AI-based model for Prediction of Power consumption in smart grid-smart way towards smart city using blockchain technology by Emran Aljarrah

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Parallel VMamba and Attention-Based Pneumonia Severity Prediction from CXRs: A Robust Model with Segmented Lung Replacement Augmentation by Bouthaina Slika, Fadi Dornaika, Karim Hammoudi

“…Early diagnosis plays a crucial role in preventing complications, necessitating the development of fast and efficient AI-based models for automated severity assessment. <b>Methods:</b> In this study, we introduce a novel approach that leverages VMamba, a state-of-the-art vision model based on the VisualStateSpace (VSS) framework and 2D-Selective-Scan (SS2D) spatial scanning, to enhance lung severity prediction. …”
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Comparative analysis of machine learning models and explainable AI for agriculture drought prediction: A case study of the Ta-pieh mountains by Lichang Xu, Shaowei Ning, Xiaoyan Xu, Shenghan Wang, Le Chen, Rujian Long, Shengyi Zhang, Yuliang Zhou, Min Zhang, Bhesh Raj Thapa

“…The models were trained on data from 2000 to 2021, with 2022 serving as an independent case study to evaluate their prediction accuracy. …”
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The Predictive Skill of a Remote Sensing-Based Machine Learning Model for Ice Wedge and Visible Ground Ice Identification in Western Arctic Canada by Qianyu Chang, Simon Zwieback, Aaron A. Berg

“…Here, we evaluate the predictive skill of XGBoost models for identifying (1) ice wedge and (2) top-5m visible ground ice in the Tuktoyaktuk Coastlands. …”
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DeepMiRBP: a hybrid model for predicting microRNA-protein interactions based on transfer learning and cosine similarity by Sasan Azizian, Juan Cui

“…Methods We present DeepMiRBP, a novel hybrid deep learning model specifically designed to predict microRNA-binding proteins by modeling molecular interactions. …”
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Zenith Tropospheric Delay Forecasting in the European Region Using the Informer–Long Short-Term Memory Networks Hybrid Prediction Model by Zhengdao Yuan, Xu Lin, Yashi Xu, Jie Zhao, Nage Du, Xiaolong Cai, Mengkui Li

“…We then employed this interpolated data from 2016 to 2020, along with an Informer–LSTM Hybrid Prediction Model, to develop a long-term prediction model for ZTD with a prediction duration of one year. …”
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Analysis and prediction of spatiotemporal carbon storage changes in the Taihu Lake Basin in Jiangsu Province based on PLUS and InVEST model by Yu Zhu, Bing Ma, Haibo Hu, Dongxia Ding, Hongwei Zhou, Jiaxuan Liu, Jiacai Liu, Zhirong Lin

“…It aims to assess and predict spatiotemporal carbon storage changes in the Taihu Lake Basin of Jiangsu Province.thereby providing a scientific basis for regional low-carbon land-use planning under China’s ''dual carbon'' goals. …”
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A hybrid deep learning framework for global irradiance prediction using fuzzy C-Means, CNN-WNN, and Informer models by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Wissem Dimassi, Salah Hannachi

“…Artificial intelligence (AI) is revolutionizing solar energy forecasting, enabling precise irradiance prediction for electric solar vehicles (ESVs) to optimize energy efficiency and extend driving range.This study introduces a novel AI-powered hybrid deep learning framework that synergistically combines fuzzy C-means (FCM) clustering, convolutional neural networks (CNNs), wavelet neural networks (WNNs), and an Informer model to achieve superior accuracy. …”
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ARBOALVO: A Bayesian spatiotemporal learning and predictive model for dengue cases in the endemic Northeast city of Natal, Rio Grande do Norte, Brazil. by Mariane Branco Alves, Rafael Santos Erbisti, Aline Araújo Nobre, Taynãna César Simões, Alessandre de Medeiros Tavares, Márcia Cristina Melo, Rodrigo Moreira Pedreira, Jan Pierre Martins de Araújo, Marilia Sá Carvalho, Nildimar Alves Honório

