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Performance evaluation of cluster-based federated machine learning
Federated Learning (FL) is a collaborative training method for machine learning (ML) that aggregates model weights from multiple participants during...
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Multi-level trust-based secure and optimal IoT-WSN routing for environmental monitoring applications
Wireless sensor networks (WSNs) are a critical component of the Internet of Things (IoT) which can be used in various fields, including environmental...
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Efficient image restoration with style-guided context cluster and interaction
Recently, convolutional neural networks (CNNs) and vision transformers (ViTs) have emerged as powerful tools for image restoration (IR). Nonetheless,...
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Ensembling validation indices to estimate the optimal number of clusters
In unsupervised learning tasks, one of the most significant and challenging aspects is how to estimate the optimal number of clusters (NC) for a...
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Dominator Coloring and CD Coloring in Almost Cluster Graphs
In this paper, we study two popular variants of Graph Coloring – Dominator Coloring and Class Domination Coloring. In both problems, we are given a...
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Octopus: SLO-Aware Progressive Inference Serving via Deep Reinforcement Learning in Multi-tenant Edge Cluster
Deep neural network (DNN) inference service at the edge is promising, but it is still non-trivial to achieve high-throughput for multi-DNN model...
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The Fault-Tolerant Cluster-Sending Problem
The emergence of blockchains is fueling the development of resilient data management systems that can deal with Byzantine failures due to crashes,...
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Accelerate distributed deep learning with cluster-aware sketch quantization
Gradient quantization has been widely used in distributed training of deep neural network (DNN) models to reduce communication cost. However,...
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K-LionER: meta-heuristic approach for energy efficient cluster based routing for WSN-assisted IoT networks
In Internet of Things (IoT), WSNs are crucial components because they sense, acquire data and communicate with the base station. Because IoT connects...
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Node position estimation based on optimal clustering and detection of coverage hole in wireless sensor networks using hybrid deep reinforcement learning
Sensor nodes, typically small and low-power devices, are components of wireless sensor networks (WSNs). Each node monitors its surroundings for...
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A Cluster-Constrained Graph Convolutional Network for Protein-Protein Association Networks
Cluster-GCN is one of the effective methods for studying the scalability of Graph Neural Networks. The idea of this approach is to use METIS...
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HCDQN-ORA: a novel hybrid clustering and deep Q-network technique for dynamic user location-based optimal resource allocation in a fog environment
With the proliferation of the Internet of Things and smart devices, there exists an urge to address the critical computation demands of end users for...
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Adaptive Cluster Assignment for Unsupervised Semantic Segmentation
Unsupervised semantic segmentation (USS) aims to identify semantically consistent regions and assign correct categories without annotations. Since...
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Enhancing heterogeneous cluster efficiency through node-centric scheduling
This article delves into the critical realm of modern computer cluster management. It focuses on the effect that the increasing heterogeneity of the...
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Job runtime prediction of HPC cluster based on PC-Transformer
Job scheduling of high performance cluster is a crucial task that affects the efficiency and performance of the system. The accuracy of job runtime...
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VANET Cluster Based Gray Hole Attack Detection and Prevention
VANET is an emerging technology for intelligent transportation systems in smart cities. Vehicle communication raises many challenges, notably in the...
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DEEC Protocol with ACO Based Cluster Head Selection in Wireless Sensor Network
When it comes to wireless sensor networks, the routing protocols have a major bearing on the network’s power consumption, lifespan, and other...
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A State-Size Inclusive Approach to Optimizing Stream Processing Applications
In stream processing applications, accurately measuring a system’s processing capacity is critical for ensuring optimal performance and meeting...
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Instance segmentation on distributed deep learning big data cluster
Distributed deep learning is a promising approach for training and deploying large and complex deep learning models. This paper presents a...
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AdaPQ: Adaptive Exploration Product Quantization with Adversary-Aware Block Size Selection Toward Compression Efficiency
Product Quantization (PQ) has received an increasing research attention due to the effectiveness on bit-width compression for memory efficiency. PQ...