ABSTRAK Artikel ini menganalisis bagaimana mengaplikasikan teori kognitif Bruner dalam pengajaran matematika berdasar teori pembelajaran penemuan. Melalui analisis pembelajaran kasus matematika, tujuan dari pengajaran matematika adalah untuk memungkinkan siswa menguasai struktur pengetahuan matematika secara komprehensif. Guru matematika harus aktif menciptakan kondisi dalam mengajar, dan membimbing siswa untuk menemukan dan belajar aktivitas langsung, pemikiran dan representasi. Dari akuisisi pasif pengetahuan menjadi penemuan aktif mandiri, sehingga siswa menjadi objek utama belajar matematika. Kata kunci : Kognitif Bruner, Teori Belajar, Pembelajaran Matematika ABSTRACT This article analyzes how to apply Bruner's cognitive theory of discovery in teaching based on learning theory. Through the analysis of mathematics case learning, the aim of teaching mathematics is to help students master mathematical knowledge comprehensively. Mathematics teachers must actively create conditions in teaching, and guide students to discover and learn direct activity, thinking and representation. From the acquisition of knowledge to independent active discovery, so that students become the main object of learning mathematics. Keywords : Bruner's Cognitive, Learning Theory, Mathematics Learning

The objective of this paper is to define and study the concepts of strong AH-submodule, and AH-homomorphism in a refined neutrosophic module. Also, this work describes the algebraic structure of all AH-endomorphisms defined over a refined neutrosophic module.

American scientific publishing group

Modules are one of the fundamental and rich algebraic structure concerning some binary operations in the study of algebra. In this paper, some basic structures of refined neutrosophic R-modules and refined neutrosophic submodules in algebra are generalized. Some properties of refined neutrosophic R-modules and refined neutrosophic submodules are presented. More precisely, classical modules and refined neutrosophic rings are utilized. Consequently, refinedneutrosophic R- modules that are completely different from the classical modular in the structural properties are introduced. Also, neutrosophic R-module homomorphism is explained and some definitions and theorems are presented.

American scientific publishing group

The security and privacy of data in an IoT-driven intelligence landscape is a major concern. This research examines the integration of Paillier homomorphic encryption into Federated Learning to enhance security while maintaining individual data privacy in such environments. The interconnectedness of devices in IoT frameworks poses a challenge in maintaining the confidentiality of sensitive information. By using Paillier encryption within Federated Learning, this problem is solved by securing learning parameters while still keeping data private. This approach demonstrates promising improvements without violating privacy through extensive simulations and comparative analyses across different model architectures. The results of this study highlight the potential effectiveness of this method for enhancing security measures in interconnected IoT environments.