1. Introduction
Biofilms, complex communities of bacteria adhering to surfaces and encased in a self-produced extracellular polymeric substance (EPS), represent a ubiquitous and impactful facet of microbial life [
1]. Their influence extends across various domains of human activity, affecting both the environment [
2] and human health [
3]. The versatile nature of biofilms is evident in their potential to interact with heavy metals [
4], substances known to pollute water sources [
5]. While detrimental biofilms contribute to pollution, their more favorable counterparts can be harnessed for environmental remediation [
6]. This includes the immobilization of heavy metals from waterways, a critical process in mitigating water pollution [
7].
Conversely, the darker side of biofilms unfolds when they contribute to various infections, posing risks to human health [
8,
9,
10]. Biofilm-related infections manifest in different forms, ranging from lung infections [
11,
12,
13] to urinary tract infections [
14,
15,
16]. Understanding the intricate dynamics of biofilm formation and its implications is paramount for developing strategies to mitigate the negative consequences associated with biofilm-related infections [
17].
Given the pivotal role of biofilms in both positive and negative aspects of human life, there exists a pressing need for in-depth research to unravel their complexities. Traditional methods for biofilm investigation involve sophisticated imaging techniques such as confocal laser scanning microscopy [
18,
19], structured illumination microscopy [
20], and mass spectrometry [
21,
22]. However, in this paper, we adopt a bibliographic research method [
23] to delve into the vast realm of biofilm research methods. This approach involves an extensive survey of existing literature, aiming to identify the most crucial keywords, elucidate the core countries and regions leading in biofilm research, and recognize the pivotal organizations contributing to this field.
Through the utilization of the bibliography approach, our goal is to offer a thorough insight into the various research methods employed worldwide in the study of biofilms. This document acts as a valuable reference, illuminating the complex procedures associated with investigating biofilms and the techniques utilized by researchers to unravel their intricacies. By delving deeply into the current body of literature, our intention is to emphasize the principal themes, methodologies, and patterns in biofilm research, ultimately enhancing the shared knowledge within this pivotal scientific field.
2. Materials and Method
The bibliographic analysis is following previous studies [
24,
25] with slightly modifications. The research commenced with a systematic exploration of the Web of Science database [
26,
27], employing the search query "biofilm research method." A comprehensive collection of scholarly articles related to biofilm research methods was obtained, yielding a total of 1000 documents for analysis.
To provide a visual representation and gain insights into the key themes and relationships within the collected data, the state-of-the-art data visualization tool, VOSviewer, was employed [
28,
29]. This powerful tool allows for the dynamic visualization of keyword occurrences, geographical distribution, and organizational contributions within the realm of biofilm research methods.
In the process of visualization, certain parameters were established to ensure the focus on significant elements. A minimum keyword occurrence threshold of 10 was set to emphasize keywords that are recurrent and influential in the literature. Furthermore, the analysis extended to the geographical distribution of biofilm research, considering a minimum of 12 documents from each country to highlight regions with substantial contributions. In parallel, the investigation delved into the organizational landscape by setting a minimum document threshold of 4 for each organization. This criterion ensured that the analysis captured the noteworthy contributions of organizations actively engaged in biofilm research method studies.
3. Results
In
Figure 1, we delve into the intricate landscape of keywords associated with biofilm research methods, unveiling a rich tapestry of diverse elements. Among the prominent features are species-related keywords, shedding light on specific microbial participants in biofilm formation. Notable examples include Staphylococcus aureus and Candida albicans, underscoring the varied microbial compositions under investigation.
Furthermore, the keyword landscape encompasses method-related terms, providing insights into the tools and techniques employed in biofilm research. Keywords such as microscopy, PCR (polymerase chain reaction), diagnosis, and device illuminate the methodological spectrum, reflecting the varied approaches researchers employ to unravel the complexities of biofilm dynamics.
Zooming in further, the intricate cellular processes within biofilm development are elucidated through a set of keywords. These include growth, detachment, adhesion, expression, as well as specific processes like nitrification and denitrification. These keywords offer a glimpse into the dynamic cellular activities that shape the biofilm life cycle, highlighting key stages and processes that researchers focus on in their investigations.
