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Article ; Online: A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

Jalal Uddin Md Akbar / Syafiq Fauzi Kamarulzaman / Abu Jafar Md Muzahid / Md. Arafatur Rahman / Mueen Uddin

IEEE Access, Vol 12, Pp 4485-

2024 Volume 4522

Abstract: With the escalating global challenges of food security and resource sustainability, innovative solutions like deep learning and computer vision are transforming agricultural practices by enabling data-driven decision-making. This paper provides a focused ...

Abstract	With the escalating global challenges of food security and resource sustainability, innovative solutions like deep learning and computer vision are transforming agricultural practices by enabling data-driven decision-making. This paper provides a focused review of recent advancements in deep learning-enabled computer vision techniques tailored specifically for greenhouse environments. First, deep learning and computer vision fundamentals are briefly introduced. Over 100 studies from 2020 to date are then comprehensively reviewed in which these technologies were applied within greenhouses for growth monitoring, disease detection, yield estimation, and other tasks. The techniques, datasets, models, and overall performance results reported in the literature are analyzed. Tables and figures showcase real-world implementations and results synthesized from current research. Key challenges are also outlined related to aspects like model adaptability, lack of sufficient labeled greenhouse data, computational constraints, the need for multi-modal sensor fusion, and other areas needing further investigation. Future trends and prospects are discussed to provide guidance for researchers exploring computer vision in the niche greenhouse domain. By condensing prior work and elucidating the state-of-the-art, this timely review aims to promote continued progress in smart greenhouse agriculture. The focused analysis, specifically on greenhouse environments, fills a gap compared to previous agricultural surveys. Overall, this paper highlights the immense potential of computer vision and deep learning in driving the emergence of data-driven, smart greenhouse farming worldwide.
Keywords	Agricultural automation ; computer vision ; deep learning ; convolutional neural networks(CNN) ; controlled-environment agriculture (CEA) ; greenhouse farming ; Electrical engineering. Electronics. Nuclear engineering ; TK1-9971
Subject code	004
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	IEEE
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: A modified mental state assessment tool for impact analysis of virtual reality-based therapeutic interventions in patients with cognitive impairment.

Khan, Samiya / Naeem, Muhammad Kamran / Tania, Marzia Hoque / Refat, Nadia / Rahman, Md Arafatur / Patwary, Mohammad

Digital health

2023 Volume 9, Page(s) 20552076231203800

Abstract: Objectives: This work has developed a modified mental state assessment tool for impact analysis of therapeutic interventions for patients with cognitive impairment. This work includes a pilot study to validate the proposed tool and assess the impact of ... ...

Abstract	Objectives: This work has developed a modified mental state assessment tool for impact analysis of therapeutic interventions for patients with cognitive impairment. This work includes a pilot study to validate the proposed tool and assess the impact of virtual reality-based interventions on patient well-being, which includes assessment of cognitive ability and mood. Methods: The suggested tool's robustness and reliability are assessed in care home facilities with elderly residents over the age of 55. Because of the repetitive nature of the pilot study, test-retest strategy for Cronbach's alpha coefficient is employed to validate the internal consistency of the proposed tool over time. Qualitative and quantitative analyses are performed on the collected data to draw inferences on the impact of virtual reality-based interventions on patients with cognitive impairments. Results: The Cronbach's alpha coefficient value shows that the proposed tool's resilience is comparable to that of its pre-intervention counterparts. The Cronbach's alpha coefficient values are determined for Pre-virtual reality and Post-virtual reality interventions, which include 116 virtual reality sessions for 52-participant, and three cohorts of virtual reality sessions for 21 participants. These values for a majority of the interventions remained within the acceptable range of 0.6-0.8. Conclusions: The proposed modified mental state assessment tool is observed to be a reliable tool for investigating the impact of virtual reality-based interventions on patients with cognitive impairments. One of the notable significance of the proposed tool is that this allows for resource allocation for such interventions to be tailored to the needs of the patient, leading to greater therapeutic efficacy and resource efficiency.
Language	English
Publishing date	2023-11-13
Publishing country	United States
Document type	Journal Article
ZDB-ID	2819396-9
ISSN	2055-2076
ISSN	2055-2076
DOI	10.1177/20552076231203800
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A modified mental state assessment tool for impact analysis of virtual reality-based therapeutic interventions in patients with cognitive impairment

Samiya Khan / Muhammad Kamran Naeem / Marzia Hoque Tania / Nadia Refat / Md Arafatur Rahman / Mohammad Patwary

Digital Health, Vol

2023 Volume 9

Abstract: Objectives This work has developed a modified mental state assessment tool for impact analysis of therapeutic interventions for patients with cognitive impairment. This work includes a pilot study to validate the proposed tool and assess the impact of ... ...

