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24th FAI-ICDBSMD 2021
Abstract for Paper Presentations
Remark: All the absent participants are requested to join in June 27, Session 3
25 June, Session 1
Chairperson
Prof. S.Raji Reddy
Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad 500075, India
D1S1
Paper ID:
8
Blockchain & QR code technology-based secure and quick vehicle identification and authentication system for Indian traffic police.
Presentators Name:
Parikshit Rana
ABSTRACT
The traditional Indian traffic police system for verifying and validating vehicle documents on the roadside has been digitized and secured. In India, there is no safe and fast system in use to get all information on a particular vehicle from Indian traffic police. To overcome this problem, we design and create an application that allows Indian traffic police to get all information about a vehicle, including its owner driving license, RC book, insurance status, vehicle emission status, by simply scanning the vehicle's QR code. To reduce the risk of data tampering, we focused on data authentication and used blockchain to create a decentralized, secure, and fast framework. In this paper, we design a scanning application, classifying and supplying the information and documents stored against it, and the existing procedure for verifying and validating vehicle documents on the roadside used by the Indian road traffic police. We also evaluate the performance of this application by analyzing the impact of light illumination and distance factors on the QR code recognition process.
Paper ID:
18
Decision-making on Maintenance Planning using PSI by Discriminating Critical Components and Reliability Modeling through Shop-floor Failure Data
Presentators Name:
Nilesh Pancholi
ABSTRACT
The electrical power transmission wire manufacturing unit is a noticeable sector of the processing industry, based on past business volume and expected growth. Poor maintenance standards result in productivity losses of 20-25 percent. Such facts and the challenges of keeping the system operational motivate a specific maintenance plan based on a real-life failure analysis. This research paper examines a real-world case of an aluminum-based rolling machine for electrical transmission wire. The goal of this paper is to concentrate on increasing process reliability by updating existing maintenance tasks for critical components. The most critical parts are separated based on an examination of shop-floor practices and an examination of previous maintenance records. The key reliability parameters are evaluated in order to understand the failure mechanism and sudden breakdown. The criticality indices of each failure cause are calculated using a preference selection index (PSI) model based on multi-criteria decision making (MCDM). The primary output of the work is a map of the maintenance affairs and their arrangement in relation to the attained criticality indices. The work will aid in the establishment of an ideal maintenance network among significant industrial plants or processes. The novelty primarily consists in reviewing the scope of augmenting existing maintenance practices through real-life failure analysis using MCDM-based PSI.
Paper ID:
22
Distributed Stochastic Bi-Directional Long Short Memory for Live Streaming Data
Presentators Name:
S.Giridharan
ABSTRACT
In the real world, currently there are more than millions bytes of social media data being created. With the help of modern computational capacity in addition to advanced research in the field of artificial intelligence, a great improvement is shown in natural language processing using deep learning techniques. Such techniques make it possible to analyze the given tons of data. In addition to it, it also helps us to have a deep dive knowledge in people�s mindset towards the current trend in media application. This also creates an another problem of cyber bullying wherein many people start sharing and posting anonymous and fake information to make a significant impact by making riots just as fake heroes. By continuously monitoring these types of online riots will provide a clean and peaceful environment which in turn supports the government in a better way. This article focuses on performing sentiment analysis over a given real time social media data by categorizing it. The effective analysis of live streaming from social media applications is done with word2vec embedding method in natural language processing and trained using bi-directional LSTM model. The performance is compared with the LSTM model, and traditional machine learning techniques like na�ve bayes and linear regression model and Bi-LSTM model outperforms the other techniques with 80%-85% accuracy with live streaming unconventional social media data.
Paper ID:
47
CASE STUDY; BRIDGE FAILURES DUE TO IMPROPER SUBSOIL INVETIGATION
Presentators Name:
Dr. Yogita Gupta
ABSTRACT
Site investigation is normally required and carried out prior to the commencement of the design of a bridge foundation. Due to lack of or inadequacy of subsurface investigation and poor quality of site investigation work, failures of the foundation occurred [1]. These failures sometime led to catastrophic disaster and impose delay and extra construction cost. The present case is about high level bridge across river Gej on Khandgawan-Geugi-Nawapara road. The bridge was under construction. All the piers and pier caps were constructed, the deck was yet to lay. Piers were supported on the group of three piles of 1.2m diameter. It was planned to construct the deck after monsoon. During monsoon two piers were collapsed. The rain was not an extraordinary. The design of pile foundation was checked by the author for full loading and for the present stage. This paper illustrates the problem of inappropriate geotechnical investigation, importance of geotechnical supervision and inadequate knowledge of site condition.
Paper ID:
62
A Note On P-Q type Modular Equations
Presentators Name:
Dr. D. Anu Radha
ABSTRACT
S. Ramanujan documented several P-Q type modular equations for the explicit evaluations of Weber class invariants, continued fractions and many more. Motivated by his work in this paper, we establish six new modular equations by elementary mathematics methods.
25 June, Session 2
Chairperson
Dr. A. Amutha
Asst. Prof., PG & Research Dept. of Mathematics, The American College, Madurai, Tamil Nadu, India
D1S2
Paper ID:
3
Side-Channel Attack Prediction and Analysis using Deep Learning.
Presentators Name:
Payal Borkar
ABSTRACT
With the increasing growth of the Internet of Things, embedded devices equipped with a scientific discipline module, it becomes a very important issue to guard sensitive knowledge. In power-based side-channel attacks, the instant power consumption of the target is analyzed with applied math tools to draw conclusions concerning the keys that the area unit used. Many works have shown that this sort of research, referred to as guide Attacks within the side-channel domain, will be rephrased as a classical Machine Learning classification drawback with the training section. Following the present trend within the latter space, recent works have incontestable that deep learning algorithms were terribly economical to conduct security evaluations of embedded systems and had several blessings compared to the opposite strategies, within the offer planned system uses a deep learning algorithmic program with the variance within the Epoch size, Keras, Tensorflow, h5py, Python, and their dependencies to form the project and can use CNN techniques whereby we are going to tune the hyperparameters like epochs, hidden layers, and dense layers size, & use HDF (Hierarchical knowledge Format) databases (ASCAD). This project proposes a comprehensive study of deep learning algorithms once applied within the context of side-channel analysis and discusses the links with the classical guide attacks. Secondly, it addresses the question of the selection of the hyper-parameters for the category of multi-layer perceptron networks and convolutional neural networks.
