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  1. Article: Evaluation of food safety knowledge, attitude, and self-reported practices of trained and newly recruited untrained workers of two baking industries in Dhaka, Bangladesh

    Jubayer, Md. Fahad / Kayshar, Md. Shahidullah / Hossain, Md. Sajjad / Uddin, Md. Nasir / Al-Emran, Md / Akter, Syeda Sabrina

    Heliyon. 2020 Sept., v. 6, no. 9

    2020  

    Abstract: In Bangladesh, with the mounting esteem of bakery products, food safety issues in bakery industries are a paramount concern nowadays. In this regard, this current study was performed to evaluate food safety knowledge, attitude, and self-reported ... ...

    Abstract In Bangladesh, with the mounting esteem of bakery products, food safety issues in bakery industries are a paramount concern nowadays. In this regard, this current study was performed to evaluate food safety knowledge, attitude, and self-reported practices of two groups (160 trained and 55 new untrained) of workers from two popular baking industries in Dhaka, Bangladesh. A self-administrated questionnaire was used to acquire the data during the study. On food safety knowledge, attitude, and self-reported practices, trained workers' scores (33.01 ± 0.09, 14.86 ± 0.03, 10.66 ± 0.25, respectively) were significantly higher than the scores (9.82 ± 0.23, 10.44 ± 0.26, 5.91 ± 0.33, respectively) of newly appointed untrained workers. The quality assurance department displayed better knowledge, attitude, and self-reported practices scores than the rest of the departments of the industries. However, compared to knowledge and attitude, the self-reported practice was not up to a satisfactory level. According to the study, training can be proved effective for improving knowledge and attitude but does not always translate those into self-reported practice and behaviors. The results also reinforce the importance of conducting training for untrained workers and suggest further behavior-based food safety training for all employees.
    Keywords quality control ; questionnaires ; Bangladesh
    Language English
    Dates of publication 2020-09
    Publishing place Elsevier Ltd
    Document type Article
    Note NAL-AP-2-clean
    ZDB-ID 2835763-2
    ISSN 2405-8440
    ISSN 2405-8440
    DOI 10.1016/j.heliyon.2020.e05021
    Database NAL-Catalogue (AGRICOLA)

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  2. Article ; Online: Detection of mold on the food surface using YOLOv5.

    Jubayer, Fahad / Soeb, Janibul Alam / Mojumder, Abu Naser / Paul, Mitun Kanti / Barua, Pranta / Kayshar, Shahidullah / Akter, Syeda Sabrina / Rahman, Mizanur / Islam, Amirul

    Current research in food science

    2021  Volume 4, Page(s) 724–728

    Abstract: The study aimed to identify different molds that grow on various food surfaces. As a result, we conducted a case study for the detection of mold on food surfaces based on the "you only look once (YOLO) v5" principle. In this context, a dataset of 2050 ... ...

    Abstract The study aimed to identify different molds that grow on various food surfaces. As a result, we conducted a case study for the detection of mold on food surfaces based on the "you only look once (YOLO) v5" principle. In this context, a dataset of 2050 food images with mold growing on their surfaces was created. Images were obtained from our own laboratory (850 images) as well as from the internet (1200 images). The dataset was trained using the pre-trained YOLOv5 algorithm. A laboratory test was also performed to confirm that the grown organisms were mold. In comparison to YOLOv3 and YOLOv4, this current YOLOv5 model had better precision, recall, and average precision (AP), which were 98.10%, 100%, and 99.60%, respectively. The YOLOv5 algorithm was used for the first time in this study to detect mold on food surfaces. In conclusion, the proposed model successfully recognizes any kind of mold present on the food surface. Using YOLOv5, we are currently conducting research to identify the specific species of the detected mold.
    Language English
    Publishing date 2021-10-16
    Publishing country Netherlands
    Document type Journal Article
    ISSN 2665-9271
    ISSN (online) 2665-9271
    DOI 10.1016/j.crfs.2021.10.003
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic.

    Islam, Muhammad Nazrul / Inan, Toki Tahmid / Rafi, Suzzana / Akter, Syeda Sabrina / Sarker, Iqbal H / Islam, A K M Najmul

    IEEE transactions on artificial intelligence

    2021  Volume 1, Issue 3, Page(s) 258–270

    Abstract: Artificial intelligence (AI) and machine learning (ML) have caused a paradigm shift in healthcare that can be used for decision support and forecasting by exploring medical data. Recent studies have shown that AI and ML can be used to fight COVID-19. The ...

