Figure 1.
Bibliometric indicator types.
Bioremediation stands as a promising solution amid the escalating challenges posed by environmental pollution. Over the past 25 years, the influx of synthetic chemicals and hazardous contaminants into ecosystems has required innovative approaches for mitigation and restoration. The resilience of these compounds stems from their non-natural existence, distressing both human and environmental health. Microbes take center stage in this scenario, demonstrating their ability of biodegradation to catalyze environmental remediation. Currently, the scientific community supports a straight connection between biorefinery and bioremediation concepts to encourage circular bio/economy practices. This review aimed to give a pre-overview of the state of the art regarding the main microorganisms employed in bioremediation processes and the different bioremediation approaches applied. Moreover, focus has been given to the implementation of bioremediation as a novel approach to agro-industrial waste management, highlighting how it is possible to reduce environmental pollution while still obtaining value-added products with commercial value, meeting the goals of a circular bioeconomy. The main drawbacks and challenges regarding the feasibility of bioremediation were also reported.
Citation: Giuseppe Maglione, Paola Zinno, Alessia Tropea, Cassamo U. Mussagy, Laurent Dufossé, Daniele Giuffrida, Alice Mondello. Microbes' role in environmental pollution and remediation: a bioeconomy focus approach[J]. AIMS Microbiology, 2024, 10(3): 723-755. doi: 10.3934/microbiol.2024033
| [1] | Loris Nanni, Michelangelo Paci, Gianluca Maguolo, Stefano Ghidoni . Deep learning for actinic keratosis classification. AIMS Electronics and Electrical Engineering, 2020, 4(1): 47-56. doi: 10.3934/ElectrEng.2020.1.47 |
| [2] | Tamizhelakkiya K, Sabitha Gauni, Prabhu Chandhar . Modulation classification analysis of CNN model for wireless communication systems. AIMS Electronics and Electrical Engineering, 2023, 7(4): 337-353. doi: 10.3934/electreng.2023018 |
| [3] | Suriya Priya R Asaithambi, Sitalakshmi Venkatraman, Ramanathan Venkatraman . Proposed big data architecture for facial recognition using machine learning. AIMS Electronics and Electrical Engineering, 2021, 5(1): 68-92. doi: 10.3934/electreng.2021005 |
| [4] | Abdullah Yahya Abdullah Amer, Tamanna Siddiqu . A novel algorithm for sarcasm detection using supervised machine learning approach. AIMS Electronics and Electrical Engineering, 2022, 6(4): 345-369. doi: 10.3934/electreng.2022021 |
| [5] | Habib Hadj-Mabrouk . Analysis and prediction of railway accident risks using machine learning. AIMS Electronics and Electrical Engineering, 2020, 4(1): 19-46. doi: 10.3934/ElectrEng.2020.1.19 |
| [6] | Manisha Bangar, Prachi Chaudhary . A novel approach for the classification of diabetic maculopathy using discrete wavelet transforms and a support vector machine. AIMS Electronics and Electrical Engineering, 2023, 7(1): 1-13. doi: 10.3934/electreng.2023001 |
| [7] | J. Rajeshwari, M. Sughasiny . Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique. AIMS Electronics and Electrical Engineering, 2023, 7(1): 75-99. doi: 10.3934/electreng.2023005 |
| [8] | Deven Nahata, Kareem Othman . Exploring the challenges and opportunities of image processing and sensor fusion in autonomous vehicles: A comprehensive review. AIMS Electronics and Electrical Engineering, 2023, 7(4): 271-321. doi: 10.3934/electreng.2023016 |
| [9] | Amleset Kelati, Hossam Gaber, Juha Plosila, Hannu Tenhunen . Implementation of non-intrusive appliances load monitoring (NIALM) on k-nearest neighbors (k-NN) classifier. AIMS Electronics and Electrical Engineering, 2020, 4(3): 326-344. doi: 10.3934/ElectrEng.2020.3.326 |
| [10] | Abdul Yussif Seidu, Elvis Twumasi, Emmanuel Assuming Frimpong . Hybrid optimized artificial neural network using Latin hypercube sampling and Bayesian optimization for detection, classification and location of faults in transmission lines. AIMS Electronics and Electrical Engineering, 2024, 8(4): 508-541. doi: 10.3934/electreng.2024024 |
Bioremediation stands as a promising solution amid the escalating challenges posed by environmental pollution. Over the past 25 years, the influx of synthetic chemicals and hazardous contaminants into ecosystems has required innovative approaches for mitigation and restoration. The resilience of these compounds stems from their non-natural existence, distressing both human and environmental health. Microbes take center stage in this scenario, demonstrating their ability of biodegradation to catalyze environmental remediation. Currently, the scientific community supports a straight connection between biorefinery and bioremediation concepts to encourage circular bio/economy practices. This review aimed to give a pre-overview of the state of the art regarding the main microorganisms employed in bioremediation processes and the different bioremediation approaches applied. Moreover, focus has been given to the implementation of bioremediation as a novel approach to agro-industrial waste management, highlighting how it is possible to reduce environmental pollution while still obtaining value-added products with commercial value, meeting the goals of a circular bioeconomy. The main drawbacks and challenges regarding the feasibility of bioremediation were also reported.
In late 2019, the world witnessed one of the most severe health disasters that affected all countries [1,2,3,4,5,6]. Spotted in Wuhan City, Hubei Province, China, Covid-19 is a respiratory disease that is caused by the new coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) [7,8,9,10]. Coronavirus is a pathogenic disease that starts by attacking the respiratory tract and then directs itself into the cells using an enzyme called furin and impairs the immune system [10]. The primary signs and symptoms of COVID-19 include fever (38℃ or higher), dry cough, dyspnea, sore throat, tiredness, pains, and, in certain cases, diarrhea. Loss of taste and smell is reported as Covid-19 symptoms. Respiratory problems are observed in those developing more severe forms, and these can result in hospitalization in critical care as well as death [7,11,12,13]. The World Health Organization declared, on February 11, 2020, this new disease to be a pandemic after it took a few weeks to spread rapidly throughout the world [7]. While writing this review, the accumulated positive cases is 698,035,072, including 6,940,130 deaths, and 669,603,810 recovered [14] (Last updated: November 20, 2023, 17:56 GMT).
Faced with this complicated situation, many precautions have been taken to minimize the spread of Covid-19: Washing hands with soap frequently, using hydroalcoholic solution, keeping social distance (2 meters), covering mouth while sneezing, and wearing a mask [7,15,16]. The precautions taken did not stop there. Rather, movement between cities and countries was prevented, and a lockdown was imposed [17,18]. Due the demand to effective solutions of this disease and after deep research in laboratories, researchers developed Covid-19 vaccines. Indeed, different types of vaccines are available: BNT162 (Pfizer BioNTech, USA), ChAdOx1 (AstraZeneca, UK), mRNA1273 (Moderna, USA), Ad26.COV2-S (Johnson & Johnson, USA), BIBP-CorV (Sinopharm, China), CoronaVac (Sinovac, China), and Sputnik V (Gamaleya, Russia) [19,20,21,22].
Considered a gold standard to detect Covid-19, Reverse Transcription-Polymerase Chain Reaction (RT-PCR) can detect the virus's genetic material, offering high accuracy. However, the need for specialized equipment and well-trained personnel and the time to confirm the patients as well as its expensiveness are the considerable obstacles of its use for testing in this pandemic [10,23]. To overcome this issue, medical images analysis is used massively in this field and allows doctors and specialists to make accurate diagnoses and eliminate the need for laborious manual review processes [24]. Indeed, X-Ray and CT (Computed Tomography) images are the most used in processing of medical images. The utilization of these images required a very good expertise to detect Covid-19 which is taking a lot of time [8]. So, the necessity of new techniques allowing the detection of the virus effectively with minimal time is crucial. Hence, the combination of Deep Learning and Machine Learning methods with X-Ray and CT images is one of the solutions that helps to beat this problem [25]. In fact, Machine Learning and Deep Learning have recently played a major role for automated medical diagnostics by providing new solutions overcoming the shortcomings of the classical methods. Therefore, developing automated analysis system based on X-Ray and CT images using Machine Learning and Deep Learning techniques, that allow to detect the anomalies in the images, helped physicians and healthcare professionals to make accurate decision and save a lot of time [26].
We aim to focus on classification and detection of Covid-19 using X-Ray and CT images through Deep Learning and Machine Learning techniques. Based on a qualitative and quantitative analysis of published documents on this field and the use of new technologies to improve the diagnosis of Covid-19, we highlight the role of Deep Learning and Machine Learning methods on recognizing Covid-19 from medical images. Data analysis is used to get more insights from the data to determine some information's such as: The distribution of published papers in all used databases, and the most used techniques in the contributions. The participation of each country in terms of the number of published documents was considered as well as the type of publication. The rest of this paper is organized as follows. In Section 2, we describe the methodology of the bibliometric analysis employed, along with the tools used for this analysis. Section 3 delves into various aspects of bibliometric analysis, including the techniques used, annual trends, types of publications, geographic dispersion, leading authors, and predominant journals. Finally, Section 4 provides a detailed breakdown analysis for each database.
The first and the most important step in any bibliometric analysis is the selection of the relevant databases which pertain to the objectives of the study [27]. The scope of bibliometric analysis is limited by the type of accessible information [28]. It is therefore imperative that the information sources be trusted and adapted to conduct such an analysis and make the most efficient decisions [29]. For our study, we used six well known databases: IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect.
Once the databases have been selected, the second step is to pick out the appropriate indicators for evaluating the resulting samples. The published papers articles showcase a range of bibliometric measures [29]. We can distinguish three different types of bibliometric indicators: (1) Quality Indicators, which can measure the impact, (2) Quantity Indicators that allow us to measure the productivity, and (3) Structural Indicators that measure the connection [30]. Figure 1 shows the types of bibliometric indicators. In our study, we focused on quantity indicators.
Our purpose of this study is to investigate the research area of classification and detection based on X-Ray and CT images using Deep Learning and Machine Learning techniques. We conducted our review using IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect databases. In our bibliometric analysis, we covered the most prevalent areas of knowledge, the prolific authors, the journals and the most publications, the most productive countries, the number of citations, the trends of publications, document types, and countries or territories of origin. To carry out our research, we used the same advanced search string for all the selected databases:
("Covid" OR "Covid19" OR "Covid 19" OR "Covid-19" OR "Coronavirus" OR "Corona Virus") AND ("Classification" OR "Image Classification") AND ("Detection" OR "Image Detection") AND ("Deep Learning" OR "Deep Neural Networks" OR "DL" OR "Convolutional Neural Network" OR "CNN") AND ("X-Ray" OR "X-Ray Image" OR "CT" OR "Computed Tomography") AND ("Machine Learning" OR "Machine Learning Algorithms" OR "ML")
The methodology followed in our bibliometric analysis is described in Figure 2. First, it begins with selecting the databases (IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect). An advanced string search query is applied across these databases to gather research papers. Then, the results from this search undergo a cleaning process to refine the selection of documents. The specific counts of papers retrieved from each database are indicated: IEEE Xplore (128), ACM (58), MDPI (13), PubMed (35), Springer (217), and ScienceDirect (132). The bibliometric analysis software VOSviewer and Numpy, Pandas, and Matplotlib Python packages are used to conduct the data analysis and visualizations. The cleaning process involves eliminating duplicated documents and documents that do not address classification and detection of Covid-19 based on X-Ray and CT images.
After the cleaning process, we extracted the relevant information for each document and created an excel file to process the data analysis. In the excel file, we stored the following information: Title of the document, year of publication, first author, country (of the first author; in some documents, we found many authors with different countries), type of document (conference paper, journal article, and book chapter), publisher (title of conference or journal), database, number of citations (the last upgrade of number of citation was 6 November 2023), abstract, key words, and the technique used (Deep Learning, Machine Learning or Deep Learning and Machine Learning). The Excel file was used as a primary tool to conduct an initial analysis of the data, which helped us understand the dataset better. This preliminary analysis opened the point for more interpretation and advanced data visualization. With the help of Python and its powerful libraries such as Pandas, Numpy, and Matplotlib, we created detailed graphs and charts that helped us visualize our findings and understand complex patterns within the data more effectively.
The research resulted in 139, 80, 15, 39,239, and 161 documents in IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect, respectively. The cleaning process consists of removing duplicated and documents that do not exactly address the classification and detection of Covid-19 based on X-Ray and CT images using Deep Learning and Machine Learning. The total number of documents, after the cleaning process, for the databases IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect is 128, 58, 13, 35,217, and 132 documents, respectively. Figure 3 shows the distribution of total documents of each database over the years.
