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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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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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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The Fundamentals of a Two-Stage Approach to Systematic Earthquake Prediction by Gitis Valeriy, Derendyaev Alexander

“…The proposed methodology introduces the following innovations: 1 – A prediction is considered successful if all epicenters of the target earthquakes during the forecast interval fall within the alarm zone. 2 – The methodology optimizes both the probability of successfully detecting earthquake epicenters across a series of forecasts and the success rate of predictions in each individual iteration. 3 – The methodology enables the estimation of the probability of success for the next forecast interval. …”
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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

“…These results suggest the algorithm's potential for early detection to decrease the number of stunting children. …”
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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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Seismic clusters and fluids diffusion: a lesson from the 2018 Molise (Southern Italy) earthquake sequence by Stefania Gentili, Piero Brondi, Giuliana Rossi, Monica Sugan, Giuseppe Petrillo, Jiancang Zhuang, Stefano Campanella

“…We explored how the significant discrepancies in these methods’ results affect the result of NExt STrOng Related Earthquake (NESTORE) algorithm—a method to forecast strong aftershocks during an ongoing cluster—previously successfully applied to the whole Italian territory. …”
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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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Efficiency of Multi-Terminators Method to Reveal Seismic Precursors in Sub-Ionospheric VLF Transmitter Signals: Case Study of Turkey–Syria Earthquakes Mw7.8 of 6 February 2023 by Mohammed Y. Boudjada, Patrick H. M. Galopeau, Sami Sawas, Giovanni Nico, Hans U. Eichelberger, Pier F. Biagi, Michael Contadakis, Werner Magnes, Helmut Lammer, Wolfgang Voller

“…This work presents an analysis of the sub-ionospheric VLF transmitter signal disturbances which were detected more than one week before the Turkey–Syria EQ occurrence. …”
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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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Prognostic factors for tuberculosis development in children with latent tuberculous infection by N. V. Korneva, A. A. Starshinova, S. M. Аnaniev, Yu. E. Ovchinnikova, I. F. Dovgalyuk

“…Goal of the study: to detect specific immune response in children with latent tuberculous infection and define factors to forecast the development of the active disease in this group.Materials and methods. …”
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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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The optimization path of agricultural industry structure and intelligent transformation by deep learning by Xingchen Pan, Jinyu Chen

“…Additionally, in pest and disease detection, the proposed method exceeds other models in accuracy (97.5%), precision (96.8%), recall (97.2%), and F1 score (0.97), underscoring its superior performance in detecting agricultural pests and diseases. …”
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