Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals by Yazeed Alkhrijah, Yazeed Alkhrijah, Shehzad Khalid, Shehzad Khalid, Syed Muhammad Usman, Amina Jameel, Muhammad Zubair, Haya Aldossary, Aamir Anwar, Saad Arif

“…The output of these classifiers is then fed into the model-agnostic meta learner ensemble classifier with LSTM as the base classifier for the final prediction of interictal and preictal states.ResultsThe proposed methodology is trained and tested on the publicly available CHB-MIT dataset while achieving 99.34% sensitivity, 98.67% specificity, and a false positive alarm rate of 0.039.DiscussionThe proposed method not only outperforms the existing methods in terms of sensitivity and specificity but is also computationally efficient, making it suitable for real-time epileptic seizure prediction systems.…”
Get full text

GeNetFormer: Transformer-Based Framework for Gene Expression Prediction in Breast Cancer by Oumeima Thaalbi, Moulay A. Akhloufi

“…<i>Background:</i> Histopathological images are often used to diagnose breast cancer and have shown high accuracy in classifying cancer subtypes. Prediction of gene expression from whole-slide images and spatial transcriptomics data is important for cancer treatment in general and breast cancer in particular. …”
Get full text

Evaluating Remote Sensing Resolutions and Machine Learning Methods for Biomass Yield Prediction in Northern Great Plains Pastures by Srinivasagan N. Subhashree, C. Igathinathane, John Hendrickson, David Archer, Mark Liebig, Jonathan Halvorson, Scott Kronberg, David Toledo, Kevin Sedivec

“…The developed methodology of RFE for feature selection and RF for biomass yield modeling is recommended for biomass and hay forage yield prediction.…”
Get full text

Prediction of Surface Water Temperature and Its Spatial-Temporal Variation Characteristics of 11 Main Lakes in Yunnan&#x2013;Guizhou Plateau by Shanshan Xu, Kun Yang, Xiaolu Zhou, Jie Zhang, Chenghao Yang, Yi Luo

Get full text

Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting by Na Hu, Dafang Zhang, Kun Xie, Wei Liang, Meng-Yen Hsieh

Get full text

Impacts of Climate Change on the Spatial Distribution and Habitat Suitability of <i>Nitraria tangutorum</i> by Lianxing Li, Zhiqing Jia, Lingxianzi He, Dong Han, Qiankun Yang, Jialuo Li, Pingyi Zhou

Get full text

Monitoring land-use changes and predicting their spatio-temporal trends in Hamedan City by Naser Shafiei Sabet, Faranak Feyzbabaei cheshmeh sefidi

“…After land use detection and its changes, the trend of these changes was predicted in 2050 using the automatic cell model and Markov chain due to its high ability to detect spatial-spatial component changes.Results and discussion: Results indicated that the growth and development of urbanization in this metropolis have led to the city's expansion in this area. …”
Get full text

Soil Moisture Content Prediction Using Gradient Boosting Regressor (GBR) Model: Soil-Specific Modeling with Five Depths by Tarek Alahmad, Miklós Neményi, Anikó Nyéki

“…Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. …”
Get full text

Multi‐Satellite Data Assimilation for Large‐Scale Hydrological‐Hydrodynamic Prediction: Proof of Concept in the Amazon Basin by S. Wongchuig, R. Paiva, V. Siqueira, F. Papa, A. Fleischmann, S. Biancamaria, A. Paris, M. Parrens, A. Al Bitar

Get full text

Ecological risk assessment and prediction of riparian zones in the Jiangsu section of the Yangtze River from a spatiotemporal perspective by Zihan Zhu, Cheng Zhang, Yangyang Lu, Jian Ye, Guohua Fang, Changran Sun, Yun Yang