We used VOSviewer version 1.6.20 as bibliometric analysis software due to its efficiency in analyzing clustered search results. Indeed, VOSviewer is a software tool designed for creating and visualizing networks within bibliometric data. Such networks include journals, researchers, or individual publications, and are formed through various relationships such as citation, bibliometric coupling, co-citation, or co-authorship relations. It also provides text mining capabilities, allowing us to construct and visualize the patterns of the networks of important terms drawn from a body of scientific literature [31,32]. VOSviewer analyzes the text within abstracts and titles of published papers by segmenting the paragraphs into individual words and phrases and connecting them with the citation data of the corresponding papers. The outcomes are presented as a density map or term bubbles. As we can see in the figure below (Figure 4), the density map highlights key areas of research related to Covid-19, including terms like "Covid-19", "deep learning, " and "machine learning" as central nodes. These nodes are surrounded by related terms such as "classification", "computed tomography image", "lung detection, " and "chest x-ray, " suggesting they are common subjects of study in the field. This visualization helps to identify the most prominent topics and techniques addressed in the existing body of research on Covid-19.
To facilitate the bubble map, words/terms occurring at a minimum of 1 time in the publications were examined and visualized, and 148 keywords met the selected threshold. Figure 5 illustrates the obtained results that are in the form of network visualization and density diagrams. Different colors indicate thematic concentrations in the literature, and lines between terms depict the relationships and collaborative nature of these research areas. VOSviewer has generated 16 clusters of terms.
The total documents used in this review is 584 documents. While extracting the relevant information from the documents, we took into consideration the contribution of each document and the technique used by the authors for classification and detection of Covid-19 based on X-ray and CT image. As a result, authors have used three techniques: Deep Learning, Machine Learning, or the combination of these two techniques. Figure 6 illustrates the distribution of techniques applied within these documents. It reveals that most of the documents, precisely 378 or 64.7%, used Deep Learning techniques, highlighting the predominant role that this method plays in the field. Moreover, Deep Learning and Machine Learning techniques were jointly utilized in 135 documents. A total of 71 documents were reviewed that used Machine Learning alone, representing 12.2% of all documents reviewed. This distribution underscores how important it is for research trends to focus on advanced computational methods, especially Deep Learning.
Since the beginning of the Covid-19 pandemic in 2020, academic publications about the virus have increased significantly, especially those using Deep Learning and Machine Learning methods. Indeed, the trend shown by Figure 7 presents the yearly distribution of total documents categorized by the utilized techniques from 2020 to 2024. Deep Learning techniques demonstrated its presence with 39 documents, and a significant increase in 2021 (100 documents), 2022 (125 documents), 2023 (111 documents), and 6 documents have already been published as of 2024, even though we are in 2023. A modest count of 6 documents for Machine Learning in 2020 had a low increase for 2021, 2022, and 2023 with 22, 26, and 16 documents, respectively. During the observed period, the combination of Deep Learning and Machine Learning remains relatively stable, indicating a consistent integration of these two approaches in research practices. As the years progressed, the adoption of Deep Learning and Machine Learning continued to grow. This continual increase underlines the critical role that Deep Learning and Machine Learning have played in understanding and combating the Covid-19 pandemic through innovative research and analysis.
In our study, we carefully paid attention to the type of document, distinguishing three major categories: Conference paper, journal article, and book chapter. We were able, through this classification, to analyze and understand the dissemination patterns and academic impact of each type of publication within the research environment. Regularly, authors present their initial findings in conference papers and their detailed contributions to the body of knowledge in journal articles, while comprehensive insights into specific areas of study are typically reserved for book chapters. Figure 8 represents the distribution of different publication types. As we can see, with 337 (57.7%) of all publications being journal articles, they make up the majority and play a major role in publishing research findings. Conference papers contain 211 (36.1%) of the publications, which is a notable proportion, indicating their significance as a platform for showcasing preliminary research findings and promoting academic discussion. Although they are fewer in number, book chapters make up 6.2% (36) of the publication because they provide more in-depth analysis and discussion on field-related topics. This distribution demonstrates the variety of channels that the academic community uses to publish research findings.
We take the first author's country into account to determine the country of origin for the research contribution in our study. Geographic dispersion of documents across the globe is illustrated in Figure 9. India tops the countries with an impressive count of 196 documents, highlighting its robust research in the topic. Followed by China in second with 61 contributions, illustrates the volume of its academic output. Other notable contributions come from Turkey with 29 papers, and Pakistan and the USA, each contributed with 23 papers. Other noteworthy contributions come from Egypt and Bangladesh, with 21 documents each. A substantial representation of Middle Eastern nations come from Iran and Saudi Arabia with 18 and 15 articles, respectively. Morocco from north Africa contributed 14 documents. The rising forces in economy and technology have challenged the established powers' traditional dominance in the field of global research and are making significant contributions. These emergent forces are leading the way in both academic and practical advances because of their remarkable agility and innovation.
The top 5 authors who have made major contributions to the corpus of documents are the focus of this analysis. We explore the specifics of their contributions, looking at the geographical context of their affiliations as well as the volume of published works they have contributed. The number of papers published, the esteemed journals that have published their work, and the corresponding databases are used to gauge their impact. This allows us to recognize the depth of their academic impact and the size of knowledge they contribute to the forefront of their respective fields.
As mentioned in Figure 10, K. Shankar from India has published four important journal articles in three years. Shankar's research papers received 18 and 38 citations in 2021 when they were published in "ACM Transactions on Internet Technology" [32] and "Applied Soft Computing" [33], respectively, both published by ACM and ScienceDirect. His work continued to garner attention in "Multimedia Systems" [34] published by Springer the following year. Shankar's 2023 publication in "Cognitive Neurodynamics" [35] was also published by Springer. All these articles, which have appeared in respectable journals and databases, have accumulated 118 citations.
The Pakistani author Imran Ahmed published two articles in 2022: "Interdisciplinary Sciences: Computational Life Sciences" [36] published by Springer, and one in "Virtual Reality & Intelligent Hardware" [37] hosted on ScienceDirect. Imran made another significant contribution in 2023 when he published an article in "Computing" [38], which was also published by Springer. The total citation of Imran reached 63 citations.
Santosh Kumar, an Indian researcher, published two journal articles in 2022, having a total of 36 citations. One article was published in "Computer Methods and Programmes in Biomedicine" [39] which is indexed in PubMed Database. His second work was featured in "Computers and Electrical Engineering" [40] on ScienceDirect.
The Iranian author Mustafa Ghaderzadeh had a remarkable year in 2021. His research was published as a conference paper by ACM and presented at the 5th International Conference on Medical and Health Informatics in 2021 [41]. Furthermore, he published an article in the "Journal of Medical Internet Research" [42] that was indexed in PubMed. He received a total count of 59 citations.
In 2023, Aysen Degerli, from Finland, made a significant contribution by publishing one journal article in "Health Information Science and Systems" [43] indexed in Springer with 76 citations. In 2022, he published a conference paper in the IEEE International Conference on Image Processing (ICIP), which is indexed in the IEEE Xplore database and earned 9 citations [44].
The prominence of the rising academic forces is demonstrated by the four leading authors who are from India, Pakistan, and Iran. Indeed, this is demonstrated by showcasing the increasing intellectual talent of their respective nations. These researchers have highlighted an academic ascendancy trend in regions that have been strongly pushing the boundaries of innovation and research.
A comprehensive overview of the 20 most productive journals in this study with details of corresponding databases, total citations, and average citation per journal is presented in Table 1. The analysis showcases that "Multimedia Tools and Applications" (29 documents), "Computers in Biology and Medicine" (19 documents), and "Expert Systems with Applications" (13 documents) are the most top three journals within their corresponding databases (PubMed for the first journal and ScienceDirect for the two others). These articles earned total citations of 237, 3711, and 399, respectively. With their large number of citations, they established a standard for scholarly impact. A more thorough analysis identifies a trend of significant contributions on several platforms, including ScienceDirect, Springer, and IEEE Xplore. This trend across databases demonstrates how research publications are dynamic and how journals bear witness to how academic discourse is changing. These results highlight the critical role that highly regarded journals play in promoting knowledge advancement among the international research community.
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Multimedia Tools and Applications | PubMed | 29 | 237 | 8.17 |
| Computers in Biology and Medicine | ScienceDirect | 19 | 3711 | 195.32 |
| Expert Systems with Applications | ScienceDirect | 13 | 399 | 30.69 |
| Applied Soft Computing | ScienceDirect | 12 | 632 | 52.67 |
| Biomedical Signal Processing and Control | ScienceDirect | 12 | 181 | 15.08 |
| Journal of Ambient Intelligence and Humanized Computing | Springer | 10 | 290 | 29.00 |
| Scientific Reports | Springer | 9 | 462 | 51.33 |
| Applied Intelligence | Springer | 8 | 1422 | 177.75 |
| IEEE Access | IEEE Xplore | 7 | 2637 | 376.71 |
| Informatics in Medicine Unlocked | ScienceDirect | 7 | 757 | 108.14 |
| Neural Computing and Applications | Springer | 7 | 106 | 15.14 |
| SN Computer Science | Springer | 5 | 78 | 15.60 |
| ACM Transactions on Multimedia Computing, Communications, and Applications | ACM | 4 | 132 | 33.00 |
| Computers and Electrical Engineering | ScienceDirect | 4 | 29 | 7.25 |
| Computer Methods and Programs in Biomedicine | PubMed | 4 | 615 | 153.75 |
| Procedia Computer Science | ScienceDirect | 4 | 39 | 9.75 |
| Neurocomputing | ScienceDirect | 4 | 48 | 12.00 |
| Pattern Recognition | ScienceDirect | 4 | 314 | 78.50 |
| Soft Computing | Springer | 4 | 185 | 46.25 |
| Knowledge-Based Systems | ScienceDirect | 3 | 74 | 24.67 |
DownLoad:
CSV
From Figure 11, we can notice that there is no clear trend indicating a direct proportionate relationship between the number of documents published and average citations. This implies that more publications do not necessarily lead to a higher average citation count per document.
The total documents that have been published in IEEE Xplore database is 128 documents. As depicted in Figure 12, most published papers used Deep Learning techniques, accounting for 77 documents of research, which is showing its significant role in studies. Machine Learning techniques and the combined use of Deep Learning and Machine Learning are almost in the same range with 23 and 28 documents, respectively.
For the distribution of publication type in IEEE Xplore, as shown in Figure 13, the first remark that we can raise is the absence of book chapters. Only conference papers, which are predominant, with 113 documents, and journal articles with 15 documents and are published. This distribution clearly highlights conference proceedings within the IEEE community, reflecting the typical patterns of academic distribution in the field.
The graph in Figure 14 represents the top 10 contributing countries to the IEEE Xplore database. India is in the lead with a substantial 51 documents, underscoring the country's significant research output. Indonesia came in second with 10 publications, while the USA, Bangladesh, and Canada each contributed 7 documents. With four publications, Turkey follows closely in demonstrating its scholarly presence. Notable contributors include Saudi Arabia, Qatar, Morocco, and the UK, each of which has three publications in the database.
For the IEEE Xplore Database, all authors have published one single document in the IEEE Xplore database. Indeed, Abbas Mazrouei Sebdani, from Iran, published a conference paper [45] on the 5th International Conference on Pattern Recognition and Image Analysis (IPRIA) in 2021. He proposed two classification algorithms of Covid-19 using Machine Learning. In 2020, the south Korean researcher, Abdul Waheed, has published a journal article [46] where he proposed a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN). Using a deep unsupervised framework to classify lung diseases from chest CT and X-ray images, Pooja Yadav (India) published in IEEE Access [47]. Oussama El Gannourm, from Morocco, presented a Deep Learning based system for the diagnosis of COVID19 disease at the IEEE 2nd International Conference on Electronics, Control, Optimization, and Computer Science (ICECOCS) [48]. Using Deep Learning and Machine Learning, a framework named CovFrameNet., which consist of a pipelined image pre-processing method and a deep learning model for feature extraction, classification, and performance measurement, has been published by Olaide Nathaniel Oyelade from South Africa on IEEE Access [49].
According to Table 2, "IEEE Access" is the prominent journal in IEEE Xplore with a total of 7 documents and an impressive total of 2637 citations. Followed by "IEEE Journal of Biomedical and Health Informatics", "IEEE Transactions on Engineering Management", and "IEEE Transactions on Medical Imaging" participating with 2 documents each with total citations of 72, 33, and 186, respectively. With only one document, "IEEE Transactions on Big Data" shows its weight in the field with 70 citations.