Paper ID:
10
Neutrosophic Beta Omega Closed Sets in Neutrosophic Topological spaces
Presentators Name:
A. ANUSUYA
ABSTRACT
Exploring a new type of neutrosophic set in neutrosophic topological spaces is the major aim of our research. In this paper, the concept �Neutrosophic Beta Omega Closed Sets� is newly defined and their properties and some interesting theorems are discussed. We have analyzed the relationships between this newly introduced set and the already existing neutrosophic sets.
Paper ID:
13
Neutrosophic Fuzzy Bi-ideals of Near-Subtraction Semigroups
Presentators Name:
J SIVA RANJINI
ABSTRACT
The theory of Neutrosophy fuzzy set is the extension of the fuzzy set that deals with imprecise and indeterminate data. We first conceptualize the Neutrosophic fuzzy Bi-ideals of Near Subtraction Semigroups along with some of its fundamentals. In particular, we examine the direct product, homomorphism, Union, Intersection and other basic algebraic properties of the said bi-ideals.
Paper ID:
17
FUNCTIONS RELATED TO NEUTROSOPHIC gs?*- CLOSED SETS IN NEUTROSOPHIC
TOPOLOGICAL SPACES
Presentators Name:
MAHESWARI S
ABSTRACT
The aim of this paper is to introduce a new class of Neutrosophic functions namely Neutrosophic gs?*- continuous functions , Neutrosophic gs?*- irresolute functions in Neutrosophic topological spaces . Additionally we relate the properties of these functions with other functions in Neutrosophic topological spaces .
Paper ID:
60
INDUCED MAPPING THEOREM USING IN TRIANGULAR FUZZY SYSTEM
Presentators Name:
G.Veeramalai
ABSTRACT
This article discusses the fundamental nature of the triangular fuzzy linear space, linear independence, linear period, and fuzzy linear transformations between linear spaces. We also show that this triangular fuzzy Hamel base exists in any linear space and its cardinality are distinct. We will start with some fundamental linear space concepts as well as definitions, which will also be used throughout this article and establish the induced mapping theorem by assuming some prior knowledge of triangular fuzzy linear systems, as well as the corresponding field of axioms as well as the addition and scalar multiplication operations
25 June, Session 3
Chairperson
Dr. S. Pious Missier
Secretary, FAI Tamilnadu(South) Chapter, Tuticorin, India
D1S3
Paper ID:
26
Nano Topology Induced by Vector Space
Presentators Name:
Gracy A
ABSTRACT
The theory of nano topology was introduced by Lellis Thivagar, on a non empty universe U with the aid of lower, upper and boundary approximation of a subset of U with an equivalence relation R on U. A topological vector space (TVS) V is a vector space over a topological field K that is endowed with a topology such that the vector addition '+' defined on V X V to V and the scalar multiplication '.' defined on K X V into V are continuous, where the domains of these functions are endowed with product topologies. The principle goal of this paper is to introduce a brand new notion called nano topological vector space (In short, NTVS) on a given non empty vector space V. Connection among TVS and NTVS have been examined. Further more nano linear subspace and continuity on NTVS are investigated and few examples are offered to illustrate the proposed concept.
Paper ID:
27
On Neurtosophic g # - Continuous Functions and Neurtosophic g #? Irresolute Functions
Presentators Name:
Babisha Julit R.L
ABSTRACT
The purpose of this paper is to introduce a new class of functions in Neutrosophic Topological space,N g#? continuous functions and N g#? irresolute functions . Basic characterizations and properties of these functions are studied. Also the inter relationship between N g#? continuous function and other known Neutrosophic continuous functions are discussed.
Paper ID:
28
A Study on Hyperconnectedness in N-Topological Spaces
Presentators Name:
Dr. MINU SARATHY
ABSTRACT
In this paper the notions of N_τ-hyperconnectedness, and N_τ-hyperconnected components have been studied in N-topological spaces , and also extend it to N_τ- pointwise hyperconnectedness in the space. The relation between N_τ-hyperconnected spaces and N_τ-pointwise hyperconnectedness has also been examined. With the help of N_τ -hyperconnected components, the dimension of a N-topological space has been obtained, and also a new space namely N_τ-noetherian space have been studied as an application to N_τ-hyperconnectedness and N_τ -hyperconnected components.
Paper ID:
49
Plithogenic FUCOM-MAIRCA to prioritize key sustainable factors and suppliers for transforming business sectors to Green Globe Creators
Presentators Name:
S.SUDHA
ABSTRACT
Multi-Criteria Decision Making (MCDM) plays an important role to make optimal selection of alternatives that highly satisfies all criteria. This paper proposes Plithogenic FUCOM (Full Consistency Method) & MAIRCA (The Multi Attributive Ideal-Real Comparative Analysis) A decision-making model to determine the significant green globe sustainability factors for the business sectors to materialize in making the globe turn green. The method of FUCOM is used to find the criterion weights and the method of MAIRCA is used to rank the best alternatives of suppliers, the partners of green globe creation. This paper also presents a comparative analysis of the proposed model under plithogenic neutrosophic and neutrosophic representations of expert�s opinion. The results are compared and observed to be compatible under plithogenic environment. The proposed model appears to be efficient as it consists of plithogenic aggregate operators to obtain the aggregate decision-making matrix.
Paper ID:
61
Theorems on Fixed Points in Parametric
cone-metric space
Presentators Name:
SUBASH V
ABSTRACT
In this work, some sets of fixed points are examined in a new environment of metric space, which is called parametric cone-metric space. Examples also studied to validate the results.
D1S4
25 June, Session 4
Chairperson
Dr. Meenakshi Srivastava
Asst. Prof., Amity Institute of Information Technology, Amity University, Lucknow, India
Paper ID:
2
Suicidal Ideation Prediction In Farmer Data Using Machine Learning Techniques
Presentators Name:
Iram Abdul Majid Qureshi
ABSTRACT
In recent year suicides square measure proliferating owing to stress and depression. Currently, day suicide of farmer is one major issue, Indian farmer is one amongst the backbone of our country, and thus our main focus to distinguishing and analysis of various forms of suicides square measure performed supported mentality of the person. Primary identification and detection square measure viewed as a good approach to avoid self-destructive try and self-destructive ideation- 2 basic hazards inflicting effective suicide. We tend to planned exhibits completely different techniques to grasp self-destructive intellection through online user contents in notably by considering National Crime information for past years as the associate objective of detection by suggests that of sentiment analysis and supervised learning ways. Analyzing the text descriptions and users language exposes wealthy information that may be used as a primary cautioning system for self-destructive detection. Many specific tasks and datasets square measure introduced and summarized to facilitate additional analysis. Finally, we tend to summarize the constraints of current work and supply associate outlook of additional analysis directions.
