Advances in the pilot point inverse method: Où En Sommes-Nous maintenant? by White, Jeremy, Lavenue, Marsh

“…The paper ends with newly developed applications of the PPM, given modern machine learning capabilities, and some foreshadowing as to where the PPM might evolve.…”
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Integrated Platform for Bio-Image Processing and Identification of Cervical Cancer by Larisa-Dalina LAZĂR, Diogen BABUC, Todor IVAŞCU

“…Following this, the cervical cells are segmented and classified using a combination of machine learning algorithms, such as shallow machine learning models and Convolutional Neural Networks (CNN). …”
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A Data-Driven Analysis of Electric Vehicle Adoption Barriers in the Philippines: Combining SEM and ANNs by Charmine Sheena R. Saflor, Klint Allen Mariñas, Ma. Janice Gumasing, Jazmin Tangsoc

“…This study investigates the factors influencing the adoption of electric vehicles in the Philippines, focusing on key barriers through an integrated approach using machine learning and structural equation modeling (SEM). …”
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Synergistic Integration of Edge Computing and 6G Networks for Real-Time IoT Applications by Ahmed M. Alwakeel

“…Our comprehensive methodology involves collecting and preprocessing data from IoT devices, extracting relevant features, predicting resource demand, optimizing task offloading, and continuously monitoring and adjusting resource allocation using advanced machine learning techniques. The results demonstrate significant improvements, including a 47% reduction in average latency, a 40% decrease in total energy consumption, and a 20% increase in resource utilization. …”
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A Method to Detect Concealed Damage in Concrete Tunnels Using a Radar Feature Vector and Bayesian Analysis of Ground-Penetrating Radar Data by Junfang Wang, Heng Chen, Jianfu Lin, Xiangxiong Li

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Elevation Error Prediction of Continuous Beam Cantilever Construction Phase Based on LS-SVM by Xilong Zheng, Yunfeng Hu, Baitao Sun

“…I compare the Least Squares-Support Vector Machines (LS-SVM) prediction value with the measured value, the SVM model predictions, the BP neural network model predictions and the dimensionality reduction model predictions, so that predict elevation errors during cantilever construction phases by established models. …”
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From bites to bytes: understanding how and why individual malaria risk varies using artificial intelligence and causal inference by Adèle Helena Ribeiro, Júlia M. P. Soler, Rodrigo M. Corder, Marcelo U. Ferreira, Marcelo U. Ferreira, Dominik Heider

“…This work shows how integrating Artificial Intelligence (AI), Machine Learning (ML), and Causal Inference can advance malaria research by identifying context-specific risk factors, uncovering causal mechanisms, and informing more effective, targeted interventions. …”
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Optimizing EEG - based Emotion Recognition with a multi-modal ensemble approach by Kavitha K V, L R Sudha, J S Jayasudha

“…These advancements make a significant contribution to EEG-based emotion recognition, facilitating more accurate mental health diagnostics, improved BCI applications, and enhanced human–machine interactions. The findings underscore the potential of ensemble deep learning models in addressing complex classification tasks where precision is critical.…”
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Software Package for Optimization of Burner Devices on Dispersed Working Fluids by Ruslan V. Fedorov, Igor I. Shepelev, Mariia A. Malyoshina, Dmitry A. Generalov, Vyacheslav V. Sherkunov, Valeriy V. Sapunov

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Sparse Feature-Weighted Double Laplacian Rank Constraint Non-Negative Matrix Factorization for Image Clustering by Hu Ma, Ziping Ma, Huirong Li, Jingyu Wang

“…As an extension of non-negative matrix factorization (NMF), graph-regularized non-negative matrix factorization (GNMF) has been widely applied in data mining and machine learning, particularly for tasks such as clustering and feature selection. …”
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Integrated GBR–NSGA-II Optimization Framework for Sustainable Utilization of Steel Slag in Road Base Layers by Merve Akbas

