A hybrid Hadoop-based sentiment analysis classifier for tweets associated with COVID-19 utilizing two machine learning algorithms: CNN, and fuzzy C4.5 by Fatima Es-sabery, Ibrahim Es-sabery, Junaid Qadir, Beatriz Sainz-de-Abajo, Begonya Garcia-Zapirain

“…Many researchers prefer using machine and deep learning techniques for this analysis. …”
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Multi-keyword partial matching algorithm based on text fragments by LIAO Wei-qi, ZOU Wei

“…In order to resolve the problem, a new concept of pattern par-tial-matching based on text fragments was presented. …”
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Using Machine Learning to Identify the Dynamic Evolution Patterns of Negative Emotions in Perinatal Women: A Longitudinal Study in Southwest China by Yuan Zhang, Wenlong Li, Jian Zou, Guohui Yang, Xiaoni Zhong, Biao Xie

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Snow Distribution Patterns Revisited: A Physics‐Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub‐Arctic by R. L. Crumley, C. L. Bachand, K. E. Bennett

“…Abstract Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year‐to‐year patterns due to local topographic, weather, and vegetation characteristics. …”
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Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Induced by Microwave Radiation and Their Associated Strength Reduction Patterns: Employing Meta-Heuristic Opti... by Zhongyuan Gu, Xin Xiong, Chengye Yang, Miaocong Cao

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Identification of anoikis-related genes classification patterns and immune infiltration characterization in chronic rhinosinusitis with nasal polyps based on machine learning by Ziqi Chen, Lingmei Qu, Qing Hao, Shuang Teng, Shuo Liu, Qin Wu, Hongtian Yi, Xianji Shen, Liang Li, Zhaonan Xu, Yanan Sun

“…Immune cell infiltration was analysed using the CIBERSORT algorithm. CRSwNP samples were classified via consensus clustering. …”
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3D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification Technology by Chongyang YAO, Yongxin CHOU, Zhiwei LIANG, Haiping YANG, Jicheng LIU, Dongmei LIN

“…On this basis, nine features are extracted from the 3D pulse signals and features selection is performed using a two-sample Kolmogorov-Smirnov test. Finally, machine learning algorithms such as decision trees and random forests are used to identify the five types of pulse conditions: deep pulse, intermittent pulse, flooding pulse, slippery pulse, and rapid pulse. …”
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Diel and Annual Patterns of Vocal Activity of Three Neotropical Wetland Birds Revealed via BirdNET by Cristian Pérez-Granados, Karl-L. Schuchmann

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Audiogram Digitization Tool for Audiological Reports by Francois Charih, James R. Green

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Construction and Application of Carbon Emissions Estimation Model for China Based on Gradient Boosting Algorithm by Dongjie Guan, Yitong Shi, Lilei Zhou, Xusen Zhu, Demei Zhao, Guochuan Peng, Xiujuan He

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Programmable friction control in 3D printed patterned multi-materials: a flexible design strategy by Xinle Yao, Yuxiong Guo, Mingyang Wang, Yaozhong Lu, Zhibin Lu, Xin Jia, Yu Gao, Xiaolong Wang

“…The explainable ML model (linear regression algorithm) analyzes composite-specific tribological and physicochemical data (100 data) to autonomously design patterning surfaces with programmable friction coefficients, validated experimentally (μ = 0.07 ∼ 0.49). …”
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Reinventing the Trochoidal Toolpath Pattern by Adaptive Rounding Radius Loop Adjustments for Precision and Performance in End Milling Operations by Santhakumar Jayakumar, Sathish Kannan, Poongavanam Ganeshkumar, U. Mohammed Iqbal

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T‐SNERF: A novel high accuracy machine learning approach for Intrusion Detection Systems by Mohamed Hammad, Nabil Hewahi, Wael Elmedany

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Machine Learning-Based Classification of Programming Logic Understanding Levels by Mouse-Tracking Heatmaps by Attaporn Khaesawad, Yosuke Fukuchi, Vibol Yem, Nobuyuki Nishiuchi

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Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder by Lei Tang, Liling Wu, Mengqin Dai, Nian Liu, Lu liu

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The impacts of specific place visitations on theft patterns: a case study in Greater London, UK by Tongxin Chen, Kate Bowers, Tao Cheng

“…We utilised geo big data (mobile phone GPS trajectory records) collected from millions of anonymous users to measure footfalls (counts of visitations) attached to place types on weekdays and weekends. An explainable machine learning approach was applied to analyse the impacts of place visitations on theft levels: the ‘XGBoost’ algorithm trained a high-performance regression model and ‘SHapley Additive exPlanations’ (SHAP) values were measured to identify the contributions of different visitation variables to theft levels at specific spatial and temporal scales. …”
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Leveraging multiple cell-death patterns based on machine learning to decipher the prognosis, immune, and immune therapeutic response of soft tissue sarcoma by Binfeng Liu, Shasha He, Chenbei Li, Zijian Xiong, Zhaoqi Li, Chengyao Feng, Hua Wang, Chao Tu, Zhihong Li

“…Nonetheless, the precise role of multiple cell death patterns in STS is yet to be clarified. We employed 96 machine-learning algorithm combination frameworks to identify novel cell death-related signatures (CDSigs) with the highest mean c-index, indicating their excellence. …”
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HashTrie:a space-efficient multiple string matching algorithm by Ping ZHANG, Yan-bing LIU, Jing YU, Jian-long TAN

“…The famous multiple string matching algorithm AC consumed huge memory when the string signatures were massive,thus unable to process high speed network traffic efficiently.To solve this problem,a space-efficient multiple string matching algorithm-HashTrie was proposed.This algorithm adopted recursive hash function to store the patterns in bit-vectors in place of the state transition table in order to reduce space consumption.Further more it made use of the rank operation for fast verification.Theoretic analysis shows that the space complexity of HashTrie is O(|P|),which is linear with the size of pattern set |P|and is independent of the alphabetsize σ.The space complexity is superior to the complexity O(|P|σlog|P|)of AC.Experiments on synthetic datasets and real-world datasets(such as Snort,ClamAV and URL)show that HashTrie saves up to 99.6% storage cost compared with AC,and in the meanwhile it runs at a matching speed that is about half of AC.HashTrie is a space-efficient multiple string matching algorithm that is appropriate to search large scale pattern strings with short lengths.…”
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Machine learning classifiers to detect data pattern change of continuous emission monitoring system: A typical chemical industrial park as an example by Zhefeng Xu, Xiahong Shi, Wei Shu, Yilu Xin, Xuan Zan, Zhaonian Si, Jinping Cheng

“…By categorizing outlets into 12 datasets based on monitoring parameters, 17 machine learning models were evaluated to identify emission patterns and detect potential data anomalies. …”
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Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study by Zheng Zhang, Jian Wu, Yi Duan, Linwei Liu, Yaru Liu, Jinghan Wang, Li Xiao, Zhifeng Gao

“…We applied four ML algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Logistic Regression (LR)—to classify patients with or without HIBPV. …”
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