PlantLncBoost: key features for plant lncRNA identification and significant improvement in accuracy and generalization

Συγγραφείς: Tian, Xue-Chan, Nie, Shuai, Domingues, Douglas, Rossi Paschoal, Alexandre, Jiang, Li-Bo, Mao, Jian-Feng, 1977

Πηγή: New Phytologist. 247(3):1538-1549

Θεματικοί όροι: feature selection, Fourier transform, gradient boosting algorithms, long noncoding RNAs (lncRNAs), model selection, ORF coverage

Περιγραφή αρχείου: electronic

Σύνδεσμος πρόσβασης: https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-240920
https://doi.org/10.1111/nph.70211

Advanced Machine Learning Techniques Revolutionize Credit Card Fraud Detection

Συγγραφείς: A, Balamanikandan, D, Jayakumar, M, Dhanalakshmi, G, Sudharsan, Prabha, Ankala Satya, V, Venkata Karthika

Πηγή: 2025 International Conference on Networks & Advances in Computational Technologies (NetACT) Networks & Advances in Computational Technologies (NetACT), 2025 International Conference on. :1-6 Aug, 2025

Relation: 2025 International Conference on Networks & Advances in Computational Technologies (NetACT)

Early Detection of Student Dropout Risk via Hybrid Deep Learning and Gradient Boosting Algorithms

Συγγραφείς: Rajput, Vaibhav, Singh, Aryan, Gupta, Subhash Chand

Πηγή: 2025 International Conference on Engineering, Technology & Management (ICETM) Engineering, Technology & Management (ICETM), 2025 International Conference on. :1-6 May, 2025

Relation: 2025 International Conference on Engineering, Technology & Management (ICETM)

Classification and Prediction of Wafer Yield Using Gradient Boosting Algorithms: A Focus on Lithography Inline Parameters

Συγγραφείς: Islam, Mohammed Nizamul, Islam, Md Tarekul, Dhali, Maruf A

Πηγή: 2025 IEEE 4th International Conference on Computing and Machine Intelligence (ICMI) Computing and Machine Intelligence (ICMI), 2025 IEEE 4th International Conference on. :1-6 Apr, 2025

Relation: 2025 IEEE 4th International Conference on Computing and Machine Intelligence (ICMI)

Enhancing Liver Cirrhosis Prognosis: A Machine Learning and Explainable AI Approach

Συγγραφείς: Jeyabalan, Jeyalakshmi, Karthikeyan

Πηγή: 2024 Third International Conference on Artificial Intelligence, Computational Electronics and Communication System (AICECS) Artificial Intelligence, Computational Electronics and Communication System (AICECS), 2024 Third International Conference on. :1-5 Dec, 2024

Relation: 2024 Third International Conference on Artificial Intelligence, Computational Electronics and Communication System (AICECS)

Meta Learning Strategies for Comparative and Efficient Adaptation to Financial Datasets

Συγγραφείς: Noor, K., Fatima, U.

Πηγή: IEEE Access Access, IEEE. 13:24158-24170 2025

Revolutionizing Credit Risk: A Deep Dive into Gradient-Boosting Techniques in AI-Driven Finance

Συγγραφείς: Tareaf, Raad Bin, AbuJarour, Mohammed, Zinn, Fabian

Πηγή: 2024 International Conference on Information Networking (ICOIN) Information Networking (ICOIN), 2024 International Conference on. :322-327 Jan, 2024

Relation: 2024 International Conference on Information Networking (ICOIN)

Analysis and development of risk prediction models for chronic opioid use after surgery: a cohort study using the nationwide database

Συγγραφείς: Jonghae Kim, Hyun-Lim Yang, Eugene Kim, Hyung-Chul Lee, Hyun-Kyu Yoon, Yun Jin Kim, Kyu-Nam Kim, Ji-Yoon Kim, Jeong Min Sung, Tagkeun Lee

Πηγή: Korean Journal of Anesthesiology, Vol 78, Iss 5, Pp 429-442 (2025)

Θεματικοί όροι: dependence, opioid, gradient boosting algorithms, machine learning, postoperative period, prediction methods, machine, surgical procedures, operative, Anesthesiology, RD78.3-87.3

Περιγραφή αρχείου: electronic resource

Relation: http://ekja.org/upload/pdf/kja-24831.pdf; https://doaj.org/toc/2005-6419; https://doaj.org/toc/2005-7563

Σύνδεσμος πρόσβασης: https://doaj.org/article/52378890d3e74f4f9ba94ad2ab5a4bc7

A Comparison of Machine Learning Algorithms for Customer Churn Prediction

Συγγραφείς: Pulkundwar, Parth, Rudani, Krishna, Rane, Omkar, Shah, Chintan, Virnodkar, Shyamal

Πηγή: 2023 6th International Conference on Advances in Science and Technology (ICAST) Advances in Science and Technology (ICAST), 2023 6th International Conference on. :437-442 Dec, 2023

Relation: 2023 6th International Conference on Advances in Science and Technology (ICAST)

AI-Automated System for Ingredient Planner using Machine Learning Algorithms

Συγγραφείς: Arvindaraj, G., Manikandan, B., Rokesh, R., Abinaya, M.