“…This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. …”
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Beyond condoms: unpacking the complex web of stigma, cost and gender disparities in STI healthcare-seeking among high-risk subgroups in Sub-Saharan Africa by Joseph Opeolu Ashaolu, Steven Ogunbola, Kehinde R. Odewumi, Oluwayemisi Bukola Tanimowo, Sylvain Y. M. Some

“…Equitable intervention strategies using machine learning (XGBoost, Random Forest, Logistic Regression) on a retrospective database (N=400), from Ekiti State Teaching Hospital, Nigeria (January–October 2024) was modelled. …”
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Do indigenous people get left behind? An innovative methodology for measuring the unmeasurable economic conditions and poverty from the poorest region of Luzon, Philippines by Emmanuel A. Onsay, Jomar F. Rabajante

“…This work puts forth fresh approaches to quantify the incalculable multifaceted poverty and socioeconomic conditions: (i) a thorough statistical analysis using diagnostic and descriptive analytics to examine socioeconomic situations; (ii) combining sophisticated econometrics and predictive analytics to measure multidimensional poverty; and (iii) integrating machine learning to model socioeconomic situations and prescriptive analytics to develop policy. …”
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Development and validation of a LASSO logistic regression based nomogram for predicting live births in women with polycystic ovary syndrome: a retrospective cohort study by Yue Liu, Jingshu Gao, Hang Ge, Hang Ge, Jiaxing Feng, Yu Wang, Xiaoke Wu, Xiaoke Wu

“…The mean-filling method was used to address missing data, and Lasso-Logistic regression was combined with machine learning models to identify the most significant predictors of live births. …”
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Impacts of Spatial Expansion of Urban and Rural Construction on Typhoon-Directed Economic Losses: Should Land Use Data Be Included in the Assessment? by Siyi Zhou, Zikai Zhao, Jiayue Hu, Fengbao Liu, Kunyuan Zheng

“…Results demonstrate three key findings: (1) By introducing prototype learning, a meta-learning approach, to guide model updates, we achieved precise assessments with small training samples, attaining an MAE of 1.02, representing 58.5–76.1% error reduction compared to conventional machine learning algorithms. …”
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Copper Stress Levels Classification in Oilseed Rape Using Deep Residual Networks and Hyperspectral False-Color Images by Yifei Peng, Jun Sun, Zhentao Cai, Lei Shi, Xiaohong Wu, Chunxia Dai, Yubin Xie

“…The classification performance of models based on 1D spectral sequences was compared from two perspectives: first, between machine learning and deep learning methods (best accuracy: 93.49% vs. 96.69%); and second, between shallow and deep convolutional neural networks (CNNs) (best accuracy: 95.15% vs. 96.69%). …”
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Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms by Xia Zhao, Wei Chen, Paraskevas Tsangaratos, Ioanna Ilia, Qingfeng He

“…Optimization of landslide susceptibility model driven by geological environment: a key challenge for disaster reduction in mountainous areas. …”
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Advancing Marine Surveillance: A Hybrid Approach of Physics Infused Neural Network for Enhanced Vessel Tracking Using Automatic Identification System Data by Tasmiah Haque, Md Asif Bin Syed, Srinjoy Das, Imtiaz Ahmed

“…Recognizing the strengths and limitations of the LSTM model, we propose a hybrid machine-learning algorithm that integrates LSTM with a physics-based model. …”
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Automatic 3D Reconstruction: Mesh Extraction Based on Gaussian Splatting from Romanesque–Mudéjar Churches by Nelson Montas-Laracuente, Emilio Delgado Martos, Carlos Pesqueira-Calvo, Giovanni Intra Sidola, Ana Maitín, Alberto Nogales, Álvaro José García-Tejedor

“…It reviews recent advancements in machine learning and deep learning—particularly neural radiance fields (NeRFs) and its successor, Gaussian splatting (GS)—as state-of-the-art techniques in the domain. …”
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Uncovering Key Factors That Drive the Impressions of Online Emerging Technology Narratives by Lowri Williams, Eirini Anthi, Pete Burnap

“…Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. …”
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