Automated Support Generation for Fixed Partial Dentures and Impact of Bone Loss, Bone Quality and Support Types: Parametric Cad and Finite Element Analysis by Hassen Jemaa, Michael Eisenburger, Andreas Greuling

“…<b>Methods</b>: The algorithm was implemented in Rhinoceros/Grasshopper to automatically generate geometries with varying bone loss (0 mm to 3 mm), support type (tooth–tooth and implant–implant support) and bone quality (D1 to D4) for a 4-unit FPD. …”
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Elevating Accuracy: Enhanced Feature Selection Methods for Type 2 Diabetes Prediction by Ghazaleh Kakavand Teimoory, MohammadReza Keyvanpour

“…Previous research has utilized various algorithms like Naïve Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and decision trees for patient classification. …”
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Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics by Guoda Han, Xu Liu, Tian Gao, Lei Zhang, Xiaoling Zhang, Xiaonan Wei, Yecheng Lin, Bohong Yin

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A hybrid machine learning method of support vector regression with particle swarm optimization for predicting IRI in continuously reinforced concrete pavement by Ali Alnaqbi, Waleed Zeiada, Ghazi Al-Khateeb

“…The proposed PSO-SVR model achieved outstanding predictive performance with an average RMSE of 0.04116 and an R2 of 0.99058 across fivefold cross-validation, outperforming benchmark models including Decision Tree, Random Forest, and XGBoost. By highlighting important input characteristics affecting IRI, variable importance analysis and 3D interaction plots improved the interpretability of the model even more. …”
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Hybrid weighted fuzzy production rule extraction utilizing modified harmony search and BPNN by Feng Qin, Azlan Mohd Zain, Kai-Qing Zhou, De-Bing Zhuo

“…Evaluated against existing optimization methods including Harmony Search (HS), Cuckoo Search (CS), Adaptive Global Optimal Harmony Search (AGOHS), and Harmony Search with Cuckoo Search (HSCS) Algorithms, DDA-HS achieves 74.48% accuracy for BPNN classification and 77.08% for WFPR classification on the PIMA dataset, representing improvements of 3.6% and 6.5%, respectively. …”
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AI-driven dynamic orthodontic treatment management: personalized progress tracking and adjustments—a narrative review by Xuanchi Guo, Yuhan Shao

“…This review will focus on the fusion of AI-driven multimodal data analysis (e.g., cone-beam CT, intraoral scanning, and 3D facial images) and deep learning algorithms (e.g., convolutional neural networks) to elucidate the technological breakthroughs in key aspects such as tooth movement trajectory prediction and early detection of root resorption. …”
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A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools by Rifa Andriani Saputri, Asrianda Asrianda, Lidya Rosnita

“…This study aims to develop a system for predicting academic achievement using the Random Forest algorithm, with a case study at SMAN 1 Aceh Barat Daya and SMAN 3 Aceh Barat Daya. …”
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Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models by Nikos Themelis, George Nikolaidis, Vasilios Zagkas

“…The power prediction models are data-driven based on machine learning algorithms. The process includes feature engineering, filtering, and data smoothing, while an evaluation of regression algorithms of the decision tree family is performed. …”
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Blind Frequency Offset Estimation Based on Phase Rotation For Coherent Transceiver by Qingzhao Tan, Aiying Yang, Peng Guo, Zhao Zhao

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Big Data Analytics in IoT, social media, NLP, and information security: trends, challenges, and applications by Kamal Taha

“…Key findings reveal that: (1) GNN and Self-Supervised Learning (SSL) are top performers in terms of predictive performance and efficiency in domains such as IoT and Social Media, (2) XGBoost and CNN offer superior accuracy and robustness across structured and unstructured data tasks, though CNN incurs higher computational costs, (3) ELM and Decision Trees are better suited for lightweight or interpretable applications, and (4) KNN generally underperforms in scalability and predictive strength for large-scale tasks. …”
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Performance of 256QAM in LTE-Hi indoor scenario by Shan LIU, Cheng TAO, Liu LIU

“…In order to solve the traffic explosion of indoor and hotspots,LTE-Hi was put forward by 3GPP organization in Release12.Due to the coverage particularity of indoor and hotspots scenarios,the organization introduced 256QAM to improve system performance.According to the 256QAM constellation given in the standard,a soft demodulation algorithm based on border distance decision was deduced.Through a simulation of the UE SINR distribution in the LTE-Hi scenario,the feasibility of the 256QAM was proved and according to the CQI/MCS/TBS signaling table changed for 256QAM,link level simulation showed the 256QAM and 64QAM throughput in comparison under different EVM index.Finally,the system performance with and without the introduction of 256QAM under different scenarios was evaluated.…”
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Performance of 256QAM in LTE-Hi indoor scenario by Shan LIU, Cheng TAO, Liu LIU