Πηγή: 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2023 7th International Conference on. :511-515 Oct, 2023

Relation: 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

Machine Learning Techniques for The Detection of Imposters in Credit Card Fraudulence

Συγγραφείς: Jeyanathan, Josephine Selle, Kavyasree, Nayani, Pranathi, Thalla, Gowthami, Male, Monalisa, Rapuri

Πηγή: 2023 World Conference on Communication & Computing (WCONF) Communication & Computing (WCONF), 2023 World Conference on. :1-5 Jul, 2023

Relation: 2023 World Conference on Communication & Computing (WCONF)

Fraud Detection in Online Payments using Machine Learning Techniques

Συγγραφείς: Siddaiah, U., Anjaneyulu, P., Haritha, Y., Ramesh, M

Πηγή: 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS) Intelligent Computing and Control Systems (ICICCS), 2023 7th International Conference on. :268-273 May, 2023

Relation: 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS)

Optimization of Prediction Results Based on Ensemble Methods of Machine Learning

Συγγραφείς: Nazarov, Fayzullo Makhmadiyarovich, Yarmatov, Sherzodjon

Πηγή: 2023 International Russian Smart Industry Conference (SmartIndustryCon) Russian Smart Industry Conference (SmartIndustryCon), 2023 International. :181-185 Mar, 2023

Relation: 2023 International Russian Smart Industry Conference (SmartIndustryCon)

Using Gradient Boosting Algorithms in Predicting Student Academic Performance

Συγγραφείς: Mashagba, Esraa, Al-Saqqar, Faisal, Al-Shatnawi, Atallah

Πηγή: 2023 International Conference on Business Analytics for Technology and Security (ICBATS) Business Analytics for Technology and Security (ICBATS), 2023 International Conference on. :1-7 Mar, 2023

Relation: 2023 International Conference on Business Analytics for Technology and Security (ICBATS)

Design of an Intrusion Detection Model for IoT-Enabled Smart Home

Συγγραφείς: Rani, D., Gill, N.S., Gulia, P., Arena, F., Pau, G.

Πηγή: IEEE Access Access, IEEE. 11:52509-52526 2023

Competitive Analysis of the Top Gradient Boosting Machine Learning Algorithms

Συγγραφείς: R, Shyam, Ayachit, Sai Sanjay, Patil, Vinayak, Singh, Anubhav

Πηγή: 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) Computing, Communication Control and Networking (ICACCCN), 2020 2nd International Conference on. :191-196 Dec, 2020

Relation: 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)

Predicting H1N1 and Seasonal Flu : Vaccine Cases using Ensemble Learning approach

Συγγραφείς: Ayachit, Sai Sanjay, Kumar, Tanmay, Deshpande, Shriya, Sharma, Nayan, Chaurasia, Kuldeep, Dixit, Mayank

Πηγή: 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) Computing, Communication Control and Networking (ICACCCN), 2020 2nd International Conference on. :172-176 Dec, 2020

Relation: 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)

Construction and Application of Carbon Emissions Estimation Model for China Based on Gradient Boosting Algorithm

Συγγραφείς: Dongjie Guan, Yitong Shi, Lilei Zhou, Xusen Zhu, Demei Zhao, Guochuan Peng, Xiujuan He

Πηγή: Remote Sensing, Vol 17, Iss 14, p 2383 (2025)

Θεματικοί όροι: carbon emission forecasting, county level, gradient boosting algorithms, spatiotemporal patterns, SHAP interpretation, Science

Περιγραφή αρχείου: electronic resource

Relation: https://www.mdpi.com/2072-4292/17/14/2383; https://doaj.org/toc/2072-4292

Σύνδεσμος πρόσβασης: https://doaj.org/article/4acc578eb0134741bd02b2a727f372df

Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting

Συνεισφορές: Harrou, Fouzi, Sun, Ying, Taghezouit, Bilal, Dairi, Abdelkader

Θεματικοί όροι: two-diode model, parameter estimation, gray wolf optimizer, photovoltaic (PV), incremental conductance (InC), dragonfly (DA), maximum power point tracking (MPPT), perturb and observe (P&O), adaptive cuckoo search optimization (ACS), particle swarm optimization (PSO), local maxima (LM), complex partial shading (CPS), partial shading (PS), photovoltaic power forecast, solar energy, Temporal Fusion Transformer, deep learning, artificial intelligence, deep reinforcement learning, double deep Q network, photovoltaic mathematical model, photovoltaic systems, ensemble bagged trees, anomaly detection, shading, electrical faults, statistical control charts, shading ratio estimation, photovoltaics, total-sky imaging, cloud estimation, artificial intelligence (AI), photovoltaic (PV) systems, dust cleaning, renewable energy, optimization, cost minimization, BIPV, PV power forecasting, machine learning, gradient boosting algorithms, maximum power point tracker (MPPT), photovoltaic, partial shading conditions (PSCs), dandelion optimizer, monitoring system, fault diagnosis, internet of things, shallow neural networks, recurrent neural networks, predictive hybrid model, photovoltaic energy, photovoltaic energy prediction, Technology: general issues

Περιγραφή αρχείου: application/octet-stream

Σύνδεσμος πρόσβασης: https://directory.doabooks.org/handle/20.500.12854/137728
https://mdpi.com/books/pdfview/book/8957

Machine Learning-Based Lightning Localization Algorithm Using Lightning-Induced Voltages on Transmission Lines

Συγγραφείς: Karami, H., Mostajabi, A., Azadifar, M., Rubinstein, M., Zhuang, C., Rachidi, F.

Πηγή: IEEE Transactions on Electromagnetic Compatibility IEEE Trans. Electromagn. Compat. Electromagnetic Compatibility, IEEE Transactions on. 62(6):2512-2519 Dec, 2020