Voice activity detection in noisy conditions using tiny convolutional neural network by R. S. Vashkevich, E. S. Azarov

Get full text

JAX‐CanVeg: A Differentiable Land Surface Model by Peishi Jiang, Patrick Kidger, Toshiyuki Bandai, Dennis Baldocchi, Heping Liu, Yi Xiao, Qianyu Zhang, Carlos Tianxin Wang, Carl Steefel, Xingyuan Chen

“…Differentiable modeling provides a new opportunity to capture these complex interactions by seamlessly hybridizing process‐based models with deep neural networks (DNNs), benefiting both worlds, that is, the physical interpretation of process‐based models and the learning power of DNNs. …”
Get full text

Effects of 2010–2045 climate change on ozone levels in China under a carbon neutrality scenario: key meteorological parameters and processes by L. Kang, H. Liao, K. Li, X. Yue, Y. Yang, Y. Wang

“…Analysis showed net chemical production was the most important process that increases O<span class="inline-formula">₃</span>, accounting for 34.0 %–62.5 % of the sum of all processes within the boundary layer. …”
Get full text

Neural network based AI model for lung health assessment by Umaisa Hassan, Amit Singhal, Gunjan Gupta

“…The NN architecture consists of three fully connected layers and an output layer for classification. Our proposed approach attains 100% accuracy, specificity, and sensitivity, performing consistently well across all four datasets, which highlights the model’s strong generalizability. …”
Get full text

Feature-adaptive anomaly detection model for onion inspection system by Ziwei Song, Prawit Buayai, Koji Makino, Xiaoyang Mao

“…The VBIM introduces a new feature-adaptive anomaly detection method, which optimizes feature layer weights in a student-teacher anomaly detection model. …”
Get full text

On the Readability of Kernel-based Deep Learning Models in Semantic Role Labeling Tasks over Multiple Languages by Daniele Rossini, Danilo Croce, Roberto Basili

“…Unfortunately, most of such decision processes are epistemologically opaque as for the limited interpretability of the acquired neural models based on the involved embeddings. …”
Get full text

Large-Language-Model-Enabled Text Semantic Communication Systems by Zhenyi Wang, Li Zou, Shengyun Wei, Kai Li, Feifan Liao, Haibo Mi, Rongxuan Lai

“…Large language models (LLMs) have recently demonstrated state-of-the-art performance in various natural language processing (NLP) tasks, achieving near-human levels in multiple language understanding challenges and aligning closely with the core principles of semantic communication Inspired by LLMs’ advancements in semantic processing, we propose LLM-SC, an innovative LLM-enabled semantic communication system framework which applies LLMs directly to the physical layer coding and decoding for the first time. …”
Get full text

Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction by Yu Liang, Dalei Wu

“…All the learning agents are fabricated in a hierarchical architecture, which enables agents to collaborate with each other in peer-to-peer and cross-layer way. This layered architecture shares the burden of large-scale data processing on machine learning models of all units. …”
Get full text

An Improved Artificial Neural Network Model for Effective Diabetes Prediction by Muhammad Mazhar Bukhari, Bader Fahad Alkhamees, Saddam Hussain, Abdu Gumaei, Adel Assiri, Syed Sajid Ullah

“…We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. …”
Get full text

Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing by Jason E. Johnson, Ishat Raihan Jamil, Liang Pan, Guang Lin, Xianfan Xu

“…The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model. This model then serves as a surrogate for the manufacturing process: predicting optimal process parameters for achieving a target geometry, e.g., the 2D geometry of each printed layer. …”
Get full text

Recent advances in the inverse design of silicon photonic devices and related platforms using deep generative models by Sun Jae Baek, Minhyeok Lee

“…We analyze their applications in the inverse design of photonic devices, comparing their effectiveness and integration in the design process. Our findings indicate that while MLP-based methods were commonly used in early research, recent studies have increasingly employed CNN, GAN, AE, and RL methods, as well as advanced MLP models. …”
Get full text

