Knee Osteoarthritis Diagnosis With Unimodal and Multi-Modal Neural Networks: Data From the Osteoarthritis Initiative by Xin Yu Teh, Pauline Shan Qing Yeoh, Tao Wang, Xiang Wu, Khairunnisa Hasikin, Khin Wee Lai

“…Contrary to existing literature, our findings reveal that unimodal neural networks using only imaging features outperform multi-modal networks, suggesting unimodal models might suffice in certain applications.…”
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A Weighted-Transfer Domain-Adaptation Network Applied to Unmanned Aerial Vehicle Fault Diagnosis by Jian Yang, Hairong Chu, Lihong Guo, Xinhong Ge

“…With the development of UAV technology, the composition of UAVs has become increasingly complex, interconnected, and tightly coupled. Fault features are characterized by weakness, nonlinearity, coupling, and uncertainty. …”
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Comparing 2D and 3D Feature Extraction Methods for Lung Adenocarcinoma Prediction Using CT Scans: A Cross-Cohort Study by Margarida Gouveia, Tânia Mendes, Eduardo M. Rodrigues, Hélder P. Oliveira, Tania Pereira

“…Next, a deep learning approach, based on a Residual Neural Network and a Transformer-based architecture, was utilised. …”
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Employing convolutional neural networks and explainable artificial intelligence for the detection of seizures from electroencephalogram signal by Tamilarasi Kathirvel Murugan, Anush Kameswaran

“…Evaluation criteria like specificity and accuracy are used to assess the models' performance. …”
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SeisDetNet: Artificial neural network for seismic event detection. Part 1: Architecture by S.A. Imashev, A.V. Aladev

“…Specifically, we selected waveforms from 27 seismic stations located in Kyrgyzstan and surrounding areas from the KRNET, KNET, KZ, G, and TJ networks, present in the STEAD database. The model architecture is a combination of a convolutional network, designed to extract key features for class separation, and a fully connected network for the task of classifying the input record as either a seismic event or seismic noise. …”
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Graph neural network structural limitation for thermal simulation and architecture optimization through rating system by Pierre Hembert, Chady Ghnatios, Julien Cotton, Francisco Chinesta

“…Abstract Graph neural networks are well suited for physics based simulation. …”
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Corrosion type identification in flanged joints using recurrent neural networks on electrochemical noise measurements by Soroosh Hakimian, Abdel-Hakim Bouzid, Lucas A. Hof

“…This study applies recurrent neural networks (RNNs) to automate corrosion type identification on flange surfaces using raw EN signals from spontaneous electrochemical reactions. …”
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A Case Study on Monolith to Microservices Decomposition with Variational Autoencoder-Based Graph Neural Network by Rokin Maharjan, Korn Sooksatra, Tomas Cerny, Yudeep Rajbhandari, Sakshi Shrestha

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A PEFKS- and CP-ABE-Based Distributed Security Scheme in Interest-Centric Opportunistic Networks by Fei Wang, YongJun Xu, Lin Wu, Longyijia Li, Dan Liu, Liehuang Zhu

“…Security is a crucial issue in distributed applications of multihop wireless opportunistic network due to the features of exposed on the fly communication, relaxed end-to-end connectivity, and vague destinations literately. …”
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Application of Artificial Neural Network Models in Segmentation and Classification of Nodules in Breast Ultrasound Digital Images by Karem D. Marcomini, Antonio A. O. Carneiro, Homero Schiabel

“…Then, five segmentation techniques were investigated to determine the most concise representation of the lesion contour, enabling us to consider the neural network SOM as the most relevant. After the delimitation of the object, the most expressive features were defined to the morphological description of the finding, generating the input data to the neural Multilayer Perceptron (MLP) classifier. …”
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An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method by Deepa Devasenapathy, Kathiravan Kannan

“…Using these methods, low featured information is generated with respect to the user in the road network. …”
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Kinship Recognition based on Deep Scattering Wavelet Convolutional Neural Network on Wild Facial Image by Somayeh Arab Najafabadi, Sara Nazari, Nafiseh Osati Eraghi

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Causal spatially heterogeneous Bayesian networks with GPs and normalizing flows for seismic multi-hazard estimation by Xuechun Li, Shan Gao, Runyu Gao, Susu Xu

“…Our framework integrates sensing observations, latent variables, and spatial heterogeneity through a novel combination of Gaussian Processes with normalizing flows, enabling us to capture how same earthquake produces different effects across varied geological and topographical features. Evaluations across three earthquakes demonstrate Spatial-VCBN achieves Area Under the Curve (AUC) improvements of up to 35.2% over existing methods. …”
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Anomaly-Based Intrusion Detection System in Wireless Sensor Networks Using Machine Learning Algorithms by Belal Al-Fuhaidi, Zainab Farae, Farouk Al-Fahaidy, Gawed Nagi, Abdullatif Ghallab, Abdu Alameri

“…One of the most significant issues in wireless sensor networks (WSNs) is security, which must be addressed to keep WSNs safe from malicious attacks. …”
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A global object-oriented dynamic network for low-altitude remote sensing object detection by Daoze Tang, Shuyun Tang, Yalin Wang, Shaoyun Guan, Yining Jin

“…This study introduces the Global Object-Oriented Dynamic Network (GOOD-Net) algorithm, comprising three fundamental components: an object-oriented, dynamically adaptive backbone network; a neck network designed to optimize the utilization of global information; and a task-specific processing head augmented for detailed feature refinement. …”
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XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer by P. Naga Srinivasu, G. Jaya Lakshmi, Abhishek Gudipalli, Sujatha Canavoy Narahari, Jana Shafi, Marcin Woźniak, Muhammad Fazal Ijaz

“…The proposed CatBoost+MLP has been evaluated using the Shapley additive explanations values to analyze the feature significance in decision-making. …”
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Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition. by Pranjul Gupta, Katharina Dobs

“…To test this hypothesis, we used task-optimized deep convolutional neural networks (CNNs) and evaluated their alignment with human behavioral signatures and neural responses, measured via magnetoencephalography (MEG), related to pareidolia processing. …”
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LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection by Anjun Yu, Hongrui Fan, Yonghua Xiong, Longsheng Wei, Jinhua She

“…Finally, a bidirectional feature pyramid network (BiFPN) is introduced in the neck to effectively reduce the parameters and computational complexity and improve the overall performance of rockfall detection. …”
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A Multiconstrained QoS Aware MAC Protocol for Cluster-Based Cognitive Radio Sensor Networks by Mir Mehedi Ahsan Pritom, Sujan Sarker, Md. Abdur Razzaque, Mohammad Mehedi Hassan, M. Anwar Hossain, Abdulhameed Alelaiwi

“…Recently, cognitive radio based sensor networks (CRSNs) have been envisioned as a promising type of implementation that provides quality-of-service (QoS) features for data transmissions. …”
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Abnormal resting-state effective connectivity of triple network predicts smoking motivations among males by Mengzhe Zhang, Jieping Sun, Qiuying Tao, Jinghan Dang, Weijian Wang, Shaoqiang Han, Yarui Wei, Jingliang Cheng, Yong Zhang

“…BackgroundThe causal or direct connectivity alterations of triple network including salience network (SN), central executive network (CEN), and default mode network (DMN) in tobacco use disorder (TUD) and the neurobiological features associated with smoking motivation are still unclear, which hampered the development of a targeted intervention for TUD.MethodWe recruited 93 male smokers and 55 male non-smokers and obtained their resting-state functional MRI (rs-fMRI) and smoking-related clinical scales. …”
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