“…Focusing on the Yangtze River riparian zone in Jiangsu Province, an ecological risk assessment system with 20 indicators was developed based on a systematic analysis of the ecological risk exposure–response process. The temporal and spatial characteristics of ecological risks in the riparian zone from 2003 to 2023 were analysed using the ecological risk composite index model, Moran index, and Getis-Ord Gi* cold hotspot analysis method, while ecological risks for 2028 and 2033 were predicted using the Grey–Markov chain and PLUS models. …”
Get full text

Predicting changes in land use and land cover using remote sensing and land change modeler by Brijmohan Bairwa, Rashmi Sharma, Arnab Kundu, Saad Sh. Sammen, Fahad Alshehri, Chaitanya Baliram Pande, Chaitanya Baliram Pande, Zoltan Orban, Ali Salem, Ali Salem

“…The integration of geo-spatial and remote sensing technologies is pivotal in comprehending these dynamics and formulating strategies for future natural resource management. …”
Get full text

Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S. by Lifeng Yuan, Kenneth J Forshay

“…However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. …”
Get full text

Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction by Wesam Ibrahim Hajim, Suhaila Zainudin, Kauthar Mohd Daud, Khattab Alheeti

Get full text

Precise Spatial Prediction of Rice Seedlings From Large-Scale Airborne Remote Sensing Data Using Optimized Li-YOLOv9 by Jayakrishnan Anandakrishnan, Arun Kumar Sangaiah, Hendri Darmawan, Nguyen Khanh Son, Yi-Bing Lin, Mohammed J. F. Alenazi

Get full text

Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, china by Qimeng Ren, Ming Sun

“…By using multi-source big data, this paper analyzes the population distribution, traffic organization, infrastructure, land use and regional economy of Jinan urban area, China, and constructs a comprehensive evaluation index system to predict the spatial demand of PCS for EVs. We analyse: (1) Distribution of population activities on weekday and rest days, the closeness and betweenness of road network, high-density area, commerce, public service facilities, parks, transportation facilities, residential area, building coverage, floor area ratio, economic development area and housing price level. (2) Correlation and influence weights of 14 evaluation indexes and PCS layout. (3) Prediction of spatial demand distribution of PCS. (4) Comparison of current PCS distribution and spatial demand prediction results. …”
Get full text

Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study by Juan Chen, Xiong Chen, Kai Fu, Lan Zhou, Shichao Long, Mengsi Li, Linhui Zhong, Aerzuguli Abudulimu, Wenguang Liu, Deng Pan, Ganmian Dai, Yigang Pei, Wenzheng Li

“…This study aimed to elucidate biological differences related to pTLS density and develop a radiomic classifier for predicting pTLS density in HCC, offering new insights for clinical diagnosis and treatment.Methods Spatial transcriptomics (n=4) and RNA sequencing data (n=952) were used to identify critical regulators of pTLS density and evaluate their prognostic significance in HCC. …”
Get full text

Computational predictions of magnetic resonance acoustic radiation force imaging for breast cancer focused ultrasound therapy by Chloe K. Nelson, Michelle Kline, Allison Payne, Christopher R. Dillon

Get full text

Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

“…Our approach leverages cutting-edge ML techniques, including Decision Trees (DT), Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM), to develop highly accurate predictive models. The performance of these models was rigorously evaluated using comprehensive statistical metrics, such as Receiver Operating Characteristic (ROC), Area Under the Curve (AUC), precision, recall, and overall accuracy (OA). …”
Get full text

Skillful seasonal prediction of the boreal summer Pacific–Japan teleconnection pattern by Kan Yi, Chenqi Wang, Yunfei Zhang, Xiang Li, Jian Wang, Renqiang Wen, Mengjiao Du

“…Our findings elucidate that the spatial structure of the PJ pattern simulated by models introduces substantial diversities in prediction skills. …”
Get full text

Spatio-Temporal Predictive Learning Using Crossover Attention for Communications and Networking Applications by Ke He, Thang Xuan Vu, Lisheng Fan, Symeon Chatzinotas, Bjorn Ottersten

“…This limitation reduces their prediction accuracy in spatio-temporal predictive learning, where understanding both spatial and temporal dependencies is essential. …”
Get full text