Data-driven modelling of a commercial cold storage system using subspace system identification by Adesola Temitope Bankole, Muhammed Bashir Mu’azu, Habeeb Bello-Salau, Zaharuddeen Haruna

“…Results show that the best identified model has a goodness of fit of 98.66 % and 90.42 % for both outputs, final prediction error of 4.11e-15 and mean square error of 0.0005660. …”
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Evaluation on nature-connected environment in building embedded landscape: theory, detection, and case design by Shu Zhong, Jiao Ren

“…Principal component analysis was used for verification, and the GBT algorithm surpassed MLR in regression performance. …”
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Deep Learning in Power Systems: A Bibliometric Analysis and Future Trends by Seyed Mahdi Miraftabzadeh, Andrea Di Martino, Michela Longo, Dario Zaninelli

“…Advancements in deep learning enable sophisticated algorithms to learn from vast data sets, driving innovation and efficiency in power systems. …”
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Development of a semantic map for an unmanned vehicle using a simultaneous localisation and mapping method by O. A. Rodionov, B. Rasheed

“…Introduction: The field of unmanned technologies is rapidly developing and a lot of research is being conducted on the practical application of artificial intelligence algorithms to solve complex problems on the road. The difficulties in the perception of the surrounding world by the machine led to the appearance of special High definition maps. …”
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Evaluation of hydraulic fracturing using machine learning by Ali Akbari, Ali Karami, Yousef Kazemzadeh, Ali Ranjbar

“…This study presents a comprehensive machine learning (ML)-based framework to address this challenge by predicting HF efficiency using three widely used algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN). …”
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Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity. by J R Managbanag, Tarynn M Witten, Danail Bonchev, Lindsay A Fox, Mitsuhiro Tsuchiya, Brian K Kennedy, Matt Kaeberlein

“…To date, however, targeted efforts at identifying longevity-associated genes have been limited by a lack of predictive power, and useful algorithms for candidate gene-identification have also been lacking.…”
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Adverse childhood experiences: terms, concepts, and study methods by Diana S. Shumskaia, Anna V. Trusova, Alexander O. Kibitov

“…Thus, correct analysis of the spectrum, intensity, and severity of ACE is extremely important for the construction of complex multidimensional predicting models of the level of suicide risk in patients with mental disorders. …”
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Identification of M2 macrophage-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches by Jiayi Zhang, Zhixiang Jia, Jiahui Zhang, Xiaohui Mu, Limei Ai

“…Subsequently, the constructed logistic regression model and nomogram demonstrated robust predictive performance. We further investigated the expression levels, prognostic values, and biological functions of these biomarkers. …”
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Intelligent data-driven system for mold manufacturing using reinforcement learning and knowledge graph personalized optimization for customized production by Chengcai He, Jiaxing Deng, Jingchun Wu, Beicheng Qin, Jinxiang Chen, Yan Li, Qiangsheng Huang

“…When actual qualification rates exceed 88.1%, the model’s regression fit also surpasses 88.1%, indicating strong alignment between predicted and actual performance. (2) Compared with other algorithmic models, the proposed approach achieves a predictive accuracy of over 94.7%. …”
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Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival by Ebru Emsen, Bahadir Baran Odevci, Muzeyyen Kutluca Korkmaz

“…Several machine learning algorithms, including Random Forest, Decision Trees, Logistic Regression, and Support Vector Machines (SVM), were evaluated for predictive accuracy. …”
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Transfer and deep learning models for daily reference evapotranspiration estimation and forecasting in Spain from local to national scale by Yu Ye, Aurora González-Vidal, Miguel A. Zamora-Izquierdo, Antonio F. Skarmeta

“…During forecasting, we used predicted weather data as input, and despite inherent biases in some variables, the TL models successfully adapted using 9-36 days of new data, significantly improving predictive performance (reducing MAE from -1.1% to 134.3%). …”
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PLOD3 as a novel oncogene in prognostic and immune infiltration risk model based on multi-machine learning in cervical cancer by Lingling Qiu, Xiuchai Qiu, Xiaoyi Yang