    Abstract Artificial intelligence (AI) and machine learning (ML) have caused a paradigm shift in healthcare that can be used for decision support and forecasting by exploring medical data. Recent studies have shown that AI and ML can be used to fight COVID-19. The objective of this article is to summarize the recent AI- and ML-based studies that have addressed the pandemic. From an initial set of 634 articles, a total of 49 articles were finally selected through an inclusion-exclusion process. In this article, we have explored the objectives of the existing studies (i.e., the role of AI/ML in fighting the COVID-19 pandemic); the context of the studies (i.e., whether it was focused on a specific country-context or with a global perspective; the type and volume of the dataset; and the methodology, algorithms, and techniques adopted in the prediction or diagnosis processes). We have mapped the algorithms and techniques with the data type by highlighting their prediction/classification accuracy. From our analysis, we categorized the objectives of the studies into four groups: disease detection, epidemic forecasting, sustainable development, and disease diagnosis. We observed that most of these studies used deep learning algorithms on image-data, more specifically on chest X-rays and CT scans. We have identified six future research opportunities that we have summarized in this paper.
    Language English
    Publishing date 2021-03-01
    Publishing country United States
    Document type Journal Article
    ISSN 2691-4581
    ISSN (online) 2691-4581
    DOI 10.1109/TAI.2021.3062771
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Article: Detection of mold on the food surface using YOLOv5

    Jubayer, Fahad / Soeb, Janibul Alam / Mojumder, Abu Naser / Paul, Mitun Kanti / Barua, Pranta / Kayshar, Shahidullah / Akter, Syeda Sabrina / Rahman, Mizanur / Islam, Amirul

    Current research in food science. 2021, v. 4

    2021  

    Abstract: The study aimed to identify different molds that grow on various food surfaces. As a result, we conducted a case study for the detection of mold on food surfaces based on the “you only look once (YOLO) v5” principle. In this context, a dataset of 2050 ... ...

    Abstract The study aimed to identify different molds that grow on various food surfaces. As a result, we conducted a case study for the detection of mold on food surfaces based on the “you only look once (YOLO) v5” principle. In this context, a dataset of 2050 food images with mold growing on their surfaces was created. Images were obtained from our own laboratory (850 images) as well as from the internet (1200 images). The dataset was trained using the pre-trained YOLOv5 algorithm. A laboratory test was also performed to confirm that the grown organisms were mold. In comparison to YOLOv3 and YOLOv4, this current YOLOv5 model had better precision, recall, and average precision (AP), which were 98.10%, 100%, and 99.60%, respectively. The YOLOv5 algorithm was used for the first time in this study to detect mold on food surfaces. In conclusion, the proposed model successfully recognizes any kind of mold present on the food surface. Using YOLOv5, we are currently conducting research to identify the specific species of the detected mold.
    Keywords Internet ; algorithms ; case studies ; data collection ; laboratory experimentation ; models ; research
    Language English
    Size p. 724-728.
    Publishing place Elsevier B.V.
    Document type Article
    ISSN 2665-9271
    DOI 10.1016/j.crfs.2021.10.003
    Database NAL-Catalogue (AGRICOLA)

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    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Article: Evaluation of food safety knowledge, attitude, and self-reported practices of trained and newly recruited untrained workers of two baking industries in Dhaka, Bangladesh.

    Jubayer, Md Fahad / Kayshar, Md Shahidullah / Hossain, Md Sajjad / Uddin, Md Nasir / Al-Emran, Md / Akter, Syeda Sabrina

    Heliyon

    2020  Volume 6, Issue 9, Page(s) e05021

    Abstract: In Bangladesh, with the mounting esteem of bakery products, food safety issues in bakery industries are a paramount concern nowadays. In this regard, this current study was performed to evaluate food safety knowledge, attitude, and self-reported ... ...