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| IEEE Access | IEEE Xplore | 7 | 2637 | 376.71 |
| IEEE Journal of Biomedical and Health Informatics | IEEE Xplore | 2 | 72 | 36.00 |
| IEEE Transactions on Engineering Management | IEEE Xplore | 2 | 33 | 16.50 |
| IEEE Transactions on Medical Imaging | IEEE Xplore | 2 | 186 | 93.00 |
| IEEE Transactions on Big Data | IEEE Xplore | 1 | 70 | 70.00 |
DownLoad:
CSV
58 documents have been published in ACM database. Figure 15 illustrates that most documents used Deep Learning technique (46 documents) while Machine Learning and combined Deep Learning and Machine Learning techniques are less used accounting for 8 and 4, respectively. This makes us conclude the importance of Deep Learning techniques in ACM platform.
For the ACM database (Figure 16), only conferences articles and journal articles have been published with a dominance of conference articles around 76% (44 documents) and only 24% (14 documents) for journal articles. This distribution highlights the dual focuses of ACM database, which are to contribute to the body of knowledge with journal articles and to promote the academic record through the conferences.
Figure 17 is demonstrating China's remarkable advantage with 14 documents. India comes in second with 9 documents, while the USA contributes 6 publications. Greece, Thailand, Taiwan, and Saudi Arabia participated with 4 documents. With 2 documents, the UK contributed to the publication, although Brazil and Canada marked their presence with 1 document. This data shows a geographically varied collection of contributions to the ACM database, illustrating the international scope and cooperative character of modern research initiatives among different countries.
As for the ACM database, we do not have duplicated authors. Indeed, the paper of Abhishek Kumar, from India, was published for the 13 Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP) [50]. He presented a comparative analysis of the most well-known CNN models to classify X-Ray images. The 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics was an opportunity for the Georgian Tarun Naren [51] to publish his work where he developed a new variant of Model-Agnostic Meta-Learning (MAML) algorithm, named MAML++, in order to classify X-Ray images. From Vietnam, Pham Ngoc Ha [52] in 2023, participated in the 8th International Conference on Intelligent Information Technology and published his paper. He used Vision transformer architecture to extract data characteristics and classify images. In 2020, a system for automatic detection of COVID-19 from CXR images using machine learning techniques was proposed by Phongsathorn Kittiworapanya [53] on the Eleventh International Conference on Computational Systems-Biology and Bioinformatics. The year 2023 was when Robert Hertel [54] published a hit paper in ACM Computing Surveys. He presented an in-depth review of deep learning techniques on the purpose of diagnosis and prognosis of COVID-19 patients.
Table 3 presents the top 5 Journals Ranked by Productivity for ACM database. Indeed, "ACM Transactions on Multimedia Computing, Communications, and Applications" stands out as the most productive journal in the ACM database with 4 papers and 132 citations overall, followed by "ACM Transactions on Management Information Systems" having 3 publications and a total of 47 citations. Publishing 2 documents, "ACM Transactions on Internet Technology" and "IEEE/ACM Transactions on Computational Biology and Bioinformatics" got overall citations of 33 and 957, respectively. "ACM Computing Surveys" has completed the list of the top five productive journals with one contribution totaling five citations.
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| ACM Transactions on Multimedia Computing, Communications, and Applications | ACM | 4 | 132 | 33.00 |
| ACM Transactions on Management Information Systems | ACM | 3 | 47 | 15.67 |
| ACM Transactions on Internet Technology | ACM | 2 | 33 | 16.50 |
| IEEE/ACM Transactions on Computational Biology and Bioinformatics | ACM | 2 | 957 | 478.50 |
| ACM Computing Surveys | ACM | 1 | 5 | 5.00 |
DownLoad:
CSV
Participating with only 13 documents in this study, MDPI possesses the smallest collection of documents. The distribution of published documents (Figure 18) according to the techniques used is that more than 61% (8 documents) of the techniques used are Deep Learning, and the mixture of Deep Learning and Machine Learning is highlighted in four documents (30.8%). The use of Machine Learning was used only in one document.
Journal articles make up 100% of the publications and are the only contributor. The complete dominance of journal articles across all 13 documents in the dataset suggests that the ACM database places a high value on formal, peer-reviewed articles. The focused nature of this collection is highlighted by the absence of other publication types like conference papers or book chapters.
The top 10 nations that have contributed to the MDPI database are shown in Figure 19. Turkey leads the count with 3 documents. Following with 2 contributions, China and Pakistan show that they are actively involved in academic publishing within MDPI. Additionally, Bangladesh, Egypt, Iraq, Jordan, Saudi Arabia, and South Korea, contributed one document each.
There was one contribution per author from the MDPI database. Indeed, Ali Tariq Nagi [55], from Pakistan, has a paper that involves an evaluation of the performance of deep learning models for COVID-19 diagnosis using chest X-ray images from a dataset containing the largest number of COVID-19 images ever used in the literature. The Iraqi, Awf Ramdhan [56] proposed an approach that uses image cropping methods and a deep learning technique that classifies three public datasets. In [57], Bayan Alsaaidah from Jordan conducted a comprehensive review of Deep Learning and Machine Learning methods for detection and classification of Covid-19. Using Deep Learning techniques, a computer-aided diagnostic system for chest X-ray and CT images for Covid-19 classification was developed by Heba M. Emara [58] from Egypt. A Turkish researcher, Jawad Rasheed [59], proposed a computer vision and artificial-intelligence-based hybrid approach aid in efficient detection and control of the COVID-19 disease.
The top 5 Journals Ranked by Productivity for the MDPI database are presented in Table 4. With 3 published documents, "Diagnostics" leads the 5 productive journals in the MDPI database, and having 10 citations, was followed by "Applied Sciences" and "Sensors" with a count of 2 documents and an overall of citation of 33 and 27, respectively. "Bioengineering" with 15 citations and "Cancers" had 6 citations as their publications, with one document each.
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Diagnostics | MDPI | 3 | 10 | 3.33 |
| Applied Sciences | MDPI | 2 | 33 | 16.50 |
| Sensors | MDPI | 2 | 27 | 13.50 |
| Bioengineering | MDPI | 1 | 15 | 15.00 |
| Cancers | MDPI | 1 | 6 | 6.00 |
DownLoad:
CSV
In this study, 35 documents were selected in the PubMed database. Figure 20 displays the distribution of techniques used. Having almost 50% (17 documents), Deep Learning was used as a technique for detection and classification of Covid-19 in the PubMed database. Deep Learning and Machine Learning were utilized jointly with a count of 14 documents (40%). The use of Machine Learning as a technique appeared only 4 times with a percentage of 11.4%. This brings us to the superiority of the use of Deep Learning techniques in the PubMed database.
Same as for the ACM database, only journal articles were published in the PubMed database. With the absence of other publication types, such as book chapters or conference papers, we can underline that peer-reviewed articles are the focused nature of the PubMed database.
Leading the top 10 countries that contributed to publication, India published 8 documents, followed by the UK with 5 publications. Three published documents were the participation of China and Iran each. Not far from them is South Africa, with 3 published documents. Other countries include Pakistan and the USA with 2 publications, and Australia, Greece, and Italy contributed with 1 document each. Figure 21 displays the top 10 Countries that contributed to publications for the PubMed database.
As displayed in Figure 22, from Saudi Arabia, Mahmoud Ragad published 2 journal articles in 2021 and 2022. In his first paper [60], he presented an artificial intelligence-based ensemble model for detection and classification (AIEM-DC) that aims to accurately detect and classify the COVID-19 using an ensemble of DL models. In [61], Mahmoud Ragad proposed a new Quantum Seagull Optimization Algorithm with a DL-based COVID-19 diagnosis model, named the QSGOA-DL technique to detect and classify COVID-19 with the help of CXR images. Abdullah Alen, from Turkey, proposed a Residual Convolutional Neural Network (ResCNN) used as a deep learning method, with the assistance of machine learning algorithms and extracted features from images [62]. An effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images (chest radiograph, CT-scan, and ECG-trace images) was proposed by the Pakistani Naeem Ullah [63]. The main idea of Linh T Duong, from Vietnam, in [64] is exploiting cutting-edge Machine Learning techniques for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images. From Italy, Luca Saba [65] presented a comparative study between Deep Learning and Machine Learning algorithms for the detection of Covid-19 based on radiological computed tomography (CT) lung scans.
Table 5 displays the 5 most productive journals in the PubMed database. With 3 documents, "Diagnostics (Basel)" ranks first with 29 citations, followed by "Computational Intelligence and Neuroscience" with 2 published papers, which got 8 citations. "Mathematical Biosciences and Engineering", "Journal of Healthcare Engineering", and "Journal of Medical Internet Research" have participated in this field by publishing 1 article each with total citations of 8, 10, and 59, respectively.
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Diagnostics (Basel) | PubMed | 3 | 29 | 9.67 |
| Computational Intelligence and Neuroscience | PubMed | 2 | 8 | 4.00 |
| Mathematical Biosciences and Engineering | PubMed | 1 | 8 | 8.00 |
| Journal of Healthcare Engineering | PubMed | 1 | 10 | 10.00 |
| Journal of Medical Internet Research | PubMed | 1 | 59 | 59.00 |
DownLoad:
CSV
Springer is the database that has the highest number of documents in this study by contributing 217 documents. With 148 journal articles, Deep Learning dominated the techniques used in Springer database with a percentage of 68.2%. Merged Deep Learning and Machine Learning techniques have been used in 55 documents, achieving 25.3% of all documents. Moreover, the use of Machine Learning techniques occurred only in 14 documents (6.5%). This distribution used techniques that signify the research trends and priorities within the Springer database. Figure 23 shows the distribution of techniques used for the Springer database.
With a percentage of 60.8% (132 documents), the publication type of published documents in Springer are journal articles with a huge part (see Figure 24). A considerable number of types of publications are conference papers, accounting for 54 documents or 24.9%. Book chapters got a significant part of publication type with 31 documents (14.3%). This means that springer is a database that opens all the opportunities to researchers to publish their works.
As we can see in Figure 25, Indian researchers lead the top 10 countries that contributed to publication with a very high number of documents (90 publications). This carries us to India's superiority in the field of scientific research in recent years. With a very big gap between India, China came second with 18 documents, followed by Pakistan, Egypt, and Turkey in the same range of published documents, with a count of 13, 11, and 10 documents, respectively. Morocco and the USA marked their presence with 8 publications each. Furthermore, Bangladesh, Iraq, and Algeria have participated with 5 documents each.
The top 5 researchers in Springer participated with 2 documents each. Indeed, an Indian researcher, K. Shankar, who came first with publications [66], presented a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images using Weiner filtering for preprocessing, grey level co-occurrence matrix for feature extraction, and artificial neural network for classification. In 2023, an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model (FM-CNN) was developed in his second paper [67]. In 2021, Sourabh Shastri, from India, designed a nested ensemble model using deep learning methods based on long short-term memory (LSTM) [68]. A CheXImageNet model has been introduced for detecting Covid-19 disease using digital images of Chest X-ray with the help of an openly accessible dataset in his second paper [69]. The work done by the Pakistani Kainat Khero in [70] was a review of ML and DL-based techniques for early detection of Covid using chest X-rays. A comparison study of 9 state-of-the-art CNN architectures through a transfer learning approach for detection of Covid-19 was presented in his second work [71]. Siddharth Gupta, from India, presented pre-trained CNN models (VGG16 and VGG19) that were used for transfer learning and several machine learning classifiers for the classification of images, such as Covid19 [72]. In [73], he employed DL-based automated techniques (VGG16, VGG19, and Inception V3) to process the chest X-ray images for the detection of Covid-19. The Indian author Ebenezer Jangam [74] proposed a stacked ensemble of heterogenous pre-trained computer vision models where 4 pre-trained DL models were considered (VGG19, ResNet101, DenseNet169, and WideResNet502). His second work [75] was about a novel multi-class classification framework that can minimize either false positives or false negatives, and is useful in computer aided diagnosis or computer aided detection, respectively.
Publishing 28 documents with a total citation of 222 citations, "Multimedia Tools and Applications" came out on top of the productive journal in Springer (see Table 6). With a considerable difference of 18 documents, "Journal of Ambient Intelligence and Humanized Computing" took the second rank, with 290 citations. "Applied Intelligence" and "Scientific Reports" have published 8 documents each with 1422 and 462 citations respectively. Six publications and 106 citations allowed "Neural Computing and Applications" to be in the top 5 productive journal in Springer.
