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  1. 1181

    OLAP Techniques for Approximation and Mining Query Answering by Murtadaha Hamd, Waleed Hassan

    Published 2010-12-01
    “…Through this research, theoretical concepts which are related with OLAP and Data Warehouse are studied in detail. …”
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    Article
  2. 1182

    An Assessment of Land Use Land Cover Using Machine Learning Technique by V. Pushpalatha, H. N. Mahendra, A. M. Prasad, N. Sharmila, D. Mahesh Kumar, N. M. Basavaraju, G. S. Pavithra and S. Mallikarjunaswamy

    Published 2024-12-01
    “…This research paper presents a comprehensive assessment of the built-up area in Mysuru City over the decade spanning from 2010 to 2020, employing advanced geospatial techniques. …”
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    Article
  3. 1183
  4. 1184

    Optimal Design of the Feeder-Bus Network Based on the Transfer System by Lianbo Deng, Wei Gao, Yanbing Fu, Wenliang Zhou

    Published 2013-01-01
    “…We extended the demand pattern of M-to-1 in most existing researches to M-to-M. …”
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    Article
  5. 1185

    An Approach for Predicting the Shape and Size of a Buried Basic Object on Surface Ground Penetrating Radar System by Nana Rachmana Syambas

    Published 2012-01-01
    “…However, the output target is only hyperbolic representation. This research develops a system to identify a buried object on surface GPR based on decision tree method. …”
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  6. 1186
  7. 1187

    Robust development of data-driven models for methane and hydrogen mixture solubility in brine by Kashif Saleem, Abhinav Kumar, K. D. V. Prasad, Ahmad Alkhayyat, T. Ramachandran, Protyay Dey, Navdeep Kaur, R. Sivaranjani, I. B. Sapaev, Mehrdad Mottaghi

    Published 2025-04-01
    “…In this paper, we aim to form robust data-driven intelligent algorithms founded on various machine learning methods of Support Vector Machine, Random Forest, AdaBoost, Decision Tree, K-nearest Neighbors, Multilayer Perceptron Artificial Neural Network and Convolutional Neural Network to model solubility of hydrogen/methane blend in brine under realistic conditions of underground hydrogen storage projects by utilizing an experimental dataset collected from the existing body of published research. …”
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  8. 1188

    Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

    Published 2025-07-01
    “…This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. …”
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  9. 1189
  10. 1190

    A predictive analytics framework for opportunity sensing in stock market by Shruti Mittal, C.K. Nagpal

    Published 2022-06-01
    “…Thus research papers in this area suffer from multiple limitations: Very short prediction period from one day to one week, consideration of few stocks only instead of whole of stock market spectrum, exploration of more suitable machine learning algorithms. …”
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  11. 1191
  12. 1192

    Explainable Anomaly Detection Based on Operational Sequences in Industrial Control Systems by Ka-Kyung Kim, Joon-Seok Kim, Ieck-Chae Euom

    Published 2025-01-01
    “…Anomaly detection systems, initially implemented for detecting device faults or failures, have increasingly become the focus of research aimed at identifying attack patterns as cyberattack techniques become more sophisticated and intelligent. …”
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  13. 1193
  14. 1194

    Integrated Evaluation and error decomposition of four gridded precipitation products using dense rain gauge observations over the Yunnan-Kweichow Plateau, China by Tianjian Lu, Qingquan Xiao, Hanyu Lu, Jintong Ren, Yongyi Yuan, Xiaoshan Luo, Zhijie Zhang

    Published 2024-12-01
    “…The main findings are as follows: (1) AERA5-Asia, AIMERG, and IMERG-Final show similar precipitation patterns, with ERA5-Land overestimating. While all display minor seasonal variations, AERA5-Asia underestimates summer rain. …”
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  15. 1195

    An optimized ensemble model with advanced feature selection for network intrusion detection by Afaq Ahmed, Muhammad Asim, Irshad Ullah, Zainulabidin, Abdelhamied A. Ateya

    Published 2024-11-01
    “…However, these methods often fall short in detecting sophisticated and evolving threats, particularly those involving subtle variations or mutations of known attack patterns. To address this challenge, our study presents the “Optimized Random Forest (Opt-Forest),” an innovative ensemble model that combines decision forest approaches with genetic algorithms (GAs) for enhanced intrusion detection. …”
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    Article
  16. 1196

    Prognostic model identification of ribosome biogenesis-related genes in pancreatic cancer based on multiple machine learning analyses by Yuan Sun, Yan Li, Anlan Zhang, Tao Hu, Ming Li

    Published 2025-05-01
    “…Prognostic gene sets were screened using machine learning algorithms to construct a risk model, which was externally validated via GEO database. …”
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  17. 1197

    Narrowband Theta Investigations for Detecting Cognitive Mental Load by Silviu Ionita, Daniela Andreea Coman

    Published 2025-06-01
    “…Another measure of the signal considered as a discriminator is energy. In this research, it was used just for model comparison. A cognitive load detection algorithm based on these signal metrics was developed and tested on original experimental data. …”
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  18. 1198
  19. 1199

    Spatiotemporal Analysis of Multi-UAV Persistent Search and Retrieval with Stochastic Target Appearance by Ryan Day, John L. Salmon

    Published 2025-02-01
    “…Additionally, we introduce trellis charts for integrated visualization and analysis of combined spatiotemporal patterns. This research builds on a case study developed in a previous case study of multi-UAV PSR-STA. …”
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  20. 1200

    Integrating digital and narrative medicine in modern healthcare: a systematic review by Efthymia Efthymiou

    Published 2025-12-01
    “…This integrated approach supports technological proficiency and humanistic values, for advancements in digital health to improve rather than displace the interpersonal foundations of patient care. Further empirical research might assess long-term outcomes and guide implementation into curricula, faculty development, and institutional policy.Background: The increasing integration of digital technologies in healthcare, such as electronic health records, telemedicine, and diagnostic algorithms, has improved efficiency but raised concerns about the depersonalization of care. …”
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