In
Figure 2, a comprehensive panorama of the primary countries and regions engaged in biofilm research methods is depicted, showcasing the global landscape of this pivotal scientific field. Notably, China and the United States emerge as central players, occupying key positions at the forefront of biofilm research endeavors. However, the significance of this field is by no means confined to these two giants; rather, a multitude of nations contribute significantly to the advancement of biofilm research methods.
Among these noteworthy contributors, countries such as Pakistan, Poland, Germany, Canada, the United Kingdom, Portugal, Belgium, Turkey, Italy, Spain, Switzerland, France, the Netherlands, Sweden, Denmark, Japan, Malaysia, Australia, Iran, Thailand, Saudi Arabia, and more, play crucial roles in shaping the trajectory of research in this field. The extensive international participation underscores the global relevance and impact of biofilm research, emphasizing the collaborative nature of scientific exploration.
International cooperation stands out as an integral aspect of biofilm research, fostering synergy among researchers from diverse cultural and academic backgrounds. Collaborative efforts enhance research efficiency, promote knowledge exchange, and contribute to the collective understanding of biofilm dynamics. As the biofilm research community continues to expand and diversify, the role of international collaboration becomes increasingly indispensable, offering a pathway to address complex challenges and advance scientific frontiers collectively. The rich and varied contributions from countries and regions worldwide underscore the truly global nature of biofilm research and the necessity of collaborative endeavors to propel this field into new realms of discovery and innovation.
In
Figure 3, a detailed depiction is provided, showcasing the critical organizations that form the backbone of advancements in the field of biofilm research methods. At the very heart of this network of institutions lies Montana State University, assuming a central role in shaping the trajectory of biofilm research. Serving as a hub for innovation and collaboration, Montana State University stands out as a key contributor to the evolving landscape of biofilm studies.
The significance of the contributions extends beyond Montana State University, encompassing a diverse array of institutions globally. The Chinese Academy of Sciences, with its strong research foundation, stands as a prominent player in advancing biofilm research methodologies. Similarly, the University of Porto, Rigshospitalet, Shanghai Jiao Tong University, King Saud University, Technical University of Denmark, University of Michigan, University of Hong Kong, University of Belgrade, Tabriz University of Medical Sciences, Islamic Azad University, and University of Tehran Medical Sciences each bring their unique strengths and expertise to the collective pursuit of understanding and unraveling the complexities of biofilm research.
What stands out in this collaborative network is the fusion of perspectives and methodologies from different corners of the world. The global collaboration not only broadens the scope of research in biofilm studies but also enhances the efficiency of scientific investigations. Through this cross-cultural collaboration, the exchange of ideas, methodologies, and findings becomes a catalyst for breakthroughs in the field. The diverse cultural and academic backgrounds represented by these institutions contribute to a rich tapestry of biofilm research, fostering an environment where innovative solutions and novel approaches can flourish.
4. Discussion
4.1. Unveiling Biofilm Complexity: Exploring the Intricacies with Traditional Research Methods
Traditional biofilm research methods encompass techniques such as confocal laser scanning microscopy [
30], structured illumination microscopy [
31], mass spectrometry [
32], and more. These methodologies have been instrumental in unraveling the intricate nature of biofilms. A well-formed biofilm holds tremendous potential for various applications, including the removal of heavy metals from water [
30], the generation of electricity through microbial fuel cells [
33,
34,
35], and even the repair of cracks in concrete structures [
36,
37,
38]. Conversely, the presence of harmful biofilms can lead to issues such as lung infections [
39] and urinary tract infections [
40], underscoring the dual nature of these microbial communities.
The utilization of traditional biofilm research methods enables a deeper understanding of the processes involved in biofilm formation and function. By employing advanced microscopy and analytical techniques [
41,
42], researchers gain insights into the structural composition, microbial interactions, and metabolic activities within biofilms. This knowledge, in turn, facilitates the harnessing of beneficial biofilms for practical applications [
43] while also providing strategies to prevent harmful bacterial or biofilm growth [
44,
45,
46].