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Multimedia Tools and Applications | Springer | 28 | 222 | 7.93 |
| Journal of Ambient Intelligence and Humanized Computing | Springer | 10 | 290 | 29.00 |
| Applied Intelligence | Springer | 8 | 1422 | 177.75 |
| Scientific Reports | Springer | 8 | 462 | 57.75 |
| Neural Computing and Applications | Springer | 6 | 106 | 17.67 |
DownLoad:
CSV
With 132 collected documents, ScienceDirect comes in second of all the databases concerned in this study. From Figure 26, we can conclude that Deep Learning techniques are the most used with a total count of 81 documents, which is almost a huge portion of all technique used. The use of Deep Learning and Machine Learning together appeared in 30 (22.7%) papers while Machine Learning as type of technique occurred in 21 (15.9%) documents.
As illustrated in Figure 27, more than 96% (127 papers) of documents published in ScienceDirect are journal articles. This shows the total dominance of this type of publication, and it is the main way for researchers to publish their works in ScienceDirect. Even though they are less in number, book chapters make up 3.8% of all publications (5 documents).
Like almost all the databases, India tops the 10 countries that contributed to publications in ScienceDirect with 38 documents, which confirms its dominance in this study. India is followed by China, with a significant gab accounting for 21 published papers. Turkey maintained its presence among the countries contributing to publication in this database. Bangladesh and Iran contributed 8 documents, while Egypt and Pakistan h 7 and 5 times, respectively. South Korea, UAE, and Saudi Arabia insisted on being included among the countries with 3 documents each. Figure 28 displays the top countries that contributed to the publications for the ScienceDirect database.
The year 2021 was a special year for the Pakistani Saddam Hussain Khan when he published two papers (Figure 29). In [76], he proposed two new deep learning frameworks: Deep Hybrid Learning (DHL) and Deep Boosted Hybrid Learning (DBHL) for effective COVID-19 detection in X-ray dataset. He developed, in his second work [77], two novel custom CNN architectures (COVID-RENet-1 and COVID-RENet-2) for COVID-19 specific pneumonia analysis. The Indian researcher, Aakarsh Malhotra [78], presented a COVID-19 Multi-Task Network (COMiT-Net), which is an automated end-to-end network for COVID-19 screening and that can perform semantic segmentation of the regions of interest to make the model explainable. Originally from India, Sakthivel R [79] built an efficient hardware architecture based on an ensemble deep learning model to identify the COVID-19 using chest X-ray (CXR) records. Saeed Iqbal [80] suggested a technique allowing a class balancing algorithm to understand and detect lung disease signs from chest X-ray and CT images. He used Deep learning techniques to train and evaluate images, enabling the extraction of basic visual attributes. Roa'a Mohammedqasem [81], from Turkey, proposed a real-time detection system based on the Internet of Things framework. The system can collect real-time data from users to determine potential coronavirus cases, analyze treatment responses for people who have been treated, and accurately collect and analyse the datasets.
As presented in Table 7, the journal "Computers in Biology and Medicine" came in the top 5 productive journals with 19 published documents and accumulating a total of 3711 citations. With 13 papers having 221 citations, "Expert Systems with Applications" ranked in second place, followed by "Biomedical Signal Processing and Control" and "Applied Soft Computing" with 12 and 11 documents and receiving 181 and 622, respectively. "Informatics in Medicine Unlocked" announced its position within the productive journals with 7 documents accounting for a total of 757 citations.
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Computers in Biology and Medicine | ScienceDirect | 19 | 3711 | 195.32 |
| Expert Systems with Applications | ScienceDirect | 13 | 221 | 17.00 |
| Biomedical Signal Processing and Control | ScienceDirect | 12 | 181 | 15.08 |
| Applied Soft Computing | ScienceDirect | 11 | 622 | 56.55 |
| Informatics in Medicine Unlocked | ScienceDirect | 7 | 757 | 108.14 |
DownLoad:
CSV
In this review, we highlighted the use of Deep Learning and Machine Learning techniques to detect and classify Covid-19 using X-Ray and CT images. The bibliometric analysis carried out for this study revealed a significant number of documents describing the importance of the use of Deep Learning in this field compared to Machine learning methods. Moreover, one conclusion we can draw from this study is the rising forces in economy and technology, especially India, China, Turkey, and Pakistan, which began to compete in the field of scientific research. Additionally, journal articles, as a publication type, dominated the type of publication compared to Conference Papers and Book Chapters. This means that authors prefer to publish their detailed contributions to the field of knowledge in journals than publish their initial findings in conference papers or their comprehensive insights into specific areas in Book Chapters. Furthermore, authors focused on the Springer database to submit their works for publication, which is explained by the high number of published documents in this database compared to other databases.
The authors declare that they have not used Artificial Intelligence (AI) tools in the creation of this article.
The authors declare that there are no conflicts of interest in this paper.
| [1] |
Sharma P, Bano A, Singh SP, et al. (2022) Recent advancements in microbial-assisted remediation strategies for toxic contaminants. Cleaner Chem Eng 2: 100020. https://doi.org/10.1016/j.clce.2022.100020 
|
| [2] | Malaviya P, Sharma R, Sharma S, et al. (2023) Role of microorganisms in environmental remediation and resource recovery through microbe-based technologies having major potentials. Good Microbes in Medicine, Food Production, Biotechnology, Bioremediation, and Agriculture . John Wiley & Sons Ltd. 247-264. https://doi.org/10.1002/9781119762621.ch20 |
| [3] | Mishra B, Varjani S, Kumar G, et al. (2020) Microbial approaches for remediation of pollutants: Innovations, future outlook, and challenges. Energy Environ 32: 1-30. https://doi.org/10.1177/0958305X19896781 |
| [4] |
Bilal M, Iqbal MNH (2020) Microbial bioremediation as a robust process to mitigate pollutants of environmental concern. Case Stud Chem Environ Eng 2: 100011. https://doi.org/10.1016/j.cscee.2020.100011
|
| [5] |
Bala S, Garg D, Thirumalesh BV, et al. (2022) Recent strategies for bioremediation of emerging pollutants: a review for a green and sustainable environment. Toxics 10: 484. https://doi.org/10.3390/toxics10080484
|
| [6] |
Kour D, Kaur T, Devi R, et al. (2021) Beneficial microbiomes for bioremediation of diverse contaminated environments for environmental sustainability: Present status and future challenges. Environ Sci Pollut Res 28: 24917-24939. https://doi.org/10.1007/s11356-021-13252-7
|
| [7] |
Sangwan S, Dukare A (2018) Microbe-mediated bioremediation: an eco-friendly sustainable approach for environmental clean-up. Advances in Soil Microbiology: Recent Trends and Future Prospects . Springer Nature Singapore Pte Ltd. 145-162. https://doi.org/10.1007/978-981-10-6178-3_8
|
| [8] |
Azubuike CC, Chikere CB, Okpokwasili GB (2016) Bioremediation techniques–classification based on site of application: principles, advantages, limitations and prospects. World J Microbiol Biotechnol 32: 180. https://doi.org/1 0.1007/s11274-016-2137-x
|
| [9] |
Swapnil P, Singh LA, Mandal C, et al. (2023) Functional characterization of microbes and their association with unwanted substance for wastewater treatment processes. J Water Process Eng 54: 103983. https://doi.org/10.1016/j.jwpe.2023.103983
|
| [10] |
Xu G, Zhao S, Liu J, et al. (2023) Bioremediation of organohalide pollutants: progress, microbial ecology, and emerging computational tools. Curr Opin Environl Sci Health 32: 100452. https://doi.org/10.1016/j.coesh.2023.10045
|
| [11] |
Kour D, Khan SS, Kour H, et al. (2022) Microbe-mediated bioremediation: Current research and future challenges. J Appl Biol Biotechnol 10: 6-24. https://doi.org/10.7324/JABB.2022.10s202
|
| [12] |
Leong HY, Chang CK, Khoo KS, et al. (2021) Waste biorefinery towards a sustainable circular bioeconomy: a solution to global issues. Biotechnol Biofuels 14: 87. https://doi.org/10.1186/s13068-021-01939-5
|
| [13] |
Ayilara MS, Babalola OO (2023) Bioremediation of environmental wastes: the role of microorganisms. Front Agron 5: 1183691. https://doi.org/10.3389/fagro.2023.1183691
|
| [14] |
Vivien FD, Nieddu M, Befort N, et al. (2019) The hijacking of the bioeconomy. Ecol Econ 159: 189-197. https://doi.org/10.1016/j.ecolecon.2019.01.027
|
| [15] |
Dahiya S, Kumar AN, Sravan JS, et al. (2018) Food waste biorefinery: Sustainable strategy for circular bioeconomy. Bioresour Technol 248: 2-12. https://doi.org/10.1016/j.biortech.2017.07.176
|
| [16] |
Ubando AT, Felix CB, Chen WH (2020) Biorefineries in circular bioeconomy: A comprehensive review. Bioresour Technol 299: 122585. https://doi.org/10.1016/j.biortech.2019.122585
|
| [17] | Paranthaman SR, Karthikeyan B (2015) Bioremediation of heavy metal in paper mill effluent using Pseudomonas spp. Int J Microbiol 1: 1-5. |
| [18] |
Guo SY, Xiao CQ, Zhou N, et al. (2021) Speciation, toxicity, microbial remediation and phytoremediation of soil chromium contamination. Environ Chem Lett 19: 1413-1431. https://doi.org/10.1007/s10311-020-01114-6
|
| [19] |
Zheng YT, Xiao CQ, Chi RA (2021) Remediation of soil cadmium pollution by biomineralization using microbial-induced precipitation: a review. World J Microbiol Biotechnol 37: 208. https://doi.org/10.1007/s11274-021-03176-2
|
| [20] |
Taghavi N, Singhal N, Zhuang WQ, et al. (2021) Degradation of plastic waste using stimulated and naturally occurring microbial strains. Chemosphere 263: 127975. https://doi.org/10.1016/j.chemosphere.2020.127975
|
| [21] |
Pinheiro LRS, Gradíssimo DG, Xavier LP, et al. (2022) Degradation of azo dyes: bacterial potential for bioremediation. Sustainability 14: 1510. https://doi.org/10.3390/su14031510
|
| [22] |
Zeenat A, Elahi DA, Bukhari S, et al. (2021) Plastics degradation by microbes: A sustainable approach. J King Saud Univ Sci 33: 101538. https://doi.org/10.1016/j.jksus.2021.101538
|
| [23] |
Liu F, Tu T, Li S, et al. (2019) Relationship between plankton-based β-carotene and biodegradable adaptablity to petroleum-derived hydrocarbon. Chemosphere 237: 124430. https://doi.org/10.1016/j.chemosphere.2019.124430
|
| [24] |
King P, Anuradha K, Lahari SB, et al. (2008) Biosorption of zinc from aqueous solution using Azadirachta indica bark: Equilibrium and kinetic studies. J Hazard Mater 152: 324-329. https://doi.org/10.1016/j.jhazmat.2007.06.101
|
| [25] | Tigini V, Prigione V, Giansanti P, et al. (2010) Fungal biosorption, an innovative treatment for the decolourisation and detoxification of textile effluents. Water (Basel) 2: 550-565. https://doi.org/10.3390/w2030550 |
