Extracting neutron skin from elastic proton-nucleus scattering with deep neural network by G.H. Yang, Y. Kuang, Z.X. Yang, Z.P. Li

“…Based on the relativistic impulse approximation of proton-nucleus elastic scattering theory, the neutron density distribution and neutron skin thickness of 48Ca are estimated via the deep learning method. The neural-network-generated neutron densities are mainly compressed to be higher inside the nucleus compared with the results from the relativistic PC-PK1 density functional, resulting in a significant improvement on the large-angle scattering observables, both for the differential cross section and analyzing power. …”
Get full text

Computational intelligence in the identification of Covid-19 patients  by using KNN-SVM Classifier by shaymaa adnan

“…This research presents an advanced methodology employing deep learning techniques for the analysis of medical pictures pertaining to respiratory disorders. …”
Get full text

Artificial intelligence in green management and sustainability: A bibliometric survey by Niftiyev Ibrahim

“…The results show that the focus of AI and sustainability research has shifted from, for example, water management to broader environmental concerns and then to new analytical tools such as deep learning and blockchain. The most prolific and collaborative researchers come from India, China and the US and publish in already established international journals. …”
Get full text

Stock Price Prediction in the Financial Market Using Machine Learning Models by Diogo M. Teixeira, Ramiro S. Barbosa

“…This paper presents an analysis of stock price forecasting in the financial market, with an emphasis on approaches based on time series models and deep learning techniques. Fundamental concepts of technical analysis are explored, such as exponential and simple averages, and various global indices are analyzed to be used as inputs for machine learning models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and XGBoost. …”
Get full text

A Noble Classification Framework for Data Glove Classification of a Large Number of Hand Movements by Yuhuang Zheng

“…The movement classification algorithm is composed of downsampling in data preparation and a new deep learning network named the DBDF network. Bidirectional Long Short-Term Memory (BiLSTM) is the main part of the DBDF network. …”
Get full text

Evaluation of Image Enhancement Techniques for Vision-Based Navigation under Low Illumination by Mohamed Aladem, Stanley Baek, Samir A. Rawashdeh

“…The findings are relevant to a wide range of feature-based vision systems, such as tracking for augmented reality, image registration, localization, and mapping, as well as deep learning-based object detectors. As autonomous mobile robots are expected to operate under low-illumination conditions at night, evaluation is based on state-of-the-art systems for motion estimation, localization, and object detection.…”
Get full text

Performance and Analysis of FCN, U-Net, and SegNet in Remote Sensing Image Segmentation Based on the LoveDA Dataset by Yang Shuhao

“…This study utilizes the LoveDA dataset to investigate the segmentation performance of three classic deep learning models: Fully Convolutional Networks(FCN), U-Net, and SegNet, in both urban and rural scenarios. …”
Get full text

Utilizing Statistical Tests for Comparing Machine Learning Algorithms by Hozan Khalid Hamarashid

“…With classification and regression prediction models it can be conducted by utilizing traditional machine learning and deep learning methods. The difficulty is to identify whether or not the difference between two models is accurate. …”
Get full text

Can Artificial Intelligence Technology Help Achieving Good Governance: A Public Policy Evaluation Method Based on Artificial Neural Network by Zhinan Xu, Zijun Liu, Hang Luo

“…By leveraging empirical data and a deep learning model based on convolutional neural networks (CNN), the model achieves a high accuracy of 93.40%, surpassing most comparable models. …”
Get full text

The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network by Yinping Gao, Daofang Chang, Ting Fang, Yiqun Fan

“…The effective forecast of container volumes can provide decision support for port scheduling and operating. In this work, by deep learning the historical dataset, the long short-term memory (LSTM) recurrent neural network (RNN) is used to predict daily volumes of containers which will enter the storage yard. …”
Get full text

Single and Multiwavelength Detection of Coronal Dimming and Coronal Wave Using Faster R-CNN by Zongxia Xie, Chunyang Ji

“…Different from the methods used before, we introduce the idea of deep learning. We train single-wavelength and multiwavelength models based on Faster R-CNN. …”
Get full text

Dual Domain Swin Transformer based Reconstruction method for Sparse-View Computed Tomography by Jonas Van der Rauwelaert, Caroline Bossuyt, Jan Sijbers

“…Our method comprises three key blocks: sinogram upsampling via linear interpolation, initial reconstruction using deep learning in both domains, and residual refinement. …”
Get full text

Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3 by Chun Liu, Jian Li

“…Combining the target HSV color histogram features and LBP local features’ target, object recognition and selection are realized by using the deep learning model due to its efficiency in extracting object characteristics. …”
Get full text

Novel image registration algorithm for scene-matching navigation by Hongrui YANG, Qiju ZHU, Peixian CAO, Hao GU, Dongdong ZHAO

“…The results demonstrate that our method considerably improves computational efficiency while maintaining matching precision. Moreover, unlike deep learning algorithms that require extensive data training for generalization, our algorithm achieves the necessary level of generalization without such extensive training. …”
Get full text

Perbandingan Prediksi Penggunaan Listrik dengan Menggunakan Metode Long Short Term Memory (LSTM) dan Recurrent Neural Network (RNN) by Nurfatima Selle, Novanto Yudistira, Candra Dewi

“…Our method uses are Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM), which is a deep learning architecture that able to capture time-series data. …”
Get full text

Neural Network and Hybrid Methods in Aircraft Modeling, Identification, and Control Problems by Gaurav Dhiman, Andrew Yu. Tiumentsev, Yury V. Tiumentsev

“…Such a variant opens up the possibility of involving deep learning technology in the construction of motion models for controlled systems. …”
Get full text

Framework for smartphone-based grape detection and vineyard management using UAV-trained AI by Sergio Vélez, Mar Ariza-Sentís, Mario Triviño, Antonio Carlos Cob-Parro, Miquel Mila, João Valente

“…Recent technological and machine learning advancements, particularly in deep learning, have provided the tools necessary to create more efficient, automated processes that significantly reduce the time and effort required for these tasks. …”
Get full text

Automatic MRI Lymph Node Annotation From CT Labels by Souraja Kundu, Yuji Iwahori, M. K. Bhuyan, Manish Bhatt, Boonserm Kijsirikul, Aili Wang, Akira Ouchi, Yasuhiro Shimizu

“…This study focuses on creating automatic lymph node annotation in MRI images using available CT annotations via deep-learning models. Training such models typically requires partial MRI labels for semi-supervision. …”
Get full text

Perbandingan Arsitektur Convolutional Neural Network Pada Klasifikasi Pneumonia, COVID-19, Lung Opacity, dan Normal Menggunakan Citra Sinar-X Thoraks by Agung Wahyu Setiawan

“…Several studies have been conducted using a deep learning approach based on Convolutional Neural Networks (CNN) architecture. …”
Get full text

AiGPro: a multi-tasks model for profiling of GPCRs for agonist and antagonist by Rahul Brahma, Sunghyun Moon, Jae-Min Shin, Kwang-Hwi Cho

“…Scientific Contribution We introduce a deep learning-based multi-task model to generalize the agonist and antagonist bioactivity prediction for GPCRs accurately. …”
Get full text