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1

  • COMMON FIXED POINT THEOREMS IN COMPLEX VALUED METRIC SPACE USING IMPLICIT RELATION



Dr.Preeti Sengar

Abstract:
Banach contraction principle in gives appropriate and simple conditions to establish the existence and uniqueness of a solution of an operator equation 𝑇𝑥 = 𝑥. Later, a number of papers were devoted to the improvement and generalization of that result. Most of these results deal with the generalizations of the different contractive conditions in metric spaces. The aim of this paper is to prove the existence and uniqueness of a common fixed point for a pair of mappings satisfying occasionally weakly compatible maps in complex valued metric space using implicit relations.


1-9
2

  • ADVANCES IN ENVELOPING SUMMABILITY THEORY OF SOME SEQUENCE SPACES



Kunwar Pal Singh

Abstract:
The theory of sequence spaces depends upon various result from topological vector spaces and their ramifications. In several branches of analysis, the study of sequence spaces occupies a very vigorous position. One can sees the construction of numerous examples of locally convex spaces obtained as a consequence of the dual structure displayed by several pairs of distinct sequence spaces. We rediscover the wide applicability of sequence spaces to several branches of functional analysis.


10-18
3

  • Forecasting the Future of Crypto currency: A Machine Learning Approach for Price Prediction



Venkata Ravi Kiran Kolla

Abstract:
virtual forex is said as one of the economic properties extensively recognized as change foreign money. Crypto currency buying and selling has stuck the attention of buyers as crypto currencies may be viewed as extraordinarily profitable investments. correct charge forecasting is essential for optimizing your crypto currency investment returns. for the reason that price prediction is a time-collection challenge, a hybrid deep mastering version has been proposed to are expecting destiny costs of crypto currencies. The hybrid version integrates a one-dimensional convolutional neural community and a Stacked Gated Recurrent Unit (1DCNN-GRU). Given crypto currency charge information through the years, a one-dimensional convolutional neural network encodes the information right into an excessive-degree identification illustration. Stack gate recursion devices then capture lengthy-time period dependencies in expressions. The proposed hybrid version changed into evaluated on 3 one-of-a-kind crypto currency datasets: Bitcoin, Ethereum, and Ripple. Experimental outcomes show that the proposed 1DCNN-GRU version outperforms present techniques with the smallest RMSE values of forty-three.933 for Bitcoin dataset, three.511 for Ethereum dataset, and 0.00128 for Ripple dataset.


19-26
4

  • THE ROLE OF FUZZIFICATION AND DEFUZZIFICATION IN TRAFFIC LIGHT CONTROLLER



1Dharmendra Kumar, 2J. P. Tripathi*, 3R. K. Shukla

Abstract:
Today, the traffic light controller plays very important role in our daily life. Since traffic congestion is a problem based on uncertainty and ambiguity and fuzzy set theory deals uncertainty and ambiguity in the best sense, therefore we introduce the important part of fuzzy logic controller as “The Role of Fuzzification and Defuzzification in Traffic Light Controller.”


19-27
5

  • A STUDY ON THE FORMULATION OF LINEAR ALGEBRA



DR. M.K. SHARMA

Abstract:
This research aims to design a Linear Algebra learning material that can facilitate the enhancing of students’ mathematical understanding and representation. The research method is research and development (R&D) which consists of three main stage, namely the preliminary, development, and dissemination. The research was limited to the development stage. The results concluded that the assessment of the experts (validator) on learning materials is in the category of valid with a small revision of the exercise questions part. The result of a practical test of learning material is 87,81% (very practices). Meanwhile, the result of limited trials indicates that learning material can be completed students’ mathematical understanding classically and individually. Besides, the mathematical representation of student has not reached the mastery both in classically and individual.


27-36
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