Dempster Shafer-Empowered Machine Learning-Based Scheme for Reducing Fire Risks in IoT-Enabled Industrial Environments by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Saurabh Singh, Byungun Yoon

“…This research proposes an advanced fire prediction approach aiming to enhance decision-making accuracy with uncertain or incomplete fire sensor data in an edge computing IoT complex industrial environment that integrates multiple supervised machine learning algorithms for each sensor types and Dempster-Shafer theory (DST) with multi-sensor fusion. …”
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Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases by Miad Boodaghidizaji, Thaisa Jungles, Tingting Chen, Bin Zhang, Tianming Yao, Alan Landay, Ali Keshavarzian, Bruce Hamaker, Arezoo Ardekani

“…This approach has become feasible with the advent of machine learning, which can uncover hidden patterns in human microbiome data and enable disease prediction. Accordingly, the aim of our study was to test the hypothesis that machine learning algorithms can distinguish stool microbiota patterns—and their responses to fiber—across diseases with previously reported overlapping dysbiotic microbiota profiles. …”
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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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Interpretable machine learning approaches to assess the compressive strength of metakaolin blended sustainable cement mortar by Naseer Muhammad Khan, Liqiang Ma, Waleed Bin Inqiad, Muhammad Saud Khan, Imtiaz Iqbal, Muhammad Zaka Emad, Saad S. Alarifi

“…Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. …”
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Multi-UAV DMPC Cooperative Guidance with Constraints of Terminal Angle and Obstacle Avoidance by Zijie Jiang, Xiuxia Yang, Cong Wang, Yi Zhang, Hao Yu

“…This paper studies the salvo attack problem for multiple unmanned aerial vehicles (UAVs) against a maneuvering target, and a guidance scheme based on distributed model predictive control (DMPC) is presented to achieve cooperative interception with constraints of terminal impact angle and no-fly zone (or obstacle) avoidance. …”
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Pseudo-Labeling and Time-Series Data Analysis Model for Device Status Diagnostics in Smart Agriculture by Minwoo Jung, Dae-Young Kim

“…This study proposes an automated data-labeling model that combines a pseudo-labeling algorithm with waveform segmentation based on Long Short-Term Memory (LSTM) to effectively label time-series data in smart agriculture. …”
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Performance Evaluation of Data Compression Systems Applied to Satellite Imagery by Lilian N. Faria, Leila M. G. Fonseca, Max H. M. Costa

“…This paper presents an overview and evaluation of some compression algorithms suitable for remote sensing applications. …”
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Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran by Ali Imamalipour, Hamed Ebrahimi, Amir reza Abdollahpur

“…Machine Learning algorithms are widely used in various fields due to their strong capability to extract and display high-level features of training samples. …”
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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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IHML: Incremental Heuristic Meta-Learner by Onur Karadeli, Kıymet Kaya, Şule Gündüz Öğüdücü

“…The landscape of machine learning constantly demands innovative approaches to enhance algorithms’ performance across diverse tasks. Meta-learning, known as “learning to learn” is a promising way to overcome these diversity challenges by blending multiple algorithms. …”
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Quantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimation by Radhey Lal, Rajiv Kumar Singh, Dinesh Kumar Nishad, Saifullah Khalid

“…The system efficiently models radiation transport and predicts patient-specific dose distributions by integrating quantum algorithms, deep learning, and Monte Carlo simulations. …”
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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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A METHOD FOR INVESTIGATING MACHINE LEARNING ATTACKS ON ARBITER-TYPE PHYSICALLY UNCLONABLE FUNCTIONS by Yuri A. Korotaev

“…The research infers that ANNs outperform traditional machine learning algorithms in carrying out attacks on Arbiter PUFs. …”
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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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Reversible data hiding in encrypted domain based on fine-grained access control by ZHANG Minqing, PENG Shen, JIANG Chao, DI Fuqiang, DONG Yufeng