“…In order to solve the traffic explosion of indoor and hotspots,LTE-Hi was put forward by 3GPP organization in Release12.Due to the coverage particularity of indoor and hotspots scenarios,the organization introduced 256QAM to improve system performance.According to the 256QAM constellation given in the standard,a soft demodulation algorithm based on border distance decision was deduced.Through a simulation of the UE SINR distribution in the LTE-Hi scenario,the feasibility of the 256QAM was proved and according to the CQI/MCS/TBS signaling table changed for 256QAM,link level simulation showed the 256QAM and 64QAM throughput in comparison under different EVM index.Finally,the system performance with and without the introduction of 256QAM under different scenarios was evaluated.…”
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Radiomic Features of Mesorectal Fat as Indicators of Response in Rectal Cancer Patients Undergoing Neoadjuvant Therapy by Francesca Treballi, Ginevra Danti, Sofia Boccioli, Sebastiano Paolucci, Simone Busoni, Linda Calistri, Vittorio Miele

“…The pre-treatment MRI analysis identified significant features (out of 107) for each sequence based on the Mann–Whitney test and <i>t</i>-test. The LASSO algorithm selected three features (shape_Sphericity, shape_Maximum2DDiameterSlice, and glcm_Imc2) for the construction of the radiomic logistic regression model, and ROC curves were subsequently generated for each model (AUC: 0.76). …”
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Relationship between stress hyperglycemia ratio and the incidence of atrial fibrillation in patients after coronary artery bypass grafting: a retrospective study based on the MIMIC... by Runjia Liu, Jiatong Li, Jing Zeng, Yuxuan Tao, Dong Chen, Haixia Li

“…We employed logistic regression models, restricted cubic splines (RCS), threshold effect analysis, ubgroup analysis, Boruta algorithm, lasso algorithm, and receiver operating characteristics (ROC) to analyze the relationship between SHR and POAF incidence comprehensively. …”
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An interpretable CT-based machine learning model for predicting recurrence risk in stage II colorectal cancer by Ziqi Wu, Liya Gong, Jingwen Luo, Xiaobo Chen, Fan Yang, Junyan Wen, Yanyu Hao, Zhishan Wang, Ruozhen Gu, Yuqin Zhang, Hai Liao, Ge Wen

“…Abstract Objectives This study aimed to develop an interpretable 3-year disease-free survival risk prediction tool to stratify patients with stage II colorectal cancer (CRC) by integrating CT images and clinicopathological factors. …”
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Predicting postoperative pulmonary infection in elderly patients undergoing major surgery: a study based on logistic regression and machine learning models by Jie Liu, Xia Li, Yanting Wang, Zhenzhen Xu, Yong Lv, Yuyao He, Lu Chen, Yiqi Feng, Guoyang Liu, Yunxiao Bai, Wanli Xie, Qingping Wu

“…Among the different algorithms, LR performed the best with an AUC of 0.80, whereas the decision tree performed the worst with an AUC of 0.75. …”
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An Explainable Fuzzy Framework for Assessing Preeclampsia Classification by Matías Salinas, Daira Velandia, Leondry Mayeta-Revilla, Ayleen Bertini, Marvin Querales, Fabian Pardo, Rodrigo Salas

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Multi-Agent Coordinated Dispatch of Power Grid and Pumped Hydro Storage with Embedded Market Game Model by LOU Wei, HU Rong, YU Jinming, ZHANG Xipeng, FAN Feilong, LIU Songyuan

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From Brain to Blood: Uncovering Potential Therapeutical Targets and Biomarkers for Huntington’s Disease Using an Integrative RNA-Seq Analytical Platform (BDASeq^®) by João Rafael Dias Pinto, Benedito Faustinoni Neto, Luciana Munhoz, Irina Kerkis, Rodrigo Pinheiro Araldi

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Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks by Bipasha Kashyap, Pubudu N. Pathirana, Malcolm Horne, Laura Power, David J. Szmulewicz

“…The algorithm builds machine-learning models to support a 3-tier diagnostic categorisation for distinguishing Ataxic Speech from healthy speech, rating the severity of Ataxic Speech, and nomogram-based supporting scoring charts for Ataxic Speech diagnosis and severity prediction. …”
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