Leveraging machine learning for sustainable solid waste management: A global perspective by Ajaya Subedi, Sahil Shrestha, Anish Ghimire, Shukra Raj Paudel

“…ML models offer promising solutions for projecting waste composition and generation trends while optimizing resource distribution by analyzing key influential factors. …”
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Genomic Selection in Alfalfa Across Multiple Ploidy Levels: A Comparative Study Using Machine Learning and Bayesian Methods by Xiaoyue Zhu, Ruixin Zhang, Tianxiang Zhang, Changhong Guo, Yongjun Shu

“…A total of 11 Bayesian and machine learning models and nine different reference genomes were used to conduct genomic selection on five traits in 385 alfalfa accessions. …”
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Optimizing Heart Disease Prediction: A Comparative Analysis of Tree-Based Ensembles With Feature Expansion and Selection by K. Aswini, Kriti Arya

“…This study examines the efficacy of tree-based ensemble machine learning models that have been improved using Feature Expansion and Selection (FES-EM). …”
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Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods by Bhaktishree Nayak, Pallavi Nayak

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Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security by Harshala Shingne, Diptee Chikmurge, Priya Parkhi, Poorva Agrawal

“…This work addresses these challenges by proposing the integration of AI and machine learning optimization techniques into quantum communication protocols to enhance both security and efficiency. …”
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Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia by Yi Yang, Yao Dong, Yanhua Chen, Caihong Li

“…In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO) based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and support vector machine (SVM) to improve the forecasting accuracy. …”
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Improving DOA Estimation via an Optimal Deep Residual Neural Network Classifier on Uniform Linear Arrays by Haya Al Kassir, Nikolaos V. Kantartzis, Pavlos I. Lazaridis, Panagiotis Sarigiannidis, Sotirios K. Goudos, Christos G. Christodoulou, Zaharias D. Zaharis

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Generalizable Solar Irradiance Prediction for Battery Operation Optimization in IoT-Based Microgrid Environments by Ray Colucci, Imad Mahgoub

“…The objective of this paper is to accurately predict the solar irradiance for battery operation optimization in microgrids. Using satellite data from weather sensors, we trained machine learning models to enhance solar irradiance predictions. …”
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A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm by G. Ramkumar, Satyajeet Sahoo, T. M. Amirthalakshmi, S. Ramesh, R. Thandaiah Prabu, Kasipandian Kasirajan, Antony V. Samrot, A. Ranjith

“…This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. …”
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Snow depth estimation in Northeast China based on space-borne scatterometer data and ML model with optimal features by Wenfei Chen, Lingjia Gu, Xiaofeng Li, Xintong Fan

“…This study explores its application to SD estimation in Northeast China. Multiple machine learning (ML) models, including support vector regression (SVR), k-nearest neighbors (KNN), XGBoost, and random forest (RF), were deployed and contrasted for SD estimation. …”
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Optimization of engine parameters and emission profiles through bio-additives: Insights from ANFIS Modeling of Diesel Combustion by Abbas Rohani, Javad Zareei, Kourosh Ghadamkheir, Seyed Alireza Farkhondeh

“…Various machine learning configurations and training algorithms were employed to optimize the model's performance. …”
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Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm by Sandeep Samantaray, Abinash Sahoo, Falguni Baliarsingh

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Optimization of Offshore Saline Aquifer CO₂ Storage in Smeaheia Using Surrogate Reservoir Models by Behzad Amiri, Ashkan Jahanbani Ghahfarokhi, Vera Rocca, Cuthbert Shang Wui Ng

“…Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. …”
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Research on the Liquor Yield Prediction Model Integrating Hybrid Kernel Support Vector Regression and Dung Beetle Optimizer by Qinwen Deng, Yibo Xu, Qiang Han, Suyi Zhang, Xianguo Tuo

“…The swarm intelligence optimization model exhibits smaller prediction errors than traditional machine learning methods. …”
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Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models by Mehmet Tahir Ucar, Asim Kaygusuz

“…Afterwards, this study was carried out with 264 separate models produced using four architectures, 11 optimization methods, and six activation functions in order. …”
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Fine Resolution Mapping of Forest Soil Organic Carbon Based on Feature Selection and Machine Learning Algorithm by Yanan Li, Jing Li, Jun Tan, Tianyue Ma, Xingguang Yan, Zongyang Chen, Kunheng Li

“…The performance of Boruta and SHAP (SHapley Additive exPlanations) in optimizing feature selection was evaluated. Ultimately, the optimal machine learning model and feature selection method were applied to map the SOC distribution, with variable contributions quantified using SHAP. …”
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School-level prediction and management of myopia in children and adolescents by Shengsong Xu, Linling Li, Yingting Zhu, Zhenbang Ruan, Yiwen Qu, Zhuandi Zhou, Yin Hu, Zhidong Li, Fei Hou, Xiaohua Zhuo, Yunxia Leng, Xuelin Huang, Yamei Lu, Zhirong Wang, Shuifeng Deng, Yehong Zhuo, Guoming Zhang, Min Fu, Xiao Yang

“…In two years, the occurrence of myopia was 45.5% and the overall myopic shift was − 0.97 ± 1.32D. The optimal machine learning models were established for predicting myopia occurrence in non-myopic individuals; and predicting progression of any amount (defined as an annual progression of < − 0.25D) in myopic individuals. …”
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ANALYSIS OF THE APPLICATIONS OF THE DATA-DRIVEN APPROACH IN EVALUATING THE THERMAL-PHYSICAL PROPERTIES OF COMPOSITES by Ruslan Lavshchenko, Gennadiy Lvov

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Evaluation and Optimization Strategies for Forest Landscape Stability in Different Landform Types of the Loess Plateau by Mei Zhang, Peng Liu, Zhong Zhao

“…This study aims to develop a forest landscape stability assessment framework that integrates structure, function, and resilience to assess forest landscape stability under different landform types on the Loess Plateau, and to propose differentiated optimization strategies. Remote sensing images and ground survey data were combined to compare the effectiveness of different machine learning models in aboveground biomass (AGB) inversion. …”
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Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Ibrahim Elbatal, Ehab M. Almetwally, Marwa M. Eid, El-Sayed M. El-Kenawy

“…Experimental results also demonstrate that the proposed NiOA-DPRNNs framework gets the highest value of R2 (0.9736), lowest error rates and fitness values than other existing models and optimization methods. From the Wilcoxon and ANOVA analyses, one can approve the specificity and consistency of the findings. …”
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