Financial Evolution and Interdisciplinary Research by Ana Njegovanović

“…This paper (summary of the second chapter of the manuscript "quantum dance") talks about the multidimensionality of finance through evolution, philosophy with interdisciplinary features (interweaving of neuroscience, mathematics, quantum physics, biology and artificial intelligence). …”
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An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition by Nguyen Hai Thanh, Truong Nguyen Nhat, Pham Linh Thuy Thi, Pham Ngoc Huynh

“…Therefore, we present an approach grounded in skeleton-based feature extraction and neural networks to find a solution to the recognition of yoga postures, creating a premise for researching a smart virtual trainer that supports home workouts for users from input image data converted into skeleton data through MoveNet. …”
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Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks by Md. Rajib Hossain, Mohammed Moshiul Hoque, M. Ali Akber Dewan, Nazmul Siddique, Md. Nazmul Islam, Iqbal H. Sarker

“…This paper presents an authorship classification approach made of Convolution Neural Networks (CNN) comprising four modules: embedding model generation, feature representation, classifier training and classifier testing. …”
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A model for shale gas well production prediction based on improved artificial neural network by LIN Hun, SUN Xinyi, SONG Xixiang, MENG Chun, XIONG Wenxin, HUANG Junhe, LIU Hongbo, LIU Cheng

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Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout by Mehdi Ghayoumi

“…<b>Methods:</b> We introduce <i>Probabilistic Feature Importance Dropout</i> (PFID), a novel regularization method that assigns dropout rates based on the probabilistic significance of individual features. …”
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A fake news detection model using the integration of multimodal attention mechanism and residual convolutional network by Ying Lu, Naiwei Yao

“…Abstract To improve the accuracy and efficiency of fake news detection, this study proposes a deep learning model that integrates residual networks with attention mechanisms. Building on traditional convolutional neural networks, the model incorporates multi-head attention mechanisms to enhance the extraction of key features from multimodal data such as text, images, and videos. …”
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EEG-based epilepsy detection using CNN-SVM and DNN-SVM with feature dimensionality reduction by PCA by Yousra Berrich, Zouhair Guennoun

“…The integration of Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) with Support Vector Machines (SVM) is explored, with a particular emphasis on the role of Principal Component Analysis (PCA) in simplifying feature dimensions. …”
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Fine-grained crop pest classification based on multi-scale feature fusion and mixed attention mechanisms by Yiheng Qian, Zhiyong Xiao, Zhaohong Deng

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Study on Photovoltaic Plant Site Selection Models Based on Geographic and Environmental Features by RAO Zhi, YANG Zaimin, YANG Xiongping, LI Jiaming, YANG Ping, WEI Zhichu

“…Then， the model leveraged the temporal feature extraction capability of the TCN to perform deep representation learning on preprocessed multisource input data， whereas the former network was employed to capture long-term dependencies. …”
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Photoplethysmogram (PPG)-Based Biometric Identification Using 2D Signal Transformation and Multi-Scale Feature Fusion by Yuanyuan Xu, Zhi Wang, Xiaochang Liu

“…Compared to existing state-of-the-art methods, the proposed model demonstrates significant improvements across all evaluation metrics, highlighting its significance in terms of network architecture and performance.…”
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Spatiotemporal pattern evolution and quantitative prediction of electrical carbon emissions from a demand-side perspective in urban areas by Ying Tian, Hui Cao, Dapeng Yan, Jinmei Chen, Yayan Hua

“…Utilizing high-frequency monitoring data from  3000 distribution network stations (May–Sept 2018), it creates an integrated ’spatiotemporal evolution-data driven prediction’ framework to reveal emission dynamics and enhance forecast accuracy. …”
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MSBiLSTM-Attention: EEG Emotion Recognition Model Based on Spatiotemporal Feature Fusion by Yahong Ma, Zhentao Huang, Yuyao Yang, Zuowen Chen, Qi Dong, Shanwen Zhang, Yuan Li

“…By using raw EEG data, the method applies multi-scale convolutional neural networks and bidirectional long short-term memory networks to extract and merge features, selects key features via an attention mechanism, and classifies emotional EEG signals through a fully connected layer. …”
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Construction of a feature gene and machine prediction model for inflammatory bowel disease based on multichip joint analysis by Yan Chaosheng, Sun Haowen, Rao Jingjing, Dai Yuanyuan, Duan Wenhui, Sheng Yingyue, Xue Yuzheng

“…On the basis of methods such as artificial neural networks (ANNs), machine learning techniques, and the SHAP model, we developed a diagnostic model for IBD. …”
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PEYOLO a perception efficient network for multiscale surface defects detection by Xun Li, Yuzhen Zhao, Xiangke Jiao, Qingzhe Meng, Zhun Guo, Ruijuan Yao, Yaqiao Yang, Baoxi Yuan

“…First, we introduce the Defect Capture Path Aggregation Network, which enhances the feature fusion network’s ability to learn multi-scale representations. …”
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Development and evaluation of deep neural networks for the classification of subtypes of renal cell carcinoma from kidney histopathology images by Amit Kumar Chanchal, Shyam Lal, Shilpa Suresh

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Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments by G. Nalinipriya, S. Rama Sree, K. Radhika, E. Laxmi Lydia, Faten Khalid Karim, Mohamad Khairi Ishak, Samih M. Mostafa

“…The insights and hidden trends detected from network data and the architecture of a data-driven ML to avoid this attack are essential to establishing an intelligent security system. …”
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LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement by Zhipeng Fan, Yayun Wu, Wei Liu, Ming Chen, Zeguo Qiu

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A novel multi-scale and fine-grained network for large choroidal vessels segmentation in OCT by Wei Huang, Qifeng Yan, Lei Mou, Yitian Zhao, Wei Chen, Wei Chen, Wei Chen

“…In this paper, we propose a novel multi-scale and fine-grained network called MFGNet. Since choroidal vessels are small targets, long-range dependencies need to be considered, therefore, we developed a two-branch fine-grained feature extraction module that can mix the long-range information extracted by TransFormer with the local information extracted by convolution in parallel, introducing information exchange between the two branches. …”
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A Fuzzy Neural Network-Based Intelligent Prediction Model for Useful Lifespan of Lithium-Ion Batteries by Yaowei Xu, Zhenxing Hao, Wei Li, Angang Cao

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Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework by Liang Yu, Yuanshen Hong, Hua Lin, Xu Jiang, Ziming Song

“…The experimental results demonstrate that the BP neural network model incorporating multi-scale features outperforms traditional BP neural network models and other control models in several evaluation metrics. …”
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