“…These models also enable predictions to support timely interventions for arboviruses like dengue, chikungunya, and Zika.…”
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Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals by Hakan Uyanik, Mehmet Kokum, Erman Senturk, Mohamed Freeshah, Salih T. A. Ozcelik, Muhammed Halil Akpinar, Serenay Celik, Abdulkadir Sengur

“…Performance metrics, including classification accuracy, sensitivity, specificity, and F1-score, are used for evaluation. Our model achieved a classification accuracy of 97.49%, demonstrating its potential for earthquake prediction systems. …”
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Remote Sensing-Derived Environmental Variables to Estimate Transmission Risk and Predict Malaria Cases in Argentina: A Pre-Certification Study (1986–2005) by Ana C. Cuéllar, Roberto D. Coello-Peralta, Davis Calle-Atariguana, Martha Palacios-Macias, Paul L. Duque, Liliana M. Galindo, Mario O. Zaidenberg, María J. Dantur-Juri

“…Early warning systems rely on statistical prediction models, with environmental risks and remote sensing data serving as essential sources of information for their development. …”
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Predicting sport event outcomes using deep learning by Jianxiong Gao, Yi Cheng, Jianwei Gao

“…In this study, we present a deep learning framework that combines a one-dimensional convolutional neural network (1D CNN) with a Transformer architecture to improve prediction accuracy. The 1D CNN effectively captures local spatial patterns in structured match data, while the Transformer leverages self-attention mechanisms to model long-range dependencies. …”
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Pulsed Focused Nonlinear Acoustic Fields from Clinically Relevant Therapeutic Sources in Layered Media: Experimental Data and Numerical Prediction Results by Tamara KUJAWSKA

“…The comparison of the experimental results with those simulated numerically has shown that the model based on the TAWE approach predicts well both the spatial-peak and spatial-spectral pressure variations in the pulsed focused nonlinear beams produced by the transducer used in water for all excitation levels complying with the condition corresponding to weak or moderate source-pressure levels. …”
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A Deep Learning-Based Trajectory and Collision Prediction Framework for Safe Urban Air Mobility by Junghoon Kim, Hyewon Yoon, Seungwon Yoon, Yongmin Kwon, Kyuchul Lee

“…To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. …”
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Spatial Modeling of Douglas‐Fir Plantations in Italy After 120 Years of Experimentation by Maurizio Marchi, Martina Cocozza, Gabriele Bucci, Paolo Iovieno

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Geospatial Robust Wheat Yield Prediction Using Machine Learning and Integrated Crop Growth Model and Time-Series Satellite Data by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Guifei Jing, Syed Roshaan Ali Shah, Aamir Ali, Muhammad Imran, Hongzhi Jiang, Obaid-ur-Rehman

“…This underscores the necessity of multi-trait-based CYM approaches. Crop growth models enable trait dynamics with reflectance data and spectral indices as proxies for crop health and traits, respectively, to have real-time, spatially explicit monitoring. …”
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Breaks in the Arctic ice cover: from observations to predictions by A. A. Ershova, L. N. Dyment, T. A. Alekseeva

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Deep Learning for Predicting Biomolecular Binding Sites of Proteins by Minjie Mou, Zhichao Zhang, Ziqi Pan, Feng Zhu

“…Emerging trends in hybrid models that combine multimodal data, such as integrating sequence and structural information, along with innovations in geometric deep learning, present promising directions for improving prediction accuracy. …”
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Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections by Md․ Ashraful Islam, Musabbir Ahmed Arrafi, Mehedi Hasan Peas, Tanvir Hossain, Md Mehedi Hasan, Sanzida Murshed, Monira Jahan Tania

“…The model was trained using diverse geospatial parameters including topographical, hydrological, soil, and geological parameters, along with an updated landslide inventory, enabling spatially explicit predictions of landslide susceptibility. …”
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A Fluid Flow‐Based Deep Learning (FFDL) Architecture for Subsurface Flow Systems With Application to Geologic CO2 Storage by Zhen Qin, Yingxiang Liu, Fangning Zheng, Behnam Jafarpour

“…Abstract Prediction of the spatial‐temporal dynamics of the fluid flow in complex subsurface systems, such as geologic CO2 storage, is typically performed using advanced numerical simulation methods that solve the underlying governing physical equations. …”
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