Noise Processing Method of MEMS Tilt Sensor Using Improved Kalman Filter Based on Quantum Particle Swarm Optimization by Yutong Ge, Weizheng Ren, Kaile Yu, Yiran Zhang, Yuxiao Li

“…The parameters of Kalman filter are adaptively optimized by QPSO, which addresses the issues of local optima and premature convergence during the optimization and correction process of prediction system. Then Kalman filter is carried out with the optimal parameters to achieve the effect of denoising. …”
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A novel smart baby cradle system utilizing IoT sensors and machine learning for optimized parental care by Kunal Chandnani, Suryakant Tripathy, Ashutosh Krishna Parbhakar, Kshitij Takiar, Urvi Singhal, P. Sasikumar, S. Maheswari

“…In contrast to traditional solutions that often require manual intervention or provide limited automation, this smart cradle uses predictive analytics to proactively address potential discomforts and hazards, thus presenting a more reliable, intelligent, and user-friendly solution for modern parenting.…”
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Unveiling tumor-infiltrating immune cell-driven immune-mediated drug resistance in clear cell renal cell carcinoma: prognostic insights and therapeutic strategies by Jifeng Yang, Yixuan Xing, Jiusong Luan, Wenbo Yang, Xin Zhang, Yanhua Tian, Haisong Zhang

“…Results The TIIC-based model demonstrated superior predictive performance for patient outcomes compared to 53 published models. …”
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Integrating Machine Learning and Geospatial Data for Mapping Socioeconomic Vulnerability to Urban Natural Hazard by Esaie Dufitimana, Paterne Gahungu, Ernest Uwayezu, Emmy Mugisha, Jean Pierre Bizimana

“…Among these, MLP achieved the best predictive performance, with an AUC score of 0.902 and an F1-score of 0.86. …”
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Methodological aspects of inventory management in logistics under modern conditions by A. G. Sakharov, A. E. Trubin, O. P. Kultygin, A. Yu. Anisimov

“…The article aims to design optimal inventory management algorithms in logistics in the current environment. …”
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Nutritional markers of undiagnosed type 2 diabetes in adults: Findings of a machine learning analysis with external validation and benchmarking. by Kushan De Silva, Siew Lim, Aya Mousa, Helena Teede, Andrew Forbes, Ryan T Demmer, Daniel Jönsson, Joanne Enticott

“…From this, the derived 12 predictive models were validated on internal- and external validation cohorts. …”
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A Novel Dataset for Early Cardiovascular Risk Detection in School Children Using Machine Learning by Rafael Alejandro Olivera Solís, Emilio Francisco González Rodríguez, Roberto Castañeda Sheissa, Juan Valentín Lorenzo-Ginori, José García

“…We conducted a rigorous performance evaluation of 10 machine learning (ML) algorithms to classify cardiovascular risk into two categories: at risk and not at risk. …”
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Does metaverse improve recommendations quality and customer trust? A user-centric evaluation framework based on the cognitive-affective-behavioural theory by Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Osama Halabi, Raian Ali

“…They can analyse large amounts of data using machine learning algorithms and predictive analytics capabilities to provide highly relevant recommendations to users. …”
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Precision in practice: exploring the impact of ai and machine learning on ultrasound guided regional anaesthesia by Noor Ul Huda Bhatti, Syed Ghazi Ali Kirmani, Maryam Butt

“…It is used to conduct predictive tasks without programming instructions by creating algorithms. …”
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Sensor‐Guided Smart Irrigation for Tomato Production: Comparing Low and Optimum Soil Moisture in Greenhouse Environments by Ibrahim Dirlik, Ferhat Uğurlar, Cengiz Kaya

“…Future research should explore the integration of advanced sensors, machine learning algorithms, and predictive models to further optimize irrigation strategies, with an emphasis on scalability and environmental impact. …”
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Multi-omics integration and machine learning uncover molecular basal-like subtype of pancreatic cancer and implicate A2ML1 in promoting tumor epithelial-mesenchymal transition by Jiachen Ge, Jianping Cai, Gaolei Zhang, Deyu Li, Lianyuan Tao

“…Prognostic genes were identified to construct predictive models through various machine learning approaches. …”
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Oil Development Engineering Company, Tehran, Iran by Seyed Ali Mohammad Tajalli, Mazda Moattari, Vahid Naghavi, Mohammad Reza Salehizadeh

