LRA-UNet: A Lightweight Residual Attention Network for SAR Marine Oil Spill Detection by Yu Cai, Jingjing Su, Jun Song, Dekai Xu, Liankang Zhang, Gaoyuan Shen

“…Our model integrates depthwise separable convolutions to reduce feature redundancy and computational cost, while adopting a residual encoder enhanced with the Simple Attention Module (SimAM) to improve the precise extraction of target features. …”
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Temperature Prediction and Fault Warning of High-Speed Shaft of Wind Turbine Gearbox Based on Hybrid Deep Learning Model by Min Zhang, Jijie Wei, Zhenli Sui, Kun Xu, Wenyong Yuan

“…By offering reliable, cost-effective prognostics without additional hardware, this approach significantly improves wind turbine health management and fault prevention.…”
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MultiFG: integrating molecular fingerprints and graph embeddings via attention mechanisms for robust drug side effect prediction by Zuhai Hu, Jinxiang Yang, Linghao Ni, Liyuan Zhang, Bin Peng

“…Abstract Accurate prediction of drug side effect frequencies is critical for drug safety assessment but remains challenging due to the high cost of clinical trials and the limited generalizability of existing models. …”
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MemoCMT: multimodal emotion recognition using cross-modal transformer-based feature fusion by Mustaqeem Khan, Phuong-Nam Tran, Nhat Truong Pham, Abdulmotaleb El Saddik, Alice Othmani

“…However, these models come at a computational cost. In contrast, convolutional neural networks are faster but struggle with capturing these long-range relationships. …”
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A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing by Larissa Silva Piauilino, Vanderlei Coelho Oliveira Junior, Valeria Cristina Ferreira Barbosa

“…We demonstrate the potential of using the convolutional equivalent layer to jointly process large gravity-gradient datasets. …”
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Lightweight Explicit 3D Human Digitization via Normal Integration by Jiaxuan Liu, Jingyi Wu, Ruiyang Jing, Han Yu, Jing Liu, Liang Song

“…We propose a lightweight and efficient 3D human reconstruction model that balances reconstruction accuracy and computational cost. Specifically, our model integrates Dilated Convolutions and the Cross-Covariance Attention mechanism into its architecture to construct a lightweight generative network. …”
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Generative Artificial Intelligence for Hyperspectral Sensor Data: A Review by Diaa Addeen Abuhani, Imran Zualkernan, Raghad Aldamani, Mohamed Alshafai

“…Generative neural networks, including generative adversarial networks and denoising diffusion probabilistic models, are highlighted for their superior performance in classification, segmentation, and object identification tasks, often surpassing traditional approaches, such as U-Nets, autoencoders, and deep convolutional neural networks. Diffusion models showed competitive performance in tasks, such as feature extraction and image resolution enhancement, particularly in terms of inference time and computational cost. …”
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Unsupervised Binary Classifier-Based Object Detection Algorithm with Integrated Background Subtraction Suitable for Use with Aerial Imagery by Gabija Veličkaitė, Ignas Daugėla, Ivan Suzdalev

“…This bias is intentional, as missing a human target in search and rescue applications carries a higher cost than producing additional false detections. …”
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A Lightweight Citrus Ripeness Detection Algorithm Based on Visual Saliency Priors and Improved RT-DETR by Yutong Huang, Xianyao Wang, Xinyao Liu, Liping Cai, Xuefei Feng, Xiaoyan Chen

“…Experiments on our dataset show that LightSal-RTDETR achieves a mAP@50 of 81%, improving by 1.9% over the original model while reducing parameters by 28.1% and computational cost by 26.5%. Therefore, LightSal-RTDETR effectively solves the citrus ripeness detection problem in orchard scenes with high complexity, offering an efficient solution for smart agriculture applications.…”
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Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters by Yong Qing, Ke Li, Peng-Fei Zhou, Shi-Ju Ran

“…The complexity of NNs, if unbounded or unconstrained, might unpredictably cause severe issues including overfitting, loss of generalization power, and excessive cost of hardware. In this study, we propose a general compression scheme that considerably reduces the variational parameters of NNs, regardless of their specific types (linear, convolutional, etc.), by encoding them into deep automatically differentiable tensor networks (ADTNs) that contain exponentially fewer free parameters. …”
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A Scalable All-Digital Near-Memory Computing Architecture for Edge AIoT Applications by Masoud Nouripayam, Arturo Prieto, Joachim Rodrigues

