A Study of Futures Price Forecasting with a Focus on the Role of Different Economic Markets by Yongxiang Wang, Qingyang Liu, Yanrong Hu, Hongjiu Liu

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Augmented self attention network for liver tumour segmentation of CT images by Bilga Jacob, R.S Vinod Kumar, S.S. Kumar

“…Here ASAneXt50 is proposed that makes use of map of convolutional features with attention feature maps in ResNeXt, leading to the development of an adept method for liver tumour segmentation. …”
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Evaluation of the Effectiveness of the UNet Model with Different Backbones in the Semantic Segmentation of Tomato Leaves and Fruits by Juan Pablo Guerra Ibarra, Francisco Javier Cuevas de la Rosa, Julieta Raquel Hernandez Vidales

“…This task is accomplished through training various models of Convolutional Neural Networks. This paper presents a comparative analysis of semantic segmentation performance using a convolutional neural network model with different backbone architectures. …”
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ADI Compact Difference Scheme for the Two-Dimensional Integro-Differential Equation with Two Fractional Riemann–Liouville Integral Kernels by Ziyi Chen, Haixiang Zhang, Hu Chen

“…The integral terms are approximated by a second-order convolution quadrature formula. The alternating direction implicit (ADI) compact difference scheme reduces the CPU time for two-dimensional problems. …”
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State of Health Prediction for Lithium-Ion Batteries Based on Gated Temporal Network Assisted by Improved Grasshopper Optimization by Xiankun Wei, Silun Peng, Mingli Mo

“…However, novel algorithms are still needed because few studies have considered the correlations between monitored parameters in Euclidean space and non-Euclidean space at different time points. To address this challenge, a novel gated-temporal network assisted by improved grasshopper optimization (IGOA-GGNN-TCN) is developed. …”
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Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques. by Yang Wu, Ming Chen, Yufang Qin

“…Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. …”
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Enhancing sound-based classification of birds and anurans with spectrogram representations and acoustic indices in neural network architectures by Fábio Felix Dias, Moacir Antonelli Ponti, Rosane Minghim

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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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Deep LBLS: Accelerated Sky Region Segmentation Using Hybrid Deep CNNs and Lattice Boltzmann Level-Set Model by Fatema A. Albalooshi, M. R. Qader, Yasser Ismail, Wael Elmedany, Hesham Al-Ammal, Muttukrishnan Rajarajan, Vijayan K. Asari

“…Our algorithm is implemented by leveraging three types of CNNs, namely DeepLabV3+, Fully Convolutional Network (FCN), and SegNet. Additionally, we utilize a local image fitting level-set function to characterize the region-based active contour model. …”
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Evaluation Model Based on the SGCNiFormer for the Influence of Different Storage Environments on Wheat Quality by Qingchuan Zhang, Zexi Song, Mingwen Bi

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Analysis of tensile properties in tempered martensite steels with different cementite particle size distributions by Kengo Sawai, Keiya Sugiura, Toshio Ogawa, Ta-Te Chen, Fei Sun, Yoshitaka Adachi

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Hyperparameter Optimization for Problem-Based Custom CNN Architectures Using a Smart Grid Search Method by H. Aktas

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An innovative methodology for segmenting vessel like structures using artificial intelligence and image processing by Reynaldo Villarreal, Sindy Chamorro-Solano, Steffen Cantillo, Roberto Pestana-Nobles, Sair Arquez, Yolanda Vega-Sampayo, Leonardo Pacheco-Londoño, Jheifer Paez, Nataly Galan-Freyle, Cristian Ayala, Paola Amar

“…The method was applied on different datasets containing images of eye fundus, citrus leaves, printed circuit boards to test how well it could segment the capillary structures. …”
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AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation by Jie Li, Changchun Yang

“…In contemporary society, individuals are inundated with a vast amount of redundant information, and recommendation systems have undoubtedly opened up new avenues for managing irrelevant data. Graph convolutional networks (GCNs) have demonstrated remarkable performance in the field of recommendation systems by iteratively performing node convolutions to capture information from neighboring nodes, thereby enhancing recommendation efficacy. …”
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DFANet: A Deep Feature Attention Network for Building Change Detection in Remote Sensing Imagery by Peigeng Lu, Haiyong Ding, Xiang Tian

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Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic wave manipulation by Chen Junzai, Li Weiran, Gong Kailuo, Lu Xiaojie, Tong Mei Song, Wang Xiaoyi, Yang Guo-Min

“…By recognizing the sEMG signals of user gestures through a pre-trained convolutional neural network (CNN) model, the system dynamically modulates the metasurface, enabling precise control of the deflection direction and polarization state of electromagnetic waves. …”
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Spatial–Spectral Interaction Super-Resolution CNN–Mamba Network for Fusion of Satellite Hyperspectral and Multispectral Image by Guangwei Zhao, Haitao Wu, Dexiang Luo, Xu Ou, Yu Zhang

“…To solve the above problems, we designed a spatial–spectral interaction super-resolution convolutional neural network (CNN)–Mamba fusion network for satellite HSI and MSI, which uses mutual guidance to improve the spatial and spectral resolution of different data, and obtains the final fused image through feature fusion. …”
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TFTformer: A novel transformer based model for short-term load forecasting by Ahmad Ahmad, Xun Xiao, Huadong Mo, Daoyi Dong

“…Additionally, a Temporal Convolutional Network is integrated within the Transformer’s encoder, employing causal convolutions and dilation to adapt to the sequential nature of data with an expanded receptive field. …”
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Research on Fine-Grained Visual Classification Method Based on Dual-Attention Feature Complementation by Min Huang, Ke Li, Xiaoyan Yu, Chen Yang

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Identifying Bias in Deep Neural Networks Using Image Transforms by Sai Teja Erukude, Akhil Joshi, Lior Shamir

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