Application of machine learning in predicting adolescent Internet behavioral addiction by Yao Gan, Li Kuang, Xiao-Ming Xu, Ming Ai, Jing-Lan He, Wo Wang, Su Hong, Jian mei Chen, Jun Cao, Qi Zhang

“…The model’s indicators under each algorithm were compared, evaluated with a confusion matrix, and the optimal model was selected.ResultThe proportion of male adolescents, urban household registration, and scores on the family function, planning, action, and cognitive subscales, along with psychoticism, introversion-extroversion, neuroticism, somatization, obsessive-compulsiveness, interpersonal sensitivity, depression, anxiety, hostility, paranoia, and psychosis, were significantly higher in the Internet addiction group than in the non-Internet addiction group (P < 0.05). …”
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Structural-methodical model of computer program for control of theoretical knowledge of cadets by V. V. Bulgakov

“…Basically, readymade computer programs are used, which are universal and are intended for wide application, without taking into account the specifics of Universities.Research and development of structural and methodological model was carried out with the use of theoretical analysis of the provisions of pedagogy on the problems of control, evaluation and analysis of the level of theoretical knowledge, methods of theory of algorithms and decision-making, methods of synthesis and analysis of information processes. …”
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Alpine Meadow Fractional Vegetation Cover Estimation Using UAV-Aided Sentinel-2 Imagery by Kai Du, Yi Shao, Naixin Yao, Hongyan Yu, Shaozhong Ma, Xufeng Mao, Litao Wang, Jianjun Wang

“…The performance of these estimates was evaluated against reference FVC values derived from centimeter-level UAV data. …”
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Sensor-Based Yield Prediction in Durum Wheat Under Semi-Arid Conditions Using Machine Learning Across Zadoks Growth Stages by Süreyya Betül Rufaioğlu, Ali Volkan Bilgili, Erdinç Savaşlı, İrfan Özberk, Salih Aydemir, Amjad Mohamed Ismael, Yunus Kaya, João P. Matos-Carvalho

“…Five different machine learning algorithms (Random Forest, Gradient Boosting, AdaBoost, LightGBM, and XGBoost) were tested individually for each stage, and the model performances were evaluated using statistical metrics such as R²%, RMSE t/ha, and MAE t/ha. …”
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Land Cover Transformations in Mining-Influenced Areas Using PlanetScope Imagery, Spectral Indices, and Machine Learning: A Case Study in the Hinterlands de Pernambuco, Brazil by Admilson da Penha Pacheco, João Alexandre Silva do Nascimento, Antonio Miguel Ruiz-Armenteros, Ubiratan Joaquim da Silva Junior, Juarez Antonio da Silva Junior, Leidjane Maria Maciel de Oliveira, Sylvana Melo dos Santos, Fernando Dacal Reis Filho, Carlos Alberto Pessoa Mello Galdino

“…The methodology consisted of monitoring and evaluating environmental impacts using the k-Nearest Neighbors (kNN) algorithm, spectral indices (Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and hydrological data, covering the period from 2018 to 2023. …”
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A Perception-Driven Transcale Display Scheme for Space Image Sequences by Lingling Zi, Xin Cong, Yanfei Peng, Pei Yang

“…On this basis, the latter focuses on high-quality display of space movements under different scales, including three algorithms, namely, target trajectory computing, space transcale display, and space movement display. …”
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A novel ensemble model for multi-temporal forest vegetation classification: integrating spectral-temporal features and topographic constraints by Rongfei Duan, Chunlin Huang, Peng Dou, Jinliang Hou, Ying Zhang

“…Outputting and integrating the results of multiple classifiers provides insights into the model’s classification mechanism, results proved the effectiveness of the feature extraction module and the differences among the integration strategies. The Simpson index was employed for visual evaluation, overcoming sample labelling limitations, and the influence of elevation and slope on vegetation was analysed, highlighting the necessity for topographic features in mountainous areas.…”
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COMPARISON BETWEEN MANUAL AND AUTOMATIC SEGMENTATION OF THE WHOLE-BRAIN AND CEREBELLUM by Marcos Paulo Dias de Sá e Silva, Maria Emília Seren TAKAHASHI, Tiago Pessolo Dos Santos, Carmino Antonio De Souza, Celso Darío Ramos

“…The Beth Israel Plugin in ImageJ allows both manual and automatic segmentation, making it relevant for evaluating differences in brain and cerebellum analysis. …”
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On the generalisation capabilities of fingerprint presentation attack detection methods in the short wave infrared domain by Jascha Kolberg, Marta Gomez‐Barrero, Christoph Busch

