Multiset Research Articles

Generalized Soft Multi Functions

Objectives: To acquaint generalized soft multi functions and graphs. Methods: Generalized semi soft multi sets was introduced using semi closure operators and open soft multi sets. As a consequence, this study acquaints generalized semi soft multi continuous functions with the help of generalized semi soft multi sets. Also the concept of generalized soft multi homeomorphisms are introduced via generalized semi soft multi continuous, open and closed mappings and also the thought of closed soft multi graph is introduced through the open soft multi sets and soft multi points. Findings: This study reveals that composition of two generalized semi soft multi continuous functions do not necessarily generalized semi soft multi continuous but composition of generalized semi soft multi continuous function with any of its stronger forms be a generalized semi soft multi continuous function and also several interesting results are found. Novelty: This study acquaints soft multi graph by using soft multi functions and also acquaint generalized semi closed soft multi graph. Keywords: Generalized Semi Soft Multi Continuous Functions, Generalized Semi Soft Multi Open Functions, Generalized Semi Soft Multi Closed Functions, Generalized Semi Soft Multi Homeomorphisms And Generalized Semi Closed Soft Multi Graphs

(α, γ)-Anti-Multi-Fuzzy Subgroups and Some of Its Properties

Recently, fuzzy multi-sets have come to the forefront of scientists’ interest and have been used in algebraic structures such as multi-groups, multi-rings, anti-fuzzy multigroup and (α, γ)-anti-fuzzy subgroups. In this paper, we first summarize the knowledge about the algebraic structure of fuzzy multi-sets such as (α, γ)-anti-multi-fuzzy subgroups. In a way, the notion of anti-fuzzy multigroup is an application of anti-fuzzy multi sets to the theory of group. The concept of anti-fuzzy multigroup is a complement of an algebraic structure of a fuzzy multi set that generalizes both the theories of classical group and fuzzy group. The aim of this paper is to highlight the connection between fuzzy multi-sets and algebraic structures from an anti-fuzzification point of view. Therefore, in this paper, we define (α, γ)-anti-multi-fuzzy subgroups, (α, γ)-anti-multi-fuzzy normal subgroups, (α, γ)-anti-multi-fuzzy homomorphism on (α, γ)-anti-multi-fuzzy subgroups and these been explicated some algebraic structures. Then, we introduce the concept (α, γ)-anti-multi-fuzzy subgroups and (α, γ)-anti-multi-fuzzy normal subgroups and of their properties. This new concept of homomorphism as a bridge among set theory, fuzzy set theory, anti-fuzzy multi sets theory and group theory and also shows the effect of anti-fuzzy multi sets on a group structure. Certain results that discuss the (α, γ) cuts of anti-fuzzy multigroup are explored.

COMPLETENESS IN MULTI METRIC SPACES

In the present paper a notion of convergence in multi metric space is presented. Complete multi metric space is introduced and some properties are studied. Cantor’s intersection theorem and Banach’s fixed point theorem are es- tablished in multi set settings.

Einstein-operations on fuzzy soft multi sets and decision making

In this paper, we define Einstein product and Einstein sum of fuzzy soft multi sets (FSM-sets) and using these products, we introduce an adjustable approach to FSM-set based decision-making, for solving decision-making in an uncertain situation. The feasibility of our proposed FSM-set based decision-making procedure in practical application is shown by some numerical examples.

Explosive Hypertrophy Development: 5rd Method, Advanced Way for Explosive Hypertrophy, Cases Report Series Research