| [26] |
Peña-Montenegro TD, Lozano L, Dussán J (2015) Genome sequence and description of the mosquitocidal and heavy metal tolerant strain Lysinibacillus sphaericus CBAM5. Stand Genomic Sci 10. https://doi.org/10.1186/1944-3277-10-2
|
| [27] |
Fazli MM, Soleimani N, Mehrasbi M, et al. (2015) Highly cadmium tolerant fungi: their tolerance and removal potential. J Environ Health Sci Eng 13: 19. https://doi.org/10.1186/s40201-015-0176-0
|
| [28] |
Wu YH, Zhou P, Cheng H, et al. (2015) Draft genome sequence of Microbacterium profundi Shh49 T, an actinobacterium isolated from deep-sea sediment of a polymetallic nodule environment. Genome Announc 3: e00642-15. https://doi.org/10.1128/genomeA.00642-15
|
| [29] |
Phulpoto AH, Qazi MA, Mangi S, et al. (2016) Biodegradation of oil-based paint by Bacillus species monocultures isolated from the paint warehouses. Int J Environ Sci Technol 13: 125-134. https://doi.org/10.1007/s13762-015-0851-9
|
| [30] |
Rai R, Vijayakumar BS (2023) Myco-remediation of textile dyes via biosorption by Aspergillus tamarii isolated from domestic wastewater. Water Air Soil Pollut 234: 542. https://doi.org/10.1007/s11270-023-06535-x
|
| [31] |
Góralczyk-Bińkowska A, Długoński A, Bernat P, et al. (2021) Environmental and molecular approach to dye industry waste degradation by the ascomycete fungus Nectriella pironii. Sci Rep 11: 23829. https://doi.org/10.1038/s41598-021-03446-x
|
| [32] |
Srinivasan S, Sadasivam SK (2021) Biodegradation of textile azo dyes by textile effluent non-adapted and adapted Aeromonas hydrophila. Environ Res 194: 110643. https://doi.org/10.1016/j.envres.2020.110643
|
| [33] |
Ikram M, Zahoor MG, Batiha ES (2021) Biodegradation and decolorization of textile dyes by bacterial strains: a biological approach for wastewater treatment. Zeitschrift Für Physikalische Chemie 235: 1381-1393. https://doi.org/10.1515/zpch-2020-1708
|
| [34] |
Ikram M, Naeem M, Zahoor M, et al. (2022) Bacillus subtilis: as an efficient bacterial strain for the reclamation of water loaded with Textile Azo Dye, Orange II. Int J Mol Sci 23: 10637. https://doi.org/10.3390/ijms231810637
|
| [35] |
Priyanka JV, Rajalakshmi S, Kumar PS, et al. (2022) Bioremediation of soil contaminated with toxic mixed reactive azo dyes by co-cultured cells of Enterobacter cloacae and Bacillus subtilis. Environ Res 204: 112136. https://doi.org/10.1016/j.envres.2021.112136
|
| [36] |
Saha P, Sivaramakrishna A, Rao KVB (2022) Bioremediation of reactive orange 16 by industrial effluent-adapted bacterial consortium VITPBC6: process optimization using response surface methodology (RSM), enzyme kinetics, pathway elucidation, and detoxification. Environ Sci Pollut Res 30: 35450-35477. https://doi.org/10.1007/s11356-022-24501-8
|
| [37] |
Barathi S, Aruljothi KN, Karthik C, et al. (2020) Optimization for enhanced ecofriendly decolorization and detoxification of Reactive Blue160 textile dye by Bacillus subtilis. Biotechnol Rep 28: e00522. https://doi.org/10.1016/j.btre.2020.e00522
|
| [38] |
Xaaldi Kalhor A, Movafeghi A, Mohammadi-Nassab AD, et al. (2017) Potential of the green alga Chlorella vulgaris for biodegradation of crude oil hydrocarbons. Mar Pollut Bull 123: 286-290. https://doi.org/10.1016/j.marpolbul.2017.08.045
|
| [39] |
Varjani SJ, Upasani VN (2016) Biodegradation of petroleum hydrocarbons by oleophilic strain of Pseudomonas aeruginosa NCIM 5514. Bioresour Technol 222: 195-201. https://doi.org/10.1016/j.biortech.2016.10.006
|
| [40] |
Baoune H, Hadj-Khelil AOE, Pucci G, et al. (2018) Petroleum degradation by endophytic Streptomyces spp. isolated from plants grown in contaminated soil of southern Algeria. Ecotoxicol Environ Saf 147: 602-609. https://doi.org/10.1016/j.ecoenv.2017.09.013
|
| [41] | Li SW, Liu MY, Yang RQ (2019) Comparative genome characterization of a petroleum-degrading Bacillus subtilis strain DM2. Int J Genomics 8: 1-16. https://doi.org/10.1155/2019/7410823 |
| [42] |
Wang D, Lin J, Lin J, et al. (2019) Biodegradation of petroleum hydrocarbons by Bacillus subtilis BL-27, a strain with weak hydrophobicity. Molecules 24: 3021. https://doi.org/10.3390/molecules24173021
|
| [43] |
Othman AR, Ismail NS, Abdullah SRS, et al. (2022) Potential of indigenous biosurfactant-producing fungi from real crude oil sludge in total petroleum hydrocarbon degradation and its future research prospects. J Environ Chem Eng 10: 107621. https://doi.org/10.1016/j.jece.2022.107621
|
| [44] |
Horton AA (2021) Plastic pollution: When do we know enough?. J Hazard Mater 422: 126885. https://doi.org/10.1016/j.jhazmat.2021.126885
|
| [45] |
Li P, Wang X, Su M, et al. (2021) Characteristics of plastic pollution in the environment: a review. Bull Environ Contam Toxicol 107: 577-584. https://doi.org/10.1007/s00128-020-02820-1
|
| [46] |
Iroegbu AOC, Ray SS, Mbarane V, et al. (2021) Plastic pollution: a perspective on matters arising: challenges and opportunities. ACS Omega 6: 19343-19355. https://doi.org/10.1021/acsomega.1c02760
|
| [47] | Maroof L, Khan I, Yoo HS, et al. (2020) Identification and characterization of low density polyethylene-degrading bacteria isolated from soils of waste disposal sites. Environ Eng Res . https://doi.org/10.4491/eer.2020.167 |
| [48] |
Munir E, Harefa RSM, Priyani N, et al. (2018) Plastic degrading fungi Trichoderma viride and Aspergillus nomius isolated from local landfill soil in Medan. IOP Conf Ser Earth Environ Sci 126: 012145. https://doi.org/10.1088/1755-1315/126/1/012145
|
| [49] |
Muhonja CN, Makonde H, Magoma G, et al. (2018) Biodegradability of polyethylene by bacteria and fungi from Dandora dumpsite Nairobi-Kenya. PLoS One 13: e0198446. https://doi.org/10.1371/journal.pone.0198446
|
| [50] | Awasthi S, Srivastava P, Singh P, et al. (2017) Biodegradation of thermally treated high-density polyethylene (HDPE) by Klebsiella pneumoniae CH0013. Biotech 7: 332. https://doi.org/10.1007/s13205-017-0959-3 |
| [51] |
Zhou H, Gao X, Wang S, et al. (2023) Enhanced bioremediation of aged polycyclic aromatic hydrocarbons in soil using immobilized microbial consortia combined with strengthening remediation strategies. Int J Environ Res Public Health 20: 1766. https://doi.org/10.3390/ijerph20031766
|
| [52] |
Lü H, Wei JL, Tang GX, et al. (2024) Microbial consortium degrading of organic pollutants: Source, degradation efficiency, pathway, mechanism and application. J Clean Prod 451: 141913. https://doi.org/10.1016/j.jclepro.2024.141913
|
| [53] |
Kulshreshtha S (2013) Genetically engineered microorganisms: a problem solving approach for bioremediation. J Bioremed Biodeg 4: e133. https://doi.org/10.4172/2155-6199.1000e133
|
| [54] | Cases I, de Lorenzo V (2005) Genetically modified organisms for the environment: stories of success and failure and what we have learned from them. Int Microbiol 8: 213-222. |
| [55] |
Mejare M, Bulow L (2001) Metal-binding proteins and peptides in bioremediation and phytoremediation of heavy metals. Trends Biotech 19: 67-73. https://doi.org/10.1016/S0167-7799(00)01534-1
|
| [56] | Shukla KP, Singh NK, Sharma S (2010) Bioremediation: developments. Curr Pract Perspect Genet Eng Biotechnol J 2010: 1-20. |
| [57] |
Liu S, Zhang F, Chen J, et al. (2011) Arsenic removal from contaminated soil via biovolatilization by genetically engineered bacteria under laboratory conditions. J Environ Sci 9: 1544-1550. https://doi.org/10.1016/S1001-0742(10)60570-0
|
| [58] |
Sharma B, Shukla P (2022) Futuristic avenues of metabolic engineering techniques in bioremediation. Biotechnol Appl Biochem 69: 51-60. https://doi.org/10.1002/bab.2080
|
| [59] |
Gupta S, Shukla P (2017) Gene editing for cell engineering: trends and applications. Crit Rev Biotechnol 37: 672-684. https://doi.org/10.1080/07388551.2016.1214557
|
| [60] | Wuana RA, Okieimen FE (2011) Heavy metals in contaminated soils: a review of sources, chemistry, risks and best available strategies for remediation. Int Scholarly Res Not 2011: 402647. https://doi.org/10.5402/2011/402647 |
| [61] | Bayata A (2023) The future insight on bioremediation of contaminated soils-review. Intl J Eng Sci Technol Innovation 3: 1-32. |
| [62] |
Singh S, Mulchandani A, Chen W (2008) Highly selective and rapid arsenic removal by metabolically engineered Escherichia coli cells expressing Fucus vesiculosus Metallothionein. Appl Environ Microbiol 74: 2924-2927. https://doi.org/10.1128/AEM.02871-07
|
| [63] |
Yuan C, Lu X, Qin J, et al. (2008) Volatile arsenic species released from Escherichia coli expressing the asiii s-adenosylmethionine methyltransferase gene. Environ Sci Technol 42: 3201-3206. https://doi.org/10.1021/es702910g
|
| [64] |
Kostal J, Yang R, Wu CH, et al. (2004) Enhanced arsenic accumulation in engineered bacterial cells expressing ArsR. Appl Environ Microbiol 70: 4582-4587. https://doi.org/10.1128/AEM.70.8.4582-4587.2004
|
| [65] |
Zhang J, Xu Y, Cao T, et al. (2017) Arsenic methylation by a genetically engineered Rhizobium-legume symbiont. Plant Soil 416: 259-269. https://doi.org/10.1007/s11104-017-3207-z
|
| [66] |
Ivask A, Dubourguier HC, Põllumaa L, et al. (2011) Bioavailability of Cd in 110 polluted top soils to recombinant bioluminescent sensor bacteria: effect of soil particulate matter. J Soils Sediments 11: 231-237. https://doi.org/10.1007/s11368-010-0292-5
|
| [67] |
Patel J, Zhang Q, McKay RML, et al. (2010) Genetic engineering of Caulobacter crescentus for removal of cadmium from water. Appl Biochem Biotechnol 160: 232-243. https://doi.org/10.1007/s12010-009-8540-0
|
| [68] |
Bondarenko O, Rõlova T, Kahru A, et al. (2008) Bioavailability of Cd, Zn and Hg in soil to nine recombinant luminescent metal sensor bacteria. Sensors 8: 6899-6923. https://doi.org/10.3390/s8116899
|
| [69] |
Kang SH, Singh S, Kim JY, et al. (2007) Bacteria metabolically engineered for enhanced phytochelatin production and cadmium accumulation. Appl Environ Microbiol 73: 6317-6320. https://doi.org/10.1128/AEM.01237-07
|
| [70] |
Deng X, Yi XE, Liu G (2007) Cadmium removal from aqueous solution by gene-modified Escherichia coli JM109. J Hazard Mater 139: 340-344. https://doi.org/10.1016/j.jhazmat.2006.06.043
|
| [71] |
Wu CH, Wood TK, Mulchandani A, et al. (2006) Engineering plant-microbe symbiosis for rhizoremediation of heavy metals. Appl Environ Microbiol 72: 1129-1134. https://doi.org/10.1128/AEM.72.2.1129-1134.2006
|
| [72] |
Sriprang R, Hayashi M, Ono H, et al. (2003) Enhanced accumulation of Cd2+ by a Mesorhizobium sp. transformed with a gene from arabidopsis thaliana coding for phytochelatin synthase. Appl Environ Microbiol 69: 1791-1796. https://doi.org/10.1128/AEM.69.3.1791-1796.2003
|
| [73] |
Valls M, Atrian S, de Lorenzo V, et al. (2000) Engineering a mouse metallothionein on the cell surface of Ralstonia eutropha CH34 for immobilization of heavy metals in soil. Nat Biotechnol 18: 661-665. https://doi.org/10.1038/76516
|
| [74] |
Freeman JL, Persans MW, Nieman K, et al. (2005) Nickel and cobalt resistance engineered in Escherichia coli by overexpression of serine acetyltransferase from the nickel hyperaccumulator plant Thlaspi goesingense. Appl Environ Microbiol 71: 8627-8633. https://doi.org/10.1128/AEM.71.12.8627-8633.2005