“…Leveraging a hybrid approach that combines the Extended Kalman Filter (EKF), Real-Time Transient Modeling (RTTM), and machine learning algorithms, the framework offers accurate insights into the pipeline’s operational status. …”
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Mapping forest types along ecological gradient in Pakistan by Naveed Ahmad, Syed Ghias Ali

“…Maxent modeling showed impressive predictive performance of all forest types (STPF, MTF and DTF) along ecological gradient with average AUC values of 0.968, 0.918, and 0.940 respectively and climatic variables have highest gain compared to topographic and soil predictors. …”
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MISHTI study evaluating hepatic fibrosis in type 2 diabetes patients in India by Debasis Datta, Krishna G. Seshadri, Samit Ghosal

“…Logistic regression models were used to identify independent predictors of significant fibrosis. The predictive performance of the models was evaluated using ROC curves. …”
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The Inversion of SPAD Value in Pear Tree Leaves by Integrating Unmanned Aerial Vehicle Spectral Information and Textural Features by Ning Yan, Yasen Qin, Haotian Wang, Qi Wang, Fangyu Hu, Yuwei Wu, Xuedong Zhang, Xu Li

“…The results showed the following: (1) both vegetation indices and textural features were significantly correlated with SPAD values, which were important indicators for estimating the SPAD values of pear leaves; (2) combining vegetation indices and textural features significantly improved the accuracy of SPAD value estimation compared with a single feature type; (3) the four machine learning algorithms demonstrated good predictive ability, and the OIA model outperformed the single model, with the model based on the OIA inversion model combining vegetation indices and textural features having the best accuracy, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> values of 0.931 and 0.877 for the training and validation sets, respectively. …”
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Model Development for Estimating Sub-Daily Urban Air Temperature Patterns in China Using Land Surface Temperature and Auxiliary Data from 2013 to 2023 by Yuchen Guo, János Unger, Tamás Gál

“…Our study offered a novel method for estimating sub-daily Tair patterns using open-source data and revealed the impacts of predictive variables on Tair estimation, which has important implications for urban thermal environment research.…”
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From support to recovery: the evolving role of LVAD in reversing heart failure by Bingqi Sun, Zhigang Liu

“…However, challenges persist, including the lack of predictive biomarkers, suboptimal GDMT adherence, and unresolved debates regarding concomitant cardiac procedures. …”
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A Review and Comparative Analysis of Solar Tracking Systems by Reza Sadeghi, Mattia Parenti, Samuele Memme, Marco Fossa, Stefano Morchio

“…Intelligent tracking systems, especially those leveraging machine learning and predictive analytics, demonstrate additional energy gains up to 7.83% under cloudy conditions compared to conventional algorithms. …”
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Problematic points of anticoagulant therapy for atrial fibrillation in certain Russian regions in 2016- 2019: results of a retrospective big data analysis by M. A. Druzhilov, O. Yu. Druzhilova, Z. A. Ilchenko, P. I. Chalaya, T. Yu. Kuznetsova

“…Anonymized data of patients with AF aged ³18 years (n=144431; men, 42,0%; mean age, 68,9±15,7 years; mean CHA2DS2-VASc score, 3,2±1,8) were extracted from the Webiomed predictive analytics service using continuous sampling from electronic health records in medical facilities of 6 Russian constituent entities (Republic of Bashkortostan, Republic of Buryatia, Republic of Karelia, Republic of Sakha (Yakutia), Perm Krai, Yamalo-Nenets Autonomous Okrug) for 2016-2019 using artificial intelligence technologies. …”
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The use of culturally adapted and translated depression screening questionnaires with South Asian haemodialysis patients in England. by Shivani Sharma, Sam Norton, Kamaldeep Bhui, Roisin Mooney, Emma Caton, Tarun Bansal, Clara Day, Andrew Davenport, Neill Duncan, Philip A Kalra, Maria Da Silva-Gane, Gurch Randhawa, Graham Warwick, David Wellsted, Magdi Yaqoob, Ken Farrington

“…Alternative screening cut-offs did not improve positive predictive values.<h4>Conclusions</h4>Culturally adapted translations of depression screening questionnaires are useful to explore symptom endorsement amongst South Asian patients. …”
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