Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke by Gökalp Çınarer

“…A total of 30 different object detection models, including the proposed model, were run with the extended Wildfire Smoke dataset, and the results were compared. …”
Get full text

Detection of IPv6 routing attacks using ANN and a novel IoT dataset by Murat Emec

“…Using artificial intelligence and machine-learning techniques, a performance evaluation was performed on four different artificial neural network models (convolutional neural network, deep neural network, multilayer perceptron structure, and routing attack detection-fed forward neural network [RaD-FFNN]). …”
Get full text

Detection of Welding Defects Tracked by YOLOv4 Algorithm by Yunxia Chen, Yan Wu

“…The improvements include optimizing the stacking method of residual blocks, modifying the activation functions for different convolutional layers, and eliminating the downsampling layer in the PANet (Pyramid Attention Network) to preserve edge information. …”
Get full text

An Improved CEEMDAN-FE-TCN Model for Highway Traffic Flow Prediction by Heyao Gao, Hongfei Jia, Lili Yang

“…Finally, the Temporal Convolutional Network (TCN) is adopted to predict the recombined subsequences, and the final prediction result is reconstructed. …”
Get full text

D2D cooperative caching strategy based on graph collaborative filtering model by Ningjiang CHEN, Linming LIAN, Pingjie OU, Xuemei YUAN

“…A D2D cooperative caching strategy based on graph collaborative filtering model was proposed for the problem of difficulty in obtaining sufficient data to predict user preferences in device-to-device (D2D) caching due to the limited signal coverage of base stations.Firstly, a graph collaborative filtering model was constructed, which captured the higher-order connectivity information in the user-content interaction graph through a multilayer graph convolutional neural network, and a multilayer perceptron was used to learn the nonlinear relationship between users and content to predict user preferences.Secondly, in order to minimize the average access delay, considering user preference and cache delay benefit, the cache content placement problem was modeled as a Markov decision process model, and a cooperative cache algorithm based on deep reinforcement learning was designed to solve it.Simulation experiments show that the proposed caching strategy achieves optimal performance compared with existing caching strategies for different content types, user densities, and D2D communication distance parameters.…”
Get full text

Series-arc-fault diagnosis using feature fusion-based deep learning model by Won-Kyu Choi, Se-Han Kim, Ji-Hoon Bae

“…The model is trained stagewise for various features in the time and frequency domains using a one-dimensional convolutional neural network combined with a long short-term memory model that uses an attention mechanism to accurately detect arc-fault features. …”
Get full text

A deep learning approach for accurate COVID-19 diagnosis from x-ray images using OBLMPA by Xiaohua Li, Shuai Fu

“…The suggested method proposes an optimal Convolutional Neural Network (CNN) to provide a diagnosis system with higher accuracy. …”
Get full text

Towards real-world monitoring scenarios: An improved point prediction method for crowd counting based on contrastive learning. by Rundong Cao, Jiazhong Yu, Ziwei Liu, Qinghua Liang

“…Initially, we construct contrastive cropped samples and feed them into a convolutional neural network to predict head points of each image patch. …”
Get full text

Meta-Learning-Based Lightweight Method for Food Calorie Estimation by Jinlin Ma, Yuetong Wan, Ziping Ma

“…Secondly, within the feature extraction module, a large convolutional kernel is proposed to provide a larger receptive field, which aims to capture more shape and semantic information and minimize information loss. …”
Get full text

Using deep learning models to decode emotional states in horses by Romane Phelipon, Lea Lansade, Misbah Razzaq

“…We perform data exploration and use different cropping methods, mainly based on Yolo and Faster R-CNN, to create two new datasets: 1) the cropped body, and 2) the cropped head dataset. …”
Get full text

Statistical Data-Generative Machine Learning-Based Credit Card Fraud Detection Systems by Xiaomei Feng, Song-Kyoo Kim

“…We analyzed two distinct datasets: one consisting of European cardholders and the other from American Express, applying multiple machine learning algorithms, including Artificial Neural Networks, Convolutional Neural Networks, and Gradient Boosted Decision Trees, as well as others. …”
Get full text

