Research on Data-driven PET-CT Gating Imaging Method by XU Shidong1, 2, 3, 4, SUN Xiaoli1, 3, 4, , LIU Shuangquan1, 3, 4, ZHENG Yushuang1, 3, 4, LI Mohan1, 3, 4, WEI Cunfeng1, 2, 3, 4

“…An improved data-driven gating method, partial data principal component analysis (PD-PCA), which optimizes the gating signal extraction approach and preprocesses the raw data was proposed. …”

A hybrid deep learning framework for short-term load forecasting with improved data cleansing and preprocessing techniques by Muhammad Sajid Iqbal, Muhammad Adnan, Salah Eldeen Gasim Mohamed, Muhammad Tariq

“…The process unfolds with data collection, followed by rigorous standardization, preprocessing, and cleansing of demand and generation data. …”
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Flight-Safe Inference: SVD-Compressed LSTM Acceleration for Real-Time UAV Engine Monitoring Using Custom FPGA Hardware Architecture by Sreevalliputhuru Siri Priya, Penneru Shaswathi Sanjana, Rama Muni Reddy Yanamala, Rayappa David Amar Raj, Archana Pallakonda, Christian Napoli, Cristian Randieri

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A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model by Xiaoliang Jia, Guoyan Zhang, Zhiqiang Wang, Huacong Li, Jing Hu, Songlin Zhu, Caiwei Liu

“…Subsequently, the Gated Recurrent Unit (GRU) network utilizes data from other sensors to reconstruct the IMFs and residuals, ultimately producing the data reconstruction results. …”
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Smart Organization of Imbalanced Traffic Datasets for Long-Term Traffic Forecasting by Mustafa M. Kara, H. Irem Turkmen, M. Amac Guvensan

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Utilizing active learning and attention-CNN to classify vegetation based on UAV multispectral data by Sheng Miao, Chuanlong Wang, Guangze Kong, Xiuhe Yuan, Xiang Shen, Chao Liu

“…The model achieves accurate identification of vegetation types in the study area by utilizing multispectral data obtained from preprocessing of unmanned aerial vehicle (UAV) remote sensing equipment. …”
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Deep Learning-Based Sentiment Analysis Using Gated Recurrent Unit by Najeem Olawale Adelakun, Mariam Adenike Lasisi

“…The research employs a systematic methodology that begins with data collection from various financial sources. This is followed by rigorous preprocessing, including data cleaning, tokenization, and downsampling to balance sentiment classes. …”
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Stroke risk prediction: a deep learning approach for identifying high-risk patients by Afeez A. Soladoye, Kazeem M. Olagunju, Sunday A. Ajagbe, Ibrahim A. Adeyanju, Precious I. Ogie, Pragasen Mudali

“…This study developed a stroke prediction system with a modified Gated Recurrent Unit (GRU), a structured stroke dataset was gotten from Kaggle, which went through different preprocessing techniques like label Encoder, Normalization with MinMax, dropping of irrelevant values. …”
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Fault Prediction of Bearing Based on Dual Dimensional Perception and Composite Gated Recurrent Network by Wang Weiping, Xue Shibei

“…In the trend dimension, 7 preprocessed vibration waveform feature quantities were proposed from 33 feature data as the feature data for the trend dimension. …”
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Hybrid Optimized Feature Selection and Deep Learning Method for Emotion Recognition That Uses EEG Data by asmaa Bashar Hmaza, Rajaa K. Hasoun

“…The process begins with collecting and preprocessing EEG information to use the data for training and testing the proposed system. …”
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Blockchain smart contract vulnerability detection based on GR algorithm by WANG Deguang, SHAN Mengtao, LI Fengqi, TONG Ning

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Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles by Abdul Hussain Ali Hussain, Montadar Abas Taher, Omar Abdulkareem Mahmood, Yousif I. Hammadi, Reem Alkanhel, Ammar Muthanna, Andrey Koucheryavy

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An Intelligent Track Segment Association Method Based on Characteristic-Aware Attention LSTM Network by Jiadi Qi, Xiaoke Lu, Jinping Sun

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DDoS classification of network traffic in software defined networking SDN using a hybrid convolutional and gated recurrent neural network by Ahmed M. Elshewey, Safia Abbas, Ahmed M. Osman, Eman Abdullah Aldakheel, Yasser Fouad

“…To address this, Synthetic Minority Over-sampling Technique (SMOTE) was applied, resulting in a balanced dataset of 24,500 samples (12,250 benign and 12,250 attacks). A robust preprocessing pipeline followed, including missing value verification (no missing values were found), feature normalization using StandardScaler to standardize numerical values, reshaping the data into 3D format to fit temporal models like CNN and GRU, and stratified train-test split (80% training, 20% testing) to maintain class distribution. …”
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Design of a Drivable Area Segmentation Network Using a Field Programmable Gate Array Based on Light Detection and Ranging by Xue-Qian Lin, Jyun-Yu Jhang, Cheng-Jian Lin, Sheng-Fu Liang

“…In order to reduce point cloud density without compromising essential information, we perform sampling and fusion on the point clouds in both Cartesian and spherical coordinate spaces during data preprocessing. The fused point cloud serves as the input to DASNet, while the output is the drivable area map. …”
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Remaining Useful Life Prediction of Rolling Bearings Based on an Improved U-Net and a Multi-Dimensional Hybrid Gated Attention Mechanism by Hengdi Wang, Aodi Shi

“…To address these issues, this paper first proposes a method for predicting the remaining useful life (RUL) of bearings, which combines an improved U-Net for enhancing vibration signals and a multi-dimensional hybrid gated attention mechanism (MHGAM) for dynamic feature fusion. …”
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Context-Aware Deep Learning Model for Yield Prediction in Potato Using Time-Series UAS Multispectral Data by Suraj A. Yadav, Xin Zhang, Nuwan K. Wijewardane, Max Feldman, Ruijun Qin, Yanbo Huang, Sathishkumar Samiappan, Wyatt Young, Francisco G. Tapia

“…</italic>) growing seasons under varied nitrogen (N)-rates ranging from 0 to 639 kg/ha. The raw data were preprocessed using Pix4Dmapper and the quantum geographic information system. …”
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GA-AGN: A generative adversarial network and attention gated network model for enhanced lung cancer detection using chest CT scans by Shenson Joseph, Herat Joshi, Meetu Malhotra, Shazia Fathima, Madhao Wagh, Kirankumar Kulkarni, Somya Singh, Onkar Mayekar, Mehedi Hassan

“…Initially, the chest CT scan images are subjected to the pre-processing phase, where image resizing and normalization are used to preprocess the images. Then, the data augmentation is performed using the GAN model that is trained by Elk Herd Optimizer (EHO). …”
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GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition by Adnan Saeed, Khurram Shehzad, Shahzad Sarwar Bhatti, Saim Ahmed, Ahmad Taher Azar

“…Additionally, we utilized an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to generate images that balance MRI data and implemented multiple preprocessing techniques to tackle inherent noise in MRI images. …”
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Optimizing Cancer Detection: Swarm Algorithms Combined with Deep Learning in Colon and Lung Cancer using Biomedical Images by HariKrishna Pathipati, Lova Naga Babu Ramisetti, Desidi Narsimha Reddy, Swetha Pesaru, Mashetty Balakrishna, Thota Anitha

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