Transcriptome analysis provides new insights into the berry size in ‘Summer Black’ grape under a two-crop-a-year cultivation system by Peiyi Ni, Shengdi Yang, Yunzhang Yuan, Chunyang Zhang, Hengliang Zhu, Jing Ma, Shuangjiang Li, Guoshun Yang, Miao Bai

“…Moreover, based on the results of interactive analysis of TO-GCN and transcriptional regulation prediction of L1–L3 genes, we constructed a unique hierarchical regulatory network for the heat stress regulation of berry size. …”
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Classifcation of events in information security systems based on neural networks by A. A. Mikryukov, A. V. Babash, V. A. Sizov

“…To address the problem more effectively, collective methods based on collective neural ensembles aligned with an advanced complex approach were implemented.Materials and methods: When solving complex classification problems, often none of the classification algorithms provides the required accuracy. In such cases, it seems reasonable to build compositions of algorithms, mutually compensating errors of individual algorithms. …”
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Multimodal data integration in early-stage breast cancer by Arnau Llinas-Bertran, Maria Butjosa-Espín, Vittoria Barberi, Jose A. Seoane

“…However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-negative tumors.The integration of multi-omics and multimodal data has the potential to provide new insights into biological processes, to improve breast cancer patient stratification, enhance prognosis and response prediction, and identify new biomarkers.This review presents a comprehensive overview of the state-of-the-art multimodal (including molecular and image) data integration algorithms developed and with applicability to breast cancer stratification, prognosis, or biomarker identification. …”
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Contrastive Disentangled Variational Autoencoder for Collaborative Filtering by Woo-Seong Yun, Seong-Min Kang, Yoon-Sik Cho

“…Recommender systems aim to accurately predict user preferences in order to provide potential items of interests. …”
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The Impact of Snow Cover on River Discharge Simulation: Insights from the Barandozchay River Basin by Haleh Hashemi, Hossein Rezaie, Keivan Khalili, Amin Amini

“…This study presents a comprehensive analysis aimed at predicting the discharge of the Barandozchay River using machine learning algorithms and meteorological data from both satellite and ground sources over the period from 2002 to 2022. …”
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Clinically validated graphical approaches identify hepatosplenic multimorbidity in individuals at risk of schistosomiasis by Yin-Cong Zhi, Simon Mpooya, Narcis B. Kabatereine, Betty Nabatte, Christopher K. Opio, Goylette F. Chami

“…Co-occurrence graphs were clinically uninformative with low predictive capacity. Graph learning algorithms with statistical assumptions, e.g. graphical lasso, enabled accurate and clinically valid multimorbidity representations. …”
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Financing Mechanisms and Preferences of Technology-Driven Small- and Medium-Sized Enterprises in the Digitalization Context by Jing Hu, Lianming Huang, Weifu Li, Hongyi Xu

“…Additionally, six machine learning (ML) algorithms were employed to predict financing preferences. …”
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Sentiment Analysis Using Stacking Ensemble After the 2024 Indonesian Election Results by Andy Victor Pakpahan, Fahmi Reza Ferdiansyah, Robby Gustian, Muhammad Nur Faiz, Sukma Aji

“…This improvement highlights the effectiveness of ensemble learning in integrating different algorithmic perspectives to enhance predictive performance. …”
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Developing an alternative data-driven model to resemble geomorphologic rainfall-runoff models by Pin-Chun Huang, Kwan Tun Lee

“…The topological distribution inherent in the input data space is analyzed to improve predictive accuracy. The proposed artificial intelligence (AI) model, which incorporates a classification algorithm for preprocessing input features prior to training a model based on the recurrent neural network, exhibits outstanding performance in runoff discharge prediction. …”
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DNN-Based ADNMPC of an Industrial Pickling Cold-Rolled Titanium Process via Field Enhancement Heat Exchange by Biao Yang, Jinhui Peng, Wei Li, Youling Li, Huilong Luo, Zhuming Zhang, Shenghui Guo, Shimin Zhang, Hezhou Su, Yaming Shi