|
| [75] |
López A, Lázaro N, Morales S, et al. (2002) Nickel biosorption by free and immobilized cells of Pseudomonas fluorescens 4F39: a comparative study. Water Air Soil Pollut 135: 157-172. https://doi.org/10.1023/A:1014706827124
|
| [76] |
Kim IH, Choi JH, Joo JO, et al. (2015) Development of a microbe-zeolite carrier for the effective elimination of heavy metals from seawater. J Microbiol Biotechnol 25: 1542-1546. https://doi.org/10.4014/jmb.1504.04067
|
| [77] |
Deng X, Li QB, Lu YH, et al. (2005) Genetic engineering of Escherichia coli SE5000 and its potential for Ni2+ bioremediation. Process Biochem 40: 425-430. https://doi.org/10.1016/j.procbio.2004.01.019
|
| [78] |
Akkurt Ş, Oğuz M, Alkan Uçkun A (2022) Bioreduction and bioremoval of hexavalent chromium by genetically engineered strains (Escherichia coli MT2A and Escherichia coli MT3). World J Microbiol Biotechnol 38: 45. https://doi.org/10.1007/s11274-022-03235-2
|
| [79] |
Li Q, Li J, Kang KL, et al. (2020) A safety type of genetically engineered bacterium that degrades chemical pesticides. AMB Express 10: 33. https://doi.org/10.1186/s13568-020-00967-y
|
| [80] |
Feng F, Ge J, Li Y, et al. (2017) Isolation, colonization, and chlorpyrifos degradation mediation of the endophytic Bacterium Sphingomonas strain hjy in chinese chives (Allium tuberosum). J Agric Food Chem 65: 1131-1138. https://doi.org/10.1021/acs.jafc.6b05283
|
| [81] |
Luo X, Zhang D, Zhou X, et al. (2018) Cloning and characterization of a pyrethroid pesticide decomposing esterase gene, Est3385, from Rhodopseudomonas palustris PSB-S. Sci Rep 8: 7384. https://doi.org/10.1038/s41598-018-25734-9
|
| [82] |
Barac T, Taghavi S, Borremans B, et al. (2004) Engineered endophytic bacteria improve phytoremediation of water-soluble, volatile, organic pollutants. Nat Biotechnol 22: 583-588. https://doi.org/10.1038/nbt960
|
| [83] |
Sun Y, Zhao X, Zhang D, et al. (2017) New naphthalene whole-cell bioreporter for measuring and assessing naphthalene in polycyclic aromatic hydrocarbons contaminated site. Chemosphere 186: 510-518. https://doi.org/10.1016/j.chemosphere.2017.08.027
|
| [84] |
Samin G, Pavlova M, Arif MI, et al. (2014) A Pseudomonas putida strain genetically engineered for 1,2,3-trichloropropane bioremediation. Appl Environ Microbiol 80: 5467-5476. https://doi.org/10.1128/AEM.01620-14
|
| [85] |
Qiao W, Chu J, Ding S, et al. (2017) Characterization of a thermo-alkali-stable laccase from Bacillus subtilis cjp3 and its application in dyes decolorization. J Environ Sci Health 52: 710-717. https://doi.org/10.1080/10934529.2017.1301747
|
| [86] |
Suzuki T, Nishizawa A, Kikuchi M, et al. (2019) Biphenyl degradation by recombinant photosynthetic cyanobacterium Synechocystis sp. PCC6803 in an oligotrophic environment using unphysiological electron transfer. Biochem J 12476: 3615-3630. https://doi.org/10.1042/BCJ20190731
|
| [87] |
Wang Y, Jiang Q, Zhou C, et al. (2014) In-situ remediation of contaminated farmland by horizontal transfer of degradative plasmids among rhizosphere bacteria. Soil Use and Manage 30: 303-309. https://doi.org/10.1111/sum.12105
|
| [88] |
Jussila MM, Zhao J, Suominen L, et al. (2007) TOL plasmid transfer during bacterial conjugation in vitro and rhizoremediation of oil compounds in vivo. Environ Pollut 146: 510-524. https://doi.org/10.1016/j.envpol.2006.07.012
|
| [89] |
Maj A, Dziewit L, Drewniak L, et al. (2020) In vivo creation of plasmid pCRT01 and its use for the construction of carotenoid-producing Paracoccus spp. strains that grow efficiently on industrial wastes. Microb Cell Fact 19: 141. https://doi.org/10.1186/s12934-020-01396-z
|
| [90] |
Furubayashi M, Kubo A, Takemura M, et al. (2021) Capsanthin production in Escherichia coli by overexpression of Capsanthin/Capsorubin synthase from Capsicum annuum. J Agric Food Chem 69: 5076-5085. https://doi.org/10.1021/acs.jafc.1c00083
|
| [91] |
Zheng X, Hu R, Chen D, et al. (2021) Lipid and carotenoid production by the Rhodosporidium toruloides mutant in cane molasses. Bioresour Technol 326: 124816. https://doi.org/10.1016/j.biortech.2021.124816
|
| [92] |
Yang P, Wu Y, Zheng Z, et al. (2018) CRISPR-Cas9 approach constructing cellulase sestc-engineered Saccharomyces cerevisiae for the production of orange peel ethanol. Front Microbiol 9: 2436. https://doi.org/10.3389/fmicb.2018.02436
|
| [93] |
Cunha JT, Soares PO, Baptista SL, et al. (2020) Engineered Saccharomyces cerevisiae for lignocellulosic valorization: a review and perspectives on bioethanol production. Bioengineered 11: 883-903. https://doi.org/10.1080/21655979.2020.1801178
|
| [94] |
Saravanan A, Kumar PS, Duc PA, et al. (2023) Strategies for microbial bioremediation of environmental pollutants from industrial wastewater: A sustainable approach. Chemosphere 313: 137323. https://doi.org/10.1016/j.chemosphere.2022.137323
|
| [95] |
Lahel A, Fanta AB, Sergienko N, et al. (2016) Effect of process parameters on the bioremediation of diesel contaminated soil by mixed microbial consortia. Int Biodeterior Biodegrad 113: 375-385. https://doi.org/10.1016/j.ibiod.2016.05.005
|
| [96] |
Abatenh E, Gizaw B, Tsegaye Z, et al. (2017) The role of microorganisms in bioremediation-A review. Open J Environ Biol 2: 030-046. https://doi.org/10.17352/ojeb.000007
|
| [97] |
Leong YK, Chang JS (2020) Bioremediation of heavy metals using microalgae: Recent advances and mechanisms. Bioresour Technol 303: 122886. https://doi.org/10.1016/j.biortech.2020.122886
|
| [98] | Ali U, Mudasir S, Farooq S, et al. (2015) Factors affecting bioremediation. J Res Dev 15. |
| [99] | Naidu R, Semple KT, Megharaj M, et al. (2008) Bioavailability, definition, assessment and implications for risk assessment. Dev Soil Sci 32: 39-52. https://doi.org/10.1016/S0166-2481(07)32003-5 |
| [100] |
Delille D, Coulon F, Pelletier E (2004) Effects of temperature warming during a bioremediation study of natural and nutrient-amended hydrocarbon-contaminated sub-Antarctic soils. Cold Reg Sci Technol 40: 61-70. https://doi.org/10.1016/j.coldregions.2004.05.005
|
| [101] | Macaulay BM (2015) Understanding the behavior of oil-degrading microorganisms to enhance the microbial remediation of spilled petroleum. Appl Ecol Environ Res 13: 247-262. https://doi.org/10.15666/aeer/1301_247262 |
| [102] |
da Cruz GF, de Vasconcellos SP, Angolini CF, et al. (2011) Could petroleum biodegradation be a joint achievement of aerobic and anaerobic microrganisms in deep sea reservoirs?. AMB Express 1: 47. https://doi.org/10.1186/2191-0855-1-47
|
| [103] | Meena T, Neelam D, Gupta V, et al. (2021) Bioremediation-an overview. Int J Sci Res 12: 41125-41133. |
| [104] |
Srivastava J, Naraian R, Kalra SJS, et al. (2013) Advances in microbial bioremediation and the factors influencing the process. Int J Environl Sci Technol 11: 1787-1800. https://doi.org/10.1007/s13762-013-0412-z
|
| [105] |
Margesin R, Fonteyne P, Schinner F, et al. (2007) Rhodotorula psychrophila sp. nov., Rhodotorula psychrophenolica sp. nov. and Rhodotorula glacialis sp. nov., novel psychrophilic basidiomycetous yeast species isolated from alpine environments. Int J Syst Evol Microbiol 57: 2179-2184. https://doi.org/10.1099/ijs.0.65111-0
|
| [106] |
Krallish I, Gonta S, Savenkova L, et al. (2006) Phenol degradation by immobilized cold-adapted yeast strains of Cryptococcus terreus and Rhodotorula creatinivora. Extremophiles 10: 441-449. https://doi.org/10.1007/s00792-006-0517-
|
| [107] |
Shrestha P, Karmacharya J, Han SR, et al. (2023) Elucidation of cold adaptation in Glaciimonas sp. PAMC28666 with special focus on trehalose biosynthesis. Front Microbiol 14: 1280775. https://doi.org/10.3389/fmicb.2023.1280775
|
| [108] | Koning M, Hupe K, Stegmann R (2000) Thermal processes, scrubbing/extraction, bioremediation and disposal. Biotechnology 11: 306-317. https://doi.org/10.1002/9783527620999.ch12m |
| [109] | Tahir ML, Bubarai U, Bapetel U (2022) Bioremediation process and techniques a strategy to restore agricultural soil productivity: A Review. Int J Res Trends Innovation . |
| [110] |
Machackova JZ, Wittlingerova KV, Zima J (2012) Major factors affecting in situ biodegradation rates of jet-fuel during large-scale biosparging project in sedimentary bedrock. J Environ Sci Health 47: 1152-1165. https://doi.org/10.1080/10934529.2012.668379
|
| [111] |
Khudur LS, Shahsavari E, Miranda AF, et al. (2015) Evaluating the efficacy of bioremediating a diesel-contaminated soil using ecotoxicological and bacterial community indices. Environ Sci Pollut Res 22: 14809-14819. https://doi.org/10.1007/s11356-015-4624-2
|
| [112] | Feola R (2020) Biosparging: efficacia ed economicità per i siti contaminati da idrocarburi. Authorea . https://doi.org/10.22541/au.159318793.37106111 |
| [113] |
Chakrabartty M, Harun-Or-Rashid GM (2021) Feasibility study of the soil remediation technologies in the natural environment. Am J Civ Eng 9: 91-98. https://doi.org/10.11648/j.ajce.20210904.11
|
| [114] |
Benyahia F, Abdulkarim M, Zekri A, et al. (2005) Bioremediation of crude oil contaminated soils. Process Saf Environ Prot 83: 364-370. https://doi.org/10.1205/psep.04388
|
| [115] |
Naeem U, Qazi MA (2020) Leading edges in bioremediation technologies for removal of petroleum hydrocarbons. Environ Sci Pollut Res 27: 27370-27382. https://doi.org/10.1007/s11356-019-06124-8
|
| [116] | Pandey K, Shrivastava A (2018) Bioremediation of lead contaminated soil using bacteria. Res J Life Sci Bioinfomatic Pharm Chem Sci 10: 3528. https://doi.org/10.26479/2018.0404.31 |
| [117] |
Conteratto C, Dalzotto AF, Benedetti SO, et al. (2021) Biorefinery: A comprehensive concept for the sociotechnical transition toward bioeconomy. Renewable Sustainable Energy Rev 151: 111527. https://doi.org/10.1016/j.rser.2021.111527
|
| [118] |
Moncada J, El-Halwagi MM, Cardona CA (2013) Techno-economic analysis for a sugarcane biorefinery: Colombian case. Bioresour Technol 135: 533-543. https://doi.org/10.1016/j.biortech.2012.08.137
|
| [119] |
Yaashikaa PR, Senthil KP, Varjani S (2022) Valorization of agro-industrial wastes for biorefinery process and circular bioeconomy: A critical review. Bioresour Technol 343: 126126. https://doi.org/10.1016/j.biortech.2021.126126
|
| [120] |
Usmani Z, Sharma M, Sudheer S, et al. (2020) Engineered microbes for pigment production using waste biomass. Curr Genomics 21: 80-95. https://doi.org/10.2174/1389202921999200330152007
|
| [121] | Sodhi AS, Sharma N, Bhatia S, et al. (2021) Insights on sustainable approaches for production and applications of value added products. Chemosphere 2021: 131623. https://doi.org/10.1016/j.chemosphere.2021.131623 |
| [122] | Wu KC, Ho KC, Tang CC, et al. (2018) The potential of foodwaste leachate as a phycoremediation substrate for microalgal CO2 fixation and biodiesel production. Environ Sci Pollut Res 25: 40724-40734. https://doi.org/10.1007/s11356-018-1242-9 |
| [123] |
Hadj SJ, Bertani G, Levante A, et al. (2021) Fermentation of agri-food waste: a promising route for the production of aroma compounds. Foods 10: 707. https://doi.org/10.3390/foods10040707
|