For instance, the ability to visualize biofilm structures through confocal laser scanning microscopy allows researchers to observe the spatial organization of microbial communities [
47,
48]. Structured illumination microscopy provides detailed insights into the three-dimensional architecture of biofilms [
49,
50]. Mass spectrometry aids in the identification of biofilm components and their potential roles in various biological and environmental processes [
51,
52].
As we delve deeper into understanding biofilm dynamics, the integration of interdisciplinary approaches and emerging technologies promises to open new avenues for research and applications [
53]. The continuous refinement of biofilm research methods not only expands our knowledge but also presents opportunities to harness the positive aspects of biofilms for sustainable solutions in diverse fields [
54]. Through these endeavors, scientists and researchers aim to strike a balance between leveraging the benefits of biofilms and mitigating the potential risks associated with their detrimental counterparts [
55].
4.2. Navigating the Future: Biofilm Research Method in the Era of Big Data and Machine Learning
The future trajectory of biofilm research methods is poised to intertwine seamlessly with cutting-edge technologies such as big data and machine learning. These transformative technologies have already found widespread applications in diverse fields, ranging from autonomous driving [
56,
57] and facial recognition [
58,
59] to global species distribution prediction [
60], educational psychology database establishing [
61,
62] and forecasting [
63]. Much like these domains, biofilm research methods stand to benefit significantly from the synergistic integration with big data and machine learning [
64,
65].
Consider the potential synergy between biofilm research methods and these advanced technologies. By leveraging big data and machine learning, researchers can embark on a journey of enhanced understanding and prediction within the realm of biofilm dynamics. Imagine establishing a comprehensive database encompassing variables such as media composition, species diversity, temperature, humidity, heavy metal distribution, and heavy metal concentration. This expansive dataset becomes the foundation for the application of machine learning models.
As an illustrative example, let's focus on the biofilm's interaction with heavy metals. With the data-rich environment provided by big data, a machine learning model can be trained to predict the efficacy of biofilm-mediated heavy metal removal. The model can analyze intricate relationships between various parameters, allowing for accurate predictions of how biofilms respond to different environmental conditions and metal concentrations. This predictive capability extends further, offering insights into strategies for enhancing heavy metal removal efficiency.
The integration of biofilm research methods with big data and machine learning heralds a new era of precision and efficiency in understanding and manipulating these microbial communities. Researchers can harness the power of data-driven insights to not only comprehend biofilm behaviors but also to optimize their applications. The interconnectedness of biofilm research with advanced technologies not only propels scientific exploration but also paves the way for sustainable solutions in areas such as environmental remediation, healthcare, and industrial processes.
In this evolving landscape, the collaboration between biofilm research, big data, and machine learning is not merely a technological advancement; it is a paradigm shift. It signifies a departure from traditional approaches towards a more dynamic and adaptive understanding of biofilm interactions. As researchers delve into this interdisciplinary frontier, the potential for groundbreaking discoveries and innovations becomes boundless, unlocking novel avenues for addressing complex challenges in diverse scientific domains.
