A Collision Avoidance Method for Intelligent Ship Based on the Improved Bacterial Foraging Optimization Algorithm by Xingzhong Wang, Xinghua Kou, Jinfeng Huang, Xianchun Tan

“…The bacterial foraging optimization algorithm (BFOA) is an intelligent population optimization algorithm widely used in collision avoidance problems; however, the BFOA is inappropriate for the intelligent ship collision avoidance planning with high safety requirements because BFOA converges slowly, optimizes inaccurately, and has low stability. …”
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Multimodal Deep Learning for Differentiating Bacterial and Fungal Keratitis Using Prospective Representative Data by N.V. Prajna, MD, Jad Assaf, MD, Nisha R. Acharya, MD, MS, Jennifer Rose-Nussbaumer, MD, Thomas M. Lietman, MD, J. Peter Campbell, MD, MPH, Jeremy D. Keenan, MD, MPH, Xubo Song, PhD, Travis K. Redd, MD, MPH

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Performance of deep learning models in predicting the nugent score to diagnose bacterial vaginosis by Naoki Watanabe, Tomohisa Watari, Kenji Akamatsu, Isao Miyatsuka, Yoshihito Otsuka

Subjects: “…artificial Intelligence…”
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Current Challenges in Bacterial Transcriptomics by Suhyung Cho, Yoobok Cho, Sooin Lee, Jayoung Kim, Hyeji Yum, Sun Chang Kim, Byung-Kwan Cho

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An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning by Xiaohui Yan, Yunlong Zhu, Hao Zhang, Hanning Chen, Ben Niu

“…Bacterial Foraging Algorithm (BFO) is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. …”
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CanKiwi: A Mechanistic Competition Model of Kiwifruit Bacterial Canker Disease Dynamics by Oussama Hadj Abdelkader, Hadjer Bouzebiba, Miguel G. Santos, Danilo Pena, António Pedro Aguiar, Susana M. P. Carvalho

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Unleashing the Power of Artificial Intelligence-Driven Drug Discovery in Streptomyces by Wei-Shan Ang, Jodi Woan-Fei Law, Yatinesh Kumari, Vengadesh Letchumanan, Loh Teng-Hern Tan

“…Fortunately, artificial intelligence (AI) and machine learning (ML) models enable rapid exploration and prediction of potential antibiotic compounds, increasing the probability of discovering new antibacterial compounds. …”
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I-PD Controller Tuning for Unstable System Using Bacterial Foraging Algorithm: A Study Based on Various Error Criterion by V. Rajinikanth, K. Latha

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Closing the loop: establishing an autonomous test-learn cycle to optimize induction of bacterial systems using a robotic platform by Jan Benedict Spannenkrebs, Aron Eiermann, Thomas Zoll, Silke Hackenschmidt, Johannes Kabisch, Johannes Kabisch, Johannes Kabisch

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Resveratrol-derived carbon dots integrated into gelatin/chitosan multifunctional films for intelligent packaging by Tianxin Fu, Yuchao Feng, Shu Zhang, Yanan Sheng, Changyuan Wang

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Optimal Commissioning of Power Plant Units by Including an Energy Storage System and Demand-Side Program Using a Hybrid Bacterial Foraging and Honey Bee Optimization Method by Afshar Shokri, Hamid Shakibi, Behrooz Sobhani

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Understanding phage BX-1 resistance in Vibrio alginolyticus AP-1 and the role of quorum-sensing regulation by Xiaoyu Li, Xin Liu, Tianyi Ma, Haochen Su, Bingrui Sui, Lili Wang, Bilal Murtaza, Yongping Xu, Na Li, Demeng Tan

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Effects of carbon sources and temperature on the formation and structural characteristics of food-related Staphylococcus epidermidis biofilms by Mingming Zou, Donghong Liu

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NERVE 2.0: boosting the new enhanced reverse vaccinology environment via artificial intelligence and a user-friendly web interface by Andrea Conte, Nicola Gulmini, Francesco Costa, Matteo Cartura, Felix Bröhl, Francesco Patanè, Francesco Filippini

“…Conclusions With its redesigned and updated environment, NERVE 2.0 allows customisable and refinable bacterial protein vaccine analyses to all different kinds of users.…”
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SHASI-ML: a machine learning-based approach for immunogenicity prediction in Salmonella vaccine development by Ottavia Spiga, Ottavia Spiga, Ottavia Spiga, Anna Visibelli, Francesco Pettini, Bianca Roncaglia, Annalisa Santucci, Annalisa Santucci, Annalisa Santucci

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Quality assessment of chicken using machine learning and electronic nose by Hassan Anwar, Talha Anwar

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Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligen... by Yu-Tzu Lin, Hsiu-Hsien Lin, Chih-Hao Chen, Kun-Hao Tseng, Pang-Chien Hsu, Ya-Lun Wu, Wei-Cheng Chang, Nai-Shun Liao, Yi-Fan Chou, Chun-Yi Hsu, Yu-Hui Liao, Mao-Wang Ho, Shih-Sheng Chang, Po-Ren Hsueh, Der-Yang Cho

“…Methods: In this study, the artificial intelligent Raman detection and identification system (AIRDIS) was implemented to identify bacterial species, including Staphylococcus aureus (n = 1290), Enterococcus faecium (n = 1020), Klebsiella pneumoniae (n = 1366), Pseudomonas aeruginosa (n = 1067), and Acinetobacter baumannii (n = 811). …”
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Clinical validation and optimization of machine learning models for early prediction of sepsis by Xi Liu, Meiyi Li, Xu Liu, Yuting Luo, Dong Yang, Hui Ouyang, Jiaoling He, Jinyu Xia, Fei Xiao, Fei Xiao, Fei Xiao

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Detection of Viable but Nonculturable E. coli Induced by Low-Level Antimicrobials Using AI-Enabled Hyperspectral Microscopy by MeiLi Papa, Aarham Wasit, Justin Pecora, Teresa M. Bergholz, Jiyoon Yi

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Novel Adaptive Bacteria Foraging Algorithms for Global Optimization by Ahmad N. K. Nasir, M. O. Tokhi, N. Maniha Abd. Ghani

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