Paper ID:
5
An Intuitive Comparison of Boosting Algorithms in Ensemble Machine Learning
Presentators Name:
Ashish Tiwari
ABSTRACT
Artificial Intelligence provides ability to systems that they can automatically learn and make experiences improvisations by using machine learning. There are many techniques that come up to give accurate results one of them is ensemble technique which isfamous and mostly used. Ensemble techniques are much useful in order to get accurate and efficient results. As this techniqueuses the concept to generate and allows the integration of base models to produce efficient results. Ensemble technique is further categorized into different methods like: Bagging, Boosting and Stacking. Bagging is the type of technique in which learning is to be done in a parallel manner, each and every learner learns from each other and then their result is combined to predict best one. Boosting is the technique in which learners� learning depends upon the previous one and later the results are combined in a determined result, further the boosting technique divide into models like AdaBoost and Gradient boost. In Stacking, a meta-model is used to identify the output after the training has been performed. This paper gives the ephemeral of different boosting models for classification. Also,comparative analysis of different data sets had been performed using Gradient Boosting and AdaBoost Algorithm based on percentage accuracy and cross validation criteria.
Paper ID:
20
AN EFFECTIVE PRIVACY-PRESERVING ALGORITHM BASED ON RUBIK'S CUBE PRINCIPAL
Presentators Name:
Ankita Ashok Giradkar
ABSTRACT
In the past few years, several encryption algorithms is a based in chaotic systems has proposed means to protect digital images against cryptographic attacks. These encryption algorithms typically relatively use small key spaces and thus offer limited security, especially they are one-dimensional. In this paper, we proposed a novel image encryption algorithm is a based on Rubik's cube principle. The original image scrambled using the principle of Rubik's cube. Then, XOR operator is applied and rows, columns of the scrambled image using two secret keys public, private. Finally, the experimental results and security analysis show that the proposed encryption image scheme not only achieve good encryption and perfect hiding ability but also it can resist exhaustive attack, statistical attack, and differential attack.
Paper ID:
32
Machine Learning Approach for Ethereum Fraud Detection
Presentators Name:
Mohammed Farhan Baluch
ABSTRACT
Ethereum is a software platform that uses the concept of blockchain and decentralizes data by distributing copies of smart contracts to thousands of individuals worldwide. Ethereum, as a currency is utilized for exchanging value all around the world in the absence of a third party to monitor or intervene. However, as online commerce grows, a slew of fraudulent activities, such as money laundering, bribery, and phishing, emerge as the primary threat to trade security. Researchers may easily access it. Ethereum transaction data and smart contracts thanks to Ethereum's openness, which opens up unparalleled prospects for detecting and analyzing Ethereum fraud. This paper conducted a detailed examination of different models such as Random Forest (RF), Multi-Layer Perceptron (MLP), etc. based on machine learning and soft computing algorithm for classifying Ethereum fraud detection dataset with limited attributes correctly. With four used so far, Light Gradient Boosting Machine (LGBM) gives a maximum accuracy of 98.60% for the stated dataset scenarios. Further optimizing LGBM, an accuracy of 99.03% was achieved.
Paper ID:
33
Machine Learning Model for Crime Data Prediction by Using Soft Computing Regression Analysis
Presentators Name:
Prajwal Sharma
ABSTRACT
The crime rate in India is considerably increasing day by day. And with time data associated with crime is also increasing opening doors for data-driven approaches to be used on these data to extract insightful knowledge which can help police and other law enforcement agencies of the country in preventing and controlling criminal activities. Crime prediction is one such approach, using machine learning algorithms on crime data we can predict region-wise crime counts. With the help of this police and other law enforcement agencies can detect probable future crime hot spots which will help them to take required measures for crime prevention. In this paper, we have used different regression algorithms namely, simple linear regression, multiple linear regression, Decision Tree Regression (DTR), Support Vector Regression (SVR), and Random Forest Regression (RFR) to build regression models which can predict a total number of Indian Penal Code (IPC) crime count and crime counts of different types of crime (murder, rape, kidnapping and abduction, riots, etc.) region-wise and all over the country. We have used adjusted R squared and mean absolute error for the evaluation of our regression models. We have used district-wise crime data from 2001-2012 we collected from the official website of NCRB. For the chosen data, we found that for region wise total IPC crime prediction random forest regression model fitted the best with adjusted R squared value 0.98 and error of 0.23, whereas for riots crime count prediction in all over the country we got decision tree regression algorithm as the best fit with 0.96 adjusted R squared value.
26 June, Session 1
Chairperson
Prof. Sunil Joshi
MANIPAL UNIVERSITY JAIPUR, JAIPUR, RAJASTHAN
D2S1
Paper ID:
35
INTERVAL VALUED FUZZY STRONG BI-IDEAL OF NEAR-RINGS
Presentators Name:
R.SUMITHA
ABSTRACT
The fundamental concept of fuzzy set was introduced by Zadeh [9] in 1965. Again
he introduced the notion of interval valued (in short i�v) fuzzy subsets in 1975 where the values of the membership functions are closed intervals of numbers instead of a single value. In 1971, Rosenfeld [3] introduced fuzzy subgroup and gave some of its properties. In 1991, Abou-Zaid [1] introduced the notion of fuzzy subnear-rings and ideals in near-rings. Jun and Kim [5] and Davvaz [4] applied a few concepts of fuzzy ideals and i�v fuzzy ideals in near-rings. Moreover, Manikantan [6] introduced the notion of fuzzy bi-ideals of near-rings and discussed some of its properties. Yong Uk Cho et al. [8] introduced the concept of weak bi-ideals applied to near-rings. Thillaigovindan et al. [7] introduced interval valued fuzzy ideals of near rings..(7)S.Usha devi S.Jayalakshmi ,T.Tamih Chelvam introduced the concept of strong bi-ideals applied tonear-rings.In this paper, we define a new notion of interval valued fuzzy strong bi-ideals of near-rings, which is a generalized concept of interval valued fuzzy ideals of near rings. We also investigate some of its properties with examples.
In this present paper, we introduce the notion of interval valued fuzzy strong bi-ideals of near-rings. We have characterized and investigated some related properties of interval valued fuzzy strong bi-ideals of near-rings.
Paper ID:
42
Prime Anti-Fuzzy Bi-ideals in Near-Subtraction Semigroups
Presentators Name:
K.Mumtha
ABSTRACT
Our primary focus is to examine the notion of anti-fuzzy prime ideals in near-subtraction semigroups. This is a continuation of our earlier research regarding anti-fuzzy prime ideals in near-subtraction semigroups. In this paper, we made an attempt to define anti-fuzzy weak bi-ideals in near-subtraction semigroups and proved various results related to the anti-fuzzy weak bi-ideals in near-subtraction semigroups.
