Desain Penilaian Risiko Privasi pada Aplikasi Seluler Melalui Model Machine Learning Berbasis Ensemble Learning dan Multiple Application Attributes by R. Ahmad Imanullah Zakariya, Kalamullah Ramli

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Predictive performance of count regression models versus machine learning techniques: A comparative analysis using an automobile insurance claims frequency dataset. by Gadir Alomair

“…Zero inflation occurs when more zeros are observed than expected under standard Poisson or negative binomial (NB) models. While machine learning (ML) techniques have been explored for predictive analytics in other contexts, their application to zero-inflated insurance data remains limited. …”
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FArSS: Fast and Efficient Semantic Question Similarity in Arabic by Mohamed Alkaoud

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Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches by Kamal Hossain Nahin, Jamal Hossain Nirob, Akil Ahmad Taki, Md Ashraful Haque, Narinderjit Sawaran SinghSingh, Liton Chandra Paul, Reem Ibrahim Alkanhel, Hanaa A. Abdallah, Abdelhamied A. Ateya, Ahmed A. Abd El-Latif

“…Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. …”
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Exploring the most important factors related to self-perceived health among older men in Sweden: a cross-sectional study using machine learning by David C Currow, Magnus Per Ekström, Max Olsson

“…Objective To evaluate which factors are the most strongly related to self-perceived health among older men and describe the shape of the association between the related factors and self-perceived health using machine learning.Design and setting This is a cross-sectional study within the population-based VAScular and Chronic Obstructive Lung disease study (VASCOL) conducted in southern Sweden in 2019.Participants A total of 475 older men aged 73 years from the VASCOL dataset.Measures Self-perceived health was measured using the first item of the Short Form 12. …”
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Machine learning methods of satellite image analysis for mapping geologic landforms in Niger: A comparison of the Aïr mountains, Niger River basin and Djado Plateau by Lemenkova Polina

Subjects: “…machine learning…”
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Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis by Clara K Chow, Aravinda Thiagalingam, Rohan Jayasinghe, Sarah Zaman, Stephen Bacchi, Justin Chan, Aashray Gupta, Shaun Evans, Pramesh Kovoor, Brandon Stretton, Jayme Bennetts, Ammar Zaka, Naim Mridha, Joshua Kovoor, Gopal Sivagangabalan, Cecil Mustafiz, Daud Mutahar, Shreyans Sinhal, James Gorcilov, Benjamin Muston, Fabio Ramponi, Dale J Murdoch

“…Surgical risk models have demonstrated modest discriminative value for patients undergoing TAVI and are typically poorly calibrated, with incremental improvements seen in TAVI-specific models. Machine learning (ML) models offer an alternative risk stratification that may offer improved predictive accuracy.Methods PubMed, EMBASE, Web of Science and Cochrane databases were searched until 16 December 2023 for studies comparing ML models with traditional statistical methods for event prediction after TAVI. …”
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Machine Learning-Based Radiomics Analysis for Identifying KRAS Mutations in Non-Small-Cell Lung Cancer from CT Images: Challenges, Insights and Implications by Mirjam Schöneck, Nicolas Rehbach, Lars Lotter-Becker, Thorsten Persigehl, Simon Lennartz, Liliana Lourenco Caldeira

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Structure- and machine learning-guided engineering demonstrate that a non-canonical disulfide in an anti-PD-1 rabbit antibody does not impede antibody developability by Wei-Ching Liang, Hongkang Xi, Dawei Sun, Luigi D’Ascenzo, Jonathan Zarzar, Nicole Stephens, Ryan Cook, Yinyin Li, Zhengmao Ye, Marissa Matsumoto, Jian Payandeh, Matthieu Masureel, Yan Wu

“…Next, and prompted by recent developments in machine learning (ML)-guided protein engineering, we used an unbiased ML- and structure-guided approach to rapidly and efficiently generate a different variant with recovered affinity. …”
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Data-Driven Approach for the Prediction of In Situ Gas Content of Deep Coalbed Methane Reservoirs Using Machine Learning: Insights from Well Logging Data by Qian Zhang, Shuheng Tang, Songhang Zhang, Zhaodong Xi, Tengfei Jia, Xiongxiong Yang, Donglin Lin, Wenfu Yang

