Snow Depth Extraction From Time‐Lapse Imagery Using a Keypoint Deep Learning Model by C. M. Breen, W. R. Currier, C. Vuyovich, Z. Miao, L. R. Prugh

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Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector by Ruixing Ming, Osama Mohamad, Nisreen Innab, Mohamed Hanafy

“…Addressing the pressing challenge of insurance fraud, which significantly impacts financial losses and trust within the insurance industry, this study introduces an innovative automated detection system utilizing ensemble machine learning (EML) algorithms. …”
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Methodology for Data Integration in 3D-HBIM Digital Models. Case Study: the Holy Chalice Chapel of Valencia Cathedral by Pablo Ariel Escudero, Concepción López González, Jorge Luis García Valldecabres

“…This phase involves the use of various machine learning algorithms, including Random Sample Consensus (RANSAC) and K-Means, for data classification. …”
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Optical Sensor-Based Approaches in Obesity Detection: A Literature Review of Gait Analysis, Pose Estimation, and Human Voxel Modeling by Sabrine Dhaouadi, Mohamed Moncef Ben Khelifa, Ala Balti, Pascale Duché

“…Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. …”
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Automatic identification and characteristics analysis of crack tips in rocks with prefabricated defects based on deep learning methods. by Mingtao Gao, Minhui Li, Lu Chen, Zihao Guo, Chengyang Guo, Liping Li, Changsen Bu

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Metaparameter optimized hybrid deep learning model for next generation cybersecurity in software defined networking environment by C. Labesh Kumar, Suresh Betam, Denis Pustokhin, E. Laxmi Lydia, Kanchan Bala, Rajanikanth Aluvalu, Bhawani Sankar Panigrahi

“…To ensure optimal performance of the CNN-BiGRU-AM model, hyperparameter tuning is performed by utilizing the seagull optimization algorithm (SOA) model to enhance the efficiency and robustness of the detection system. …”
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Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application by Eko Abdul Goffar, Rosa Eliviani, Lili Ayu Wulandhari

“…This study addresses the challenge of stunting by developing a predictive model using machine learning techniques to forecast stunting risks based on public health data. …”
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Research on fault localization method of valve area in converter station based on metric learning and knowledge reasoning by WEI Yun, LI Xianwei, ZHANG Yanlong, CAO Hui, XU Dan, QIU Junhong

“…A data dimensionality reduction network based on metric learning is constructed to achieve feature extraction of recorded electrical quantities by maximizing inter class distance and minimizing intra class distance. …”
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Multivariate Machine Learning Model Based on YOLOv8 for Traffic Flow Prediction in Intelligent Transportation Systems by Fukui Wu, Hanzhong Tan, Linfeng Zhang, Shuangbing Wen, Tao Hu

“…In conclusion, by integrating real-time data collection from cameras with the predictive advantages of machine learning models, this algorithm offers a more efficient and sustainable solution for urban traffic management.…”
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Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques by Joan D. Gonzalez-Franco, Alejandro Galaviz-Mosqueda, Salvador Villarreal-Reyes, Jose E. Lozano-Rizk, Raul Rivera-Rodriguez, Jose E. Gonzalez-Trejo, Alexei-Fedorovish Licea-Navarro, Jorge Lozoya-Arandia, Edgar A. Ibarra-Flores

“…Cardiovascular diseases stand as the leading cause of mortality worldwide, underscoring the urgent need for effective tools that enable early detection and monitoring of at-risk patients. This study combines Artificial Intelligence (AI) techniques—specifically the k-means clustering algorithm—alongside dimensionality reduction methods like Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) to identify patient groups with varying levels of heart attack risk. …”
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Community Clustering and Recommendation Toward Elderly Health Records: Probabilistic Representation Learning and Large Graph Search by Huijiong Ding

“…This approach enables more precise, efficient, and personalized healthcare interventions, wherein the key steps include: 1) Geometry-based feature selection from high-dimensional health data-This effectively reduces dimensionality while preserving discriminative health patterns. 2) Probabilistic multi-topic representations of individual health profiles-These latent space embeddings capture complex health-need interactions. 3) Graph-based community detection via shared clinical/behavioral traits-The algorithm identifies natural clusters with medical relevance. 4) Personalized intervention ranking by community relevance-This ensures tailored care plan recommendations for each subgroup. …”
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Ensemble Learning-Based Alzheimer’s Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images by Young Jae Huh, Jun-ha Park, Young Jae Kim, Kwang Gi Kim

“…In this research, we demonstrate that three machine learning algorithms, trained on an ensemble of electroencephalogram (EEG) and clock drawing test (CDT) feature data for an AD classification task, show improved AD detection accuracy compared to when either the EEG or CDT dataset is used independently. …”
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Machine learning unveils multiple Pauli blockades in the transport spectroscopy of bilayer graphene double-quantum dots by Ankan Mukherjee, Anuranan Das, Adil Anwar Khan, Bhaskaran Muralidharan

“…Leveraging our model to train a machine learning algorithm, we successfully develop an automated method for the real-time detection of multiple Pauli blockade regimes. …”
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Enhancing glaucoma diagnosis: Generative adversarial networks in synthesized imagery and classification with pretrained MobileNetV2 by I. Govindharaj, D. Santhakumar, K. Pugazharasi, S. Ravichandran, R. Vijaya Prabhu, J. Raja

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Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network by Binay Kumar Pandey, Digvijay Pandey, Mesfin Esayas Lelisho

“…Using the retrieved attributes, a Weighted Naive Bayes Classification (WNBC) detected masks in the images. Additionally, a deep neural network-based, the faster Region-Based Convolutional Neural Networks (R-CNN) algorithm combined with Adaptive Galactic Swarm Optimization (AGSO) is being used to identify appropriate and incorrect face mask wear in images, as well as to monitor social distancing among individuals in crowded areas. …”
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Machine learning modeling of cancer treatment-related cardiac events in breast cancer: utilizing dosiomics and radiomics by Sefika Dincer, Muge Akmansu, Oya Akyol

“…Radiomics and dosiomics were extracted using PyRadiomics. Machine learning models were optimized using the Tree-based Pipeline Optimization Tool (TPOT), identifying the gradient-boosted classification as the best-performing algorithm. …”
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Predicting diabetic peripheral neuropathy through advanced plantar pressure analysis: a machine learning approach by Mehewish Musheer Sheikh, Mamatha Balachandra, Narendra V. G., Arun G. Maiya

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deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria by Yao Xiao, Yan Zhang

“…In this study, we have developed a deep learning-based algorithm, deep-Sep, for quickly and precisely identifying selenoprotein genes in bacterial genomic sequences. …”
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Aboveground biomass estimation using multimodal remote sensing observations and machine learning in mixed temperate forest by Shashika Himandi Gardeye Lamahewage, Chandi Witharana, Rachel Riemann, Robert Fahey, Thomas Worthley

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TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks by Mohammed Baz

“…This paper introduces Tensor Visibility Graph-enhanced Attention Networks (TVGeAN), a novel graph autoencoder model specifically designed for MTS learning tasks. The underlying approach of TVGeAN is to combine the power of complex networks in representing time series as graphs with the strengths of Graph Neural Networks (GNNs) in learning from graph data. …”
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