Abstract	Objectives This work has developed a modified mental state assessment tool for impact analysis of therapeutic interventions for patients with cognitive impairment. This work includes a pilot study to validate the proposed tool and assess the impact of virtual reality-based interventions on patient well-being, which includes assessment of cognitive ability and mood. Methods The suggested tool’s robustness and reliability are assessed in care home facilities with elderly residents over the age of 55. Because of the repetitive nature of the pilot study, test-retest strategy for Cronbach’s alpha coefficient is employed to validate the internal consistency of the proposed tool over time. Qualitative and quantitative analyses are performed on the collected data to draw inferences on the impact of virtual reality-based interventions on patients with cognitive impairments. Results The Cronbach’s alpha coefficient value shows that the proposed tool’s resilience is comparable to that of its pre-intervention counterparts. The Cronbach’s alpha coefficient values are determined for Pre-virtual reality and Post-virtual reality interventions, which include 116 virtual reality sessions for 52-participant, and three cohorts of virtual reality sessions for 21 participants. These values for a majority of the interventions remained within the acceptable range of 0.6–0.8. Conclusions The proposed modified mental state assessment tool is observed to be a reliable tool for investigating the impact of virtual reality-based interventions on patients with cognitive impairments. One of the notable significance of the proposed tool is that this allows for resource allocation for such interventions to be tailored to the needs of the patient, leading to greater therapeutic efficacy and resource efficiency.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	629
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	SAGE Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Classification of fungal genera from microscopic images using artificial intelligence.

Rahman, Md Arafatur / Clinch, Madelyn / Reynolds, Jordan / Dangott, Bryan / Meza Villegas, Diana M / Nassar, Aziza / Hata, D Jane / Akkus, Zeynettin

Journal of pathology informatics

2023 Volume 14, Page(s) 100314

Abstract: Microscopic image examination is fundamental to clinical microbiology and often used as the first step to diagnose fungal infections. In this study, we present classification of pathogenic fungi from microscopic images using deep convolutional neural ... ...

Abstract	Microscopic image examination is fundamental to clinical microbiology and often used as the first step to diagnose fungal infections. In this study, we present classification of pathogenic fungi from microscopic images using deep convolutional neural networks (CNN). We trained well-known CNN architectures such as DenseNet, Inception ResNet, InceptionV3, Xception, ResNet50, VGG16, and VGG19 to identify fungal species, and compared their performances. We collected 1079 images of 89 fungi genera and split our data into training, validation, and test datasets by 7:1:2 ratio. The DenseNet CNN model provided the best performance among other CNN architectures with overall accuracy of 65.35% for top 1 prediction and 75.19% accuracy for top 3 predictions for classification of 89 genera. The performance is further improved (>80%) after excluding rare genera with low sample occurrence and applying data augmentation techniques. For some particular fungal genera, we obtained 100% prediction accuracy. In summary, we present a deep learning approach that shows promising results in prediction of filamentous fungi identification from culture, which could be used to enhance diagnostic accuracy and decrease turnaround time to identification.
Language	English
Publishing date	2023-04-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	2579241-6
ISSN	2153-3539 ; 2229-5089
ISSN (online)	2153-3539
ISSN	2229-5089
DOI	10.1016/j.jpi.2023.100314
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Artificial Intelligence Advances in Transplant Pathology.

Rahman, Md Arafatur / Yilmaz, Ibrahim / Albadri, Sam T / Salem, Fadi E / Dangott, Bryan J / Taner, C Burcin / Nassar, Aziza / Akkus, Zeynettin

Bioengineering (Basel, Switzerland)

2023 Volume 10, Issue 9

Abstract: Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are ... ...

