An Efficient Random Forest Classifier for Detecting Malicious Docker Images in Docker Hub Repository by Maram Aldiabat, Qussai M. Yaseen, Qusai Abu Ein

“…Therefore, developing a machine learning classifier that effectively predicts and classifies whether a Docker image contains injected malicious behaviors is crucial as a proactive approach. …”
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Modelling Itasy Lake Water Quality by Long Short Term Memory (LSTM) using Landsat8 Data by Randrianiaina Jerry Jean Christien Frederick, Rakotonirina Rija Itokiana, Jean Robertin Rasoloariniaina, Fils Lahatra Razafindramisa

“…This work used The Long Short-Term Memory (LSTM) deep learning (DL) architecture to obtain models for modeling and predicting water quality parameters of Lake Itasy depending on the reflectance of Landsat8 OLI. …”
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Defect detection in textiles using back propagation neural classifier by Subrata Das, Amitabh Wahi, Suresh Jayaram

“…This paper presents a classification method to detect defects such as holes and thick places in knitted fabric by applying artificial neural network algorithm. The artificial neural network algorithms learn from the input data after successful training process, it predicts the nature of the unknown samples in very fast and accurate way. …”
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The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials by Vera Malheiro, Beatriz Santos, Ana Figueiras, Filipa Mascarenhas-Melo

“…AI has revolutionized drug discovery and development by enabling rapid and effective analysis of vast volumes of biological and chemical data during the identification of new therapeutic compounds. The algorithms developed can predict the efficacy, toxicity, and possible adverse effects of new drugs, optimize the steps involved in clinical trials, reduce associated time and costs, and facilitate the implementation of innovative drugs in the market, making it easier to develop precise therapies tailored to the individual genetic profile of patients. …”
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High-Performance Multi-Object Tracking for Autonomous Driving in Urban Scenarios With Heterogeneous Embedded Boards by Alessio Medaglini, Biagio Peccerillo, Sandro Bartolini

“…Common stages of Autonomous Driving systems are the identification of objects in the scene (Object Detection), and the ability to predict the evolution of the tracked objects’ states – usually, positions and velocities (Multi-Object Tracking). …”
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An Intelligent Technique for Android Malware Identification Using Fuzzy Rank-Based Fusion by Altyeb Taha, Ahmed Hamza Osman, Yakubu Suleiman Baguda

“…Second, the fuzzy rank-based fusion approach was employed to adaptively integrate the classification results obtained from the base machine learning algorithms. By leveraging rankings instead of explicit class labels, the proposed ANDFRF method reduces the impact of anomalies and noisy predictions, leading to more accurate ensemble outcomes. …”
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Combination of Feature Selection and Learning Methods for IoT Data Fusion by V. Sattari-Naeini, Zahra Parizi-Nejad

“…All the schemes consist of four stages, including preprocessingthe data set based on curve fitting, reducing the data dimension and identifying the most effective featuresets according to data correlation, training classification algorithms, and finally predicting new databased on classification algorithms. …”
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Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman, Maxime Leduc

“…Our findings showed that XGB and RF could predict alfalfa crop height with an R² of 0.79 and a mean absolute error (MAE) of around 4 cm Our findings indicated that SVR exhibited the lowest accuracy among the three algorithms tested, with R² of 0.69 and an MAE of 4.63 cm. …”
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FXLMS with Multiple Error Switching for Active Noise-Cancelling Casings by Krzysztof MAZUR, Stanisław WRONA, Anna CHRAPOŃSKA, Jarosław RZEPECKI, Marek PAWEŁCZYK

“…Passive noise reduction methods require thick and heavy barriers to be effective for low frequencies and those clasical ones are thus not suitable for reduction of low frequency noise generated by devices. …”
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Modified tree-based selection in hierarchical mixed-effect models with trees: A simulation study and real-data application by Asrirawan, Khairil Anwar Notodiputro, Budi Susetyo, Sachnaz Desta Oktarina

“…These methods utilize the classification and regression trees (CART) algorithm to select the best tree through a backfitting algorithm. …”
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Representation Learning of Multi-Spectral Earth Observation Time Series and Evaluation for Crop Type Classification by Andrea González-Ramírez, Clement Atzberger, Deni Torres-Roman, Josué López

“…To develop accurate solutions for RS-based applications, often supervised shallow/deep learning algorithms are used. However, such approaches usually require fixed-length inputs and large labeled datasets. …”
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Artificial Intelligence–Enabled ECG Screening for LVSD in LBBB by Hak Seung Lee, MD, Sooyeon Lee, MD, Sora Kang, MS, Ga In Han, MS, Ah-Hyun Yoo, MS, Jong-Hwan Jang, PhD, Yong-Yeon Jo, PhD, Jeong Min Son, MD, Min Sung Lee, MD, MS, Joon-myoung Kwon, MD, MS, Kyung-Hee Kim, MD, PhD