“…In this context, the authors evaluate the generalisability of multiple PAD algorithms on a dataset of 19,711 bona fide and 4339 PA samples, including 45 different PAI species. …”
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Path Loss Characterization Using Machine Learning Models for GS-to-UAV-Enabled Communication in Smart Farming Scenarios by Sarun Duangsuwan, Phakamon Juengkittikul, Myo Myint Maw

“…The performances of the path loss prediction models were evaluated using the statistical error indicators in different measurement locations and UAV trajectories. …”
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Land Surface Condition-Driven Emissivity Variation and Its Impact on Diurnal Land Surface Temperature Retrieval Uncertainty by Lijuan Wang, Ping Yue, Yang Yang, Sha Sha, Die Hu, Xueyuan Ren, Xiaoping Wang, Hui Han, Xiaoyu Jiang

“…This study utilizes thermal infrared emissivity and hyperspectral data collected from diverse underlying surfaces from 2017 to 2024 to analyze LSE variation characteristics across different surface types, spectral bands, and temporal scales. …”
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Integrating microplastic research in sustainable agriculture: Challenges and future directions for food production by Marcelo Illanes, María-Trinidad Toro, Mauricio Schoebitz, Nelson Zapata, Diego A. Moreno, María Dolores López-Belchí

“…Furthermore, machine learning algorithms can be employed to analyze complex datasets, enhancing our ability to predict the impacts of MPs on plant health and crop performance under different environmental conditions. …”
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Advancing Transportation Routing Decisions Using Riemannian Manifold Surfaces by Emre Tokgöz, Iddrisu Awudu, Saravanan Kuppusamy

“…Though these factors increase the complexity of modeling, they provide the flexibility to evaluate the routing solutions from different perspectives: cost, distance, and time, to name a few. …”
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Human Performance-Aware Scheduling and Routing of a Multi-Skilled Workforce by Maikel L. van Eck, Murat Firat, Wim P. M. Nuijten, Natalia Sidorova, Wil M. P. van der Aalst

“…Planning human activities within business processes often happens based on the same methods and algorithms as are used in the area of manufacturing systems. …”
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Monitoring poultry social dynamics using colored tags: Avian visual perception, behavioral effects, and artificial intelligence precision by Florencia B. Rossi, Nicola Rossi, Gabriel Orso, Lucas Barberis, Raul H. Marin, Jackelyn M. Kembro

“…In separate experiments, we aim to: (i) evaluate avian visual perception of the different colored tags; (ii) assess the potential impact of tag colors on social behavior; and (iii) test the ability of the YOLO model to accurately distinguish between different colored tags on Japanese quail in social group settings. …”
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Multiomics-Based Deep Learning Prediction of Prognosis and Therapeutic Response in Patients With Extensive-Stage Small Cell Lung Cancer Receiving Chemoimmunotherapy: A Retrospectiv... by Nie F, Pei X, Du J, Shi W, Wang J, Feng L, Liu Y

“…The model’s predictive ability was evaluated using the receiver operating characteristic (ROC) curve and clinical decision curve analysis(DCA). …”
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A Comparative Study for Speech Summarization Based on Machine Learning: A Survey by Hiba Altememi, Yusra Al-Irhaim

“…As newer deep learning approaches were not included in earlier surveys, this is a new survey in this discipline where different approaches with various datasets were explored for speech summarization and evaluated using subjective or objective methods.…”
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A Critical Comparison of Machine Learning Classifiers to Predict Match Outcomes in the NFL by Beal Ryan, Norman Timothy J., Ramchurn Sarvapali D.

“…Specifically, we implement and test nine techniques using real-world datasets of 1280 games over 5 seasons from the National Football League (NFL). We test the nine different classifier techniques using a total of 42 features for each team and we find that the best performing algorithms are able to improve one previous published works. …”
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Using machine learning to predict gamma shielding properties: a comparative study by T A Nahool, A M Abdelmonem, M S Ali, A M Yasser

“…This study employed machine learning (ML) algorithms to predict the linear attenuation coefficients (LACs) of materials in inorganic scintillation detectors, which are crucial for evaluating self-shielding properties. …”
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AI-Driven Advancements in Bioinformatics: Transforming Healthcare and Science by Sunil Namdev Thitame, Ashwini Ashok Aher

“…The sorting and analysis of large biological data sets benefit from Original Artificial Intelligence and its subcategories including machine learning and deep learning which previously struggled with data interpretation. This review evaluates the fundamental AI enabled bioinformatics approaches with their deployment for genomic and protein structure prediction and drug discovery algorithms and diagnostic solutions. …”
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