A Spatial Long-Term Load Forecast Using a Multiple Delineated Machine Learning Approach by Terence Kibula Lukong, Derick Nganyu Tanyu, Yannick Nkongtchou, Thomas Tamo Tatietse, Detlef Schulz

“…Advanced methods like spatiotemporal graph transformers, graph convolutional networks, and improved scale-limited dynamic time warping better capture these dependencies, thereby enhancing prediction accuracy. …”
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Deep Learning in Wireless Communication Receivers: A Survey by Shadman Rahman Doha, Ahmed Abdelhadi

“…The design of wireless communication receivers to enhance signal processing in complex and dynamic environments is going through a transformation by leveraging deep neural networks (DNNs). …”
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A unified approach for weakly supervised crack detection via affine transformation and pseudo label refinement by Zhongmin Huangfu, Yibo Jiao, Fupeng Wei, Ge Shi, Hangcheng Dong

“…The information flow in the samples is utilized to invert and eliminate the checkerboard noise produced during integration spatially; in the third stage, a dynamic range compression mechanism is employed to augment the prominence of the cracked areas by compressing the highlighted regions in the saliency map and diminishing the influence of background noise. …”
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An improved method of AUD-YOLO for surface damage detection of wind turbine blades by Li Zou, Anqi Chen, Xinhua Yang, Yibo Sun

“…Secondly, the model incorporates the UniRepLKNet large convolution kernel with the C2f module, enabling it to learn complex image features more comprehensively. …”
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State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States by Osman Ozer, Hayri Arabaci

“…These results demonstrate the effectiveness of the proposed approach in accurately and reliably estimating SOC in dynamic battery applications.…”
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Application of CycleGAN-based low-light image enhancement algorithm in foreign object detection on belt conveyors in underground mines by Anxin Zhao, Qiuhong Zheng, Liang Li

“…In the generator network, first, a multi-scale convolutional feature enhancement module is designed to extract features from low-light images at multiple scales, obtaining richer feature information. …”
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An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks by Zhifeng Ma, Zhanjun Hao, Zhenya Zhao

“…Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor’s operational mode and communication frequency according to the node’s energy status and task urgency. …”
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A novel explainable deep learning framework for reconstructing South Asian palaeomonsoons by K. M. R. Hunt, K. M. R. Hunt, S. P. Harrison

“…By applying explainability techniques, we show that the models make use of both local hydroclimate and synoptic-scale dynamical relationships. Our findings offer insights into the historical variability of the Indian summer monsoon and highlight the potential of deep learning techniques in palaeoclimate reconstruction.…”
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Deep Learning for Traffic Scene Understanding: A Review by Parya Dolatyabi, Jacob Regan, Mahdi Khodayar

“…The review also addresses key challenges in traffic scene understanding, such as occlusions, the dynamic nature of urban traffic, and environmental complexities like varying weather and lighting conditions. …”
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Gait Phase Recognition in Multi-Task Scenarios Based on sEMG Signals by Xin Shi, Xiaheng Zhang, Pengjie Qin, Liangwen Huang, Yaqin Zhu, Zixiang Yang

“…MFAREn is used to extract the dynamic complexity and energy intensity features of sEMG signals, serving as the input matrix for EMACNN to achieve fast and accurate gait phase recognition. …”
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Cross-Layer Analysis of Machine Learning Models for Secure and Energy-Efficient IoT Networks by Rashid Mustafa, Nurul I. Sarkar, Mahsa Mohaghegh, Shahbaz Pervez, Ovesh Vohra

“…We integrate layer-specific ML models, such as long short-term memory networks for temporal anomaly detection and decision trees for application-layer validation, along with adaptive speck encryption for the dynamic adjustment of cryptographic overheads. We then introduce a granular RBAC system that incorporates energy-aware policies. …”
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Adapting Cross-Sensor High-Resolution Remote Sensing Imagery for Land Use Classification by Wangbin Li, Kaimin Sun, Jinjiang Wei

“…Additionally, we integrate convolutional neural networks and Transformers to enhance the model’s feature extraction capabilities, and employ a fine-tuning strategy with dynamic pseudo-labels to reduce the reliance on annotated data from the target domain. …”
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An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images by Muhammad Zubair, Muhammad Owais, Taimur Hassan, Malika Bendechache, Muzammil Hussain, Irfan Hussain, Naoufel Werghi

“…The proposed framework uses three different attention mechanism channels and convolutional neural networks to extract multichannel features during the classification process. …”
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Collaborative Forecasting of Multiple Energy Loads in Integrated Energy Systems Based on Feature Extraction and Deep Learning by Zhe Wang, Jiali Duan, Fengzhang Luo, Xiaoyu Qiu

“…Then, a multi-task parallel learning framework combining a regression convolutional neural network and long short-term memory networks is developed to predict reconstructed subsequences. …”
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A deep learning‐based approach for software vulnerability detection using code metrics by Fazli Subhan, Xiaoxue Wu, Lili Bo, Xiaobing Sun, Muhammad Rahman

“…From traditional manual vulnerability mining techniques to static and dynamic detection, all rely on human experts for feature definition. …”
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Memristor-Based Artificial Neural Networks for Hardware Neuromorphic Computing by Boyan Jin, Zhenlong Wang, Tianyu Wang, Jialin Meng

“…We provide a comprehensive background on the evolution of neural network models and memristors, as well as introduce the principles of memristive devices, which mimic the dynamic behavior of biological synapses. Various neural network architectures, including convolutional, recurrent, and spiking models, are discussed, highlighting the advantages of integrating memristors for in-memory computing and parallel processing. …”
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VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security by Yonas Teweldemedhin Gebrezgiher, Sekione Reward Jeremiah, Stefanos Gritzalis, Jong Hyuk Park

“…This paper proposes a real-time anomaly detection framework that integrates the reconstruction capabilities of Variational Autoencoders (VAEs) with the feature extraction power of Convolutional Neural Networks (CNNs). Our model processes streaming data using a sliding window mechanism, ensuring prompt detection of anomalies in the dynamic V2X environment. …”
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Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation by A. S. M. Sharifuzzaman Sagar, Muhammad Zubair Islam, Jawad Tanveer, Hyung Seok Kim

“…The framework introduces a Contextual Dynamic Encoder (CDE) for extracting multi-scale features and capturing long-range dependencies. …”
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Fast Identification and Detection Algorithm for Maneuverable Unmanned Aircraft Based on Multimodal Data Fusion by Tian Luan, Shixiong Zhou, Yicheng Zhang, Weijun Pan

“…Building upon the YOLO v11 architecture, we develop an enhanced model incorporating four key innovations: (1) A dual-path RGB-IR fusion architecture that exploits complementary multi-modal data; (2) C3k2-DATB dynamic attention modules for enhanced feature extraction and semantic perception; (3) A bilevel routing attention mechanism with agent queries (BRSA) for precise target localization; (4) A semantic-detail injection (SDI) module coupled with windmill-shaped convolutional detection heads (PCHead) and Wasserstein Distance loss to expand receptive fields and accelerate convergence. …”
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Attention-enhanced and integrated deep learning approach for fishing vessel classification based on multiple features by Xin Cheng, Jintao Wang, Xinjun Chen, Fan Zhang

“…Then, a multidimensional feature vector was constructed by combining the geometric, static and dynamic characteristics of fishing vessels to explain the behavioral differences of various types of fishing vessels more effectively. …”
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