DeepOmicsSurv: a deep learning-based model for survival prediction of oral cancer by Deepali, Neelam Goel, Padmavati Khandnor

“…This study aims to propose a deep learning-based model, DeepOmicsSurv, to predict survival in oral cancer patients using clinical and multi-omics data. Methods DeepOmicsSurv builds on the DeepSurv model, incorporating multi-head attention convolutional layers, dropout, pooling, and batch normalization to boost its strength and precision. …”
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Optimizing Large Railway Vision Models for Efficient In-Context Learning by Xuemei Zhan, Xubo Wu, Hua Ma

“…Drawing inspiration from Efficient-ViT, we incorporate the CGA layer as part of the LRVM’s building blocks to alleviate computational bottlenecks of the multi-head self-attention (MHSA). Additionally, we implement a multi-level feature concatenation technique to enhance transferability across different railway-related tasks and reduce the computation cost. …”
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A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment by Jinyin Bai, Wei Zhu, Shuhong Liu, Chenhao Ye, Peng Zheng, Xiangchen Wang

“…The TCN layer incorporates batch normalization, an optimized activation function (Leaky ReLU), and a dropout mechanism to enhance its ability to capture multi-scale temporal features. …”
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Big Data-Driven 3D Visualization Analysis System for Promoting Regional-Scale Digital Geological Exploration by Yiping Tian, Jiongqi Wu, Genshen Chen, Gang Liu, Xialin Zhang

“…The system integrates three core technological components: (1) a heterogeneous cloud resource scheduling method employing an optimized CMMN algorithm with unified cloud API standardization to enhance task distribution efficiency; (2) a block model-based dynamic data aggregation approach utilizing semantic unification and attribute mapping for multi-source geological data integration; (3) a GPU-accelerated rendering framework implementing occlusion culling and batch processing to optimize 3D visualization performance. …”
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Establishment and Implementation of Sponge City Monitoring System— A Case Study of Zhenjiang by WANG Yan, YU Meixiu, LU Zhicong

“…In order to effectively deal with urban water pollution and waterlogging,China proposes the concept of sponge city for stormwater management,which takes the control rate of total annual runoff,urban non-point pollution control,and urban rainstorm and waterlogging prevention as evaluation indicators to comprehensively improve the water resources,water security,water environment,and water ecological quality of cities.In the process of sponge city planning,construction,operation,and maintenance,it is necessary to establish a complete online monitoring system to assist the whole life cycle management of the sponge city construction.With Zhenjiang city listed in the first batch of pilot sponge cities in China as an example,a multi-level and organically nested sponge city monitoring system is constructed,and it mainly monitors rainfall,underlying surface,land area (sponge facilities,project sites,drainage zones),rivers and lakes,and basins.In addition,its monitoring indicator,distribution principle,monitoring frequency,as well as selection,recognition,and effective utilization of data are specified.The construction and implementation of the sponge city monitoring system are of great theoretical significance and application value for evaluating the construction and operation optimization of sponge cities,strengthening urban waterlogging prevention and control,and systematically promoting sponge cities in the whole area.…”
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An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes by Qian Yi, Xin Wu, Junkang Zhuo, Congbo Li, Chuanjiang Li, Huajun Cao

“…Finally, using real data collected in enterprises for four consecutive years, it is verified that the predictive model can be effectively applied to the working conditions of multi-variety, small batch, and incremental heat treatment data.…”
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Towards a unified framework for single‐cell ‐omics‐based disease prediction through AI by Matteo Barberis, Jinkun Xie

“…Machine learning pipelines or deep learning architectures can then be trained in a multi‐task fashion, classifying both disease identity and disease stage. …”
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Adaptive multilevel attention deeplabv3+ with heuristic based frame work for semantic segmentation of aerial images using improved golden jackal optimization algorithm by Anilkumar P, Venugopal P, Satheesh Kumar S, Jagannadha Naidu K

“…To addressing the issue in deeplab series, an adaptive multi-level attention based deeplabv3+ (AMLA-Deeplabv3+) with improved golden jackal optimization algorithm is implemented in this paper. …”
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GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8 by Qiang Hu, Yunhua Zhang

