Using a Hybrid of AI and Template-Based Method in Automatic Item Generation to Create Multiple-Choice Questions in Medical Education: Hybrid AIG by Yavuz Selim Kıyak, Andrzej A Kononowicz

“… Abstract BackgroundTemplate-based automatic item generation (AIG) is more efficient than traditional item writing but it still heavily relies on expert effort in model development. While nontemplate-based AIG, leveraging artificial intelligence (AI), offers efficiency, it faces accuracy challenges. …”
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Integrative machine learning and bioinformatics analysis to identify cellular senescence-related genes and potential therapeutic targets in ulcerative colitis and colorectal cancer by Tianle Xue, Yunpeng Chen, Xiaomeng Li, Zhixiang Zhou, Qiyang Chen

“…An integrated machine learning (IML) model—utilizing 12 algorithms with 10-fold cross-validation—was constructed to pinpoint key diagnostic biomarkers. …”
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Multi-crop plant counting and geolocation using a YOLO-Powered GUI System by Renato Herrig Furlanetto, Nathan Schawn Boyd, Ana Claudia Buzanini

“…Crop counting has traditionally relied on manual field assessments or complex machine learning algorithms, which often struggle to identify small objects or underestimate the total count of objects present in the field. …”
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An interpretable electrocardiogram-based model for predicting arrhythmia and ischemia in cardiovascular disease by Tanjila Alam Sathi, Rafsan Jany, Razia Zaman Ela, AKM Azad, Salem Ali Alyami, Md Azam Hossain, Iqram Hussain

“…Ischemia, due to reduced myocardial blood flow, can lead to sudden cardiac death, while arrhythmias, marked by abnormal heart rhythms, are common in the elderly. …”
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Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease by Daryll Philip, Rebecca Hodgkiss, Swarnima Kollampallath Radhakrishnan, Akshat Sinha, Animesh Acharjee

“…(CRC: n = 150, Healthy: n = 127) were subjected to three machine learning algorithms, eXtreme gradient boosting (XGBoost), Random Forest, and Least Absolute Shrinkage and Selection Operator (LASSO). …”
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Using correlative science, open access big data and ensemble machine learning to track contamination signals in the wild: A first landscape-scale prediction for the Himalayan vultu... by Dikpal Krishna Karmacharya, Ganesh Puri, Ganga Ram Regmi, Madan Krishna Suwal, Falk Huettmann

“…We used latest machine learning algorithms to obtain the best possible prediction for inference. …”
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Recent Advances in Resistive Gas Sensors: Fundamentals, Material and Device Design, and Intelligent Applications by Peiqingfeng Wang, Shusheng Xu, Xuerong Shi, Jiaqing Zhu, Haichao Xiong, Huimin Wen

“…Machine learning (ML) algorithms have enabled intelligent design of novel sensing materials, optimized multi-gas identification, and enhanced data reliability in complex environments. …”
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An integration of ensemble deep learning with hybrid optimization approaches for effective underwater object detection and classification model by G. Abirami, S. Nagadevi, J. D. Dorathi Jayaseeli, T. Prabhakara Rao, R S M Lakshmi Patibandla, Rajanikanth Aluvalu, K Srihari

“…In this manuscript, an Underwater Object Detection and Classification Utilizing the Ensemble Deep Learning Approach and Hybrid Optimization Algorithms (UODC-EDLHOA) technique is developed. The UODC-EDLHOA technique mainly detects and classifies underwater objects using advanced DL and hyperparameter models. …”
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Artificial intelligence for surgical outcome prediction in glaucoma: a systematic review by Zeena Kailani, Lauren Kim, Joshua Bierbrier, Michael Balas, David J. Mathew, David J. Mathew

“…Studies were included if they applied AI models to glaucoma surgery outcome prediction.ResultsSix studies met inclusion criteria, collectively analyzing 4,630 surgeries. A variety of algorithms were applied, including random forests, support vector machines, and neural networks. …”
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Sustainable financial infrastructure and governance: A fuzzy multi-criteria decision-making analysis of open service innovation in unstable economies by Homa Molavi, Lihong Zhang, Mohammad Khanbabaei

