Application of CNN-LSTM Model for Vehicle Acceleration Prediction Using Car-following Behavior Data by Shuning Tang, Yajie Zou, Hao Zhang, Yue Zhang, Xiaoqiang Kong

“…Then the convolutional neural network (CNN) and long short-term memory (LSTM) network are applied to predict vehicle acceleration. …”
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Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network by Xiaolei Ru, Xiangdong Xu, Yang Zhou, Chao Yang

“…To identify these critical segments, we assume that the states of floating cars within different road segments are correlative and mutually representative and design a heuristic algorithm utilizing the attention mechanism embedding in the graph neural network (GNN). The results show that the designed model achieves a high accuracy compared to the conventional method using only two critical segments which account for 2.7% in the road networks. …”
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The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings by Esther Kok, Aneesh Chauhan, Michele Tufano, Edith Feskens, Guido Camps

“…We explored the applicability of hyperspectral imaging and machine learning for dietary assessment of home-prepared meals, by building a proof-of-concept, which automatically detects food ingredients inside closed sandwiches.MethodsIndividual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.ResultsThe resulting best performing models had an accuracy score of ~80% for predicting type of bread, ~60% for butter, and ~ 28% for filling type. …”
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Optimization of dried garlic physicochemical properties using a self-organizing map and the development of an artificial intelligence prediction model by Hany S. El-Mesery, Mohamed Qenawy, Mona Ali, Merit Rostom, Ahmed Elbeltagi, Ali Salem, Abdallah Elshawadfy Elwakeel

“…The relationships between the input process factors and response factors’ physicochemical properties of dried garlic were optimized by a self-organizing map (SOM), and the model was developed using machine learning. Artificial Neural Network (ANN) with 99% predicting accuracy and Self-Organizing Maps (SOM) with 97% clustering accuracy were used to determine the quality characteristics of garlic. …”
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Applying Hybrid Deep Learning Models to Assess Upper Limb Rehabilitation by Sheng Miao, Zitong Liu, Dezhen Wang, Xiang Shen, Nana Shen

“…The method uses a monocular camera to capture video data from the patient’s upper limb rehabilitation training, utilizes Faster R-CNN and HRNet to recognize the human body position and upper limb bone key point information, and then builds a long short-term memory (LSTM) neural network model incorporating the ProbSparse Self-Attention mechanism to evaluate the rehabilitation training movements. …”
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A disproportionality analysis of FDA adverse event reporting system events for misoprostol by Li Yang, Wenting Xu

“…This study used proportional disequilibrium methods such as reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM) to detect AEs. …”
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A Modified Time Reversal Method for Guided Wave Detection of Bolt Loosening in Simulated Thermal Protection System Panels by Guan-nan Wu, Chao Xu, Fei Du, Wei-dong Zhu

“…To analyze a large number of tightness indices, a principle component analysis method is introduced, and a neural network-based loosening detection method is proposed. …”
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Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features by Weidong Song, Guohui Jia, Hong Zhu, Di Jia, Lin Gao

“…To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. …”
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Application of Improved Deep Learning Method in Intelligent Power System by HuiJie Liu, Yang Liu, ChengWen Xu

“…The method uses the convolutional neural network to establish the energy prediction calculation model, uses CNN adaptive data features to mine characteristics, quantifies power uncertainty, uses drop regularization to optimize the deep network structure, uses the deep forest to learn the extracted data features, and builds a prediction model, in order to achieve accurate prediction of power load and solve the problem that the accuracy of existing forecasting methods decreases due to random fluctuations of power. …”
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Artificial Intelligence Scribe and Large Language Model Technology in Healthcare Documentation: Advantages, Limitations, and Recommendations by Sarah A. Mess, MD, Alison J. Mackey, PhD, David E. Yarowsky, PhD

“…Insidious and potentially significant errors of omission, fabrication, or substitution may occur. The neural network algorithms of LLMs have unpredictable sensitivity to user input and inherent variability in their output. …”
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MultiPhys: Heterogeneous Fusion of Mamba and Transformer for Video-Based Multi-Task Physiological Measurement by Chaoyang Huo, Pengbo Yin, Bo Fu

