Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making. by Rawan Abulibdeh, Karen Tu, Debra A Butt, Anthony Train, Noah Crampton, Ervin Sejdić

“…The association between the completeness rates of the sociodemographic data and the various clinics, electronic medical record vendors, and physician characteristics was analyzed. Supervised machine learning models were used to determine the absence or presence of each characteristic for all adult patients over the age of 18 in the database. …”
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Modeling the gameplay actions of elite volleyball players and teams based on statistical match reports by Sergii Iermakov, Tetiana Yermakova, Krzysztof Prusik

“…The pandas library was used for data analysis and statistical operations, and 'scikit-learn' for machine learning. Results. Models are presented that best predict the results for teams and volleyball players. …”
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Quantum distance approximation for persistence diagrams by Bernardo Ameneyro, Rebekah Herrman, George Siopsis, Vasileios Maroulas

“…The space of persistence diagrams can be endowed with various metrics, which admit a statistical structure and allow to use these summaries for machine learning algorithms, e.g. the Wasserstein distance. …”
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A comprehensive image dataset for the identification of lemon leaf diseases and computer vision applicationsMendeley Data by A K M Fazlul Kobir Siam, Prayma Bishshash, Md. Asraful Sharker Nirob, Sajib Bin Mamun, Md Assaduzzaman, Sheak Rashed Haider Noori

“…The dataset will not only act as a benchmark in developing accurate machine learning models for early disease detection, but it will also contribute to the cause of sustainable lemon cultivation practices by facilitating timely and effective disease management interventions.…”
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Experimental and analytical study on axial behaviour of square corrugated concrete filled single and double skin tube stub columns by Aya Mohsen Handousa, Mohamed Abdellatief, Fikry Abdo Salem, Nabil Mahmoud, Mohamed Ghannam

“…Furthermore, the study proposed two machine-learning models, namely Artificial Neural Network (ANN) and Gaussian Process Regression (GPR), to estimate the ultimate compressive strength of square CFDST columns. …”
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Artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers by Bentegri Houcine, Rabehi Mohamed, Kherfane Samir, Boukansous Sarra

“…Using various validation criteria such as coefficient of determination (R), root mean squared error (RMSE) and mean absolute error (MAE), the ANN model was validated and compared to two machine learning (ML) Random Forest (RF) techniques and Multilayer Perceptron (MLP). …”
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Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review by Kaelan Lockhart, Juan Sandino, Narmilan Amarasingam, Richard Hann, Barbara Bollard, Felipe Gonzalez

“…Despite the potential of established Machine-Learning (ML) classifiers such as Random Forest, K Nearest Neighbour, and Support Vector Machine, and gradient boosting in the semantic segmentation of UAV-captured images, there is a notable scarcity of research employing Deep Learning (DL) models in these extreme environments. …”
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Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors by C. L. Bachand, C. L. Bachand, C. Wang, B. Dafflon, L. N. Thomas, L. N. Thomas, I. Shirley, S. Maebius, S. Maebius, C. M. Iversen, K. E. Bennett

“…We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. …”
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Two-Level Automatic Adaptation of a Distributed User Profile for Personalized News Content Delivery by Maria Papadogiorgaki, Vasileios Papastathis, Evangelia Nidelkou, Simon Waddington, Ben Bratu, Myriam Ribiere, Ioannis Kompatsiaris

“…It involves the use of machine learning algorithms applied to the implicit and explicit user feedback. …”
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Automated Segmentation and Object Classification of CT Images: Application to In Vivo Molecular Imaging of Avian Embryos by Alexander Heidrich, Jana Schmidt, Johannes Zimmermann, Hans Peter Saluz

“…The classification engine was implemented using the WEKA machine learning tool. Results. Our system reduces analysis time and observer bias while maintaining high accuracy. …”
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Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings by Özge Öztürk Hacar, Müslüm Hacar, Fatih Gülgen, Luca Pappalardo

“…The study employs a random forest machine learning model to predict review volumes and ratings, categorized into high and low classes. …”
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Human Trajectory Imputation Model: A Hybrid Deep Learning Approach for Pedestrian Trajectory Imputation by Deb Kanti Barua, Mithun Halder, Shayanta Shopnil, Md. Motaharul Islam