References
- Flemming, H.C.; Wingender, J. Relevance of microbial extracellular polymeric substances (EPSs)-Part I: Structural and ecological aspects. Water science and technology 2001, 43, 1–8. [Google Scholar] [CrossRef] [PubMed]
- Rao, T.S. Comparative effect of temperature on biofilm formation in natural and modified marine environment. Aquatic Ecology 2010, 44, 463–478. [Google Scholar] [CrossRef]
- Srivastava, S.; Bhargava, A. Biofilms and human health. Biotechnology letters 2016, 38, 1–22. [Google Scholar] [CrossRef]
- Jasu, A.; Ray, R.R. Biofilm mediated strategies to mitigate heavy metal pollution: A critical review in metal bioremediation. Biocatalysis and Agricultural Biotechnology 2021, 37, 102183. [Google Scholar] [CrossRef]
- Ding, Y. Heavy metal pollution and transboundary issues in ASEAN countries. Water Policy 2019, 21, 1096–1106. [Google Scholar] [CrossRef]
- Mahto, K.U.; Kumari, S.; Das, S. Unraveling the complex regulatory networks in biofilm formation in bacteria and relevance of biofilms in environmental remediation. Critical Reviews in Biochemistry and Molecular Biology 2022, 57, 305–332. [Google Scholar] [CrossRef]
- Syed, Z.; Sogani, M.; Rajvanshi, J.; Sonu, K. Microbial biofilms for environmental bioremediation of heavy metals: a review. Applied Biochemistry and Biotechnology 2023, 195, 5693–5711. [Google Scholar] [CrossRef]
- Hall-Stoodley, L.; Stoodley, P. Evolving concepts in biofilm infections. Cellular microbiology 2009, 11, 1034–1043. [Google Scholar] [CrossRef]
- Wu, H.; Moser, C.; Wang, H.-Z.; Høiby, N.; Song, Z.-J. Strategies for combating bacterial biofilm infections. International journal of oral science 2015, 7, 1–7. [Google Scholar] [CrossRef]
- Stewart, P.S. Biophysics of biofilm infection. Pathogens and disease 2014, 70, 212–218. [Google Scholar] [CrossRef]
- Maurice, N.M.; Bedi, B.; Sadikot, R.T. Pseudomonas aeruginosa biofilms: host response and clinical implications in lung infections. American journal of respiratory cell and molecular biology 2018, 58, 428–439. [Google Scholar] [CrossRef]
- Kolpen, M.; Kragh, K.N.; Enciso, J.B.; Faurholt-Jepsen, D.; Lindegaard, B.; Egelund, G.B.; Jensen, A.V.; Ravn, P.; Mathiesen, I.H.M.; Gheorge, A.G. Bacterial biofilms predominate in both acute and chronic human lung infections. Thorax 2022, 77, 1015–1022. [Google Scholar] [CrossRef]
- Yang, L.; Haagensen, J.A.J.; Jelsbak, L.; Johansen, H.K.; Sternberg, C.; Høiby, N.; Molin, S. In situ growth rates and biofilm development of Pseudomonas aeruginosa populations in chronic lung infections. 2008. [Google Scholar] [CrossRef]
- Trautner, B.W.; Darouiche, R.O. Role of biofilm in catheter-associated urinary tract infection. American journal of infection control 2004, 32, 177–183. [Google Scholar] [CrossRef]
- Tenke, P.; Köves, B.; Nagy, K.; Hultgren, S.J.; Mendling, W.; Wullt, B.; Grabe, M.; Wagenlehner, F.M.E.; Cek, M.; Pickard, R. Update on biofilm infections in the urinary tract. World journal of urology 2012, 30, 51–57. [Google Scholar] [CrossRef]
- Nickel, J.C.; Costerton, J.W.; McLean, R.J.C.; Olson, M. Bacterial biofilms: influence on the pathogenesis, diagnosis and treatment of urinary tract infections. Journal of Antimicrobial Chemotherapy 1994, 33, 31–41. [Google Scholar] [CrossRef]
- Del Pozo, J.L. Biofilm-related disease. Expert review of anti-infective therapy 2018, 16, 51–65. [Google Scholar] [CrossRef] [PubMed]
