Big data clustering on Spark is a practical method that makes use of Apache Spark's distributed computing capabilities to handle clustering tasks on massive datasets such as big data sets. Using the unsupervised l...
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For the efficiency and precision of autonomous exploration, this paper proposes an optimized algorithm for mobile robots in unknown confined environment. However, the traditional algorithms frequently lead to excessiv...
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The development of verbal exchange systems has up to date be a vital research up-to-date with the upward push of latest styles of computing networks. Independent conversation systems offer the ability up to date updat...
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This paper proposes a vector similarity search acceleration by leveraging DRAM-based Processing In Memory (PIM), which is a key component in Retrieval-Augmented Generation (RAG) used to address limitations in large la...
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This paper points out a vacuum in the literature on text summarization: not enough attention has been paid to methods for Indian languages. This research investigates the current state-of-the-art techniques for summar...
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The proceedings contain 289 papers. The topics discussed include: employing multifaceted bioinformatics strategies for the discovery of novel ASK1 inhibitors targeting neurodegenerative disorders;sailfish optimizer al...
ISBN:
(纸本)9798350359688
The proceedings contain 289 papers. The topics discussed include: employing multifaceted bioinformatics strategies for the discovery of novel ASK1 inhibitors targeting neurodegenerative disorders;sailfish optimizer algorithm for effective toxic gas detection sensor placement in IioT;a comprehensive study on satellite-based data communication for big earth observation systems;APIs insight for phenotype classification and hive health forecasting using IoT and deep learning;the future of teaching: exploring the integration of machine learning in higher education;person recognition using ear images based on fractional gannet sparrow optimization enabled deep learning;restaurant recommendation system using machine learning algorithms;and AI-driven remote Parkinson's diagnosis with BPNN framework and cloud-based data security.
Dynamic partial reconfiguration (DPR) enables the design and implementation of flexible, scalable and robust adaptive systems. We present an FPGA-based DPR flow for partially reconfigurable heterogeneous SoCs that use...
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ISBN:
(数字)9781665472968
ISBN:
(纸本)9781665472968
Dynamic partial reconfiguration (DPR) enables the design and implementation of flexible, scalable and robust adaptive systems. We present an FPGA-based DPR flow for partially reconfigurable heterogeneous SoCs that uses an incremental compilation technique to reduce the total FPGA compilation time.
An advantage of this methodology to sixth generation (6G) mobile systems is reconfigurable intelligence surface (RIS)-enabled communication. Enhancing the capacities, range, power generation, physical layer safety, &a...
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Predictive process monitoring plays a critical role in process mining by predicting the dynamics of ongoing processes. Recent trends employ deep learning techniques that use event sequences to make highly accurate pre...
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ISBN:
(纸本)9783031610561;9783031610578
Predictive process monitoring plays a critical role in process mining by predicting the dynamics of ongoing processes. Recent trends employ deep learning techniques that use event sequences to make highly accurate predictions. However, this focus often overshadows the significant advantages of lightweight, transparent algorithms. This study explores the potential of traditional regression algorithms, namely kNN, SVM, and RF, enhanced by event time feature engineering. We integrate existing and novel time-related features to augment these algorithms and compare their performance against the well-known LSTM network. Our results show that these enhanced lightweight models not only compete with LSTM in terms of predictive accuracy but also excel in scenarios requiring online, real-time decision-making and explanation. Furthermore, despite incorporating additional feature extraction processes, these algorithms maintain superior computational efficiency compared to their deep learning counterparts, making them more viable for time-critical and resource-constrained environments.
This research explores the mathematical model of ergodic secrecy capacity in the context of security analysis of wireless communication networks with reconfigurable intelligent surfaces (RISs). Particularly, the study...
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