The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma by Atsuto Katano

“…Clinical studies have demonstrated notable benefits, such as a reduction in contouring time and improved dosimetric quality using machine learning–based optimization algorithms. …”
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Effectiveness and user experience of a virtual reality intervention in a cohort of patients with chronic musculoskeletal pain syndromes. by Tiffany Prétat, Pedro Ming Azevedo, Chris Lovejoy, Thomas Hügle

“…Follow-up interviews were conducted after one month. An unsupervised machine learning model explored response patterns. …”
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Unveiling the carbon neutrality pathways of compact cities: a simulation-based scenario analysis from China by Tianhui Fan, Yujie Ren, Andrew Chapman

“…By employing machine learning models, we analyze how key features of compact cities—such as population density, land-use mix, and public transportation development—contribute to the achievement of carbon neutrality. …”
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Electricity nowcasting and forecasting efficiency in blockchain-integrated energy trading platforms by Ameni Boumaiza

“…Our methodology integrates real-time nowcasting algorithms and machine learning-based forecasting models to optimize electricity distribution, battery storage utilization, and cost efficiency within the marketplace. …”
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Text classification using SVD, BERT, and GRU optimized by improved Seagull optimization (ISO) algorithm by Yuanyuan Chen, Nan Sun, Yuanbang Li, Rong Peng, Abbas Habibi

“…Over time, techniques for text classification have progressed from rule-based methods to more advanced deep learning and machine learning approaches. Conventional approaches often struggled with the language intricacies, including issues like contextual links, polysemy, and the ambiguity among words. …”
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A densely connected framework for cancer subtype classification by Yu Li, Denggao Zheng, Kaijie Sun, Chi Qin, Yuchen Duan, Qingqing Zhou, Yunxia Yin, Hongxing Kan, Jili Hu

“…Experimental results demonstrate that DEGCN achieves a cross-validated classification accuracy of 97.06% ± 2.04% on renal cancer data, outperforming conventional machine learning algorithms and state-of-the-art deep learning models. …”
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Multidimensional bioinformatics analysis of chondrosarcoma subtypes and TGF-β signaling networks using big data approaches by Shengke Li, Junteng Chen, Fuping He, Maosheng Wang, Jun Liu, Hui Xie

“…Pseudotime analysis charted differentiation trajectories, while machine learning models evaluated the classification accuracy of gene expression patterns. …”
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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

“…Future studies are encouraged to expand the use of machine learning models to unravel the complexities of arid climate hydrology further.…”
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ANN and Multilayer-ELM based prediction of combustion, performance and emission characteristics of a diesel engine fuelled with Diesel-DTBP blends by Hiren Dave, Vinay Vakharia, Hitesh Panchal, Md Irfanul Haque Siddiqui, Dan Dobrotă

“…The experiments were carried out at a fixed engine load of 80 % and three different engine speeds of 1600 rpm, 2000 rpm and 2400 rpm respectively. Two machine learning (ML) models: Artificial Neural Network (ANN) and Multilayer Extreme Learning Machine (MELM) were developed to predict the key engine characteristics such as brake specific fuel consumption (BSFC), maximum cylinder pressure (Pmax), smoke emissions and nitrogen oxides (NOx) emissions. …”
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Improving prediction of solar radiation using Cheetah Optimizer and Random Forest. by Ibrahim Al-Shourbaji, Pramod H Kachare, Abdoh Jabbari, Raimund Kirner, Digambar Puri, Mostafa Mehanawi, Abdalla Alameen

“…In the contemporary context of a burgeoning energy crisis, the accurate and dependable prediction of Solar Radiation (SR) has emerged as an indispensable component within thermal systems to facilitate renewable energy generation. Machine Learning (ML) models have gained widespread recognition for their precision and computational efficiency in addressing SR prediction challenges. …”
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Chronological age estimation from human microbiomes with transformer-based Robust Principal Component Analysis by Tyler Myers, Se Jin Song, Yang Chen, Britta De Pessemier, Lora Khatib, Daniel McDonald, Shi Huang, Richard Gallo, Chris Callewaert, Aki S. Havulinna, Leo Lahti, Guus Roeselers, Manolo Laiola, Sudarshan A. Shetty, Scott T. Kelley, Rob Knight, Andrew Bartko

