Graph neural network-based water contamination detection from community housing information by Raphael Anaadumba, Yigit Bozkurt, Connor Sullivan, Madhavi Pagare, Pradeep Kurup, Benyuan Liu, Mohammad Arif Ul Alam

“…Specifically, the GAT achieved an accuracy of 0.80, precision of 0.71, and recall of 0.93, outperforming XGBoost, a classical machine learning algorithm, which had an accuracy of 0.70, precision of 0.66, and recall of 0.67.Discussion: In addition to its predictive capabilities, the GAT model identifies key factors contributing to lead contamination, enabling more precise targeting of at-risk areas. …”
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MAD-RAPPEL: Mobility Aware Data Replacement And Prefetching Policy Enrooted LBS by Ajay K. Gupta, Udai Shanker

“…The cache replacement and invalidation module uses the next location prediction algorithm by the application of the mobility Markov model on mobile user’s trajectories. …”
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Radar Working State Recognition Based on Improved HPSO-BP by Huiqin Li, Yanling Li, Xuemei Wang, Zhe Xu, Xinli Yin

“…First, the model improves the HPSO algorithm through the nonlinear decreasing inertia weight by adding the deceleration factor and asynchronous learning factor. …”
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Field calibration of fine particulate matter low-cost sensors in a highly industrialized semi-arid conurbation by Mariana Villarreal-Marines, Michael Pérez-Rodríguez, Yasmany Mancilla, Gabriela Ortiz, Alberto Mendoza

“…When using rank-level confusion matrices, True Positive air quality classification of predicted PM2.5 levels by XGBoost rated between 71% and 88%.…”
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An Innovative Proposal for Developing a Dynamic Urban Growth Model Through Adaptive Vector Cellular Automata by Ahmet Emir Yakup, Ismail Ercument Ayazli

“…For the simulation phase, an adaptive VCA-based urban growth model was developed to predict LULC changes through to 2040. The results demonstrate that the proposed algorithm can achieve a satisfactory level of accuracy in modeling urban growth.…”
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From word models to executable models of signaling networks using automated assembly by Benjamin M Gyori, John A Bachman, Kartik Subramanian, Jeremy L Muhlich, Lucian Galescu, Peter K Sorger

“…Abstract Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. …”
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Probabilistic Routing Based on Two-Hop Information in Delay/Disruption Tolerant Networks by Xu Wang, Rongxi He, Bin Lin, Ying Wang

“…Probabilistic routing protocol using history of encounters and transitivity (PRoPHET) is an efficient history-based routing protocol specifically proposed for DTNs, which only utilizes the delivery predictability of one-hop neighbors to make a decision for message forwarding. …”
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Historical Reconstruction of Astronaut Cancer Risk: Context for Recent Solar Minima by Tony C. Slaba

“…., sunspot number records and radionuclide yields from ice cores and/or tree rings) combined with model predictions have also been used to infer that the Sun may be entering into a quiet period similar to the Dalton or Gleissberg minima when GCR fluences reached historically high levels. …”
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Machine learning discovery of the dielectric properties of strontium-containing condensed matter by Dongyang Huang, Jiaxing Fu, Chenghao Yu

“…Traditional theoretical predictions or first-principles calculations of dielectric constants are often challenging and require significant time and computational resources. …”
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An explainable and federated deep learning framework for skin cancer diagnosis. by Shuvo Biswas, Sajeeb Saha, Muhammad Shahin Uddin, Rafid Mostafiz

“…To ensure model interpretability, an XAI-based algorithm named Local Interpretable Model-Agnostic Explanations (LIME) was used to explain the predictions of the proposed framework. …”
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Comparison of Random Forest, XGBoost, and LightGBM Methods for the Human Development Index Classification by Yunna Mentari Indah, Rafika Aristawidya, Anwar Fitrianto, Erfiani Erfiani, L.M. Risman Dwi Jumansyah

“…The study applied these algorithms to analyze the most influential variables affecting HDI. …”
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Adaptive Kalman Filter Fusion Positioning Based on Wi-Fi and Vision by Shuxin Zhong, Li Cheng, Haiwen Yuan, Xuan Li

“…In the experimental section, the real trajectory data and the predicted trajectory data generated using different positioning methods are compared. …”
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Classifying software security requirements into confidentiality, integrity, and availability using machine learning approaches by Taghreed Bagies

“…Our results revealed that SVM with the sentence-transformer technique outperformed all classifiers by 87% accuracy in predicting a type of security dimension.…”
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COVID-19 Pandemic Forecasting Using CNN-LSTM: A Hybrid Approach by Zuhaira M. Zain, Nazik M. Alturki

“…Furthermore, we demonstrated that the suggested model produced satisfactory predicting results even with a small amount of data.…”
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Research of Large Inflow Angles BEMT-Based Analytical–Numerical Performance Evaluation Model by Carlos Sosa Henríquez, Martynas Lendraitis

“…Drawing upon the theoretical framework presented by Stahlhut, the algorithm enables a more complex understanding of proprotor dynamics, facilitating accurate predictions of the loads at each blade section. …”
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ANN-Based Maximum Power Tracking for a Grid-Synchronized Wind Turbine-Driven Doubly Fed Induction Generator Fed by Matrix Converter by Mohamed A. Alarabi, Sedat Sünter

“…To address this, an artificial neural network (ANN) is adopted to predict the optimal shaft speed, ensuring maximum power point tracking (MPPT) for a wind energy-driven DFIG connected to a matrix converter (MC). …”
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Unraveling the Drivers of ESG Performance in Chinese Firms: An Explainable Machine-Learning Approach by Hyojin Kim, Myounggu Lee

“…This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. We analyze comprehensive ESG data of 1608 Chinese listed companies over 13 years (2009–2021), integrating financial and non-financial determinants traditionally examined in isolation. …”
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A numerical framework for enhancing the seismic resilience of urban infrastructure in clayey basins using finite element modeling and AI optimization by Kaveh Dehghanian

“…AI-based sensitivity analysis has singled out key parameters influencing seismic resilience; real earthquake data validates model predictions. This study provides a systemic approach toward enhancing the seismic resilience of urban infrastructure in clayey basins, with information applicable for earthquake risk mitigation and infrastructure design.…”
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Optimized CNN-LSTM with hybrid metaheuristic approaches for solar radiation forecasting by İrem Fatma Şener, İhsan Tuğal

“…A hybrid ensemble approach is then proposed, integrating the predictions of the three optimized CNN-LSTM models to reduce error and enhance forecasting stability. …”
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A machine learning driven computationally efficient horse shoe shaped antenna design for internet of medical things. by Umhara Rasool Khan, Javaid A Sheikh, Aqib Junaid, Shazia Ashraf, Altaf A Balkhi

“…In this article, regression-based Machine learning (ML) techniques are used to facilitate the design of Horse shoe shaped patch antenna to predict the frequency of operation, radiation efficiency and Specific Absorption Rate (SAR) values to accelerate its design process for on-body applications. …”
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