Automated Analysis of Vertebral Body Surface Roughness for Adult Age Estimation: Ellipse Fitting and Machine-Learning Approach by Erhan Kartal, Yasin Etli

“…<b>Conclusions:</b> Automated analysis of vertebral cortical roughness provides a transparent, observer-independent means of estimating adult age with accuracy approaching that of more complex deep learning pipelines. Streamlining image preparation and validating the approach across diverse populations are the next steps toward forensic adoption.…”
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Optimized Breast Cancer Classification Using PCA-LASSO Feature Selection and Ensemble Learning Strategies With Optuna Optimization by Prabhat Kumar Sahu, Taiyaba Fatma

“…This study presents a novel and optimized breast cancer classification system using machine learning models enhanced through advanced hyperparameter tuning techniques and statistical validation methods. …”
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Standing balance of single-legged hopping robot model using reinforcement learning approach in the presence of external disturbances by S. Mohamad Hoseinifard, Majid Sadedel

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Operations management on the front line of COVID-19 vaccination: building capability at scale via technology-enhanced learning by Iain M Smith, Elaine Bayliss, Hollie Salisbury, Ali Wheeler

“…Lean focuses explicitly on process efficiency through the elimination of non-value adding steps to optimise processes for those who use and depend on them.Technology-enhanced learning can be a strategy to build improvement capability at scale. …”
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Development of an imitation learning method for a neural network system of mobile robot’s movement on example of the maze solving by T. Yu. Kim, R. A. Prakapovich

“…The proposed method is based on the work of two agents interacting with each other: the first directly implements the search algorithm and searches for an exit from the maze, and the second, following it, tries to learn using the imitation learning method. The expert agent, implementing a discrete algorithm for moving through the maze, makes precise discrete steps and moves almost independently of the second agent. …”
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Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies by Felix O. Hofmann, Christian Heiliger, Tengis Tschaidse, Stefanie Jarmusch, Liv A. Auhage, Ughur Aghamaliyev, Alena B. Gesenhues, Tobias S. Schiergens, Hanno Niess, Matthias Ilmer, Jens Werner, Bernhard W. Renz

“…In this study, we developed and validated a flexible, open-access pipeline integrating available deep learning-based segmentation models with pre- and postprocessing steps to extract body composition measures from routine computed tomography (CT) scans. …”
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Explainable machine learning to compare the overall survival status between patients receiving mastectomy and breast conserving surgeries by Betelhem Bizuneh Asfaw, Eyachew Misganew Tegaw

“…Two surgical options, Mastectomy and Breast Conserving Surgery (BCS), share the same survival outcomes, clinical or molecular factors; and explainable Machine Learning (ML) techniques like SHapley Additive exPlanations (SHAP) offer further insights. …”
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Improving appendix cancer prediction with SHAP-based feature engineering for machine learning models: a prediction study by Ji Yoon Kim

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Goal-oriented autonomous decision-making for social robots via collaborative interactive inverse reinforcement learning approach by Mingyue Luo, Hui Li, Wanbo Luo, Hewei Li, Jianan Li

“…Abstract Since the practical constraints of unknown pedestrian goal information, research on inverse reinforcement learning (IRL) applied to social robots has focused on trajectory planning based on current motion direction, other pedestrians, and obstacles. …”
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Learning programme for on-the-job reflection on guideline use in district nursing practice: a protocol for an action research study by Lisette Schoonhoven, Pieterbas Lalleman, Nienke Bleijenberg, Inge Wolbers, Arjan van Os

“…Data analysis will occur in two steps: parallel data collection and analysis during the intervention, followed by a longitudinal qualitative approach to identify learning processes over time and evaluate the intervention’s impact.Ethics and dissemination Ethics approval has been obtained from the Ethical Committee Research of the HU University of Applied Sciences Utrecht (reference numbers 165-001-2022 and 157-001-2022). …”
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Design a path – planning strategy for mobile robot in multi-structured environment based on distributional reinforcement learning by Anh-Tu Nguyen, Duc-Duy Pham, Van-Nghia Le, Vu-Hai Luu

“…The article proposes a novel path-planning strategy for mobile robots moving in unknown environments by integrating Lightweight Learned Image Denoising with Instance Adaptation (LIDIA) and Quantile Regression Deep Q-Network (QR-DQN) within a unified framework. …”
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A Machine Learning-Based Method for Lithology Identification of Outcrops Using TLS-Derived Spectral and Geometric Features by Yanlin Shao, Peijin Li, Ran Jing, Yaxiong Shao, Lang Liu, Kunpeng Zhao, Binqing Gan, Xiaolei Duan, Longfan Li

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Automated classification of midpalatal suture maturation stages from CBCTs using an end-to-end deep learning framework by Omid Halimi Milani, Lauren Mills, Amanda Nikho, Marouane Tliba, Veerasathpurush Allareddy, Rashid Ansari, Ahmet Enis Cetin, Mohammed H. Elnagar

“…Our preprocessing steps include region-of-interest extraction, followed by high-pass and Sobel filtering for emphasis of low-level features. …”
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Applying Wearable Sensors and Machine Learning to the Diagnostic Challenge of Distinguishing Parkinson’s Disease from Other Forms of Parkinsonism by Rana M. Khalil, Lisa M. Shulman, Ann L. Gruber-Baldini, Stephen G. Reich, Joseph M. Savitt, Jeffrey M. Hausdorff, Rainer von Coelln, Michael P. Cummings

“…We also suggested potential steps to address the dataset challenges which limited the models’ performance. …”
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Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment by Lanlan Li, Yeying Yang, Qi Zhang, Jiao Wang, Jiehui Jiang, Alzheimer’s Disease Neuroimaging Initiative

“…In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. …”
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Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome by Pedro Pons-Suñer, François Signol, Noemi Alvarez, Claudia Sargas, Sara Dorado, Jose Vicente Gil Ortí, Juan A. Delgado Sanchis, Marta Llop, Laura Arnal, Rafael Llobet, Juan-Carlos Perez-Cortes, Rosa Ayala, Eva Barragán

“…Second, to validate machine learning models that predict the risk of complications in patients with acute myeloid leukemia (AML) using data available at diagnosis. …”
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Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform by Sajjad Maleki Lonbar, Akram Beigi, Nasour Bagheri, Nasour Bagheri, Pedro Peris-Lopez, Carmen Camara

“…For recognition, deep learning techniques, particularly convolutional neural networks (CNNs), are applied. …”
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MEDIA PEMBELAJARAN MATEMATIKA BERDIFERENSIASI UNTUK MATEMATIKA SMP [DIFFERENTIATED MATH LEARNING MEDIA FOR JUNIOR HIGH SCHOOL MATH] by Dira Salsabila, Usfandi Haryaka

“… This study is grounded in the problems, namely: students' difficulties in understanding the material using data, limited learning facilities in the form of LCD projectors, learning media used have not implemented differentiated learning, and the widespread use of smartphones with Android operating systems among students. …”
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Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm by Xuehui Fan, Ruixue Ye, Yan Gao, Kaiwen Xue, Zeyu Zhang, Jing Xu, Jingpu Zhao, Jun Feng, Yulong Wang

“…The pandas library was used for data cleaning and preprocessing, involving 68 categorical and 12 continuous variables. The steps included handling missing values, data normalization, and encoding conversion. …”
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Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory by Kgothatso Makubyane, Daniel Maposa

“…Over the past couple of decades, the academic literature has transitioned from conventional statistical time series models to embracing EVT and machine learning algorithms for the modelling of environmental variables. …”
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