Employing Streaming Machine Learning for Modeling Workload Patterns in Multi-Tiered Data Storage Systems by Edson Ramiro Lucas Filho, George Savva, Lun Yang, Kebo Fu, Jianqiang Shen, Herodotos Herodotou

“…Modern multi-tiered data storage systems optimize file access by managing data across a hybrid composition of caches and storage tiers while using policies whose decisions can severely impact the storage system’s performance. Recently, different Machine-Learning (ML) algorithms have been used to model access patterns from complex workloads. …”
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Improvement of classification accuracy of functional near-infrared spectroscopy signals for hand motion and motor imagery using a common spatial pattern algorithm by Omid Asadi, Mahsan Hajihosseini, Sima Shirzadi, Zahra Einalou, Mehrdad Dadgostar

“…This study aimed to address this challenge by employing the common spatial pattern (CSP) algorithm to reduce input dimensions for support vector machine (SVM) and linear discriminant analysis (LDA) classifiers. …”
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Efficient and Effective Detection of Repeated Pattern from Fronto-Parallel Images with Unknown Visual Contents by Hong Qu, Yanghong Zhou, P. Y. Mok, Gerhard Flatz, Li Li

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Modulation pattern recognition method of wireless communication automatic system based on IABLN algorithm in intelligent system. by Ting Xie, Xing Han

“…The Kappa coefficient in the Communication Signal Processing Benchmark for Machine Learning (CSPB.ML2018) 2018 dataset was 0.62, representing an average increase of 10.32% over other algorithms. …”
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Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy by Jing Zhang, Lulu Cheng, Shan Jiang, Duosheng Zhu

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Pattern and semantic analysis to improve unsupervised techniques for opinion target identification by Khairullah khan, Ashraf Ullah, Baharum Baharudin

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Spatiotemporal estimation of ambient forest phytoncides: Unveiling patterns through geospatial-based machine learning approach by Aji Kusumaning Asri, Hao-Ting Chang, Chia-Pin Yu, Wan-Yu Liu, Yinq-Rong Chern, Rui-Hao Xie, Shih-Chun Candice Lung, Kai Hsien Chi, Yu-Cheng Chen, Sen-Sung Cheng, Gary Adamkiewicz, John D. Spengler, Chih-Da Wu

“…The robustness of these models was confirmed through extensive validation. Spatial pattern analysis revealed that variations in these biogenic compound concentrations were linked to meteorological conditions and vegetation types. …”
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A Model for Detecting <i>Xanthomonas campestris</i> Using Machine Learning Techniques Enhanced by Optimization Algorithms by Daniel-David Leal-Lara, Julio Barón-Velandia, Lina-María Molina-Parra, Ana-Carolina Cabrera-Blandón

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Evaluation of the Proposed Hand Vein Authentication System using Machine Learning by rajaa ahmed, Ziyad Tariq Mustafa Al-Ta’i

“…The features are extracted through PCA Net for acquiring the most distinctive attributes of hand veins. The different machine learning algorithms used in this evaluation for classification of the extracted features include SVM, Logistic Regression, and Naive Bayes. …”
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Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning models by Hongxia Hao, Jiemin Chen, Yifei Yan, Yifei Yan, Qi Zhang, Qi Zhang, Zhilu Zhou, Wentao Xia

“…PurposeTo develop effective machine learning models that analyze pattern visual evoked potentials (PVEPs) to predict the stabilized visual acuity (VA) of patients with treated ocular trauma.MethodsThis experiment included 260 patients (220 males, average age 42.54 years) with unilateral ocular trauma. …”
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Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases by Miad Boodaghidizaji, Thaisa Jungles, Tingting Chen, Bin Zhang, Tianming Yao, Alan Landay, Ali Keshavarzian, Bruce Hamaker, Arezoo Ardekani

“…Accordingly, the aim of our study was to test the hypothesis that machine learning algorithms can distinguish stool microbiota patterns—and their responses to fiber—across diseases with previously reported overlapping dysbiotic microbiota profiles. …”
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Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis by Ana Rocha, Cristina Costeira, Raul Barbosa, Florbela Gonçalves, Miguel Castelo-Branco, Joaquim Viana, Margarida Gaudêncio, Filipa Ventura

“…Statistical analyses were performed using SPSS and machine learning tools, specifically KMeans clustering and Random Forest algorithms. …”
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Muses or Stereotypes? Identifying Historical Patterns of Sexism in a Corpus of Brazilian Lyrics by Janaina Nogueira de Souza Lopes, Vitória Pereira Firmino, Valéria Quadros dos Reis

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The Determination of Distances between Images by de Rham Currents Method by Sergey N. Chukanov

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Analyzing Student Graduation and Dropout Patterns Using Artificial Intelligence and Survival Strategies by Behrouz Alefy, Vahid Babazadeh

“…The study applies state-of-the-art machine learning techniques to establish dominant patterns and offer forecasts using a wide range of student records. …”
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An interpretable machine learning model with demographic variables and dietary patterns for ASCVD identification: from U.S. NHANES 1999–2018 by Qun Tang, Yong Wang, Yan Luo

“…This study aimed to construct a machine learning (ML) algorithm that can accurately and transparently establish correlations between demographic variables, dietary habits, and ASCVD. …”
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Information entropy based match field cutting algorithm by Peng-hao SUN, Ju-long LAN, Shao-jun ZHANG, Jun-fei LI

“…With the increasing diversity of network functions,packet classification had a higher demand on the number of match fields and depth of match table,which placed a severe burden on the storage capacity of hardware.To ensure the efficiency of matching process while at the same time improve the usage of storage devices,an information entropy based cutting algorithm on match fields was proposed.By the analysis on the redundancy of match fields and distribution pattern in a rule set,a match field cutting model was proposed.With the mapping of matching process to the process of entropy reduction,the complexity of optimal match field cutting was reduced from NP-hard to linear complexity.Experiment results show that compared to existing schemes,this scheme can need 40% less TCAM storage space,and on the other side,with the growing of table size,the time complexity of this algorithm is also far less than other algorithms.…”
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Mapping Nationwide Subfield Division Dynamics in Saudi Arabia Using Temporal Patterns of Sentinel-2 NDVI and Machine Learning by Ting Li, Oliver Miguel Lopez Valencia, Matthew F. McCabe

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Towards Automated Cadastral Map Improvement: A Clustering Approach for Error Pattern Recognition by Konstantinos Vantas, Vasiliki Mirkopoulou

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Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes by Cillian Thompson, Oscar Higgins

“…As a powerful data mining tool, neural networks are a suitable method for discovering hidden patterns in the information of diabetic patients. In this study, in order to discover hidden patterns and diagnose diabetes, a particle swarm intelligence algorithm has been used along with a neural network to increase the accuracy of diabetes diagnosis. …”
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