The knowledge of muscle function plays a major role in bodybuilding training and makes big difference for bodybuilders. Muscle monitoring is of great interest in areas such as clinical assessment, and sport science. Understanding, ideally quantifying, when a muscle state has changed in relation to whole body motion can assist in physical training and muscle conditioning. There are lot of different methods to gain muscle mass and improve weak parts; bodybuilders been using those methods for decades such: drop-set, rest-pause, super-set, tri-set, giant sets, contrast time, multi hold pump set, intra set load contrast, regressive range of motion, iso dynamic contrast Our method is based on scientific work; our main goal is to improve bodybuilder’s weak muscles and increase muscle mass in very short period of time, to asses our work we used electromyography (EMG) to assess the skeletal muscles activity and ultrasonography (US) to measure muscle thickness. Bodybuilding is the use of progressive resistance exercise to develop the musculature, is the process of developing muscle fibres through the combination of weight training, increased caloric intake, and rest. Bodybuilding decreases risk of developing diseases, Improve Mental Health, simply Weight training exercise, aerobic exercise and right nutrition have beneficial effects for strong mind and body. Muscle hypertrophy is a term for the growth and increase of the size of muscle cells. Hypertrophy is, by definition, the enlargement of an organ or tissue from the increase in size of its cells. Not to be confused withhyperplasia, the process of increasing the number of cells, hypertrophy is the process of increasing the size of the cells that are already there. By that a hormonal response is triggered, so a release of growth factor, cortisol, and testosterone. Growth factors help stimulate muscle hypertrophy while testosterone increases protein synthesis. This process makes satellite cells multiplying and their daughter cells go to the damaged tissue. To build muscle through weight lifting, you need to have both mechanical damage and metabolic fatigue. When you lift a heavy weight, the contractile proteins in the muscles must generate force to overturn the resistance provided by the weight.

Generalized mapping for multiset rewriting systems

Generalized mapping for multiset rewriting systems

Pattern recognition based on multiple attribute decision making in intuitionistic fuzzy environment

In this paper we adapt an algorithm from the multiple attribute decision making field in order to be used in pattern recognition, working with intuitionistic fuzzy sets and intuitionistic fuzzy multi sets. Our method is implemented in two versions: using the score matrix (that is characteristic of decision making problems) and using a similarity measure (that is characteristic of pattern recognition problems). Firstly, the method is built to work with intuitionistic fuzzy sets and after it is extended for intuitionistic fuzzy multi sets. Experimental results demonstrate the superiority of the second versions in pattern recognition problems. For each example we compare our results with those given by other measures whose accuracy has been validated by the respective examples and we conclude that our method can be successfully used in pattern recognition instead of some specific methods in this area.

An multi-BSS multi-user full duplex MAC protocol based on AP cooperation for the next generation WLAN

Aiming at the problems of low efficiency of buffer information collection and sharp drop of system throughput caused by the aggravation of conflicts in the multi basic service set (BSS) high density deployment overlap coverage scenario of next generation wireless local access network (WLAN), an access point cooperation based multi-BSS multi-user full duplex multiple access protocol named as CMMFD is proposed. Firstly, a channel resource allocation algorithm for stations (STA) in overlapping coverage area is designed, in which, the access control (AC) equipment allocates the channel resources for all STA in response to buffer state and interference information (BI) according to the proportion of STA in the overlapped coverage area. Secondly, a BI information collection protocol process based on access point (AP) cooperation sending trigger frame is designed to collect all STA information. After receiving trigger frame, STA will access the channel and report BI information in p-probability according to the channel resources allocated by AC. Finally, a multi-BSS multi-user channel resource allocation algorithm based on full duplex is designed. AC allocates channel resources according to all BI information reported by AP, and schedules STA in the multi-BSS to carry out multi-user full duplex transmission over the sub-channel. Simulation results show that the throughput of the CMMFD protocol improves by 29.6%, compared with Mu-FuPlex protocol and EnFD-OMAX protocol.

Seizure Detection using Deep Multiset Canonical Correlation Analysis and Bayesian Optimization

As one of the most common neurological diseases in the world, epilepsy seizure is difficult to ignore. Seizure detection is receiving more and more attention from researchers. Feature extraction is one of the key steps in automatic seizure detection. Lots of features have been proposed to detect seizure using EEG signal. However, few works focus on feature fusion. In this paper, deep multi set CCA is explored for seizure detection. Since deep neural network architecture has a great impact on performance of deep multiset CCA, bayesian optimization is employed to search architecture parameters automatically. Preliminary experiments show it is effective for seizure detection using deep multiset CCA and bayesian optimization. Satisfactory seizure classification results are achieved with little manual intervention.