Paper ID:
43
Comparitive study on conventional FCM and fuzzy sociogram decision-making approaches of Cognitive maps
Presentators Name:
R.PRIYA
ABSTRACT
Fuzzy Cognitive Maps (FCM) are the decision making tools applied in social setting.Deductive and Inductive modelling approaches were used to develop FCM models.FCM model aims in determining the cause-effect relationship between the factors and also the most significant factor of the research problem.This paper compares the FCM's expert based method with the newly proposed fuzzy sociogram approach in cognitive maps.The efficiency of the new approach and its limitation over the classical FCM approach are studied with the illustrations of the factors associated with emotional intelligence of the learners present age.It was inferred that the results obtained from the new approach are in the consensus with the results derived from the conventional approach of FCM to a great extent.This research work suggests to use this fuzzy sociogram approach of FCM modelling as an alternative approach to rank the factors of the research problem and to investigate the relational impacts.
Paper ID:
50
Inventory model with Quality assurance costs parameters to combat pandemic challenges of food Processing Industries
Presentators Name:
M.KASI MAYAN
ABSTRACT
The continuous waves of the pandemic are disrupting the functioning flow of all the business setting and the food processing industries (FPI) are not an exception. Curfews to combat this deadly virus have led FPI to devise various strategical plans making these industries witness a paradigm shift in their production and distribution systems. The objective of these industries is to enhance their sustainability at this pandemic time by adopting different quality-assuring strategies and materializing will certainly incur costs. The industries are presently working on costs minimization than marginal profit maximization. In line with it, an inventory model to optimize the costs associated with quality assurance of food processing industries is developed in this paper. The proposed inventory model encompasses quality production costs, quality sustenance costs, quality testing costs, quality deliverance costs and the related sub-costs. The model put forth is validated with secondary data and this model will lay a platform for the decision-makers and the researchers to explore the changes made by the pandemic in the trends of food processing production and distribution processes.
Paper ID:
53
Nano Almost ?*AS -Continuous Functions in Nano Topological Spaces
Presentators Name:
I.Sahaya Dani
ABSTRACT
The properties of new class of function, namely nano almost ?*AS - continuous function in nano topological space are analysed in this paper. The relation of these functions with already existing well known functions are studied.
Paper ID:
59
A note on Eisenstein series and convolution of sums
Presentators Name:
Shruthi Karanth
ABSTRACT
In this paper, we obtain a Eisenstein series of level 6 by using Borweins' cubic theta functions and further, we deduce convolution sums of the form $\displaystyle{\sum_{2i+3j=m}\sigma(i)\sigma(j)}$, $\displaystyle{\sum_{i+6j=m}\sigma(i)\sigma(j)}$ and $\displaystyle{\sum_{i+8j=m}\sigma(i)\sigma(j)}$.
Paper ID:
64
Two parameter evaluation of some of the theta-function identities
Presentators Name:
NARENDRA R
ABSTRACT
In this paper, we evaluate two parameters g_{k,n} and g'_{k,n} of some P-Q type theta-functions /psi(q) for some positive real numbers k and n. During this process, we also evaluate Ramanujan-Gollnitz-Gordon continued fraction.
D2S2
26 June, Session 2
Chairperson
Dr. Ramakant Bhardwaj
Associate Professor, Department of Applied Science, Amty University, Kolkata
Paper ID:
31
New class of continuous function in soft topological spaces
Presentators Name:
S. Chitra
ABSTRACT
The classical mathematical theories have dificulties for solving complicated problems which include uncertain data in many areas. To deal with these uncertainities Molodtsov defined soft sets.The conception of soft sets became stable when Shabir and Naz introduced soft topological spaces in 2011, which are defined over an initial universe with a fixed set of parameters. The authors of this paper paved a new pathway by introducing a new class of soft generalized closed sets called soft Jc closed sets in soft topological spaces, based on the researches on soft set theory and soft topology. We introduce a new class of continuous functions namely soft Jc-continuous functions, in order to obtain a generalization of the well known embedding theorem to the class of soft topological spaces. Further We introduce soft TJc spaces, soft Jc-neighbourhood and analyze their properties in this paper.
Paper ID:
34
Pythagorean Fuzzy Approach to Game Theory
Presentators Name:
ANJU THOMAS
ABSTRACT
Pythagorean fuzzy set is an innovative tool to tackle imprecise nature of decision making. The wider scope of Pythagorean fuzzy set in diverse fields has motivated me to investigate matrix games under Pythagorean fuzzy environment. The mathematical model of Pythagorean fuzzy matrix game, Pythagorean fuzzy expected payoff function has been outlined. The necessary and sufficient condition for the existence of Pythagorean fuzzy saddle point has also been developed with its proof. A novel algorithm to solve Pythagorean fuzzy game problem with two numerical examples has also been stated substantiating the credibility of the proposed theory.
Paper ID:
44
A Review on IOT Botnet Techniques and Future challenges
Presentators Name:
Ms. Falaq Jeelani
ABSTRACT
Internet of things(IOT) is becoming the most important part of human life in a short span of time. Humans are having their surroundings with lots of IoT devices like sensors, actuators, sending devices and many more. The major criteria of IOT devices are to provide connectivity to the devices, human or things which do not have any processing element. However, security is one of the main challenge in IOT due to its always connected feature. The botnets are kind of malware which creates its team of compromised devices and make it large by affecting other devices also. There are several attacks which may affect the devices like DDOS, click fraud, phishing and ransomware. This paper covers several components of IOT botnet and with its detection techniques. It also covers several open challenges and future work in the field of detection of IOT botnet
Paper ID:
48
Intelligent Tourism Recommendations System
Presentators Name:
Saurabh Srivastav
ABSTRACT
The purpose of the current work is to propose a tourist destination recommendation system based on personalized criteria of tourists which will suggest the preference order of tourist destinations. In the present work, four experts were involved in assessing linguistic inputs. Further, these linguistic inputs were converted to triangular fuzzy numbers and, the soft computing technique: Fuzzy TOPSIS, was applied to obtain the order of preference for all the tourist destinations. The research provides a personalized recommendation system and the preferences of tourist destinations in Varanasi, India. The rankings produced by the proposed method would be useful to visitors, as well as the government, administrative bodies, and various tour operators, in formulating regulations based on the rating of each tourist site, which will aid tourism growth. A visitor can build or change his schedule according to particular criteria using the personalized tourist locations suggestion system, which is novel in the tourism sector. Another advantage of the current study is that policymakers may use the customized recommendation system to determine the order of preference of tourist locations, allowing them to develop legislation to enhance the characteristics that are badly ranked.
