A Lightweight Model for Weed Detection Based on the Improved YOLOv8s Network in Maize Fields by Jinyong Huang, Xu Xia, Zhihua Diao, Xingyi Li, Suna Zhao, Jingcheng Zhang, Baohua Zhang, Guoqiang Li

“…Finally, Dualconv was employed instead of the conventional convolution for downsampling, further diminishing the network load. …”
Get full text

YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu, Ende Zhang

“…Secondly, within the neck network, multi-scale feature extraction is facilitated through the design of novel composite convolutions, ConvX and MConv, based on a “split–differentiate–concatenate” paradigm. …”
Get full text

Satellite-Based Validation of Contrail Prediction Models for Sustainable Aviation by Baddireddi Sree Chandana, Pelleti Nandieswar Reddy, Rithvika Alapati, Sai Aswath Reddy, Radha Doraisamy, Uma Sankari

“…U-Net variants, a convolutional neural network architecture, is utilized for image segmentation to identify contrails in satellite imagery. …”
Get full text

Integrating Machine Learning and Multi-Objective Optimization in Biofuel Systems: A Review by Ivan P. Malashin, Dmitry A. Martysyuk, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

“…The optimization of biofuel production involves balancing multiple conflicting objectives such as yield maximization, cost minimization, and environmental impact reduction. …”
Get full text

ScarNet: Development and Validation of a Novel Deep CNN Model for Acne Scar Classification With a New Dataset by Masum Shah Junayed, Md Baharul Islam, Afsana Ahsan Jeny, Arezoo Sadeghzadeh, Topu Biswas, A. F. M. Shahen Shah

“…In this paper, a novel automated acne scar classification system is proposed based on a deep Convolutional Neural Network (CNN) model. First, a dataset of 250 images from five different classes is collected and labeled by four well-experienced dermatologists. …”
Get full text

Avnet: learning attitude and velocity for vehicular dead reckoning using smartphone by adapting an invariant EKF by Long Qian, Xinchuang Lin, Xiaoguang Niu, Qihai Huang, Leilei Li, Guangyi Guo, Zexin Wang, Ruizhi Chen

“…The key components of the method are a Kalman filter with data-driven parameters adapter and a deep neural network that provides data-driven measurement estimation. A combined convolutional neural network and gated recurrent unit deep learning network, termed AVNet, is proposed to estimate the attitude and velocity of the vehicle. …”
Get full text

Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…Multiple ML models were constructed and optimized, with the convolutional neural network (CNN) model achieving the highest prediction accuracy at 99%. …”
Get full text

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. …”
Get full text

Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion by Abdolraheem Khader, Jingxiang Yang, Sara Abdelwahab Ghorashi, Ali Ahmed, Zeinab Dehghan, Liang Xiao

“…With the revolution of deep learning, the recent HS-MS image fusion techniques gained good outcomes by utilizing the power of the convolutional neural network (CNN) for feature extraction. …”
Get full text

Learning Spectral–Spatial-Former Deep Prior for Hyperspectral Image Superresolution by Zeinab Dehghan, Jingxiang Yang, Abdolraheem Khader, Jian Fang, Liang Xiao

“…To overcome this, we integrated our model into a deep convolutional neural network enhanced by a Transformer module for regularization. …”
Get full text

A Novel Federated Learning Framework for Sustainable and Efficient Breast Cancer Classification System (FL-L₂CNN-BCDet) by Nesma Abd El-Mawla, Mohamed A. Berbar, Nawal A. El-Fishawy, Mohamed A. El-Rashidy

“…The proposed DL architecture employs attention mechanisms, convolutional layers, and LSTMs to extract and analyze features and uses techniques to prevent overfitting and manage imbalanced data. …”
Get full text

SIGNETS: Neural Network Architectures for m-QAM Soft Demodulation by Aravind R. Voggu, Kanish R, Nishith Akula, Lohitaksh Maruvada, Takanori Shimizu, Madhav Rao

“…Through systematic Neural Architecture Search and Hyper-parameter optimization, we develop a family of Convolutional Neural Network architectures that demonstrate robust performance across challenging channel conditions, including multi-path fading, inter-symbol interference, and non-linear distortions, without requiring explicit channel estimation. …”
Get full text

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. …”
Get full text

Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres by Ahmed Rady, Oliver Fisher, Aly A. A. El-Banna, Haitham H. Emasih, Nicholas J. Watson

“…This study investigates colour vision and transfer learning to classify the grade of five long (Giza 86, Giza 90, and Giza 94) and extra-long (Giza 87 and Giza 96) staple cotton cultivars. Five Convolutional Neural networks (CNNs)—AlexNet, GoogleNet, SqueezeNet, VGG16, and VGG19—were fine-tuned, optimised, and tested on independent datasets. …”
Get full text

Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions by Thais de Castro Moraes, Jiancheng Qin, Xue-Ming Yuan, Ek Peng Chew

“…<i>Methods:</i> Addressing this gap, we introduce novel E2E deep learning frameworks that combine Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for resolving single-period inventory ordering decisions, also termed the Newsvendor Problem (NVP). …”
Get full text

Comparative Analysis of Machine Learning Algorithms for Antenna Alignments by Mohammad Al Bataineh, Dana I. Abu Abdoun, Mahmoud Al Ahmad

“…This study presents a comprehensive comparative analysis of three advanced machine learning models&#x2014;Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP)&#x2014;to predict antenna alignments based on S-parameter measurements. …”
Get full text

Innovative Expert-Based Tools for Spatiotemporal Shallow Landslides Mapping: Field Validation of the GOGIRA System and Ex-MAD Framework in Western Greece by Michele Licata, Francesco Seitone, Efthimios Karymbalis, Konstantinos Tsanakas, Giandomenico Fubelli

“…ExMAD applied a pre-trained U-Net convolutional neural network for automated temporal trend detection of landslide events. …”
Get full text

Machine Learning to Recognise ACL Tears: A Systematic Review by Julius Michael Wolfgart, Ulf Krister Hofmann, Maximilian Praster, Marina Danalache, Filippo Migliorini, Martina Feierabend

“…Deep learning algorithms in the form of convolutional neural networks (CNNs) were most frequently used. …”
Get full text

Investigation of a transformer-based hybrid artificial neural networks for climate data prediction and analysis by Shangke Liu, Ke Liu, Zheng Wang, Yuanyuan Liu, Bin Bai, Rui Zhao

“…While they have achieved some success, these models still face issues such as complexity, high computational cost, and insufficient handling of multivariable nonlinear relationships.MethodsIn light of this, this paper proposes a hybrid deep learning model based on Transformer-Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) to improve the accuracy of climate predictions. …”
Get full text

Synergistic image and point cloud processing of UAV data for urban flood modeling: point cloud smart thinning and curb mapping by P. A. P. Zamboni, H. Hendrickx, D. Sprute, H. Flatt, M. I. Rushdi, F. Brodrecht, A. Eltner

“…UAV flights were conducted to generate an initial orthoimage, which was used to train a convolutional neural network (CNN) segmentation model. …”
Get full text