“…All models were externally validated on 1,334 ECGs from another hospital, with performance assessed by area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and predictive values. Results: In external validation, the transfer learning model achieved the highest AUROC (0.903; 95% CI: 0.887-0.918), closely followed by the general model (0.899; 95% CI: 0.883-0.915); the difference was not significant. …”
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Nondestructive estimation of leaf chlorophyll content in banana based on unmanned aerial vehicle hyperspectral images using image feature combination methods by Weiping Kong, Weiping Kong, Lingling Ma, Huichun Ye, Huichun Ye, Jingjing Wang, Chaojia Nie, Binbin Chen, Xianfeng Zhou, Wenjiang Huang, Zikun Fan

“…We concluded that the nonlinear Gaussian process regression model with the VIs and TFs-PC1 combination selected by maximal information coefficient as input achieved the highest accuracy in LCC prediction for banana, with the highest R2 of 0.776 and lowest RMSE of 2.04. …”
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A comprehensive investigation of the relationship between dietary fatty acid intake and preserved ratio impaired spirometry: multimethodology based on NHANES by Chenyuan Deng, Yu Jiang, Yuechun Lin, Hengrui Liang, Wei Wang, Jianxing He, Ying Huang

“…Subsequently, innovative implementation of the principal component analysis (PCA), Weighted Quantile Sum (WQS) regression, and Bayesian Kernel Machine Regression (BKMR) approaches were employed to assess the joint impact of the various intake of FAs, as well as total saturated, monounsaturated, and polyunsaturated FAs on PRISm. To facilitate the prediction of PRISm, six distinct machine learning algorithms were constructed, followed by the application of SHAP analysis to elucidate the contribution of individual predictors. …”
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Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation by Yoonsung Kwon, Asta Blazyte, Yeonsu Jeon, Yeo Jin Kim, Kyungwhan An, Sungwon Jeon, Hyojung Ryu, Dong-Hyun Shin, Jihye Ahn, Hyojin Um, Younghui Kang, Hyebin Bak, Byoung-Chul Kim, Semin Lee, Hyung-Tae Jung, Eun-Seok Shin, Jong Bhak

“…We further developed a robust diagnostic risk prediction system to classify anxiety disorders from healthy controls using the 17 biomarkers. …”
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Digital-twin driven alignment control method for marine shafting with air spring vibration isolation system by Song Liu, Liang Shi, Wei Xu, ZeChao Hu

“…First, we design a digital twin prediction model based on the neural network to describe the data mapping relationship between the air spring pressures and shafting alignment state. …”
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Generative AI for drug discovery and protein design: the next frontier in AI-driven molecular science by Uddalak Das

“…Generative artificial intelligence (AI) has emerged as a disruptive paradigm in molecular science, enabling algorithmic navigation and construction of chemical and proteomic spaces through data-driven modeling. …”
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Implementation of Machine Vision Methods for Cattle Detection and Activity Monitoring by Roman Bumbálek, Tomáš Zoubek, Jean de Dieu Marcel Ufitikirezi, Sandra Nicole Umurungi, Radim Stehlík, Zbyněk Havelka, Radim Kuneš, Petr Bartoš

“…It also focused on finding the optimal hyperparameter settings for training the model, as balancing prediction accuracy, training time, and computational demands is crucial for real-world implementation. …”
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Integrating Street View Images, Deep Learning, and sDNA for Evaluating University Campus Outdoor Public Spaces: A Focus on Restorative Benefits and Accessibility by Tingjin Wu, Deqing Lin, Yi Chen, Jinxiu Wu

“…On this basis, restorative benefit evaluation models were established, including the explanatory and predictive models. The explanatory model used Pearson’s correlation and multiple linear regression analysis to identify the key indicators affecting restorative benefits, and the predictive model used the XGBoost 1.7.3 algorithm to predict the restorative benefit scores on the campus scale. …”
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Prognostic Value of a Classification and Regression Tree Model in Patients with Open-Globe Injuries by Danica T. Esteban, MD, Karlo Marco D. Claudio, MD, Cheryl A. Arcinue, MD

“…Purposive sampling of hospital medical records was done to collect data from both in- and out-patient cases. The CART algorithm was utilized to determine the predicted visual outcome for each case, and the accuracy of prognostication was measured by computing for sensitivity, specificity, positive predictive value, and negative predictive value. …”
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