Optimisation of sparse deep autoencoders for dynamic network embedding by Huimei Tang, Yutao Zhang, Lijia Ma, Qiuzhen Lin, Liping Huang, Jianqiang Li, Maoguo Gong

“…A sparse deep autoencoder (called SPDNE) for dynamic NE is proposed, aiming to learn the network structures while preserving the node evolution with a low computational complexity. SPDNE tries to use an optimal sparse architecture to replace the fully connected architecture in the deep autoencoder while maintaining the performance of these models in the dynamic NE. …”
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MythicVision: a deep learning powered mobile application for understanding Indian mythological deities using weight centric decision approach by Tauseef Khan, Aditya Nitin Patil, Aviral Singh, Gitesh Prashant Bhavsar, Kanakagiri Sujay Ashrith, Sachi Nandan Mohanty

“…Key features of “MythicVision” include model-wise weight computation and a weight-centric decision mechanism, which deliver more accurate results compared to traditional majority voting in multi-class image classification. …”
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A Survey of Matrix Completion Methods for Recommendation Systems by Andy Ramlatchan, Mengyun Yang, Quan Liu, Min Li, Jianxin Wang, Yaohang Li

“…We focus on the mathematical models for matrix completion and the corresponding computational algorithms as well as their characteristics and potential issues. …”
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High-Transmittance Subwavelength Metal Grating with Relief Structure Composed of Multiple Steps by Zhongfei Wang, Dawei Zhang, Qi Wang, Banglian Xu, Qingyong Tang, Yuanshen Huang, Songlin Zhuang

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KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning by Huimin Luo, Huimin Luo, Hui Yang, Hui Yang, Ge Zhang, Ge Zhang, Jianlin Wang, Jianlin Wang, Junwei Luo, Chaokun Yan, Chaokun Yan, Chaokun Yan

“…Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. …”
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CDPMF-DDA: contrastive deep probabilistic matrix factorization for drug-disease association prediction by Xianfang Tang, Yawen Hou, Yajie Meng, Zhaojing Wang, Changcheng Lu, Juan Lv, Xinrong Hu, Junlin Xu, Jialiang Yang

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TransDeep: Transformer-Integrated DeepLabV3+ for Image Semantic Segmentation by Tengfei Chai, Zhiguo Xiao, Xiangfeng Shen, Qian Liu, NianFeng Li, Tong Guan, Jia Tian

“…Aiming at problems such as the inability of many image segmentation algorithms to fully capture global context information, low computational efficiency, and insufficient context information fusion. …”
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Distributed Storage System for Electric Power Data Based on HBase by Jiahui Jin, Aibo Song, Huan Gong, Yingying Xue, Mingyang Du, Fang Dong, Junzhou Luo

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A Strategic Study about Quality Characteristics in e-Health Systems Based on a Systematic Literature Review by F. J. Domínguez-Mayo, M. J. Escalona, M. Mejías, G. Aragón, J. A. García-García, J. Torres, J. G. Enríquez

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A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method by S. M. Nuruzzaman Nobel, S. M. Masfequier Rahman Swapno, Md. Rajibul Islam, Mejdl Safran, Sultan Alfarhood, M. F. Mridha

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Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection by Amal Alshardan, Hany Mahgoub, Nuha Alruwais, Abdulbasit A. Darem, Wafa Sulaiman Almukadi, Abdullah Mohamed

“…Abstract Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathematical modelling to unravel complex data embedded within medical images. …”
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Efficient Spectral-Spatial Fusion With Multiscale and Adaptive Attention for Hyperspectral Image Classification by Xiaoqing Wan, Feng Chen, Weizhe Gao, Yupeng He, Hui Liu, Zhize Li

“…In order to reduce the computational cost and improve the classification accuracy of land cover categories, an efficient spectral-spatial fusion method (ESSF) is proposed, which is based on the following modules: a multiscale feature fusion module (MSFFM), an efficient adaptive spectral-spatial feature extraction module (EASSFEM), and a context-aware fusion network (CFN). …”
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Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations by Farida Siddiqi Prity, MD. Mehadi Hasan, Shakhawat Hossain Saif, Md. Maruf Hossain, Sazzad Hossain Bhuiyan, Md. Ariful Islam, Md Tousif Hasan Lavlu

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Diagnosis and detection of bone fracture in radiographic images using deep learning approaches by Theyazn Aldhyani, Zeyad A. T. Ahmed, Bayan M. Alsharbi, Sultan Ahmad, Sultan Ahmad, Mosleh Hmoud Al-Adhaileh, Ahmed Hassan Kamal, Mohammed Almaiah, Jabeen Nazeer

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THERMO-SENSOR DIAGNOSTICS OF AK9CH ALLOY SOLIDIFICATION PROCESS IN THE DEVELOPMENT OF TECHNOLOGY FOR DEFECT-FREE CASTINGS by P. E. Lushchik

“…This paper shows the practical application of algorithm for calculating the volume fraction of solids for the computer simulation of aluminum castings technology…”
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PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection by Zhou Wang, Yuting Su, Feng Kang, Lijin Wang, Yaohua Lin, Qingshou Wu, Huicheng Li, Zhiling Cai

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TMBocelot: an omnibus statistical control model optimizing the TMB thresholds with systematic measurement errors by Xin Lai, Shaoliang Wang, Xuanping Zhang, Xiaoyan Zhu, Yuqian Liu, Zhili Chang, Zhili Chang, Xiaonan Wang, Xiaonan Wang, Yang Shao, Yang Shao, Jiayin Wang, Yixuan Wang

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RvXmBlendNet: A Multi-architecture Hybrid Model for Improved Skin Cancer Detection by Farida Siddiqi Prity, Ahmed Jabid Hasan, Md Mehedi Hassan Anik, Rakib Hossain, Md. Maruf Hossain, Sazzad Hossain Bhuiyan, Md. Ariful Islam, Md Tousif Hasan Lavlu

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Deep learning in microbiome analysis: a comprehensive review of neural network models by Piotr Przymus, Krzysztof Rykaczewski, Adrián Martín-Segura, Jaak Truu, Enrique Carrillo De Santa Pau, Mikhail Kolev, Mikhail Kolev, Irina Naskinova, Aleksandra Gruca, Alexia Sampri, Alexia Sampri, Marcus Frohme, Alina Nechyporenko, Alina Nechyporenko

“…Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. …”
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VGG-16, VGG-16 With Random Forest, Resnet50 With SVM, and EfficientNetB0 With XGBoost-Enhancing Bone Fracture Classification in X-Ray Using Deep Learning Models by Spoorthy Torne, Dasharathraj K. Shetty, Krishnamoorthi Makkithaya, Prasiddh Hegde, Manu Sudhi, Phani Kumar Pullela, T Tamil Eniyan, Ritesh Kamath, Staissy Salu, Pranav Bhat, S. Girisha, P. S. Priya

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