Network Intrusion Detection and Prevention System Using Hybrid Machine Learning with Supervised Ensemble Stacking Model by Godfrey A. Mills, Daniel K. Acquah, Robert A. Sowah

“…In this paper, we present a hybrid intrusion detection system that combines supervised and unsupervised learning models through an ensemble stacking model to increase the detection accuracy rates of attacks in networks while minimising false alarms. Three machine learning algorithms comprising a multilayer perceptron neural network, a modified self-organizing map, and a decision tree were used for the detection framework. …”
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Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine) by Chuin-Hen Liew, Song-Quan Ong, David Chun-Ern Ng

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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

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Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning by Jian Wang, Chengsong Duan, Qiao Yu, Cheng Yang

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Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning by Qiqiang Liang, Xin Xu, Shuo Ding, Jin Wu, Man Huang

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Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning by Gege Cai, Jiamei Zhang, Wanlu Li, Jiejun Zhang, Yun Liu, Shanshan Xi, Guolian Li, Haibin Li, Xing Chen, Fanhao Song, Fazhi Xie

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Two-centers machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum tests by Jichong Zhu, Yong Zhao, Chengqian Huang, Chenxing Zhou, Shaofeng Wu, Tianyou Chen, Xinli Zhan

Subjects: “…Machine learning…”
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Searching for new genes associated with the familial hypercholesterolemia phenotype using whole-genome sequencing and machine learning by D. E. Ivanoshchuk, A. B. Kolker, O. V. Timoshchenko, S. E. Semaev, E. V. Shakhtshneider

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Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning by Jindou Shi, Alexander Ho, Corey E. Snyder, Eric J. Chaney, Janet E. Sorrells, Aneesh Alex, Remben Talaban, Darold R. Spillman, Marina Marjanovic, Minh Doan, Gary Finka, Steve R. Hood, Stephen A. Boppart

“…This integrated optical bioimaging and machine learning approach presents a promising solution to expedite cell line selection process while ensuring identification of high-performing biopharmaceutical cell lines. …”
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Machine learning models reveal microbial signatures in healthy human tissues, challenging the sterility of human organs by Anargyros Skoulakis, Anargyros Skoulakis, Giorgos Skoufos, Giorgos Skoufos, Armen Ovsepian, Armen Ovsepian, Artemis G. Hatzigeorgiou, Artemis G. Hatzigeorgiou

“…Our study endeavors to discern microbial signatures within normal human internal tissues using data from the Genotype-Tissue Expression (GTEx) consortium. Machine learning (ML) models were developed to classify each tissue type based solely on microbial profiles, with the identification of tissue-specific microbial signatures suggesting the presence of distinct microbial communities inside tissues.MethodsWe analyzed 13,871 normal RNA-seq samples from 28 tissues obtained from the GTEx consortium. …”
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Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS) by Saghar Tabib, Seyed Danial Alizadeh, Aref Andishgar, Babak Pezeshki, Omid Keshavarzian, Reza Tabrizi

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A Client-Centric Evaluation System to Evaluate Guest’s Satisfaction on Airbnb Using Machine Learning and NLP by Mohamed Chiny, Omar Bencharef, Moulay Youssef Hadi, Younes Chihab

“…In this study, we took a machine learning-based approach to examine 100,000 customer reviews left on the Airbnb platform to identify different dimensions that shape customer satisfaction according to each category studied (individuals, couples, and families). …”
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Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia. by Dyah Aryani Perwitasari, Imaniar Noor Faridah, Haafizah Dania, Didik Setiawan, Triantoro Safaria

“…We used the ORANGE Data mining as the machine learning to predict the adherence. We recruited 201 patients, whereas the male participants and less than 61 years old as the dominant participants. …”
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Predicting the Progress of Tuberculosis by Inflammatory Response-Related Genes Based on Multiple Machine Learning Comprehensive Analysis by Shuai Ma, Peifei Peng, Zhihao Duan, Yifeng Fan, Xinzhi Li

“…The TB datasets were downloaded from the GEO database. Three machine learning models, namely LASSO, RF, and SVM-RFE, were used to identify the key characteristic genes related to inflammation during the progression of LTBI to ATB. …”
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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

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Modeling of winter wheat yield prediction based on solar-induced chlorophyll fluorescence by machine learning methods by Minxue Zheng, Han Hu, Yue Niu, Qiu Shen, Feng Jia, Xiaolei Geng

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Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning by Zahra Saeidi Mobarakeh, Hossein Amoozadkhalili

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Enhancing water pump failure prediction using machine learning: a focus on less-explored variables by Reza Rasinojehdehi, Goran Cirovic

Subjects: “…machine learning…”
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RETRACTED ARTICLE: Construction and validation of a machine learning-based immune-related prognostic model for glioma by Qi Mao, Zhi Qiao, Qiang Wang, Wei Zhao, Haitao Ju

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Edge intelligence for poultry welfare: Utilizing tiny machine learning neural network processors for vocalization analysis by Ramasamy Srinivasagan, Mohammed Shawky El Sayed, Mohammed Ibrahim Al-Rasheed, Ali Saeed Alzahrani

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