“…Specifically, a Convolutional Neural Network (CNN) is used to quickly extract local features in the early stage, a transformer captures global context and long-distance dependencies, and Mamba is used to compensate for the transformer’s deficiencies, reducing the computational complexity and improving the accuracy of the model. …”
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Computer Vision-Based Fire–Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits by Peng Chen, Xutong Shao, Guangyu Wen, Yaowu Song, Rao Fu, Xiaoyan Xiao, Tulin Lu, Peina Zhou, Qiaosheng Guo, Hongzhuan Shi, Chenghao Fei

“…To further validate the reliability of the algorithm, Back Propagation Neural Network (BP), Support Vector Machine (SVM), Deep Belief Network (DBN), and Random Forest (RF) were used for reverse validation, and the accuracy of the training set and test set reached 98.83–100% and 95.89–99.32%, respectively. …”
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Benchmarking human face similarity using identical twins by Shoaib Meraj Sami, John McCauley, Sobhan Soleymani, Nasser Nasrabadi, Jeremy Dawson

“…The facial similarity measure is determined via a deep convolutional neural network. This network is trained on a tailored verification task designed to encourage the network to group together highly similar face pairs in the embedding space and achieves a test AUC of 0.9799. …”
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A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants by Ahmad Shaheryar, Xu-Cheng Yin, Hong-Wei Hao, Hazrat Ali, Khalid Iqbal

“…Sensors health monitoring is essentially important for reliable functioning of safety-critical chemical and nuclear power plants. Autoassociative neural network (AANN) based empirical sensor models have widely been reported for sensor calibration monitoring. …”
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Estimating traffic flow at urban intersections using low occupancy floating vehicle data by Lili Zhang, Kang Yang, Ke Zhang, Wei Wei, Jing Li, Hongxin Tan

“…These estimated flow rates are then refined using the proposed Radial Basis Function (RBF) neural network approximation method to achieve higher accuracy. …”
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When Remote Sensing Meets Foundation Model: A Survey and Beyond by Chunlei Huo, Keming Chen, Shuaihao Zhang, Zeyu Wang, Heyu Yan, Jing Shen, Yuyang Hong, Geqi Qi, Hongmei Fang, Zihan Wang

“…Most deep-learning-based vision tasks rely heavily on crowd-labeled data, and a deep neural network (DNN) is usually impacted by the laborious and time-consuming labeling paradigm. …”
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Intelligent On/Off Dynamic Link Management for On-Chip Networks by Andreas G. Savva, Theocharis Theocharides, Vassos Soteriou

“…., expected future utilization link levels), where links are turned off and on via the use of a small and scalable neural network. Simulation results with various synthetic traffic models over various network topologies show that the proposed work can reach up to 13% power savings when compared to a trivial threshold computation, at very low (<4%) hardware overheads.…”
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An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning by S. M. Taslim Uddin Raju, Amlan Sarker, Apurba Das, Md. Milon Islam, Mabrook S. Al-Rakhami, Atif M. Al-Amri, Tasniah Mohiuddin, Fahad R. Albogamy

“…Different machine learning (ML) approaches, including support vector regression (SVR), extreme learning machine (ELM), and multilayer perceptron neural network (MLP), are adopted as reference models. …”
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Data‐Driven Forecasting of Low‐Latitude Ionospheric Total Electron Content Using the Random Forest and LSTM Machine Learning Methods by Gebreab K. Zewdie, Cesar Valladares, Morris B. Cohen, David J. Lary, Dhanya Ramani, Gizaw M. Tsidu

“…Abstract In this research, we present data‐driven forecasting of ionospheric total electron content (TEC) using the Long‐Short Term Memory (LSTM) deep recurrent neural network method. The random forest machine learning method was used to perform a regression analysis and estimate the variable importance of the input parameters. …”
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Application of deconvolutional networks for feature interpretability in epilepsy detection by Sihao Shao, Yu Zhou, Ruiheng Wu, Aiping Yang, Qiang Li

“…The Fully Convolutional Network (FCN) can provide the model’s interpretability but has not been applied in seizure detection.MethodsTo address these challenges, a novel convolutional neural network (CNN) model, combining SE (Squeeze-and-Excitation) modules, was proposed on top of the FCN. …”
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