Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, E. N. Dzhakhnaeva

“…The abstract describes the construction of a binary classification model for predicting the type of job advertisement in cloud-based ATS (Applicant Tracking Systems) as either legitimate or fraudulent. …”
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Comparative analysis of the performance of mixing rules for density prediction of simple chemical mixtures by Babalola F. U., Akanji I. O., Oyegoke T.

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Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy by Chenlong Fan, Ying Liu, Tao Cui, Mengmeng Qiao, Yang Yu, Weijun Xie, Yuping Huang

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Eigenvector biomarker for prediction of epileptogenic zones and surgical success from interictal data by Sayantika Roy, Armelle Varillas, Armelle Varillas, Emily A. Pereira, Patrick Myers, Patrick Myers, Golnoosh Kamali, Kristin M. Gunnarsdottir, Kristin M. Gunnarsdottir, Nathan E. Crone, Adam G. Rouse, Jennifer J. Cheng, Michael J. Kinsman, Patrick Landazuri, Patrick Landazuri, Utku Uysal, Carol M. Ulloa, Nathaniel Cameron, Sara Inati, Kareem A. Zaghloul, Varina L. Boerwinkle, Sarah Wyckoff, Niravkumar Barot, Jorge González-Martínez, Joon Y. Kang, Sridevi V. Sarma, Sridevi V. Sarma

“…However, one limitation of the SSI is that it is computed heuristically from the parameters of dynamical network models (DNMs).Methods:In this work, we propose a formal method for detecting sink regions from DNMs, which has a strong foundation in linear systems theory. …”
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Phenotype augmentation using generative AI for isocitrate dehydrogenase mutation prediction in glioma by Ha Kyung Jung, Changyong Choi, Ji Eun Park, Seo Young Park, Jae Ho Lee, Namkug Kim, Ho Sung Kim

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ML-Driven Alzheimer’s disease prediction: A deep ensemble modeling approach by Mustafa Lateef Fadhil Jumaili, Emrullah Sonuç

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Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete by Omobolaji Opafola, Abisola Olayiwola, Ositola Osifeko, Adekunle David, Ajibola Oyedejı

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Predicted distribution of Metaparasitylenchus hypothenemi (Tylenchida: Allantonematidae), parasite of the coffee berry borer by Simota-Ruiz M., Castillo A., Cisneros-Hernández J., Carmona-Castro O.

“…Four species distribution models were generated for the Neotropical region with environmental variables for sites with parasite presence data, predicting a range of possible distribution with a high probability of occurrence in southeastern Mexico and southwestern Guatemala and a low probability in areas of Central and South America. …”
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External validation of the prediction model of intradialytic hypotension: a multicenter prospective cohort study by Yuhe Xiang, Guoting Ma, Qin Yang, Min Cao, Wenbin Xu, Lin Li, Qian Yang

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Artificial Neural Networks for Flood Prediction in Current and CMIP6 Climate Change Scenarios by Abderraman R. Amorim Brandão, Dimaghi Schwamback, Frederico C. M. deMenezes Filho, Paulo T. S. Oliveira, Maria Clara Fava

Subjects: “…climate change models…”
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Topological analysis and predictive modeling of amino acid structures with implications for bioinformatics and structural biology by Huili Li, Anisa Naeem, Shamaila Yousaf, Adnan Aslam, Fairouz Tchier, Keneni Abera Tola

“…The findings reveal novel insights into the structural determinants of amino acid properties and present efficient predictive models for various attributes. This research contributes towards better understanding amino acid structures and offers practical applications in bioinformatics, drug design, and structural biology, enhancing the ability to manipulate and comprehend the molecular world.…”
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Can a Non-Destructive Method Predict the Leaf Area of Species in the Caatinga Biome? by Toshik Iarley da Silva, João Everthon da Silva Ribeiro, Thainan Sipriano dos Santos, Marcos Roberto Santos Correia, Maria Carolina Borges de Oliveira Ribeiro, Allysson Jonhnny Torres Mendonça, Antonio Gideilson Correia da Silva, Pablo Henrique de Almeida Oliveira, Ester dos Santos Coêlho, Aurélio Paes Barros Júnior, Elania Freire da Silva, Alfredo Emilio Rubio-Casal, João L. M. P. de Lima, Thieres George Freire da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim

“…The best equations for all species were constructed using linear and power models, which were indicated for accurate prediction of leaf area. …”
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Predictive value of ASTRAL score modified by triglyceride glucose index for prognosis in ischemic stroke by PEI Lu-lu, CHAI Yuan, YANG Jun-zhe, YANG Jun-zhe, XU Yu-ming, SONG Bo

“…Conclusions The ASTRAL score modified by the TyG index (ASTRAL‐TyG model) improved the predictive value for poor prognosis in ischemic stroke patients.…”
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Development of Mathematical Functions to Predict Deflection of Radial and Bias Tractor Tires on Rigid Surface by Firat Komekci, Adnan Degirmencioglu

“…The model selection was achieved by three different criteria and % differences between the measured and predicted data. …”
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High entropy alloy property predictions using a transformer-based language model by Spyros Kamnis, Konstantinos Delibasis

“…Abstract This study introduces a language transformer-based machine learning model to predict key mechanical properties of high-entropy alloys (HEAs), addressing the challenges due to their complex, multi-principal element compositions and limited experimental data. …”
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Construction of a model for predicting sensory attributes of cosmetic creams using instrumental parameters based on machine learning by He Jingru, Qian Xuedan, Huang Hu, Lin Bao, Zhang Jun, Zhang Chunxiao, Chen Yuyan

“…The results showed that K-Nearest Neighbors, AdaBoost, and LightGBM were the algorithms with the best performance for most sensory attributes, and the overall model achieved over 95% prediction accuracy for 80% of the sensory dimensions, demonstrating strong reproducibility and accuracy in the verification test, with predicted sensory scores closely aligning with actual values. …”
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Predicting CO2 adsorption in KOH-activated biochar using advanced machine learning techniques by Raouf Hassan, Alireza Baghban

“…Detailed analysis, utilizing the Taylor Diagram and performance metrics, confirmed that SVR and CatBoost models achieved the highest accuracy in predicting CO2 adsorption. …”
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A Comprehensive Study of Deep Learning Approaches for Predicting Reciprocal Traffic Dynamics and Climate Variability by Abrar Ali, Wadhah R. Baiee

“…Climate variability integrated into traffic models increases the prediction of long-term traffic trends by 12%, justifying the significance of the influence of climate factors in traffic management systems …”
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Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies by Nuray Vakitbilir, Abrar Islam, Alwyn Gomez, Kevin Y. Stein, Logan Froese, Tobias Bergmann, Amanjyot Singh Sainbhi, Davis McClarty, Rahul Raj, Frederick A. Zeiler

“…Ensemble learning techniques, which aggregate predictions from diverse models, further enhance predictive accuracy and robustness. …”
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From Data to Diagnosis: Machine Learning Revolutionizes Epidemiological Predictions by Abdul Aziz Abdul Rahman, Gowri Rajasekaran, Rathipriya Ramalingam, Abdelrhman Meero, Dhamodharavadhani Seetharaman

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