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ON INTERVAL VALUED FUZZY QUASI-IDEALS OF SEMIGROUPS
Thillaigovindan, N.,Chinnadurai, V. The Youngnam Mathematical Society 2009 East Asian mathematical journal Vol.25 No.4
In this paper we shall introduce the notion of an i-v fuzzy interior ideal, an i-v fuzzy quasi-ideal and an i-v fuzzy bi-ideal in a semi-group. We study some properties of i-v fuzzy subsets and using their properties we characterize regular semigroups.
On interval valued fuzzy quasi-ideals of semigroups
N. Thillaigovindan,V. Chinnadurai 영남수학회 2009 East Asian mathematical journal Vol.25 No.4
In this paper we shall introduce the notion of an i-v fuzzy interior ideal, an i-v fuzzy quasi-ideal and an i-v fuzzy bi-ideal in a semigroup. We study some properties of i-v fuzzy subsets and using their properties we characterize regular semigroups.
INTUITIONISTIC FUZZY n-NORMED LINEAR SPACE
Vijayabalaji, Srinivasan,Thillaigovindan, Natesan,Jun, Young-Bae Korean Mathematical Society 2007 대한수학회보 Vol.44 No.2
The motivation of this paper is to present a new and interesting notion of intuitionistic fuzzy n-normed linear space. Cauchy sequence and convergent sequence in intuitionistic fuzzy n-normed linear space are introduced and we provide some results onit. Furthermore we introduce generalized cartesian product of the intuitionistic fuzzy n-normed linear space and establish some of its properties.
Vijayabalaji, Srinivasan,Thillaigovindan, Natesan Korean Mathematical Society 2007 대한수학회보 Vol.44 No.3
The purpose of this paper is to introduce the notion of fuzzy n-inner product space. Ascending family of quasi ${\alpha}$-n-norms corresponding to fuzzy quasi n-norm is introduced and we provide some results on it.
Nanoindentation of aluminum (100) at various temperatures
Murugavel Rathinam,Ramesh Thillaigovindan,Prema Paramasivam 대한기계학회 2009 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.23 No.10
High temperatures generally affect materials in some form. In this regard, the capability to perform nanoscale measurements at elevated temperatures opens up new possibilities for investigating the temperature dependence of materials’ mechanical properties. Particularly, the responses of aluminum’s different mechanical properties to indentation at various temperatures have been studied experimentally. In this paper, aluminum response to different room temperatures was examined. The behaviors of a single crystal aluminum during loading and unloading were observed. Nanoindentation experiments on a single crystal aluminum (100) sample at temperatures of 265 K and 388 K were performed with different loading conditions. At the start of the first burst of the dislocation glide, which was indicated by a sudden increase in displacement with no increase in loading, evidence of plastic properties and softening effects on aluminum was identified. The ductile to brittle transition was observed at temperatures below 273 K. Generally, there was a significant increase in the penetration depth and a decrease in hardness, elastic modulus, and elastic recovery as the testing temperature increased.
Srinivasan Vijayabalaji,Natesan Thillaigovindan 대한수학회 2007 대한수학회보 Vol.44 No.3
The purpose of this paper is to introduce the notion of fuzzyn-inner product space. Ascending family of quasi -n-norms correspond-ing to fuzzy quasi n-norm is introduced and we provide some results onit.
Syed Anwar Hussainy F,Senthil Kumar Thillaigovindan 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.2
Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.