Self-Correlation Network With Triple Contrastive Learning for Hyperspectral Image Classification With Noisy Labels by Kwabena Sarpong, Mohammad Awrangjeb, Md. Saiful Islam, Islam Helmy

“…Furthermore, we incorporate structure-level representation learning to address inconsistencies across different projections. By doing so, we mitigate the classifier from overfitting to noisy labels. …”
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Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism by Wangyong Guo, Shijin Liu, Liguo Weng, Xingyu Liang

“…The Channel Attention module is then applied to weight different feature channels, highlighting important information and reducing redundancy. …”
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Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations by Kai-Cheng Chuang, Ping-Sung Cheng, Yu-Hung Tsai, Meng-Hsiun Tsai

“…Conclusions By introducing innovative label-preprocessing methods, this study addressed the relationships between miRNAs and diseases, and improved the ambiguity of the results in different experiments. Based on these refined label definitions, we developed a DL-based model to refine and predict the results of associations between miRNAs and diseases. …”
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Synergistic use of SAR satellites with deep learning model interpolation for investigating of active landslides in Cuenca, Ecuador by Mohammad Amin Khalili, Silvio Coda, Domenico Calcaterra, Diego Di Martire

“…To this aim, we have used Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) as two different Deep Learning Algorithms (DLAs) to integrate results in the temporal and spatial domain, respectively. …”
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SSMSFuse: A Spectral and Spatial Multiscale Coupling Fusion Model for Hyperspectral and Multispectral Image by Siyuan Liu, Yingchao Fan, Qi Hu, Bing Li, Yudong Zhang, Shuaiqi Liu

“…Finally, to achieve interactive coupling of dual-branch information, we designed a spatial–spectral guidance fusion block to fuse features at different scales to avoid loss of spatial and spectral details. …”
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Astronomical Image Superresolution Reconstruction with Deep Learning for Better Identification of Interacting Galaxies by Jiawei Miao, Liangping Tu, Hao Liu, Jian Zhao

“…High-resolution images of galaxies can identify fine structures within galaxies, which are essential for identifying and distinguishing different substructures within merging systems. However, due to observational and instrumental limitations, galaxy data is often collected at low resolution. …”
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Comprehensive Evaluation of Techniques for Intelligent Chatter Detection in Micro-Milling Processes by Guilherme Serpa Sestito, Wesley Angelino De Souza, Alessandro Roger Rodrigues, Maira Martins Da Silva

“…This study exploits experimental data from chatter and chatter-free cuts during machining operations with commercial COSAR-60 low-carbon steel under two different grain sizes (as received and ultra-fined). …”
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Evaluation of the precision and accuracy in the classification of breast histopathology images using the MobileNetV3 model by Kenneth DeVoe, Gary Takahashi, Ebrahim Tarshizi, Allan Sacker

“…This visual assessment is repeated on numerous slides taken at various sections through the resected tumor, each at different magnifications. Computer vision models have been proposed to assist human pathologists in classification tasks such as these. …”
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Models and methods for analyzing complex networks and social network structures by Ju. P. Perova, V. P. Grigoriev, D. O. Zhukov

“…Compared with other methods, the network approach has the undeniable advantage of operating with data at different levels of research to ensure its continuity. …”
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Multi-scale window transformer for cervical cytopathology image recognition by Jiaxiang Yi, Xiuli Liu, Shenghua Cheng, Li Chen, Shaoqun Zeng

“…We design multi-scale window multi-head self-attention (MW-MSA) to simultaneously integrate cell features of different scales. Small window self-attention is used to extract local cell detail features, and large window self-attention aims to integrate features from smaller-scale window attention to achieve window-to-window information interaction. …”
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Lightweight GAN-Assisted Class Imbalance Mitigation for Apple Flower Bud Detection by Wenan Yuan, Peng Li

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Multi-Layer Modeling and Visualization of Functional Network Connectivity Shows High Performance for the Classification of Schizophrenia and Cognitive Performance via Resting fMRI by Duc My Vo, Anees Abrol, Zening Fu, Vince D. Calhoun

“…In the first, a deep convolutional neural network (DCNN) is trained to produce heatmaps from multiple convolution layers. …”
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An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification by Khaoula Taji, Ali Sohail, Tariq Shahzad, Bilal Shoaib Khan, Muhammad Adnan Khan, Khmaies Ouahada

“…The ensemble feature vector is optimized using three different meta-heuristic algorithms that are Binary Dragonfly algorithm (BDA), Ant Colony Optimization algorithm and Moth Flame Optimization algorithm (MFO). …”
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Predicting microbe-disease associations via graph neural network and contrastive learning by Cong Jiang, Cong Jiang, Junxuan Feng, Junxuan Feng, Bingshen Shan, Bingshen Shan, Qiyue Chen, Jian Yang, Jian Yang, Gang Wang, Gang Wang, Xiaogang Peng, Xiaozheng Li, Xiaozheng Li

“…Next, we apply the feature encoder separately to the microbe similarity network, disease similarity network, and microbe-disease association network, and enhance the consistency of features for the same nodes across different association networks through contrastive learning. …”
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MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification by Md. Eshmam Rayed, Jamin Rahman Jim, Md Juniadul Islam, M. F. Mridha, Md Mohsin Kabir, Md. Jakir Hossen

“…Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. …”
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Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNN by Simanto Saha, Mathias Baumert, Alistair Mcewan

“…This study proposed a motor imagery (MI) classification pipeline featuring a 1−dimensional convolutional neural network (1D-CNN) with different time/frequency feature representation techniques. …”
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MMAgentRec, a personalized multi-modal recommendation agent with large language model by Xiaochen Xiao

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A Lightweight Fault Diagnosis Framework for Hydro-Turbine Main Shaft Bearing Under Noise Interference by Hongwei Zhang, Zhao Liu, Hansong Si, Kaipeng Yu, Shuaifang Li, Zhenwu Yan

“…Second, a parameter-free Light Global Attention mechanism is proposed, which distinguishes key features from noise interference by minimizing the energy differences among similar features. Comparative experiments demonstrate that the lightweight method proposed in this paper exhibits superior diagnostic performance and noise robustness.…”
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An Optimized Model for Identification of Cerebral Palsy Using Deep Learning by Md Anjar Ahsan, Hassan bin Mohammed, Abhilash Maroju, Nurhafizah Moziyana Mohd Yusop, Wan Su Emi Yusnita Wan Yusof, Najjah Salwa Abd Razak, Mohd Arif Dar

“…Individualized therapy and rehabilitation programs are necessary to treat these differences effectively. Therefore, early-stage CP categorization is crucial to ensuring timely and targeted treatment efforts. …”
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Assessment of the Diagnostic Accuracy of Artificial Intelligence Software in Identifying Common Periodontal and Restorative Dental Conditions (Marginal Bone Loss, Periapical Lesion... by Wael I. Ibraheem, Saurabh Jain, Mohammed Naji Ayoub, Mohammed Ahmed Namazi, Amjad Ismail Alfaqih, Aparna Aggarwal, Abdullah A. Meshni, Ammar Almarghlani, Abdulkareem Abdullah Alhumaidan

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