Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS by Mengyuan Zhao, Beibei Cui, Yuehao Yu, Xiaoyi Zhang, Jiaxin Xu, Fengzheng Shi, Liang Zhao

“…To achieve accurate detection of tomato fruit maturity and enable automated harvesting in natural environments, this paper presents a more lightweight and efficient maturity detection algorithm, YOLO-DGS, addressing the challenges of subtle maturity differences between regular and cherry tomatoes, as well as fruit occlusion. …”
Get full text

Analysis of baseball behavior recognition model based on Dual-GCN improved by motion weights by Ji Li

“…A motion weight improvement model based on dual-graph convolutional network is proposed. The new model takes a dual-graph convolutional network for behavior recognition and key region segmentation of baseball video images, and enhances the correlation and contribution between characters through motion weights. …”
Get full text

Enhancing the Power of CNN Using Data Augmentation Techniques for Odia Handwritten Character Recognition by Mamatarani Das, Mrutyunjaya Panda, Shreela Dash

“…This paper shows the performance of five different machine learning models that uses a convolutional neural network to identify handwritten characters in response to handwritten datasets that are manipulated and expanded using several augmentation techniques to create variation and increase the volume of the data in the given dataset. …”
Get full text

FAULT DIAGNOSIS METHOD OF DIESEL ENGINE BASED ON PCA-EDT-CNN (MT) by BAI YunJie, JIA XiSheng, LIANG QingHai, MA YunFei, WEN Liang

“…Aiming at the problem that the vibration signal in diesel engine fault diagnosis is non-stationary and nonlinear, and the original signal is directly input into the Convolutional Neural Network（CNN） for fault diagnosis with poor effect, a new method based on PCA-EDT-CNN is proposed. …”
Get full text

Quantum‐inspired Arecanut X‐ray image classification using transfer learning by Praveen M. Naik, Bhawana Rudra

“…A comparative analysis of transfer learning‐based classification, employing both a traditional convolutional neural network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. …”
Get full text

A lightweight CNN-LSTM hybrid model for land cover classification in satellite imagery by Nowshad Hasan, Md. Saiful Islam

“…However, traditional Convolutional Neural Networks (CNNs) require high computational demand, a large number of parameters, and a long training time for classification tasks. …”
Get full text

Research and development of thick plate shape prediction system based on industrial big data by Yufei MA, Changxin LIU, Wei KONG, Jinliang DING

“…Thick plate shape is one of the important indicators to measure the quality of thick plate products.The timely prediction of the final plate shape in production is of great significance for adjusting the operation and control of thick plate production.In actual industrial production, thick plate data has many characteristics, such as multiple coupling information, large amount of redundant information, and multi-source heterogeneity of data.Combining the needs of thick plate shape prediction, a thick plate shape prediction system was designed and developed.The data dump function was used to filter and preprocess the industrial big data to remove the coupling information and redundant variables in the data.LSTM neural network, convolutional neural network and 3D convolutional neural network were used to extract data features from data of different dimensions, and the features were fused based on the maximum mutual information coefficient to establish an integrated learning prediction model, which effectively solved the modeling difficulties caused by multi-source heterogeneous data.The actual industrial data of a domestic thick plate production line was used for verification, and the results showed the effectiveness of the developed system.…”
Get full text

Optimized AlexNet Pruning for Edge-Based Medical Diagnostics by Yasser A. Amer, Hassan I. Saleh, Omar A. Nasr

“…The results reveal a clear difference between fully connected (FC) and convolutional layers: pruning FC layers substantially reduces memory consumption, while pruning convolutional layers significantly boosts inference speed. …”
Get full text

LMVT: A hybrid vision transformer with attention mechanisms for efficient and explainable lung cancer diagnosis by Jesika Debnath, Al Shahriar Uddin Khondakar Pranta, Amira Hossain, Anamul Sakib, Hamdadur Rahman, Rezaul Haque, Md.Redwan Ahmed, Ahmed Wasif Reza, S M Masfequier.Rahman Swapno, Abhishek Appaji

“…Furthermore, we integrate attention mechanisms based on the Convolutional Block Attention Module (CBAM) and feature selection techniques derived from the Simple Gray Level Difference Method (SGLDM) to improve discriminative focus and minimize redundancy. …”
Get full text

Noise-Robust Local Ternary Pattern Center for Noisy Texture Classification by Farhan A. Alenizi, Mokhtar Mohammadi, Mohammad Hossein Shakoor