Abstract	Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians' decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.
Language	English
Publishing date	2023-09-04
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2746191-9
ISSN	2306-5354
ISSN	2306-5354
DOI	10.3390/bioengineering10091041
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The Emergence of Internet of Things (IoT)

Md Arafatur Rahman / A. Taufiq Asyhari

Computers, Vol 8, Iss 2, p

Connecting Anything, Anywhere

2019 Volume 40

Abstract: Internet of Things (IoT) plays the role of an expert’s technical tool by empowering physical resources into smart entities through existing network infrastructures. Its prime focus is to provide smart and seamless services at the user end without any ... ...

Abstract	Internet of Things (IoT) plays the role of an expert’s technical tool by empowering physical resources into smart entities through existing network infrastructures. Its prime focus is to provide smart and seamless services at the user end without any interruption. The IoT paradigm is aimed at formulating a complex information system with the combination of sensor data acquisition, efficient data exchange through networking, machine learning, artificial intelligence, big data, and clouds. Conversely, collecting information and maintaining the confidentiality of an independent entity, and then running together with privacy and security provision in IoT is the main concerning issue. Thus, new challenges of using and advancing existing technologies, such as new applications and using policies, cloud computing, smart vehicular system, protective protocols, analytics tools for IoT-generated data, communication protocols, etc., deserve further investigation. This Special Issue reviews the latest contributions of IoT application frameworks and the advancement of their supporting technology. It is extremely imperative for academic and industrial stakeholders to propagate solutions that can leverage the opportunities and minimize the challenges in terms of using this state-of-the-art technological development.
Keywords	IoT ; smart environment ; security and surveillance ; Electronic computers. Computer science ; QA75.5-76.95
Language	English
Publishing date	2019-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Measuring student motivation on the use of a mobile assisted grammar learning tool.

Nadia Refat / Hafizoah Kassim / Md Arafatur Rahman / Ramdan Bin Razali

PLoS ONE, Vol 15, Iss 8, p e

2020 Volume 0236862

Abstract: Language learning is an emerging research area where researchers have done significant contributions by incorporating technological assistantship (i.e., computer- and mobile-assistant learning). However, it has been revealed from the recent empirical ... ...

Abstract	Language learning is an emerging research area where researchers have done significant contributions by incorporating technological assistantship (i.e., computer- and mobile-assistant learning). However, it has been revealed from the recent empirical studies that little attention is given on grammar learning with the proper instructional materials design and the motivational framework for designing an efficient mobile-assisted grammar learning tool. This paper hence, reports a preliminary study that investigated learner motivation when a mobile-assisted tool for tense learning was used. This study applied the Attention-Relevance-Confidence-Satisfaction (ARCS) model. It was hypothesized that with the use of the designed mobile- assisted tense learning tool students would be motivated to learn grammar (English tense). In addition, with the increase of motivation, performance outcome in paper- based test would also be improved. With the purpose to investigate the impact of the tool, a sequential mixed-method research design was employed with the use of three research instruments; Instructional Materials Motivation Survey (IMMS), a paper-based test and an interview protocol using a semi-structured interview. Participants were 115 undergraduate students, who were enrolled in a remedial English course. The findings showed that with the effective design of instructional materials, students were motivated to learn grammar, where they were positive at improving their attitude towards learning (male 86%, female 80%). The IMMS findings revealed that students' motivation increased after using the tool. Moreover, students improved their performance level that was revealed from the outcome of paper-based instrument. Therefore, it is confirmed that the study contributed to designing an effective multimedia based instructions for a mobile-assisted tool that increased learners' motivational attitude which resulted in an improved learning performance.
Keywords	Medicine ; R ; Science ; Q
Subject code	420
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep Learning enabled Fall Detection exploiting Gait Analysis.

Anwary, Arif Reza / Rahman, Md Arafatur / Muzahid, Abu Jafar Md / Ul Ashraf, Akanda Wahid / Patwary, Mohammad / Hussain, Amir

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2022 Volume 2022, Page(s) 4683–4686

Abstract: Falls associated injuries often result not only increasing the medical-, social- and care-cost but also loss of mobility, impair chronic health and even potential risk of fatality. Because of elderly population growth, it is one of the major global ... ...

