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Diversity of Pollen Grain Fertility and Crossing of Potato in Tajikistan
Published in Hasnain Nangyal, Muhammad Saleem Khan, Environmental Pollution, Biodiversity, and Sustainable Development, 2020
K. Partoev, K. Melikov, A. Naimov, H. Nangyal, F.A. Khan
It should be noted that some clones sampled individually from hybrid swarm F1 and propagated from one plant by culture method (in-vitro plants) had stamen filaments of different shapes. For example, clones 43, 48, and 54 had different morphological attributes of stamen filaments (Fig. 6.2). Clone 43 had plants with two types of stamen filament shape. One part of the plant had a regular stamen filament; the other part of the plant had a modified stamen filament when stamen filaments are not pressed to the pistil stem as it is with regular plants. They are detached from it and look friable.
Stochastic modeling and meta-heuristic multivariate optimization of bioprocess conditions for co-valorization of feather and waste frying oil toward prodigiosin production
Published in Preparative Biochemistry & Biotechnology, 2023
Atim Asitok, Maurice Ekpenyong, Ubong Ben, Richard Antigha, Nkpa Ogarekpe, Anitha Rao, Anthony Akpan, Nsikak Benson, Joseph Essien, Sylvester Antai
Design of experiment (DoE), in conjunction with response surface methodology (RSM) or artificial neural network (ANN), has been harnessed to optimize conditions toward improved microbial metabolite production.[16,17] A comparison between RSM and ANN has frequently favored ANN because of its ability to handle non-linear stochastic relationships such as observed in microbial production processes.[18] ANN is a biologically-inspired technique that simulates how neurons work in human brain, and tries to solve complicated data issues like classification, regression, and pattern recognition. Its efficiency depends on the choice of weights and biases, and several techniques have been employed to optimize them including back-propagation and feed-forward approaches.[19] In recent times, a number of meta-heuristic algorithms especially ant colony optimization, manta-ray foraging optimization and particle swarm optimization (PSO) are employed to efficiently optimize these weights and biases for ANN.[20–22] These hybrid swarm intelligence techniques have proven their efficiency in bioprocess optimizations.[23]
Symbiotic Organisms Search Optimization based Faster RCNN for Secure Data Storage in Cloud
Published in IETE Journal of Research, 2023
J. Thresa Jeniffer, A. Chandrasekar, S. Jothi
Thabit et al. [9] proposed data security based on the lightweight homomorphic cryptographic algorithm in cloud computing. In this paper, the algorithm comprises two layers; a lightweight cryptographic algorithm was employed in the first layer and the multiplicative homomorphic method was used in the second layer for enhancing data security. Swetha et al. [10] discussed the convolutional neural network (CNN) based on securing multimedia medical data. The system was effective but it was required to provide security and privacy for users. The results showed that the scheme acquired excellent performance compared to other methods. Elhoseny et al. [11] evaluated the security of medical images based on the IoT by utilizing the cryptographic design with optimization methods. Utilizing hybrid swarm optimization the optimal key was selected for enhancing the security levels of the decryption and encryption process. It was revealed that the peak signal-to-noise ratio was extremely high. Govindarajalu et al.[12] introduced the intelligent preserving phrase search with data encryption in the cloud-based IoT. The real-time dataset was utilized in this paper. The probability phrase recognition algorithm was utilized for protecting the users.
Optimized PV Fed Zeta Converter Integrated with MPPT Algorithm for Islanding Mode Operation
Published in Electric Power Components and Systems, 2023
The paper organization is as follows: Section 2 highlighted the recent techniques in MPPT tracking in PV units. Section 3 highlighted the drawbacks in the existing system. The proposed Hybrid swarm intelligence approach for MPPT tracking in PV is explained in Section 4. Section 5 explained the implementation and result analysis. Section 6 and 7 summarize advantages of the method proposed and its future works.