Real-time event detection using recurrent neural network in social sensors by Van Quan Nguyen, Tien Nguyen Anh, Hyung-Jeong Yang

“…We proposed an approach for temporal event detection using deep learning and multi-embedding on a set of text data from social media. …”
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Survey of artificial intelligence data security and privacy protection by Kui REN, Quanrun MENG, Shoukun YAN, Zhan QIN

“…Artificial intelligence and deep learning algorithms are developing rapidly.These emerging techniques have been widely used in audio and video recognition, natural language processing and other fields.However, in recent years, researchers have found that there are many security risks in the current mainstream artificial intelligence model, and these problems will limit the development of AI.Therefore, the data security and privacy protection was studied in AI.For data and privacy leakage, the model output based and model update based problem of data leakage were studied.In the model output based problem of data leakage, the principles and research status of model extraction attack, model inversion attack and membership inference attack were discussed.In the model update based problem of data leakage, how attackers steal private data in the process of distributed training was discussed.For data and privacy protection, three kinds of defense methods, namely model structure defense, information confusion defense and query control defense were studied.In summarize, the theoretical foundations, classic algorithms of data inference attack techniques were introduced.A few research efforts on the defense techniques were described in order to provoke further research efforts in this critical area.…”
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Adapting physics-informed neural networks to improve ODE optimization in mosquito population dynamics. by Dinh Viet Cuong, Branislava Lalić, Mina Petrić, Nguyen Thanh Binh, Mark Roantree

“…The integration of physics principles enables the method to require less data while maintaining the robustness of deep learning in modelling complex dynamical systems. …”
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Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks by Meng-Hui Wang, Jing-Xuan Hong, Shiue-Der Lu

“…The signal is processed by the SDP method to generate characteristic images for fault diagnosis. Finally, a deep learning algorithm is used to evaluate the state of the lithium battery. …”
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Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data by Feng Zhihao

“…Through an analysis of these methods, the research demonstrates how applying advanced deep learning algorithms and cross-attention processes has significantly improved prediction robustness and accuracy. …”
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Overview of Feature Extraction and Recognition Methods for Fiber Optic Vibration Signals by QIAN Junxia, GUO Jiaxing

“…This article reviews the feature extraction methods combining the time domain, frequency domain, and time-frequency domain of optical fiber perimeter signals, and the classification and recognition methods based on vector machines, neural networks, and deep learning. It specifically discusses the principles and application scenarios of various algorithms, and conducts a comparative analysis of their advantages and disadvantages.…”
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A Depth Camera-Based Intelligent Method for Identifying and Quantifying Pavement Diseases by Hao Bai, Xiangyu Hu, Fei Chen, Zhiyong Liao, Kai Li, Guangjiong Ran, Fengni Wei

“…The results show that the sensor can achieve plane fitting at investigated working distances by means of a deep learning network. In addition, two pavement examples show that the detection method can save a lot of manpower and improve the detection efficiency with certain accuracy.…”
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Predicting Cardiovascular Diseases Using Neural Networks: Validation With the SCORE2 Risk Assessment Tool by Mohammed Marouane Saim, Hassan Ammor, Mohamed Alami

“…Given the significant global health impact of CVDs, our research aims to assess the effectiveness of deep learning-based models compared with traditional methods. …”
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IRSnet: An Implicit Residual Solver and Its Unfolding Neural Network With 0.003M Parameters for Total Variation Models by Yuanhao Gong

“…However, the traditional iterative solvers require a large number of iterations to converge, while deep learning solvers have a huge number of parameters, hampering their practical deployment. …”
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A Survey on Automatic Face Recognition Using Side-View Face Images by Pinar Santemiz, Luuk J. Spreeuwers, Raymond N. J. Veldhuis

“…Traditionally overlooked, recent advancements in deep learning have brought side-view poses to the forefront of research attention. …”
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Harnessing artificial intelligence and machine learning for fraud detection and prevention in Nigeria by Oluwaseun Isaac Odufisan, Osekhonmen Victory Abhulimen, Erastus Olarenwaju Ogunti

