Using N-Version Architectures for Railway Segmentation with Deep Neural Networks by Philipp Jaß, Carsten Thomas

“…Our results show that the N-version architecture not only enables a detection of erroneous predictions by utilizing those adjusted confidence values, but it can also partially improve the predictions by using the PMV combination algorithm. …”
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Incidence and Predictors of Acute Kidney Injury Following Advanced Ovarian Cancer Cytoreduction at a Tertiary UK Centre: An Exploratory Analysis and Insights from Explainable Artif... by Elizabeth Ratcliffe, Ciara Devlin, Sarika Munot, Timothy Broadhead, Amudha Thangavelu, Michela Quaranta, David Nugent, Evangelos Kalampokis, Diederick De Jong, Alexandros Laios

“…Mortality rates were similar between patients with and without AKI. AI-driven algorithms highlighted the complexity of AKI prediction and provided individual risk profiles, enabling future stratification and prompting different frequencies of AKI monitoring following cytoreduction. …”
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Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities by Mahdieh Shokrollahi Barough, Mohammad Darzi, Masoud Yunesian, Danesh Amini Panah, Yekta Ghane, Sam Mottahedan, Sohrab Sakinehpour, Tahereh Kowsarirad, Zahra Hosseini-Farjam, Mohammad Reza Amirzargar, Samaneh Dehghani, Fahimeh Shahriyary, Mohammad Mahdi Kabiri, Marzieh Nojomi, Neda Saraygord-Afshari, Seyedeh Ghazal Mostofi, Zeynab Yassin, Nazanin Mojtabavi

“…Clinical and laboratory data, along with comorbidity information, were collected and analyzed using advanced coding, data alignment, and regression analyses. Machine learning algorithms were employed to identify relevant features and calculate predictive probability scores. …”
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Groundwater Pollution Concentration Estimation with Modified Kalman Filter Method by Nenik Estuningsih, Fatmawati Fatmawati

“…The modified Kalman filter method is a method that collaborates the Kalman filter estimation algorithm with the model order reduction method. The model order reduction method used in this research is the LMI (Linear Matrix Inequality) method because the model reduction error using the LMI method is the smallest error compared to the reduction error using the Balanced Truncation method or the Singular Pertrubation Approximation method. …”
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A Novel Method of Self-Healing Concrete to Improve Durability and Extend the Service Life of Civil Infrastructure by Yan Xue, Weiliang Gao, Yanming Zhao

“…Building upon this, the enhanced concrete durability prediction model based on the NSGA-II algorithm proves to be highly effective in predicting the optimal concrete mix proportion scheme. …”
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METHODOLOGY OF DETERMINING FORECASTING CONTROLLERS OF DISTRIBUTED GENERATION PLANTS by Yu. N. Bulatov, A. V. Kryukov, N. V. Huan

“…Therefore, the predictive algorithms built on the basis of the model laws of regulation may prove to be very promising for real systems of technological process control, especially in the need to accelerate the commissioning of objects, such as distributed generation (DG) plants, working on the basis of synchronous generators with automatic excitation and speed controls (AEC and ASC). …”
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A method for the identification of lactate metabolism-related prognostic biomarkers and its validations in non-small cell lung cancer by Weiyang Yang, Miao Gu, Yabin Zhang, Yunfan Zhang, Tao Liu, Di Wu, Juntao Deng, Min Liu, Youwei Zhang

“…We proposed a Cox elastic-net regression combined with genetic algorithm (GA-EnCox) to predict prognosis and optimize the selection of key biomarkers. …”
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Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems by Ali Basem, Hanaa Kadhim Abdulaali, As’ad Alizadeh, Pradeep Kumar Singh, Komal Parashar, Ali E. Anqi, Husam Rajab, Pancham Cajla, H. Maleki

“…Progress in artificial intelligence and machine learning has significantly improved the capability to accurately predict the properties of nano-enhanced phase change materials (NePCMs). …”
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Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model by Elhoucine Ouassam, Yassine Dabachine, Nabil Hmina, Belaid Bouikhalene

“…To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. …”
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Multi-stage Optimization Forecast of Short-term Power Load Based on VMD and PSO-SVR by Wenwu LI, Qiang SHI, Dan LI, Qunyong HU, Yun TANG, Jinchao MEI

