Applied Artificial Intelligence (AI) in engineering is gaining significant traction. AI object detection methods can be applied in the engineering industry to extract information from engineering drawings, offering im...
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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
The compressed code of Absolute Moment Block Truncation Coding (AMBTC) consists of quantized values (QVs) and bitmaps. The QVs exhibit greater predictability, and the bitmaps themselves carry more randomness. While ex...
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In the engineering, Procurement, and Construction (EPC) sector, accurate cost estimations during the tendering phase are crucial for maintaining competitiveness, especially with constrained project schedules and risin...
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For point cloud registration, the purpose of this article is to propose a novel centralized random sample consensus (RANSAC) (C-RANSAC) registration with fast convergence and high accuracy. In our algorithm, the novel...
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Money laundering is a serious threat to global financial systems, causing instability and inflation, and especially hurting middle-class savings. This paper suggests a new way to tackle these problems by using blockch...
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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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Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,...
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Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.
Zinc metal batteries(ZnBs)are poised as the next-generation energy storage solution,complementing lithium-ion batteries,thanks to their costeffectiveness and safety *** benefits originate from the abundance of zinc an...
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Zinc metal batteries(ZnBs)are poised as the next-generation energy storage solution,complementing lithium-ion batteries,thanks to their costeffectiveness and safety *** benefits originate from the abundance of zinc and its compatibility with non-flammable aqueous ***,the inherent instability of zinc in aqueous environments,manifested through hydrogen evolution reactions(HER)and dendritic growth,has hindered commercialization due to poor cycling *** potassium polyacrylate(PAAK)-based water-in-polymer salt electrolyte(WiPSE),a novel variant of water-in-salt electrolytes(WiSE),designed to mitigate side reactions associated with water redox processes,thereby enhancing the cyclic stability of *** this study,WiPSE was employed in ZnBs featuring lignin and carbon composites as cathode *** research highlights the crucial function of acrylate groups from WiPSE in stabilizing the ionic flux on the surface of the Zn *** stabilization promotes the parallel deposition of Zn along the(002)plane,resulting in a significant reduction in dendritic ***,our sustainable Zn-lignin battery showcases remarkable cyclic stability,retaining 80%of its initial capacity after 8000 cycles at a high current rate(1 A g^(-1))and maintaining over 75%capacity retention up to 2000 cycles at a low current rate(0.2 A g^(-1)).This study showcases the practical application of WiPSE for the development of low-cost,dendrite-free,and scalable ZnBs.
Cloud computing has gained significant popularity as a platform for processing large-scale data analytics, offering benefits such as high availability, robustness, and cost-effectiveness. However, job scheduling in cl...
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