Paper ID:
58
Properties of n-independent sets of a graph
Presentators Name:
Dr.Ravi Shankar Bhat S
ABSTRACT
The open neighbourhood N(v) of a vertex v 2 V; is the set of all vertices adjacent
to v. Then N[v] = N(v) [ fvg is called the enclave of v. We say that a vertex v 2 V ,
n-covers an edge x 2 X if x 2 hN[v]i, the subgraph induced by the set N[v]. The n-
covering number n(G) introduced by Sampathkumar and Neeralagi [?] is the minimum
number of vertices needed to n-cover all the edges of G: A set S V is said to be
n-independent if every edge x 2 hSi is n-covered by a vertex in V ?? S. On the other
hand, S is n-complete if, for every pair of nonadjacent vertices u; v 2 S there exists a
vertex w 2 V ??S such that fu; v;wg is independent. The n-independence (n-complete)
number N(G)(!N(G)) is the maximum order of n-independent (n-complete) set of G:
In this paper, a Gallai's theorem type result n(G)+N(G) = p is proved. In addition
to getting several bounds on n-independence number, we show that N(G) = !N(G)
and the chromatic number (G) N(G) + 1:
Paper ID:
63
New Sort of Nano Derived Sets
Presentators Name:
G. Kabin Antony
ABSTRACT
The concepts of θ-closure and θ-interior operators were first introduced by Velicko[7] and further studies of these operators have been made by many authors. The notion of nano topological spaces and also the operators nano-interior and nano closure was introduced by Lellis Thivagar et al.[3]. The main purpose of this paper deals with the new notion called nano θ-derived set which is stronger than nano derived set. Further, we studied its characterizations and different forms of nano θ-derived sets under various cases of approximations are also derived.
Paper ID:
67
gw-CLOSED SETS IN WEAK STRUCTURE SPACES
Presentators Name:
Dr. V. SANGEETHASUBHA
ABSTRACT
In this paper, we introduce the concepts of gw-closed sets and gw-open sets in weak structure spaces. Further, we study some of their properties.
D2S3
26 June, Session 3
Chairperson
Dr. V. Ramesh
Secretary, FAI Tamilnadu(CENTRAL) Chapter, Madurai Kamaraj University, India
Paper ID:
55
A Numerical Approach towards Atangana-Baleanu and Caputo-Fabrizio Fractional Derivatives on MHD Couette Flow
Presentators Name:
Dr. Dipen Saikia
ABSTRACT
A numerical investigation has been carried out to analyze the consequences of variable viscosity and thermal conductivity using fractional derivatives of Atangana-Baleanu (AB) and Caputo-Fabrizio (CF) on MHD coquette flow with heat transfer in presence of constant heat source. In this paper we have considered an unsteady two-dimensional free convection couette flow of an incompressible, viscous, and electrically conducting fluid under the influence of magnetic field bounded by two infinite horizontal parallel plates. The leading partial differential equations together with the boundary conditions are transformed
into ordinary form by similarity transformations ensure that physical parameters show up in the equations and interpretations on these parameters can be achieved appropriately. By the use of ordinary finite difference scheme, the equations thus obtained are discretized. These discretized equations are numerically solved by the approach derived from Gauss-Seidel iteration scheme. The consequences of all the physical parameters involved in the problem on velocity, temperature and magnetic field distribution are presented with the aid of graphs and offered in tabular form. It has been found that each parameters effects are prominent enough. An assessment on AB and CF techniques has also been depicted in tabular form. It is noticed that both approaches have been well agreed upon.
Paper ID:
66
TRAPEZOIDAL FUZZY NUMBER AND MEDICAL DIAGNOSTIC SOFT COMPUTING MODEL
Presentators Name:
Neeraja Sharma
ABSTRACT
The present paper is focused on design and development of a soft computing model for an effective medical knowledge in medical diagnosis decision making problems using trapezoidal fuzzy number in tensorial fuzzy judgement.
Paper ID:
74
Fuzzy Utility Matrix based Intelligent Decision-Making Mathematical model and its application in Diet recommendation system for Metabolic Disorders patient
Presentators Name:
Rajkrishna Mondal
ABSTRACT
Objective: Developed an Intelligent decision-making mathematical model with the help of fuzzy utility matrix, that will take a decision as human way of thinking. With the help of this model built a Diet recommendation system for Metabolic Disorders patient.
Study Design: The key features of this system have developed by using Fuzzy utility matrix and maximizing Fuzzy set concepts. This system is mainly divided into three phases.
1st phase, we calculate the daily needed calorie according to patient’s personal information (like sex, age, height, weight), physical activity, environmental situations, and food habits.
2nd phase, build up knowledge set with the information of the patient’s foods habit.
3rd phase, we discussed the recommendation system of the dinner menu using a decision-making mechanism to maintain daily proper nutritional macros percentage.
Result: Through the degree of match algorithm and from various Nutritional Experts we have validated our result.
Conclusion: It will help the people living in rural areas those who have not primary nutritional knowledge about their daily food items to maintain his(her) daily nutritional macros (like carbohydrate, protein, fat) percentage without help of nutritional experts.
D2S4
26 June, Session 4
Chairperson
Prof. A. Senthilrajan
Alagappa University
Paper ID:
19
Neural Network based Error Prediction in FE Analysis of Prestressed Concrete Beams
Presentators Name:
Dr. Pushpa Pathote
ABSTRACT
Prestressed concrete is one of the most important construction material. It is being used in all sorts of small to big structures like, railway sleepers, buildings, nuclear reactors, bridges etc. In prestressed concrete high tensile strength steel bars are used which are called tendon or prestressing cable. For realistic analysis of prestressed concrete beams, in this study, prestressing cable is modeled by B-spline curve by finite element analysis. Finite element analysis is a numerical technique in which a chain of mathematical operation takes place. In any computer based analysis, errors accumulate due to various sources like input data, processing, truncation or round-off. To minimize errors higher precision computational analysis is required. But higher precision computation is time consuming and required more memory space. Artificial neural network is nonlinear statistical data modeling tool which can be used to model complex relationship between inputs and outputs. In this study, with the help of neural network, error models are developed which will predict error in single precision computation for given input parameters (Poisson�s ratio and Young�s modulus). Effect of friction is also taken into account.
