This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)*** distinct machine learning approache...
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This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)*** distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter *** improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and *** study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among *** Trees and Random Forests exhibited stable performance throughout the *** enhancing accuracy,hyperparameter optimization also led to increased execution *** representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular *** research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.
Vision sensors are versatile and can capture a wide range of visual cues, such as color, texture, shape, and depth. This versatility, along with the relatively inexpensive availability of machine vision cameras, playe...
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Vision sensors are versatile and can capture a wide range of visual cues, such as color, texture, shape, and depth. This versatility, along with the relatively inexpensive availability of machine vision cameras, played an important role in adopting vision-based environment perception systems in autonomous vehicles (AVs). However, vision-based perception systems can be easily affected by glare in the presence of a bright source of light, such as the sun or the headlights of the oncoming vehicle at night or simply by light reflecting off snow or ice-covered surfaces;scenarios encountered frequently during driving. In this paper, we investigate various glare reduction techniques, including the proposed saturated pixel-aware glare reduction technique for improved performance of the computer vision (CV) tasks employed by the perception layer of AVs. We evaluate these glare reduction methods based on various performance metrics of the CV algorithms used by the perception layer. Specifically, we considered object detection, object recognition, object tracking, depth estimation, and lane detection which are crucial for autonomous driving. The experimental findings validate the efficacy of the proposed glare reduction approach, showcasing enhanced performance across diverse perception tasks and remarkable resilience against varying levels of glare. IEEE
This paper presents a research study on the use of Convolutional Neural Network (CNN), ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile models to efficiently detect brain tumors in order to reduce the time requi...
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Large-quantity and high-quality data is critical to the success of machine learning in diverse *** with the dilemma of data silos where data is difficult to circulate,emerging data markets attempt to break the dilemma...
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Large-quantity and high-quality data is critical to the success of machine learning in diverse *** with the dilemma of data silos where data is difficult to circulate,emerging data markets attempt to break the dilemma by facilitating data exchange on the ***,on the other hand,is one of the important methods to efficiently collect large amounts of data with high-value in data *** this paper,we investigate the joint problem of efficient data acquisition and fair budget distribution across the crowdsourcing and data *** propose a new metric of data value as the uncertainty reduction of a Bayesian machine learning model by integrating the data into model *** by this data value metric,we design a mechanism called Shapley Value Mechanism with Individual Rationality(SV-IR),in which we design a greedy algorithm with a constant approximation ratio to greedily select the most cost-efficient data brokers,and a fair compensation determination rule based on the Shapley value,respecting the individual rationality *** further propose a fair reward distribution method for the data holders with various effort levels under the charge of a data *** demonstrate the fairness of the compensation determination rule and reward distribution rule by evaluating our mechanisms on two real-world *** evaluation results also show that the selection algorithm in SV-IR could approach the optimal solution,and outperforms state-of-the-art methods.
Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless *** this paper,a robust transmission scheme for ...
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Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless *** this paper,a robust transmission scheme for an AirCompbased FL system with imperfect channel state information(CSI)is *** model CSI uncertainty,an expectation-based error model is *** main objective is to maximize the number of selected devices that meet mean-squared error(MSE)requirements for model broadcast and model *** problem is formulated as a combinatorial optimization problem and is solved in two ***,the priority order of devices is determined by a sparsity-inducing ***,a feasibility detection scheme is used to select the maximum number of devices to guarantee that the MSE requirements are *** alternating optimization(AO)scheme is used to transform the resulting nonconvex problem into two convex *** results illustrate the effectiveness and robustness of the proposed scheme.
We evaluate different Neural Radiance Field(NeRF)techniques for the 3D reconstruction of plants in varied environments,from indoor settings to outdoor *** methods usually fail to capture the complex geometric details ...
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We evaluate different Neural Radiance Field(NeRF)techniques for the 3D reconstruction of plants in varied environments,from indoor settings to outdoor *** methods usually fail to capture the complex geometric details of plants,which is crucial for phenotyping and breeding *** evaluate the reconstruction fidelity of NeRFs in 3 scenarios with increasing complexity and compare the results with the point cloud obtained using light detection and ranging as ground *** the most realistic field scenario,the NeRF models achieve a 74.6％F1 score after 30 min of training on the graphics processing unit,highlighting the efficacy of NeRFs for 3D reconstruction in challenging ***,we propose an early stopping technique for NeRF training that almost halves the training time while achieving only a reduction of 7.4％in the average F1 *** optimization process substantially enhances the speed and efficiency of 3D reconstruction using *** findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process.
Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can...
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Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can potentially address these problems by allowing systems trained on labelled datasets from the source domain(including less expensive synthetic domain)to be adapted to a novel target *** conventional approach involves automatic extraction and alignment of the representations of source and target domains *** limitation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be more easily extracted and aligned between the source and target domains than others,limiting the adaptation over all ***,we address:this problem by introducing a Class-Conditional Domain Adaptation(CCDA)*** incorporates a class-conditional multi-scale discriminator and class-conditional losses for both segmentation and ***,they measure the segmentation,shift the domain in a classconditional manner,and equalize the loss over *** results demonstrate that the performance of our CCDA method matches,and in some cases,surpasses that of state-of-the-art methods.
Automatic Speech Recognition (ASR) has been the regnant research area in the domain of Natural Language Processing for the last few decades. Past years’ advancement provides progress in this area of research. The acc...
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Deep learning methods, which form the backbone of neural network architectures, have not only demonstrated exceptional capabilities in classifying data but also in reducing false predictions when handling vast dataset...
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In real-world materials research,machine learning(ML)models are usually expected to predict and discover novel exceptional materials that deviate from the known *** is thus a pressing question to provide an objective ...
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In real-world materials research,machine learning(ML)models are usually expected to predict and discover novel exceptional materials that deviate from the known *** is thus a pressing question to provide an objective evaluation ofMLmodel performances in property prediction of out-ofdistribution(OOD)materials that are different fromthe training *** performance evaluation of materials property prediction models through the random splitting of the dataset frequently results in artificially high-performance assessments due to the inherent redundancy of typical material datasets.
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