| [124] |
Grewal J, Wołacewicz M, Pyter W, et al. (2022) Colorful treasure from agro-industrial wastes: a sustainable chassis for microbial pigment production. Front Microbiol 13: 832918. https://doi.org/10.3389/fmicb.2022.832918
|
| [125] |
Jatoi AS, Abbasi SA, Hashmi Z, et al. (2021) Recent trends and future perspectives of lignocellulose biomass for biofuel production: a comprehensive review. Biomass Convers Biorefinery 13: 6457-6469. https://doi.org/doi:10.1007/s13399-021-01853-8
|
| [126] |
Tropea A (2022) Biofuels production and processing technology. Fermentation 8: 319. https://doi.org/10.3390/fermentation8070319
|
| [127] |
Grewal J, Khare SK (2017) 2-Pyrrolidone synthesis from g-aminobutyric acid produced by Lactobacillus brevis under solid-state fermentation utilizing toxic deoiled cottonseed cake. Bioproc Biosyst Eng 40: 145-152. https://doi.org/10.1007/s00449-016-1683-9
|
| [128] |
Grewal J, Khare SK (2018) One-pot bioprocess for lactic acid production from lignocellulosic agro-wastes by using ionic liquid stable Lactobacillus brevis. Bioresour Technol 251: 268-273. https://doi.org/10.1016/j.biortech.2017.12.056
|
| [129] |
Grewal J, Tiwari R, Khare SK (2020) Secretome analysis and bioprospecting of lignocellulolytic fungal consortium for valorization of waste cottonseed cake by hydrolase production and simultaneous gossypol degradation. Waste Biomass Valorizat 11: 2533-2548. https://doi.org/10.1007/s12649-019-00620-1
|
| [130] |
Lu H, Yadav V, Bilal M, et al. (2022) Bioprospecting microbial hosts to valorize lignocellulose biomass–Environmental perspectives and value-added bioproducts. Chemosphere 288: 132574. https://doi.org/10.1016/J.CHEMOSPHERE.2021.132574
|
| [131] |
Banu JR, Kavitha S, Tyagi VK, et al. (2021) Lignocellulosic biomass based biorefinery: A successful platform towards circular bioeconomy. Fuel 302: 121086. https://doi.org/10.1016/j.fuel.2021.121086
|
| [132] | Muscat A, de Olde EM, Ripoll-Bosch R, et al. (2021) Principles, drivers and opportunities of a circular bioeconomy. Nat Food 2: 1-6. https://doi.org/10.1038/s43016-021-00340-7 |
| [133] |
Balbino TR, Sanchez-Munoz S, Díaz-Ruíz E, et al. (2023) Lignocellulosic biorefineries as a platform for the production of high-value yeast derived pigments–A review. Bioresour Technol 386: 129549. https://doi.org/10.1016/j.biortech.2023.129549
|
| [134] |
Tropea A (2022) Foodwaste. valorization. Fermentation 8: 168. https://doi.org/10.3390/fermentation8040168
|
| [135] |
Anupong W, Jutamas K, On-uma R, et al. (2022) Sustainable bioremediation approach to treat the sago industry effluents and evaluate the possibility of yielded biomass as a single cell protein (SCP) using cyanide tolerant Streptomyces tritici D5. Chemosphere 304: 135248. https://doi.org/10.1016/j.chemosphere.2022.135248
|
| [136] |
Ayadi I, Kamoun O, Trigui‑Lahiani H, et al. (2016) Single cell oil production from a newly isolated Candida viswanathii Y‑E4 and agro‑industrial by‑products valorization. J Ind Microbiol Biotechnol 43: 901-914. https://doi.org/10.1007/s10295-016-1772-4
|
| [137] |
Kothri M, Mavrommati M, Elazzazy AM, et al. (2020) Microbial sources of polyunsaturated fatty acids (PUFAs) and the prospect of organic residues and wastes as growth media for PUFA-producing microorganisms. FEMS Microbiol Lett 367: fnaa028. https://doi.org/10.1093/femsle/fnaa028
|
| [138] |
Rafiq S, Bhat MI, Sofi SA, et al. (2023) Bioconversion of agri-food waste and by-products into microbial lipids: Mechanism, cultivation strategies and potential in food applications. Trends Food Sci Technol 139: 104118. https://doi.org/10.1016/j.tifs.2023.07.015
|
| [139] |
Sekoai PT, Roets-Dlamini Y, O'Brien F, et al. (2024) Valorization of food waste into single-cell protein: an innovative technological strategy for sustainable protein production. Microorganisms 12: 166. https://doi.org/10.3390/microorganisms12010166
|
| [140] |
Alves Cardoso S, Díaz-Ruiz E, Lisboa B, et al. (2023) Microbial meat: A sustainable vegan protein source produced from agri-waste to feed the world. Food Res Int 166: 112596. https://doi.org/10.1016/j.foodres.2023.112596
|
| [141] |
LaTurner W, Zachary BNG, San KY, et al. (2020) Single cell protein production from food waste using purple nonsulfur bacteria shows economically viable protein products have higher environmental impacts. J Cleaner Prod 276: 123114. https://doi.org/10.1016/j.jclepro.2020.123114
|
| [142] |
Koukoumaki DI, Tsouko E, Papanikolaou S, et al. (2024) Recent advances in the production of single cell protein from renewable resources and applications. Carbon Resour Convers 7: 100195. https://doi.org/10.1016/j.crcon.2023.07.004
|
| [143] |
Kumar Y, Kaur S, Kheto A, et al. (2022) Cultivation of microalgae on food waste: Recent advances and way forward. Bioresour Technol 363: 127834. https://doi.org/10.1016/j.biortech.2022.127834
|
| [144] |
Gaykawad SS, Ramanand SS, Blomqvist J, et al. (2021) Submerged fermentation of animal fat by-products by oleaginous filamentous fungi for the production of unsaturated single cell oil. Fermentation 7: 300. https://doi.org/10.3390/fermentation7040300
|
| [145] |
Saad MG, Dosoky NS, Zoromba MS, et al. (2019) Algal biofuels: current status and key challenges. Energies 12: 1920. https://doi.org/10.3390/en12101920
|
| [146] |
Das PK, Rani J, Rawat S, et al. (2021) Microalgal co-cultivation for biofuel production and bioremediation: current status and benefits. BioEnergy Res 15: 1-26. https://doi.org/10.1007/s12155-021-10254-8
|
| [147] |
Wang Y, Le Quyet V, Yang H, et al. (2021) Progress in microbial biomass conversion into green energy. Chemosphere 281: 130835. https://doi.org/10.1016/j.chemosphere.2021.130835
|
| [148] |
Salafia F, Ferracane A, Tropea A (2022) Pineapple waste cell wall sugar fermentation by Saccharomyces cerevisiae for second generation bioethanol production. Fermentation 8: 100. https://doi.org/10.3390/fermentation8030100
|
| [149] |
Yadav M, Joshi C, Paritosh K, et al. (2022) Organic waste conversion through anaerobic digestion: A critical insight into the metabolic pathways and microbial interactions. Metab Eng 69: 323-337. https://doi.org/10.1016/j.ymben.2021.11.014
|
| [150] |
Huang J, Feng H, Huang L, et al. (2020) Continuous hydrogen production from food waste by anaerobic digestion (AD) coupled single-chamber microbial electrolysis cell (MEC) under negative pressure. Waste Manag 103: 61-66. https://doi.org/10.1016/j.wasman.2019.12.015
|
| [151] |
Gautam R, Nayak JK, Ress NV, et al. (2023) Bio-hydrogen production through microbial electrolysis cell: Structural components and influencing factors. Chem Eng J 455: 140535. https://doi.org/10.1016/j.cej.2022.140535
|
| [152] |
Teoh TP, Ong SA, Ho LN, et al. (2020) Up-flow constructed wetland-microbial fuel cell: Influence of floating plant, aeration and circuit connection on wastewater treatment performance and bioelectricity generation. J Water Process Eng 36: 101371. https://doi.org/10.1016/j.jwpe.2020.101371
|
| [153] |
Pandit S, Savla N, Sonawane JM, et al. (2021) Agricultural waste and wastewater as feedstock for bioelectricity generation using microbial fuel cells: recent advances. Fermentation 7: 169. https://doi.org/10.3390/fermentation7030169
|
| [154] |
Kumar SD, Yasasve M, Karthigadevi G, et al. (2022) Efficiency of microbial fuel cells in the treatment and energy recovery from food wastes: Trends and applications-A review. Chemosphere 287: 132439. https://doi.org/10.1016/j.chemosphere.2021.132439
|
| [155] |
Saravanan K, Umesh M, Kathirvel P (2022) Microbial Polyhydroxyalkanoates (PHAs): a review on biosynthesis, properties, fermentation strategies and its prospective applications for sustainable future. J Polym Environ 30: 4903-4935. https://doi.org/10.1007/s10924-022-02562-7
|
| [156] |
Kalathinathan P, Muthukaliannan GK (2021) Characterisation of a potential probiotic strain Paracoccus marcusii KGP and its application in whey bioremediation. Folia Microbiol 66: 819-830. https://doi.org/10.1007/s12223-021-00886-w
|
| [157] |
Javaid H, Nawaz A, Riaz N, et al. (2020) Biosynthesis of polyhydroxyalkanoates (PHAs) by the valorization of biomass and synthetic waste. Molecules 25: 5539. https://doi.org/10.3390/molecules2 52355 39
|
| [158] |
Kumar V, Lakkaboyana SK, Tsouko E, et al. (2023) Commercialization potential of agro-based polyhydroxyalkanoates biorefinery: A technical perspective on advances and critical barriers. Int J Biol Macromol 234: 123733. https://doi.org/10.1016/j.ijbiomac.2023.123733
|
| [159] |
Kalia VC, Patel SKS, Shanmugam R, et al. (2021) Polyhydroxyalkanoates: Trends and advances toward biotechnological applications. Bioresour Technol 326: 124737. https://doi.org/10.1016/j.biortech.2021.124737
|
| [160] |
Khomlaem C, Aloui H, Singhvi M, et al. (2023) Production of polyhydroxyalkanoates and astaxanthin from lignocellulosic biomass in high cell density membrane bioreactor. Chem Eng J 451: 138641. https://doi.org/10.1016/j.cej.2022.138641
|
| [161] | Mozejko-Ciesielska J, Marciniak P, Moraczewski K, et al. (2022) Cheese whey mother liquor as dairy waste with potential value for polyhydroxyalkanoate production by extremophilic Paracoccus homiensis. Sustain Mater Technol 33: e00449. https://doi.org/10.1016/j.susmat.2022.e00449 |
| [162] |
Aruldass CA, Dufossè L, Ahmad WA (2018) Current perspective of yellowish-orange pigments from microorganisms-a review. J Cleaner Prod 180: 168-182. https://doi.org/10.1016/j.jclepro.2018.01.093
|
| [163] |
Liu J, Luo Y, Guo T, et al. (2020) Cost-effective pigment production by Monascus purpureus using rice straw hydrolysate as substrate in submerged fermentation. J Biosci Bioeng 129: 229-236. https://doi.org/10.1016/j.jbiosc.2019.08.007
|
| [164] |
Lopes FC, Tichota DM, Pereira JQ, et al. (2013) Pigment production by filamentous fungi on agroindustrial byproducts: an eco-friendly alternative. Appl Biochem Biotechnol 171: 616-625. https://doi.org/10.1007/s12010-013-0392-y
|
| [165] |
Rapoport A, Guzhova I, Bernetti L, et al. (2021) Carotenoids and some other pigments from fungi and yeasts. Metabolites 11: 92. https://doi.org/10.3390/metabo11020092
|
| [166] |
Pailliè-Jiménez ME, Stincone P, Brandelli A (2020) Natural pigments of microbial origin. Front Sustain Food Syst 4: 160. https://doi.org/10.3389/fsufs.2020.590439
|
| [167] |
Arashiro LT, Boto-Ordóñez M, Van Hulle SWH, et al. (2020) Natural pigments from microalgae grown in industrial wastewater. Bioresour Technol 303: 122894. https://doi.org/10.1016/j.biortech.2020.122894
|
| [168] |
Rishu K, Suchitra G, Mayurika G (2021) Microalgae bioremediation: A perspective towards wastewater treatment along with industrial carotenoids production. J Water Process Eng 40: 101794. https://doi.org/10.1016/j.jwpe.2020.101794
|
| [169] | de Bruijn FJ, Smidt H, Cocolin LS, et al. (2023) Good Microbes in Medicine, Food Production, Biotechnology, Bioremediation, and Agriculture. John Wiley & Sons Ltd. https://doi.org/10.1002/9781119762621 |
| [170] |
Sundaram T, Govindarajan RK, Vinayagam S, et al. (2024) Advancements in biosurfactant production using agroindustrial waste for industrial and environmental applications. Front Microbiol 15: 1357302. https://doi.org/10.3389/fmicb.2024.1357302