Paper ID:
41
Classification of Skin Disease using Machine Learning and Deep Learning algorithms
Presentators Name:
M KALAIYARIVU
ABSTRACT
Abstract
There are innumerable miracles and wonders going on in the medical field during this 21st century. Technology development is one of the major contributors to this. Dermatitis is a skin inflammation or disorders that is affecting many people. The skin diseases or disorders that was commonly affected by humans, spread throughout the society. There are many types of skin diseases , affecting the humans. This paper illustrates the classification of seven skin diseases(Melanocytic Nevi, Melanoma,Benign Keratosis- like lesions, Basal cell carcinoma, Actinic Keratoses, Vascular lesions, Dermatofibroma). HAM10000 dataset is a public dataset collected used for this work. The proposed technique for skin disease classification has two phases: Feature extraction and Classification models. Segmentation process is done by using Canny edge-detection. This work used has two feature extraction techniques such as texture feature (LBP, GLCM) and Color feature(Color Historgram, Color Space) are extracted from the lesion image and extracted features are trained with various Machine Learning models (K-nearst neighbors(KNN), Decision-tree(DT), Support Vector Machine(SVM), Light Gradient Boosting Machine (LightGBM)). In the feature extraction Deep Learning model(Convolutional Neural Network) accuracy is used as a performance measure to access the accuracy of skin disease classification from the experimental results. It�s shows that the Deep Learning model Convolutional Neural Network(CNN) has an accuracy of 85.2%. From the measure noted that Convolutional Neural Network gives more accurate than Machine Learning models (63.2%, 67.5%, 81.3%, 79.4%).
M. Kalaiyarivu
Research Scholar,
Computer and Information Science,
Annamalai University, Annamalai Nagar, Chidambaram-608002, India.
kalaiyarivu2016.c@gmail.com
9486655909
Dr. N. J. Nalini
Associate Professor and Research Supervisor
Computer Science and Engineering,
Faculty of Engineering and Technology,
Annamalai University, Annamalai Nagar, Chidambaram-608002, India.
njnsce78@gmail.com
Paper ID:
51
FOREST MISFORTUNE MITIGATION SYSTEM USING CNN
Presentators Name:
GEORGY T REJI
ABSTRACT
Forest is the major source of survival for humans. Forest helps us to provide fresh air, climate change mitigation and livelihood for humans. In spite of these much dependences humans are note taking care for the protection of forest. Many studies reflects that, animals are capable to sense major changes that is going to be happening in the environment, even hours before its actual occurrence. In response to this, they always produce a sound. Taking this in account, the project predicts disasters from animal sounds and gives out warning messages to the stations about the place where disasters like forest fire, hunting, earthquake and deforestation is taking place. In addition, to improve accuracy of the system we use DHT sensor to sense temperature and humidity of the forest land. Our proposed system is to create a forest misfortune mitigation technique using machine learning and CNN as framework. The sounds of animals from the forest area are continuously monitored using microphones. This data is been fed into the trained ML which predicts the output. Once the presence of disaster is sensed, the warning messages will be sent to the station along with exact location map. Here, the black box used is raspberry pi. This system can be implemented in large-scale in places such as deep forest & amazon rain forest, as these regions are difficult to be guarded by humans due to harsh environment and carnivorous attacks.
Paper ID:
52
A Convergence Theorem for lshikawa Iteration of Continuous Generalized Soft Hemi Contractive Mapping
Presentators Name:
Dr. Pankaj Kumar
ABSTRACT
The first aim of this paper is to study some important properties of soft Banach spaces and second is to set up new concept of soft continuous mappings and inspect some important properties of soft continuous generalized soft Hemi Contractive mappings and to establish Convergence Theorem for lshikawa Iteration of Continuous Generalized soft Hemi Contractive Mapsin soft contractive mappings on soft Banach spaces
Paper ID:
56
Design and analysis of low power Hybrid Full adder using CMOS 45nm Technology
Presentators Name:
SUNILKUMAR J
ABSTRACT
The updated logic method is mostly utilized in the designing process of an arithmetic logic
circuitry.The working capacity of a full adder having few different factors like holding time, potential charge utility and the capacity of the circuit is highly based on how efficiently the circuit will work.From this project, a large speed, low power consuming count of ten transistors logic circuit is designed,and it generates effective fluctuations at the same time with effective delay at output.The performance efficiency of the designed circuit is calculated by simulating it in a tanner software using 45nm technology.The established circuit decreases the PDP factor at least by 15% than already existing models.In this project we are introducing two different designs of full adders those are designed in this article by using the already designed XOR XNOR circuitry and existed sum and carry generating
blocks.The designed full adders provides 11%�45% betterment in words of power fluctuation product in the comparison of other models.To calculating the driving capacities the proposed full adders are fixed in multistage full adder circuits.Outputs show that two of the designed full adders generate the better results for a larger count of data bits in all the full adders.
