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  1. 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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  2. 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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  3. 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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  4. 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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  5. Article ; Online: Performance Evaluation of Wireless Routing Protocols in Mobile WiMAX Environment

    Md. Saiful Azad / Mohammad Moshee Uddin / Farhat Anwar / Md. Arafatur Rahman

    Lecture Notes in Engineering and Computer Science, Vol 2169, Iss 1, Pp 1109-

    2008  Volume 1114

    Keywords Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q
    Language English
    Publishing date 2008-03-01T00:00:00Z
    Publisher Newswood and International Association of Engineers
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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