Genomics, phenomics, and machine learning in transforming plant research: Advancements and challenges by Sheikh Mansoor, Ekanayaka M.B.M. Karunathilake, Thai Thanh Tuan, Yong Suk Chung

“…Advances in gene editing and natural genetic variability present significant opportunities to generate novel alleles and select natural sources of genetic variation for horticulture crop improvement. …”
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A novel deep learning approach to field-road semantic segmentation by Bei Wang, Wenze Wang

“…This AI-driven discrimination is crucial for accurately monitoring the field operations of agricultural machinery. This study presents a deep learning framework that implements a novel semantic segmentation model designed to segment field-road trajectories, addressing the marked differences in spatial characteristics. …”
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Students’ perception of community as classroom for learning Geography in middle secondary school by Karma Sonam Rigdel, Mani Raj Thapa

“…Given these contexts, the present study seeks to explore the use of Community as Classroom (CAC) as a teaching strategy in learning Geography in middle secondary schools. …”
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Application of Machine Learning for Adaptive Trajectory Control of UAVs Under Uncertainty by Alexander S. Ermilov, Olga A. Saltykova

“…The article explores the potential of applying machine learning (ML) for adaptive trajectory control of unmanned aerial vehicles (UAVs) under uncertainty. …”
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The Changing Attitudes Towards Distance Learning: The New Demand for the Format by the Faculty by O. B. Solodovnikova, E. E. Malkova

“…The article presents an analysis of the results of the monitoring of faculty’s attitudes towards the digitalization of education conducted by the INSAP Center of IPEI RANEPA since 2020. …”
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When more is less: the impact of multimorphemic words on learning word meaning by Niveen Omar, Karen Banai, Bracha Nir

“…., affixes), while the entire words encode lower categories in the hierarchical structure. In the present study, we asked whether sub-lexical forms influence the learning of the meaning encoded by the entire word they underlie. …”
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Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning by Bimal Bhattarai, Rohan Kumar Yadav, Hui-Seon Gang, Jae-Young Pyun

“…Therefore, we can divide the indoor space into landmarks with magnetic field values and find the position of the user in a particular area inside the building. We present long short-term memory DRNNs for spatial/temporal sequence learning of magnetic patterns and evaluate their positioning performance on our testbed datasets. …”
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Prioritizing perturbation-responsive gene patterns using interpretable deep learning by Yan Cui, Zhiyuan Yuan

“…We introduce DSEP gene prioritization as a new analytical task and present River, an interpretable deep learning framework that identifies genes exhibiting condition-relevant spatial changes. …”
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Enhanced Diagnosis of Thyroid Diseases Through Advanced Machine Learning Methodologies by Osasere Oture, Muhammad Zahid Iqbal, Xining (Ning) Wang

“…This research developed an automated system capable of classifying three thyroid conditions using five machine learning models and a deep learning model. Resampling techniques, such as SMOTE oversampling and Random undersampling, are utilised to correct the issue of class imbalance in the dataset. …”
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Optimization based machine learning algorithms for software reliability growth models by Myeongguen Shin, Juwon Jung, Jihyun Lee, Insoo Ryu, Sanggun Park

“…However, many previous studies have relied on single optimization methods or deep learning approaches, which are prone to local optima and extrapolation issues, reducing prediction accuracy. …”
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Online ECG Biometrics for Streaming Data with Prototypes Learning and Memory Enhancement by Kuikui Wang, Na Wang

“…To enhance the discriminative power of to-be-learned sample representations, we introduce two novel modules: bidirectional regression and prototype learning. …”
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The learning curve of V-NOTES hysterectomy: a single-surgeon experience by François Charles, Mathieu Luyckx, Nathanael Dubois

“…Operative time (OT) served as an indicator of surgical proficiency. The learning curve was analyzed using the cumulative sum method of operative time (CUSUMot).ResultsThe mean OT was 59.32 ± 21.13 min. …”
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Undergraduate Nursing Students' Satisfaction of The Clinical Learning Environment in Iraq-Kurdistan by Zuhair Rushdi Mustafa, Safiya Sabri Piro

“…The researchers of the current study adopted the clinical learning environment inventory (CLEI) questionnaire to investigate nursing students' satisfaction of their clinical learning environment (CLE) for the purpose of data collection. …”
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Developing an Optimize Deep Learning Framework for Brain Tumors Detection and Classification by Ghosh Sayani, Mazumder Souvik, Kundu Prasenjit, Dey Arpita

“…Brain tumors are considered one of the present health challenges. Perhaps the early spotting could be an important factor in their effective treatment and in the good outcome of the patients. …”
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Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets by Amin Salemnia, Seyedehmaryam Hosseini Boldaji, Vida Atashi, Manoochehr Fathi-Moghadam

“…Vertical water jets present significant challenges for hydraulic structures due to their potential to cause erosion and structural damage. …”
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Transformer-Driven Active Transfer Learning for Cross-Hyperspectral Image Classification by Muhammad Ahmad, Francesco Mauro, Rana Aamir Raza, Manuel Mazzara, Salvatore Distefano, Adil Mehmood Khan, Silvia Liberata Ullo

“…Hyperspectral image (HSI) classification presents inherent challenges due to high spectral dimensionality, significant domain shifts, and limited availability of labeled data. …”
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An interpretable deep learning framework for medical diagnosis using spectrogram analysis by Shagufta Henna, Juan Miguel Lopez Alcaraz, Upaka Rathnayake, Mohamed Amjath

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Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks by Ying Yu, Min Li, Liangliang Liu, Yaohang Li, Jianxin Wang

“…This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. …”
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Power Theft Detection in Smart Grids Using Quantum Machine Learning by Konstantinos Blazakis, Nikolaos Schetakis, Mahmoud M. Badr, Davit Aghamalyan, Konstantinos Stavrakakis, Georgios Stavrakakis

“…This study proposes a novel electricity theft detection framework based on quantum machine learning (QML). The elegant field of QML has been used to demonstrate that data classification becomes more efficient in higher-dimensional spaces. …”
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Development and validation of the learning schedule adherence scale (LSAS) for university students by Shakira Khatun, Mim Mariha Nisha Mridha, Md. Minhaz Uddin, Abdul Muktadir, Anika Tarannum, Md. Romiz Uddin Rudro, Mohd. Ashik Shahrier

“…To achieve this goal, the present research developed a new scale, Learning Schedule Adherence Scale (LSAS), and examined its factor structure and validity in the higher education context of Bangladesh. …”
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