“…The more level of noise the more number of applying average kernel must be convolved to each texture. Convolution is a time-consuming operation, so here, a fast average method is proposed that can do this operation around 2 to 3 times faster than the traditional convolution method. …”
Get full text

Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification by Stefano Ferretti, Gabriele D'Angelo, Vittorio Ghini

“…Based on the dataset analysis, we experiment with different subsets of features. Our findings suggest that the use of Graph Neural Network convolutions, combined with a final linear layer and skip connections, allow for an improvement in the state-of-the-art results, especially when Chebyshev and GATv2 convolutions are used.…”
Get full text

Scan path visualization and comparison using visual aggregation techniques by Vsevolod Peysakhovich, Christophe Hurter

“…We demonstrate the use of different visual aggregation techniques to obtain non-cluttered visual representations of scanpaths. …”
Get full text

Dynamic focusing of an ultrasonic be am by means of a phased annular array using a pulse technique by Tamara KUJAWSKA

“…The time-dependent pressure for any piston velocity motion may then be computed by a convolution of the piston velocity with the appropriate impulse response. …”
Get full text

A Tank Experiment of the Autonomous Detection of Seabed-Contacting Segments for Submarine Pipelaying Operations by Bo Wang, Jie Wang, Chen Zheng, Ye Li, Jian Cao, Yueming Li

“…To address this problem, we propose a cascade attention module and a prefusion module with a convolutional neural network. The cascade attention module samples the feature maps in a non-convolutional form to realize the interaction between structure and channels, and the attention map is generated by cascading attention. …”
Get full text

Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology Images by Amit Kumar Chanchal, Shyam Lal, Shilpa Suresh

“…The proposed Robust CNN (RoCNN) architecture is capable of learning features at varying convolutional filter sizes because of the inception modules employed in it. …”
Get full text

High-Precision Qiantang River Water Body Recognition Based on Remote Sensing Image by Hongcui Wang, Yihong Zheng, Ouxiang Chen

“…River water body identification plays an important role in flood monitoring, urban planning, Thus, it attracts more interests of studying and investigating, especially based on remote sensing technology, The traditional NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) methods are widely used, However, these methods need manual intervention to select the threshold, In order to achieve automatic water body recognition, deep learning methods, such as CNN, VGG, Unet etc., are applied, Currently there are few works on the water body identification of Qiantang River, Here, one major challenge for high-precision Qiantang water body recognition is the real complex water body features and complicated geological environment, They are the dense distribution of small water bodies in the Qiantang River Basin, large differences in water body nutrition, and the high complexity of surface environments such as mountains and plains, We investigated two traditional and several deep learning methods and found that WatNet was the most effective model for Qiantang River, This model adopts the structure based on encoder-decoder convolutional network, It uses MobileNetV2 as the encoder, which makes it extract more water feature information while being lightweight and uses ASPP module to capture global multi-scale features in deep layers, Experimental results show that the MIoU and OA (Overall Accuracy) can reach 0. 97 and 0. 99 respectively.…”
Get full text

Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis by Kevin Barrera-Llanga, Jordi Burriel-Valencia, Angel Sapena-Bano, Javier Martinez-Roman

“…This analysis introduces a new approach by demonstrating how different convolutional blocks capture particular features: the first convolutional block captures signal shape, while the second identifies background features. …”
Get full text

Ulnar variance detection from radiographic images using deep learning by Sahar Nooh, Abdelrahim Koura, Mohammed Kayed

“…Abstract Ulnar variance is a relative length difference in the wrist between the ulna and radius bones. …”
Get full text

Rancang Bangun Aplikasi Berbasis Android untuk Perbaikan Kualitas Citra Tanaman Atas Perbedaan Spesifikasi Kamera Smartphone pada Prediksi Kandungan Pigmen Fotosintesis Secara Real... by Felix Adrian Tjokro Atmodjo, Kestrilia Rega Prilianti, Hendry Setiawan

“…However, Fuzzy Piction is still not invariant to differences in image quality that can occur due to differences in smartphone camera specifications. …”
Get full text

Classification of pulmonary diseases from chest radiographs using deep transfer learning. by Muneeba Shamas, Huma Tauseef, Ashfaq Ahmad, Ali Raza, Yazeed Yasin Ghadi, Orken Mamyrbayev, Kymbat Momynzhanova, Tahani Jaser Alahmadi

“…This paper has explored the effectiveness of Convolutional Neural Networks and transfer learning to improve the predictive outcomes of fifteen different pulmonary diseases using chest radiographs. …”
Get full text