Deep learning has achieved good results in the field of image recognition due to the key role of the optimizer in a deep learning network. In this work, the optimizers of dynamical system models are established,and th...
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Deep learning has achieved good results in the field of image recognition due to the key role of the optimizer in a deep learning network. In this work, the optimizers of dynamical system models are established,and the influence of parameter adjustments on the dynamic performance of the system is proposed. This is a useful supplement to the theoretical control models of optimizers. First, the system control model is derived based on the iterative formula of the optimizer, the optimizer model is expressed by differential equations, and the control equation of the optimizer is established. Second, based on the system control model of the optimizer, the phase trajectory process of the optimizer model and the influence of different hyperparameters on the system performance of the learning model are analyzed. Finally, controllers with different optimizers and different hyperparameters are used to classify the MNIST and CIFAR-10 datasets to verify the effects of different optimizers on the model learning performance and compare them with related methods. Experimental results show that selecting appropriate optimizers can accelerate the convergence speed of the model and improve the accuracy of model recognition. Furthermore, the convergence speed and performance of the stochastic gradient descent(SGD) optimizer are better than those of the stochastic gradient descent-momentum(SGD-M) and Nesterov accelerated gradient(NAG) optimizers.
Freezing of gait (FoG) refers to sudden, relatively brief episodes of gait arrest in Parkinson’s disease, known to manifest in the advanced stages of the condition. Events of freezing are associated with tumbles, tra...
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Effective manipulations of thermal expansion and conductivity are significant for improving operational performances of protective coatings,thermoelectric,and *** work uncovers determinant mechanisms of the thermal ex...
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Effective manipulations of thermal expansion and conductivity are significant for improving operational performances of protective coatings,thermoelectric,and *** work uncovers determinant mechanisms of the thermal expansion and conductivity of symbiotic ScTaO_(4)/SmTaO_(4) composites as thermal/environmental barrier coatings(T/EBCs),and we consider the effects of interface stress and thermal *** weak bonding and interface stress among composite grains manipulate coefficient of thermal expansion(CTE)stretching from 6.4×10^(−6) to 10.7×10^(−6) K^(−1) at 1300℃,which gets close to that of substrates in T/EBC *** multiscale effects,including phonon scattering at the interface,mitigation of the phonon speed(vp),and lattice point defects,synergistically depress phonon thermal transports,and we estimate the proportions of different *** interface thermal resistance(R)reduces the thermal conductivity(k)by depressing phonon speed and scattering phonons because of different acoustic properties and weak bonding between symbiotic ScTaO_(4) and SmTaO_(4) ceramics in the *** study proves that CTE of tantalates can be artificially regulated to match those of different substrates to expand their applications,and the uncovered multiscale effects can be used to manipulate thermal transports of various materials.
Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors t...
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With the development of deep learning and computer vision, face detection has achieved rapid progress owing. Face detection has several application domains, including identity authentication, security protection, medi...
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Although thermography has been proposed over the past decade as an effective method for breast cancer diagnosis, the complexity of thermograms presents a significant obstacle, making their interpretation challenging. ...
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This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic *** in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from ...
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This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic *** in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault *** biologically inspired strategies allow for effective solutions to intricate physical *** its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization *** utility and benefits have found traction in numerous academic *** its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference *** paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization *** trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.
Recognizing and analyzing medical images is crucial for disease early detection and treatment planning with appropriate treatment options based on the patient's individual needs and disease history. Deep learning ...
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Recognizing and analyzing medical images is crucial for disease early detection and treatment planning with appropriate treatment options based on the patient's individual needs and disease history. Deep learning technologies are widely used in the field of healthcare because they can analyze images rapidly and precisely. However, because each object on the image has the potential to hold illness information in medical images, it is critical to analyze the images with minimal information loss. In this context, Capsule Network (CapsNet) architecture is an important approach that aims to reduce information loss by storing the location and properties of objects in images as capsules. However, because CapsNet maintains information on each object in the image, the existence of several objects in complicated images can impair CapsNet's performance. This work proposes a new model called HMedCaps to improve the performance of CapsNet. In the proposed model, it is aimed to develop a deeper and hybrid structure by using Residual Block and FractalNet module together in the feature extraction layer. While it is aimed to obtain rich feature maps by increasing the number of features extracted by deepening the network, it is aimed to prevent the vanishing gradient problem that may occur in the network with increasing depth with these modules with skip connections. Furthermore, a new squash function is proposed to make distinctive capsules more prominent by customizing capsule activation. The CIFAR10 dataset of complex images, RFMiD dataset of retinal images, and Blood Cell Count Dataset dataset of blood cell images were used to evaluate the study. When the proposed model was compared with the basic CapsNet and studies in the literature, it was observed that the performance in complex images was improved and more accurate classification results were obtained in the field of medical image analysis. The proposed hybrid HMedCaps architecture has the potential to make more accurate dia
Femoral stem hip prostheses and plates for long bone fractures play a vital role in restoring the functionality of the patients. One of the key factors that can determine the positive result of restoring their mobilit...
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Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence ...
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