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SLO-Aware DL Job Scheduling for Efficient FPGA-GPU Edge Cloud Computing
Deep learning applications have become increasingly popular in recent years, leading to the development of specialized hardware accelerators such as...
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DL-SkLSTM approach for cyber security threats detection in 5G enabled IIoT
The advancement of 5G technology has enabled the IIoT (Industrial Internet of Things) to integrate artificial intelligence, cloud computing, and edge...
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Efficient facial expression recognition framework based on edge computing
Facial expression recognition (FER) is a technology that recognizes human emotions based on biometric markers. Over the past decade, FER has been a...
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Applying DDDAS Principles for Realizing Optimized and Robust Deep Learning Models at the Edge
Edge computing is an attractive avenue to support low-latency applications including those that leverage deep learning (DL)-based model inferencing....
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Toward efficient resource utilization at edge nodes in federated learning
Federated learning (FL) enables edge nodes to collaboratively contribute to constructing a global model without sharing their data. This is...
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An Effective Prediction of Resource Using Machine Learning in Edge Environments for the Smart Healthcare Industry
Recent modern computing and trends in digital transformation provide a smart healthcare system for predicting diseases at an early stage. In...
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Inference serving with end-to-end latency SLOs over dynamic edge networks
While high accuracy is of paramount importance for deep learning (DL) inference, serving inference requests on time is equally critical but has not...
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A Novel Semi-Supervised Learning for Industrial Edge Computing Platforms in Quality Prediction
The manufacturing industry is embracing Deep Learning (DL) and Edge Computing (EC) solutions to escalate productivity and computing efficiency....
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Reasoning in DL- \(Lite_R\) Based Knowledge Base Under Category Semantics
We propose in this paper a rewriting of the usual set-theoretical semantics of the Description Logic DL-...
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A survey: contribution of ML & DL to the detection & prevention of botnet attacks
Machine Learning (ML) and Deep Learning (DL) are transforming the detection and prevention of botnets, significant threats in cybersecurity. In this...
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CADS-ML/DL: efficient cloud-based multi-attack detection system
With the increasing adoption of cloud computing, securing cloud-based systems and applications has become a critical concern for almost every...
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Edge resource slicing approaches for latency optimization in AI-edge orchestration
Edge service computing is an emerging paradigm for computing, storage, and communication services to optimize edge framework latency and cost based...
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A deep-learning framework running on edge devices for handgun and knife detection from indoor video-surveillance cameras
The early detection of handguns and knives from surveillance videos is crucial to enhance people’s safety. Despite the increasing development of Deep...
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AI-empowered mobile edge computing: inducing balanced federated learning strategy over edge for balanced data and optimized computation cost
In Mobile Edge Computing, the framework of federated learning can enable collaborative learning models across edge nodes, without necessitating the...
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Dense residual network for image edge detection
The major challenge of edge detection in denoised images is smoothing the edges, thus confusing algorithms to classify a true edge with false one....
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Benchmarking ML and DL Models for Mango Leaf Disease Detection: A Comparative Analysis
Mango leaf diseases can have detrimental effects on the productivity and health of mango trees, leading to significant economic losses. Early and...
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Edge artificial intelligence for big data: a systematic review
Edge computing, artificial intelligence (AI), and machine learning (ML) concepts have become increasingly prevalent in Internet of Things (IoT)...
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Energy data classification at the edge: a comparative study for energy efficiency applications
As the global economy is increasingly influenced by energy policy and efficiency, the opportunities of energy data classification are broadening....
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Intelligent computational offloading for mobile-edge server computing and hybrid optimal resource allocation
To get beyond the limitations of mobile devices, the mobile cloud is an emerging technology. Offloading resource-intensive applications to distant...
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Intelligent detection for sustainable agriculture: A review of IoT-based embedded systems, cloud platforms, DL, and ML for plant disease detection
Plant diseases pose a significant threat to the sustainability of the environment and global food security. With an increasing population density and...
