SVM), Decision Trees (DT), Artificial Neural N...
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the proceedings contain 165 papers. the topics discussed include: dynamic feature selection and classification for threat mitigation in wireless sensor networks;optimized scheduling of electric vehicles charging in sm...
ISBN:
(纸本)9798350385793
the proceedings contain 165 papers. the topics discussed include: dynamic feature selection and classification for threat mitigation in wireless sensor networks;optimized scheduling of electric vehicles charging in smart grid using deep learning;cloud-based data protection: a framework for authorizing data movement;efficient video streaming on raspberry PI 4B: reducing CPU utilization through optimization techniques;electronic medical images security and privacy techniques;stress detection during social interactions with natural language processing and machine learning;adaptive network traffic reduction for optimal performance;dynamic clustering algorithm of remote VHR images for object cost estimation;and comprehensive analysis based on signal processing for cardiac arrhythmia detection.
this study analyses and compares the performance of six heuristic algorithms: Genetic Algorithm (GA), Simulated Annealing (SA), Hybrid (SA+GA), Tabu Search (TS), Ant Colony optimization (ACO), and Particle Swarm Optim...
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this research focuses on developing an Intrusion Detection System (IDS) for Internet of Medical things (IoMT) applications, utilizing the CICIoMT-2024 dataset. It approaches the problem as a classification challenge, ...
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the proceedings contain 108 papers. the topics discussed include: fuzzy PID control based on genetic algorithm optimization inverted pendulum system;multi-task recognition of modulation types and arrival directions of...
the proceedings contain 108 papers. the topics discussed include: fuzzy PID control based on genetic algorithm optimization inverted pendulum system;multi-task recognition of modulation types and arrival directions of underwater acoustic signals based on convolutional neural networks;localization and detection of underwater acoustic communication signals using convolutional recursive neural networks;online trajectory anomaly detection model based on graph neural networks and variational autoencoder;an improved dynamical variational autoencoder framework for predicting aero-engine remaining useful life;construction of digital twin workshop integrated with edge computing and deep learning;research on electromagnetic pulse signal detection method based on intelligent compressed sensing technology;and path planning and collision avoidance approach for a multi-agent system in grid environments.
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