PYTHAGOREAN FUZZY MULTI SET AND ITS APPLICATIONS IN FISH FEED FOR INDIAN MAJOR CARP

Pythagorean fuzzy set is an extension of Intutionistic fuzzy set, which is more capable of expressing and handling the uncertainty under uncertain environments, so that it was broadly applied in various fields. In this paper, we explored the concept of Pythagorean fuzzy multi set (PFMS). We describe some basic set operations of Pythagorean fuzzy multi set and also, we proposed sine exponential distance function. Finally, through an illustrative example it is shown how the proposed distance works in decision-making problem.

The Robot Intelligent Spraying Glazing System for Sanitary Ceramics Industry

Aiming at the current situation and existing problems of the sanitary ceramics industry, the paper presents the R&D of a set of multi - robot cooperation in the spraying glazing system. Firstly, the paper introduces the requirement of spraying glazing and puts forward the technics of spraying grazing robot. Then the paper introduces the system solution of spraying glazing robot, the working mode of the cooperative multi-robot, and the method of robot trajectory planning. Finally, according to the real experimentation of established spraying glazing robot system, the paper tested and verified the performance of the system. This system plays a positive demonstration role in the sanitary ceramics industry.

Rule-Based Classification using Multi Soft Set Theory

One of the important data mining functionality is classification. Presently, different methods exist for implementing classification. Rule-based classification using decision tree induction method is a conventional and simple method for identifying an unknown class of a given object. This method has a set of demerits and to remove these demerits, we depend on a soft computing tool which is known as soft set theory. One branch of soft set theory is called - multi soft theory- and it has a wide range of applications in the area of classification. We made a certain alteration in the rule-based classification using decision tree induction method by applying multi soft set theory. These changes will simplify the difficulties of the rule-based classification using decision tree induction method. The first two sections of this research work discuss introduction and preliminaries. In the remaining sections, the authors describe the multi soft set theory and its applications in rule base classification. Lastly, the paper finishes with a new algorithm, which the research scholars implemented as software using python programming. The suggested work experts can use in data mining industry. It has massive use in the fields of business, agriculture, health, education and many more.

On Fuzzy Soft Multi Set and Its Application in Information Systems

On Fuzzy Soft Multi Set and Its Application in Information Systems

(1904-5132) General Definitions for the Union and Intersection of Ordered Fuzzy Multisets

Since its original formulation, the theory of fuzzy sets has spawned a number of extensions where the role of membershipvalues in the real unit interval [0; 1] is handed over to more complex mathematical entities. Amongst the many existingextensions, two similar ones, the fuzzy multisets and the hesitant fuzzy sets, rely on collections of several distinct valuesto represent fuzzy membership, the key difference being that the fuzzy multisets allow for repeated membership valueswhereas the hesitant fuzzy sets do not. But in neither case are these collections of values ordered, as they are simplyrepresented through multisets or sets. In this paper, we study ordered fuzzy multisets, where the membership valuecan be an ordered n-tuple of values, thus accounting for both order and repetition. We present some basic de finitionsand results and explore the relation between these ordered fuzzy multisets and the fuzzy multisets and hesitant fuzzysets.

An Outranking Approach for MCDM-Problems with Neutrosophic Multi-Sets

In this paper, we introduced a new outranking approach for multi-criteria decision making (MCDM) problems to handle uncertain situations in neutrosophic multi environment. Therefore, we give some outranking relations of neutrosophic multi sets. We also examined some desired properties of the outranking relations and developed a ranking method for MCDM problems. Moreover, we describe a numerical example to verify the practicality and effectiveness of the proposed method.

Mining Multi-Dimensional Intra and Inter-Association Patterns of Call Records for Targeted Advertising using Multi-Granulation Rough Sets

Customer contacts to various businesses identified in telecom call records convey their interest in availing those services. Multi-dimensional attribute dependence with day and time of such communications generate useful insights for targeted advertising. Also, frequent and significant inter patterns of service associations give the probability that takers of one service may also be the prospects of the other. This work presents a multi granulation rough sets model to address the issue of prospect discovery from interest traits depicted in call records. The proposed method solves problems like higher computational complexity and large statistically insignificant patterns space inherent in traditional intra and inter-pattern mining methods. The algorithm is tested to generate target audience for food and restaurant business using one-month data of anonymous call records of a Thailand based telecom service provider. Some interesting mathematical properties of underlying knowledge structures are also validated.