    Abstract In Bangladesh, with the mounting esteem of bakery products, food safety issues in bakery industries are a paramount concern nowadays. In this regard, this current study was performed to evaluate food safety knowledge, attitude, and self-reported practices of two groups (160 trained and 55 new untrained) of workers from two popular baking industries in Dhaka, Bangladesh. A self-administrated questionnaire was used to acquire the data during the study. On food safety knowledge, attitude, and self-reported practices, trained workers' scores (33.01 ± 0.09, 14.86 ± 0.03, 10.66 ± 0.25, respectively) were significantly higher than the scores (9.82 ± 0.23, 10.44 ± 0.26, 5.91 ± 0.33, respectively) of newly appointed untrained workers. The quality assurance department displayed better knowledge, attitude, and self-reported practices scores than the rest of the departments of the industries. However, compared to knowledge and attitude, the self-reported practice was not up to a satisfactory level. According to the study, training can be proved effective for improving knowledge and attitude but does not always translate those into self-reported practice and behaviors. The results also reinforce the importance of conducting training for untrained workers and suggest further behavior-based food safety training for all employees.
    Language English
    Publishing date 2020-09-23
    Publishing country England
    Document type Journal Article
    ZDB-ID 2835763-2
    ISSN 2405-8440
    ISSN 2405-8440
    DOI 10.1016/j.heliyon.2020.e05021
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  6. Article: A Survey on the Use of AI and ML for Fighting the COVID-19 Pandemic

    Islam, Muhammad Nazrul / Inan, Toki Tahmid / Rafi, Suzzana / Akter, Syeda Sabrina / Sarker, Iqbal H. / Islam, A. K. M. Najmul

    Abstract: Artificial intelligence (AI) and machine learning (ML) have made a paradigm shift in health care which, eventually can be used for decision support and forecasting by exploring the medical data. Recent studies showed that AI and ML can be used to fight ... ...

    Abstract Artificial intelligence (AI) and machine learning (ML) have made a paradigm shift in health care which, eventually can be used for decision support and forecasting by exploring the medical data. Recent studies showed that AI and ML can be used to fight against the COVID-19 pandemic. Therefore, the objective of this review study is to summarize the recent AI and ML based studies that have focused to fight against COVID-19 pandemic. From an initial set of 634 articles, a total of 35 articles were finally selected through an extensive inclusion-exclusion process. In our review, we have explored the objectives/aims of the existing studies (i.e., the role of AI/ML in fighting COVID-19 pandemic); context of the study (i.e., study focused to a specific country-context or with a global perspective); type and volume of dataset; methodology, algorithms or techniques adopted in the prediction or diagnosis processes; and mapping the algorithms/techniques with the data type highlighting their prediction/classification accuracy. We particularly focused on the uses of AI/ML in analyzing the pandemic data in order to depict the most recent progress of AI for fighting against COVID-19 and pointed out the potential scope of further research.
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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  7. Book ; Online: A Survey on the Use of AI and ML for Fighting the COVID-19 Pandemic

    Islam, Muhammad Nazrul / Inan, Toki Tahmid / Rafi, Suzzana / Akter, Syeda Sabrina / Sarker, Iqbal H. / Islam, A. K. M. Najmul

    2020  

    Abstract: Artificial intelligence (AI) and machine learning (ML) have made a paradigm shift in health care which, eventually can be used for decision support and forecasting by exploring the medical data. Recent studies showed that AI and ML can be used to fight ... ...

    Abstract Artificial intelligence (AI) and machine learning (ML) have made a paradigm shift in health care which, eventually can be used for decision support and forecasting by exploring the medical data. Recent studies showed that AI and ML can be used to fight against the COVID-19 pandemic. Therefore, the objective of this review study is to summarize the recent AI and ML based studies that have focused to fight against COVID-19 pandemic. From an initial set of 634 articles, a total of 35 articles were finally selected through an extensive inclusion-exclusion process. In our review, we have explored the objectives/aims of the existing studies (i.e., the role of AI/ML in fighting COVID-19 pandemic); context of the study (i.e., study focused to a specific country-context or with a global perspective); type and volume of dataset; methodology, algorithms or techniques adopted in the prediction or diagnosis processes; and mapping the algorithms/techniques with the data type highlighting their prediction/classification accuracy. We particularly focused on the uses of AI/ML in analyzing the pandemic data in order to depict the most recent progress of AI for fighting against COVID-19 and pointed out the potential scope of further research.

    Comment: 10 pages, 6 figures, 5 tables
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computers and Society ; covid19
    Subject code 006
    Publishing date 2020-08-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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