Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review by Mahmoud Saleh Al-Khafaji, Layth Abdulameer, Muthanna M. A. AL-Shammari, Najah M. L. Al Maimuri, Anmar Dulaimi, Dhiya Al‑Jumeily

“…Our analysis reveals a 13-fold increase in AI adoption since 2011, with innovations such as adaptive neuro-fuzzy inference systems (ANFIS) and deep neural networks (DNNs) facilitating real-time anomaly detection and contamination forecasting. The novelty of this review lies in its dual focus—quantifying AI's scalability for global water security while critically addressing unresolved challenges in data standardization, model interpretability, and ethical governance. …”
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Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Abdul Hameed Kalifullah, Arife Tugsan Isiacik Colak, Md Redzuan Zoolfakar

“…Traditional deterministic methods often fail to capture the dynamic interactions within wind farms, thereby underscoring the need for ML-integrated approaches that enhance precision in energy forecasting, fault detection, and exergy analysis. …”
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Prognostic Factors in Postoperative Brain Metastases Derive From Non-small Cell Lung Cancer: A Retrospective Analysis by Haibin Chen, Liang Sun, Zhi Yang, Yuanyuan Qu, Nanyang Tong, Caixing Sun, Liang Xia

“…Background: Brain metastases are crucial in cancer progression, requiring focused efforts on the screening, early detection, and treatment. However, accurate forecasting the postoperative prognosis of patients with non-small cell lung cancer brain metastasis remains a challenge. …”
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FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach by Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey

“…The results reveal that U-Net evaluation achieves greater sensitivity to active region presence in the Sun’s far hemisphere compared to conventional methods.…”
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Genomic investigation of a non-travel Plasmodium falciparum case linked to imported malaria in China’s post-elimination era by Fei Luo, Dan Jiang, Jing-Ru Xu, Yan Tan, Meng-Ping Yang, Jun Xie, Sen-Ping Yang, Hai-Mo Shen, Shuang Zhou, Jun-Hu Chen

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A Novel Fault Diagnosis of Induction Motor by Using Various Soft Computation Techniques: BESO-RDFA by Kapu V. Sri Ram Prasad, K. Dhananjay Rao, Guruvulu Naidu Ponnada, Umit Cali, Taha Selim Ustun

“…Simulation analysis shows the detection and isolation method with great sensitivity indicating the incipient winding failures.…”
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SPPMFN: Efficient Multimodal Financial Time-Series Prediction Network With Self-Supervised Learning by Ningxin Li, Gang Chao, Jianke Zou, Gaozhe Jiang

“…Traditional statistical methods like ARIMA and GARCH struggle with non-linear data. …”
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Over-activation and dysfunction of platelet-NK cell aggregates in HIV-infected individuals by Meiju Deng, Ruojia Biao, Meiqing Jiang, Jiantao Fu, Hongxin Zhao, Juan Du

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MODELI NG CLINICAL AND ECONOMIC OUTCOMES OF TESTING FOR LTBI WITH T-SPOT.TB IN IMMUNOCOMPROMISED CHILDREN by V. I. Ignatyeva, M. V. Avxentyeva, V. V. Omelyanovsky, G. R. Khachatryan

“…The aim of the study was to evaluate the cl inical and economic outcomes of testingfor LTBI with T-SPOT.TB in immunocompromised children. Methods: we develope d a model, forecasting the testing results, probable cases of tuberculosis activation and related costs for scenarios with alternative use of T-SPOT.TB and TST for diagnosing LTBI in immunocompromised children. …”
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Toward a Time-Bounded Solution for Real-Time Latency Prediction in Dynamic 5G Communication by Andrea Nota, Lisa Maile, Selma Saidi

“…Even with limited knowledge about future channel conditions, using state-of-the-art forecasting methods, our approach still bounds 99.8% of actual latencies, demonstrating its robustness. …”
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A two-year wastewater-based surveillance of SARS-CoV-2 trends within the Tshwane region, South Africa by Miss Gugulethu Dhlamini, Doctor David-Noel Nogbou, Doctor John Y Bolukaoto, Miss Prudence F Ndzinisa, Proffesor Maphoshane Nchabeleng, Doctor Andrew M Musyoki

“…Introduction: Wastewater-Based epidemiology uses biological and chemical indicators for detection of infectious pathogens. This surveillance strategy forecast information on the presence, distribution, or the resurgence of diseases in countries with centralize sewage infrastructure. …”
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Predictive analysis of heart disease using quantum-assisted machine learning by Mehroush Banday, Sherin Zafar, Parul Agarwal, M. Afshar Alam, Siddhartha Sankar Biswas, Imran Hussain, K. M. Abubeker

“…Techniques for QML have the potential to forecast cardiac disease and help in early detection. …”
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Probing cosmic voids with emission-line galaxies by Yuka Yamada, Naoki Yoshida, Naoki Yoshida

“…We study how the measured physical properties depend on the observational methods for void detection and identification.MethodsWe generate mock intensity maps targeting the far-infrared CO(3–2) emission line by assigning the line luminosities to dark matter halos in cosmological simulations. …”
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Meteorological factors and normalized difference vegetation index drivers of scrub typhus incidence in Shandong Province based on a 16-year time-frequency analysis by Zhisong Ni, Shufen Li, Rui Xi, Kemeng Liang, Sihao Song, Chuanlong Cheng, Hui Zuo, Liang Lu, Xiujun Li

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Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review by Jialing Song, Jiajin Leng, Jian Li, Hui Wei, Shangru Li, Feiyue Wang

“…Traditional monitoring approaches, such as geodetic, geotechnical, and geophysical methods, have limitations in providing early warning capabilities due to their inability to detect precursory subsurface deformations. …”
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Predictors of severe forms of rotaviral infection in children by K. D. Ermolenko, A. I. Konev, K. Yu. Ermolenko, Yu. V. Lobzin

“…Comparison of the frequency of occurrence of signs in the groups was performed using the Pearson χ2 test and Fisher’s exact method. The forecasting model was developed using discriminant analysis of the statistical package Statistica for Windows.Results. …”
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Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition by Zeinab Hassani, Davud Mohammadpur, Hossein Safari

“…A sliding window technique is employed to detect temporal quasi-patterns in both irregular and regularized flare time series. …”
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Development of a neural network for diagnosing the risk of depression according to the experimental data of the stop signal paradigm by M. O. Zelenskih, A. E. Saprygin, S. S. Tamozhnikov, P. D. Rudych, D. A. Lebedkin, A.  N. Savostyanov

“…Improving the quality and volume of information, complicating its presentation, the need to detect hidden connections makes it ineffective, and most often impossible, to use classical statistical forecasting methods. …”
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Analysis of lightning localization errors based on different station layouts by Yingting ZENG, Jiaying GU, Ruoyun ZHANG, Qinbo YANG, Jinquan ZENG

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Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis by Mahreen Kiran, Ying Xie, Nasreen Anjum, Graham Ball, Barbara Pierscionek, Duncan Russell

“…Ensemble methods (e.g., Random Forest, Gradient Boosting) and deep learning models (e.g., Convolutional Neural Networks) dominate recent advancements. …”
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