In recent years, road traffic safety has been a major concern. Research on road traffic accidents is crucial to address people's worries about their daily commutes. This study focuses on California, using U.S. tra...
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We study decentralized federated learning (DFL) in edge computing networks where edge nodes (ENs) collaboratively train their artificial intelligence (AI) models in a serverless manner without sharing local data. We c...
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Neural network pruning is a popular approach to reducing the computational complexity of deep neural *** recent years,as growing evidence shows that conventional network pruning methods employ inappropriate proxy metr...
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Neural network pruning is a popular approach to reducing the computational complexity of deep neural *** recent years,as growing evidence shows that conventional network pruning methods employ inappropriate proxy metrics,and as new types of hardware become increasingly available,hardware-aware network pruning that incorporates hardware characteristics in the loop of network pruning has gained growing attention,Both network accuracy and hardware efficiency(latency,memory consumption,etc.)are critical objectives to the success of network pruning,but the conflict between the multiple objectives makes it impossible to find a single optimal *** studies mostly convert the hardware-aware network pruning to optimization problems with a single *** this paper,we propose to solve the hardware-aware network pruning problem with Multi-Objective Evolutionary Algorithms(MOEAs).Specifically,we formulate the problem as a multi-objective optimization problem,and propose a novel memetic MOEA,namely HAMP,that combines an efficient portfoliobased selection and a surrogate-assisted local search,to solve *** studies demonstrate the potential of MOEAs in providing simultaneously a set of alternative solutions and the superiority of HAMP compared to the state-of-the-art hardware-aware network pruning method.
We propose an approach for the early detection of COVID-19 and other related lung diseases using artificial intelligence (AI) and deep learning-based methods. The proposed approach involves utilizing transfer learning...
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With the exponential growth of big data and advancements in large-scale foundation model techniques, the field of machine learning has embarked on an unprecedented golden era. This period is characterized by significa...
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With the exponential growth of big data and advancements in large-scale foundation model techniques, the field of machine learning has embarked on an unprecedented golden era. This period is characterized by significant innovations across various aspects of machine learning, including data exploitation, network architecture development, loss function settings and algorithmic innovation.
Wheat is the most widely grown crop in the world,and its yield is closely related to global food *** number of ears is important for wheat breeding and yield ***,automated wheat ear counting techniques are essential f...
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Wheat is the most widely grown crop in the world,and its yield is closely related to global food *** number of ears is important for wheat breeding and yield ***,automated wheat ear counting techniques are essential for breeding high-yield varieties and increasing grain ***,all existing methods require position-level annotation for training,implying that a large amount of labor is required for annotation,limiting the application and development of deep learning technology in the agricultural *** address this problem,we propose a count-supervised multiscale perceptive wheat counting network(CSNet,count-supervised network),which aims to achieve accurate counting of wheat ears using quantity *** particular,in the absence of location information,CSNet adopts MLP-Mixer to construct a multiscale perception module with a global receptive field that implements the learning of small target attention maps between wheat ear *** conduct comparative experiments on a publicly available global wheat head detection dataset,showing that the proposed count-supervised strategy outperforms existing position-supervised methods in terms of mean absolute error(MAE)and root mean square error(RMSE).This superior performance indicates that the proposed approach has a positive impact on improving ear counts and reducing labeling costs,demonstrating its great potential for agricultural counting *** code is available at .
Different from classical one-model-fits-all strategy, individualized models allow parameters to vary across samples and are gaining popularity in various fields, particularly in personalized medicine. Motivated by med...
The rapid growth of 5G/6G networks requires resilient solutions to optimize network performance while ensuring adaptability against failures. This paper introduces a novel Adaptive Hybrid Genetic-Ant Colony Optimizati...
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Multi-access edge computing has become an effective paradigm to provide offloading services for computation-intensive and delay-sensitive tasks on vehicles. However, high mobility of vehicles usually incurs spatio-tem...
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The rising cost of food presents a significant challenge for households in developing countries, prompting experts to explore effective policies to mitigate price volatility. This concern has recently gained attention...
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ISBN:
(纸本)9798350384864
The rising cost of food presents a significant challenge for households in developing countries, prompting experts to explore effective policies to mitigate price volatility. This concern has recently gained attention in Sri Lanka, sparking discussions on the current and future trajectory of food commodity prices. The increase in agricultural commodity prices is partially attributed to the rising costs of petroleum. This study aims to understand the relationship between coconut prices and fluctuations in the price of Lanka Petrol (LP92), as well as the connection between coconut prices and changes in the price of Lanka Auto Diesel (LAD). It also investigates variations in Samba prices concerning LP92 price changes, given their significance as highly consumed food products and widely used petroleum commodities. Utilizing data from January 2008 to November 2022 sourced from the official website of the Central Bank of Sri Lanka, the study employs Vector Auto Regressive (VAR) models. Model selection is determined by criteria such as Akaike Information Criterion (AIC), Schwarz-Bayesian Information Criterion (SC), and Hannan-Quinn (HQ). The study finds that VAR (2) is optimal for identifying Samba price variations with LP92, VAR (3) for Coconut price variations with LAD, and VAR (3) for Coconut price variations with LP92. The dataset is split into an 80% training set and a 20% test set for model evaluation, utilizing metrics like Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). Further investigations into the impact of fuel costs on Samba and Coconut prices, using Granger causality and cointegration tests with LAD and LP92 price data, yield no conclusive evidence of causation. The study proceeds to remodel the original datasets using machine learning techniques, with the models demonstrating superior performance compared to traditional VAR models. Based on all the models, the models created through machine learning tech
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