A multi-scale cross-dimension interaction approach with adaptive dilated TCN for RUL prediction by Zhe Lu, Bing Li, Changyu Fu, Liang Xu, Bai Jiang, Zelong Li, Junbao Wu, Siye Jia

“…To address the aforementioned issues, this paper proposes an Adaptive Dilated Temporal Convolutional Network (AD-TCN) approach, incorporating a Multi-Scale Cross-Dimension Interaction Module (MSCDIM) to enhance feature extraction and interaction. First, a dynamic adaptive dilation factor is incorporated into the TCN, thereby enabling the model to adjust its receptive field dynamically, which facilitates the capture of long- and short-term dependencies across different scales, allowing a more comprehensive representation of equipment degradation patterns. …”
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An Adaptive Convolutional Neural Network With Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP) for Robust EEG-Based Motor Imagery Classification by Aaqib Raza, Mohd Zuki Yusoff

“…Furthermore, the model introduces three key innovations: Dynamic Multi-Scale Convolutional Learning for adaptive kernel selection, Unified Spatio-Temporal Attention (USTA) for efficient feature recalibration, and AFB for multi-scale feature fusion while preserving long-range dependencies. …”
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Multimodal lightweight neural network for Alzheimer's disease diagnosis integrating neuroimaging and cognitive scores by Bhoomi Gupta, Ganesh Kanna Jegannathan, Mohammad Shabbir Alam, Kottala Sri Yogi, Janjhyam Venkata Naga Ramesh, Vemula Jasmine Sowmya, Isa Bayhan

“…To address these challenges, we propose Light-Mo-DAD, a lightweight multimodal diagnostic neural network designed to integrate MRI, PET imaging, and neuropsychological assessment scores for enhanced AD detection. In the neuroimaging feature extraction module, redundancy-reduced convolutional operations are employed to capture fine-grained local features, while a global filtering mechanism enables the extraction of holistic spatial patterns. …”
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Social mates dynamically coordinate aggressive behavior to produce strategic territorial defense. by Nicole M Moody, Cole M Williams, Sohini Ramachandran, Matthew J Fuxjager

“…One way to make these contextual adjustments is by arranging behavioral output into intentional patterns. Yet, few studies explore how behavioral patterns vary across a wide range of contexts, or how allies might interlace their behavior to produce a coordinated response. …”
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Wearable Sensors and Computational Intelligence in Alpine Skiing Analysis by Ales Prochazka, Hana Charvatova

“…This paper focuses on motion analysis in alpine skiing using real accelerometric, gyroscopic, positioning, and video data to evaluate ski movement patterns. The proposed methodology employs functional transforms to estimate motion patterns and utilizes artificial intelligence for signal segmentation and feature classification related to lower limb movement. …”
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ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network by Xiao Feng, Xiao Feng, Zhongyuan Guo, Zhongyuan Guo, Sam Kwong

“…To address these issues, we propose a novel interpretable residual shrinkage network, namely, ID3RSNet, for cross-subject driver drowsiness detection using single-channel EEG signals. First, a base feature extractor is employed to extract the essential features of EEG frequencies; to enhance the discriminative feature learning ability, the residual shrinkage building unit with attention mechanism is adopted to perform adaptive feature recalibration and soft threshold denoising inside the residual network is further applied to achieve automatic feature extraction. …”
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Convolutional Neural Networks—Long Short-Term Memory—Attention: A Novel Model for Wear State Prediction Based on Oil Monitoring Data by Ying Du, Hui Wei, Tao Shao, Shishuai Chen, Jianlei Wang, Chunguo Zhou, Yanchao Zhang

“…Initially, the CNN performs hierarchical extraction of localized patterns from multi-sensor tribological signals. Subsequently, the self-attention mechanism conducts adaptive recalibration of feature saliency, prioritizing diagnostically critical feature channels. …”
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A deep Reinforcement learning-based robust Intrusion Detection System for securing IoMT Healthcare Networks by Jamshed Ali Shaikh, Chengliang Wang, Muhammad Wajeeh Us Sima, Muhammad Arshad, Muhammad Owais, Dina S. M. Hassan, Reem Alkanhel, Mohammed Saleh Ali Muthanna

“…The CNN captures spatial patterns in network traffic, while the LSTM identifies temporal patterns. …”
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Detection-Oriented Evaluation of SAR Dexterous Barrage Jamming Effectiveness by Hai Zhu, Sinong Quan, Shiqi Xing, Haoyu Zhang, Yun Ren