“…In the second stage, phase space reconstruction is used to optimize and reorganize each sequence component, and establish support vector regression（SVR）prediction model for each component. In the third stage, the particle swarm optimization（PSO）algorithm is applied to optimize the internal parameters of the SVR model to facilitate better training and forecasting. …”
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MRI quantified enlarged perivascular space volumes as imaging biomarkers correlating with severity of anxiety depression in young adults with long-time mobile phone use by Li Li, Yalan Wu, Jiaojiao Wu, Bin Li, Rui Hua, Feng Shi, Lizhou Chen, Yeke Wu

“…In the current study, we aim to develop a predictive model utilizing MRI-quantified EPVS metrics and machine learning algorithms to assess the severity of anxiety and depression symptoms in patients with LTMPU.MethodsEighty-two participants with LTMPU were included, with 37 suffering from anxiety and 44 suffering from depression. …”
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Learning-based early detection of post-hepatectomy liver failure using temporal perioperative data: a nationwide multicenter retrospective study in ChinaResearch in context by Kai Wang, Qian Yang, Kang Li, Shanhua Tang, Baoluhe Zhang, Xiangyun Liao, Shunda Du, Wenguang Fu, Zhiwei Li, Huanwei Chen, Haorong Xie, Pengxiang Huang, Jieyuan Li, Qiuting Wang, Haiqing Liu, Zhiwei Huang, Pheng Ann Heng, Xueshuai Wan, Chuanjiang Li, Weixin Si

“…Importantly, our model demonstrates a high capacity for predicting clinically relevant PHLF. The clinicians' prediction assisted by our model was substantially improved over the clinician-only predictions (AUC = 0.778 vs. 0.637, P = 0.009). …”
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Machine learning insights on activities of daily living disorders in Chinese older adults by Huanting Zhang, Wenhao Zhou, Jianan He, Xingyou Liu, Jie Shen

“…Nine machine learning algorithms, including neural networks and an ensemble model, were employed with a 2/3 training and 1/3 testing split. …”
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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…Due to their limits, machine learning algorithms outperform traditional methods in real-time fault diagnosis, predictive maintenance, and multi-fault categorization. …”
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A Machine Learning Approach to Analyze Manpower Sleep Disorder by Reza Amiri

“…Moreover, a combination of machine learning and metaheuristic algorithms such as eXtreme Gradient Boosting and particle swarm optimization are used to make an accurate predictive model. …”
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Classification and Application of Roof Stability of Bolt Supporting Coal Roadway Based on BP Neural Network by Heng Ren, Yongjian Zhu, Ping Wang, Peng Li, Yuqun Zhang, Xizhi Wang, Yingying Li

“…Through on-site investigation, numerical analysis, and other research methods, 6 evaluation indicators were determined, and according to the relevant evaluation factors and four types of coal roadway roof stability, a neural network structure for roof stability prediction was constructed to realize the quantitative prediction of the roof stability of bolt-supported coal roadway. …”
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Hierarchical Classification of Variable Stars Using Deep Convolutional Neural Networks by Mahdi Abdollahi, Nooshin Torabi, Sadegh Raeisi, Sohrab Rahvar

“…There have been many attempts to classify variable stars by traditional algorithms like Random Forest. In recent years, neural networks as classifiers have come to notice because of their lower computational cost compared to traditional algorithms. …”
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Development of an Artificial Intelligent Lighting System for Protected Crops by Basil Mohammed Al-Hadithi, Cecilia E. García Cena, Raquel Cedazo León, Carlos Loor Loor

“…It also allows selecting the appropriate control strategy with a choice of selecting a predictive control or PD control system. Both algorithms make use of a mathematical model of the lamps which is responsible for transforming the signals generated by the drivers in digital signals that govern the operation of the implemented electronic system. …”
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On the Importance of Learning Non‐Local Dynamics for Stable Data‐Driven Climate Modeling: A 1D Gravity Wave‐QBO Testbed by Hamid A. Pahlavan, Pedram Hassanzadeh, M. Joan Alexander

“…Abstract Model instability remains a core challenge for data‐driven parameterizations, especially those developed with supervised algorithms, and rigorous methods to address it are lacking. …”
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Soft Schemes for Earthquake-Geotechnical Dilemmas by Silvia García

“…Models make it possible to predict or simulate a system’s behavior; in earthquake geotechnical engineering, they are required for the design of new constructions and for the analysis of those that exist. …”
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