“…Previous attempts to address this issue, such as statistical inference and machine learning approaches, have shown promise. Yet, the landscape of deep learning is rapidly evolving, with new and more robust models emerging. …”
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Assessing Driving Risk Level: Harnessing Deep Learning Hybrid Model With Intercity Bus Naturalistic Driving Data by Wei-Hsun Lee, Che-Yu Chang

“…Additionally, the high-risk level prediction F1-score reaches 0.728 for the proposed model, which is up to 9.3 times better than the performance of the machine learning baseline model. This breakthrough in driving risk prediction not only represents a major advancement in traffic safety management but also has practical implications for fleet scheduling management among transportation companies in the future. …”
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Identification of Proteins and Genes Associated with Hedgehog Signaling Pathway Involved in Neoplasm Formation Using Text-Mining Approach by Nadezhda Yu. Biziukova, Sergey M. Ivanov, Olga A. Tarasova

“…For recognition of the Hedgehog pathway proteins and genes and neoplastic diseases we use a dictionary-based named entity recognition approach, while for all other proteins and genes machine learning method is used. For association extraction, we develop a set of semantic rules. …”
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A Performance Analysis of Business Intelligence Techniques on Crime Prediction by Ivan, Niyonzima, Emmanuel Ahishakiye, Elisha Opiyo Omulo, Ruth Wario

“…The dataset was acquired from UCI machine learning repository website with a title ‘Crime and Communities’. …”
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Global insights into MRSA bacteremia: a bibliometric analysis and future outlook by Jia-Yi Lin, Jia-Yi Lin, Jia-Kai Lai, Jia-Kai Lai, Jian-Yi Chen, Jian-Yi Chen, Jia-Yu Cai, Jia-Yu Cai, Zhan-Dong Yang, Zhan-Dong Yang, Liu-Qingqing Yang, Liu-Qingqing Yang, Liu-Qingqing Yang, Ze-Tao Zheng, Ze-Tao Zheng, Xu-Guang Guo, Xu-Guang Guo, Xu-Guang Guo

“…Future research will rely on integrating genomics, AI, and machine learning to drive personalized treatment. Strengthening global cooperation, particularly in resource-limited countries, will be key to effectively addressing MRSA BSIs.…”
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Artificial Neural Network (ANN) Approach to Predict Tensile Properties of Longitudinally Placed Fiber Reinforced Polymeric Composites including Interphase by Sagar Chokshi, Piyush Gohil, Vijay Parmar, Vijaykumar Chaudhary

“…Machine Learning has become prevalent nowadays for predicting data on the mechanical properties of various materials and is widely used in various polymeric applications. …”
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Do indigenous people get left behind? An innovative methodology for measuring the unmeasurable economic conditions and poverty from the poorest region of Luzon, Philippines by Emmanuel A. Onsay, Jomar F. Rabajante

“…This work puts forth fresh approaches to quantify the incalculable multifaceted poverty and socioeconomic conditions: (i) a thorough statistical analysis using diagnostic and descriptive analytics to examine socioeconomic situations; (ii) combining sophisticated econometrics and predictive analytics to measure multidimensional poverty; and (iii) integrating machine learning to model socioeconomic situations and prescriptive analytics to develop policy. …”
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An online intelligent electronic medical record system via speech recognition by Xin Xia, Yunlong Ma, Ye Luo, Jianwei Lu

“…On the data sets from real clinical scenarios, our proposed algorithm significantly outperforms other machine learning algorithms. Furthermore, compared to traditional electronic medical record systems that rely on keyboard inputs, our system is much more efficient, and its accuracy rate increases with the increasing online time of the proposed system. …”
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Designing and planning a bioethanol supply chain network under uncertainty using a data-driven robust optimization model under disjunctive uncertainty sets by Farzaneh MansooriMooseloo, Maghsoud Amiri, Mohammad Taghi Taghavi Fard, Mostafa Hajiaghaei-Keshteli

“…Therefore, the aim of this study is to design and optimize the biomass-to-bioethanol supply chain network using data-driven robust optimization methods and disjunctive uncertainty sets.Methodology: The methodology of this study is a multi-methodology approach based on mathematical modeling and machine learning algorithms. Initially, uncertainty sets for the non-deterministic model parameter were created using K-means and SVC methods. …”
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