“…The NMC co-processor, operating alongside the general-purpose RISC-V core, forms a multi-core system-on-chip that combines low hardware cost with high energy efficiency, while maintaining a high degree of flexibility. …”
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A Low Complexity Algorithm for 3D-HEVC Depth Map Intra Coding Based on MAD and ResNet by Erlin Tian, Jiabao Zhang, Qiuwen Zhang

“…In intra coding, quadtree partitioning is determined recursively through rate-distortion cost calculations. This process demands extensive computational resources and results in high encoding complexity. …”
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Methods of security situation prediction for industrial internet fused attention mechanism and BSRU by Xiangdong HU, Zhengguo TIAN

“…The security situation prediction plays an important role in balanced and reliable work for industrial internet.In the face of massive, high-dimensional and time-series data generated in the industrial production process, traditional prediction models are difficult to accurately and efficiently predict the network security situation.Therefore, the methods of security situation prediction for industrial internet fused attention mechanism and bi-directional simple recurrent unit (BSRU) were proposed to meet the real-time and accuracy requirements of industrial production.Each security element was analyzed and processed, so that it could reflect the current network state and facilitate the calculation of the situation value.One-dimensional convolutional network was used to extract the spatial dimension features between each security element and preserve the temporal correlation between features.The BSRU network was used to extract the time dimension features between the data information and reduced the loss of historical information.Meanwhile, with the powerful parallel capability of SRU network, the training time of model was reduced.Attention mechanism was introduced to optimize the correlation weight of BSRU hidden state to highlight strong correlation factors, reduced the influence of weak correlation factors, and realized the prediction of industrial internet security situation combining attention mechanism and BSRU.The comparative experimental results show that the model reduces the training time and training error by 13.1% and 28.5% than the model using bidirectional long short-term memory network and bidirectional gated recurrent unit.Compared with the convolutional and BSRU network fusion model without attention mechanism, the prediction error is reduced by 28.8% despite the training time increased by 2%.The prediction effect under different prediction time is better than other models.Compared with other prediction network models, this model achieves the optimization of time performance and uses the attention mechanism to improve the prediction accuracy of the model under the premise of increasing a small amount of time cost.The proposed model can well fit the trend of network security situation, meanwhile, it has some advantages in multistep prediction.…”
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Time-series visual representations for sleep stages classification. by Rebeca Padovani Ederli, Didier A Vega-Oliveros, Aurea Soriano-Vargas, Anderson Rocha, Zanoni Dias

“…Polysomnography is the standard method for sleep stage classification; however, it is costly and requires controlled environments, which can disrupt natural sleep patterns. …”
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Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods by Mehmet Kızıldağ, Fatih Abut, Mehmet Fatih Akay

“…The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions. …”
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Multimode Fiber Specklegram Sensor for Multi-Position Loads Recognition Using Traversal Occlusion by Bohao Shen, Jianzhi Li, Zhe Ji

“…Our method represents a significant advancement in this field, offering a cost-effective and efficient solution for distributed sensing applications.…”
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AC-YOLO: A lightweight ship detection model for SAR images based on YOLO11. by Rui He, Dezhi Han, Xiang Shen, Bing Han, Zhongdai Wu, Xiaohu Huang

“…However, existing SAR ship detection algorithms encounter two major challenges: limited detection accuracy and high computational cost, primarily due to the wide range of target scales, indistinct contour features, and complex background interference. …”
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Fingerprint Classification Based on Multilayer Extreme Learning Machines by Axel Quinteros, David Zabala-Blanco

“…A comparison is made with the aforementioned approaches in terms of accuracy, penetration rate, and computational cost. The results demonstrate that a two-layer hidden ELM achieves superior classification of both majority and minority fingerprint classes with remarkable computational efficiency.…”
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A novel hybrid model by integrating TCN with TVFEMD and permutation entropy for monthly non-stationary runoff prediction by Huifang Wang, Xuehua Zhao, Qiucen Guo, Xixi Wu

“…Subsequently, the complexity of each sub-component is evaluated using the permutation entropy (PE), and similar low-frequency components are clustered based on the entropy value to reduce the computational cost. Then, the temporal convolutional network (TCN) model is built for runoff prediction for each high-frequency IMFs and the reconstructed low-frequency IMF respectively. …”
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Automated assessment of simulated laparoscopic surgical skill performance using deep learning by David Power, Cathy Burke, Michael G. Madden, Ihsan Ullah

“…Lack of labeled data is a particular problem in surgery considering its complexity, as human annotation and manual assessment are both expensive in time and cost, and in most cases rely on direct intervention of clinical expertise. …”
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