Abstract	Falls associated injuries often result not only increasing the medical-, social- and care-cost but also loss of mobility, impair chronic health and even potential risk of fatality. Because of elderly population growth, it is one of the major global public health problems. To address such issue, we present a Deep Learning enabled Fall Detection (DLFD) method exploiting Gait Analysis. More in details, firstly, we propose a framework for fall detection system. Secondly, we discussed the proposed DLFD method which exploits fall and non-fall RGB video to extract gait features using MediaPipe framework, applies normalization algorithm and classifies using bi-directional Long Short-Term Memory (bi-LSTM) model. Finally, the model is tested on collected three public datasets of 434x2 videos(more than 1 million frames) which consists of different activities and varieties of falls. The experimental results show that the model can achieve the accuracy of 96.35% and reveals the effectiveness of the proposal. This could play a significant role to alleviate falls problem by immediate alerting to emergency and relevant teams for taking necessary actions. This will speed up the assistance proceedings, reduce the risk of prolonged injury and save lives.
MeSH term(s)	Accidental Falls/prevention & control ; Aged ; Algorithms ; Deep Learning ; Gait ; Gait Analysis ; Humans
Language	English
Publishing date	2022-09-10
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC48229.2022.9871964
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Measuring student motivation on the use of a mobile assisted grammar learning tool.

Refat, Nadia / Kassim, Hafizoah / Rahman, Md Arafatur / Razali, Ramdan Bin

PloS one

2020 Volume 15, Issue 8, Page(s) e0236862

Abstract	Language learning is an emerging research area where researchers have done significant contributions by incorporating technological assistantship (i.e., computer- and mobile-assistant learning). However, it has been revealed from the recent empirical studies that little attention is given on grammar learning with the proper instructional materials design and the motivational framework for designing an efficient mobile-assisted grammar learning tool. This paper hence, reports a preliminary study that investigated learner motivation when a mobile-assisted tool for tense learning was used. This study applied the Attention-Relevance-Confidence-Satisfaction (ARCS) model. It was hypothesized that with the use of the designed mobile- assisted tense learning tool students would be motivated to learn grammar (English tense). In addition, with the increase of motivation, performance outcome in paper- based test would also be improved. With the purpose to investigate the impact of the tool, a sequential mixed-method research design was employed with the use of three research instruments; Instructional Materials Motivation Survey (IMMS), a paper-based test and an interview protocol using a semi-structured interview. Participants were 115 undergraduate students, who were enrolled in a remedial English course. The findings showed that with the effective design of instructional materials, students were motivated to learn grammar, where they were positive at improving their attitude towards learning (male 86%, female 80%). The IMMS findings revealed that students' motivation increased after using the tool. Moreover, students improved their performance level that was revealed from the outcome of paper-based instrument. Therefore, it is confirmed that the study contributed to designing an effective multimedia based instructions for a mobile-assisted tool that increased learners' motivational attitude which resulted in an improved learning performance.
MeSH term(s)	Attention ; Educational Measurement/methods ; Female ; Humans ; Interviews as Topic ; Language ; Learning ; Male ; Mobile Applications ; Motivation ; Personal Satisfaction ; Self Concept ; Students/psychology ; Young Adult
Language	English
Publishing date	2020-08-28
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0236862
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Need for developing a security robot-based risk management for emerging practices in the workplace using the Advanced Human-Robot Collaboration Model.

Zheyuan, Cui / Rahman, Md Arafatur / Tao, Hai / Liu, Yao / Pengxuan, Du / Yaseen, Zaher Mundher

Work (Reading, Mass.)

2021 Volume 68, Issue 3, Page(s) 825–834

Abstract: Background: The increasing use of robotics in the work of co-workers poses some new problems in terms of occupational safety and health. In the workplace, industrial robots are being used increasingly. During operations such as repairs, unmanageable, ... ...

Abstract	Background: The increasing use of robotics in the work of co-workers poses some new problems in terms of occupational safety and health. In the workplace, industrial robots are being used increasingly. During operations such as repairs, unmanageable, adjustment, and set-up, robots can cause serious and fatal injuries to workers. Collaborative robotics recently plays a rising role in the manufacturing filed, warehouses, mining agriculture, and much more in modern industrial environments. This development advances with many benefits, like higher efficiency, increased productivity, and new challenges like new hazards and risks from the elimination of human and robotic barriers. Objectives: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace. Results: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk. Conclusion: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.
MeSH term(s)	Artificial Intelligence ; Humans ; Occupational Health ; Risk Assessment ; Robotics ; Workplace
Language	English
Publishing date	2021-02-15
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1394194-x
ISSN	1875-9270 ; 1051-9815
ISSN (online)	1875-9270
ISSN	1051-9815
DOI	10.3233/WOR-203416
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