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  1. Article ; Online: Correction

    Minilu Dejene / Hemalatha Palanivel / Heeravathi Senthamarai / Venkatramanan Varadharajan / S. Venkatesa Prabhu / Alazar Yeshitila / Solomon Benor / Shipra Shah

    Applied Biological Chemistry, Vol 66, Iss 1, Pp 1-

    Optimisation of culture conditions for gesho (Rhamnus prinoides.L) callus differentiation using Artificial Neural Network-Genetic Algorithm (ANN-GA) Techniques

    2023  Volume 1

    Keywords Agriculture (General) ; S1-972 ; Chemistry ; QD1-999
    Language English
    Publishing date 2023-10-01T00:00:00Z
    Publisher SpringerOpen
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  2. Article ; Online: Optimisation of culture conditions for gesho (Rhamnus prinoides.L) callus differentiation using Artificial Neural Network-Genetic Algorithm (ANN-GA) Techniques

    Minilu Dejene / Hemalatha Palanivel / Heeravathi Senthamarai / Venkatramanan Varadharajan / S. Venkatesa Prabhu / Alazar Yeshitila / Solomon Benor / Shipra Shah

    Applied Biological Chemistry, Vol 66, Iss 1, Pp 1-

    2023  Volume 14

    Abstract: Abstract Gesho (Rhamnus prinoides) is a medicinal plant with antioxidant and anti-inflammatory activities commonly used in the ethnomedicinal systems of Africa. Using a three-layer neural network, four culture conditions viz., concentration of agar, ... ...

    Abstract Abstract Gesho (Rhamnus prinoides) is a medicinal plant with antioxidant and anti-inflammatory activities commonly used in the ethnomedicinal systems of Africa. Using a three-layer neural network, four culture conditions viz., concentration of agar, duration of light exposure, temperature of culture, and relative humidity were used to calculate the callus differentiation rate of gesho. With the ability to quickly identify optimal solutions using high-speed computers, synthetic neural networks have emerged as a rapid, reliable, and accurate fitting technique. They also have the self-directed learning capability that is essential for accurate prediction. The network's final architecture for four selected variables and its performance has been confirmed with high correlation coefficient (R2, 0.9984) between the predicted and actual outputs and the root-mean-square error of 0.0249, were developed after ten-fold cross validation as the training function. In vitro research had been conducted using the genetic algorithm’s suggestions for the optimal culture conditions. The outcomes demonstrated that the actual gesho differentiation rate was 93.87%, which was just 1.86% lesser than the genetic algorithm's predicted value. The projected induced differentiation rate was 87.62%, the actual value was 84.79%, and the predicted value was 2.83% higher than Response Surface Methods optimisation. The environment for the growth of plant tissue can be accurately and efficiently optimised using a genetic algorithm and an artificial neural network. Further biological investigations will presumably utilise this technology.
    Keywords In vitro culture conditions ; Medicinal plants ; Gesho ; Rhamnus species ; Mathematical modelling ; Response Surface Methodology ; Agriculture (General) ; S1-972 ; Chemistry ; QD1-999
    Subject code 621
    Language English
    Publishing date 2023-09-01T00:00:00Z
    Publisher SpringerOpen
    Document type Article ; 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.

  3. Article: Molecular Insights into Abiotic Stresses in Mango.

    Muthuramalingam, Pandiyan / Muthamil, Subramanian / Shilpha, Jayabalan / Venkatramanan, Varadharajan / Priya, Arumugam / Kim, Jinwook / Shin, Yunji / Chen, Jen-Tsung / Baskar, Venkidasamy / Park, Kyoungmi / Shin, Hyunsuk

    Plants (Basel, Switzerland)

    2023  Volume 12, Issue 10

    Abstract: Mango ( ...

    Abstract Mango (
    Language English
    Publishing date 2023-05-09
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2704341-1
    ISSN 2223-7747
    ISSN 2223-7747
    DOI 10.3390/plants12101939
    Database MEDical Literature Analysis and Retrieval System OnLINE

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