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

“…These improvements highlight the model’s ability to assimilate diverse climatic and topographical inputs for more accurate rainfall prediction, particularly in areas where conventional methods fall short due to sparse or irregular precipitation. …”
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Machine-Learning-Driven Approaches for Assessment, Delegation, and Optimization of Multi-Floor Building by Abtin Baghdadi, Harald Kloft

“…The significance of this research lies in its ability to automate and accelerate complex structural analysis using Adaptive Neuro-Fuzzy Inference Systems (ANFISs), achieving an average error of less than 2% in multi-variable prediction scenarios. The results were compared against reference calculations and ETABS simulations to validate its effectiveness, demonstrating deviations of less than 3%. …”
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Ensemble Learning-Based Alzheimer’s Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images by Young Jae Huh, Jun-ha Park, Young Jae Kim, Kwang Gi Kim

“…Ensemble learning (EL), a machine learning technique that combines the results of multiple learning algorithms to obtain predicted values, aims to achieve better predictive performance than a single learning algorithm alone. …”
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Optimizing additive manufacturing workflows using model-based systems engineering by Mohamed Amine Daoud, Khadija Bekkay Haouari, Meriem Hayani Mechkouri, Amine Ennawaoui, Hicham El hadraoui, Ilias Naser, Mustapha Ouardouz, Kamal Reklaoui

“…Future iterations of ASAM will incorporate advanced algorithms for real-time parameter optimization and predictive analytics. …”
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Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning by Fang-Chung Chen

“…We found that the power conversion efficiencies of the device prepared with the polymer candidates can be predicted with their structure fingerprints as the only inputs. …”
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Spectral Fingerprinting of Tencha Processing: Optimising the Detection of Total Free Amino Acid Content in Processing Lines by Hyperspectral Analysis by Qinghai He, Yihang Guo, Xiaoli Li, Yong He, Zhi Lin, Hui Zeng

“…Combining competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA) methods for characteristic band selection, specific bands were chosen to predict the amino acid content. By comparing modeling evaluation indicators for each model, the optimal model was identified: the overall model CT+CARS+PLSR, with predictive indicators Rc² = 0.9885, Rp² = 0.9566, RMSEC = 0.0956, RMSEP = 0.1749, RPD = 4.8021, enabling the visualization of total free amino acid content in processed Tencha leaves. …”
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Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans by Sheikh Fahim Faysal Sowrav, Sujit Kumar Debsarma, Mohan Kumar Das, Khan Mohammad Ibtehal, Mahfujur Rahman, Noshin Tabassum Hridita, Atika Afia Broty, Muhammad Sajid Anam Hoque

“…Key water quality parameters—Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, and pH—were predicted through ML algorithms and interpolated using the Empirical Bayesian Kriging (EBK) model in ArcGIS Pro. …”
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Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy by Xiaote Zhang, Qiaoyi Xie, Ganggang Wu

“…Given the urgent need for improved diagnostic methods and extensive characterization of risk factors for OME in AH children, developing diagnostic models represents an efficient strategy to enhance clinical identification accuracy in practice.ObjectiveThis study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.MethodsA retrospective analysis was conducted on 847 pediatric patients with AH. …”
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Oxidative stress gene expression in ulcerative colitis: implications for colon cancer biomarker discovery by Ting Yan, Ting Su, Miaomiao Zhu, Qiyuan Qing, Binjie Huang, Jun Liu, Tenghui Ma

“…The model may be beneficial in prognostic prediction and guiding treatment decisions.…”
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Clinical, genetic, and sociodemographic predictors of symptom severity after internet-delivered cognitive behavioural therapy for depression and anxiety by Olly Kravchenko, Julia Bäckman, David Mataix-Cols, James J. Crowley, Matthew Halvorsen, Patrick F. Sullivan, John Wallert, Christian Rück

“…Employing machine learning algorithms capable of capturing complex non-linear associations and interactions is a viable next step to improve prediction of post-ICBT symptom severity. …”
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Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response by Sha-Zhou Li, Hai-Ying Sun, Yuan Tian, Liu-Qing Zhou, Tao Zhou

“…Dasatinib and 7 medicine showed the superior sensitivity to the high-risk NHSCC, which had potential to the clinical.ConclusionsThe construction of MLDPM effectively eliminated artificial bias by utilizing 101 algorithm combinations. This model demonstrated high accuracy in predicting HNSCC outcomes and has the potential to identify novel therapeutic targets for HNSCC patients, thus offering significant advancements in personalized treatment strategies.…”
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Artificial intelligence for difficult airway assessment: a protocol for a systematic review with meta-analysis by Dan Liu, Tingting Li, Li Du, Jianqiao Zheng, Yujie Huang, Weiyi Zhang

“…Nevertheless, the diagnostic performance of AI algorithms for difficult airway assessment remains unclear due to the small sample sizes, insufficient image acquisition standards and poor predictive accuracies. …”
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A Cautionary Perspective on Artificial Intelligence and Novel Imaging Technologies in Patient Selection for Retrograde Intrarenal Surgery by Samir Muter, Noorulhuda Al-Ani

“… The advancement of retrograde intrarenal surgery (RIRS) has been accompanied by the evolution of many promising tools that aim at improving patient selection and predicting surgery outcomes in terms of stone fragmentation and expected complications. …”
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Knowledge and Perception of Practicing Anesthetists on Current Techniques, Clinical Applications, and Limitations of Artificial Intelligence in Anesthesiology: An Indian Study by Manasij Mitra, Maitraye Basu, Amrita Ghosh, Ranabir Pal

“…Their concepts on techniques, applications, and safety of AI including levels and potentials of use were significant so far as predictive algorithms, assessing vital parameters and perioperative care were concerned. …”
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Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique by Fatin Nabihah Jais, Mohd Zulfaezal Che Azemin, Mohd Radzi Hilmi, Mohd Izzuddin Mohd Tamrin, Khairidzan Mohd Kamal

“…A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery. …”
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Stochastic Multiple Chaotic Local Search-Incorporated Gradient-Based Optimizer by Hang Yu, Yu Zhang, Pengxing Cai, Junyan Yi, Sheng Li, Shi Wang

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Analysing the effectiveness of unsignalized crossing infrastructure in improving pedestrian safety using multiple data-driven approaches by Shengqi Liu, Harry Evdorides

“…While numerous studies have applied predictive models to traffic crash data, few have systematically analysed pedestrian crash severity at unsignalized crossings using multiple machine learning algorithms. …”
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Big Data Analytics in IoT, social media, NLP, and information security: trends, challenges, and applications by Kamal Taha

“…Key findings reveal that: (1) GNN and Self-Supervised Learning (SSL) are top performers in terms of predictive performance and efficiency in domains such as IoT and Social Media, (2) XGBoost and CNN offer superior accuracy and robustness across structured and unstructured data tasks, though CNN incurs higher computational costs, (3) ELM and Decision Trees are better suited for lightweight or interpretable applications, and (4) KNN generally underperforms in scalability and predictive strength for large-scale tasks. …”
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Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations by Bogdan Felician Abaza, Vlad Gheorghita

“…The integration of intelligent monitoring systems and self-learning algorithms is reshaping machining processes, enabling higher efficiency, precision, and sustainability. …”
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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

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