“…The identifier of the direct adaptive nonlinear model identification and the controller of the adaptive nonlinear model predictive control are designed based on series-parallel dynamic neural network training by RLS algorithm with variable incremental factor, gain, and forgetting factor. …”
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Validation of chronic obstructive pulmonary disease recording in the Clinical Practice Research Datalink (CPRD-GOLD) by John R Hurst, Jennifer K Quint, Hana Müllerova, Liam Smeeth, Harriet Forbes, Susan Eaton, Rachael L DiSantostefano, Kourtney Davis

“…All information received was reviewed independently by two respiratory physicians whose opinion was taken as the gold standard.Primary outcome measure The primary measure of accuracy was the positive predictive value (PPV), the proportion of people identified by each algorithm for whom COPD was confirmed.Results 951 questionnaires were sent and 738 (78%) returned. …”
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SOFTWARE IMPLEMENTATION OF THE ARTIFICIAL NEURAL NETWORK FOR VIRTUAL OBJECTS СONTROL by Yu. B. Popova, S. V. Yatsynovich

“…The use of an artificial neural network has reduced the use of CPU time, which is extremely important in problems where rapid decision making is required, because complex calculations and prediction algorithms can not always be invested in 20 ms, which is fraught with skipping moves and losses. …”
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Artificial intelligence in colorectal cancer: a review by G. Singh

“…Challenges in AI development are addressed, such as data standardization and the interpretability of machine learning algorithms. The potential of AI in treatment decision support, precision medicine, and prognosis prediction is discussed, emphasizing the role of AI in selecting optimal treatments and improving surgical precision. …”
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Incorporating Wave-ViT for Breast Cancer Diagnosis Using MRI Imaging by Sahil Mahey, Hamid Usefi

“…In this study, we propose a machine learning approach to enhance breast cancer prediction and diagnosis. We utilize a pre-trained multiscale vision transformer, Wave-ViT, to classify MRI slices as healthy or unhealthy. …”
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Is visual inspection with acetic acid (VIA) a useful method of finding pre-invasive cervical cancer? by Maliheh Arab, Atefeh Moridi, Ghazaleh Fazli, Robabeh Ghodssi-Ghasemabadi, Maryam Maktabi, Samaneh Saraeian, Mahdie Sanati

“…The sensitivity, specificity and negative predictive value (NPV) of the VIA were 41.9%, 73.3%, and 98.2%, respectively. …”
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Influence-Balanced XGBoost: Improving XGBoost for Imbalanced Data Using Influence Functions by Akiyoshi Sutou, Jinfang Wang

“…Decision tree boosting algorithms, such as XGBoost, have demonstrated superior predictive performance on tabular data for supervised learning compared to neural networks. …”
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Research on Resource Reservation Strategy for Edge Federation by Hengzhou Ye, Huangran Li, Jiaming Li, Qiu Lu, Gong Chen

“…Furthermore, the RRP-MADDPG strategy exhibits excellent convergence performance and outperforms both the load prediction-based reservation strategy and other similar deep reinforcement learning algorithms.…”
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The tumour histopathology "glossary" for AI developers. by Soham Mandal, Ann-Marie Baker, Trevor A Graham, Konstantin Bräutigam

“…The applications of artificial intelligence (AI) and deep learning (DL) are leading to significant advances in cancer research, particularly in analysing histopathology images for prognostic and treatment-predictive insights. However, effective translation of these computational methods requires computational researchers to have at least a basic understanding of histopathology. …”
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Adaptive Ensemble Framework With Synthetic Sampling for Tackling Class Imbalance Problem by R. Sasirekha, B. Kanisha

“…ABSTRACT Class imbalance is a critical challenge in heart disease prediction datasets, often leading to biased models with poor performance on the minority class. …”
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Intelligent monitoring and control of farmland based on edge-cloud collaboration and digital twin for digital energy management: investment benefit analysis by Zheng Liu

“…Regarding energy management, a digital twin model of the photovoltaic energy storage system is constructed to achieve accurate prediction and optimization of energy flow. Edge-cloud collaborative architecture for real-time data collection/analysis, reducing network latency by 40% compared to traditional cloud-only models; deep reinforcement learning (DRL)-driven irrigation optimization, achieving 51% crop yield increase and 18% water efficiency improvement; digital twin modeling of photovoltaic-energy storage systems, enhancing energy flow prediction accuracy to 98.2% and reducing energy waste by 9.5%; game theory-based resource allocation to balance energy supply–demand, improving system economic benefits by 15%. …”
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