Novel insights into the molecular mechanisms of sepsis-associated acute kidney injury: an integrative study of GBP2, PSMB8, PSMB9 genes and immune microenvironment characteristics by Haiting Ye, Xiang Zhang, Pengyan Li, Mei Wang, Ruolan Liu, Dingping Yang

“…The expression patterns of these three genes were well-validated through animal experiments and databases. …”
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Biomimetic Computing for Efficient Spoken Language Identification by Gaurav Kumar, Saurabh Bhardwaj

“…Further, the selection of features is performed by DBO algorithm, which removes redundant features and helps to improve efficiency and accuracy. …”
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Intelligent Photolithography Corrections Using Dimensionality Reductions by Parag Parashar, Chandni Akbar, Tejender S. Rawat, Sparsh Pratik, Rajat Butola, Shih H. Chen, Yung-Sung Chang, Sirapop Nuannimnoi, Albert S. Lin

“…Also, we implement a pure machine learning approach where the input masks are directly mapped to the output etched patterns. …”
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Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency by Yaling Hew, Duygu Kutuk, Tuba Duzcu, Yagmur Ergun, Murat Basar

“…However, integrating AI also brings ethical, regulatory, and societal challenges, including concerns about data security, algorithmic bias, and the human–machine interface in clinical decision-making. …”
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Assessing Climate and Land-Use Change Scenarios on Future Desertification in Northeast Iran: A Data Mining and Google Earth Engine-Based Approach by Weibo Yin, Qingfeng Hu, Jinping Liu, Peipei He, Dantong Zhu, Abdolhossein Boali

“…Six remote sensing indices were selected to model desertification using four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Generalized Linear Models (GLM). …”
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Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods by Bayan Banimfreg, Ernesto Damiani, Vesta Afzali Gorooh, Duncan Axisa, Luca Delle Monache, Youssef Wehbe

“…Abstract Background In the hyper-arid environment of the United Arab Emirates (UAE), understanding rainfall patterns is essential for effective water resource management, agricultural planning, and ecological conservation. …”
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Combination of Music, Artificial Intelligence, and Brainwaves for the Promotion of Mental Well-Being by Georgios Karachalios, Evina Makaroni

“…Auditory stimulation technologies—particularly those using binaural beats and other sound patterns—have already proven effective in inducing these desirable states. …”
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Analysis of Gene Expression Omnibus high-throughput sequencing data for the determination of microribonucleic acids in the blood plasma of patients with glioblastomas by A. A. Pushkin, D. Yu. Gvaldin, N. N. Timoshkina, E. E. Rostorguev, L. Yu. Vladimirova, E. A. Dzenkova

“…Determination of significant miRNAs using machine learning algorithms of the R 4.0.4 project. …”
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Geographical features and management strategies for microplastic loads in freshwater lakes by Huike Dong, Ruixuan Zhang, Xiaoping Wang, Jiamin Zeng, Lei Chai, Xuerui Niu, Li Xu, Yunqiao Zhou, Ping Gong, Qianxue Yin

“…To address this gap, our study utilizes Machine Learning (the random forest algorithm), combined with number-to-mass transformation techniques to generate a global prediction. …”
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GIS Analysis Model Integration and Service Composition Prospects by L. Ding, P. Cai, W. Huang, H. Zhang, F. Ding, W. Zhao, D. Tang, Z. Wang

“…GIS model integration involves combining diverse spatial algorithms—such as buffer analysis, network analysis, spatial regression, and machine learning models—to tackle multifaceted geographic challenges. …”
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Detailed Time-resolved Spectral and Temporal Investigations of SGR J1550–5418 Bursts Detected with Fermi Gamma-Ray Burst Monitor by Mustafa Demirer, Ersin Göğüş, Yuki Kaneko, Özge Keskin, Sinem Şaşmaz, Shotaro Yamasaki

