Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these paramete...
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Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.
Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toadd...
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Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.
Introduction: Several types of cancer can be detected early through thermography, which uses thermal profiles to image tissues in recent years, thermography has gained increasing attention due to its non-invasive and ...
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Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world *** study presents a new optimization method based on an unusual geolo...
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Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world *** study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization *** efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark ***,GEA has been applied to several real-parameter engineering optimization problems to evaluate its *** addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization *** results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and *** that the source code of the GEA is publicly available at https://***/projects/gea.
In Decentralized Machine Learning(DML)systems,system participants contribute their resources to assist others in developing machine learning *** malicious contributions in DML systems is challenging,which has led to t...
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In Decentralized Machine Learning(DML)systems,system participants contribute their resources to assist others in developing machine learning *** malicious contributions in DML systems is challenging,which has led to the exploration of blockchain *** leverages its transparency and immutability to record the provenance and reliability of training ***,storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational ***,current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading ***,less attention has been paid to the scenario where malicious requesters intentionally manipulate test data during evaluation to gain an unfair *** paper proposes a transparent and accountable training data sharing method that securely shares data among potentially malicious system ***,we introduce a blockchain-based DML system architecture that supports secure training data sharing through the IPFS ***,we design a blockchain smart contract to transparently split training datasets into training and test datasets,respectively,without involving system *** the system,transparent and accountable training data sharing can be achieved with attribute-based proxy *** demonstrate the security analysis for the system,and conduct experiments on the Ethereum and IPFS platforms to show the feasibility and practicality of the system.
Accurate and reliable wind power forecasting is of great importance for stable grid operation and advanced dispatch planning. Due to the complex, non-stationary, and highly volatile nature of wind power data, Transfor...
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Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training of models without centralized access to the raw data on local devices. In the typical FL paradigm (e.g., FedAvg), mod...
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In recent years,with the rapid development of deepfake technology,a large number of deepfake videos have emerged on the Internet,which poses a huge threat to national politics,social stability,and personal *** many ex...
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In recent years,with the rapid development of deepfake technology,a large number of deepfake videos have emerged on the Internet,which poses a huge threat to national politics,social stability,and personal *** many existing deepfake detection methods exhibit excellent performance for known manipulations,their detection capabilities are not strong when faced with unknown ***,in order to obtain better generalization ability,this paper analyzes global and local inter-frame dynamic inconsistencies from the perspective of spatial and frequency domains,and proposes a Local region Frequency Guided Dynamic Inconsistency Network(LFGDIN).The network includes two parts:Global SpatioTemporal Network(GSTN)and Local Region Frequency Guided Module(LRFGM).The GSTN is responsible for capturing the dynamic information of the entire face,while the LRFGM focuses on extracting the frequency dynamic information of the eyes and *** LRFGM guides the GTSN to concentrate on dynamic inconsistency in some significant local regions through local region alignment,so as to improve the model's detection *** on the three public datasets(FF++,DFDC,and Celeb-DF)show that compared with many recent advanced methods,the proposed method achieves better detection results when detecting deepfake videos of unknown manipulation types.
Quantitative phase imaging(QPI)recovers the exact wavefront of light from intensity *** and optical density maps of translucent microscopic bodies can be extracted from these quantified phase *** demonstrate quantitat...
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Quantitative phase imaging(QPI)recovers the exact wavefront of light from intensity *** and optical density maps of translucent microscopic bodies can be extracted from these quantified phase *** demonstrate quantitative phase imaging at the tip of a coherent fiber bundle using chromatic aberrations inherent in a silicon nitride hyperboloid *** method leverages spectral multiplexing to recover phase from multiple defocus planes in a single capture using a color *** 0.5mm aperture metalens shows robust quantitative phase imaging capability with a 28°field of view and 0.2πphase resolution(~0.1λin air)for experiments with an endoscopic fiber *** the spectral functionality is encoded directly in the imaging lens,the metalens acts both as a focusing element and a spectral *** use of a simple computational backend will enable real-time *** limitations in the adoption of phase imaging methods for endoscopy such as multiple acquisition,interferometric alignment or mechanical scanning are completely mitigated in the reported metalens based QPI.
AI(Artificial Intelligence)workloads are proliferating in modernreal-time *** the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be *** particular...
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AI(Artificial Intelligence)workloads are proliferating in modernreal-time *** the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be *** particular,it is difficult to immediately handle changes inreal-time tasks without violating the deadline *** cope with thissituation,this paper analyzes the task situations of AI workloads and findsthe following two ***,resource planning for AI workloadsis a complicated search problem that requires much time for ***,although the task set of an AI workload may change over time,thepossible combinations of the task sets are known in *** on theseobservations,this paper proposes a new resource planning scheme for AIworkloads that supports the re-planning of *** of generatingresource plans on the fly,the proposed scheme pre-determines resourceplans for various combinations of ***,in any case,the workload isimmediately executed according to the resource plan ***,the proposed scheme maintains an optimized CPU(Central Processing Unit)and memory resource plan using genetic algorithms and applies it as soonas the workload *** proposed scheme is implemented in the opensourcesimulator SimRTS for the validation of its *** show that the proposed scheme reduces the energy consumptionof CPU and memory by 45.5%on average without deadline misses.
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