“…In this study, offer a precision medicine methods for predicting patient outcomes as well as fresh insights into the metabolic foundations, which may contribute to the prognosis and immunotherapy of CC. …”
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Exploration of the shared gene signatures and molecular mechanisms between cardioembolic stroke and ischemic stroke by Xuan Wang, Xuan Wang, Xueyuan Liu, Xueyuan Liu

“…Three machine learning algorithms were employed to detect biomarkers from the core shared genes, and the diagnostic value of the hub genes was evaluated by establishing a predictive nomogram. …”
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Potential carbon stock distribution of mangrove and synergistic effect of ecosystem services in China by Shuhao Liu, Shuai He, Shang Chen

“…Our results demonstrated that tree-based algorithms exhibited high predictive accuracy. The provinces of Hainan and the Pearl River estuary in Guangdong were identified as having higher habitat suitability. …”
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Exploring happiness factors with explainable ensemble learning in a global pandemic. by Md Amir Hamja, Mahmudul Hasan, Md Abdur Rashid, Md Tanvir Hasan Shourov

“…The World Happiness Report (WHR), published annually, includes data on 'GDP per capita', 'social support', 'life expectancy', 'freedom to make life choices', 'generosity', and 'perceptions of corruption'. This paper predicts happiness scores using Machine Learning (ML), Deep Learning (DL), and ensemble ML and DL algorithms and examines the impact of individual variables on the happiness index. …”
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Assessment of mass characteristics of a wheeled chassis when designing a preventive suspension system by Malinovsky M.P., Tarichko V.I., Chikina A.K.

“…The article presents a suspension system developed by the authors, the peculiarity of which is the preventive nature of the action, consisting in the construction of a predictive algorithm from the driver’s control actions on the controls, unlike most existing stabilization systems with a corrective nature of the action. …”
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Identification of effective subdominant anti-HIV-1 CD8+ T cells within entire post-infection and post-vaccination immune responses. by Gemma Hancock, Hongbing Yang, Elisabeth Yorke, Emma Wainwright, Victoria Bourne, Alyse Frisbee, Tamika L Payne, Mark Berrong, Guido Ferrari, Denis Chopera, Tomas Hanke, Beatriz Mothe, Christian Brander, M Juliana McElrath, Andrew McMichael, Nilu Goonetilleke, Georgia D Tomaras, Nicole Frahm, Lucy Dorrell

“…These vulnerable and so-called "beneficial" regions were of low entropy overall, yet several were not predicted by stringent conservation algorithms. Consistent with this, stronger inhibition of clade-matched than mismatched viruses was observed in the majority of subjects, indicating better targeting of clade-specific than conserved epitopes. …”
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Identification of factors associated with acute malnutrition in children under 5 years and forecasting future prevalence: assessing the potential of statistical and machine learnin... by Christopher Coffey, Meike Reusken, Frans Cruijssen, Bertrand Melenberg, Cascha van Wanrooij

“…However, accurately forecasting future prevalence of cases remains challenging, with the application of predictive models being notably scarce. Addressing this gap, this paper aims to identify factors associated with Global Acute Malnutrition (GAM) and explores the potential of machine learning in predicting its prevalence using data from Somalia.Methods Survey data on GAM prevalence systematically collected in Somalia every 6 months at a district level from 2017 to 2021 were collated alongside a range of potential climatic, demographic, disease, environmental, conflict and food security-related factors over a matching time period. …”
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Machine Learning Models Decoding the Association Between Urinary Stone Diseases and Metabolic Urinary Profiles by Lin Ma, Yi Qiao, Runqiu Wang, Hualin Chen, Guanghua Liu, He Xiao, Ran Dai

“…Our analyses revealed that the Random Forest algorithm exhibited the highest predictive accuracy, with AUC values of 0.809 for kidney stones, 0.99 for ureter stones, and 0.775 for multiple location stones. …”
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Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach by Artur Krolik, Radosław Drelich, Michał Pakuła, Dariusz Mikołajewski, Izabela Rojek

“…The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. …”
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