Optimizing hybrid models for forest leaf and canopy trait mapping from EnMAP hyperspectral data with limited field samples by Nizom Farmonov, Susanne Walden, Eric Martinée, Christian Lampei, Mona Schreiber, Lars Opgenoorth, Anjaharinony Andry Ny Aina Rakotomalala, Tobias Müller, Nina Farwig, Stefan Pinkert, Lucy Saueressig, Annabell Rosemarie Wagner, Robert R. Junker, Jochem Verrelst, Jörg Bendix

“…Here, we use a combination of the PROSAIL-D leaf and canopy radiative transfer models (RTMs) and Gaussian Process Regression (GPR) optimized with Active Learning (AL) sampling to improve the retrieval accuracy while minimizing reliance on extensive in situ data. …”
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A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study by Kaihuan Zhou, Lian Qin, Yin Chen, Hanming Gao, Yicong Ling, Qianqian Qin, Chenglin Mou, Tao Qin, Junyu Lu

“…This study aimed to develop a machine learning (ML) model to predict the risk of ARDS in patients with sepsis using circulating immune cell parameters and other physiological data. …”
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An Integrated Model of Atom Search Optimization-Based Resonance Sparse Signal Decomposition and Cross-Validation SVM for Gearbox Fault Diagnosis by Fengfeng Bie, Yifan Wu, Ying Zhang, Jian Peng, Hongfei Zhu

“…To address these issues, this paper develops a novel resonance-based sparse signal decomposition (RSSD) fault diagnosis method combined with support vector machine (SVM). Based on the key of the decomposition parameters in the resonant sparse decomposition method, the influence of atom search optimization (ASO) on the quality factor in the resonance-based sparse signal decomposition method is primarily studied. …”
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Anaerobic digestion of a curious VFA complex feed for biomethane production; A study on ANN modeling optimized with genetic algorithm by Armin Rahimieh, Mohsen Nosrati, Seyed Morteza Zamir

“…This study uses ANN modeling as well as genetic algorithm optimization to explore and predict how these intermediates behave. …”
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A Model Predictive Control to Improve Grid Resilience by Joseph Young, David G. Wilson, Wayne Weaver, Rush D. Robinett

“…To accomplish this, the following article includes a description of a reduced order model (ROM) of an electric power grid based on a circuit model, an optimization formulation that describes the MPC, a collocation method for solving linear time-dependent differential algebraic equations (DAEs) that result from the ROM, and an overall strategy for iteratively refining the behavior of the MPC. …”
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Modeling the Dynamic Global Distribution of the Ring Current Oxygen Ions Using Artificial Neural Network Technique by Qiushuo Wang, Chao Yue, Jinxing Li, Jacob Bortnik, Donglai Ma, Chae‐Woo Jun

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Comparing traditional natural language processing and large language models for mental health status classification: a multi-model evaluation by Thomas Kallstenius, Andrea Johansson Capusan, Gerhard Andersson, Adam Williamson

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Advanced Machine Learning Methodology for Earthquake Magnitude Forecasting Using Comprehensive Seismic Data by Subhieh El-Salhi, Bashar Igried, Sari Awwad

“…Feature selection was performed using Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing, while ten machine learning models were implemented — ranging from Linear Regression and Decision Trees to Gradient Boosting, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) networks. …”
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Advanced internet of things enhanced activity recognition for disability people using deep learning model with nature-inspired optimization algorithms by Mohammed Maray

“…The EARDP-DLMNOA model mainly relies on improving the activity recognition model using advanced optimization algorithms. …”
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Mixed Cooperation MAC Protocol with Sleep Mechanism for Data Acquisition in Wireless Machine-to-Machine Networks by Yulei Zhao, Bing Du, Ning Ge

“…Furthermore, based on some reasonable assumptions, the proposed MS-CTDMA analysis model can be extended to the whole M2M network. Consequently, we propose an optimal source node selection strategy from the perspective of the relay node during its idle time, regarding the traffic load, residual energy, and channel state. …”
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Machine learning analysis of drug solubility via green approach to enhance drug solubility for poor soluble medications in continuous manufacturing by Ahmed A. Lahiq, Abdullah A. Alshehri, Shaker T. Alsharif

