Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review by Jie Li, Manli Zhu, Li Yan

“…Then, we comprehensively extracted relevant data related to machine learning algorithms, predictors, and predicted objectives. We subsequently performed a critical evaluation of research quality, data aggregation, and analyses.Results We screened 25 studies on predictive models for sepsis-associated acute kidney injury from a total of originally identified 2898 studies. …”
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A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach by Yuli Wang, Na Mei, Ziyi Zhou, Yuan Fang, Jiacheng Lin, Fanchen Zhao, Zhihong Fang, Yan Li

“…Finally, critical variables in the optimal model were screened based on the interpretable algorithms to build a decision tree to facilitate clinical application. …”
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Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

“…The findings demonstrate that deep learning algorithms offer more accurate and earlier detection of diseases, highlighting their potential for application in plant growing. …”
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Semantic Segmentation-Driven Knowledge Distillation-Based Infrared Visible Image Fusion Framework by Xingshuo Wang

“…However, many existing fusion algorithms overly emphasize visual quality and traditional statistical evaluation metrics while neglecting the requirements of real-world applications, especially in high-level vision tasks. …”
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A secure and efficient user selection scheme in vehicular crowdsensing by Min Zhang, Qing Ye, Zhimin Yuan, Kaihuan Deng

“…Security analysis demonstrates that under the Canetti–Krawczyk (CK) model and the Decisional Diffie–Hellman (DDH) security assumption, the SEUS-VCS scheme satisfies the security of session keys. Performance evaluation indicates that compared to other key exchange schemes, the SEUS-VCS scheme shows a significant advantage in supporting national cryptographic algorithms and anonymity. …”
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Analisis Sentimen Ulasan Pengguna Aplikasi Alfagift Menggunakan Random Forest by M. Bagus Prayogi, Gustina Masitoh

“…User feedback on the Google Play Store shows mixed sentiments, including both positive and negative responses, which can be utilized by developers as material to improve the quality of the application. This study focuses on assessing the sentiment of Alfagift app user reviews through the application of the Random Forest algorithm. …”
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QoS-Aware Fault Detection in Wireless Sensor Networks by Alessandra De Paola, Giuseppe Lo Re, Fabrizio Milazzo, Marco Ortolani

“…The experimental evaluation produced good results, showing that our algorithm is able to greatly reduce the response time at the cost of a small reduction in classification accuracy.…”
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Boosting Crowdsourced Annotation Accuracy: Small Loss Filtering and Augmentation-Driven Training by Yanming Fu, Weigeng Han, Jingsang Yang, Haodong Lu, Xin Yu

“…However, the labels provided by untrained crowdsourcing workers often contain a considerable amount of noise. Although the application of ground truth inference algorithms to deduce integrated labels effectively enhances label quality, a certain level of noise persists. …”
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Research of color models in digital graphics by Hetman Oksana, Shpetna Svitlana

“…It is established that the RGB model is an additive system optimized for screens and displays, as it provides a broad and vibrant color range suitable for digital applications. In contrast, the CMYK model is subtractive and widely used for printing purposes, ensuring high-quality color reproduction but with a limited gamut. …”
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Review of Surface-Defect Detection Methods for Industrial Products Based on Machine Vision by Quan Wang, Mengnan Wang, Jiadong Sun, Deji Chen, Pei Shi

“…The paper organizes industrial defect datasets by type (multi-product and single-product), evaluates data quality and availability, and summarizes common evaluation metrics for accuracy, efficiency by task requirements. …”
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Data augmentation of time-series data in human movement biomechanics: A scoping review. by Christina Halmich, Lucas Höschler, Christoph Schranz, Christian Borgelt

“…This understanding is crucial for assessing the impact of the augmented data set on downstream models and evaluating the quality of the data augmentation process.…”
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Approaches for handling imbalanced data used in machine learning in the healthcare field: A case study on Chagas disease database prediction. by André G Coimbra, Cleiane G Oliveira, Matheus P Libório, Hasheem Mannan, Laercio I Santos, Elisa Fusco, Marcos F S V D'Angelo

“…This study conducts a comparative analysis of techniques for handling imbalanced data and evaluates their effectiveness in combination with a set of classification algorithms, specifically focusing on stroke prediction. …”
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Machine Learning Approaches to Code Similarity Measurement: A Systematic Review by Zixian Zhang, Takfarinas Saber

“…Following a rigorous systematic review protocol, we identified and analyzed 84 primary studies on a broad spectrum of dimensions covering application type, devised Machine Learning algorithms, used code representations, datasets, and performance metrics, as well as performance evaluations. …”
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The optimization path of agricultural industry structure and intelligent transformation by deep learning by Xingchen Pan, Jinyu Chen

“…Abstract This study addresses key challenges in optimizing agricultural industry structures and facilitating intelligent transformation through the application of deep learning algorithms and advanced optimization techniques. …”
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Microgrid Resilience Enhancement with Sensor Network-Based Monitoring and Risk Assessment Involving Uncertain Data by Tangxiao Yuan, Kossigan Roland Assilevi, Kondo Hloindo Adjallah, Ayité Sénah A. Ajavon, Huifen Wang

“…Subsequently, a decision risk assessment framework is constructed, integrating data quality evaluation and operational risk considerations, to drive strategy optimization through in-depth data analysis. …”
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DEEP LEARNING-BASED SUPER-RESOLUTION TECHNIQUES: A COMPARATIVE ANALYSIS WITH RECENT INSIGHTS by Renuka Sambhaji Sindge, Maitreyee Dutta, Jagriti Saini

“…Current researchers have employed machine-learning techniques, neural networks, and Deep Learning based methods to enhance the quality of LR images obtained from different field applications. …”
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An Efficient Biobjective Heuristic for Scheduling Workflows on Heterogeneous DVS-Enabled Processors by Pengji Zhou, Wei Zheng

“…Energy consumption has recently become a major concern to multiprocessor computing systems, of which the primary performance goal has traditionally been reducing execution time of applications. In the context of scheduling, there have been increasing research interests on algorithms using dynamic voltage scaling (DVS), which allows processors to operate at lower voltage supply levels at the expense of sacrificing processing speed, to acquire a satisfactory trade-off between quality of schedule and energy consumption. …”
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Assessing chronic obstructive pulmonary disease risk based on exhalation and cough sounds by Geyi Wen, Chenshuo Wang, Wei Zhao, Jinliang Meng, Yanyan Xu, Ruiqi Wang, Zijing Zeng

“…Ultimately, we assessed the feasibility of utilizing smartphones to capture exhalation and cough sounds for the identification of COPD risks through the application of machine learning algorithms. Results We gathered valid data from 530 adults, of whom 171 met the criteria for being at risk of COPD. …”
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Inferring Travel Modes from Cellular Signaling Data Based on the Gated Recurrent Unit Neural Network by Yanchen Wang, Fei Yang, Li He, Haode Liu, Li Tan, Cheng Wang

“…However, due to data privacy issues, the empirical evaluation of the performance of different identification methods is not yet sufficient. …”
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Radiomic Feature Extraction Based on Computed Tomography for Stroke Patients by Alamgir Hossain, Jolly Sultana, Shahed Hossain, Hafizur Rahman

“…Conclusion: In summary, stroke imaging analysis benefits greatly from the deployment of complex image processing techniques, reliable feature extraction methods, and cutting-edge segmentation algorithms. Accurate stroke lesion identification and stratification are made possible by the thorough assessment of diagnostic parameters such as ROC curves and the application of visualization tools such as heatmaps.…”
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