“…Subsequently, we obtained nonoverlapping time segments with varying lengths based on their spectral evolution patterns, employing a machine learning algorithm called k -means clustering. …”
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Population status and impact of climate change on the distribution of vulnerable multipurpose plant Hippophae rhamnoides ssp. turkestanica for conservation in Trans-Himalaya, India by Shiv Paul, Shiv Paul, Khilendra Singh Kanwal, Anil Kumar, Sher Singh Samant, Sher Singh Samant, Indra Dutt Bhatt, Rakesh Chandra Sundriyal, Rakesh Chandra Sundriyal, Swaran Lata

“…Therefore, the current study aims to assess the population status and predict highly suitable areas for Trans-Himalaya species under changing climatic conditions. The machine learning algorithm showed that Bio_6 (minimum temperature of the coldest month), elevation, and slope were the best suitable variables for the habitat prediction along with the CMIP6 project’s MIROC6 and CMCC-ESM2 climate change models to identify the potential distribution area of the species for the future under the SSP245 (middle of the way) and SSP585 (fossil-fueled development) scenarios, respectively. …”
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Proactive Data Placement in Heterogeneous Storage Systems via Predictive Multi-Objective Reinforcement Learning by Suchuan Xing, Yihan Wang

“…The framework’s ability to proactively adapt to evolving access patterns while maintaining computational efficiency makes it particularly suitable for large-scale machine learning and scientific computing environments where data placement critically impacts overall system performance.…”
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An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates by J. Hu, C. Miao, J. Su, Q. Zhang, J. Gou, J. Gou, Q. Sun, Q. Sun

“…Building upon the improved inverse distance weighting interpolation method used in our previous dataset CHM_PRE V1, we integrated a machine learning algorithm – light gradient boosting machine (LGBM) – to incorporate precipitation-related covariates in a data-driven manner. …”
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Artificial Intelligence for Unstructured Data Processing by Yohanes Bowo Widodo, Febrianti Widyahastuti, Mohammad Narji, Sondang Sibuea

“…By using deep learning models and advanced algorithms, AI can identify patterns and relationships in complex data, thereby providing deeper insights for better decision making. …”
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MapReduce based big data framework using associative Kruskal poly Kernel classifier for diabetic disease prediction by R. Ramani, S. Edwin Raja, D. Dhinakaran, S. Jagan, G. Prabaharan

“…Recent trendy applications of Artificial Intelligence are Machine Learning (ML) algorithms, which have been extensively utilized for processes like pattern recognition, object classification, effective prediction of disease etc. …”
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Correlation Between Depression-Associated Genes and Cancer Types: Predicting Cancer Based on Mutation Frequencies by Fernando Patricio Carranco-Avila, Clayanela Zambrano-Caicedo, Jonathan Javier Loor-Duque, Ariana Deyaneira Jimenez-Narvaez, Ivan Galo Reyes-Chacon, Paulina Vizcaino, Isidro Rafael Amaro Martin, Manuel Eugenio Morocho-Cayamcela

“…The analysis employed advanced methodologies, including HJ biplot K-means and DBSCAN clustering algorithms for pattern grouping in 2D. This process generated a dataset, enabling the training and testing of machine learning and deep learning classification models. …”
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The role of the SOX2 gene in cervical cancer: focus on ferroptosis and construction of a predictive model by Shenping Liu, Zhi Wei, Huiqing Ding

“…Methods A multidimensional approach integrating advanced bioinformatics, comprehensive molecular profiling, and state-of-the-art machine learning algorithms was employed to assess SOX2 expression patterns and their correlation with ferroptosis marker expression patterns in cervical cancer tissues. …”
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The impact of artificial intelligence on medical diagnostics: A letter to the editor by Sahar Imtiaz, Sheikh Abdul Qadir Jillani

“… Madam, Artificial intelligence (AI) describes the generation of intelligent machines that are capable of carrying out activities that usually call for human intellect. (1) It involves creating algorithms and models that enable computers to learn from and analyze vast amounts of data, recognize patterns, make decisions, and even engage in natural language processing…”
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CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…By detecting and addressing faults early, systems can maintain optimal performance levels. Machine Learning (ML) in Solar Photovoltaic (PV) systems refers to the application of algorithms and statistical models that enable computers to perform specific tasks without using explicit instructions, relying instead on patterns and inference. …”
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