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Estudos genéticos do milho-miúdo em condições hiperáridas

Estudos genéticos do milho-miúdo em condições hiperáridas

Mukesh Kumar; Manoj Kumar; Pawan Kumar

Edicoes Nosso Conhecimento
2025
nidottu
O milho-mi do Pennisetum glaucum (L.) R. Br.] amplamente cultivado nas regi es tropicais ridas e semi ridas do subcontinente indiano e do continente africano, sob as condi es agroclim ticas mais adversas, onde outras culturas cereal feras n o conseguem produzir rendimentos econ micos. Os seus gr os s o valorizados como alimento humano, enquanto os seus caules secos constituem uma importante ra o para o gado em sistemas de agricultura e pecu ria. No futuro, o milho-mi do dever desempenhar um papel ainda mais importante na seguran a alimentar e nutricional. O milho-mi do tamb m seria um excelente recurso gen mico para o isolamento de genes candidatos respons veis pela toler ncia a stress clim ticos e ed ficos, acelerando o melhoramento gen tico desta cultura, bem como a sua poss vel utiliza o no melhoramento gen tico de outras culturas.
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Developing Hybrid Intelligence Based Recommender System

Developing Hybrid Intelligence Based Recommender System

Arup Roy; Pawan Kumar Singh

Lap Lambert Academic Publishing
2023
pokkari
The scope of harnessing information science has been experienced in personalized decision support systems. This involves minimizing the volume of information and making deductions, such as in the case of Amazon's recommendation system. Traditional recommendation systems are becoming outdated and inadequate in meeting user requirements and technological trends. New recommendation systems like contextual, group, and social recommendation have been discovered. These systems have been investigated and analyzed using nature-inspired algorithms, evolutionary algorithms, swarm intelligence algorithms, and machine learning techniques to provide more precise personalized recommendations. A community-based filtering algorithm is proposed as well as an innovative hybrid intelligent algorithm to handle non-erroneous recommendations in a context-aware framework and address threats from intruders using optimization techniques and.The work aims to provide efficient solutions to problems faced by users, including sparsity, novelty, precise recommendation, and optimum decision-making solutions. The proposed models have been extensively experimented with and show superior learning mechanisms.
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