- Neu, T.R.; Lawrence, J.R. Development and structure of microbial biofilms in river water studied by confocal laser scanning microscopy. FEMS Microbiology Ecology 1997, 24, 11–25. [Google Scholar] [CrossRef]
- Kuehn, M.; Hausner, M.; Bungartz, H.-J.; Wagner, M.; Wilderer, P.A.; Wuertz, S. Automated confocal laser scanning microscopy and semiautomated image processing for analysis of biofilms. Applied and environmental microbiology 1998, 64, 4115–4127. [Google Scholar] [CrossRef] [PubMed]
- Neu, T.R.; Manz, B.; Volke, F.; Dynes, J.J.; Hitchcock, A.P.; Lawrence, J.R. Advanced imaging techniques for assessment of structure, composition and function in biofilm systems. FEMS microbiology ecology 2010, 72, 1–21. [Google Scholar] [CrossRef] [PubMed]
- Dean, S.N.; Walsh, C.; Goodman, H.; van Hoek, M.L. Analysis of mixed biofilm (Staphylococcus aureus and Pseudomonas aeruginosa) by laser ablation electrospray ionization mass spectrometry. Biofouling 2015, 31, 151–161. [Google Scholar] [CrossRef]
- Li, B.; Comi, T.J.; Si, T.; Dunham, S.J.B.; Sweedler, J.V. A one-step matrix application method for MALDI mass spectrometry imaging of bacterial colony biofilms. Journal of mass spectrometry 2016, 51, 1030–1035. [Google Scholar] [CrossRef]
- Leonidou, L.C.; Katsikeas, C.S.; Coudounaris, D.N. Five decades of business research into exporting: A bibliographic analysis. Journal of International Management 2010, 16, 78–91. [Google Scholar] [CrossRef]
- Chen, S.; Ding, Y. Tackling Heavy Metal Pollution: Evaluating Governance Models and Frameworks. Sustainability 2023, 15, 15863. [Google Scholar] [CrossRef]
- Chen, S.; Ding, Y. A bibliography study of Shewanella oneidensis biofilm. FEMS Microbiology Ecology 2023, 99, fiad124. [Google Scholar] [CrossRef]
- Wilder, E.I.; Walters, W.H. Using conventional bibliographic databases for social science research: Web of Science and Scopus are not the only options. Scholarly Assessment Reports 2021, 3. [Google Scholar] [CrossRef]
- Pranckutė, R. Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications 2021, 9, 12. [Google Scholar] [CrossRef]
- Van Eck, N.J.; Waltman, L. Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics 2017, 111, 1053–1070. [Google Scholar] [CrossRef] [PubMed]
- Wong, D. VOSviewer. Technical Services Quarterly 2018, 35, 219–220. [Google Scholar] [CrossRef]
- Ding, Y.; Peng, N.; Du, Y.; Ji, L.; Cao, B. Disruption of putrescine biosynthesis in Shewanella oneidensis enhances biofilm cohesiveness and performance in Cr (VI) immobilization. Applied and environmental microbiology 2014, 80, 1498–1506. [Google Scholar] [CrossRef] [PubMed]
- Ding, Y.; Zhou, Y.; Yao, J.; Szymanski, C.; Fredrickson, J.; Shi, L.; Cao, B.; Zhu, Z.; Yu, X.-Y. In situ molecular imaging of the biofilm and its matrix. Analytical chemistry 2016, 88, 11244–11252. [Google Scholar] [CrossRef] [PubMed]
- Hua, X.; Yu, X.-Y.; Wang, Z.; Yang, L.; Liu, B.; Zhu, Z.; Tucker, A.E.; Chrisler, W.B.; Hill, E.A.; Thevuthasan, T. In situ molecular imaging of a hydrated biofilm in a microfluidic reactor by ToF-SIMS. Analyst 2014, 139, 1609–1613. [Google Scholar] [CrossRef] [PubMed]
- Zhao, C.e.; Wu, J.; Ding, Y.; Wang, V.B.; Zhang, Y.; Kjelleberg, S.; Loo, J.S.C.; Cao, B.; Zhang, Q. Hybrid conducting biofilm with built-in bacteria for high-performance microbial fuel cells. ChemElectroChem 2015, 2, 654–658. [Google Scholar] [CrossRef]
