the enormous volumes of data generated by Next Generation Sequencing (NGS) technology have transformed genomics and made efficient data analysis techniques necessary. To create data analytics applications, the extract...
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the proceedings contain 300 papers. the topics discussed include: generative AI in focus : a comprehensive review of leading models across modalities;improving performance of supervised machine learningalgorithms on ...
ISBN:
(纸本)9798331529635
the proceedings contain 300 papers. the topics discussed include: generative AI in focus : a comprehensive review of leading models across modalities;improving performance of supervised machine learningalgorithms on small datasets;automated detection of tight junction damage in corneal endothelium using machine learning;smart waste management system using deep learning;gesture recognition technology in smart gloves enhanced by machine learning;design and implementation of a forest flame identification system;exploring advanced approaches: a comprehensive analysis of machine learning and deep neural networks in spectrum sensing applications;leveraging deep quantum convolutional neural networks for student facial expression identification and mode assessments;and real-time vehicle detection and road condition prediction for smart urban areas.
optimization in UAV based IoT networks is the most important task for the improvement of network performance, resource utilization, and overall efficiency. UAV -based IoT networks consist of decentralized architecture...
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In recent years, deep learning has markedly enhanced the efficiency of speech signal processing, thereby facilitating the deployment of various speech applications. However, when confronted withthe complexity of huma...
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ISBN:
(纸本)9798400717840
In recent years, deep learning has markedly enhanced the efficiency of speech signal processing, thereby facilitating the deployment of various speech applications. However, when confronted withthe complexity of human dialects and accents, models often struggle to achieve accurate recognition across multiple dialects. the heterogeneity of dialects and the issue of imbalanced training data present substantial challenges, significantly impacting the quality of human-computer interaction. In light of these challenges, we propose a multi-task approach with multi-grained information extraction for dialect speech recognition. Based on the end-to-end auto speech recognition framework, we extract encoded speech features for joint optimization across multiple modules, facilitating multi-task learning. Specifically, we introduce a dialect identification module enhanced by a sparse self-attention mechanism within the transformer architecture, which improves local modeling capabilities to accurately classify dialects by focusing on pivotal information. Subsequently, leveraging the classification outcomes, the Mixture of Dialect Experts guides the extraction of dialect-specific features, thereby enhancing the model's comprehension of diverse dialects. Additionally, we integrate the cluster acoustic unit with a masked prediction approach to extract deeper acoustic information from the encoded features, which enhances the model's capability to achieve more precise speech recognition by capturing fine-grained acoustic details. Extensive results on KeSpeech dataset demonstrate that our method achieves state-of-the-art performance for multi-dialect speech recognition, achieving a reduction in Character Error Rate from 8.08% to 7.11%.
In corporate finance, a number of conventional techniques are being replaced by machine and deep learningalgorithms as a result of artificial intelligence advancements. these tools of artificial intelligence will con...
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ISBN:
(纸本)9783031686740;9783031686757
In corporate finance, a number of conventional techniques are being replaced by machine and deep learningalgorithms as a result of artificial intelligence advancements. these tools of artificial intelligence will continue to revolutionize society and various aspects of the economy, including corporate finance. this paper is dedicated to a review of the applications of machine and deep learningalgorithms in corporate finance decisions throughout the funding process. these algorithms can be used to assess current or future funding needs, explore the various funding options available to the company, choose the most appropriate funding method, prepare the funding file, and manage the risks inherent in funding. these modern technological tools are indispensable for navigating an increasingly complex and competitive financial environment. their strategic use helps optimize financial decisions and maximize growth opportunities, from the beginning stages of planning to continuous risk management. By using them, decision-making is enhanced, resource allocation is optimized, and profitable growth is guaranteed.
Withthe rapid development of drone technology, the application domains for unmanned aerial vehicles (UAVs) have expanded significantly. In practical applications, effective trajectory planning enhances the operationa...
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this paper explores the optimization of AI-driven algorithms for sustainable supply chain management by integrating Internet of things (IoT) and Blockchain technologies. We provide a comprehensive analysis of various ...
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the proceedings contain 264 papers. the topics discussed include: automatic classification of modulation types in digital communication signals with probabilistic neural networks;wireless broadband ad hoc network comm...
ISBN:
(纸本)9798350352931
the proceedings contain 264 papers. the topics discussed include: automatic classification of modulation types in digital communication signals with probabilistic neural networks;wireless broadband ad hoc network communication algorithm based on IGS technology;augmented single-frequency Ka-band patch antenna with dynamic slot configurations for optimized bandwidth and radiation propensities;reflectarray antenna using Minkowski-Unitcell with beam steering capability for wi-fi applications;a systematic analysis of load balance in the cloud computing based on optimization and deep neural network algorithms;enhancing wireless communication efficiency: channel models and optimization techniques;and lightweighted security algorithm based computer network security storage system in cloud computing.
Olive trees play a crucial role in the global agricultural landscape, serving as a primary source of olive oil production. However, olive trees are susceptible to several diseases, which can significantly impact yield...
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ISBN:
(数字)9783031774263
ISBN:
(纸本)9783031774256;9783031774263
Olive trees play a crucial role in the global agricultural landscape, serving as a primary source of olive oil production. However, olive trees are susceptible to several diseases, which can significantly impact yield and quality. this study addresses the challenge of improving the diagnosis of diseases in olive trees, specifically focusing on aculus olearius and Olive Peacock Spot diseases. Using a novel hybrid approach that combines deep learning and machine learning methodologies, the authors aimed to optimize disease classification accuracy by analyzing images of olive leaves. the presented methodology integrates Local Binary Patterns (LBP) and an adapted ResNet50 model for feature extraction, followed by classification through optimized machine learning models, including Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and Random Forest (RF). the results demonstrated that the hybrid model achieved a groundbreaking accuracy of 99.11%, outperforming existing models. this advancement underscores the potential of integrated technological approaches in agricultural disease management and sets a new benchmark for the early and accurate detection of foliar diseases.
this article discusses the key technical challenges of accurate perception, real-time response and new threat identification in the field of network security situation awareness, and studies a network security situati...
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