|
| [171] |
Datta D, Ghosh S, Kumar S, et al. (2024) Microbial biosurfactants: Multifarious applications in sustainable agriculture. Microbiol Res 279: 127551. https://doi.org/10.1016/j.micres.2023.127551
|
| [172] |
Martínez O, Sánchez A, Font X, et al. (2018) Enhancing the bioproduction of value-added aroma compounds via solid-state fermentation of sugarcane bagasse and sugar beet molasses: Operational strategies and scaling-up of the process. Bioresour Technol 263: 136-144. https://doi.org/10.1016/j.biortech.2018.04.106
|
| [173] |
Kiran EU, Trzcinski AP, Ng WJ, et al. (2014) Enzyme production from food wastes using a biorefinery concept. Waste Biomass Valor 5: 903-917. https://doi.org/10.1007/s12649-014-9311-x
|
| [174] | Kumar V, Shahi SK, Singh S (2018) Bioremediation: an eco-sustainable approach for restoration of contaminated sites. Microb Bioprospect Sustain Dev 115–136. https://doi.org/10.1007/978-981-13-0053-0_6 |
| [175] |
Caldeira C, Vlysidis A, Fiore G, et al. (2020) Sustainability of food waste biorefinery: A review on valorisation pathways, techno-economic constraints, and environmental assessment. Bioresour Technol 312: 123575. https://doi.org/10.1016/j.biortech.2020.123575
|
| [176] | Vishwakarma GS, Bhattacharjee G, Gohil N, et al. (2020) Current status, challenges and future of bioremediation. Biorem Pollut 403–415. https://doi.org/10.1016/b978-0-12-819025-8.00020-x |
| [177] |
Azubuike CC, Chikere CB, Okpokwasili GC (2020) Bioremediation: an eco-friendly sustainable technology for environmental management. Bioremediation of Industrial Waste for Environmental Safety . Springer, Singapore 19-40. https://doi.org/10.1007/978-981-13-1891-7_2
|
| [178] |
Vidali M (2001) Bioremediation: an overview. Pure Appl Chem 73: 1163-1172. https://doi.org/10.1351/pac200173071163
|
| [179] |
Alori ET, Gabasawa AI, Elenwo CE, et al. (2022) Bioremediation techniques as affected by limiting factors in soil environment. Front Soil Sci 2: 937186. https://doi.org/10.3389/fsoil.2022.937186
|
| [180] |
Hartman B, Mustian M, Cunningham C (2014) Legal and regulatory frameworks for bioremediation. Bioremediation: Applied Microbial Solutions for Real-World Environmental Cleanup . ASM Press 86-107. https://doi.org/10.1128/9781555817596.ch3
|
| [181] |
Kocher S, Levi D, Aboud R (2002) Public attitudes toward the use of bioremediation to clean up toxic contamination. J Appl Social Psychol 32: 1756-1770. https://doi.org/10.1111/j.1559-1816.2002.tb02774.x
|
| [182] |
Kumar V, Sharma N, Umesh M, et al. (2022) Emerging challenges for the agro-industrial food waste utilization: A review on food waste biorefinery. Bioresour Technol 362: 127790. https://doi.org/10.1016/j.biortech.2022.127790
|
| [183] | Bioremediation Market Size, Share & Trends Analysis Report By Type (In Situ, Ex Situ), By Technology (Biostimulation, Phytoremediation), By Service (Soil Remediation, Oilfield Remediation), By Region, And Segment Forecasts, 2022–2030. Available from: https://www.grandviewresearch.com/industry-analysis/bioremediation-market-report# |
| [184] |
Boopathy R (2000) Factors limiting bioremediation technologies. Bioresour Technol 74: 63-67. https://doi.org/10.1016/s0960-8524(99)00144-3
|
| 1. | Shaima Ibraheem Jabbar, Abathar Qahtan Omran Aladi, 2024, Fuzzy inference system for detection CT and X-Ray image’s edges, 979-8-3503-8753-7, 1, 10.1109/AICT61888.2024.10740426 |
Giuseppe Maglione, Paola Zinno, Alessia Tropea, Cassamo U. Mussagy, Laurent Dufossé, Daniele Giuffrida, Alice Mondello. Microbes' role in environmental pollution and remediation: a bioeconomy focus approach[J]. AIMS Microbiology, 2024, 10(3): 723-755. doi: 10.3934/microbiol.2024033
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Multimedia Tools and Applications | PubMed | 29 | 237 | 8.17 |
| Computers in Biology and Medicine | ScienceDirect | 19 | 3711 | 195.32 |
| Expert Systems with Applications | ScienceDirect | 13 | 399 | 30.69 |
| Applied Soft Computing | ScienceDirect | 12 | 632 | 52.67 |
| Biomedical Signal Processing and Control | ScienceDirect | 12 | 181 | 15.08 |
| Journal of Ambient Intelligence and Humanized Computing | Springer | 10 | 290 | 29.00 |
| Scientific Reports | Springer | 9 | 462 | 51.33 |
| Applied Intelligence | Springer | 8 | 1422 | 177.75 |
| IEEE Access | IEEE Xplore | 7 | 2637 | 376.71 |
| Informatics in Medicine Unlocked | ScienceDirect | 7 | 757 | 108.14 |
| Neural Computing and Applications | Springer | 7 | 106 | 15.14 |
| SN Computer Science | Springer | 5 | 78 | 15.60 |
| ACM Transactions on Multimedia Computing, Communications, and Applications | ACM | 4 | 132 | 33.00 |
| Computers and Electrical Engineering | ScienceDirect | 4 | 29 | 7.25 |
| Computer Methods and Programs in Biomedicine | PubMed | 4 | 615 | 153.75 |
| Procedia Computer Science | ScienceDirect | 4 | 39 | 9.75 |
| Neurocomputing | ScienceDirect | 4 | 48 | 12.00 |
| Pattern Recognition | ScienceDirect | 4 | 314 | 78.50 |
| Soft Computing | Springer | 4 | 185 | 46.25 |
| Knowledge-Based Systems | ScienceDirect | 3 | 74 | 24.67 |
DownLoad:
CSV
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| IEEE Access | IEEE Xplore | 7 | 2637 | 376.71 |
| IEEE Journal of Biomedical and Health Informatics | IEEE Xplore | 2 | 72 | 36.00 |
| IEEE Transactions on Engineering Management | IEEE Xplore | 2 | 33 | 16.50 |
| IEEE Transactions on Medical Imaging | IEEE Xplore | 2 | 186 | 93.00 |
| IEEE Transactions on Big Data | IEEE Xplore | 1 | 70 | 70.00 |
DownLoad:
CSV
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| ACM Transactions on Multimedia Computing, Communications, and Applications | ACM | 4 | 132 | 33.00 |
| ACM Transactions on Management Information Systems | ACM | 3 | 47 | 15.67 |
| ACM Transactions on Internet Technology | ACM | 2 | 33 | 16.50 |
| IEEE/ACM Transactions on Computational Biology and Bioinformatics | ACM | 2 | 957 | 478.50 |
| ACM Computing Surveys | ACM | 1 | 5 | 5.00 |
DownLoad:
CSV
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Diagnostics | MDPI | 3 | 10 | 3.33 |
| Applied Sciences | MDPI | 2 | 33 | 16.50 |
| Sensors | MDPI | 2 | 27 | 13.50 |
| Bioengineering | MDPI | 1 | 15 | 15.00 |
| Cancers | MDPI | 1 | 6 | 6.00 |
DownLoad:
CSV
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Diagnostics (Basel) | PubMed | 3 | 29 | 9.67 |
| Computational Intelligence and Neuroscience | PubMed | 2 | 8 | 4.00 |
| Mathematical Biosciences and Engineering | PubMed | 1 | 8 | 8.00 |
| Journal of Healthcare Engineering | PubMed | 1 | 10 | 10.00 |
| Journal of Medical Internet Research | PubMed | 1 | 59 | 59.00 |
DownLoad:
CSV
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Multimedia Tools and Applications | Springer | 28 | 222 | 7.93 |
| Journal of Ambient Intelligence and Humanized Computing | Springer | 10 | 290 | 29.00 |
| Applied Intelligence | Springer | 8 | 1422 | 177.75 |
| Scientific Reports | Springer | 8 | 462 | 57.75 |
| Neural Computing and Applications | Springer | 6 | 106 | 17.67 |
DownLoad:
CSV
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Computers in Biology and Medicine | ScienceDirect | 19 | 3711 | 195.32 |
| Expert Systems with Applications | ScienceDirect | 13 | 221 | 17.00 |
| Biomedical Signal Processing and Control | ScienceDirect | 12 | 181 | 15.08 |
| Applied Soft Computing | ScienceDirect | 11 | 622 | 56.55 |
| Informatics in Medicine Unlocked | ScienceDirect | 7 | 757 | 108.14 |
DownLoad:
CSV
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Multimedia Tools and Applications | PubMed | 29 | 237 | 8.17 |
| Computers in Biology and Medicine | ScienceDirect | 19 | 3711 | 195.32 |
| Expert Systems with Applications | ScienceDirect | 13 | 399 | 30.69 |
| Applied Soft Computing | ScienceDirect | 12 | 632 | 52.67 |
| Biomedical Signal Processing and Control | ScienceDirect | 12 | 181 | 15.08 |
| Journal of Ambient Intelligence and Humanized Computing | Springer | 10 | 290 | 29.00 |
| Scientific Reports | Springer | 9 | 462 | 51.33 |
| Applied Intelligence | Springer | 8 | 1422 | 177.75 |
| IEEE Access | IEEE Xplore | 7 | 2637 | 376.71 |
| Informatics in Medicine Unlocked | ScienceDirect | 7 | 757 | 108.14 |
| Neural Computing and Applications | Springer | 7 | 106 | 15.14 |
| SN Computer Science | Springer | 5 | 78 | 15.60 |
| ACM Transactions on Multimedia Computing, Communications, and Applications | ACM | 4 | 132 | 33.00 |
| Computers and Electrical Engineering | ScienceDirect | 4 | 29 | 7.25 |
| Computer Methods and Programs in Biomedicine | PubMed | 4 | 615 | 153.75 |
| Procedia Computer Science | ScienceDirect | 4 | 39 | 9.75 |
| Neurocomputing | ScienceDirect | 4 | 48 | 12.00 |
| Pattern Recognition | ScienceDirect | 4 | 314 | 78.50 |
| Soft Computing | Springer | 4 | 185 | 46.25 |
| Knowledge-Based Systems | ScienceDirect | 3 | 74 | 24.67 |
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| IEEE Access | IEEE Xplore | 7 | 2637 | 376.71 |
| IEEE Journal of Biomedical and Health Informatics | IEEE Xplore | 2 | 72 | 36.00 |
| IEEE Transactions on Engineering Management | IEEE Xplore | 2 | 33 | 16.50 |
| IEEE Transactions on Medical Imaging | IEEE Xplore | 2 | 186 | 93.00 |
| IEEE Transactions on Big Data | IEEE Xplore | 1 | 70 | 70.00 |
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| ACM Transactions on Multimedia Computing, Communications, and Applications | ACM | 4 | 132 | 33.00 |
| ACM Transactions on Management Information Systems | ACM | 3 | 47 | 15.67 |
| ACM Transactions on Internet Technology | ACM | 2 | 33 | 16.50 |
| IEEE/ACM Transactions on Computational Biology and Bioinformatics | ACM | 2 | 957 | 478.50 |
| ACM Computing Surveys | ACM | 1 | 5 | 5.00 |
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Diagnostics | MDPI | 3 | 10 | 3.33 |
| Applied Sciences | MDPI | 2 | 33 | 16.50 |
| Sensors | MDPI | 2 | 27 | 13.50 |
| Bioengineering | MDPI | 1 | 15 | 15.00 |
| Cancers | MDPI | 1 | 6 | 6.00 |
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Diagnostics (Basel) | PubMed | 3 | 29 | 9.67 |
| Computational Intelligence and Neuroscience | PubMed | 2 | 8 | 4.00 |
| Mathematical Biosciences and Engineering | PubMed | 1 | 8 | 8.00 |
| Journal of Healthcare Engineering | PubMed | 1 | 10 | 10.00 |
| Journal of Medical Internet Research | PubMed | 1 | 59 | 59.00 |
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Multimedia Tools and Applications | Springer | 28 | 222 | 7.93 |
| Journal of Ambient Intelligence and Humanized Computing | Springer | 10 | 290 | 29.00 |
| Applied Intelligence | Springer | 8 | 1422 | 177.75 |
| Scientific Reports | Springer | 8 | 462 | 57.75 |
| Neural Computing and Applications | Springer | 6 | 106 | 17.67 |
| Journal Title | Database | Total Documents | Total Citation | Average Citation per Journal |
| Computers in Biology and Medicine | ScienceDirect | 19 | 3711 | 195.32 |
| Expert Systems with Applications | ScienceDirect | 13 | 221 | 17.00 |
| Biomedical Signal Processing and Control | ScienceDirect | 12 | 181 | 15.08 |
| Applied Soft Computing | ScienceDirect | 11 | 622 | 56.55 |
| Informatics in Medicine Unlocked | ScienceDirect | 7 | 757 | 108.14 |
