Modeling the hillside movement in the area of Sattarkhan dam reservoir using by predictive models Logistic Regression and Neural Network by soghra andaryani, Nasrin Samandar, Ohammadreza Nikjoo

“…To use logistic regression and neural network models, it is required to use a set of data includeing both data and track their documents. …”
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Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data by Dev Dinesh, Shashi Kumar, Sameer Saran

“…Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. …”
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The abnormally increased functional connectivity of the locus coeruleus in migraine without aura patients by Bangli Shen, Jinming Cheng, Xi Zhang, Xiaoyuan Wu, Zhihong Wang, Xiaozheng Liu

“…FC was calculated based on rsfMRI data collected by a 3T MRI scanner. General linear model were used to compare whether there were differences in LC brain networks between the two groups. …”
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Non-Invasive Optoelectronic System for Color-Change Detection in Oranges to Predict Ripening by Using Artificial Neural Networks by J. D. Filoteo-Razo, J. C. Elizondo-Leal, J. R. Martinez-Angulo, J. H. Barron-Zambrano, A. Diaz-Manriquez, V. P. Saldivar-Alonso, J. M. Estudillo-Ayala, R. Rojas-Laguna

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Convolutional neural network-based classification of craniosynostosis and suture lines from multi-view cranial X-rays by Seung Min Kim, Ji Seung Yang, Jae Woong Han, Hyung Il Koo, Tae Hoon Roh, Soo Han Yoon

“…Our approach integrates X-ray-marker removal, head-pose standardisation, skull-cropping, and fine-tuning modules for CSO and suture-line classification using convolution neural networks (CNNs). It enhances the diagnostic accuracy and efficiency of identifying CSO from X-ray images, offering a promising alternative to traditional methods. …”
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AI‐Based Digital Rocks Augmentation and Assessment Metrics by Lei Liu, Bernard Chang, Maša Prodanović, Michael J. Pyrcz

“…Compared to most traditional dimensionality reduction methods that process images into a flattened vector, we propose topological image analysis for dimensionality reduction while preserving the essential geometric and topological features of the high‐dimensional data. …”
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How do the resting EEG preprocessing states affect the outcomes of postprocessing? by Shiang Hu, Jie Ruan, Pedro Antonio Valdes-Sosa, Zhao Lv

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Multiparameter MRI-based automatic segmentation and diagnostic models for the differentiation of intracranial solitary fibrous tumors and meningiomas by Lingzhen Wei, Zehong Cao, Feng Shi, Fuyan Li, Yi Cui, Yu Gu, Jinming Chen, Meilin Li, Jiahao Liu, Huaizhen Wang, Xuechun Wang, Qingshi Zeng

“…The integration of clinical and radiological data enhanced the model’s AUC to 0.957. Stratified analysis showed that the weighted AUC value reached 0.846 in the validation set.Conclusion The comprehensive system integrating automatic segmentation with diagnostic models can differentiate SFTs from meningiomas precisely.…”
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Rapid <i>Salmonella</i> Serovar Classification Using AI-Enabled Hyperspectral Microscopy with Enhanced Data Preprocessing and Multimodal Fusion by MeiLi Papa, Siddhartha Bhattacharya, Bosoon Park, Jiyoon Yi

“…Hyperspectral data cubes were collected to generate single-cell spectra and RGB composite images representing the full microscopy field. …”
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Brain dynamics alterations induced by partial sleep deprivation: An energy landscape study by Yutong Wu, Liming Fan, Wei Chen, Xing Su, Simeng An, Nan Yao, Qian Zhu, Zi-Gang Huang, Youjun Li

“…Our study applied energy landscape analysis to resting-state functional magnetic resonance imaging data to characterize the dominant brain activity patterns in 36 healthy young (19 females, 23.53 ± 2.36 years) and 33 healthy older (18 females, 68.81 ± 2.41 years) adults after full sleep (FS) and PSD. …”
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A Bidirectional Mendelian Randomization Study of Causal Relationships Between Migraine and White-Matter Structural Connectivity by Tong D, Zhang X, Xiao Z, Taothong S, Teeravarunyou P, Wu W, Wu S, Chen N, Tao S, Zhou J, Song Q, Liang F, Li Z