“…Abstract The development of continuous pharmaceutical manufacturing is crucial and can be analyzed via advanced computational models. Machine learning is a strong computational paradigm that can be integrated into a continuous process to enhance the drugs’ solubility and efficacy. …”
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Optimizing Air Pollution Forecasting Models Through Knowledge Distillation: A Novel GCN and TRANS_GRU Methodology for Indian Cities by Sudhir Kumar, Vaneet Kour, Ankit Raj, Tagru Tapung, Shivendu Mishra, Rajiv Misra, T. N. Singh

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Enhancing DDoS Attacks Mitigation Using Machine Learning and Blockchain-Based Mobile Edge Computing in IoT by Mahmoud Chaira, Abdelkader Belhenniche, Roman Chertovskih

“…We evaluate multiple machine learning models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Transformer architectures, and LightGBM, using the CICDDoS2019 dataset. …”
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Application of the VDI 2221 method in the design of 3D printer machines utilizing additive manufacturing technology by Hery Irwan, Muhammad Rusydi Fattah, Ryan Dana Gidion Tarigan, Fauzan Maulana Siddiq Aritonang, Edi Sumarya

“…The results identify the Cartesian model variant (Variant 1) as the optimal solution, selected based on functional performance, cost efficiency, and ease of assembly. …”
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Machine learning predicts improvement of functional outcomes in spinal cord injury patients after inpatient rehabilitation by Mohammad Rasoolinejad, Irene Say, Peter B. Wu, Xinran Liu, Yan Zhou, Yan Zhou, Nathan Zhang, Emily R. Rosario, Daniel C. Lu, Daniel C. Lu, Daniel C. Lu

“…The study also highlighted the superior ability of tree-based models to capture the complex, non-linear relationships between variables that impact recovery in SCI patients.DiscussionThis research underscores the potential of machine learning models to enhance the accuracy of outcome predictions in SCI rehabilitation. …”
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Enhanced anomaly network intrusion detection using an improved snow ablation optimizer with dimensionality reduction and hybrid deep learning model by Fatimah Alhayan, Asma Alshuhail, Ahmed Omer Ahmed Ismail, Othman Alrusaini, Sultan Alahmari, Abdulsamad Ebrahim Yahya, Sami Saad Albouq, Mutasim Al Sadig

“…Additionally, the classification is performed by employing the long short-term memory and autoencoder (LSTM–AE) model. Finally, the improved Snow Ablation Optimizer (ISAO) model optimally tunes the hyperparameters of the LSTM–AE model, leading to enhanced classification performance. …”
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Hybridization of deep learning models with crested porcupine optimizer algorithm-based cybersecurity detection on industrial IoT for smart city environments by Sarah A. Alzakari, Mohammed Aljebreen, Mashael M. Asiri, Wahida MANSOURI, Sultan Alahmari, Mohammed Alqahtani, Shaymaa Sorour, Wafi Bedewi

“…This study presents Cybersecurity using a Crested Porcupine Optimizer Algorithm with Hybrid Deep Learning Models (CCPOA-HDLM). …”
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Design of an Integrated Model Combining CycleGAN, PPO, and Vision Transformer for Adaptive Scene Rendering in the Metaverse by Durga Prasad Kavadi, Kailash Chandra Mishra, Sai Babu Veesam, Mudimela Madhusudhan, Palacharla Ravi Kumar, Pannangi Naresh, Yuvaraju Chinnam

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Urban flood hazard assessment using FLA-optimized boost algorithms in Ankara, Türkiye by Enes Gul

“…This approach is novel in that it incorporates FLA for hyperparameter optimization, marking a significant advancement in flood hazard modeling and achieving higher model accuracy and generalizability. …”
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A Novel Long Short-Term Memory Seq2Seq Model with Chaos-Based Optimization and Attention Mechanism for Enhanced Dam Deformation Prediction by Lei Wang, Jiajun Wang, Dawei Tong, Xiaoling Wang

“…The AOA optimizes the model’s learnable parameters by utilizing the distribution patterns of four mathematical operators, further enhanced by logistic and cubic mappings, to avoid local optima. …”
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