Ontology-based prompt tuning for news article summarization by A. R. S. Silva, Y. H. P. P. Priyadarshana

“…Despite the progress in natural language processing (NLP) and machine learning, existing methods often rely on extractive summarization, which lacks the ability to generate coherent and contextually rich summaries. …”
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Customer segmentation in the digital marketing using a Q-learning based differential evolution algorithm integrated with K-means clustering. by Guanqun Wang

“…Furthermore, four widely recognized machine learning methods are employed to classify the clustering results, achieving over 95% classification accuracy on the test set. …”
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Comparing self reported and physiological sleep quality from consumer devices to depression and neurocognitive performance by Samir Akre, Zachary D. Cohen, Amelia Welborn, Tomislav D. Zbozinek, Brunilda Balliu, Michelle G. Craske, Alex A. T. Bui

“…Correlations between self-reported and physiological sleep measures were generally weak. Machine learning models revealed that self-reported sleep quality could detect all depression symptoms measured using the Patient Health Questionnaire-14, whereas physiological sleep measures detected “sleeping too much” and low libido. …”
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Ultrasound Radiogenomics-based Prediction Models for Gene Mutation Status in Breast Cancer by Zhai Yue, Tan Dianhuan, Lin Xiaona, Lv Heng, Chen Yan, Li Yongbin, Luo Haiyu, Dan Qing, Zhao Chenyang, Xiang Hongjin, Zheng Tingting, Sun Desheng

“…By integrating clinical data with ultrasonic features, predictive models are developed using machine learning techniques, aiming to refine the capability to diagnose and personalize treatment plans for breast cancer patients. …”
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Fault analysis and fault degree evaluation via an improved ResNet method for aircraft hydraulic system by Kenan Shen, Dongbiao Zhao

“…After that the evaluate results are given and analyzed, moreover the comparison between the SE-ResNet method with the other machine learning methods are given. The results show that the method in this paper has the best accuracy and shortest test time, therefore the method proposed in this paper has effective measures to improve the reliability of the aircraft hydraulic system.…”
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Conditional similarity triplets enable covariate-informed representations of single-cell data by Chi-Jane Chen, Haidong Yi, Natalie Stanley

“…In order to translate immune signatures assayed from blood or tissue into powerful diagnostics, machine learning approaches are often employed to compute immunological summaries or per-sample featurizations, which can be used as inputs to models for outcomes of interest. …”
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ECP-IEM: Enhancing seasonal crop productivity with deep integrated models. by Ghulam Mustafa, Muhammad Ali Moazzam, Asif Nawaz, Tariq Ali, Deema Mohammed Alsekait, Ahmed Saleh Alattas, Diaa Salama AbdElminaam

“…Artificial Intelligence (AI) has increased the yield accuracy significantly. The existing Machine Learning (ML) methods are using statistical measures as regression, correlation and chi square test for predicting crop yield, all such model's leads to low accuracy when the number of factors (variables) such as the weather and soil conditions, the wind, fertilizer quantity, and the seed quality and climate are increased. …”
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Risk in management decisions of the modern organization and methods of management of this risk by Veronika Holomozenko, Yuliia Stasiuk

“…Key risk management techniques such as quantitative and qualitative risk assessment tools, scenario analysis, and the use of advanced technologies such as artificial intelligence and machine learning are discussed. The study also discusses the role of risk management committees and the importance of establishing a risk-aware culture in an organization. …”
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Design and modeling of a nanocomposite system for demineralization of sweet whey by Mina Rezapour, Mohsen Esmaiili, Mehdi Mahmoudian, Alireza Behrooz Sarand

“…The dynamic flux behavior of whey output and salt rejection from whey was modeled using convolutional neural network (CNN) machine learning tools. Linear and non-linear correlations demonstrated that the CNN model correlates well with experimental data on dynamic flux (R2–1.00). …”
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Quantum-inspired K-nearest neighbors classifier for enhanced printer source identification in forensic document analysis by Saad M. Darwish, Raad A. Ali, Adel A. Elzoghabi

“…However, identifying consistent patterns across different printers remains challenging, especially when perpetrators attempt to obscure these artifacts. Machine learning models in this field must identify discriminative features that differentiate printers while minimizing noise. …”
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Masked and Unmasked Face Recognition Model Using Deep Learning Techniques. A case of Black Race. by Mabirizi, Vicent, Ampaire, Ray Brooks, Muhoza, Gloria B.

“…The model was trained and tested on 1000 images taken from students of Kabale University using Nikon d850 camera. Machine learning techniques such as Principal Component Analysis, Geometric Feature Based Methods and double threshold techniques were used in the development phase while results were classified using CNN pre-trained models. …”
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Masked and unmasked Face Recognition Model Using Deep Learning Techniques. A case of Black Race. by Mabiriz,I, Vicent, Ampaire, Ray Brooks, Muhoza, B. Gloria

“…The model was trained and tested on 1000 images taken from students of Kabale University using a Nikon d850 camera. Machine learning techniques such as Principal Component Analysis, Geometric Feature-Based Methods, and double threshold techniques were used in the development phase while results were classified using CNN pre-trained models. …”
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Analisis Sentimen Kebijakan Penerapan Kurikulum Merdeka Sekolah Dasar dan Sekolah Menengah pada Media Sosial Twitter dengan Menggunakan Metode Word Embedding dan Long Short Term Me... by Alif Rizal Maulana, Satrio Hadi Wijoyo, Yusi Tyroni Mursityo

“…Analisis sentimen dilakukan pada opini siswa yang diutarakan di media sosial Twitter dengan menggunakan pendekatan machine learning. Arsitektur yang digunakan adalah Long Short-Term Memory Networks (LSTM). …”
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Exploring ethical considerations in medical research: Harnessing pre-generated transformers for AI-powered ethics discussions. by Takuya Mori, Takuya Watanabe, Shinji Kosugi

“…This limitation may be attributed to biased opinions resulting from machine learning optimization, preventing comprehensive identification of certain ethical issues.…”
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Prediction of High-ozone Events Using GAM, SMOTE, and Tail Dependence Approaches in Texas (2005–2019) by Benjamin Brown-Steiner, Xiong Zhou, Matthew J. Alvarado, Brook T. Russell

“…Finally, the feature selection via the tail dependence method performs comparably to other forms of machine learning-based feature selection and we find that there are multiple parameter sets that can predict MDA8 O3 with equal success.…”
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Remote sensing forest health assessment – a comprehensive literature review on a European level by Drechsel Johannes, Forkel Matthias

“…We conclude that the availability of new satellite systems and advances in artificial intelligence and machine learning should be translated into FHA practice according to ICP standards.…”
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Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes by Cillian Thompson, Oscar Higgins

“…Furthermore, in diabetic disease modelling, artificial neural networks have demonstrated outstanding accuracy compared to alternative methods such as machine learning, regression, artificial neural networks, and decision trees.…”
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The Implementation of a Support Vector Regression Model Utilizing Meta-Heuristic Algorithms for Predicting Undrained Shear Strength by Rami Al-Qasimi, Firas Al-Hajri

“…To address these limitations, this study introduces innovative machine learning techniques, employing the Support Vector Regression (SVR) model to accurately predict USS. …”
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Introducing Iterative Model Calibration (IMC) v1.0: a generalizable framework for numerical model calibration with a CAESAR-Lisflood case study by C. Banerjee, K. Nguyen, C. Fookes, G. Hancock, T. Coulthard

“…Moreover, the utility of machine-learning-based and data-driven approaches is curtailed by the requirement for the numerical model to be differentiable for optimization purposes, which challenges their generalizability across different models. …”
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