HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR Images by Dezheng Zhang, Zehan Liang, Rui Cen, Zhihong Yan, Rui Wan, Dong Wang

“…Convolutional Neural Network (CNN)-based Synthetic Aperture Radar (SAR) target detection eliminates manual feature engineering and improves robustness but suffers from high computational costs, hindering on-satellite deployment. …”
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Online Calibration Method of LiDAR and Camera Based on Fusion of Multi-Scale Cost Volume by Xiaobo Han, Jie Luo, Xiaoxu Wei, Yongsheng Wang

“…First, a multi-layer convolutional network is used to downsample and concatenate the camera RGB data and LiDAR point cloud data to obtain three-scale feature maps. …”
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A hybrid zero-reference and dehazing network for joint low-light underground image enhancement by Qing Du, Shihao Zhang, Zhipeng Wang, Jincheng Liang, Shijiao Yang

“…It addresses two key aspects: (1) enhancing low-light images by incorporating higher-order loss curves into the DCE-Net backbone and introducing a new loss function to optimize network learning for improved low-light image quality; (2) addressing the color distortion and blur caused by low light enhancement through post-processing using convolutional neural networks, with AOD-Net enhancing the clarity of downhole images. …”
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DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product Reviews by Yuanfang Dong, Xiaofei Li, Meiling He, Jun Li

“…The rapid growth of e-commerce has led to a significant increase in user feedback, especially in the form of post-purchase comments on online platforms. These reviews not only reflect customer sentiments but also crucially influence other users’ purchasing decisions due to their public accessibility. …”
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Landslide Segmentation in High-Resolution Remote Sensing Images: The Van–UPerAttnSeg Framework with Multi-Scale Feature Enhancement by Chang Li, Quan Zou, Guoqing Li, Wenyang Yu

“…The decoder consists of a pyramid pooling module (PPM) and feature pyramid network (FPN), combined with a convolutional block attention module (CBAM) module. Through this structure, the model can effectively integrate features of different scales, achieving precise positioning and recognition of landslide areas. …”
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Road Damage Detection Using YOLOv7 with Cluster Weighted Distance-IoU NMS by Rudy Rachman, Nanik Suciati, Shintami Chusnul Hidayati

“…Previous research that used images as input for pothole detection used the Faster Regional Convolutional Neural Network (R-CNN) method. It has a large inference time because it is a two-stage detection method. …”
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Deep learning based rapid X-ray fluorescence signal extraction and image reconstruction for preclinical benchtop X-ray fluorescence computed tomography applications by Amrit Kaphle, Sandun Jayarathna, Sang Hyun Cho

“…Here we propose a novel end-to-end deep learning (DL) framework that integrates a one-dimensional convolutional neural network (1D CNN) for rapid XRF signal extraction with a U-Net model for XFCT image reconstruction. …”
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Rolling Bearing Fault Diagnosis Based on SCNN and Optimized HKELM by Yulin Wang, Xianjun Du

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Vehicle Network Beamforming Method Based on Multimodal Feature Fusion by Jiali NIE, Yuanhao CUI, Di ZHANG, Ronghui ZHANG, Junsheng MU, Xiaojun JING

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YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation by Madalena de Oliveira Barbosa, Fernanda Pereira Leite Aguiar, Suely dos Santos Sousa, Luana dos Santos Cordeiro, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

“…Employing the YOLOv8m convolutional neural network, the study identified eight distinct pepper varieties: Pimento, Bode, Cambuci, Chilli, Fidalga, Habanero, Jalapeno, and Scotch Bonnet. …”
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Energy saving by Artificial intelligence-based fault detection and diagnosis: 42 chiller case studies by Yen-Tang Chen, Da-Sheng Lee, Jhih-Jie Yang, Wei-Tao Huang, Yen-Po Liu

“…This study proposes an Artificial Intelligence (AI)-based Fault Detection and Diagnosis (FDD) system for chiller fault diagnostics, utilizing a One-Dimensional Convolutional Neural Network (1D-CNN) combined with Transfer Learning to enhance generalizability across diverse sites. …”
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Generation of Shape Models of Calcified TAVR populations for Solid Mechanics Simulations by means of Deep Learning by Oldenburg Jan, Borowski Finja, Supp Laura, Öner Alper, Schmitz Klaus-Peter, Stiehm Michael