“…Starting from the detection, it divides the evaluation process into two stages: (1) for the case in which the target can be detected under the jamming scenario, two feature parameters, namely, the target exposion area and target relative magnitude, are extracted; (2) for the case in which the target cannot be detected under the jamming scenario, another three feature parameters, namely, jamming relative magnitude, average edge brightness, and local information entropy, are extracted. …”
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Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance by Deepak Kumar Singh, Dibakar Raj Pant, Ganesh Gautam, Bhanu Shrestha

“…To overcome these limitations, model frameworks, i.e., the video anomaly detector model, have been proposed, leveraging the meta-learning framework for faster adaptation using swin transformer for feature extraction to new concepts. In response, the dataset named MSAD (multi-scenario anomaly detection) having 14 different scenarios from multiple camera views, is the high resolution anomaly detection dataset that includes diverse motion patterns and challenging variations such as varying lighting and weather conditions, offering a robust foundation for training advanced anomaly detection models. …”
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A Deep Learning-Based Diagnostic Framework for Shaft Earthing Brush Faults in Large Turbine Generators by Katudi Oupa Mailula, Akshay Kumar Saha

“…A key innovation lies in the use of FFT-derived spectrograms from both voltage and current waveforms as dual-channel inputs to the CNN, enabling automatic feature extraction of time–frequency patterns associated with different SEB fault types. …”
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Electricity Theft Detection Using Rule-Based Machine Leaning (rML) Approach by Sheyda Bahrami, Erol Yumuk, Alper Kerem, Beytullah Topçu, Ahmetcan Kaya

“…Even though consumption-based models have been applied extensively to the detection of power theft, it can be difficult to reliably identify theft instances based only on patterns of usage. In this paper, a novel rule-based combined machine learning (rML) technique is developed for power theft detection to address the drawbacks of systems that rely just on consumption patterns. …”
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Multiple Chaos Synchronization System for Power Quality Classification in a Power System by Cong-Hui Huang, Chia-Hung Lin

“…Multiple detectors are used to monitor the dynamic errors between the master system and the slave system and are used to construct the feature patterns from time-domain signals. The maximum likelihood method (MLM), as a classifier, performs a comparison of the patterns of the features in the database. …”
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A multi-dilated convolution network for speech emotion recognition by Samaneh Madanian, Olayinka Adeleye, John Michael Templeton, Talen Chen, Christian Poellabauer, Enshi Zhang, Sandra L. Schneider

“…Then, the SPP layer is deployed to extract both the global-level prominent feature vector and multi-local-level feature vector, followed by an attention model to weigh the feature vectors. …”
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Flexi-YOLO: A lightweight method for road crack detection in complex environments. by Jiexiang Yang, Renjie Tian, Zexing Zhou, Xingyue Tan, Pingyang He

“…The AKConv convolution operation is employed to adaptively adjust the size of convolutions, further enhancing local feature capturing. Additionally, a lightweight network design is implemented, establishing G-Head (Ghost-Head) as the detection head to optimize the issue of feature redundancy. …”
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Multiscale deformed attention networks for white blood cell detection by Xin Zheng, Qiqi Xu, Shiyi Zheng, Luxian Zhao, Deyang Liu, Liangliang Zhang

“…The Cross-Deformation Convolution Module (CDCM) reduces feature correlation, aiding the model in capturing diverse aspects and patterns in images, thereby improving generalization. …”
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A Frequency-Aware Transformer for Multiscale Fault Diagnosis in Electrical Machines by Yurim Choi, Inwhee Joe

“…However, conventional fault diagnosis methods struggle to effectively capture complex time-frequency patterns, limiting their ability to perform early fault detection and accurate classification. …”
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Convolutional Neural Network–Vision Transformer Architecture with Gated Control Mechanism and Multi-Scale Fusion for Enhanced Pulmonary Disease Classification by Okpala Chibuike, Xiaopeng Yang

“…Furthermore, we incorporated a multi-scale fusion module (MSFM) in the proposed framework to fuse the features at different scales for more comprehensive feature representation. …”
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Transformer-Guided Serial Knowledge Distillation for High-Precision Anomaly Detection by Danyang Wang, Bingyan Wang

“…This three-stage architecture consists of a fixed pretrained teacher network serving as the upstream feature extractor, a dedicated multi-feature aggregation and filtering module integrated with Vision Transformer components as the intermediate processor, and a trainable student network functioning as the downstream reconstruction module. …”
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Machine Learning Optimization and Challenges in Used Car Price Prediction by Zheng Yufan

“…To begin with, models like XGBoost and Random Forest excel at processing large-scale data and identifying complex feature patterns, thanks to their ability to use an ensemble of decision trees to reduce bias and variance. …”
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