The development trend of China’s marine economy: a predictive analysis based on industry level by Yu Chen, Huahan Zhang, Lingling Pei

“…First, the results from multi-step experiments demonstrate that the CFGM model significantly outperforms traditional statistical, machine learning models, and grey models in the context of the sectoral added value predictions, with an average accuracy improvement of 32.14%. …”
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Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects by Ma Yuhan

“…With the widespread application of Artificial Intelligence (AI) and Machine Learning (ML) in medical field, early diagnosis of brain tumors has become increasingly significant. …”
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An Improved Feature Selection Based on Effective Range for Classification by Jianzhong Wang, Shuang Zhou, Yugen Yi, Jun Kong

“…Feature selection is a key issue in the domain of machine learning and related fields. The results of feature selection can directly affect the classifier’s classification accuracy and generalization performance. …”
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Analysis on the Particularity of Higher Education Subject Development under the Background of Artificial Intelligence by Yancai Wang

“…The subject development quality is evaluated using machine learning (ML) and artificial intelligence (AI) techniques. …”
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Technology adoption and extreme stock risk: Evidence from digital tax reform in China by Kangqi Jiang, Xiaofeng Chen, Jiayun Li, Mengling Zhou

“…After performing parallel trend test, dealing with endogeneity concerns, and estimating the double machine learning model, we confirm baseline findings again. …”
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Improving person re-identification based on two-stage training of convolutional neural networks and augmentation by S. A. Ihnatsyeva, R. P. Bohush

“…The main goal is to improve person re-identification accuracy in distributed video surveillance systems.Methods. Machine learning methods are applied.Result. A technology for two-stage training of convolutional neural networks (CNN) is presented, characterized by the use of image augmentation for the preliminary stage and fine tuning of weight coefficients based on the original images set for training. …”
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Innovative capacity building strategies for sustainable disaster risk management: A systematic review, conceptual framework, and future research directions by Saputra Nika, Ekha Putera Roni, Zetra Aidinil, Azwar

“…Findings show that advancements in machine learning, Geographic Information Systems (GIS), and Information and Communication Technologies (ICT) significantly enhance DRM, particularly in risk assessment, early warning systems, and disaster response. …”
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Bi-directional Pre-trained Network for Single-station Seismic Waveform Analysis by Yuqi CAI, Ziye YU, Weitao WANG, Yanru AN, Lu LI

“…The application of machine learning, particularly deep learning methods, is becoming increasingly widespread in seismology, achieving near-human accuracy in tasks such as phase detection and event classification. …”
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HYEI: A New Hybrid Evolutionary Imperialist Competitive Algorithm for Fuzzy Knowledge Discovery by D. Jalal Nouri, M. Saniee Abadeh, F. Ghareh Mohammadi

“…The performance of HYEI has been evaluated by using several benchmark datasets from the UCI machine learning repository. The classification accuracy attained by the proposed algorithm has the highest classification accuracy in 6 out of the 7 dataset problems and is comparative to the classification accuracy of the 5 other test problems, as compared to the best results previously published.…”
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Exploring α−ψ−ϕ contractive mapping: novel fixed point theorems in complete b-metric spaces [version 2; peer review: 2 approved, 1 approved with reservations] by Maysoon Qousini, Berhanu Seboka, Fikadu Tesgera Tolasa, Gudeta Hanchalu, Nasir Ali, Tamene Raji

“…These findings provide deeper insights into the structure of b-metric spaces and open avenues for further applications in fields such as optimization and machine learning.…”
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Multi-Objective Optimization of Injection Parameters and Energy Consumption Based on ANN-Differential Evolution by Devic Oktora, Yu-Hung Ting, Sukoyo

“…Unlike classic linear modeling techniques like simple regression, many machine learning (ML) models have the ability to adjust to the nonlinear behaviors and interactions between input and output parameters. …”
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GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism by Burak Gülmez

“…Results demonstrate that GA-Attention-Fuzzy-Stock-Net consistently outperforms traditional machine learning approaches and baseline models across different evaluation metrics (MSE, MAE, MAPE, R2). …”
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Fast Local Laplacian-Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks by Nhat-Duc Hoang, Quoc-Lam Nguyen

“…This study proposes an alternative for automating the periodic surveys of pavement condition by means of image processing and machine learning. Advanced image processing techniques including fast local Laplacian filter, Sobel filter, steerable filter, and projection integral are employed for image enhancement and analysis to extract useful features from digital images. …”
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A Novel Multi-Level Perceptron for Accurate Heart Stroke Diagnosis by Muhammad Zaman, Muhammad Khubaib, Tanzila Kahkashan, Anam Zahoor, Narges Shahbaz, Shahzad Shoukat, Fahma Nisar

“…Different types of Machine Learning and deep learning algorithms are used for heart stroke predictions. …”
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An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games by Wang, Guillaume, Chizat, Lénaïc

“…This problem arises for example in game-theory-inspired machine learning applications, such as distributionally-robust learning. …”
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Cognitive Driven Multilayer Self-Paced Learning with Misclassified Samples by Qi Zhu, Ning Yuan, Donghai Guan

“…In recent years, self-paced learning (SPL) has attracted much attention due to its improvement to nonconvex optimization based machine learning algorithms. As a methodology introduced from human learning, SPL dynamically evaluates the learning difficulty of each sample and provides the weighted learning model against the negative effects from hard-learning samples. …”
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Trivariate Stochastic Weather Model for Predicting Maize Yield by Patrick Chidzalo, Phillip O. Ngare, Joseph K. Mung’atu

“…Its aggregated values predict maize yield with R2 up to 0.9765 and MAPE=1.94% under common machine learning algorithms. Comparatively, the R2 is 0.8829% and MAPE=4.18%, under the maize yield SDE. …”
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Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models by Laurentiu Fara, Alexandru Diaconu, Dan Craciunescu, Silvian Fara

“…This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. The main contributions of the authors could be synthetized as follows: (1) analysis and discussion of the experimental and simulated results regarding solar radiation forecast, as well as energy production prediction and forecasting based on ARIMA and ANN models for two case studies: (a) laboratory BIPV system developed at the Polytechnic University of Bucharest and (b) large PV park placed in a specific site of the south of Romania. …”
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Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review. by Malontema Katchali, Edward Richard, Henri E Z Tonnang, Chrysantus M Tanga, Dennis Beesigamukama, Kennedy Senagi

“…Mathematical models such as simulation, regression, dynamics, and kinetics have been applied while computational data driven machine learning models such as random forest, support vector machines, gradient boosting, and artificial neural networks have also been applied as well. …”
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Enhancing urban air quality prediction using time-based-spatial forecasting framework by Shrikar Jayaraman, Nathezhtha T, Abirami S, Sakthivel G

“…The proposed Time-Based-Spatial (TBS) forecasting framework is integrated with spatial and temporal information using machine learning techniques on data collected from a wide range of cities. …”
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