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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Further, we make use of the $(n, delta)$-neighborhoods of functions in both classes $T^p _n (lambda, mu, A, B)$ and $K^p _n (lambda, mu, A, B)$
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper we introduce the classes $T^p _n (lambda, mu, A, B)$and $K^p _n (lambda, mu, A, B)$, and derive coefficient bounds and distortion inequalities for functions belonging to the class T%(,A,B).
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Key Findings & Data:In this paper we introduce the classes $T^p _n (lambda, mu, A, B)$and $K^p _n (lambda, mu, A, B)$, and derive coefficient bounds and distortion inequalities for functions belonging to the class T%(,A,B).
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Conclusion & Impact:Further, we make use of the $(n, delta)$-neighborhoods of functions in both classes $T^p _n (lambda, mu, A, B)$ and $K^p _n (lambda, mu, A, B)$
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
In this paper we introduce the classes $T^p _n (lambda, mu, A, B)$and $K^p _n (lambda, mu, A, B)$, and derive coefficient bounds and distortion inequalities for functions belonging to the class T%(,A,B). Further, we make use of the $(n, delta)$-neighborhoods of functions in both classes $T^p _n (lambda, mu, A, B)$ and $K^p _n (lambda, mu, A, B)$
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Cite This Article
ALTINTAŞ, O. (2022). Certain applications of subordination associated with neighborhoods. Hacettepe Journal of Mathematics and Statistics, Cilt: 39 - Sayı: 4, 527–534. https://ajindex.com/articles/530657