- Zhao, C.-e.; Chen, J.; Ding, Y.; Wang, V.B.; Bao, B.; Kjelleberg, S.; Cao, B.; Loo, S.C.J.; Wang, L.; Huang, W. Chemically functionalized conjugated oligoelectrolyte nanoparticles for enhancement of current generation in microbial fuel cells. ACS Applied Materials & Interfaces 2015, 7, 14501–14505. [Google Scholar]
- Yang, Y.; Ding, Y.; Hu, Y.; Cao, B.; Rice, S.A.; Kjelleberg, S.; Song, H. Enhancing bidirectional electron transfer of Shewanella oneidensis by a synthetic flavin pathway. ACS synthetic biology 2015, 4, 815–823. [Google Scholar] [CrossRef]
- Zhang, Z.; Weng, Y.; Ding, Y.; Qian, S. Use of genetically modified bacteria to repair cracks in concrete. Materials 2019, 12, 3912. [Google Scholar] [CrossRef]
- Zhang, Z.; Liu, D.; Ding, Y.; Wang, S. Mechanical performance of strain-hardening cementitious composites (SHCC) with bacterial addition. Journal of Infrastructure Preservation and Resilience 2022, 3, 1–11. [Google Scholar] [CrossRef]
- Zhang, Z.; Ding, Y.; Qian, S. Influence of bacterial incorporation on mechanical properties of engineered cementitious composites (ECC). Construction and Building Materials 2019, 196, 195–203. [Google Scholar] [CrossRef]
- Ciofu, O.; Rojo-Molinero, E.; Macià, M.D.; Oliver, A. Antibiotic treatment of biofilm infections. Apmis 2017, 125, 304–319. [Google Scholar] [CrossRef]
- Tapiainen, T.; Hanni, A.M.; Salo, J.; Ikäheimo, I.; Uhari, M. Escherichia coli biofilm formation and recurrences of urinary tract infections in children. European journal of clinical microbiology & infectious diseases 2014, 33, 111–115. [Google Scholar]
- Achinas, S.; Yska, S.K.; Charalampogiannis, N.; Krooneman, J.; Euverink, G.J.W. A technological understanding of biofilm detection techniques: a review. Materials 2020, 13, 3147. [Google Scholar] [CrossRef] [PubMed]
- Franklin, M.J.; Chang, C.; Akiyama, T.; Bothner, B. New technologies for studying biofilms. Microbial Biofilms 2015, 1–32. [Google Scholar]
- Philipp, L.-A.; Bühler, K.; Ulber, R.; Gescher, J. Beneficial applications of biofilms. Nature Reviews Microbiology 2023, 1–15. [Google Scholar] [CrossRef] [PubMed]
- Hamdany, A.H.; Ding, Y.; Qian, S. Graphene-Based TiO2 Cement Composites to Enhance the Antibacterial Effect of Self-Disinfecting Surfaces. Catalysts 2023, 13, 1313. [Google Scholar] [CrossRef]
- Hamdany, A.H.; Ding, Y.; Qian, S. Mechanical and antibacterial behavior of photocatalytic lightweight engineered cementitious composites. Journal of Materials in Civil Engineering 2021, 33, 04021262. [Google Scholar] [CrossRef]
- Hamdany, A.H.; Ding, Y.; Qian, S. Visible light antibacterial potential of graphene-TiO2 cementitious composites for self-sterilization surface. Journal of Sustainable Cement-Based Materials 2023, 12, 972–982. [Google Scholar] [CrossRef]
- Reichhardt, C.; Parsek, M.R. Confocal laser scanning microscopy for analysis of Pseudomonas aeruginosa biofilm architecture and matrix localization. Frontiers in microbiology 2019, 10, 677. [Google Scholar] [CrossRef]
- Villena, G.K.; Fujikawa, T.; Tsuyumu, S.; Gutiérrez-Correa, M. Structural analysis of biofilms and pellets of Aspergillus niger by confocal laser scanning microscopy and cryo scanning electron microscopy. Bioresource Technology 2010, 101, 1920–1926. [Google Scholar] [CrossRef]
- Neu, T.R.; Lawrence, J.R. Investigation of microbial biofilm structure by laser scanning microscopy. Productive Biofilms 2014, 1–51. [Google Scholar]
- Grohmann, E.; Vaishampayan, A. Techniques in studying biofilms and their characterization: microscopy to advanced imaging system in vitro and in situ. Biofilms in Plant and Soil Health 2017, 215–230. [Google Scholar]