“…The key innovation lies in the utilization of a conditional Convolutional Variational Autoencoder (cCVAE) to generate realistic calcification patterns, demonstrating promising preliminary results in matching actual cohort data. …”
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An adaptive deep learning approach based on InBNFus and CNNDen-GRU networks for breast cancer and maternal fetal classification using ultrasound images by Mamuna Fatima, Muhammad Attique Khan, Anwar M. Mirza, Jungpil Shin, Areej Alasiry, Mehrez Marzougui, Jaehyuk Cha, Byoungchol Chang

“…Abstract Convolutional Neural Networks (CNNs), a sophisticated deep learning technique, have proven highly effective in identifying and classifying abnormalities related to various diseases. …”
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A Violet‐Light‐Responsive ReRAM Based on Zn2SnO4/Ga2O3 Heterojunction as an Artificial Synapse for Visual Sensory and In‐Memory Computing by Saransh Shrivastava, Wei‐Sin Dai, Stephen Ekaputra Limantoro, Hans Juliano, Tseung‐Yuen Tseng

“…Classification of three‐channeled images corrupted with different levels (0.15–0.9) of Gaussian noise is achieved by simulating a convolutional neural network (CNN). The violet light (405 nm) illumination generates excitatory post synaptic current (EPSC), which is influenced by the persistent photoconductivity (PPC) effect after discontinuing the optical excitation. …”
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Developing a multi-variate prediction model for COVID-19 from crowd-sourced respiratory voice data by Yuyang Yan, Wafaa Aljbawi, Sami O. Simons, Visara Urovi

“…Aim: COVID-19 has affected more than 223 countries worldwide and in the post-COVID era, there is a pressing need for non-invasive, low-cost, and highly scalable solutions to detect COVID-19. …”
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A deep learning based multiple RNA methylation sites prediction across species by Sajid Shah, Saima Jabeen, Mohammed ElAffendi, Ishrat Khan, Muhammad Almas Anjum, Mohamed A. Bahloul

“…Methylation of ribonucleic acid (RNA) is an essential post-transcriptional alteration that has a major effect on many biological processes. …”
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Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments by Chuanjie Yao, Suhang Liu, Zhengjie Liu, Shuang Huang, Tiancheng Sun, Mengyi He, Gemin Xiao, Han Ouyang, Yu Tao, Yancong Qiao, Mingqiang Li, Zhou Li, Peng Shi, Hui-jiuan Chen, Xi Xie

“…Herein, an anti-noise triboelectric acoustic sensor (Anti-noise TEAS) based on flexible nanopillar structures is developed and integrated with a convolutional neural network-based deep learning model (Anti-noise TEAS-DLM). …”
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MolNexTR: a generalized deep learning model for molecular image recognition by Yufan Chen, Ching Ting Leung, Yong Huang, Jianwei Sun, Hao Chen, Hanyu Gao

“…To bridge this gap, we proposed MolNexTR, a novel image-to-graph deep learning model that collaborates to fuse the strengths of ConvNext, a powerful Convolutional Neural Network variant, and Vision-TRansformer. …”
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FD-YOLO: A YOLO Network Optimized for Fall Detection by Hoseong Hwang, Donghyun Kim, Hochul Kim

“…First, a global attention module (GAM) based on the Convolutional Block Attention Module (CBAM) was employed to improve detection performance. …”
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Ensemble Machine Learning, Deep Learning, and Time Series Forecasting: Improving Prediction Accuracy for Hourly Concentrations of Ambient Air Pollutants by Valentino Petrić, Hussain Hussain, Kristina Časni, Milana Vuckovic, Andreas Schopper, Željka Ujević Andrijić, Simonas Kecorius, Leizel Madueno, Roman Kern, Mario Lovrić

“…A diverse set of techniques was implemented to tackle this challenge, encompassing the utilisation of the prophet, random forest, and three different deep learning architectures: long short-term memory networks, convolutional neural networks, and multilayer perceptrons. …”
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