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  1. 2081

    Diagnosis and Classification of Two Common Potato Leaf Diseases (Early Blight and Late Blight) Using Image Processing and Machine Learning by H. Koroshi Talab, D. Mohammad Zamani, M. Gholami Parashkoohi

    Published 2025-03-01
    “…This study presents a framework for diagnosing early blight and late blight diseases in potatoes using a combination of Relief feature selection algorithms and Random Forest classification, along with color, texture, and shape features in three color spaces: RGB, HSV, and CIELAB (Lab*). …”
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  2. 2082

    M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy by Nalini Schaduangrat, Hathaichanok Chuntakaruk, Thanyada Rungrotmongkol, Pakpoom Mookdarsanit, Watshara Shoombuatong

    Published 2025-04-01
    “…Finally, M3S-GRPred was constructed by integrating probabilistic feature from the selected base-classifiers derived from a two-step feature selection technique. …”
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    Article
  3. 2083
  4. 2084

    Complete mitogenomes reveal high diversity and recent population dynamics in Antarctic krill by Shuai Sun, Shuo Li, Inge Seim, Xiao Du, Xianwei Yang, Kaiqiang Liu, Zhanfei Wei, Changwei Shao, Guangyi Fan, Xin Liu

    Published 2025-04-01
    “…Furthermore, our investigation into selection pressures on mitochondrial genes reveals evidence of purifying selection across all 13 protein-coding genes, underscoring the pivotal role of mtDNA conservation in maintaining mitochondrial function under extreme environments. …”
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    Article
  5. 2085

    IGF2/IGFBP4 reduces apoptosis and increases free cholesterol of chicken granulosa cells in vitro by Wenhui Zhang, Xuejiao Chen, Axiu Guo, Zongyi Zhao, Bo Zhang, Fuwei Li, Hao Zhang

    Published 2024-12-01
    “…Therefore, this study provides new evidence on the functions of IGFs and IGFBPs in chickens, establishing a crucial experimental foundation for understanding the regulatory mechanisms of follicle selection. In addition, our study contributes to understanding follicular development and improves the egg-laying performance of chickens.…”
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  6. 2086

    Delineation of genotype × environment interaction and identifying superior red sorghum [Sorghum bicolor L. Moench] genotypes via multi-trait-based stability selection methods by Sureshkumar Rajesh-Kumar, Guruswamy Anand, Subramanian Lakshmi Narayanan, Elangovan Subramanian, Madhavan Lysal Mini

    Published 2025-03-01
    “…The data collected were subjected to Additive Main Effects and Multiplicative Interaction (AMMI) and Genotype-by-Environment Interaction (GGE) biplot models to recommend a specific genotype for the particular locality. …”
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  7. 2087

    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

    Published 2025-07-01
    “…The Mayfly Optimization Algorithm (MOA) is then utilized for feature selection, effectively mitigating computational complexity. …”
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    Article
  8. 2088
  9. 2089
  10. 2090

    The Application of Near-Infrared Spectroscopy Combined with Chemometrics in the Determination of the Nutrient Composition in Chinese <i>Cyperus esculentus</i> L. by Xiaobo Jiao, Dongliang Guo, Xinjun Zhang, Yunpeng Su, Rong Ma, Lewen Chen, Kun Tian, Jingyu Su, Tangnuer Sahati, Xiahenazi Aierkenjiang, Jingjing Xia, Liqiong Xie

    Published 2025-01-01
    “…Partial least squares regression (PLSR) was utilized to establish prediction models between NIR and chemical indicators. In addition, to further enhance the prediction performance of the models, various preprocessing and variable selection algorithms were utilized to optimize the prediction models. …”
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    Article
  11. 2091

    Negative frequency-dependent selection through variations in seedling fitness due to genetic differentiation of parents’ pair in a tropical rainforest tree, Rubroshorea curtisii (D... by Naoki Tani, Naoki Tani, Chin Hong Ng, Soon Leong Lee, Chai Ting Lee, Norwati Muhammad, Toshiaki Kondo, Yoshihiko Tsumura, Saori Sugiyama, Kaoru Niiyama, Azizi Ripin, Abdul Rahman Kassim, Samsudin Musa

    Published 2025-02-01
    “…IntroductionThe role of syngameons in adaption to microgeographical environmental heterogeneity is important and could be one of the sources of rich species diversity in tropical forests. In addition, negative frequency- or density-dependent selection is one of the major processes contributing to the maintenance of genetic diversity.MethodsTo assess genetic factors that affect the fitness of seedlings of Rubroshorea curtisii, a dominant canopy tree species in hill dipterocarp forests, the inter- and intra-population genetic structure of individuals from natural populations and individuals at two permanent plots in a hill dipterocarp forest with reproductive stage was studied. …”
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  12. 2092
  13. 2093

    Whole-Genome Resequencing Analysis of Athletic Traits in Grassland-Thoroughbred by Wenqi Ding, Wendian Gong, Tugeqin Bou, Lin Shi, Yanan Lin, Xiaoyuan Shi, Zheng Li, Huize Wu, Manglai Dugarjaviin, Dongyi Bai

    Published 2025-08-01
    “…Through whole-genome resequencing, we selected 60 offspring obtained from the crossbreeding of Thoroughbred horses and Xilingol horses for this study. …”
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  14. 2094

    Elucidation of genetic diversity in hawthorn (Crataegus azarolus L.) accessions naturally growing in the Eastern Mediterranean region of Türkiye using morphological, pomological, a... by Yazgan Tunç, Mehmet Yaman, Kadir Uğurtan Yılmaz, Ali Khadivi, Somayeh Goodarzi

    Published 2025-01-01
    “…In addition, the study is expected to make significant contributions to the literature on the evaluation and conservation of hawthorn genetic resources.…”
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    Article
  15. 2095
  16. 2096

    Designing a Stock Recommender System Using the Collaborative Filtering Algorithm for the Tehran Stock Exchange by Marziyeh Nourahmadi, Ali Rahimi, Hojjatollah Sadeqi

    Published 2024-06-01
    “…The overall index returned 16 times during this period, indicating the excellent performance of the strategy over time. Additionally, the value at risk (VaR) for the selected method during the study period stood at -12.8%, indicating the lower risk of this method. …”
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  17. 2097
  18. 2098

    Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT, 18F-FDG PET/CT, DNA mutation, and CA19... by Liang Qi, Xiang Li, Jiayao Ni, Yali Du, Qing Gu, Baorui Liu, Jian He, Juan Du

    Published 2025-01-01
    “…We used short-term and long-term treatment efficacy as the prediction outcomes and performed statistical and machine learning-based feature selection and COX regression analysis to identify potentially predictive features. …”
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  19. 2099
  20. 2100