New Similarity Measure between Intuitionistic Fuzzy Multisets based on Tangent Function and its Application in Medical Diagnosis

In recent years, Intuitionistic fuzzy set is very useful in decision making problems such as medical diagnosis, pattern recognition, clustering etc., which deals with vagueness and uncertainty. Similarity measure is a tool used to find the closeness of the intuitionistic fuzzy sets by considering the membership, nonmembership and hesitation function. In this paper, we propose an effective similarity measure based on tangent function for intuitionistic fuzzy multi sets in which membership, nonmembership, hesitation function occurs more than once and also we apply this measure in medical diagnosis and pattern recognition.

A set of new multi- and many-objective test problems for continuous optimization and a comprehensive experimental evaluation

Multi- and many-objective optimization problems have wide applications in the real world, and they have received growing attention from the evolutionary computation community. To promote the algorithm development in this area, numerous studies have been devoted to designing multi- and many-objective test problems. Most of these studies focus on handling complicated Pareto fronts (PFs), and the impact of the Pareto sets (PSs) has not been well-studied, although complicated PSs are prevalent in the real world. This paper presents a set of scalable test problems according to a new principle, which considers the geometrical properties of both PF and PS. A position function with a spherical form is proposed to introduce non-linear variable dependences to the PS, so as to simulate the variable dependencies in the real-world problems. According to the proposed principle, the first m (i.e., the number of objectives) decision variables are used to form the surface of a unit hypersphere, while the rest variables are designed to optimize a certain distance function. A set of test problems are generated by the proposed principle, which are then used to investigate six representative algorithms. The experimental results indicate that the proposed test problems pose considerable difficulties to existing algorithms, calling for the need for designing new algorithms capable of handling complicated PF and PS simultaneously.

NeuroArray: A Customized aCGH for the Analysis of Copy Number Variations in Neurological Disorders.

Background: Neurological disorders are a highly heterogeneous group of pathological conditions that affect both the peripheral and the central nervous system. These pathologies are characterized by a complex and multifactorial etiology involving numerous environmental agents and genetic susceptibility factors. For this reason, the investigation of their pathogenetic basis by means of traditional methodological approaches is rather arduous. High-throughput genotyping technologies, including the microarray-based comparative genomic hybridization (aCGH), are currently replacing classical detection methods, providing powerful molecular tools to identify genomic unbalanced structural rearrangements and explore their role in the pathogenesis of many complex human diseases.Methods: In this report, we comprehensively describe the design method, the procedures, validation, and implementation of an exon-centric customized aCGH (NeuroArray 1.0), tailored to detect both single and multi-exon deletions or duplications in a large set of multi- and monogenic neurological diseases. This focused platform enables a targeted measurement of structural imbalances across the human genome, targeting the clinically relevant genes at exon-level resolution.Conclusion: An increasing use of the NeuroArray platform may offer new insights in investigating potential overlapping gene signatures among neurological conditions and defining genotype-phenotype relationships.

Algorithms for two dimensional multi set canonical correlation analysis

Multi set canonical correlation analysis (mCCA), which extends the application of canonical correlation analysis (CCA) to more than two datasets, is a data driven technique that can jointly analyze the relationship amongst multiple (more than two) datasets. However, the conventional mCCA is directly applicable only to multivariate vector data and requires the image data to be reshaped into vectors. This approach fails to consider the spatial structure of the images and in addition, leads to an increase in the computational complexity. In this paper, we propose new two dimensional mCCA algorithms that operate directly on the image data instead of vectorizing them. Face recognition experiments are presented to compare the performances of conventional mCCA and the proposed two dimensional mCCA techniques. Additionally, experiments against fMRI data are conducted to demonstrate the applicability of the proposed approach in multisubject fMRI analysis.