“…The inverse variance-weighted (IVW) method served as the primary approach for analyzing causality.Results: In the forward MR analysis, it was found that migraine had a significant effect on right hemisphere somatomotor network to amygdala WM structural connectivity (IVW-derived β = 0.11, 95% CI = [0.04, 0.17], p = 1.02e-03, FDR p = 0.04). …”
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Next-Generation Automation in Neuro-Oncology: Advanced Neural Networks for MRI-Based Brain Tumor Segmentation and Classification by Syed Sajid Hussain, Niyaz Ahmad Wani, Jasleen Kaur, Naveed Ahmad, Sadique Ahmad

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VTA projections to M1 are essential for reorganization of layer 2-3 network dynamics underlying motor learning by Amir Ghanayim, Hadas Benisty, Avigail Cohen Rimon, Sivan Schwartz, Sally Dabdoob, Shira Lifshitz, Ronen Talmon, Jackie Schiller

“…Previous studies demonstrated that skill acquisition requires dopaminergic VTA (ventral-tegmental area) signaling in M1, however little is known regarding the effect of these inputs at the neuronal and network levels. Using dexterity task, calcium imaging, chemogenetic inhibiting, and geometric data analysis, we demonstrate VTA-dependent reorganization of M1 layer 2-3 during motor learning. …”
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Multimodal data fusion AI model uncovers tumor microenvironment immunotyping heterogeneity and enhanced risk stratification of breast cancer by Yunfang Yu, Gengyi Cai, Ruichong Lin, Zehua Wang, Yongjian Chen, Yujie Tan, Zifan He, Zhuo Sun, Wenhao Ouyang, Herui Yao, Kang Zhang

“…We employed unsupervised clustering to identify distinct lncRNA expression patterns and developed an AI‐based pathology model using convolutional neural networks to predict immune–metabolic subtypes. Additionally, we created a multimodal model integrating lncRNA data, immune‐cell scores, clinical information, and pathology images for prognostic prediction. …”
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Bridging Theory and Practice: A Review of AI-Driven Techniques for Ground Penetrating Radar Interpretation by Lilong Zou, Ying Li, Kevin Munisami, Amir M. Alani

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Neurocognitive Biotypes of Risk and Resilience for Mood Disorders in Adolescents: Insights From Behavioral and Graph-Theoretic Network Markers by Ambra Coccaro, Ziwei Cheng, Luka Ruzic, Amelia D. Moser, Jenna Jones, Elena C. Peterson, Elisa F. Stern, Naomi P. Friedman, Roselinde H. Kaiser

“…Methods: Adolescents (N = 146; 13–21 years, 66% first-degree familial history of mood disorders) completed behavioral tests and magnetic resonance imaging at baseline. Biotypes were derived using cluster analysis on measures of reward sensitivity and executive functioning. …”
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Exploring the changes in functional connectivity of the limbic system in Patients with amnestic mild cognitive impairment treated by acupuncture based on fMRI by Han Yingmei, Li Yijie, Zhang Heng, Feng Ze, Li Weiqing, Zhang Hanxi, Yang Ming, Chu Bingyuan, Wang Feng

“…We conduct an investigation of the FC of limbic system networks in amnestic mild cognitive impairment (aMCI) and speculate on the brain effect mechanism of acupuncture therapy based on resting - state Functional Magnetic Resonance Imaging (rs - fMRI).Method50 patients with aMCI and 41 healthy participants (HC group) from the First Affiliated Hospital of Heilongjiang University of Chinese Medicine in Harbin City, Heilongjiang Province, China, were recruited.rs-fMRI data of all participants were collected. …”
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Adaptive Reversible Data Hiding Scheme Based on Novel Cascading Prediction Error Histogram Shifting Using Decimal Floating Stream by Reza Ghorbandost Soveiri, Maryam Rajabzadeh Asaar, Pouya Derakhshan Barjoei

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Design of tomato picking robot detection and localization system based on deep learning neural networks algorithm of Yolov5 by Jianwei Zhao, Wei Bao, Leiyu Mo, Zhiting Li, Yushuo Liu, Jiaqing Du

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Deep learning for quantitative dynamic fragmentation analysis by Erwin Cazares, Brian E. Schuster

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