“…Additionally, RepConv is incorporated into C2f-GR to avoid the complexity of multi-branch structures and enhance gradient flow capability. …”
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Leveraging multiple labeled datasets for the automated annotation of single-cell RNA and ATAC data by Joseba Sancho-Zamora, Akash Kanhirodan, Xabier Garrote, Juan Manuel Silva Rojas, Olivier Gevaert, Mikel Hernaez, Guillermo Serrano, Idoia Ochoa

“…However, assembling these atlases is challenging due to batch effects and the need for accurate and consistent cell annotation. …”
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Loss Function Optimization Method and Unsupervised Extraction Approach D-DBSCAN for Improving the Moving Target Perception of 3D Imaging Sonar by Jingfeng Yu, Aigen Huang, Zhongju Sun, Rui Huang, Gao Huang, Qianchuan Zhao

“…For rapid data annotation, a batch annotation method combining human expertise and multi-frame superposition is proposed. …”
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Towards precision agriculture tea leaf disease detection using CNNs and image processing by Irfan Sadiq Rahat, Hritwik Ghosh, Suresh Dara, Shashi Kant

“…Our model is distinguished by its complex multi-layer architecture, crafted to adeptly handle 256 × 256 pixel images across three color channels (RGB). …”
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SineKAN: Kolmogorov-Arnold Networks using sinusoidal activation functions by Eric Reinhardt, Dinesh Ramakrishnan, Sergei Gleyzer

“…Recent work has established an alternative to traditional multi-layer perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). …”
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Machine Learning Methods for Quality Prediction in Production by Sidharth Sankhye, Guiping Hu

“…While there has been thorough research on predicting the quality of specific manufacturing processes, the adoption of classification methods to predict the overall compliance of production batches has not been extensively investigated. This paper aims to design machine learning based classification methods for quality compliance and validate the models via case study of a multi-model appliance production line. …”
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Aircraft Wake Vortex Recognition Method Based on Improved Inception-VGG16 Hybrid Network by Weijun Pan, Yuhao Wang, Leilei Deng, Yanqiang Jiang, Yuanfei Leng

“…This module comprises convolution, batch normalization, ReLU activation, and max pooling operations. …”
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Development of SNAP-Tag Based Nanobodies as Secondary Antibody Mimics for Indirect Immunofluorescence Assays by Wenjie Sheng, Chaoyu Zhang, T. M. Mohiuddin, Marwah Al-Rawe, Roland Schmitz, Marcus Niebert, Lutz Konrad, Steffen Wagner, Felix Zeppernick, Ivo Meinhold-Heerlein, Ahmad Fawzi Hussain

“…Moreover, as animal-derived products, secondary antibodies are associated with ethical concerns and batch-to-batch variability. In this study, we developed fluorescence-labeled recombinant nanobodies as secondary antibodies by utilizing previously established anti–mouse and anti–rabbit IgG secondary nanobodies in combination with the self-labeling SNAP-tag. …”
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DIA-TSK: A Dynamic Incremental Adaptive Takagi–Sugeno–Kang Fuzzy Classifier by Hao Chen, Chenhui Sha, Mingqing Jiao, Changbin Shao, Shang Gao, Hualong Yu, Bin Qin

“…For large-scale data sets, DIA-TSK evolves into B-DIA-TSK, which implements batch updates for multiple samples based on the Woodbury matrix identity. …”
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Long time-series and high-frequency ecological evaluation of Henan section of the Yellow River by Jianzhong Guo, Daozhu Xu, Jian Xu, Ruoxin Zhu, Ning Li

“…So the research tends to select images from limited periods, making it difficult to conduct batch calculations of high-frequency, long-term sequence, and multi-time scales of ecological environment quality automatically, dynamically, and rapidly. …”
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A human activity recognition model based on deep neural network integrating attention mechanism by Feng Xu, Xuchen Gao, Weigang Wang

“…Experiments using data from 30 participants’ smartphone sensors (acceleration and gyroscope) show that after preprocessing and sampling, the model takes 561-dimensional feature vectors as input. With multi-scale feature extraction, residual and skip connections, and dual attention mechanisms, along with a series of optimization strategies like dropout, batch normalization, and AdamW optimizer, the model achieves an average accuracy of 99.03% in five-fold cross-validation. …”
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Fuzzy Encryption Search Scheme and Data Verification Mechanism Based on Blockchain by Kuan Li

“…First, privacy protection of the raw data is achieved through a hierarchical Mini Batch K-Means clustering algorithm and locally sensitive hash functions. …”
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