Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems by Shize Huang, Xiaowen Liu, Xiaolu Yang, Zhaoxin Zhang, Lingyu Yang

“…Trams have increasingly deployed object detectors to perceive running conditions, and deep learning networks have been widely adopted by those detectors. Growing neural networks have incurred severe attacks such as adversarial example attacks, imposing threats to tram safety. …”
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Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm by Bernard Marie Tabi Fouda, Dezhi Han, Bowen An, Xiangzhi Chen

“…A feature vector is formed, and multiple types of probabilistic neural networks (PNNs) are performed on it to determine whether intrusion events occur. …”
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Shale-pore Semantic Segmentation Network Based on Pseudo-labeling by Chenzhang WANG, Yanfei WANG, Zhijing BAI

“…Furthermore, pseudo-labeling enhances the generalizability of neural networks while addressing the high time cost of manual annotation required in previous deep-learning segmentation methods, whereas ensemble learning stably increases the model’s accuracy.…”
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Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia by Bojana Ivošević, Nina Pajević, Sanja Brdar, Rana Waqar, Maryam Khan, João Valente

“…Our method compares the efficacy and accuracy of object-based image analysis (OBIA) combined with random forest and convolutional neural networks (CNN) for land cover classification. We produced detailed land cover maps for 27 varied landscapes across Serbia, identifying nine unique land cover classes and assessing human impact on natural habitats. …”
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Experimental Analysis of Neural Approaches for Synthetic Angle-of-Attack Estimation by Angelo Lerro, Piero Gili, Mario Luca Fravolini, Marcello Napolitano

“…In the class of data-driven observers, multilayer perceptron neural networks are widely used to approximate the input-output mapping angle-of-attack function. …”
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Gesture Recognition with Residual LSTM Attention Using Millimeter-Wave Radar by Weiqing Bai, Siyu Chen, Jialiang Ma, Ying Wang, Chong Han

“…To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper proposes a gesture recognition algorithm based on <b>R</b>esNet <b>L</b>ong Short-Term Memory with an <b>A</b>ttention Mechanism (RLA). …”
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Non-intrusive load monitoring based on time-enhanced multidimensional feature visualization by Tie Chen, Yimin Yuan, Jiaqi Gao, Shinan Guo, Pingping Yang

“…The ECA-ResNet34 network model is used for load identification, avoiding the problems of network degradation and training difficulties caused by the excessive depth of traditional convolutional neural networks (CNN), and achieving efficient monitoring of household loads. …”
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Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms by Rivindu Weerasekera, Mohan Sridharan, Prakash Ranjitkar

“…As a step towards addressing these problems, this paper investigates the ability of Artificial Neural Networks, Random Forests, and Support Vector Regression algorithms to reliably model traffic flow at different data resolutions and respond to unexpected traffic incidents. …”
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A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting by Guangyuan Xing, Shaolong Sun, Jue Guo

“…According to the principle of “divide and conquer,” we propose a novel decomposition ensemble learning approach by integrating ensemble empirical mode decomposition (EEMD), artificial neural networks (ANNs), and adaptive particle swarm optimization (APSO) for forecasting PM2.5 concentrations. …”
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Design Space Approach in Optimization of Fluid Bed Granulation and Tablets Compression Process by Jelena Djuriš, Djordje Medarević, Marko Krstić, Ivana Vasiljević, Ivana Mašić, Svetlana Ibrić

“…Percent of paracetamol released and tablets hardness were determined as critical quality attributes. Artificial neural networks (ANNs) were applied in order to determine design space. …”
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Peripheral membrane protein endophilin B1 probes, perturbs and permeabilizes lipid bilayers by Arni Thorlacius, Maksim Rulev, Oscar Sundberg, Anna Sundborger-Lunna

“…Here we present the highest resolution cryo-EM structure of a BAR protein to date and the first structures of a BAR protein bound to a lipid bicelle. Using neural networks, we can effectively sort particle species of different stoichiometries, revealing the tremendous flexibility of post-membrane binding, pre-polymer BAR dimer organization and membrane deformation. …”
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Action Video Game Experience Related to Altered Large-Scale White Matter Networks by Diankun Gong, Weiyi Ma, Jinnan Gong, Hui He, Li Dong, Dan Zhang, Jianfu Li, Cheng Luo, Dezhong Yao

“…WM network modulates the distribution of action potentials, coordinating the communication between brain regions and acting as the framework of neural networks. And various types of cognitive deficits are usually accompanied by impairments of WM networks. …”
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Deep learning in defects detection of PV modules: A review by Katleho Masita, Ali Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…This review explores the application of deep learning (DL) methods, particularly convolutional neural networks (CNNs), in the identification and classification of PV module defects. …”
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EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning by Waqas Ali, Wesam Alsabban, Muhammad Shahbaz, Ali Al-Laith, Bassam Almogadwy

“…To address this, we developed a method integrating convolutional neural networks (CNN) and transformer models for direct EF estimation from ultrasound video scans. …”
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Face and Voice Recognition-Based Emotion Analysis System (EAS) to Minimize Heterogeneity in the Metaverse by Surak Son, Yina Jeong

“…It comprises three neural networks: the Facial Emotion Analysis Model (FEAM), which classifies emotions using facial landmarks; the Voice Sentiment Analysis Model (VSAM), which detects vocal emotions even in noisy environments using MCycleGAN; and the Metaverse Emotion Recognition Model (MERM), which integrates FEAM and VSAM outputs to infer overall emotional states. …”
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A Data-Driven Method for Supporting Self-Adapt Large-Scale Group Decision-Making: A Case Study on Resilient Design of Firm’s Product by Houxue Xia, Mingwei Liu, Jingyao Jiao, Huagang Tong, Haifeng Zhang

“…First, recurrent neural networks (RNNs) have been proposed as a data-driven method to effectively learn and predict experts’ preferences. …”
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Transfer Learning for CNN-Based Damage Detection in Civil Structures with Insufficient Data by Mona Chamangard, Gholamreza Ghodrati Amiri, Ehsan Darvishan, Zahra Rastin

“…In this study, compact one-dimensional (1D) convolutional neural networks (CNNs) are utilized that require less data for training. …”
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Automatic Recommendation Algorithm for Video Background Music Based on Deep Learning by Hong Kai

“…In terms of model design, this paper conducts auxiliary information mining based on the diversity and structural differences of auxiliary information, uses an improved stack autoencoder to learn user’s interests, and uses convolutional neural networks to mine hidden features of video background music. …”
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An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning by Ayesha Jabeen, Muhammad Yasir, Yasmeen Ansari, Sadaf Yasmin, Jihoon Moon, Seungmin Rho

“…Subsequently, to compute coefficients, we conducted a regression analysis using one of the more sophisticated approaches: single-layer neural networks and ordinary least square regression. …”
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Dynamic Development Analysis of Complex Network Research: A Bibliometric Analysis by Wei Zhou, Yuting Pan, Qiu Shu, Sun Meng

“…Through emergent analysis, we found that the latest hot research trends are the study of infectious diseases and applications in neural networks. At the same time, through the main path analysis, we find the essential literature to elaborate on the development context of a dynamic complex network at different time points. …”
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