Enhanced real-time Parkinson’s disease monitoring and severity prediction using a multi-faceted deep learning approach by Marreddy Naga Sabari, Deepak Ch

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Prevalence, associated risk factors and satellite imagery analysis in predicting soil-transmitted helminth infection in Nakhon Si Thammarat Province, Thailand by Jarawadee Muenjak, Jutarat Thongrod, Chanakan Choodamdee, Pongphan Pongpanitanont, Manachai Yingklang, Tongjit Thanchomnang, Sakhone Laymanivong, Penchom Janwan

“…We developed an innovative predictive model by integrating convolutional neural networks (CNNs) for land-use classification of satellite imagery with artificial neural networks (ANNs) following dimensionality reduction through principal component analysis (PCA). …”
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A Secure and Efficient Framework for Multimodal Prediction Tasks in Cloud Computing with Sliding-Window Attention Mechanisms by Weiyuan Cui, Qianye Lin, Jiaqi Shi, Xingyu Zhou, Zeyue Li, Haoyuan Zhan, Yihan Qin, Chunli Lv

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High-resolution global modeling of wheat’s water footprint using a machine learning ensemble approach by Murat Emeç, Abdullah Muratoğlu, Muhammed Sungur Demir

“…The study revealed distinct outcomes for different clustering methods, demonstrating the model's robustness across varying spatial scales. …”
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Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration by Gökhan Deveci, Özgün Yücel, Ali Bahadır Olcay

“…The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. …”
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Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment by Heru Agus Santoso, Nur Setiawati Dewi, Susilo, Arga Dwi Pambudi, Hanif Pandu Suhito, Iman Dehzangi

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Artificial Intelligence in Ovarian Cancer: A Systematic Review and Meta-Analysis of Predictive AI Models in Genomics, Radiomics, and Immunotherapy by Mauro Francesco Pio Maiorano, Gennaro Cormio, Vera Loizzi, Brigida Anna Maiorano

“…Pooled AUCs indicated strong predictive performance for genomics-based (0.78), radiomics-based (0.88), and immunotherapy-based (0.77) models. …”
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Assessing Coincidence of Satellite Acquisitions and Flood Events to Predict Suitability for Flood Map Synthesis by Lyle Prince, Riley C. Hales, Kel N. Markert, E. James Nelson, Gustavious P. Williams, Daniel P. Ames, Hyongki Lee, Amirhossein Rostami

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Attributing the spatial-temporal contributions of inflow nitrogen and phosphorus loads to lake water quality and insights for effective mitigation strategies by Zhilong Zhao, Bo Yao, Yu Li, Zhaokui Ni, Shengrui Wang

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Performance analysis of rank reduction estimator in the presence of unexpected modeling errors by WANG Ding, WU Ying

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Uncertainties in predicting the medium-term future occurrence of grapevine budburst in Europe: the role of phenological models and climate scenarios by Sergi Costafreda-Aumedes, Luisa Leolini, Christoph Menz, Chenyao Yang, Giovanni Argenti, Camilla Dibari, Marco Bindi, Helder Fraga, Joao Santos, Daniel Molitor, J. Arturo Torres-Matallana, Marko Simeunović, Marco Moriondo

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Dynamic Prediction Method of 3D Spatial Information of Coal Mining Subsidence Water Area Integrated with Landsat Remote Sensing and Knothe Time Function by Hui Liu, Yu Li

“…Taking the 1031 working face of Wugou Coal Mine in Huaibei, Anhui, China, as the research subject, (1) a three-dimensional (3D) spatial information dynamic prediction method was proposed for high-water-level coal mining subsidence areas by combining the Knothe time function based on the probability integration method (PIM) and the principle of water balance. (2) The dynamic evolution law of the water accumulation area in the high-water-level coal mining subsidence area was studied. (3) The applicability of the dynamic prediction model of the water accumulation range in the high-water-level coal mining subsidence area was verified. …”
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Spatial distribution of archeological sites in the Kingdom of Saudi Arabia and the application of the theory of planned behavior to predict pro-environmental behavioral intention a... by Nada Sulaiman Mohammed Alali, Tarek Abdel Azim Ahmed, Eman Sarhan Shaker

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Data sharing and GRA weight optimization for power prediction of distributed photovoltaic power plant considering missing NWP information by YANG Xiyun, YANG Yan, MENG Lingzhuochao, PENG Yan, WANG Chenxu

“…On this basis, this paper proposes a power prediction model for distributed photovoltaic power plant based on data sharing and grey relation analysis (GRA) weight optimization. …”
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Toward a Multi‐Representational Approach to Prediction and Understanding, in Support of Discovery in Hydrology by Luis A. De la Fuente, Hoshin V. Gupta, Laura E. Condon

“…Specifically, we test a lumped water‐balance model (GR4J), a data‐based dynamical systems model (LSTM), and a data‐based regression tree model (Random Forest). …”
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Monitoring and forecasting of land use/land cover (LULC) in Al-Hassa Oasis, Saudi Arabia based on the integration of the Cellular Automata (CA) and the Cellular Automata-Markov Mod... by Ashraf Abdelkarim

“…This study sought to integrate the Cellular Automata-Markov Model (CA-Markov) and the Cellular Automata (CA) using sensing data for land cover maps for the years: 1988, 2000, 2013 and 2020 to monitor, detect, and predict the spatial and temporal of Land Use/Land Cover (LULC) change in Al-Hassa Oasis, Saudi Arabia. …”
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On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment by Kindie Fentahun Muchie, Anthony Kibira Wanjoya, Samuel Musili Mwalili

“…Model-based Bayesian analysis is popular for its ability to combine information from several sources as well as taking account uncertainties in the analysis and spatial prediction of spatial data. …”
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Predicting climate-driven shift of the East Mediterranean endemic Cynara cornigera Lindl by Heba Bedair, Heba Bedair, Yehia Hazzazi, Asmaa Abo Hatab, Marwa Waseem A. Halmy, Mohammed A. Dakhil, Mohammed A. Dakhil, Mubaraka S. Alghariani, Mubaraka S. Alghariani, Mari Sumayli, A. El-Shabasy, Mohamed M. El-Khalafy

“…Furthermore, our models predicted that the distribution range of C. cornigera would drop by more than 25% during the next few decades. …”
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Enhancing soil total nitrogen prediction in rice fields using advanced Geo-AI integration of remote sensing data and environmental covariates by Novandi Rizky Prasetya, Aditya Nugraha Putra, Mochtar Lutfi Rayes, Sri Rahayu Utami

“…Recently, advanced Geospatial-Artificial Intelligence (Geo-AI) techniques such as the random forest (RF) algorithm have been developed to increase the accuracy and spatial representativeness of STN prediction. However, critical challenges remain in using datasets from multiple locations. …”
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A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau by Ritu Wu, Zhimin Hong, Wala Du, Yu Shan, Hong Ying, Rihan Wu, Byambakhuu Gantumur

“…The models were trained with the data of grassland fires from 2019 to 2022 in the Mongolian Plateau to predict the occurrence of grassland fires in 2023. …”
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