ViOTUcluster: A high‐speed, All‐in‐one pipeline for viromic analysis of metagenomic data by Sihang Liu, Yinyin Ye, Bing Guo, Yuxing Hu, Kaiyang Jiang, Chengyu Liang, Siqing Xia, Hong Wang

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Noise Reduction in CWRU Data Using DAE and Classification with ViT by Jun-gyo Jang, Soon-sup Lee, Se-yun Hwang, Jae-chul Lee

“…The accuracy of failure classification was the highest when the data, preprocessed using a Denoising Autoencoder (DAE), were classified by a Vision Transformer (ViT).…”
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Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web by Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong

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Class VI Database project: Drill-stem test data from Sweetwater County, Wyoming, USAMendeley Data by Abdeldjalil Latrach, Lily J. Jackson, Christian Martinez

“…For each DST, we provide both the digitized raw DST time-series data and in-situ reservoir pressures calculated using Horner’s analysis. …”
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Machine learning analysis of breast cancer treatment protocols and cycle counts: A case study at Mohammed vi hospital, Morocco by Houda AIT BRAHIM, Salah EL-HADAJ, Abdelmoutalib METRANE

“…This paper presents a new study of predicting patients' breast cancer treatment protocol and the corresponding treatment cycle based on machine learning algorithms. The data used were collected at Mohammed VI Hospital in Morocco, and it contains patient information with two targets (protocol and treatment cycle).After preparing the data and testing several machine learning algorithms, two models were developed: The first one, based on Gradient Boosting Classifier algorithm, successfully classified patient treatment protocols with an overall accuracy of 64 % across all categories and an impressive 94 % accuracy for the mode category, widely adopted in the hospital. …”
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Analyse i kvalitativ forskning – Det vi ikke skriver om by Sissel H. Eriksen, Amy Holtan

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Ornamental Potential Classification and Prediction for Pepper Plants (<i>Capsicum</i> spp.): A Comparison Using Morphological Measurements and RGB Images as Data Source by Antonia Maiara Marques do Nascimento, Ruben Ruiz-Gonzalez, Víctor Martínez-Martínez, Artur Mendes Medeiros, Fábio Sandro dos Santos, Elizanilda Ramalho do Rêgo, Samy Pimenta, Cláudia Pombo Sudré, Cintia dos Santos Bento, Carlos Cambra, Priscila Alves Barroso

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ViTAU: Facial paralysis recognition and analysis based on vision transformer and facial action units by Jia GAO, Wenhao CAI, Junli ZHAO, Fuqing DUAN

“…Initially, the ViT model utilizes its self-attention mechanism to accurately determine the presence of facial paralysis. …”
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"Vi kallar det skolverkiska" by Maria Olsson, Maria Fredriksson

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DermViT: Diagnosis-Guided Vision Transformer for Robust and Efficient Skin Lesion Classification by Xuejun Zhang, Yehui Liu, Ganxin Ouyang, Wenkang Chen, Aobo Xu, Takeshi Hara, Xiangrong Zhou, Dongbo Wu

“…By introducing a modular design that mimics a physician’s observation mode, DermViT achieves more logical feature extraction and decision-making processes for medical diagnosis, providing an efficient and reliable solution for dermoscopic image analysis.…”
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Optimization of chromium(VI) biosorption using gooseberry seeds by response surface methodology by J. Aravind, P. Kanmani, G. Sudha, R. Balan

“…Quadratic model had maximum R2 value (0.9984) and larger F value (1109.92). From the Analysis Of Variance table and R2 value, quadratic model was predicted to be the significant model with the best fit to the generated experimental data. …”
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FedViTBloc: Secure and privacy-enhanced medical image analysis with federated vision transformer and blockchain by Gabriel Chukwunonso Amaizu, Akshita Maradapu Vera Venkata Sai, Sanjay Bhardwaj, Dong-Seong Kim, Madhuri Siddula, Yingshu Li

“…This paper introduces FedViTBloc, a secure and privacy-enhanced framework for medical image analysis utilizing Federated Learning (FL) combined with Vision Transformers (ViT) and blockchain technology. …”
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Critical Analysis of Annex VI of MARPOL: Evaluating the Need for New Restriction Limits to Preserve the Ozone Layer by Sheikh Mehbuba Moitree

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Tumor ViT-GRU-XAI: Advanced Brain Tumor Diagnosis Framework: Vision Transformer and GRU Integration for Improved MRI Analysis: A Case Study of Egypt by Mohammed Aly, Abdullatif Ghallab, Islam S. Fathi

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ROC analysis data and predictors of rehabilitation effectiveness in patients with type 2 diabetes mellitus by T.H. Bakaliuk, N.R. Makarchuk, H.O. Stelmakh, V.I. Pankiv, L.B. Pavlovych, O.D. Bilous, I.I. Kamyshna

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CoViNAR: a context-aware social media dataset for pandemic severity level prediction and analysis by Soofi Shafiya, Mudasir Ahmad Wani, Suraiya Jabin, Mohammad ELAffendi, Jahiruddin

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A Multi-Scale Deep Learning Framework Combining MobileViT-ECA and LSTM for Accurate ECG Analysis by Abduljabbar S. Ba Mahel, Mehdhar S. A. M. Al-Gaashani, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Mohammed Saleh Ali Muthanna, Ammar Muthanna, Ahmed Aziz

“…Simultaneously, a Long Short-Term Memory (LSTM) network captures temporal dependencies in the data. The concatenation of the outputs from both the MobileViT-ECA block and the LSTM network allows for the extraction of both local and global features. …”
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Corrigendum: PGTransNet: a physics-guided transformer network for 3D ocean temperature and salinity predicting in tropical Pacific by Song Wu, Senliang Bao, Wei Dong, Senzhang Wang, Xiaojiang Zhang, Chengcheng Shao, Junxing Zhu, Xiaoyong Li

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PGTransNet: a physics-guided transformer network for 3D ocean temperature and salinity predicting in tropical Pacific by Song Wu, Senliang Bao, Wei Dong, Senzhang Wang, Xiaojiang Zhang, Chengcheng Shao, Junxing Zhu, Xiaoyong Li

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Hypertensive disorders of pregnancy and breastfeeding practices: A secondary analysis of data from the All Our Families Cohort by Kristin Horsley, Kathleen Chaput, Deborah Da Costa, Tuong‐Vi Nguyen, Natalie Dayan, Lianne Tomfohr‐Madsen, Suzanne Tough

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