Clinical validation of an artificial intelligence algorithm for classifying tuberculosis and pulmonary findings in chest radiographs by Thiago Fellipe Ortiz de Camargo, Thiago Fellipe Ortiz de Camargo, Guilherme Alberto Sousa Ribeiro, Guilherme Alberto Sousa Ribeiro, Maria Carolina Bueno da Silva, Luan Oliveira da Silva, Pedro Paulo Teixeira e Silva Torres, Denise do Socorro da Silva Rodrigues, Mayler Olombrada Nunes de Santos, William Salibe Filho, Marcela Emer Egypto Rosa, Magdala de Araujo Novaes, Thiago Augusto Massarutto, Osvaldo Landi Junior, Elaine Yanata, Marcio Rodrigues da Cunha Reis, Gilberto Szarf, Pedro Vieira Santana Netto, Joselisa Peres Queiroz de Paiva

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Sentiment Analysis of the Issue of Eliminating the Independent Curriculum using the Naïve Bayes Classifier Algorithm by Ainul Haq Nurridha Warahmat Hidayat, Adhitia Erfina

“…The analysis is conducted using Python 3.12.3 in Google Colab with the Naïve Bayes Classifier algorithm. …”
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A clustering approach for classifying scholars based on publication performance using bibliometric data by Ali Pişirgen, Serhat Peker

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A science-based approach to classifying light vehicles in Europe: methodology and case studies by Lorenzo Laveneziana, Andres L. Marin, Dermot O’Brien, Matteo Prussi, Georgios Fontaras

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False-Alarm-Controllable Detection of Marine Small Targets via Improved Concave Hull Classifier by Sainan Shi, Jiajun Wang, Jie Wang, Tao Li

“…In the Doppler spectrum, RCV reflects fluctuation variation in high SCR cases and WPHR describes the intensity property after clutter suppression in low SCR cases. On the other hand, in 3D feature space, an improved concave hull classifier is developed to further shrink the decision region, where a fast two-stage parameter search is designed for low computational cost and accurate control of false alarm rate. …”
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Dynamic Classifier Auditing by Unsupervised Anomaly Detection Methods: An Application in Packaging Industry Predictive Maintenance by Fernando Mateo, Joan Vila-Francés, Emilio Soria-Olivas, Marcelino Martínez-Sober, Juan Gómez-Sanchis, Antonio José Serrano-López

“…By leveraging the classifier to condense sensor data and applying anomaly detection to its output, the system optimizes the decision reliability. …”
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Statistical classification methods for estimating sex based on five skull traits: A nonmetric assessment using 3D CT models by Yun taek Shim, Deog-Im Kim, Nahyun Aum, Seung gyu Choi, Young Seok Lee, Hyung Nam Koo, Yi-Suk Kim

“…Five cranial nonmetric traits for sex estimation for sex estimation are classified by score according to geometry. The population of origin is one of the factors influencing cranial nonmetric traits. …”
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CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES by abdullah alsraratee, Ahmed Al-Azawei

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Predictive modeling of pediatric drug-induced liver injury: Dynamic classifier selection with clustering analysis by Zixin Shi, Linjun Huang, Haolin Wang

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Early Detection of Fetal Health Conditions Using Machine Learning for Classifying Imbalanced Cardiotocographic Data by Irem Nazli, Ertugrul Korbeko, Seyma Dogru, Emin Kugu, Ozgur Koray Sahingoz

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Hybrid Feature Selection and Classifying Stages through Electrocardiogram (ECG) Signal for Heart Disease Prediction by Babu Kumar, Radhakrishnan Soundararajan, Kanimozhi Natesan, Roobini Maridhas Santhi

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Explainable deep learning-based meta-classifier approach for multi-label classification of retinal diseases by Md. Moniruzzaman Hemal, Suman Saha

“…To develop and train the models, we apply a transfer learning approach to several state-of-the-art deep learning models, including MobileNetV2, InceptionV3, NASNetMobile, DenseNet169, EfficientNetB4, DenseNet121, ConvNeXt, and Xception. …”
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Multi-CNN Deep Feature Fusion and Stacking Ensemble Classifier for Breast Ultrasound Lesion Classification by Kemal PANÇ, Sümeyye SEKMEN

“…While promising for clinical decision support, the model’s lower sensitivity for malignant lesions necessitates further refinement.…”
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Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia by Ewunate Assaye Kassaw, Ewunate Assaye Kassaw, Ashenafi Kibret Sendekie, Ashenafi Kibret Sendekie, Bekele Mulat Enyew, Biruk Beletew Abate, Biruk Beletew Abate

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Using a Bayesian network to classify time to return to sport based on football injury epidemiological data. by Kate K Y Yung, Paul P Y Wu, Karen Aus der Fünten, Anne Hecksteden, Tim Meyer

“…A total of twelve variables from three categories (player's characteristics and anthropometrics, match information and injury information) were included. …”
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Hyperspectral Data Can Classify Plant Functional Groups Within New Zealand Hill Farm Pasture by Thomas A. Cushnahan, Miles C. E. Grafton, Diane Pearson, Thiagarajah Ramilan

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Assessment of Body Morphometry to Classify Two Colombian Creole Pigs Using Statistical and Machine Learning Methods by Arcesio Salamanca-Carreño, Mauricio Vélez-Terranova, Pere M. Parés-Casanova, Paula A. Toalombo-Vargas, David E. Rangel-Pachón, Andrés F. Castillo-Pérez

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A hybrid approach to predicting and classifying dental impaction: integrating regularized regression and XG boost methods by Asok Mathew, Pradeep K. Yadalam, Ahmed Radeideh, Shrouk Hady, Rona Swed, Reyyan Cheema, Majd Mousa AL-Mohammad, Mohammed Alsaegh, SR Shetty

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Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java by Silfiana Lis Setyowati, Asyifah Qalbi, Rafika Aristawidya, Bagus Sartono, Aulia Rizki Firdawanti

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A Mean Weighted Squared Error-based Neural Classifier for Intelligent Pattern Recognition in Smart Grids by Mehdi Khashei, Mehrnaz Ahmadi, Fatemeh Chahkoutahi

“…In addition, numerical results of an extensively reviewed case study indicate that the proposed classifier can also yield more accurate results than some other well-known shallow intelligent classifiers such as support vector machine, random forest, and decision tree by 2.72%, 3.34%, and 3.82% improvement, respectively. …”
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