Machine Learning Models to Predict Individual Cognitive Load in Collaborative Learning: Combining fNIRS and Eye-Tracking Data by Wenli Chen, Zirou Lin, Lishan Zheng, Mei-Yee Mavis Ho, Farhan Ali, Wei Peng Teo

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Computer-Modeling System of Agronomist’s Labor Functions by I. E. Bystrenina

“…A diagram of use cases for this notation made it possible to describe the functional requirements and interactions between the system and its user. …”
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Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer by Luqian Liu, Fengyu Zhang, Jiaye Yu, Pingping Dong, Jing Deng, Weiwei Liu, Xinlei Cao, Juanjuan Huang, Xiaoming Lin, Xiangyang Xue

“…Elevated NUBPL expression levels can impair the function of chemokines. Moreover, patients with lower NUBPL expression levels exhibit better responses to immunotherapy. …”
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Big Data in Neuroscience: Transforming Research and the Understanding of Brain Function by Adina DZIŢAC

“…Since the year 2000, artificial intelligence and machine learning have transformed neuroscience research and have revolutionized data analysis and made it possible for researchers to discover complex patterns in brain activity, relating this to neurological disorders and cognitive functions. …”
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Evaluation of Bayesian network scoring functions in polychotomous data analysis by Xuejia Ke, Katherine Keenan, V. Anne Smith

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Accelerated predictions of the sublimation enthalpy of organic materials with machine learning by Yifan Liu, Huan Tran, Chaofan Huang, Beatriz G. delRio, V. Roshan Joseph, Mark Losego, Rampi Ramprasad

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Influence of digital environment on the cognitive function of schoolchildren and students by E. A. Avdeeva, O. A. Kornilova

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Data‐Driven Materials Research and Development for Functional Coatings by Kai Xu, Xuelian Xiao, Linjing Wang, Ming Lou, Fangming Wang, Changheng Li, Hui Ren, Xue Wang, Keke Chang

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Image Reconstruction Algorithm Based on Extreme Learning Machine for Electrical Capacitance Tomography by SU Ziheng, CHEN Deyun, WANG Lili

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Metabolic profiles and prediction of failure to thrive of citrin deficiency with normal liver function based on metabolomics and machine learning by Peiyao Wang, Duo Zhou, Lingwei Hu, Pingping Ge, Ziyan Cen, Zhenzhen Hu, Qimin He, Kejun Zhou, Benqing Wu, Xinwen Huang

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Explorative learning and functional inferences on a five-step means-means-end problem in Goffin's cockatoos (Cacatuagoffini). by Alice M I Auersperg, Alex Kacelnik, Auguste M P von Bayern

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Developing Machine Learning-based Control Charts for Monitoring Different GLM-type Profiles With Different Link Functions by Patrik Hric, Hamed Sabahno

“…In this paper, we propose an innovative approach that uses different machine-learning (ML) techniques for constructing control charts and monitoring generalized linear model (GLM) profiles with three different GLM-type response distributions of Binomial, Poisson, and Gamma, and by examining different link functions for each response distribution. …”
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A Comparative Analysis of Machine Learning and Pedotransfer Functions Under Varying Data Availability in Two Greek Regions by Panagiotis Tziachris, Panagiota Louka, Eirini Metaxa, Miltiadis Iatrou, Konstantinos Tsiouplakis

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Personalized prediction model generated with machine learning for kidney function one year after living kidney donation by Rikako Oki, Toshihio Hirai, Kazuhiro Iwadoh, Yu Kijima, Hiroyuki Hashimoto, Yasunori Nishimura, Taro Banno, Kohei Unagami, Kazuya Omoto, Tomokazu Shimizu, Junichi Hoshino, Toshio Takagi, Hideki Ishida, Toshihito Hirai

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Predicting rapid decline in kidney function among type 2 diabetes patients: A machine learning approach by Eri Nakahara, Kayo Waki, Hisashi Kurasawa, Imari Mimura, Tomohisa Seki, Akinori Fujino, Nagisa Shiomi, Masaomi Nangaku, Kazuhiko Ohe

“…Background: Diabetic kidney disease (DKD) is one of the typical complications of type 2 diabetes (T2D), with approximately 10 % of DKD patients experiencing a Rapid decline (RD) in kidney function. RD leads to an increased risk of poor outcomes such as the need for dialysis. …”
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UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning by Yaxi Liu, Xulong Li, Boxin He, Meng Gu, Wei Huangfu

“…A diverse data collection framework is established where the uplink communication and sensing functions are both considered, which can also be referred to as the uplink ISAC system. …”
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Self-Supervised Learning with Trilateral Redundancy Reduction for Urban Functional Zone Identification Using Street-View Imagery by Kun Zhao, Juan Li, Shuai Xie, Lijian Zhou, Wenbin He, Xiaolin Chen

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Comparative study on Functional Machine learning and Statistical Methods in Disease detection and Weed Removal for Enhanced Agricultural Yield by Sudha D., Menaga D.

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Application of machine learning and temporal response function modeling of EEG data for differential diagnosis in primary progressive aphasia by Heather Dial, Lokesha S. Pugalenthi, G. Nike Gnanateja, Junyi Jessy Li, Maya L. Henry

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Deep Learning Integrating Scale Conversion and Pedo‐Transfer Function to Avoid Potential Errors in Cross‐Scale Transfer by Peijun Li, Yuanyuan Zha, Yonggen Zhang, Chak‐Hau Michael Tso, Sabine Attinger, Luis Samaniego, Jian Peng

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