Robust Tracking Method for Small and Weak Multiple Targets Under Dynamic Interference Based on Q-IMM-MHT by Ziqian Yang, Hongbin Nie, Yuxuan Liu, Chunjiang Bian

“…Furthermore, the algorithm utilizes Support Vector Machines (SVMs) for anomaly detection and trajectory recovery, thereby enhancing the accuracy of data association and the overall robustness of the system. …”
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Geographical features and management strategies for microplastic loads in freshwater lakes by Huike Dong, Ruixuan Zhang, Xiaoping Wang, Jiamin Zeng, Lei Chai, Xuerui Niu, Li Xu, Yunqiao Zhou, Ping Gong, Qianxue Yin

“…To address this gap, our study utilizes Machine Learning (the random forest algorithm), combined with number-to-mass transformation techniques to generate a global prediction. …”
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Nuevos modelos para la Caracterización, Detección y Diagnóstico de Fallas en Máquinas Eléctricas Rotativas by Jair Elías Araujo Vargas, Dilan Yesid Franklin Coronel, Victor Manuel Arias Ruiz

“…Therefore, modern techniques based on machine learning offer significant improvements in fault detection and prediction, allowing the identification of complex patterns and more precise diagnoses, expanding the capabilities of conventional approaches to facilitate predictive maintenance. …”
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Correlation Between Depression-Associated Genes and Cancer Types: Predicting Cancer Based on Mutation Frequencies by Fernando Patricio Carranco-Avila, Clayanela Zambrano-Caicedo, Jonathan Javier Loor-Duque, Ariana Deyaneira Jimenez-Narvaez, Ivan Galo Reyes-Chacon, Paulina Vizcaino, Isidro Rafael Amaro Martin, Manuel Eugenio Morocho-Cayamcela

“…The analysis employed advanced methodologies, including HJ biplot K-means and DBSCAN clustering algorithms for pattern grouping in 2D. This process generated a dataset, enabling the training and testing of machine learning and deep learning classification models. …”
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Detailed Time-resolved Spectral and Temporal Investigations of SGR J1550–5418 Bursts Detected with Fermi Gamma-Ray Burst Monitor by Mustafa Demirer, Ersin Göğüş, Yuki Kaneko, Özge Keskin, Sinem Şaşmaz, Shotaro Yamasaki

“…Subsequently, we obtained nonoverlapping time segments with varying lengths based on their spectral evolution patterns, employing a machine learning algorithm called k -means clustering. …”
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Population status and impact of climate change on the distribution of vulnerable multipurpose plant Hippophae rhamnoides ssp. turkestanica for conservation in Trans-Himalaya, India by Shiv Paul, Shiv Paul, Khilendra Singh Kanwal, Anil Kumar, Sher Singh Samant, Sher Singh Samant, Indra Dutt Bhatt, Rakesh Chandra Sundriyal, Rakesh Chandra Sundriyal, Swaran Lata

“…Therefore, the current study aims to assess the population status and predict highly suitable areas for Trans-Himalaya species under changing climatic conditions. The machine learning algorithm showed that Bio_6 (minimum temperature of the coldest month), elevation, and slope were the best suitable variables for the habitat prediction along with the CMIP6 project’s MIROC6 and CMCC-ESM2 climate change models to identify the potential distribution area of the species for the future under the SSP245 (middle of the way) and SSP585 (fossil-fueled development) scenarios, respectively. …”
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Assessing Climate and Land-Use Change Scenarios on Future Desertification in Northeast Iran: A Data Mining and Google Earth Engine-Based Approach by Weibo Yin, Qingfeng Hu, Jinping Liu, Peipei He, Dantong Zhu, Abdolhossein Boali

“…Six remote sensing indices were selected to model desertification using four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Generalized Linear Models (GLM). …”
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A predictive analytics framework for opportunity sensing in stock market by Shruti Mittal, C.K. Nagpal

“…Thus research papers in this area suffer from multiple limitations: Very short prediction period from one day to one week, consideration of few stocks only instead of whole of stock market spectrum, exploration of more suitable machine learning algorithms. By overcoming the problems of raw data these limitations can be conquered. …”
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Spatio-temporal distribution, prediction and relationship of three major acute cardiovascular events: Out-of-hospital cardiac arrest, ST-elevation myocardial infarction and stroke by Angelo Auricchio, Tommaso Scquizzato, Federico Ravenda, Ruggero Cresta, Stefano Peluso, Maria Luce Caputo, Stefano Tonazzi, Claudio Benvenuti, Antonietta Mira

“…Widespread implementation in clinical practice of prediction algorithms may allow to improve resource allocation, reduce treatment delays, and improve outcomes.…”
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Analysis of Gene Expression Omnibus high-throughput sequencing data for the determination of microribonucleic acids in the blood plasma of patients with glioblastomas by A. A. Pushkin, D. Yu. Gvaldin, N. N. Timoshkina, E. E. Rostorguev, L. Yu. Vladimirova, E. A. Dzenkova

“…Determination of significant miRNAs using machine learning algorithms of the R 4.0.4 project. …”
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Smart deep learning model for enhanced IoT intrusion detection by Faisal S. Alsubaei