- Pereira, F.D.E.S.; Bonatto, C.C.; Lopes, C.A.P.; Pereira, A.L.; Silva, L.P. Use of MALDI-TOF mass spectrometry to analyze the molecular profile of Pseudomonas aeruginosa biofilms grown on glass and plastic surfaces. Microbial pathogenesis 2015, 86, 32–37. [Google Scholar] [CrossRef] [PubMed]
- Guo, R.; Luo, X.; Liu, J.; Lu, H. Mass spectrometry based targeted metabolomics precisely characterized new functional metabolites that regulate biofilm formation in Escherichia coli. Analytica Chimica Acta 2021, 1145, 26–36. [Google Scholar] [CrossRef] [PubMed]
- Koshy-Chenthittayil, S.; Archambault, L.; Senthilkumar, D.; Laubenbacher, R.; Mendes, P.; Dongari-Bagtzoglou, A. Agent based models of polymicrobial biofilms and the microbiome—A review. Microorganisms 2021, 9, 417. [Google Scholar] [CrossRef] [PubMed]
- Vishwakarma, V. Impact of environmental biofilms: Industrial components and its remediation. Journal of basic microbiology 2020, 60, 198–206. [Google Scholar] [CrossRef]
- Muhammad, M.H.; Idris, A.L.; Fan, X.; Guo, Y.; Yu, Y.; Jin, X.; Qiu, J.; Guan, X.; Huang, T. Beyond risk: bacterial biofilms and their regulating approaches. Frontiers in microbiology 2020, 11, 928. [Google Scholar] [CrossRef] [PubMed]
- Bachute, M.R.; Subhedar, J.M. Autonomous driving architectures: insights of machine learning and deep learning algorithms. Machine Learning with Applications 2021, 6, 100164. [Google Scholar] [CrossRef]
- Garcia Cuenca, L.; Sanchez-Soriano, J.; Puertas, E.; Fernandez Andres, J.; Aliane, N. Machine learning techniques for undertaking roundabouts in autonomous driving. Sensors 2019, 19, 2386. [Google Scholar] [CrossRef]
- Raju, K.; Chinna Rao, B.; Saikumar, K.; Lakshman Pratap, N. An optimal hybrid solution to local and global facial recognition through machine learning. A fusion of artificial intelligence and internet of things for emerging cyber systems 2022, 203–226. [Google Scholar]
- Coe, J.; Atay, M. Evaluating impact of race in facial recognition across machine learning and deep learning algorithms. Computers 2021, 10, 113. [Google Scholar] [CrossRef]
- Chen, S.; Ding, Y. Machine Learning and Its Applications in Studying the Geographical Distribution of Ants. Diversity 2022, 14, 706. [Google Scholar] [CrossRef]
- Chen, S.; Ding, Y. Assessing the Psychometric Properties of STEAM Competence in Primary School Students: A Construct Measurement Study. Journal of Psychoeducational Assessment 2023, 41, 796–810. [Google Scholar] [CrossRef]
- Chen, S.; Ding, Y.; Liu, X. Development of the growth mindset scale: Evidence of structural validity, measurement model, direct and indirect effects in Chinese samples. Current Psychology 2023, 42, 1712–1726. [Google Scholar] [CrossRef]
- Chen, S.; Ding, Y. A Machine Learning Approach to Predicting Academic Performance in Pennsylvania’s Schools. Social Sciences 2023, 12, 118. [Google Scholar] [CrossRef]
- Wang, J.; Jiang, Z.; Wei, Y.; Wang, W.; Wang, F.; Yang, Y.; Song, H.; Yuan, Q. Multiplexed identification of bacterial biofilm infections based on machine-learning-aided lanthanide encoding. ACS nano 2022, 16, 3300–3310. [Google Scholar] [CrossRef] [PubMed]
- Dimauro, G.; Deperte, F.; Maglietta, R.; Bove, M.; La Gioia, F.; Renò, V.; Simone, L.; Gelardi, M. A novel approach for biofilm detection based on a convolutional neural network. Electronics 2020, 9, 881. [Google Scholar] [CrossRef]
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