Aiming at the problem of low precision and large cleaning error in multi-link similar data mining, a multi-link similar data mining cleaning method based on Bayesian algorithm is proposed in this paper. On the basis o...
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Machine learning methods based on fully convolutional networks have emerged as a viable choice for retinal vessel segmentation. However, when input samples significantly deviate from the training data distribution, th...
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The metal-organic framework(MOF)derived Ni–Co–C–N composite alloys(NiCCZ)were“embedded”inside the carbon cloth(CC)strands as opposed to the popular idea of growing them upward to realize ultrastable energy storag...
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The metal-organic framework(MOF)derived Ni–Co–C–N composite alloys(NiCCZ)were“embedded”inside the carbon cloth(CC)strands as opposed to the popular idea of growing them upward to realize ultrastable energy storage and conversion *** NiCCZ was then oxygen functionalized,facilitating the next step of stoichiometric sulfur anion diffusion during hydrothermal sulfurization,generating a flower-like metal hydroxysulfide structure(NiCCZOS)with strong partial implantation inside *** obtained NiCCZOS shows an excellent capacity when tested as a supercapacitor electrode in a three-electrode ***,when paired with the biomass-derived nitrogen-rich activated carbon,the asymmetric supercapacitor device shows almost 100%capacity retention even after 45,000 charge–discharge cycles with remarkable energy density(59.4 Wh kg^(-1)/263.8μWh cm^(–2))owing to a uniquely designed ***,the same electrode performed as an excellent bifunctional water-splitting electrocatalyst with an overpotential of 271 mV for oxygen evolution reaction(OER)and 168.4 mV for hydrogen evolution reaction(HER)at 10 mA cm−2 current density along with 30 h of unhinged chronopotentiometric stability performance for both HER and ***,a unique metal chalcogenide composite electrode/substrate configuration has been proposed as a highly stable electrode material for flexible energy storage and conversion applications.
Due to the strong demand of massive storage capacity, the density of flash memory has been improved in terms of technology node scaling, multi-bit per cell technique, and 3D stacking. However, these techniques also de...
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Plastic-based food packaging cannot prevent food deterioration due to microbial infestation and has a short shelf life, resulting in food being unfit for consumption after a period of time. Nanotechnology's use in...
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This study proposes a method to parallelly connect 2 Class-E resonant inverters for domestic induction cookers. The setup consists of induction heating systems, which include a full-wave rectifier, an electromagnetic ...
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Deep Learning (DL) models have demonstrated remarkable proficiency in image classification and recognition tasks, surpassing human capabilities. The observed enhancement in performance can be attributed to the utiliza...
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Deep Learning (DL) models have demonstrated remarkable proficiency in image classification and recognition tasks, surpassing human capabilities. The observed enhancement in performance can be attributed to the utilization of extensive datasets. Nevertheless, DL models have huge data requirements. Widening the learning capability of such models from limited samples even today remains a challenge, given the intrinsic constraints of small datasets. The trifecta of challenges, encompassing limited labeled datasets, privacy, poor generalization performance, and the costliness of annotations, further compounds the difficulty in achieving robust model performance. Overcoming the challenge of expanding the learning capabilities of Deep Learning models with limited sample sizes remains a pressing concern even today. To address this critical issue, our study conducts a meticulous examination of established methodologies, such as Data Augmentation and Transfer Learning, which offer promising solutions to data scarcity dilemmas. Data Augmentation, a powerful technique, amplifies the size of small datasets through a diverse array of strategies. These encompass geometric transformations, kernel filter manipulations, neural style transfer amalgamation, random erasing, Generative Adversarial Networks, augmentations in feature space, and adversarial and meta-learning training paradigms. Furthermore, Transfer Learning emerges as a crucial tool, leveraging pre-trained models to facilitate knowledge transfer between models or enabling the retraining of models on analogous datasets. Through our comprehensive investigation, we provide profound insights into how the synergistic application of these two techniques can significantly enhance the performance of classification tasks, effectively magnifying scarce datasets. This augmentation in data availability not only addresses the immediate challenges posed by limited datasets but also unlocks the full potential of working with Big Data in
We propose a family of second-order resonance-based sinusoidal oscillators with electronically tunable frequencies. Each oscillator is comprised of two amplifiers, surrounded by four impedances which must be a single ...
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The objective of the present study was to test the hypothesis that subglottal resonances (SGRs) serve as quantal boundaries that separate Arabic vowel features. Previous research suggests that SGR frequencies predict ...
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The objective of the present study was to test the hypothesis that subglottal resonances (SGRs) serve as quantal boundaries that separate Arabic vowel features. Previous research suggests that SGR frequencies predict discontinuities in vowel formant prominence in the neighborhood of SGR frequencies due to coupling between the oral and subglottal cavities. These frequencies represent the boundary between the formant frequencies of vowels with different articulatory features. All these studies have confirmed that the first subglottal resonance (SGR1) is defined as a boundary that separates high vs. low (corresponding to the phonological features [-low] and [+ low]) vowels along the F1 dimension, and the second subglottal resonance (SGR2) is defined as a boundary that separates front vs. back (corresponding to the phonological features [-back] and [+ back]) vowels along the F2 dimension. Nine adult native Algerian speakers aged 20 to 41 participated in the experiment. The four acoustic parameters (the first two formants and the first two subglottal resonances) of the six vowels [/a/ /u/ /i/ /a:/ /u:/ /i:/] were measured from the speech signal and the subglottal acoustic signal and used to test this hypothesis. To validate our experimental results, we compared SGR-based vowel classification with two machine learning (ML) models: Support Vector Machines (SVM) and Decision Stumps (DS). For [+ back]/[-back] identification, the percentages of correct identification during the training phase were 99.07% for SVM, 99.31% for DS, and 95.61% for the SGR1, indicating a slight superiority of DS over SGR1. For [+ low]/[-low] identification, the correct identification rates were 95.49% for SVM, 93.06% for DS, and 94.45% for the SGR2, showing a marginally better performance of DS compared to SGR2. However, during the testing phase, the boundaries determined by the first and second SGRs outperformed the ML models, achieving identification accuracies of 90.12% and 91.67%, respectively,
A crucial problem in cloud computing is load balancing, which makes it challenging to guarantee that services operate as intended in accordance with quality of service (QoS), performance reviews, and service contracts...
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