Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis by Tianyi Wang, Ruiyuan Chen, Ning Fan, Lei Zang, Shuo Yuan, Peng Du, Qichao Wu, Aobo Wang, Jian Li, Xiaochuan Kong, Wenyi Zhu

“…ObjectiveThis systematic review and meta-analysis aimed to pool the results and evaluate the heterogeneity of the current studies in using TML or DL models to diagnose LSS, thereby providing more comprehensive information for further clinical application. …”
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Reprogramming Heritage: An Approach for the Automatization in the Adaptative Reuse of Buildings by Marta Domènech-Rodríguez, David López López, Sergi Nadal, Anna Queralt, Còssima Cornadó

“…The proposed methodology for a computational algorithm aims to systematise spatial characteristics, assess programmatic needs, and optimise building usage, while addressing challenges such as data integration and quality assurance. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The algorithms (RF and STR) with the smallest Mean Absolute Error (MAE) and the highest residual error (RMSE) and the highest correlation coefficient (RP2) were selected for further parameter optimization and evaluation. …”
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Adaptive multi classifier traffic data interpolation model based on information entropy by Yunkai ZHANG, Jin GAO, Qing LI, Xu WANG

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Machine learning approaches in the therapeutic outcome prediction in major depressive disorder: a systematic review by Veronica Atemnkeng Ntam, Tatjana Huebner, Michael Steffens, Catharina Scholl

“…The objective of the evaluation was to assess applicability of currently published ML-approaches for clinical settings in the EU on the basis of quality, ethical, social, and legal criteria.MethodsWe performed a bibliographic search on PubMed and Google Scholar for studies from January 2016 to December 2024 on ML-applications predicting treatment outcomes in MDD. …”
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Tennis Timing Assessment by a Machine Learning-Based Acoustic Detection System: A Pilot Study by Lucio Caprioli, Amani Najlaoui, Francesca Campoli, Aatheethyaa Dhanasekaran, Saeid Edriss, Cristian Romagnoli, Andrea Zanela, Elvira Padua, Vincenzo Bonaiuto, Giuseppe Annino

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Comparing machine learning models for predicting preoperative DVT incidence in elderly hypertensive patients with hip fractures: a retrospective analysis by Xue Ge, Lan Yao, Yan Liu, Yi Wang, Fang Zhang

“…Various machine learning algorithms were also employed to build predictive models. …”
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Comparative Study on EDM Wire Cutting and CO2 Laser Cutting for High‐Precision Stainless Steel Sheet Processing by A. Parthiban, K. Ananthakumar, S. Ajith Arul Daniel, S. Sivaganesan, T. Sathish, Jayant Giri, A. Johnson Santhosh, Ahmad O. Hourani

“…This study conducts a comprehensive experimental analysis of these techniques, focusing on their performance when cutting stainless steel grades AISI 316L and 304. The research evaluates key process parameters influencing the cut quality, aiming to achieve minimal surface roughness. …”
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Assessing the Impact of the MOOC Learning Platform on the Comprehensive Development of English Teachers at College Level under “Double First-Rate” by Utilization of the SWOT Analys... by Wenjuan He

“…Then, the reliability test and the MOOC learning recommendation algorithm are used to evaluate the quality evaluation system of English teaching in the MOOC utilizing the structural equation model. …”
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Determinants and risk prediction models for frailty among community-living older adults in eastern China by Lin Qi, Lin Qi, Jianyu Liu, Xuhui Song, Xinle Wang, Mengmeng Yang, Xinyi Cao, Yan He

“…Predictive models were constructed using decision trees, random forests, and XGBoost algorithms, implemented in R software (version 4.4.2). …”
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Key geological and engineering technologies and research directions for shale gas exploration and development in western Hubei Province, China by Guangyou Zhu, Jineng Jin, Qilin Xiao, Lei Wang, Guanglong Sheng, Jun Tang, Jianghui Meng, Zhiyuan Lu, Can Chen

“…This necessitates developing region-specific evaluation systems and exploitation techniques. Drawing on previous application cases and guided by the research paradigm of geology-engineering integration, this study presents key technologies potentially applicable to shales in western Hubei Province, spanning from reservoir and sweet spot evaluation to fracturing scheme design and fracturing performance monitoring. …”
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PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things by Mutkule Prasad Raghunath, Shyam Deshmukh, Poonam Chaudhari, Sunil L. Bangare, Kishori Kasat, Mohan Awasthy, Batyrkhan Omarov, Rajesh R. Waghulde

“…After completing the preparation step, the data set is classified using several machine learning techniques such as support vector machine, linear regression, and random forest. Evaluating the veracity, exactness, and retrieval rate of different machine learning algorithms is crucial for choosing the most effective ones. …”
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Metabolomics for origin traceability of lamb: An ensemble learning approach based on random forest recursive feature elimination by Chongxin Liu, Simona Grasso, Nigel Patrick Brunton, Qi Yang, Shaobo Li, Li Chen, Dequan Zhang

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Capturing the extent of climate's dynamic effects on runoff and nutrient yields across short- and long-term scales by Di Chang, Shuo Li

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Comparative analysis of image mosaicing techniques for aerial agriculture field imaging by Maria John, S. Santhanalakshmi, J Amudha, Jianfeng Zhou

“…The Aerial images are used to evaluate distinctive feature-based and direct pixel-based mosaicing methods. …”
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Artificial intelligence for difficult airway assessment: a protocol for a systematic review with meta-analysis by Dan Liu, Tingting Li, Li Du, Jianqiao Zheng, Yujie Huang, Weiyi Zhang

“…The risk of bias will be evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. …”
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A Review on Trending Machine Learning Techniques for Type 2 Diabetes Mellitus Management by Panagiotis D. Petridis, Aleksandra S. Kristo, Angelos K. Sikalidis, Ilias K. Kitsas

“…Our work indicates that Gradient Boosting and tree-based models are the most successful ones, the SHAPley and Wrapper algorithms being quite popular feature interpretation and evaluation methods, highlighting urinary markers and dietary intake as emerging diabetes predictors besides the typical invasive ones. …”
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HOLa: HoloLens Object Labeling by Schwimmbeck Michael, Khajarian Serouj, Holzapfel Konstantin, Schmidt Johannes, Remmele Stefanie

“…As segmentation foundation models like the Segment Anything Model (SAM) begin to emerge, zero-shot segmentation requires only minimal human participation obtaining high-quality object masks. We introduce a HoloLens-Object-Labeling (HOLa) Unity and Python application based on the SAM-Track algorithm that offers fully automatic single object annotation for HoloLens 2 while requiring minimal human participation. …”
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Raman Spectra Classification of Pharmaceutical Compounds: A Benchmark of Machine Learning Models with SHAP-Based Explainability by Dimitris Kalatzis, Alkmini Nega, Yiannis Kiouvrekis

“…A diverse array of algorithms—including Support Vector Machines (SVMs), Random Forests, k-Nearest Neighbors (k-NN), Gradient Boosting (XGBoost, LightGBM), and 1D Convolutional Neural Networks (CNNs)—were evaluated on a publicly available dataset. …”
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Chaotic Hénon–Logistic Map Integration: A Powerful Approach for Safeguarding Digital Images by Abeer Al-Hyari, Mua’ad Abu-Faraj, Charlie Obimbo, Moutaz Alazab

“…Performance evaluation in terms of encryption time, throughput, and image quality metrics validates the effectiveness of the proposed integrated approach. …”
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