Paper ID:
57
Design and Analysis of GDI- Low power Booth Multiplier using CMOS 22nm Technology
Presentators Name:
P.RAVI KUMAR REDDY
ABSTRACT
GDI technique is used to design the low power circuits. Low power circuits will play an important role in digital engineering. This paper proposed low power Booth Decoder, Product Generating Unit, Full adder, Half adder and Partial generating unit. AND gate, OR gate, X-OR gate are used to implement Full adder and Half adders where as NAND gate, NOR gate, X-NOR gate, NOT gate are used to impalement Booth decoder and Partial generator units.GDI technique is used in various applications. In digital signal processing, the speed of the processor is directly proportional to the speed of the multiplier. Hence to eradicate this problem, a high speed digital multiplier is used. This module demonstrates about the low power booth multiplier perform based on a partial product, shifted nearly and addition. The technique used here is Gate Diffusion Input (GDI) which as low electricity usage. The proposed modified CMOS Booth multiplier requires less number of transistors, due to this power consumption and delay of the multiplier decreased. The speed of the partial product increases whenever the booth multiplier cuts the required partial product into half. Basically, it consists of three sections are decoder, partial product generating unit and adder circuit. By using Mentor graphics tool this technique is implemented. The technology used here is 22nm technology. The proposed schematic validated with input voltage range from 0.1V to 0.7V.Where the output is obtained here in terms of average power which is compared with the performance of GDI static CMOS technology
Paper ID:
68
STANDARDS, ROUTING PROTOCOLS AND CHALLENGES IN VEHICULAR AD-HOC NETWORKS
Presentators Name:
KOMAL KHARKWAL
ABSTRACT
In recent years there are drastic increase in road accidents due to increase in vehicular traffic. The intelligent transportation system is a branch of modern technology for delivery of Road Safety Services. Every year 1.35 Million people were killed in Road Accidents. In the field of intelligent transport system, the development is growing rapidly with the enhancement of Vehicular Ad-hoc Networks (VANETs). Vehicular Ad-hoc Networks (VANETs) have gained conspicuous attention among the researchers from industry and academia due to researches going in the smart cities. There are many existing standards for wireless access in VANETs; such as 2G, 2.5G,3G,4G, DSRC, WAVE and WiMAX. The existing topological and geo-graphical MANET routing protocols are used by Vehicles for making routing decisions. Standards for Wireless Access in VANETs are available to provide the Radio Access by the Vehicles in order to communicate via Vehicle-to-Vehicle communication, Vehicle-to-Infrastructure communication or Infrastructure-to-Infrastructure communication. The main purpose of these communication standards to improve efficiency and safety in traffic on the roads and to provide comfort to Drivers as well as Passengers. A process through which a source node finds a route for reaching the destination node in the underlying network. This process is to be done by routing protocols by collecting necessary information for the selection of routes between any two nodes. There are several existing Vehicular Routing Protocols, further they are divided into five categories: Topology Based, Position Based, Cluster Based, Geo cast Based, Multi cast Based. The main aim of routing protocols is to achieve minimum communication time with minimum consumption of network resources. At present, routing protocols have been deployed for MANET are used to test the VANETs accuracy and performance. Recently VANET research are focused on pre-defined areas such as routing and broadcasting, security, quality of service (QoS) and infotainment and information during emergencies. In Ad-hoc networks the links connect or disconnect very frequently for managing revival, robust, efficient, timely and scalable ventures in Ad-hoc networks. In smart cities we use intelligent transport systems (ITS) to improve the mobility, quality, comfort and safety through communication network and information technology. Vehicular Ad-hoc network is considered as backbone for the development of ITS. Wireless Ad-hoc networks divided into three categories which are used in VANETs: Wireless Sensor Networks (WSNs), Wireless Mesh Networks (WMNs) and Mobile Ad-hoc Networks (MANETs). Intelligent Transportation System (ITS) is the major application supported by Vehicular Ad-hoc Networks. ITS use the WAVE Standard for reducing in-convenience and avoiding danger situations. For the distribution of information and data about the road maintenance, weather forecast and road conditions we use ITS. Through VANETs the usages of advanced driver assistance system (ADAS) are possible. In Ad-hoc networks ADAS used for communication for delivery of driver assistance efficiently with the safety of the Vehicle. In this paper we discuss about existing routing protocols and standards that are most commonly used by VANETs with their details and problems or challenges associated with these protocols.
Paper ID:
69
Arm fracture detection using Deep Convolution Neural Network
Presentators Name:
Vishwanath Bijalwan
ABSTRACT
Arm fracture detection using Deep Convolution Neural Network
D3S1
27 June, Session 1
Chairperson
Dr. Neelam Srivastava
Dept. of Physics, Mahila Maha Vidyalaya, BHU, Varanasi
Paper ID:
14
Deep Learning-based System to Diagnose Learning Disabilities
Presentators Name:
Pranshu Chandra Bhushan Singh Negi
ABSTRACT
Learning Disabilities can be categorized as neurological disorders that
may hamper individuals' learning capability and intellectual progress. A considerable number of such cases have been seen in India, especially among elementary school children. The main issue in proper detection and diagnosis of learning
disabilities is its misinterpretation, lack of information, and awareness about the
disease. Diagnosis of learning disabilities is still a complex and tedious task. This
paper aims to propose a deep learning-based diagnostic system for predicting
learning disabilities. Learning Disabilities can be categorized as neurological disorders that may hamper individuals' learning capability and intellectual progress.
In the present work, deep learning algorithm-based methodology has been proposed based on a questionnaire designed to gather the data. The questionnaire
responses are fed into the deep learning model for a comprehensive assessment
of learning disabilities. The trained model has shown 100% classification accuracy for hundred students, and it is observed that the model provides a diagnostic
report in 5 seconds after the submission of the form by parent/teacher. Thus, a
deep learning-based diagnostic system is developed for predicting the learning
disabilities in a child. This model showed considerable accuracy in the prediction
of LD in a child. These findings will help in the early prediction of the extent of
LD in a child and help the parents/teachers to adjust the mode of education for
the child.
Paper ID:
54
Insights on Energy Harvesting by Lead Zirconate Titanate Materials
Presentators Name:
VIPIN PATAIT
ABSTRACT
This paper aims to study the properties of lead zirconate titanate (PZT) material and its utilization in energy harvesting devices. The distinct properties of PZT have been highlighted, which are responsible for its energy harvesting characteristics. The methodology utilized for low-frequency vibration captured from the environment, for thin-film PZT, for increasing the output performance of the PZT material, for the study of nonlinearity and temperature effect on the PZT devices, for its utilization in various applications and for finite element analysis validation is demonstrated. The statistics have been shown which indicates the various values responsible for PZT�s energy harvesting property.
Paper ID:
72
The impact of centrifugal casting processing parameters on the wear behaviour of Al alloy/Al2O3 functionally graded Materials
Presentators Name:
Bhupendra Kumar
ABSTRACT
In this study, A vertical centrifugal casting machine was used to fabrication of a functionally graded material (FGM) which was made of a non -commercial pure Al alloy (>97.9% Al) and Al2O3 reinforcement particles were produced at different speed rang 800 rpm and 1200 rpm. The reinforcement were varied with different volume percentage such as 3%, 4.5%, 6%, & 7.5%vol. A Pin on the Disc tribometer was used to investigate the sliding wear behavior of metal matrix composites. The disc speed was kept constant (0.5m/s) during the wear test, with a load was 30 N at room temperature. The disc as a counter body was made of AISI 52100 steel. For were test all the specimens were prepared as per the ASTM standards. Establish wear test outcomes have been used the expected weight loss method across the wall thickness of the cast ring which is depending on the fabrication parameters, sliding distance, velocity and the loading conditions which also exhibited in the Scanning Electron Microscopy analysis and micro hardness in that region.
Paper ID:
73
Improved TOPSIS method in wireless networks
Presentators Name:
MUDDAMALLA NARESH
ABSTRACT
In recent trends, the knowledge of various vertical handover (VHO) algorithms is important for various heterogeneous networks. In this paper, an optimized algorithm is developed by combining two existing multiple attribute decision-making models, the first one is the improved TOPSIS model and the second is the Entropy weighting model. The improved TOPSIS method is used to rank the alternative networks available and the Entropy weighting technique is used to get attribute weights. The proposed method can be implemented using NS2 (Network simulation).