Comparison and Optimization of Generalized Stamping Machine Fault Diagnosis Models Using Various Transfer Learning Methodologies by Po-Wen Hwang, Yuan-Jen Chang, Hsieh-Chih Tsai, Yu-Ta Tu, Hung-Pin Yang

“…Vibration data, acquired using accelerometers strategically placed at two distinct sensor locations on each machine, serve as the primary input for the model. Four prominent deep learning architectures—a 10-layer convolutional neural network (CNN), a CNN with residual connections (CNN-Res), VGG16, and ResNet50—were rigorously evaluated in conjunction with fine-tuning strategies to determine the optimal model architecture. …”
Get full text

Heat Resistance and Heat-and-Mass Transfer in Road Pavements by B. M. Khroustalev, . Liu Tingguo, V. D. Akeliev, . Li Zhongyu, H. Yu. Aliakseyeu, V. V. Zankаvich

Get full text

Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis by Pradeep Kumar Yadalam, Prabhu Manickam Natarajan, Carlos M. Ardila

“…The Basic AE effectively modeled RNA-seq data complexity compared to Variational and Denoising Autoencoders. …”
Get full text

Electrospun Fiber‐Based Tubular Structures as 3D Scaffolds to Generate In Vitro Models for Small Intestine by Lorenzo Zavagna, Eligio F. Canelli, Bahareh Azimi, Fabiola Troisi, Lorenzo Scarpelli, Teresa Macchi, Giuseppe Gallone, Massimiliano Labardi, Roberto Giovannoni, Mario Milazzo, Serena Danti

“…Abstract Recently, in vitro models emerge as valuable tools in biomedical research by enabling the investigation of complex physiological processes in a controlled environment, replicating some traits of interest of the biological tissues. …”
Get full text

Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos by Shadi Jaradat, Mohammed Elhenawy, Huthaifa I. Ashqar, Alexander Paz, Richi Nayak

“…The framework consists of two key components: a deep learning model to segment video streams and identify potential near-miss or crash incidents and a multimodal large language model (MLLM) to further analyze and extract narrative information from the identified events. …”
Get full text

New method of text representation model based on neural network by Shui-fei ZENG, Xiao-yan ZHANG, Xiao-feng DU, Tian-bo LU

“…Method of text representation model was proposed to extract word-embedding from text feature.Firstly,the word-embedding of the dual word-embedding list based on dictionary index and the corresponding part of speech index was created.Then,feature vectors was obtained further from these extracted word-embeddings by using Bi-LSTM recurrent neural network.Finally,the sentence vectors were processed by mean-pooling layer and text categorization was classified by softmax layer.The training effects and extraction performance of the combination model of Bi-LSTM and double word-embedding neural network were verified.The experimental results show that this model not only performs well in dealing with the high-quality text feature vector and the expression sequence,but also significantly outperforms other three kinds of neural networks,which includes LSTM,LSTM+context window and Bi-LSTM.…”
Get full text

Hierarchical Motion Field Alignment for Robust Optical Flow Estimation by Dianbo Ma, Kousuke Imamura, Ziyan Gao, Xiangjie Wang, Satoshi Yamane

Get full text

Compressed CNN Plant Leaf Ｒecognition Model Fused with Bayesian by YAN Ming, ZHU Liang-kuan, JING Wei-peng

“…Aiming at the problem that there are many parameters in the process of plant leaf recognition and it is easy to produce over-fitting，in order to reduce the cost of storage and calculation，this paper proposes a plant leaf recognition convolutional neural network model based on Bayesian fusion. …”
Get full text

Welding Image Data Augmentation Method Based on LRGAN Model by Ying Wang, Zhe Dai, Qiang Zhang, Zihao Han

“…First, a five-layer spectral normalization neural network was designed as the discriminator of the model. …”
Get full text