“…We explore various AI methodologies, including supervised, unsupervised, and deep learning. We discuss their applications in anomaly detection, behavioural analysis, risk scoring, and network analysis. …”
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Emotion Monitoring for Preschool Children Based on Face Recognition and Emotion Recognition Algorithms by Guiping Yu

“…For previous emotion recognition focusing on faces, we propose to obtain more comprehensive information from faces, gestures, and contexts. Using the deep learning approach, we design a more lightweight network structure to reduce the number of parameters and save computational resources. …”
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Telemedicine in Diabetic Retinal Screening: Pre- and Post-COVID-19 Challenges a New Perspective by Arshi Baig, Azhar Zafar

“…The image analysis by AI and deep-learning algorithms offers insight into the future of screening in diabetes. …”
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Research on unsupervised domain adaptive bearing fault diagnosis method by WU ShengKai, SHAO Xing, WANG CuiXiang, GAO Jun

“…Aiming at the problem that the bearing fault diagnosis algorithm based on deep learning has poor diagnosis performance when the fault samples are lack of labels in different working conditions and real environmentsly, an unsupervised domain adaptive bearing fault diagnosis method was proposed to realize the unsupervised fault diagnosis of bearings under different working conditions. …”
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Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets by Kumar Abhishek, Aditi Jain, Ghassan Hamarneh

“…Abstract The remarkable progress of deep learning in dermatological tasks has brought us closer to achieving diagnostic accuracies comparable to those of human experts. …”
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Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning by Spyridon Chavlis, Panayiota Poirazi

“…Abstract Artificial neural networks (ANNs) are at the core of most Deep Learning (DL) algorithms that successfully tackle complex problems like image recognition, autonomous driving, and natural language processing. …”
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Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN by Mengmeng Huang, Fang Liu, Xianfa Meng

“…The development of deep learning has enabled it to be applied to SAR ATR. …”
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Recognition of life-threatening arrhythmias by ECG scalograms by A.P. Nemirko, A.S. Ba Mahel, L.A. Manilo

“…For arrhythmia classification, the AlexNet neural network with a well-known deep learning architecture, which is commonly used in image classification tasks, is used. …”
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EEG emotion recognition based on parallel separable convolution and label smoothing regularization by Yong ZHANG, Jikui LIU, Wenlong KE

“…In recent years, emotion recognition methods based on deep learning and electroencephalogram (EEG) have achieved good results.However, existing methods still have issues such as incomplete extraction of emotional features from EEG and significant impact from artificially mislabeled emotional labels.A parallel separable convolution and label smoothing regularization (PSC-LSR) network model was proposed.Firstly, through the attention mechanism, EEG important time points and important channels were given greater weight to obtain shallow emotional features of EEG.Secondly, a parallel separable convolution module was used to comprehensively extract EEG emotional information and obtain deep emotional features.Finally, the emotion label smoothing regularization method was used to optimize the model parameters, which increased model’s fault tolerance probability for incorrect labels, enhanced the generalization and robustness of the network model, and improved accuracy of EEG emotion recognition.The proposed method has been validated in two datasets, in which the average accuracy rates of arousal and valence dimensions in the DEAP dataset reaches 99.23% and 99.13%, respectively.In the Dreamer dataset, the average accuracy rates for both arousal and valence dimensions reaches 97.33% and 97.25%.…”
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A Comparative Study of Supervised and Self-Supervised Denoising Techniques for Defect Segmentation in Industrial CT Imaging by Virginia Florian, Jiayang Shi, Willem Jan Palestijn, Daniël M. Pelt, K. Joost Batenburg, Thomas Lang, Christoph Heinzl, Christian Kretzer, Stefan Kasperl, Dominik Wolfschläger, Robert H. Schmitt

“…Different methods, the most recent ones based on deep learning, have been proposed to address both CT artifacts and noise. …”
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