D3S2
27 June, Session 2
Chairperson
Prof. Raghu Raman
Ex. Faculty, School of Business Management ,ICT, Oman
Paper ID:
21
Composition of Non-Farm activities and the extent of Employment Diversification in rural areas of Jammu District
Presentators Name:
Priya Sharma
ABSTRACT
In India, the majority of the population lives in rural areas and is mostly dependent upon the farm sector for their survival. But the farming sector does not give a satisfactory means of survival to a greater part of the rural population. There arises a need for diversification because completely depending on the farm sector is a greater risk for livelihood. Households diversify by adopting different types of activities which may consist of farm and non-farm activities. Keeping this in mind the present study mainly focuses on examining the Composition of rural non-farm employment and the extent of employment diversification in rural areas of Jammu District. This study is based on primary data which has been collected from the selected households with the help of a well-structured questionnaire and about 200 respondents have been surveyed. The data has analyzed by using descriptive statistics and the Simpson index of Diversity. It has been found that there are various non-farm activities in the study area which are divided into six activities i.e. business, Govt. Job, Private Job, Local shop, labour and other and the majority of the households follow some level of employment diversification. The share of the non-farm sector to total employment/earnings is higher as compared to the share of employment/earnings from the farm sector. The average value of SID has increased with the increased farm size. The implication of the study is that the opportunities of non-farm employment should be increased to decrease the rural unemployment problem and to combat the poor households� susceptibility to shocks and income fluctuations. It is also recommended to provide more attention to landless households for diversifying their employment and increasing income.
Paper ID:
25
Innovative approach for assessing sustainability of the medicinal plant- Enicostema littorale_Blume
Presentators Name:
Dr. Dolly Parmar
ABSTRACT
Innovative approach for assessing sustainability of the medicinal plant- Enicostema littorale_Blume
Paper ID:
30
Role of Big Data Analytics in Business Management
Presentators Name:
Satyam Prakash Srivastava
ABSTRACT
Abstract: Data analytics have brought a paradigm shift in the entire business management and gained paramount importance for big business tycoons. In the past few years role of data analytics have shown phenomenal growth in business. It has become quite evident from the current scenario that when tremendous data and information are analyzed collectively it plays influential role for business in enormous areas and assist them to take pertinent decisions. These decisions could be concerned with consumers� tastes, preferences and judging their traits in making choices. In this study, an endeavor is made to comprehend, exhibit and review the role of data analytics in the entire business arena and how it facilitate corporate at large in making business decisions, thinking strategically and reviewing the performance at different levels. An attempt is also made to develop and trigger a holistic approach through data analytics. It is also intended to study the perspective of business entity in utilizing the very nature of data for different purposes.
Paper ID:
46
E - Waste management with reference to hilly region of Himalayas: A Case Study
Presentators Name:
Dr PRADEEP MAMGAIN
ABSTRACT
Abstract: E-waste management is a recent concept emerging with more and more use of electronic gadgets. In the post covid situation larger quantity of electronic waste emphasizes the proper need of E-waste management. The present paper highlights the scenario of E-waste with reference to Garhwal region of Himalayas. The study show how the used computers, mobile phones etc. has contributed the largest portion of E-waste in the region. Pilling up of E-waste is deteriorating the environment and biodiversity of Garhwal region thereby causing the environmental and health associated problem in the region. The Paper highlighted the government policies need to be framed for the efficient recycling of E-waste management in Garhwal region. Further it may be linked with the employment generation in the region.
ABSENT
27 June, Session 3
(Only for Absent Participants)
Chairperson
Dr. V. Ramesh
Dr. Ramakant Bhardwaj
Madurai Kamaraj University, India
Amty University, Kolkata
Paper ID:
20
AN EFFECTIVE PRIVACY-PRESERVING ALGORITHM BASED ON RUBIK'S CUBE PRINCIPAL
Presentators Name:
Ankita Ashok Giradkar
ABSTRACT
In the past few years, several encryption algorithms is a based in chaotic systems has proposed means to protect digital images against cryptographic attacks. These encryption algorithms typically relatively use small key spaces and thus offer limited security, especially they are one-dimensional. In this paper, we proposed a novel image encryption algorithm is a based on Rubik's cube principle. The original image scrambled using the principle of Rubik's cube. Then, XOR operator is applied and rows, columns of the scrambled image using two secret keys public, private. Finally, the experimental results and security analysis show that the proposed encryption image scheme not only achieve good encryption and perfect hiding ability but also it can resist exhaustive attack, statistical attack, and differential attack.
Paper ID:
35
INTERVAL VALUED FUZZY STRONG BI-IDEAL OF NEAR-RINGS
Presentators Name:
R.SUMITHA
ABSTRACT
The fundamental concept of fuzzy set was introduced by Zadeh [9] in 1965. Again
he introduced the notion of interval valued (in short i�v) fuzzy subsets in 1975 where the values of the membership functions are closed intervals of numbers instead of a single value. In 1971, Rosenfeld [3] introduced fuzzy subgroup and gave some of its properties. In 1991, Abou-Zaid [1] introduced the notion of fuzzy subnear-rings and ideals in near-rings. Jun and Kim [5] and Davvaz [4] applied a few concepts of fuzzy ideals and i�v fuzzy ideals in near-rings. Moreover, Manikantan [6] introduced the notion of fuzzy bi-ideals of near-rings and discussed some of its properties. Yong Uk Cho et al. [8] introduced the concept of weak bi-ideals applied to near-rings. Thillaigovindan et al. [7] introduced interval valued fuzzy ideals of near rings..(7)S.Usha devi S.Jayalakshmi ,T.Tamih Chelvam introduced the concept of strong bi-ideals applied tonear-rings.In this paper, we define a new notion of interval valued fuzzy strong bi-ideals of near-rings, which is a generalized concept of interval valued fuzzy ideals of near rings. We also investigate some of its properties with examples.
In this present paper, we introduce the notion of interval valued fuzzy strong bi-ideals of near-rings. We have characterized and investigated some related properties of interval valued fuzzy strong bi-ideals of near-rings.
Paper ID:
63
New Sort of Nano Derived Sets
Presentators Name:
G. Kabin Antony
ABSTRACT
The concepts of θ-closure and θ-interior operators were first introduced by Velicko[7] and further studies of these operators have been made by many authors. The notion of nano topological spaces and also the operators nano-interior and nano closure was introduced by Lellis Thivagar et al.[3]. The main purpose of this paper deals with the new notion called nano θ-derived set which is stronger than nano derived set. Further, we studied its characterizations and different forms of nano θ-derived sets under various cases of approximations are also derived.
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