Research on information extraction methods for historical classics under the threshold of digital humanities by Lifan HAN, Zijing JI, Zirui CHEN, Xin WANG

“…Digital humanities aims to use modern computer network technology to help traditional humanities research.Classical Chinese historical books are the important basis for historical research and learning, but since their writing language is classical Chinese, it is quite different from the vernacular Chinese in grammar and meaning, so it is not easy to read and understand.In view of the above problems, the solution to extract entities and relations in historical books based on pre-trained models was proposed to obtain the rich information contained in historical texts effectively.The model usedmulti-level pre-training tasks instead of BERT's original pre-training tasks to fully capture semantic information.And the model added some structures such as convolutional layers and sentence-level aggregations on the basis of the BERT model to optimize the generated word representation further.Then, in view of the scarcity of classical Chinese annotation data, a crowdsourcing system for the task of labeling historical classics was constructed, high-quality, large-scale entity and relation data was obtained and the classical Chinese knowledge extraction dataset was constructed.So it helped to evaluate the performance of the model and fine-tune the model.Experiments on the dataset constructed in this paper and on the GulianNER dataset demonstrated the effectiveness of the model proposed in this paper.…”
Get full text

Advancements in Image Classification: From Machine Learning to Deep Learning by Cheng Haoran

“…By comparing the performance of different methods, this paper aims to provide references for researchers in the realm of image classification, promoting further development in this area.…”
Get full text

ECG Signal Classification Using MODWT and CNN for Early Detection of Cardiac Abnormalities by Mohammad Yusuf Hamadani, Zainul Abidin, Muhammad Fauzan Edy Purnomo

“…This study proposes a method that integrates Maximal Overlap Discrete Wavelet Transform (MODWT) for feature extraction with a Convolutional Neural Network (CNN) to enhance classification performance. …”
Get full text

Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. by Yuan Wang, Shaolin Hu

“…Additionally, it presents an intelligent system design method for fault tracing in compressors and localization of faults from different sources. This method starts from petrochemical big data and consists of three parts: fault dynamic knowledge graph construction, instrument data sliding fault-tolerant filtering, and the fusion and reasoning of fault dynamic knowledge graph and instrument data variation monitoring. …”
Get full text

A Comprehensive Investigation of Fraud Detection Behavior in Federated Learning by Sun Rui

“…This research delves into the application of Federated Learning (FL) models for detecting fraud across different financial bodies. FL facilitates decentralized training of models using local data, ensuring privacy, crucial for handling sensitive financial data. …”
Get full text

Internet of Things-Based Smart Infant-Incubators Using Machine Learning Analysis by Mahmoud Gamal, Ibrahim Radi, Amr Yousef, and Ali Gaber Mohamed Ali

“…A convolutional neural network (CNN) algorithm is used to take neonatal care a step further. …”
Get full text

Adversarial liveness detector: Leveraging adversarial perturbations in fingerprint liveness detection by Antonio Galli, Michela Gravina, Stefano Marrone, Domenico Mattiello, Carlo Sansone

“…In this work, a novel FPAD approach based on Convolutional Neural Networks (CNNs) and on an ad hoc adversarial data augmentation strategy designed to iteratively increase the considered detector robustness is proposed. …”
Get full text

DMA‐Net: A dual branch encoder and multi‐scale cross attention fusion network for skin lesion segmentation by Guangyao Zhai, Guanglei Wang, Qinghua Shang, Yan Li, Hongrui Wang

“…Additionally, to enhance the feature interaction and fusion of local and global information, a multi‐scale cross attention fusion module is adopted to cross‐merge features in different directions and at different scales, maximizing the advantages of the dual‐branch encoder and achieving precise segmentation of skin lesions. …”
Get full text

Fast segmentation and multiplexing imaging of organelles in live cells by Karl Zhanghao, Meiqi Li, Xingye Chen, Wenhui Liu, Tianling Li, Yiming Wang, Fei Su, Zihan Wu, Chunyan Shan, Jiamin Wu, Yan Zhang, Jingyan Fu, Peng Xi, Dayong Jin

“…We further show that transfer learning can predict both 3D and 2D datasets from different microscopes, different cell types, and even complex systems of living tissues. …”
Get full text