Apple Yield Estimation Method Based on CBAM-ECA-Deeplabv3+ Image Segmentation and Multi-Source Feature Fusion by Wenhao Cui, Yubin Lan, Jingqian Li, Lei Yang, Qi Zhou, Guotao Han, Xiao Xiao, Jing Zhao, Yongliang Qiao

“…Four structural feature ratios were extracted, visible-light and multispectral vegetation indices were calculated, and feature selection was performed using Pearson’s correlation coefficient analysis. …”
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Spatio-Temporal Fusion of Landsat and MODIS Data for Monitoring of High-Intensity Fire Traces in Karst Landscapes: A Case Study in China by Xiaodong Zhang, Jingyi Zhao, Guanzhou Chen, Tong Wang, Qing Wang, Kui Wang, Tingxuan Miao

“…Moreover, in our karst landscape study area, our proposed Hybrid Strategy selection framework can dynamically optimize the fusion process of multi-source satellite data, which is significantly better than a single fusion strategy. (3) The dNBR-based extraction achieved 90.00% producer accuracy, 69.23% user accuracy, and a Kappa coefficient of 0.718 when validated against field data. …”
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A Triple-Channel Network for Maritime Radar Targets Detection Based on Multi-Modal Features by Kaiqi Wang, Zeyu Wang

“…This method comprehensively improves the traditional multi-channel inputs by extracting highly complementary multi-modal features from radar echoes, namely, time-frequency image, phase sequence and correlation coefficient sequence. Appropriate networks are selected to construct a triple-channel network according to the internal data structure of each feature. …”
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Multi-Level Thresholding Based on Composite Local Contour Shannon Entropy Under Multiscale Multiplication Transform by Xianzhao Li, Yaobin Zou

“…Experimental results on synthetic images and real-world images with complex backgrounds, low contrast, blurred boundaries, and unbalanced sizes demonstrate that the proposed method outperforms six recently proposed multi-level thresholding methods based on the Matthew’s correlation coefficient, indicating stronger adaptability and robustness for segmentation without requiring complex parameter tuning.…”
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ASSESSMENT OF GYMNASTIC SKILLS AT PHYSICAL EDUCATION – THE CASE OF BACKWARD ROLL by Marjeta Kovač

“…For formative assessment, it has to be mentioned that the measuring scales and criteria should differ according to the purpose of evaluation, the developmental stage of pupils and the complexity of evaluated movement. …”
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Graph cut-based segmentation for intervertebral disc in human MRI by Leena Silvoster, R. Mathusoothan S. Kumar

“…The polynomial time complexity of this approach enables the exploration of a globally optimal solution, eliminating the need for user interaction in seed point selection. …”
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Fine-Tuned Machine Learning Classifiers for Diagnosing Parkinson’s Disease Using Vocal Characteristics: A Comparative Analysis by Mehmet Meral, Ferdi Ozbilgin, Fatih Durmus

“…Ensemble models proved particularly effective in handling complex datasets, demonstrating robust diagnostic performance. …”
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Solving Volterra-Fredholm integral equations by non-polynomial spline function based on weighted residual methods by S.H. Salim, R.K. Saeed, K.H.F. Jwamer

“…The approach begins with the selection of a series of knots along the integration interval. …”
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Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning Algorithms by Chao Li, Jinhui Li, Zhongqi Shi, Li Li, Mingxiong Li, Dianqi Jin, Guo Dong

“…Three indicators, mean absolute error (MAE), accuracy (ACC), and coefficient of determination (R2), are selected to evaluate the prediction performance. …”
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Modified Energy-Based Design Method of the Precast Partially Steel-Reinforced Concrete Beam–CFST Column Eccentrically Braced Frame by Fugui Hou, Weiguang Chong, Yu Lin, Xijun He, Guanglei Zhang

“…Current related design methods focus on the concrete and steel structures rather than on the complex composite structure. In addition, they tend to overlook the contribution of the energy-dissipation unit and its corresponding additional influence on the structure. …”
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Exploring implications of input parameter uncertainties in glacial lake outburst flood (GLOF) modelling results using the modelling code r.avaflow by S. Rinzin, S. Dunning, R. J. Carr, A. Sattar, M. Mergili

