Reconstructing Molecular Networks by Causal Diffusion Do‐Calculus Analysis with Deep Learning by Jiachen Wang, Yuelei Zhang, Luonan Chen, Xiaoping Liu

“…Presently, most methods for inferring gene regulatory and biological networks rely on association studies or observational causal‐analysis approaches. This study introduces a novel approach that combines intervention operations and diffusion models within a do‐calculus framework by deep learning, i.e., Causal Diffusion Do‐calculus (CDD) analysis, to infer causal networks between molecules. …”
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Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis by Yongxin Ge, Jiake Leng, Ziyang Tang, Kanran Wang, Kaicheng U, Sophia Meixuan Zhang, Sen Han, Yiyan Zhang, Jinxi Xiang, Sen Yang, Xiang Liu, Yi Song, Xiyue Wang, Yuchen Li, Junhan Zhao

“…Validated with datasets from human lung, breast, and colorectal cancers, GIST effectively reveals spatial domains and substantially improves the accuracy of segmenting the microenvironment after denoising transcriptomics data. This enhancement enables more accurate gene expression analysis and aids in identifying prognostic marker genes, outperforming state-of-the-art deep learning methods with a total improvement of up to 49.72%. …”
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Identifying the Origin of Cyber Attacks Using Machine Learning and Network Traffic Analysis by Du Tianqing

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Systematic Analysis of Federated Learning Approaches for Intrusion Detection in the Internet of Things Environment by Nuha A. Hamad, Khairul Azmi Abu Bakar, Faizan Qamar, Ahmed Mahdi Jubair, Rajina R. Mohamed, Mohamad Afendee Mohamed

“…Traditional IDS solutions typically rely on centralized learning architectures, where data from edge nodes are transferred to central servers for analysis. …”
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A Synergy Between Machine Learning and Formal Concept Analysis for Crowd Detection by Anas M. Al-Oraiqat, Oleksandr Drieiev, Sattam Almatarneh, Mohammadnoor Injadat, Karim A. Al-Oraiqat, Hanna Drieieva, Yassin M. Y. Hasan

“…Recent systems take advantage of the synergy between machine learning, data mining, and image processing to extract/analyze features from crowded zones and recognize patterns and anomalies from the crowd behavior. …”
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Advanced machine learning for regional potato yield prediction: analysis of essential drivers by Dania Tamayo-Vera, Morteza Mesbah, Yinsuo Zhang, Xiuquan Wang

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Analysis of the Suitability of Implementing Project Based Learning for Elementary School Teacher in Salatiga by Gamaliel Septian Airlanda, Yari Dwikurnaningsih, Agustina Tyas Asri Hardini

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Leveraging machine learning to predict residential location choice: A comparative analysis by Vahid Noferesti, Hamid Mirzahossein

“…A comparative analysis of various machine learning algorithms reveals that XGBoost and gradient boosting models significantly outperform traditional methods, achieving a 42 % accuracy rate in predicting residential location choices on the 33 % validation data of household travel survey data from MWCOG. …”
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Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification by Md Takbir Alam, Md Ashibul Islam Khan, Nahian Nakiba Dola, Tahia Tazin, Mohammad Monirujjaman Khan, Amani Abdulrahman Albraikan, Faris A. Almalki

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The Application of Machine Learning Algorithms for Text Mining based on Sentiment Analysis Approach by Reza Samizade, Elnaz Mahmoudi Saeid Abad

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Earthquake induced liquefaction hazard analysis for Chittagong City using machine learning by Md. Mehedi Hasan, Md. Zillur Rahman, Abul Kashem Faruki Fahim

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Comparative analysis of machine learning algorithms for predicting depression among individuals with diabetes by Hind Bourkhime, Noura Qarmiche, Soumaya Benmaamar, Nada Lazar, Mohammed Omari, Mohamed Berraho, Nabil Tachfouti, Samira El Fakir, Hanan El Ouahabi, Nada Otmani

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Internet of Things-Based Smart Infant-Incubators Using Machine Learning Analysis by Mahmoud Gamal, Ibrahim Radi, Amr Yousef, and Ali Gaber Mohamed Ali

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Meta-analysis of Jelajah Alam Sekitar (JAS) Approach Implementation in Learning Procces by S. Ngabekti, S. Ridlo, E. Peniati, R. Martanto

“…Then, how the effectiveness of the implementation of the JAS approach in improving the learning process. This study uses meta-analysis-data in the form of descriptive exploratory qualitative. …”
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Deep Learning Shield Attitude Prediction Model Based on Grey Correlation Analysis by Ke MAN, Zongxu LIU, Yan SHANG, Zhifei SONG, Xiaoli LIU, Bao SU

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Advanced Deep Learning Based Predictive Maintenance of DC Microgrids: Correlative Analysis by M. Y. Arafat, M. J. Hossain, Li Li

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The nursing process and total health cost variability: an analysis using machine learning by Maria Consuelo Company-Sancho, Victor M. González-Chordá, Maria Isabel Orts-Cortés

“…To improve prediction accuracy and account for non-linear relationships, the analysis was completed using two machine learning models. …”
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Propagation routing for hybrid bandwidth allocation with reinforcement learning in VANET infrastructure analysis by G. Michael, A. Selvakumar, Ramesh Sundar, S. Manikandan, R. Gnanajeyaraman, U. Arul

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Generating learning guides for medical education with LLMs and statistical analysis of test results by Iván Roselló Atanet, Mihaela Tomova, Miriam Sieg, Victoria Sehy, Patrick Mäder, Maren März

“…The feedback generated with data from students in later stages of their studies was more detailed, as they tend to face more questions matching their knowledge level.…”
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Using Mathlink Cubes to Introduce Data Wrangling with Examples in R by Lucy D’Agostino McGowan

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