“…To improve the embedding rate and security of reversible data hiding algorithms in encrypted images for cloud environments, ciphertext-policy attribute-based encryption and reversible data hiding in encrypted images were organically integrated, and a reversible data hiding algorithm in encrypted images based on fine-grained access control was proposed. …”
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An Integrative Computational Approach for Identifying Cotton Host Plant MicroRNAs with Potential to Abate CLCuKoV-Bur Infection by Muhammad Aleem Ashraf, Imran Shahid, Judith K. Brown, Naitong Yu

“…Using this suite of robust algorithms, the predicted repertoire of the cotton microRNA-binding landscape was determined for a CLCuKoV-Bur consensus genome sequence. …”
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Improving Mental Health Diagnosis with Hybrid Ensemble Models: A Data-Driven Approach by Malave Sachin, Khemani Bharti, Kelkar Rucha, Balekundri Urvi, Bogar Shravani, Kolekar Areen

“…This study examines how emotional and behavioural indicators might be used to predict mental health issues using machine learning (ML) algorithms. …”
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Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network by Hui Zhou, Manmohan Kamboj, Yi Guo, Angela D Liese, Rebecca Anthopolos, Lu Zhang, John Chang, Anna Roberts, Tessa Crume, Brian E Dixon, Hui Shao, David C Lee, Lorna E Thorpe, Dimitri Christakis, Eneida A Mendonca, Katie Allen, Dana Dabelea, Giuseppina Imperatore, Mark Weiner, Meredith Akerman, Rong Wei, Kristi Reynolds, Annemarie G Hirsch, Jasmin Divers, Tianchen Lyu, Alex Ewing, Shaun Grannis, Yuan Luo, Bo Cai, Anthony Wong, Brian S Schwartz, Meda Pavkov, Meredith Lewis, Sarah Conderino, Jiang Bian, Yonghui Wu, Jihad S Obeid, Harold P Lehmann, Charles Bailey, Theresa Anderson, Elizabeth A Shenkman, Elizabeth Nauman, Christopher Forrest, Mattia Prosperi, Seho Park, Cara M Nordberg, Tessa L Crume, Anna Bellatorre, Stefanie Bendik, Marc Rosenman, Levon Utidjian, Mitch Maltenfort, Amy Shah, G Todd Alonso, Sara Deakyne-Davies, Tim Bunnell, Anne Kazak, Melody Kitzmiller, Daksha Ranade, Joseph J DeWalle, H Lester Kirchner, Dione G Mercer, Amy Poissant, Nimish Valvi, Jeff Warvel, Ashley Wiensch, Tamara Hannon, Eva Lustigova, Don McCarthy, Matthew T Mefford, George Lales, Allison Zelinski, Pedro Rivera, Thomas Carton, Victor W Zhong, Andrew Fair, Jessica Guillaume, Shahidul Islam, Alan Jacobson, Chinyere Okpara, Anand Rajan, Andrea Titus, Rebecca Conway, Toan Ong, Jack Pattee, Shawna Burgett, Bethlehem Shiferaw, Sarah J Bost, William T Donahoo, William R Hogan, Piaopiao Li, Lisa Knight, Caroline Rudisill, Jessica Stucker, Deborah Bowlby, Elaine Apperson, Deborah B Rolka

“…The network will assess the validity of computable phenotype definitions to determine diabetes status and type based on sensitivity, specificity, positive predictive value and negative predictive value of the phenotypes against the gold standard of manually abstracted medical charts. …”
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The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis by Hatice Kabaoğlu, Emine Uçar, Fecir Duran

“…Subsequently, the RF algorithm classifies these predictions into specific error conditions. …”
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Prognostic implications of the high sensitivity C-reactive protein to albumin ratio for major cardiovascular adverse events in patients undergoing noncardiac surgery by Yingjie Chen, Jingfang Lin, Dongxu Chen, Jundan Jiang, Yongxin Huang, Cansheng Gong, Yawen Xie, Xiaohui Chen, Ting Zheng, Yanling Tang, Xiaochun Zheng, Yanling Liao

“…The feature significance and predictive models were developed via the Boruta algorithm and machine learning techniques. …”
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