Sql injection detection using Naïve Bayes classifier: A probabilistic approach for web application security by Lu Zhexi

“…This collection of attributes is employed to generate a feature vector that serves as the input for the Naive Bayes classification algorithms. The classifier is trained using a labeled dataset and then learns to distinguish between benign and malicious requests by assessing their computed probabilities. …”
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Determination of high-confidence germline genetic variants in next-generation sequencing through machine learning models: an approach to reduce the burden of orthogonal confirmatio... by Muqing Yan, Qiandong Zeng, Zhenxi Zhang, Patricia Okamoto, Stanley Letovsky, Angela Kenyon, Natalia Leach, Jennifer Reiner

“…Results WES variant calls from Genome in a Bottle (GIAB) cell lines and their associated quality features were used to train five different machine learning models to predict whether a variant was a true positive or false positive based on quality metrics. …”
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Enhancing the Performance of YOLOv9t Through a Knowledge Distillation Approach for Real-Time Detection of Bloomed Damask Roses in the Field by Farhad Fatehi, Hossein Bagherpour, Jafar Amiri Parian

“…Recent developments in deep learning algorithms, especially in convolutional models, have shown significant promise for object detection, highlighting strong possibilities for improving the efficiency of this process. …”
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Application of explainable machine learning for estimating direct and diffuse components of solar irradiance by Rial A. Rajagukguk, Hyunjin Lee

“…The present study introduces a novel separation approach for direct and diffuse irradiance, employing machine learning algorithms and utilizing data with a temporal resolution of 1 min. …”
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Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models by Marta Terrados-Cristos, Marina Diaz-Piloneta, Francisco Ortega-Fernández, Gemma Marta Martinez-Huerta, José Valeriano Alvarez-Cabal

“…Among the models tested, tree-based algorithms, particularly gradient boosting, provided the highest prediction accuracy (F1 score: 0.8673). …”
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Machine Learning-Based Ensemble Feature Selection and Nested Cross-Validation for miRNA Biomarker Discovery in Usher Syndrome by Rama Krishna Thelagathoti, Dinesh S. Chandel, Wesley A. Tom, Chao Jiang, Gary Krzyzanowski, Appolinaire Olou, M. Rohan Fernando

“…We employed ensemble feature selection techniques to select the top miRNAs appearing in at least three algorithms. Machine learning models were trained and tested using this subset, followed by validation on an independent 10% sample. …”
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MMPred: a tool to predict peptide mimicry events in MHC class II recognition by Filippo Guerri, Filippo Guerri, Valentin Junet, Valentin Junet, Judith Farrés, Xavier Daura, Xavier Daura, Xavier Daura

“…However, the tool is easily extendable to MHC class I predictions by incorporating pre-trained models from CNN-PepPred and NetMHCpan. To evaluate MMPred’s ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving i) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, ii) interpreting these predictions within a systems biology framework to identify potential functional links between the predicted autoantigens and pathophysiological pathways related to autoimmune diseases, and iii) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. …”
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Real-Time AI Posture Correction for Powerlifting Exercises Using YOLOv5 and MediaPipe by Yeong-Min Ko, Aziz Nasridinov, So-Hyun Park

“…This data is used to train machine learning and deep learning models for detailed posture classification and real-time feedback. …”
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Stock Price Prediction Using Machine Learning: Evidence from Pakistan Stock Exchange by Zafar Akhter, Dr. Hassan Raza

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Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study by Zheng Zhang, Jian Wu, Yi Duan, Linwei Liu, Yaru Liu, Jinghan Wang, Li Xiao, Zhifeng Gao

“…Background High intraoperative blood pressure variability (HIBPV) is significantly associated with postoperative adverse complications. …”
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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model's final outcome prediction.…”
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Identification method for wheel/rail tread defects based on integrated partial convolutional network by CHENG Xiang, HE Jing, ZHANG Changfan, JIA Lin

“…Given the difficulties associated with accurately detecting minor wheelset damages, an enhanced adaptive spatial feature fusion (E-ASFF) detection approach was introduced. …”
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Leveraging machine learning to identify determinants of zero utilization of maternal continuum of care in Ethiopia: Insights from SHAP analysis and the 2019 mini DHS. by Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem, Meron Asmamaw Alemayehu

“…The dataset was preprocessed and modeled using various machine learning algorithms through the PyCaret library, with lightGBM emerging as the best model after various models trained and evaluated based on classification performance metrics. …”
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An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data by Amena Mahmoud, Eiko Takaoka

“…We employed a feature selection process to identify the most relevant gene expressions associated with liver cancer. This approach reduced the dimensionality of the data while preserving crucial biological information. …”
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Semi-Supervised Learned Autoencoder for Classification of Events in Distributed Fibre Acoustic Sensors by Artem Kozmin, Oleg Kalashev, Alexey Chernenko, Alexey Redyuk

“…However, deploying these systems is challenging due to the high costs associated with dataset creation. Additionally, advanced signal processing algorithms are necessary for accurately determining the location and nature of detected events. …”
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Innovations in Proteomic Technologies and Artificial Neural Networks: Unlocking Milk Origin Identification by Achilleas Karamoutsios, Emmanouil D. Oikonomou, Chrysoula (Chrysa) Voidarou, Lampros Hatzizisis, Konstantina Fotou, Konstantina Nikolaou, Evangelia Gouva, Evangelia Gkiza, Nikolaos Giannakeas, Ioannis Skoufos, Athina Tzora

“…The current study presents an innovative approach utilising proteomics and neural networks to classify and distinguish bovine, ovine and caprine milk samples by employing advanced machine learning techniques; we developed a precise and reliable model capable of distinguishing the unique mass spectral signatures associated with each species. Our dataset includes a diverse range of mass spectra collected from milk samples after MALDI-TOF MS (Matrix-assisted laser desorption/ionization-time of flight mass spectrometry) analysis, which were used to train, validate, and test the neural network model. …”
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Performance prediction of radio frequency based negative ion source using fusion neural network model by Yu Gu, Chundong Hu, Yang Li, Yuwen Yang, Yahong Xie, Qinglong Cui, Yuanzhe Zhao

“…Notably, the theoretical foundations and associated algorithms of the model are not limited to this ion source. …”
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HoRNS-CNN model: an energy-efficient fully homomorphic residue number system convolutional neural network model for privacy-preserving classification of dyslexia neural-biomarkers by Opeyemi Lateef Usman, Ravie Chandren Muniyandi, Khairuddin Omar, Mazlyfarina Mohamad, Ayoade Akeem Owoade, Morufat Adebola Kareem

“…Abstract Recent advancements in cloud-based machine learning (ML) now allow for the rapid and remote identification of neural-biomarkers associated with common neuro-developmental disorders from neuroimaging datasets. …”
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Detection and monitoring of Melampsora spp. Damage in multiclonal poplar plantations coupling biophysical models and Sentinel-2 time series by Carlos Camino, Alexey Valero-Jorge, Erika García Lima, Ramón Álvarez, Pieter S.A. Beck, Flor Álvarez-Taboada

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Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods by Eylem Gul Ates, Gokcen Coban, Jale Karakaya

“…Various feature selection algorithms were applied to identify the most relevant features, which were then used to train and evaluate machine learning classification models. …”
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