“…Existing approaches, however, are usually hampered by the inability to effectively counter the sophisticated and evolving nature of such threats, especially in preprocessing optimization and hyperparameter tuning, which typically adopt conventional machine learning and deep learning models. This paper addresses these limitations with large preprocessing steps followed by hyperparameter tuning of machine learning XGBoost and deep learning Sequential Neural Network (OSNN) algorithms through Grid Search for their best values to improve multiclass intrusion detection across varied datasets. …”
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Determinants of depressive symptoms in multinational middle-aged and older adults by Can Lu, Shenwei Wan, Zhiyong Liu

“…Stratified analyses by income and sex revealed marked heterogeneity: wealth, employment, digital inclusion, and marital status exerted greater influence in lower-income groups, with distinct gender-specific patterns. These findings highlight machine learning’s capacity to reveal nuanced, context-dependent risk profiles beyond traditional models, emphasizing the need for tailored interventions that address the diverse vulnerabilities of aging populations, particularly those socioeconomically disadvantaged.…”
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Deep Learning Hybrid Architecture Based on Vision Transformer for Phase Analysis of Moiré Fringes by Dajie Yu, Junbo Liu, Chuan Jin, Yuyang Li, Kairui Zhang, Ji Zhou

“…Overlay accuracy is a fundamental indicator of a photolithography machine performance. Misalignment between the mask and wafer is the main factor affecting overlay accuracy. …”
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Small-sample-data augmentation and transfer strategies for forest cover change monitoring by Kun Feng, Shaoxiu Ma, Haiyang Xi, Linhao Liang, Weiqi Liu, Atsushi Tsunekawa

“…The large sample data were used to develop the 30-meter annual forest cover dataset (AFD_QLM) from 1986 to 2023, using a locally adaptive machine learning algorithm at 1°×1° grid cells. This approach offers an effective technical framework for forest mapping in regions with scarce sample data and complex terrain. …”
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Sparse Feature-Weighted Double Laplacian Rank Constraint Non-Negative Matrix Factorization for Image Clustering by Hu Ma, Ziping Ma, Huirong Li, Jingyu Wang

“…Consequently, the SFLRNMTF approach becomes more robust in capturing data patterns and achieving high-quality clustering results in complex datasets. …”
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Mitigating malicious denial of wallet attack using attribute reduction with deep learning approach for serverless computing on next generation applications by Amal K. Alkhalifa, Mohammed Aljebreen, Rakan Alanazi, Nazir Ahmad, Sultan Alahmari, Othman Alrusaini, Ali Alqazzaz, Hassan Alkhiri

“…The deep learning (DL) model, a part of the machine learning (ML) technique, has developed as an effectual device in cybersecurity, permitting more effectual recognition of anomalous behaviour and classifying patterns indicative of threats. …”
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Integrating structured and unstructured data for livestock price forecasting: a sustainability study from South Korea by Yifan Zhu, Yifan Zhu, Yifan Zhu, Tserenpurev Chuluunsaikhan, Jong-Hyeok Choi, Aziz Nasridinov

“…SASD framework, which systematically decomposes complex livestock price time series into trend, seasonal, and residual components, improving the forecasting accuracy by isolating seasonal patterns and irregular fluctuations. Additionally, we develop a Korean-language sentiment lexicon using an improved Term Frequency–Inverse Document Frequency (ITF-IDF) algorithm, enabling morpheme-level sentiment analysis for better sentiment extraction in Korean contexts. …”
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Associations of accelerometer-measured physical activity, sedentary behaviour, and sleep with next-day cognitive performance in older adults: a micro-longitudinal study by Mikaela Bloomberg, Laura Brocklebank, Aiden Doherty, Mark Hamer, Andrew Steptoe

“…Physical behaviour (time spent in moderate-to-vigorous physical activity [MVPA], light physical activity [LPA], and sedentary behaviour [SB]) and sleep characteristics (overnight sleep duration, time spent in rapid eye movement [REM] sleep and slow wave sleep [SWS]) were extracted from accelerometers, with sleep stages derived using a novel polysomnography-validated machine learning algorithm. We used linear mixed models to examine associations of physical activity and sleep with next-day cognitive performance, after accounting for habitual physical activity and sleep patterns during the study period and other temporal and contextual factors. …”
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Whole genome resequencing reveals genetic diversity, population structure, and selection signatures in local duck breeds by Pengwei Ren, Yongdong Peng, Liu Yang, Muhammad Zahoor Khan, Yadi Jing, Chao Qi, Zhansheng Liu, Shuer Zhang, Nenzhu Zheng, Meixia Zhang, Xiang Liu, Zhiming Zhu, Mingxia Zhu

“…Conclusions This study, utilizing genome sequencing data and machine learning algorithms, provides a comprehensive evaluation of the genetic resources of Shandong’s local duck breeds. …”
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An analysis of spatial changes in the manufacturing industry in china’s three major urban clusters from 2015 to 2019 using POI data by Chenxi Jin, Chenjing Fan, Yiwen Gong, Xinran Huang, Shiqi Li, Runhan Liu, Chunwei Guo, Yuxin Liu

“…Here, we analyzed spatial patterns of the manufacturing industry using point-of-interest (POI) data and a machine learning classification algorithm based on the Naive Bayes classifier. …”
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