A Parts Detection Network for Switch Machine Parts in Complex Rail Transit Scenarios by Jiu Yong, Jianwu Dang, Wenxuan Deng

“…This article proposes a complex scene rail transit switch machine parts detection network YOLO-SMPDNet (YOLO-based Switch Machine Parts Detecting Network). …”
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Augmented Filtering Based on Information Weighted Consensus Fusion for Simultaneous Localization and Tracking via Wireless Sensor Networks by Xiangyuan Jiang, Baozhou Lu, Peng Ren, Chunbo Luo, Xinheng Wang

“…This paper develops a novel augmented filtering framework based on information weighted consensus fusion, to achieve the simultaneous localization and tracking (SLAT) via wireless sensor networks (WSNs). …”
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Spillover effects in transit networks: A parameterized weight matrix spatial lagged approach by Paraskevas Nikolaou, Loukas Dimitriou

“…The novelty and the importance of the proposed model rely on the ability to introduce transit network structure within the framework of spatial econometric regression, fostering the explanatory statistical analysis over networked information. …”
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Neural-Augmented Hybrid Dynamic Modeling of a Series-Parallel Elastic Actuator by Timur Umurzakov, Sharafatdin Yessirkepov, Michele Folgheraiter

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CyclicAugment: Optimized Medical Image Analysis via Adaptive Augmentation Intensity by Min-Jun Kim, Jung-Woo Chae, Hyun-Chong Cho

“…The method gradually transitions from using original data as weak augmentation, subsequently applying strong augmentation before reverting to weak augmentation again, reintroducing the original data. …”
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Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations by Jing Xiong, Youchao Sun, Junzhou Sun, Yongbing Wan, Gang Yu

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Efficient and Stable Learning for Distribution Network Operation: A Model-Based Reinforcement Learning Approach by Dong Yan, Zhan Shi, Xinying Wang, Yiying Gao, Tianjiao Pu, Jiye Wang

“…Incorporating a state transition model, the proposed algorithm augments data and enhances dynamic deduction, improving training efficiency. …”
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Demonstration of accurate ID-VG characteristics modeling in SiC mosfets using separated artificial neural networks with small training dataset by Manopat Chankla, Bang-Ren Chen, Shivendra Kumar Singh, Yogesh Singh Chauhan, Wen-Jay Lee, Nan-Yow Chen, Songphol Kanjanachuchai, Tian-Li Wu

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Fusion of Masked Autoencoder for Adaptive Augmentation Sequential Recommendation by SUN Xiujuan, SUN Fuzhen, LI Pengcheng, WANG Aofei, WANG Shaoqing

“…In order to address the issue of poor-quality contrast views generated by contrastive learning methods in sequential recommendation tasks, a model called GATSR, which is based on graph attention networks for sequential recommendation, is proposed. …”
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Robust real-time detection of right whale upcalls using neural networks on the edge by Matthew D. Hyer, Austin T. Anderson, David A. Mann, T. Aran Mooney, Nadège Aoki, Frants H. Jensen

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Study on Thermal Conductivity Prediction of Granites Using Data Augmentation and Machine Learning by Yongjie Ma, Lin Tian, Fuhang Hu, Jingyong Wang, Echuan Yan, Yanjun Zhang

“…Thermal conductivity prediction models were constructed using Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network(BPNN). Results showed that data augmentation significantly improved model performance: the RF model exhibited the best improvement, with its coefficient of determination R² increasing from 0.7489 to 0.9765, Root Mean Square Error (RMSE) decreasing from 0.1870 to 0.1271, and Mean Absolute Error (MAE) reducing from 0.1453 to 0.0993. …”
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AI augmented edge and fog computing for Internet of Health Things (IoHT) by Deepika Rajagopal, Pradeep Kumar Thimma Subramanian

“…Previous surveys related to healthcare mainly focused on architecture and networking, which left untouched important aspects of smart systems like optimal computing techniques such as artificial intelligence, deep learning, advanced technologies, and services that includes 5G and unified communication as a service (UCaaS). …”
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Two forecasting model selection methods based on time series image feature augmentation by Wentao Jiang, Quan Wang, Hongbo Li

“…Specifically, we utilize Gramian Angular Fields (GAF), Markov Transition Fields (MTF), and Recurrence Plots (RP) to transform time series data into image representations. …”
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Toward AI-Augmented Formal Verification: A Preliminary Investigation of ENGRU and Its Challenges by Chanon Dechsupa, Teerapong Panboonyuen, Wiwat Vatanawood, Praisan Padungweang, Chakchai So-In

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Artificial intelligence (AI) or augmented intelligence? How big data and AI are transforming healthcare: Challenges and opportunities by K Moodley

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Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models by Cecile Valsecchi, Jose A. Arjona-Medina, Natalia Dyubankova, Ramil Nugmanov

“…Graph-based representations offer a more realistic depiction and support 3D geometry and conformer-based augmentation. Graph Neural Networks (GNNs) and Graph-based Transformer models (GTs) represent two paradigms in this field, with GT models emerging as a flexible alternative. …”
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Augmenting TikTok shop adoption: The mediating role of Key Opinion Consumers (KOCs) in live streaming, video content, and Artificial Intelligence (AI) recommendations by Minh Thi Hong Le, Hieu Ngoc Minh Pham, Ly Thi Truc Nguyen, Thuy Thi Xuan Le, Suong Thi Mai Nguyen

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Information Propagation in Hypergraph-Based Social Networks by Hai-Bing Xiao, Feng Hu, Peng-Yue Li, Yu-Rong Song, Zi-Ke Zhang

“…Social networks, functioning as core platforms for modern information dissemination, manifest distinctive user clustering behaviors and state transition mechanisms, thereby presenting new challenges to traditional information propagation models. …”
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Fault Estimation for Semi-Markov Jump Neural Networks Based on the Extended State Method by Lihong Rong, Yuexin Pan, Zhimin Tong

“…Unlike studies considering only constant transition rates, this work investigates s-MJNNs with time-varying transition probabilities, which more closely reflect practical situations. …”
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Prediction of carbon dioxide phase at bottomhole by adaptive factorization network considering well geometry by Sungil Kim, Tea-Woo Kim, Yongjun Hong, Hoonyoung Jeong

“…This study addresses these challenges by introducing a deep learning framework based on the adaptive factorization network (AFN), which enhances CO2 phase prediction accuracy by leveraging feature interactions. …”
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