The future of optoelectronics is directed towards small-area light sources,foremost being ***,their use has been inhibited so far primarily due to fabrication and integration challenges,which impair efficiency and ***...
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The future of optoelectronics is directed towards small-area light sources,foremost being ***,their use has been inhibited so far primarily due to fabrication and integration challenges,which impair efficiency and ***,bottom-up nanostructures grown using selective area epitaxy have garnered attention as a solution to the aforementioned *** Fu *** used this technique to demonstrate uniform p-i-n core-shell InGaAs/InP nanowire array light emitting *** devices are capable of voltage and geometry-controlled multi-wavelength and high-speed *** publication accentuates the wide capabilities of bottom-up nanostructures to resolve the difficulties of nanoscale optoelectronics.
Row-scale Composable Disaggregated Infrastructure (CDI) is a heterogeneous high performance computing (HPC) architecture that relocates the GPUs to a single chassis which CPU nodes can then request compute resources f...
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Extracting cancer-related information from unstructured text presents challenges that require accurate identification and extraction techniques. This study compares three methods: keyword-based matching, regular expre...
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Pre-training on a large dataset such as ImageNet followed by supervised fine-tuning has brought success in various deep learning-based tasks. However, the modalities of natural images and ultrasound images have consid...
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In the past few years, image processing has been widely adopted for symptom diagnosis of medical application. To achieve accurate analysis, the medical applications require high quality image for applying to the sympt...
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In this article, we focus on solving a class of distributed optimization problems involving n agents with the local objective function at every agent i given by the difference of two convex functions fi and gi (differ...
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While standard evaluation scores for generative models are mostly reference-based, a reference-dependent assessment of generative models could be generally difficult due to the unavailability of applicable reference d...
This paper presents an optimization framework for routing in software-defined elastic optical networks using reinforcement learning algorithms. We specifically implement and compare the epsilon-greedy bandit, upper co...
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Accurate detection of the Physical Cell Identity (PCI) is critical for rapid synchronization and connection establishment in 5G New Radio (5G-NR) systems. This paper introduces a deep learning-based approach for PCI c...
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The neurological disease known as autism spectrum disorder (ASD) is characterized by impaired social interaction, communication issues, and constrained and repetitive behavior patterns. For the benefit of early interv...
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ISBN:
(纸本)9798350367461
The neurological disease known as autism spectrum disorder (ASD) is characterized by impaired social interaction, communication issues, and constrained and repetitive behavior patterns. For the benefit of early interventions and support for afflicted persons, timely and accurate ASD prognosis is essential. Deep learning methods have become effective tools for predictive modeling across a range of industries, including healthcare. This study examines the use of deep learning and transfer learning to forecast ASD using a large dataset of clinical and behavioral variables. In this study, the effectiveness of three well-known deep learning architectures VGG16, DenseNet121, and MobileNetv2 in predicting ASDs is compared. A sizable dataset with a variety of ASD-related variables, such as demographic data, medical histories, and behavioral assessments, is used to train the models. To take use of pre-learned weights from models trained on extensive generic image recognition tasks, transfer learning is used. With accuracy rates of 97% apiece, the experimental results show remarkable prediction performance for VGG16 and DenseNet121. These models have significant generalization abilities that make it possible to make reliable predictions for identifying those who are at risk for ASD. In contrast to the other architectures, MobileNetv2 only obtains an accuracy of 73%. The results show that deeper architectures like VGG16 and DenseNet121 capture the rich patterns and fine details of the input data, resulting in more precise predictions. Additionally, thorough investigations are carried out to look into the models' learned representations and pinpoint the primary features that influence ASD prediction. These revelations aid in a better comprehension of the underlying causes and potential biomarkers of ASD. The information gleaned from these studies can direct ongoing research projects and support the creation of individualized interventions and therapies. Overall, the study empha
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