“…<p>Modelling complex mass flow processes, such as glacial lake outburst floods (GLOFs), for hazard and risk assessments requires extensive data and computational resources. …”
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Comparative analysis of machine learning models for predicting water quality index in Dhaka’s rivers of Bangladesh by Mosaraf Hosan Nishat, Md. Habibur Rahman Bejoy Khan, Tahmeed Ahmed, Syed Nahin Hossain, Amimul Ahsan, M. M. El-Sergany, Md. Shafiquzzaman, Monzur Alam Imteaz, Mohammad T. Alresheedi

“…The ANN model demonstrated superior predictive capability, achieving a Root Mean Squared Error (RMSE) of 2.34, a Mean Absolute Error (MAE) of 1.24, a Nash–Sutcliffe Efficiency (NSE) of 0.97, and a Coefficient of Determination (R2) of 0.97. Furthermore, an Adjusted R 2 value of 0.965 further confirmed its ability to capture complex patterns in water quality data with remarkable accuracy. …”
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A graph convolutional network approach for hyperspectral image analysis of blueberries physiological traits under drought stress by Md. Hasibur Rahman, Savannah Busby, Sajid Hanif, Md Mesbahul Maruf, Faraz Ahmad, Sushan Ru, Alvaro Sanz-Saez, Jingyi Zheng, Tanzeel U. Rehman

“…The Plant-GCN model utilizes graph convolutional layers that aggregate information from neighboring nodes, effectively capturing complex interactions in the spectral signature and enhancing the prediction of physiological traits. …”
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Assessment of Microsatellite Genetic Diversity of Cultured Eriocheir sinensis Populations from Jiangsu and Anhui by Yuting HU, Jun LING, He JIANG, Huan WANG, Tingshuang PAN, Guoqing DUAN, Huaxing ZHOU, Min YANG, Tong LI

“…Analysis of molecular variance (AMOVA) and the coefficient of genetic differentiation (Fst) were performed using Arlequin 3.5 software. …”
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Construction of a sugar and acid content estimation model for Miliang-1 kiwifruit during storage by LIU Li, YANG Tianyi, DONG Congying, SHI Caiyun, SI Peng, WEI Zhifeng, GAO Dengtao

“…[Objective] Traditional chemical methods for assessing the storage quality of kiwifruit typically involve complex procedures and high costs, which may hinder their widespread use. …”
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COMPARISON OF CORONARY HEART DISEASE MORTALITY IN MEN AND WOMEN AGE 50 YEARS AND OLDER IN RUSSIA AND USA by S. A. Boytsov, O. V. Zayratiants, E. M. Andreev, I. V. Samorodskaya

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Maize and soybean yield prediction using machine learning methods: a systematic literature review by Ramandeep Kumar Sharma, Jasleen Kaur, Gary Feng, Yanbo Huang, Chandan Kumar, Yi Wang, Sandhir Sharma, Johnie Jenkins, Jagmandeep Dhillon

“…This SLR also reported major challenges with obtaining high quantity/quality data, model complexity tackling, and incorporating the inclusion of farm management factors in yield prediction process. …”
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Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security by Hanadi Hakami, Muhammad Faheem, Majid Bashir Ahmad

“…The approach optimizes data preprocessing by integrating SMOTE for effective data balancing and Pearson&#x2019;s Correlation Coefficient (PCC) for feature selection. We compared several ML and DL techniques to detect and address the most efficient one for our pipeline. …”
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The level of integration of moral values in secondary education: Instrument validation by Florina-Gratiela Schiopu-Constantin

“…Item analysis, exploratory factorial analysis, and reliability analysis led to item selection and also the final design. The instrument was given the name of RIS- Scale. …”
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Calculation Method of Phenotypic Traits for Tomato Canopy in Greenhouse Based on the Extraction of Branch Skeleton by Xiaodan Ma, Qiu Jiang, Haiou Guan, Lu Wang, Xia Wu

“…Automatic acquisition of phenotypic traits in tomato plants is important for tomato variety selection and scientific cultivation. Because of time-consuming and labor-intensive traditional manual measurements, the lack of complete structural information in two-dimensional (2D) images, and the complex structure of the plants, it is difficult to automatically obtain the phenotypic traits of the tomato canopy. …”
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