Nitrous oxide prediction through machine learning and field-based experimentation: A novel strategy for data-driven insights by Muhammad Hassan, Khabat Khosravi, Travis J. Esau, Gurjit S. Randhawa, Aitazaz A. Farooque, Seyyed Ebrahim Hashemi Garmdareh, Yulin Hu, Nauman Yaqoob, Asad T. Jappa

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Enhancing corn yield prediction: Optimizing data quality or model complexity? by Yuting Zhou, Shengfang Ma, Huihui Zhang, Sathyanarayanan Aakur

“…However, the cost of data acquisition and training requirements for machine or deep learning models depend on various factors, such as equipment (multispectral vs. …”
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Prevalence and clinical characteristics of patients with rheumatoid arthritis with interstitial lung disease using unstructured healthcare data and machine learning by Raul Castellanos-Moreira, Diego Benavent, Alejandra López Robles, Ernesto Trallero-Araguás, Lucía Silva-Fernández, Juliana Restrepo, Jose A Román Ivorra, Maria Lopez Lasanta, Laura Cebrián, Leticia Lojo, Belén López-Muñíz, Julia Fernández-Melon, Belén Núñez, Raúl Veiga Cabello, Pilar Ahijado, Isabel De la Morena Barrio, Nerea Costas Torrijo, Belén Safont, Enrique Ornilla, Arantxa Campo, Jose L Andreu, Elvira Díez, Elena Bollo, David Vilanova, Sara Luján Valdés

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A Workload Characterization Methodology Using Supervised and Unsupervised Deep Learning by Bing Hu, Karl Kempf, Nicholas Mason

“…To fill this gap, this study proposes a deep-learning workload analysis tool (DLWAT) to capture complex workload dynamics. …”
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A systematic review of effective data augmentation in cervical cancer detection by Betelhem Zewdu Wubineh, Andrzej Rusiecki, Krzysztof Halawa

“…The rapid progress of AI has made computer-assisted systems essential in medical fields like cervical cytology analysis. Deep learning requires large datasets, but data scarcity and privacy concerns pose challenges. …”
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Revolutionizing Sperm Analysis with AI: A Review of Computer-Aided Sperm Analysis Systems by Francisco J. Baldán, Diego García-Gil, Carlos Fernandez-Basso

“…By employing a spectrum of techniques, from classic machine learning (ML), often valued for its interpretability and efficiency with structured data, to deep learning (DL), which excels at extracting intricate features directly from image and video data, the field now achieves more accurate, automated, and high-throughput evaluations. …”
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Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model by Yi Wu, Yuwen Pan

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Recent innovations in machine learning for skin cancer lesion analysis and classification: A comprehensive analysis of computer‐aided diagnosis by Syeda Shamaila Zareen, Md Shamim Hossain, Junsong Wang, Yan Kang

“…This review article offers a thorough overview of current breakthroughs in machine learning (ML) and computer‐aided diagnostics (CAD) for the aim of analysis and classification of skin cancer lesions over the past 6 years. …”
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Association between serum hypertriglyceridemia and hematological indices: data mining approaches by Somayeh Ghiasi Hafezi, Amin Mansoori, Alireza Kooshki, Marzieh Hosseini, Sahar Ghoflchi, Mark Ghamsary, Gordon Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…Method Our sample size was 9704 participants beginning in 2007 and ending in 2020 aged between 35 and 65 years, sourced from the MASHAD cohort (northeastern Iran). Machine learning methodologies, specifically logistic regression, decision tree, and random forest algorithms, were utilized for data analysis in the investigation of individuals with normal and high TG levels. …”
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Partial-Net: A Method for Data Gaps Reconstruction on Mars Images by Depei Gu, Dingruibo Miao, Jianguo Yan, Zhigang Tu, Jean-Pierre Barriot

“…Data gaps appear in images of the Martian surface taken by the HiRISE camera on Mars orbiter reconnaissance, affecting downstream missions, e.g., terrain analysis. …”
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IMPROVING PREDICTION OF DEPRESSION AN ANALYTICAL COMPARISON BETWEEN HYBRID AI, MACHINE LEARNING AND DEEP LEARNING APPROACHES by Kanchapogu Naga Raju, Sachi Nandan Mohanty

“…This study provides a substantial addition by conducting an in-depth analysis of individual machine learning and deep learning models and comparing them to the integrated artificial intelligence solutions that we have built. …”
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FCEEG: federated learning-based seizure diagnosis through electroencephalogram (EEG) analysis by Zheng You Lim, Ying Han Pang, Shih Yin Ooi, Sarmela Raja Sekaran, Yee Jian Chew

“…Thus, we propose FCEEG, a convolutional-based deep learning with federated learning (FL) to diagnose seizures with EEG signals while preserving data privacy. …”
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Learning designs for work and power materials using didactical situation analysis approach by Dwi Ajni Shafarwati, Asep Herry Hermawan, Laksmi Dewi

“…Efforts to improve the sequence and/or stages of curriculum presentation have been made by examining various alternatives in accordance with students' previous learning experiences as well as their capacity and potential through didactical situation analysis. …”
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Federated Learning-Based Load Forecasting for Energy Aggregation Service Providers by HUANG Yichuan, SONG Yuhui, JING Zhaoxia

“…To address this issue，this study proposes a load-forecasting model based on federated learning，which achieves an effective improvement in load-forecasting accuracy while ensuring data security and privacy. …”
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Development of digital twin of CNC unit based on machine learning methods by Yu. G. Kabaldin, D. A. Shatagin, M. S. Anosov, A. M. Kuzmishina

“…In addition, equipment failures can be identified in real time on the basis of the intelligent analysis of the distributed sensor system data.…”
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Reliability analysis in curriculum development for social science education driven by machine learning by Rui Mao

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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

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Experiential-informed data reconstruction for fishery sustainability and policies in the Azores by Brenda Nogueira, Gui M. Menezes, Rita P. Ribeiro, Nuno Moniz

“…Our main objective is to tackle the task of data set reconstruction, leveraging domain knowledge and machine learning methods to retrieve or associate metier-related information to each fish landing. …”
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The analysis of motion recognition model for badminton player movements using machine learning by Xuanmin Zhu, Lizhi Liu, Jingshuo Huang, Genyan Chen, Xi Ling, Yanshuo Chen

“…The results indicate that QCNN has strong adaptability to noisy data. Further feature analysis reveals that arm angle, twist angle, and step position are key factors affecting classification accuracy, with the highest contribution in the QCNN model. …”
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Analysis of Learning Obstacles for Junior High School Students in Understanding SPLDV Concepts by Firda Luthfiatika Jannah, Neneng Aminah, Surya Amami Pramuditya, Cita Dwi Rosita, M. Subali Noto

“…The research instruments used were concept understanding tests and interview guidelines. Data analysis in this research includes data reduction, data presentation, and concluding. …”
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