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'Medical Minds and Machine Learning': Awareness and Opinions on Artificial Intelligence in Healthcare among Undergraduate Medical Students of a Tertiary Care Institute of Kolkata, India by Pattanayak Shalini, Basu Mausumi, Sinha Debasish, Kerketta Prince

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Analisis Sentimen untuk Evaluasi Reputasi Merek Motor XYZ Berkaitan dengan Isu Rangka Motor di Twitter Menggunakan Pendekatan Machine Learning by Ferdian Maulana Akbar, Robby Hermansyah, Sofian Lusa, Dana Indra Sensuse, Nadya Safitri, Damayanti Elisabeth

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RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients by Xiliang Zhang, Hanjie Guo, Xiaolong Li, Wei Tao, Xiaoqing Ma, Yuxing Zhang, Weidong Xiao

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Machine Learning and Multi-Omics Integration to Reveal Biomarkers and Microbial Community Assembly Differences in Abnormal Stacking Fermentation of Sauce-Flavor <i>Baijiu</i> by Shuai Li, Yueran Han, Ming Yan, Shuyi Qiu, Jun Lu

Subjects: “…machine learning…”
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A novel machine learning approach for numerical simulation on the hybrid nanofluid flow past a converging/diverging channel: Properties of tantalum and alumina nanoparticles by Shilpa B, Jasgurpreet Singh Chohan, N Beemkumar, Ankur Kulshreshta, Refka Ghodhbani, Nashwan Adnan Othman, Barno Abdullaeva, M Ijaz Khan

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Global trends and research frontiers on machine learning in sustainable animal production in times of climate change: Bibliometric analysis aimed at insights and orientations for the coming decades by Robson Mateus Freitas Silveira, Concepta Mcmanus, Iran José Oliveira da Siva

“…The present pioneering review provides a longitudinal perspective on the current state of academic research in the emerging machine learning field linked to sustainable animal production in times of climate change. …”
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A Step Forward in Identifying Socially Desirable Respondents: An Integrated Machine Learning Model Considering T-Scores, Response Time, Kinematic Indicators, and Eye Movements by Cristina Mazza, Irene Ceccato, Loreta Cannito, Merylin Monaro, Eleonora Ricci, Emanuela Bartolini, Alessandra Cardinale, Adolfo Di Crosta, Matteo Cardaioli, Pasquale La Malva, Marco Colasanti, Renata Tambelli, Luciano Giromini, Rocco Palumbo, Riccardo Palumbo, Alberto Di Domenico, Paolo Roma

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Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol by Jiawen Deng, Hemang Yadav, Kiyan Heybati

“…Early identification of patients at risk for propofol-associated hypertriglyceridemia is crucial for optimising sedation strategies and preventing adverse outcomes. Machine-learning (ML) models offer a promising approach for predicting individualised patient risks of propofol-associated hypertriglyceridemia.Methods and analysis We propose the development of an ML model aimed at predicting the risk of propofol-associated hypertriglyceridemia in ICU patients receiving IMV. …”
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A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data by M. Atzeni, G. Cappon, J. K. Quint, F. Kelly, B. Barratt, M. Vettoretti

“…To address this, we designed a machine learning (ML) framework that leverages data from personal air quality monitors, health records, lifestyle, and living condition information to build models that perform short-term prediction of COPD exacerbations. …”
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Performance evaluation and comparative analysis of different machine learning algorithms in predicting postnatal care utilization: Evidence from the ethiopian demographic and health survey 2016. by Daniel Niguse Mamo, Agmasie Damtew Walle, Eden Ketema Woldekidan, Jibril Bashir Adem, Yosef Haile Gebremariam, Meron Asmamaw Alemayehu, Ermias Bekele Enyew, Shimels Derso Kebede

“…This study assessed machine learning models for forecasting postnatal care usage. …”
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