“…To investigate benefits of TRPCA over conventional machine learning models, we benchmarked performance on age prediction from three body sites(skin, oral, gut), with 16S rRNA gene amplicon(16S) and whole-genome sequencing(WGS) data. …”
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XI2S-IDS: An Explainable Intelligent 2-Stage Intrusion Detection System by Maiada M. Mahmoud, Yasser Omar Youssef, Ayman A. Abdel-Hamid

“…The challenge is further compounded by the fact that most IDS rely on black-box machine learning (ML) and deep learning (DL) models, making it difficult for security teams to interpret their decisions. …”
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Sample Weighting Methods for Compensating Class Imbalance in Elephant Flow Classification by Piotr Jurkiewicz, Robert Wojcik, Jerzy Domzal

“…However, the inherent class imbalance between elephant and mouse flows presents a challenge for machine learning models, often leading to poor classification accuracy. …”
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ML-ROM wall shear stress prediction in patient-specific vascular pathologies under a limited clinical training data regime. by Chotirawee Chatpattanasiri, Federica Ninno, Catriona Stokes, Alan Dardik, David Strosberg, Edouard Aboian, Hendrik von Tengg-Kobligk, Vanessa Díaz-Zuccarini, Stavroula Balabani

“…Data-driven approaches such as Reduced Order Modeling (ROM) and Machine Learning (ML) are increasingly being explored alongside CFD to advance biomechanical research and application. …”
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Civil Aircraft Landing Attitude Ultra-Limit Warning System Based on mRMR-LSTM by Fei Lu, Tong Jing, Chunsheng Xie, Haonan Chen

“…The early warning system includes four modules: data pretreatment, feature dimensionality reduction, prediction, and judgment. Subsequently, through data pretreatment methods such as data cleaning, frequency normalization, data standardization, and feature classification, the experimental dataset is transformed into a form recognizable by machine learning algorithms and neural network models. …”
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Linear regressive weighted Gaussian kernel liquid neural network for brain tumor disease prediction using time series data by Firoz Khan, Sardar Irfanullah Amanullah, Shitharth Selvarajan

“…However, conventional machine learning and deep learning detection models face challenges in achieving high accuracy in brain tumor disease prediction while minimizing time complexity. …”
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Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy by Junlang Yuan, Ke Yang, Taiwei Yang, Haoran Xu, Ting Xiong, Shidong Fan

“…The calculation method of the effective specific cutting energy is given, which is calculated by the cutter motor power, slurry concentration, and slurry flow rate. Based on the machine learning framework, a model framework for predicting the specific cutting energy according to the relevant parameters of the suction-lifting system is constructed. …”
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CoMSeC: A Comparative Analysis of Various Service Classification Techniques by Malabika Das, Ansh Sarkar, Sujata Swain

“…The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has significantly impacted Web Service Classification, a critical task for service discovery, composition, and selection in various applications. …”
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Accuracy and clinical effectiveness of risk prediction tools for pressure injury occurrence: An umbrella review. by Bethany Hillier, Katie Scandrett, April Coombe, Tina Hernandez-Boussard, Ewout Steyerberg, Yemisi Takwoingi, Vladica M Veličković, Jacqueline Dinnes

“…The 19 SRs of prognostic accuracy evaluated 70 tools (39 scales and 31 machine learning (ML) models), with the Braden, Norton, Waterlow, Cubbin-Jackson scales (and modifications thereof) the most evaluated tools. …”
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Comparative life cycle assessments of laboratory and Pilot-scale Mechanochemical processes for producing carbonated mineral products as cement substitutes by Mohamed Katish, Xinyuan Ke, Phil Renforth

“…Life cycle assessments consistently revealed GWP reductions for OPC-BFA mixtures, with additional emissions reductions when incorporating flow sheet modelling and machine learning data. …”
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