A comparison of personality type congruence with learning achievement of students from the primary school teacher education program at Adzkia University by Irwan Prayitno, Ismira Ismira

“…The hypothesis test result shows that personality type has no impact on learning achievement.…”
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Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks by Hanae Loutfi, Ahmed Bernatchou, Younès Raoui, Rachid Tadili

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The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis by Samer Tohme, Hamza O Yazdani, Amaan Rahman, Sanah Handu, Sidrah Khan, Tanner Wilson, David A Geller, Richard L Simmons, Michele Molinari, Christof Kaltenmeier

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Examination of empirical and Machine Learning methods for regression of missing or invalid solar radiation data using routine meteorological data as predictors by Konstantinos X Soulis, Evangelos E Nikitakis, Aikaterini N Katsogiannou, Dionissios P Kalivas

Subjects: “…machine learning…”
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MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images by Tianhu Zhao, Tianhu Zhao, Yong Yue, Hang Sun, Jingxu Li, Yanhua Wen, Yudong Yao, Wei Qian, Yubao Guan, Shouliang Qi, Shouliang Qi

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Protocol for functional screening of CFTR-targeted genetic therapies in patient-derived organoids using DETECTOR deep-learning-based analysis by Mattijs Bulcaen, Ronald B. Liu, Kasper Gryspeert, Sam Thierie, Anabela S. Ramalho, François Vermeulen, Xavier Casadevall I Solvas, Marianne S. Carlon

“…Summary: Here, we present a protocol for the rapid functional screening of gene editing and addition strategies in patient-derived organoids using the deep-learning-based tool DETECTOR (detection of targeted editing of cystic fibrosis transmembrane conductance regulator [CFTR] in organoids). …”
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Effects of 3R, 16S-2-hydroxyethyl apovincaminate (HEAPO), donepezil and galantamine on learning and memory retention in naïve Wistar rats by Dimitrova Darinka, Getova Damianka, Saracheva Kremena

“…All the studied compounds showed positive effects in the learning and memory tests, compared to the controls. …”
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Improved COVID-19 Diagnosis Using a Hybrid Transfer Learning Model with Fuzzy Edge Detection on CT Scan Images by Hassan Salarabadi, Mohammad Saber Iraji, Mehdi Salimi, Mehdi Zoberi

“…Consequently, there is a need for a quick recognition system based on deep learning techniques to detect COVID-19 infection and prevent its spread. …”
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Efficient Feature Selection and Hyperparameter Tuning for Improved Speech Signal-Based Parkinson’s Disease Diagnosis via Machine Learning Techniques by Deepak Painuli, Suyash Bhardwaj, Utku Kose

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Accurate estimation of Jujube leaf chlorophyll content using optimized spectral indices and machine learning methods integrating geospatial information by Nigela Tuerxun, Sulei Naibi, Jianghua Zheng, Renjun Wang, Lei Wang, Binbin Lu, Danlin Yu

“…Hyperspectral data enable precise LCC monitoring by extracting spectral indices through optimal band combination (OBC) and predicting LCC with machine learning. However, OBC faces dimensionality issues, and machine learning models often overlook geographical influences, potentially reducing prediction accuracy. …”
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AtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets by Jaime Gonzalo Flor Flores, Jim Solomon, Connor Nasseraddin, Talha Yerebakan, Andrey B Matsko, Chee Wei Wong

“…Machine Learning: Science and Technology…”
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HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures by M. Rus, M. Rus, H. Mihanović, M. Ličer, M. Ličer, M. Kristan

“…A recently proposed deep-learning model, HIDRA2 (HIgh-performance Deep tidal Residual estimation method using Atmospheric data, version 2), avoids numerical simulations while delivering competitive forecasts. …”
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Vis/NIR Spectroscopy and Vis/NIR Hyperspectral Imaging for Non-Destructive Monitoring of Apricot Fruit Internal Quality with Machine Learning by Tiziana Amoriello, Roberto Ciorba, Gaia Ruggiero, Francesca Masciola, Daniela Scutaru, Roberto Ciccoritti

“…In recent years, machine learning techniques, such as artificial neural networks (ANNs), have been successfully applied to more efficiently extract valuable information from spectral data and to accurately predict quality traits. …”
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Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention by Fawaz Khaled Alarfaj, Shabnam Shahzadi

Subjects: “…Deep learning…”
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Enhanced detection of mild cognitive impairment in Alzheimer’s disease: a hybrid model integrating dual biomarkers and advanced machine learning by John Sahaya Rani Alex, R. Roshini, G. Maneesha, Jeetashree Aparajeeta, B. Priyadarshini, Chih-Yang Lin, Chi-Wen Lung

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Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) by Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano-Garcia

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FedBFGCN: A Graph Federated Learning Framework Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution by Hefei Wang, Ruichun Gu, Jingyu Wang, Xiaolin Zhang, Hui Wei

Subjects: “…Graph federated learning…”
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A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance by Wei-Wen Chen, Ling Kuo, Yi-Xun Lin, Wen-Chung Yu, Chien-Chao Tseng, Yenn-Jiang Lin, Ching-Chun Huang, Shih-Lin Chang, Jacky Chung-Hao Wu, Chun-Ku Chen, Ching-Yao Weng, Siwa Chan, Wei-Wen Lin, Yu-Cheng Hsieh, Ming-Chih Lin, Yun-Ching Fu, Tsung Chen, Shih-Ann Chen, Henry Horng-Shing Lu

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A topology-guided high-quality solution learning framework for security-constraint unit commitment based on graph convolutional network by Liqian Gao, Lishen Wei, Shichang Cui, Jiakun Fang, Xiaomeng Ai, Wei Yao, Jinyu Wen

“…In this sense, this paper proposes a topology-guided high-quality solution learning framework based on graph convolutional network (GCN) and neighborhood search (NS). …”
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Exploring the role of oxidative stress in carotid atherosclerosis: insights from transcriptomic data and single-cell sequencing combined with machine learning by Yiqin Yang, Mei Dong

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