Integration of deep learning and railway big data for environmental risk prediction models and analysis of their limitations by Liuhui Quan, Minjie Wang, Lyu Baihang, Zhang Ziwen, Zhang Ziwen, Zhang Ziwen

“…To address these gaps, we propose a novel framework leveraging deep learning techniques tailored to railway big data. Our method integrates temporal encoders and spatial graph neural networks, combined with domain-specific knowledge and contextual awareness, to achieve robust anomaly detection, predictive maintenance, and passenger demand forecasting. …”
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The role of computational intelligence techniques in the advancements of solar photovoltaic systems for sustainable development: a review by Ranganai Tawanda Moyo, Mendon Dewa

“…The study includes the involvement of CI techniques for parameter identification of solar cells, PV system sizing, maximum power point tracking (MPPT), forecasting, fault detection and diagnosis, inverter control and solar tracking systems. …”
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Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar by Raouf Hassan, Mohammad Reza Kazemi

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A Pilot Study on Single-Cell Raman Spectroscopy Combined with Machine Learning for Phenotypic Characterization of <i>Staphylococcus aureus</i> by Li Liu, Junjing Xue, Yang Song, Taijie Zhan, Yang Liu, Xiaohui Song, Li Mei, Duochun Wang, Yu Vincent Fu, Qiang Wei

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Estimating Snow Coverage Percentage on Solar Panels Using Drone Imagery and Machine Learning for Enhanced Energy Efficiency by Ashraf Saleem, Ali Awad, Amna Mazen, Zoe Mazurkiewicz, Ana Dyreson

“…While prior studies have explored snow detection using fixed-camera setups, these methods suffer from scalability limitations, stationary viewpoints, and the need for reference images. …”
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Robust development of data-driven models for methane and hydrogen mixture solubility in brine by Kashif Saleem, Abhinav Kumar, K. D. V. Prasad, Ahmad Alkhayyat, T. Ramachandran, Protyay Dey, Navdeep Kaur, R. Sivaranjani, I. B. Sapaev, Mehrdad Mottaghi

“…An outlier detection method is utilized for checking out the data reliability for the model development. …”
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Deep learning-based crop health enhancement through early disease prediction by Venkata Santhosh Yakkala, Krishna Vamsi Nusimala, Badisa Gayathri, Sriya Kanamarlapudi, S. S. Aravinth, Ayodeji Olalekan Salau, S. Srithar

“…Manual disease detection methods currently in use are laborious, time-intensive, and heavily reliant on specialized knowledge. …”
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Condition monitoring of heterogeneous landslide deformation in spatio-temporal domain using advanced graph attention network by Huajin Li, Yu Zhu, Qiang Xu, Ran Tang, Chuanhao Pu, Yusen He

“…Validation using datasets from three landslides in China (Muyubao, Baishuihe, and Shuping) demonstrates that our approach significantly outperforms existing methods in identifying heterogeneous deformation states. …”
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Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

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Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children by Satrio Agung Wicaksono, Satrio Hadi Wijoyo, Fatmawati Fatmawati, Tri Afirianto, Diva Kurnianingtyas, Mochammad Chandra Saputra

“…Six test scenarios were conducted using different percentages of training data (90%, 80%, 70%, 60%, 50%, and 40%) to evaluate the reliability of the Naïve Bayes method. Results indicated that the highest accuracy of 78.84% was achieved in the sixth test scenario. …”
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Time series analysis of energy usage patterns by Tanzania large power users by Diana Rwegasira, Edward Ngailo, Elimboto Yohana, Noel Masasi

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Modeling the Analysis of the Financial Result of an Entity by Alexandra A. Derbicheva

“…Methodology. The methods used are system and complex approaches, dialectic method of cognition, scientific abstraction, analysis and synthesis, comparison methods and others. …”
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EXPERIENCE OF USING ELECTRICAL TOMOGRAPHY, RADON SURVEY AND MICROSEISMIC SOUNDING IN SEARCH FOR PIPES CONTROLLED BY FAULT ZONES by K. Zh. Seminsky, S. A. Bornyakov, A. A. Bobrov, A. N. Shagun

“…The complex of geophysical methods was successfully applied in the Alakit-Markha kimberlite field of the Yakutsk diamondiferous province. …”
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Optimized AI and IoT-Driven Framework for Intelligent Water Resource Management by Mahmoud Badee Rokaya Mahmoud, Dalia Ismaeil Ibrahim Hemdan, Samah Hazzaa Alajmani, Raneem Yousif Alyami, Ghada Elmarhomy, Hassan Hashim, El-Sayed Atlam

“…By virtue of the empirical validation and comparative analysis of the framework, it has been shown to perform better than the regular methods, proving that the methodology can act as a step forward in the field of real-time, AI-assisted irrigation and leak detection systems.…”
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Automatic Reading and Reporting Weather Information from Surface Fax Charts for Ships Sailing in Actual Northern Pacific and Atlantic Oceans by Jun Jian, Yingxiang Zhang, Ke Xu, Peter J. Webster

“…This study employed many artificial intelligent (AI) methods including a vectorization approach and target recognition algorithm to automatically detect the severe weather information from Japanese and US weather charts. …”
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AI-Assisted Real-Time Monitoring of Infectious Diseases in Urban Areas by Mohammed M. Alwakeel

“…The findings confirm that AI-driven real-time surveillance significantly improves outbreak detection and forecasting, enabling timely public health interventions. …”
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MSRGAN: A Multi-Scale Residual GAN for High-Resolution Precipitation Downscaling by Yida Liu, Zhuang Li, Guangzhen Cao, Qiong Wang, Yizhe Li, Zhenyu Lu

“…Experiments conducted using Weather Research and Forecasting (WRF) simulation data over the continental United States demonstrate that MSRGAN outperforms traditional interpolation methods and state-of-the-art deep learning models across various metrics, including Critical Success Index (CSI), Heidke Skill Score (HSS), False Alarm Rate (FAR), and Jensen–Shannon divergence. …”
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Selective protection of cereals using artificial neural networks by Wiesław Golka, Magdalena Szechyńska-Hebda, Jolanta Kowalska, Adrian Golka, Robert Grolik, Magdalena Jakubowska, Mateusz Góra, Aleksander Bomberski

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LSTM-ANN-GA A HYBRID DEEP LEARNING MODEL FOR PREDICTIVE MAINTENANCE OF INDUSTRIAL EQUIPEMENT by Farouk Noumich, Abouchabaka Jaafar, Amrani Ayoub

“…Unlike traditional predictive approaches based on existing data for fault detection, which rely on a single deep model and often struggle to maintain satisfactory generalization performance across various forecasting scenarios, these approaches encounter difficulties in effectively initializing and optimizing reset parameters, impeding performance and accuracy. …”
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Using Cuckoo Search Algorithm to Predict Corporate Financial Risks and Alleviate Economic Uncertainty by Muqiao Cai

“…Conventional methods, such as logistic regression, decision trees, and linear discriminant analysis, often struggle to accurately detect early financial risks because they are deterministic and unsuited for exploring global optimal solutions. …”
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