“…This includes highlighting the role of regulatory sandboxes in fostering innovation while ensuring compliance, the importance of data governance in maintaining trust and stability in the financial system, and the impact of AI implementation and AI-driven algorithms in driving open innovation within an unstable banking and economic system. …”
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Machine-Learning-Driven Approaches for Assessment, Delegation, and Optimization of Multi-Floor Building by Abtin Baghdadi, Harald Kloft

“…Extensive parametric studies reveal the dominant impact of core wall dimensions and floor number on structural efficiency, while the application of stiffness reduction factors (e.g., <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mn>11</mn></msub></semantics></math></inline-formula>) proves effective in reducing material without compromising performance. …”
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Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic by Muhammed Ballı, Asli Ercan Dogan, Sevin Hun Senol, Hale Yapici Eser

“…Data from 924 university students seeking mental health services were analyzed using seven machine learning algorithms. Suicidal ideation was assessed through the 9th item of the Patient Health Questionnaire-9. …”
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Early detection of woody plant encroachment in Canadian prairies using UAV imagery and transformer-based deep learning by Yihan Pu, Xiaojing Lu, Irini Soubry, Xulin Guo

“…Spatial resolution degradation (from 0.73 cm to 3 cm) reduced detection accuracy for small shrub patches (diameters ∼10–20 cm), while showing minimal impact on larger patches (diameters >1 m). …”
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A Hybrid Artificial Intelligence Approach for Down Syndrome Risk Prediction in First Trimester Screening by Emre Yalçın, Serpil Aslan, Mesut Toğaçar, Süleyman Cansun Demir

“…The final features are classified using machine learning algorithms, including Bagged Trees and Naive Bayes. …”
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Mathematical model for prediction of Tuberculosis in Nigeria using hybrid fractional differential equations and artificial neural network methods by Samson Linus Manu, Shikaa Samuel, Taparki Richard, Eshi Priebe Dovi

“…Training of the NN involves minimizing a loss function combining data fit and system constraints, optimized using the Adam and L-BFGS algorithms, achieving a high degree of accuracy with an MSE of 6.005×10−6. …”
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Clinical applications of photon-counting CT in neuroimaging by LÜ Haiying, LU Yong, HE Naying

“…Photon-counting computed tomography (PCCT) is a next-generation CT imaging technology that markedly improves image quality while reducing radiation dose and image noise through single-photon detection and energy discrimination by photon detectors. …”
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Grey Wolf Optimization- and Particle Swarm Optimization-Based PD/I Controllers and DC/DC Buck Converters Designed for PEM Fuel Cell-Powered Quadrotor by Habibe Gursoy Demir

“…Both optimizers work together in the system and try to minimize tracking errors while also minimizing power consumption by using suitable objective functions. …”
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Dual-hybrid intrusion detection system to detect False Data Injection in smart grids. by Saad Hammood Mohammed, Mandeep S Jit Singh, Abdulmajeed Al-Jumaily, Mohammad Tariqul Islam, Md Shabiul Islam, Abdulmajeed M Alenezi, Mohamed S Soliman

“…Recall is enhanced by capturing critical features relevant to all attack types, while precision is improved by reducing false positives, leading to fewer unnecessary interventions. …”
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A Machine Learning Based Framework for a Stage-Wise Classification of Date Palm White Scale Disease by Abdelaaziz Hessane, Ahmed El Youssefi, Yousef Farhaoui, Badraddine Aghoutane, Fatima Amounas

“…To classify the WSD into its four classes (healthy, low infestation degree, medium infestation degree, and high infestation degree), two types of ML algorithms were tested; classical machine learning methods, namely, support vector machine (SVM) and k-nearest neighbors (KNN), and ensemble learning methods such as random forest (RF) and light gradient boosting machine (LightGBM). …”
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3D anatomical digital twins: New generation virtual models to navigate robotic partial nephrectomy by Daniele Amparore, Alberto Piana, Federico Piramide, Sabrina De Cillis, Enrico Checcucci, Cristian Fiori, Francesco Porpiglia

“…Textures are created from intraoperative robotic surgery images using machine learning algorithms. The result is a 3D model ‐ digital twin that replicates the organ's shape and appearance. …”
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